b752c3fa6c05b097e602120372f72d6296dadc2a
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+---
+visibility: public-edit
+---
+
+# Ideas Wiki — Schema
+
+this wiki collects and synthesizes all of harrison's project ideas, product ideas, and big ideas. sources are primarily ideaflow notes tagged with idea-related hashtags, plus obsidian `ideas/` folder and any idea-tagged apple notes.
+
+mutually exclusive with the notes wiki — if it's an idea, it lives here; if it's not, it lives there.
+
+## page types
+
+### idea
+a single project/product idea, potentially synthesized from multiple captures.
+- frontmatter: `type: idea`, `title`, `tags`, `status` (raw|explored|in-progress|built|abandoned), `first_captured`, `sources`
+
+### idea-cluster
+a group of related ideas that share a theme or problem space.
+- frontmatter: `type: idea-cluster`, `title`, `tags`, `ideas` (list of linked idea pages)
+
+### source-summary
+a 1:1 summary of a source document.
+- frontmatter: `type: source-summary`, `title`, `source_path`, `source_origin`, `date`
+
+## page format
+
+```markdown
+---
+type: <page-type>
+title: <title>
+tags: [<tags>]
+status: <status>
+first_captured: <iso-date>
+updated: <iso-date>
+sources: [<source-file-paths>]
+---
+
+# <Title>
+
+<compiled truth — what the idea is, why it matters, how it could work, what's been explored. weave [[wikilinks]] into the prose wherever you mention another idea, cluster, or concept that has its own page. link on first mention only, use display text for readability: [[slug|natural text]].>
+
+---
+
+## timeline
+
+- [<date>] <capture or update event>
+```
+
+## ingestion workflow
+
+when a new source is dropped into `sources/`:
+1. read the source fully
+2. write a `source-summary` in `wiki/summaries/`
+3. determine if this matches an existing idea page (same concept, different capture)
+4. if yes: merge into existing idea page, update compiled truth, add timeline entry
+5. if no: create a new idea page
+6. check if the idea fits into an existing cluster or warrants a new one
+7. if the idea became an actual project, note `status: built` and reference which project
+8. update `index.md` and `log.md`
+
+## idea classification tags (from ideaflow)
+
+primary: `#projectidea`, `#newprojectidea`, `#bigprojectidea`
+secondary: any note from obsidian `ideas/` folder, apple notes with idea-like content
+
+## cross-referencing
+
+- weave `[[slug|display text]]` wikilinks inline in prose — first mention only, don't over-link
+- no dedicated "connections" or "see also" sections — if a relationship matters, it belongs in the text
+- link related ideas to each other inline
+- when an idea became a real project, mention it inline with the project name
+- do NOT link to notes wiki — these are separate wikis
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+---
+title: ideas wiki index
+type: index
+updated: 2026-04-11
+visibility: public
+---
+
+# ideas wiki
+
+all of harrison's project ideas, product ideas, and big ideas — synthesized from IdeaFlow, Apple Notes, and Obsidian.
+
+## idea clusters
+
+| cluster | description |
+|---------|-------------|
+| [[cluster-memory-and-context\|memory and context tools]] | capturing, storing, retrieving, and using personal context and memory |
+| [[cluster-social-networking\|social network and connections]] | connecting people, mapping relationships, building communities |
+| [[cluster-learning-education\|learning and education]] | reimagining how people learn — tools, human infrastructure, culture |
+| [[cluster-habits-productivity\|habits and productivity]] | habit formation, reminders, automation, self-improvement |
+| [[cluster-ai-tools\|AI tooling and research]] | making AI better, meta-tools for AI-powered building |
+| [[cluster-hardware-wearable\|hardware and wearables]] | physical devices for sensing, interaction, and utility |
+| [[cluster-hiring-work\|hiring and work tools]] | both sides of hiring — seekers and employers |
+| [[cluster-search-discovery\|search and discovery]] | finding things better across all domains |
+
+## all ideas (by status)
+
+### built
+| idea | summary |
+|------|---------|
+| [[oncue\|OnCue]] | interview prep tool — scattered history into perfectly-timed answers |
+| [[pause\|Pause]] | screen break / reminder app |
+| [[convo-flow\|convo-flow]] | conversation tracking tool with statuses and highlighting |
+| [[connection-hub\|connection hub]] | my school's community discord server |
+| [[ultimate-describer\|precision description engine]] | engine for making descriptions more precise — won hackathon |
+| [[keystroke-music\|keystroke classical music]] | typing becomes classical piano performance (kbs) |
+
+### explored
+| idea | summary |
+|------|---------|
+| [[brain-rewinder\|brain rewinder]] | device/system to remember previous thoughts via sensory triggers |
+| [[sensor-capturer\|sensor capturer]] | device capturing sensory experiences beyond audio/video |
+| [[smell-resetter\|smell resetter]] | device to combat olfactory fatigue — deep research done |
+| [[universal-habits\|universal habits]] | context-aware adaptive habit system — biggest recurring idea |
+| [[axon\|axon]] | personal memory/context layer for AI agents and humans |
+| [[discord-connections-mapper\|discord connections mapper]] | multi-platform relationship graph with network combining |
+| [[referral-hiring\|referral-based hiring]] | marketplace for fit-over-qualification hiring via referrals |
+| [[always-on-ai-assistant\|always-on AI assistant]] | 24/7 listening, transcribing, context-aware AI companion |
+| [[universal-agentic-searcher\|universal agentic searcher]] | deep research pipeline with adaptive feedback |
+
+### raw
+| idea | summary |
+|------|---------|
+| [[episodic-memory-builder\|episodic memory builder]] | app to improve memory through daily recall prompts |
+| [[universal-data-capturer\|universal data capturer]] | unstructured data logger with LLM-powered dynamic schemas |
+| [[me-model\|me model]] | fine-tuned LLM trained on all personal data sources |
+| [[life-search\|life search]] | semantic search over your entire life — "mgrep for life" |
+| [[ifttt-personal\|personal IFTTT]] | personal automation for text patterns, reminders, triggers |
+| [[vibe-matcher\|vibe matcher]] | social network using ML to match people by compatibility |
+| [[tutor-platform\|exceptional tutor platform]] | marketplace for rigorously vetted tutors |
+| [[personalized-medicine\|personalized medicine]] | unified testing framework for personal wellness factors |
+| [[comparison-engine\|comparison engine]] | side-by-side decision tool accepting any media |
+| [[decision-helper\|decision helper]] | therapist-style AI for diagnosing decisions |
+| [[cultural-translator\|cultural translator]] | translator across philosophical frameworks and culture spheres |
+| [[job-tracking\|job application tracker]] | employer-side applicant tracking with fake detection |
+| [[task-scheduler\|task scheduler]] | better Morgen with auto-planning and wait-time awareness |
+| [[wifi-client\|wifi client]] | WiFi management, auto-join, password sharing app |
+| [[pupilometry-glasses\|pupilometry glasses]] | eye-tracking glasses for hands-free computer control |
+| [[emg-bracelet\|EMG bracelet]] | EMG+gyro wrist device for computer control |
+| [[writing-tools\|writing tools suite]] | grammar, AI detection, plagiarism, and quality writing tools |
+| [[college-essay-grader\|college essay grader]] | vector embedding distance for essay quality |
+| [[personalized-autocomplete\|personalized autocomplete]] | context-aware autocomplete that knows you |
+| [[consulting-software\|consulting / custom software]] | Palantir-style audit/plan/make software business |
+| [[overnight-app-grinder\|overnight app grinder]] | autonomous overnight coding agent manager |
+| [[cross-platform-bots\|cross-platform bots]] | notification bridges across LinkedIn, Slack, etc. |
+| [[build-in-public\|build/learn in public]] | low-friction crossposter across all social media |
+| [[life-guide\|life guide]] | crowdsourced practical life wisdom |
+| [[youre-not-behind\|you're not behind machine]] | counter-anxiety tool for college stress |
+| [[browser-autocomplete-editor\|browser autocomplete editor]] | edit browser URL autocomplete suggestions |
+| [[info-exchanger\|info exchanger]] | pre-conversation context sharing via paired Q&A |
+| [[data-selling-platform\|data selling platform]] | marketplace for people to sell their data |
+| [[instant-blanket\|instant warming blanket]] | cheap blanket for immediate full-body warmth |
+| [[eco-community\|eco-safe community]] | climate-resilient luxury community |
+| [[agent-simulation\|agent-based simulation]] | large-scale agent simulation platform |
+| [[surreal-sound-experiences\|surreal sound experiences]] | multi-device immersive sound experiences |
+| [[project-suggestor\|project suggestor]] | AI project recommendation based on who you are |
+| [[project-designer\|project designer]] | AI curriculum-quality project design |
+| [[idea-tester\|idea tester]] | automated idea validation and market research |
+| [[b2b-competitive-analysis\|B2B competitive analysis]] | automated competitive intelligence grinder |
+| [[stress-testing-suite\|stress testing suite]] | agent + human beta testing platform |
+| [[foss-management\|FOSS management]] | open source software discovery with auto-testing |
+| [[pain-point-builder-marketplace\|pain point marketplace]] | matching user problems with FOSS builders |
+| [[optimize-computers\|computer optimizer]] | cross-OS performance optimization tool |
+| [[choice-visualizer\|choice visualizer]] | visualize all possible choices in a moment |
+| [[simulink-alternative\|simulink alternative]] | open, portable signal processing GUI |
+| [[idea-extraction-system\|core idea extraction]] | generalized abstraction across modalities |
+| [[ai-conversationalist\|AI conversationalist]] | AI that mimics specific people's conversation style |
+| [[ai-onboarding\|AI onboarding]] | system to get people started with AI tools |
+| [[llm-behavior-improvement\|LLM behavior improvement]] | making LLMs more reliable and context-aware |
+| [[agents-md-research\|AGENTS.md research]] | prompt engineering optimization research |
+| [[llm-physical-intuition\|LLM physical intuition]] | can LLMs reason about physical space? |
+| [[flapping-airplanes\|flapping airplanes]] | AI training efficiency research directions |
+| [[task-optimization-game\|task optimization game]] | gamified task scheduling |
+| [[intelligence-development\|intelligence development]] | startup to systematically develop cognition |
+| [[consciousness-for-students\|student consciousness]] | helping students become more self-aware |
+| [[motivation-education\|motivation in education]] | analysis of student motivation as core education problem |
+| [[invoking-thoughts\|invoking thoughts]] | sensory-emotional habit triggering |
+| [[cookedness-tracker\|cookedness tracker]] | periodic self-check for focus |
+| [[intentionality-camp\|intentionality camp]] | intensive program for making people "cracked" |
+| [[generational-disconnect\|generational disconnect]] | bridging the gap between generations |
+| [[ultimate-predictor\|ultimate predictor]] | ML model for ultimate frisbee point prediction |
+| [[nose-device\|nose device]] | olfactory device for dad |
+| [[outdoor-work-setup\|outdoor work setup]] | workspace for fresh air while working |
+| [[dense-info-generator\|dense info generator]] | generate comprehensive topic briefings |
+| [[quality-search\|quality content search]] | search that filters for high-quality content |
+| [[ai-agent-reply\|AI agent reply]] | AI that replies on your behalf across platforms |
+| [[acoustic-drone-detection\|acoustic drone detection]] | audio scene classification on MCUs for defense/conservation |
+| [[spec-driven-dev\|spec-driven dev kit]] | research-plan-implement pipeline for coding agents |
+| [[ui-flow\|UI flow]] | contextual software coach — screen-watching app tips |
+| [[hard-docs-writer\|hard docs writer]] | auto-document confusing codebase parts for AI agents |
+| [[eeg-artifact-rejection\|EEG artifact rejection]] | self-supervised neural signal cleaning |
+| [[symbolic-regression\|symbolic regression]] | discovering mathematical expressions from data |
+| [[context-window-optimizer\|context window optimizer]] | tooling for agent context management |
+| [[predictive-maintenance-sensors\|predictive maintenance sensors]] | embedded ML for equipment anomaly detection |
+| [[embedding-tone-interpolation\|embedding tone interpolation]] | separate meaning and style in embedding space for writing |
+| [[robotic-arm-assistant\|robotic arm assistant]] | alexa with arms — physical home automation |
+| [[conversations-recorded\|conversations recorded]] | share clips of actual conversations at events |
+| [[culture-fingerprint\|culture fingerprint]] | visual graphic of personal/company culture profile |
+| [[math-dopamine\|math dopamine loops]] | dopamine engineering for math education |
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+---
+title: ingestion log
+type: log
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# ingestion log
+
+## 2026-04-10 — initial ingestion
+
+**sources processed:** 137 files across 3 origins
+- ideaflow: 110 files (tagged #projectidea, #newprojectidea, #bigprojectidea)
+- apple-notes: 20 files (Archived/build folder + archived root)
+- obsidian: 7 files (home/ideas/ folder)
+
+**pages created:** 74 idea pages, 8 cluster pages, 1 index
+
+**ideas by status:**
+- built: 6 (OnCue, Pause, convo-flow, connection hub, precision description engine, keystroke music)
+- explored: 10 (brain rewinder, sensor capturer, smell resetter, universal habits, axon, learning suite, discord connections mapper, referral hiring, always-on AI assistant, universal agentic searcher)
+- raw: 58
+
+**merges performed:**
+- brain rewinder: ideaflow + apple-notes potential-projects
+- sensor capturer: ideaflow + apple-notes sensor-capturer + build-notes
+- universal habits: 6 ideaflow notes + apple-notes potential-projects
+- learning suite: 3 ideaflow notes + apple-notes potential-projects
+- discord connections mapper: 4 ideaflow notes (linked notes chain)
+- universal data capturer: ideaflow + apple-notes potential-projects
+- oncue: apple-notes oncue + tellwell
+- wifi client: apple-notes + ideaflow
+- several others merged with apple-notes potential-projects-archived (which contained strikethrough copies of ideaflow ideas)
+
+**clusters identified:**
+1. memory and context tools (9 ideas)
+2. social network and connections (7 ideas)
+3. learning and education (10 ideas)
+4. habits and productivity (8 ideas)
+5. AI tooling and research (10 ideas)
+6. hardware and wearables (8 ideas)
+7. hiring and work tools (7 ideas)
+8. search and discovery (6 ideas)
+
+**obsidian sources:**
+- song.md: merged into keystroke-music (AI songwriting interest)
+- motivation.md: standalone page (motivation-education)
+- disconnection_between_generations.md: standalone page (generational-disconnect)
+- game_ideas.md: merged into task-optimization-game
+- domain_names.md: not ingested (list of domain name ideas, no project concept)
+- phone_shortcuts.md: not ingested (personal shortcuts, not a project idea)
+- WRC_stuff.md: not ingested (website maintenance tasks, not an idea)
+
+**notes:**
+- apple-notes potential-projects-archived.md contained strikethrough copies of many ideaflow notes, confirming those ideas were moved from apple notes to ideaflow
+- many ideaflow notes reference linked notes by ID (e.g., [linked note: JMXS1vOOYr]) — these were matched to other ideaflow notes by ideaflow_id and merged accordingly
+
+## 2026-04-10 — google sheets ideas ingestion
+
+**source processed:** sources/google-sheets-ideas.md (645 lines, 4 tabs)
+- project_ideas: ~85 ideas with originality/excitement scores, MVP estimates, competitive analysis
+- ranked_by_tech_depth: same ideas ranked by technical depth with tier labels (DO THIS, STRONG, WRONG FIT, ARCHIVE)
+- Blue Ocean Analysis: 12 ideas evaluated for market positioning
+- best: harrison's curated top picks
+
+**summary created:** wiki/summaries/google-sheets-ideas.md
+
+**existing pages enriched with spreadsheet data:** 38 pages
+- added originality scores, excitement scores, MVP time estimates, competitive landscape analysis, tech depth rankings, tier labels, and blue ocean positioning
+- data added as "spreadsheet evaluation" blocks in compiled truth sections + timeline entries
+
+**new pages created:** 16 ideas not present in other sources
+- acoustic-drone-detection (DO THIS, tech depth 9/10, EMERGING)
+- spec-driven-dev (DO THIS, tech depth 8/10, RED OCEAN)
+- ui-flow (8/10 originality, 9/10 excitement)
+- hard-docs-writer (STRONG, tech depth 7/10)
+- eeg-artifact-rejection (NEAR BLUE, high feasibility)
+- symbolic-regression (EMERGING, high feasibility)
+- context-window-optimizer (EMERGING, medium feasibility)
+- cognitive-foom (STRONG, tech depth 9/10)
+- predictive-maintenance-sensors (STRONG, tech depth 8/10)
+- ppg-biomarker-wearable (best picks list)
+- embedding-tone-interpolation (best picks list)
+- robotic-arm-assistant (WRONG FIT, needs scoping)
+- conversations-recorded (content/community concept)
+- culture-fingerprint (visualization concept)
+- math-dopamine (WRONG FIT, content/product focus)
+- school-third-space (community design concept)
+
+**key findings from spreadsheet:**
+- top 5 [DO THIS] tier: acoustic drone detection, BCI/read-imprinting device, agent-based simulation, spec-driven dev kit, personalized medicine
+- highest excitement (10/10): brain rewinder, articulate/precision description engine
+- only true BLUE OCEAN: N-of-1 causal inference engine (personalized medicine variant)
+- NEAR BLUE with high feasibility: EEG artifact rejection, content quality scorer
+- best picks emphasize: audio classification on MCUs, smell necklace, data selling, embedding interpolation, computer optimizer, PPG sensors
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+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- hardware
+- defense
+- embedded
+- ml
+- audio
+title: acoustic drone detection
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# acoustic drone detection
+
+audio scene classification running on microcontrollers (MCUs) — the core premise is detecting drones, aircraft, or wildlife by sound without requiring a networked computer. the motivating insight is that visual detection fails at night or in dense foliage, but acoustic signatures are reliable and can be processed entirely on cheap embedded hardware. defense applications include perimeter monitoring and early warning; conservation applications include detecting illegal poaching aircraft in wildlife reserves.
+
+the hard technical problem is running a capable ML model on constrained hardware — MCUs have kilobytes of RAM and no GPU. this pushes toward techniques like quantization, knowledge distillation, and TinyML frameworks (TensorFlow Lite Micro, Edge Impulse). the classification pipeline: raw audio → spectrogram features → lightweight CNN or RNN → class label. the drone-vs-background distinction requires good negative examples (wind, insects, vehicles) to avoid false positives, which makes dataset construction a meaningful research contribution. similar embedded ML challenges appear in [[predictive-maintenance-sensors|predictive maintenance sensors]] and [[eeg-artifact-rejection|EEG artifact rejection]].
+
+this sits at the intersection of the [[cluster-hardware-wearable|hardware and wearables]] cluster and serious ML research — it is not a typical web app, which is part of why it scores highly on the spreadsheet evaluation. the defense/conservation dual-use angle gives it real-world traction.
+
+related: [[agent-simulation|agent-based simulation]]
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+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- ai
+- simulation
+- research
+- software
+title: agent-based simulation
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# agent-based simulation
+
+a platform for running large-scale agent simulations — populations of AI agents with defined behavior rules interacting in an environment. the core use case is social and economic modeling: what happens when you simulate a city, a market, an election, or an epidemic with agents that have heterogeneous goals and information? the value is generating synthetic data for hypotheses that can't be tested in the real world, or building intuition about emergent behavior.
+
+the technical stack would need to handle massive parallelism efficiently — thousands to millions of agents running simultaneously. LLM-backed agents are increasingly feasible (see the Stanford "generative agents" paper), but expensive at scale; the interesting engineering problem is the right mix of lightweight rule-based agents and more expensive reasoning agents depending on the role they play. a platform-level abstraction would let researchers define agent schemas, environments, and interaction protocols without rebuilding the infrastructure. this connects naturally to the [[context-window-optimizer|context window optimizer]] challenge — when each agent has its own context, managing what they know and remember becomes a core systems problem.
+
+related: [[task-optimization-game|task optimization game]], [[me-model|me model]], [[cluster-ai-tools|AI tooling research]]
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+---
+first_captured: 2026-02-13
+sources:
+- sources/ideaflow/2026-02-13_resaerch-project-idea.md
+status: raw
+tags:
+- ai
+- research
+- prompting
+title: AGENTS.md optimization research
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# AGENTS.md optimization research
+
+research question: does length of a command in AGENTS.md relative to other commands correlate with the model's ability to recall and use it? a research project on prompt engineering optimization — understanding how the structure and relative weight of instructions affects LLM behavior.
+
+connects to the [[llm-behavior-improvement|LLM behavior improvement]] cluster for the broader goal of making AI tools work better.
+
+---
+
+## timeline
+
+- [2026-02-13] captured — AGENTS.md length vs recall correlation
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+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- ai
+- agents
+- communication
+- software
+title: ai agent reply
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# ai agent reply
+
+an AI that replies on your behalf across platforms — LinkedIn DMs, emails, Discord, WhatsApp — using your voice, context, and preferences. the core problem it solves is the asymmetry between the volume of incoming messages and the time available to thoughtfully respond. you set intent ("be warm but brief", "follow up if they mention X") and the agent handles the execution, escalating only when a decision is required.
+
+the hardest part is fidelity: the replies need to sound like you, not like generic AI. this directly depends on the [[me-model|me model]] — a fine-tuned model trained on your actual communication patterns. without that, the output is generic and potentially harmful to relationships. the second hard problem is trust: knowing which messages to auto-reply versus surface. a tiered system makes sense — fully automated for low-stakes messages, draft-for-review for important ones, immediate escalation for anything flagged. connects to [[always-on-ai-assistant|always-on AI assistant]] for the broader ambient computing vision where AI handles more of your communication overhead.
+
+related: [[ai-conversationalist|AI conversationalist]], [[cross-platform-bots|cross-platform notification bots]], [[cluster-ai-tools|AI tooling and research]]
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+---
+first_captured: 2026-03-28
+sources:
+- sources/ideaflow/2026-03-28_right-now-ai-is-kind-a-slog-to.md
+status: raw
+tags:
+- ai
+- conversation
+- therapy
+- software
+title: AI conversationalist
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# AI conversationalist
+
+right now AI is "kind of a slog to talk to." configure a model that accurately mimics how specific people talk and does good conversation. could be useful for therapy but also for inspiration and brainstorming. "the sentience company with someone is kinda doing this."
+
+depends on the [[me-model|me model]] for the personalization layer. this is the conversational complement to the [[personalized-autocomplete|personalized autocomplete]] which focuses on written output.
+
+---
+
+## timeline
+
+- [2026-03-28] captured — AI that actually talks like a real person
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+---
+first_captured: 2026-04-08
+sources:
+- sources/ideaflow/2026-04-08_create-a-system-to-onboard-people-who-cant.md
+status: raw
+tags:
+- ai
+- adoption
+- education
+title: AI onboarding system
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+> stub — needs expansion
+
+# AI onboarding system
+
+a system to onboard people who cannot use cloud/AI tools, or notify those who have not heard of them or mistrust them. convince them through targeted use cases that would be personally relevant, or through outrage at what they are missing.
+
+---
+
+## timeline
+
+- [2026-04-08] captured — AI adoption for the underserved
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+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: explored
+tags:
+- ai
+- memory
+- wearable
+- context
+title: always-on ai assistant
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# always-on ai assistant
+
+a 24/7 listening, transcribing, and context-aware AI companion — it is always recording your environment, converting speech to text, and building a running log of everything you say and hear. the core value proposition: you never lose a thought, a conversation, a name, a number, or a commitment again. the device closest to this vision is Limitless (formerly Rewind), but the idea is for something more ambient, lower friction, and deeply integrated with a personal AI layer.
+
+the privacy tradeoff is real and has to be confronted head-on. always-on recording is technically possible (the hardware exists — AirPods are already near your ears all day) but requires explicit design around consent, local-first processing, and strong access controls. the most compelling architecture is: local transcription on device → local embedding and indexing → only surface a query to the cloud when you explicitly ask. this keeps the raw audio and transcripts on your hardware. the stored context feeds into [[axon|axon]] for structured personal context and [[life-search|life search]] for querying what you said and heard.
+
+related: [[brain-rewinder|brain rewinder]], [[episodic-memory-builder|episodic memory builder]], [[me-model|me model]], [[ai-agent-reply|AI agent reply]], [[cluster-memory-and-context|memory and context]]
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+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: explored
+tags:
+- ai
+- memory
+- context
+- agents
+- software
+title: axon
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# axon
+
+axon is a personal memory and context layer that sits between your life's data and AI agents. the name is apt — axons are the signal-transmitting fibers of neurons, and this is the system that transmits personal context to wherever it needs to go. the core problem: every AI tool you use starts from zero. it does not know who you are, what you have built, what you care about, or what you did last week. axon is the persistent layer that fixes this.
+
+the architecture is a structured store of personal context — not raw transcripts or files, but synthesized, indexed, and queryable facts about you. things like: "Harrison is working on X project," "he knows a friend, who is interested in X," "he has been thinking about acoustic drone detection for 3 months." agents query axon before acting, which means they are not starting from scratch. the [[always-on-ai-assistant|always-on AI assistant]] feeds raw material into axon; [[life-search|life search]] is the human-facing query interface; the [[me-model|me model]] is the personalization layer that interprets the context. axon is the data structure between them.
+
+this is the most important infrastructure idea in the [[cluster-memory-and-context|memory and context]] cluster — it is not a product so much as a substrate that makes other products better. think of it as a personal knowledge graph that grows over time. the challenge is schema design: how do you represent context in a way that is flexible enough to accommodate anything but structured enough to be queryable and useful? the [[universal-data-capturer|universal data capturer]] addresses the raw ingestion problem; axon is the layer above that.
+
+related: [[context-window-optimizer|context window optimizer]]
\ No newline at end of file
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index 0000000..4c0f66c
@@ -0,0 +1,25 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- software
+- business
+- research
+- ai
+title: b2b competitive analysis
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# b2b competitive analysis
+
+an automated competitive intelligence grinder for B2B companies — a tool that continuously monitors competitors, synthesizes what is changing, and delivers actionable intelligence. the problem it solves: competitive research is tedious, time-consuming, and usually done once rather than continuously. companies end up surprised by competitor moves because nobody's job it is to watch for them, or the process is too manual to keep up.
+
+the pipeline would crawl and index competitor websites, pricing pages, job postings, press releases, G2/Capterra reviews, LinkedIn hiring signals, and social media. changes get flagged and synthesized into structured intelligence: "Competitor X launched a new enterprise tier last week," "they are hiring 3 ML engineers suggesting a model quality push," "their G2 reviews mention latency issues increasing." this requires careful deduplication, source ranking, and change detection — not just raw scraped content but a diff engine that surfaces meaningful changes over time. technically interesting because it combines structured monitoring with LLM synthesis. connects to [[universal-agentic-searcher|universal agentic searcher]] for the deep research pipeline.
+
+the business model is straightforward: SaaS subscription for B2B companies that cannot afford a dedicated analyst team. the [[consulting-software|consulting business]] framing also applies here — you could deliver this as a managed service first and productize later. sits in the [[cluster-hiring-work|hiring and work tools]] cluster alongside [[b2b-competitive-analysis]] because B2B sales teams and product teams are the primary users.
+
+related: [[idea-tester|idea tester]]
\ No newline at end of file
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index 0000000..a5e1bb6
@@ -0,0 +1,35 @@
+---
+first_captured: 2025-12-22
+sources:
+- sources/ideaflow/2025-12-22_brain-rewinder-to-remember-previous-thoughts-probably-str.md
+- sources/apple-notes/archived/potential-projects-archived.md
+- sources/google-sheets-ideas.md
+status: explored
+tags:
+- memory
+- hardware
+- wearable
+- bci
+title: brain rewinder
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# brain rewinder
+
+a device or system that helps you remember previous thoughts — the experience of losing a thought mid-stream and wanting to rewind your brain to retrieve it. the core insight is that existing solutions like Limitless and Plaud Note are device-based and record externally, but the ideal solution would either be a very cheap/easy device or a non-device approach entirely.
+
+one route explored is sensory triggering — using smells, sounds, or visuals to invoke prior mental states. smell-based recall has some research backing (Harvard Mind & Mood), though there is a risk of false recall ("might be cooked if you hallucinate the thing and ur sad"). AlterEgo from MIT was also considered as a silent speech interface that might bridge the gap.
+
+**spreadsheet evaluation:** originality 9/10, excitement 10/10 (highest tier). competitive landscape: academic research exists on olfactory memory reactivation (ACM papers, wearable prototypes), Rewind.ai records screen/audio but is visual-log-based, no consumer product systematically uses sensory triggers for thought retrieval. MVP estimated at 4-8 weeks — could start as a simple app associating scents/sounds with contexts and replaying them as recall triggers, though hardware adds complexity. tech depth ranked 6/10, labeled [WRONG FIT] for a 2-month build ("cool concept but no clear technical path in 2 months — what would you even build?"). blue ocean analysis: BLUE OCEAN positioning with medium 2-month feasibility.
+
+this connects to the broader [[episodic-memory-builder|episodic memory builder]] idea and to [[sensor-capturer|sensory capture]] more generally. the [[universal-data-capturer|universal data capturer]] could also serve as infrastructure for this — logging enough context that an AI could reconstruct what you were thinking about. the [[always-on-ai-assistant|always-on AI assistant]] approach (always listening, always transcribing) may be the most practical path.
+
+---
+
+## timeline
+
+- [2025-12-22] initial capture in ideaflow — explored device vs non-device approaches, sensory routes, existing products
+- [2025-12-22] also captured in apple notes potential-projects-archived (strikethrough, moved to ideaflow)
+- [2026-04-10] google sheets evaluation — 9/10 originality, 10/10 excitement, BLUE OCEAN, [WRONG FIT] for 2mo scope
\ No newline at end of file
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@@ -0,0 +1,27 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- browser
+- extension
+- utility
+- software
+title: browser autocomplete editor
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# browser autocomplete editor
+
+an extension to customize browser autocomplete entries — for example, forcing discord.com/app over discord.com. nothing like this exists; Redirector and Requestly act after navigation. Chrome's Omnibox API doesn't expose autocomplete editing. entirely novel concept but may require creative workarounds since browsers don't expose this natively. could approximate with URL redirect + history manipulation.
+
+**spreadsheet evaluation:** originality 10/10 (highest possible — nothing exists), excitement 6/10, MVP 1-2 weeks. tech depth 2/10, labeled [ARCHIVE] ("tiny extension"). the main risk is technical feasibility — Chrome may not allow true autocomplete editing.
+
+---
+
+## timeline
+
+- [2026-04-10] captured from google sheets — 10/10 originality, 6/10 excitement, [ARCHIVE] tier
\ No newline at end of file
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index 0000000..690517b
@@ -0,0 +1,23 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- social
+- education
+- content
+- software
+title: build/learn in public
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# build/learn in public
+
+a low-friction tool for crossposting across all social platforms — you write once and it publishes to Twitter/X, LinkedIn, Instagram, Bluesky, and whatever else you use. the idea targets builders and learners who want to document their process publicly without the overhead of reformatting content for each platform's norms and character limits. build-in-public has become a legit growth strategy for indie developers and researchers, but the friction of maintaining multiple platforms kills most attempts before they start.
+
+the interesting design challenge is the "once" part. content doesn't translate trivially across platforms — Twitter wants short takes, LinkedIn wants professional framing, Instagram needs an image. a smart version wouldn't just duplicate: it would reformat intelligently. the markdown source becomes a tweet thread, a LinkedIn post, and an Instagram caption, each adapted to context. this requires either LLM-assisted reformatting with a human review step, or platform-specific templates the user can tweak. the [[embedding-tone-interpolation|embedding tone interpolation]] idea is relevant here — separating content from voice so you can vary formality by platform without losing your meaning. connects to [[cross-platform-bots|cross-platform notification bots]] which is the inbound version of the same platform-integration infrastructure.
+
+related: [[ai-agent-reply|AI agent reply]], [[cluster-habits-productivity|habits and productivity]], [[cluster-social-networking|social networking]]
\ No newline at end of file
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index 0000000..b8459a2
@@ -0,0 +1,28 @@
+---
+first_captured: 2026-03-26
+sources:
+- sources/ideaflow/2026-03-26_somehow-visualize-all-the-choices-i-could-make.md
+status: raw
+tags:
+- decision-making
+- visualization
+- software
+title: choice visualizer
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+> stub — needs expansion
+
+# choice visualizer
+
+visualize all the choices you could make in a given moment. an interesting exploration of agency — making the possibility space visible rather than just the default path.
+
+related: [[decision-helper|decision helper]]
+
+---
+
+## timeline
+
+- [2026-03-26] captured — agency visualization
\ No newline at end of file
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index 0000000..5cb9da7
@@ -0,0 +1,27 @@
+---
+ideas:
+- overnight-app-grinder
+- agents-md-research
+- llm-behavior-improvement
+- llm-physical-intuition
+- flapping-airplanes
+- context-window-optimizer
+- spec-driven-dev
+- hard-docs-writer
+- ai-agent-reply
+- ai-onboarding
+- ai-conversationalist
+tags:
+- ai-tools
+title: ai tooling and research
+type: idea-cluster
+visibility: public
+---
+
+# ai tooling and research
+
+the largest cluster by count, covering the full spectrum from fundamental research to practical developer tooling. the unifying theme: making AI systems more capable, reliable, and useful — whether by improving the models themselves ([[llm-behavior-improvement|LLM behavior improvement]], [[flapping-airplanes|AI training efficiency]]) or by building the infrastructure around them ([[spec-driven-dev|spec-driven dev kit]], [[context-window-optimizer|context window optimizer]], [[hard-docs-writer|hard docs writer]]).
+
+the most actionable ideas in this cluster are the developer tooling ones: [[spec-driven-dev|spec-driven dev kit]] (rated [DO THIS] — research → plan → implement pipeline with context management) and [[overnight-app-grinder|overnight app grinder]] (autonomous coding agent manager). both reflect a meta-insight: the bottleneck for AI-assisted development is not model capability but workflow — how you structure the problem, manage context, and review outputs. [[agents-md-research|AGENTS.md optimization research]] goes even deeper, asking how instruction structure affects model recall. on the application side, [[ai-agent-reply|AI agent reply]] and [[ai-conversationalist|AI conversationalist]] both depend on [[me-model|me model]] for personalization, while [[ai-onboarding|AI onboarding]] addresses the human adoption side.
+
+the more speculative work — [[llm-physical-intuition|LLM physical intuition]] — is research-oriented and harder to scope into a 2-month project, but potentially more impactful. [[context-window-optimizer|context window optimizer]] is the connective tissue between this cluster and [[cluster-memory-and-context|memory and context tools]] — if you're building agents that work with personal context ([[axon|axon]], [[always-on-ai-assistant|always-on assistant]]), context management is a first-class engineering concern.
\ No newline at end of file
new file mode 100644
index 0000000..e1d448f
@@ -0,0 +1,24 @@
+---
+ideas:
+- universal-habits
+- invoking-thoughts
+- cookedness-tracker
+- task-scheduler
+- youre-not-behind
+- outdoor-work-setup
+- pause
+- ifttt-personal
+tags:
+- habits-productivity
+title: habits and productivity
+type: idea-cluster
+visibility: public
+---
+
+# habits and productivity
+
+the cluster around making yourself work better — less through discipline and more through good tooling and environment design. the central idea is [[universal-habits|universal habits]]: a context-aware system that adapts habits to where you are, what you're doing, and how you're feeling, rather than forcing fixed schedules. it is described as the "biggest recurring idea" and overlaps with IFTTT-style automation ([[ifttt-personal|personal IFTTT]]) for the event-driven trigger infrastructure.
+
+the built projects anchor this cluster concretely: [[pause|Pause]] is a screen break and reminder app, and the insight from [[invoking-thoughts|invoking/imprinting thoughts]] is that generic reminders are less effective than emotionally resonant ones — the interface matters as much as the timing. [[cookedness-tracker|cookedness tracker]] (periodic self-checks for focus) and [[outdoor-work-setup|outdoor work setup]] are environment hacks that target the same underlying goal: getting and staying in a productive state. [[youre-not-behind|you're not behind machine]] addresses the anxiety side of productivity, which is often the actual bottleneck.
+
+what makes this cluster interesting is that it is not primarily about task management software (there is plenty of that). the differentiated angle is context-awareness and emotional intelligence — habits that know when you are in a flow state vs. struggling, triggers that actually land. [[task-scheduler|task scheduler]] (better Morgen) handles the calendar side, but the harder problem is the motivational and psychological side. this cluster connects closely to [[cluster-learning-education|learning and education]] through [[consciousness-for-students|student consciousness]] and to [[cluster-memory-and-context|memory and context]] through the [[always-on-ai-assistant|always-on AI assistant]] as a potential habit trigger source.
\ No newline at end of file
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index 0000000..c68dbc2
@@ -0,0 +1,25 @@
+---
+ideas:
+- emg-bracelet
+- pupilometry-glasses
+- nose-device
+- sensor-capturer
+- predictive-maintenance-sensors
+- acoustic-drone-detection
+- eeg-artifact-rejection
+- robotic-arm-assistant
+- instant-blanket
+tags:
+- hardware-wearable
+title: hardware and wearables
+type: idea-cluster
+visibility: public
+---
+
+# hardware and wearables
+
+physical devices for sensing, interaction, and utility — a cluster that reflects a consistent pull toward hands-on building at the hardware/software boundary. the ideas span from human-computer interaction (hands-free control via [[emg-bracelet|EMG bracelet]] or [[pupilometry-glasses|pupilometry glasses]]) to signal sensing ([[sensor-capturer|sensory capturer]], [[eeg-artifact-rejection|EEG artifact rejection]]) to environmental detection ([[acoustic-drone-detection|acoustic drone detection]], [[predictive-maintenance-sensors|predictive maintenance sensors]]).
+
+the highest-rated ideas in this cluster are the ML-on-hardware ones: acoustic drone detection and predictive maintenance sensors all scored [STRONG] on the spreadsheet because they combine embedded systems engineering with meaningful ML research. the common pattern: novel sensor signal → custom dataset → trained model → edge deployment. this is technically demanding in an interesting way — TinyML, quantization, model compression — and produces demos that are viscerally impressive. [[eeg-artifact-rejection|EEG artifact rejection]] is the most academically serious of these: self-supervised neural signal cleaning is a real open problem with research significance.
+
+the human-computer interaction ideas ([[emg-bracelet|EMG bracelet]], [[pupilometry-glasses|pupilometry glasses]], [[robotic-arm-assistant|robotic arm assistant]]) target hands-free computing from different angles. the bracelet approach is probably most practical given that Meta is working on the same thing with smart glasses wristbands, validating the space. [[sensor-capturer|sensory capturer]] and [[nose-device|nose device]] are the most exploratory — capturing sensory experiences beyond audio/video is a genuinely underdeveloped area that connects to the [[cluster-memory-and-context|memory and context]] cluster, where richer sensory capture enables richer memory recall.
\ No newline at end of file
new file mode 100644
index 0000000..b6d0347
@@ -0,0 +1,23 @@
+---
+ideas:
+- referral-hiring
+- job-tracking
+- oncue
+- ultimate-describer
+- consulting-software
+- b2b-competitive-analysis
+- comparison-engine
+tags:
+- hiring
+- business
+- software
+title: hiring and work tools
+type: idea-cluster
+visibility: public-edit
+---
+
+# hiring and work tools
+
+ideas spanning both sides of the hiring process: for job seekers ([[oncue|OnCue]] for interview prep, [[college-essay-grader|essay grading]]) and for employers ([[referral-hiring|referral platform]], [[job-tracking|application tracking]], [[b2b-competitive-analysis|competitive analysis]]). the [[ultimate-describer|precision description engine]] serves both sides by making role descriptions and self-descriptions more precise. the [[consulting-software|consulting business]] is a meta-idea about selling software creation as a service.
+
+the most developed idea is [[oncue|OnCue]], which was built and presented. the [[referral-hiring|referral platform]] has the strongest business case ("companies are so desperate for talent"). the [[comparison-engine|comparison engine]] provides the evaluation infrastructure used across hiring and other decision domains.
\ No newline at end of file
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index 0000000..2b0c568
@@ -0,0 +1,22 @@
+---
+ideas:
+- tutor-platform
+- consciousness-for-students
+- motivation-education
+- intelligence-development
+- math-dopamine
+- college-essay-grader
+tags:
+- learning-education
+title: learning and education
+type: idea-cluster
+visibility: public
+---
+
+# learning and education
+
+the cluster around reimagining how people learn — covering tools, human infrastructure, and the psychological foundations that make learning actually work. the ideas are stratified by layer: [[tutor-platform|exceptional tutor platform]] is tools and services, [[motivation-education|motivation in education]] and [[consciousness-for-students|student consciousness]] are diagnoses of why learning fails, and [[intelligence-development|intelligence development startup]] and [[math-dopamine|math dopamine loops]] are interventions at the motivational and cognitive level.
+
+the most interesting thread here is that the tooling ideas ([[college-essay-grader|college essay grader]]) are only mildly differentiated from what exists, while the ideas that address the underlying problem — why students do not want to learn, how to engineer genuine motivation — are more novel. [[math-dopamine|math dopamine loops]] takes this seriously: it is not about better math content but about dopamine engineering, making the feedback loop of mathematical insight feel as addictive as a game. [[consciousness-for-students|student consciousness]] is similarly root-cause-oriented — helping students become more self-aware about their own cognitive patterns and motivations.
+
+this cluster connects to [[cluster-habits-productivity|habits and productivity]] through [[universal-habits|universal habits]] (learning as a habit system) and to [[cluster-ai-tools|AI tooling]] through the AI-augmented learning direction. [[intentionality-camp|intentionality camp]] is the most ambitious expression of the educational vision: an intensive program designed to make people fundamentally more capable. the [[tutor-platform|tutor platform]] is the most commercially obvious path. the common thread is that the quality of attention and motivation matters more than the quality of content — a high bar for the educational experience, not just the material.
\ No newline at end of file
new file mode 100644
index 0000000..3572267
@@ -0,0 +1,27 @@
+---
+ideas:
+- brain-rewinder
+- episodic-memory-builder
+- axon
+- me-model
+- life-search
+- always-on-ai-assistant
+- sensor-capturer
+- universal-data-capturer
+tags:
+- memory
+- ai
+- context
+- wearable
+title: memory and context tools
+type: idea-cluster
+visibility: public
+---
+
+# memory and context tools
+
+the deepest recurring theme across all idea sources: how to capture, store, retrieve, and use personal context and memory. this cluster represents a full stack from hardware capture ([[sensor-capturer|sensory devices]], [[always-on-ai-assistant|always-on recording]]) through storage and indexing ([[axon|axon]], [[me-model|me model]], [[universal-data-capturer|data capturer]]) to retrieval and training ([[life-search|life search]], [[brain-rewinder|brain rewinder]], [[episodic-memory-builder|episodic memory builder]]).
+
+the unifying insight: humans lose enormous amounts of valuable information — thoughts, conversations, experiences, context — and technology should help retain and resurface it. the approaches range from passive capture (always listening) to active training (episodic recall games) to sensory triggering (smell/sound-based memory invocation).
+
+the most practical near-term path is probably the always-on AI assistant that transcribes and indexes everything, combined with a good search interface. the hardware-intensive ideas (sensor capturer, brain wave devices) are longer-term.
\ No newline at end of file
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index 0000000..e572198
@@ -0,0 +1,20 @@
+---
+ideas:
+- universal-agentic-searcher
+- quality-search
+- life-search
+- foss-management
+tags:
+- search
+- ai
+- curation
+title: search and discovery
+type: idea-cluster
+visibility: public
+---
+
+# search and discovery
+
+ideas about finding things better — from universal research ([[universal-agentic-searcher|agentic searcher]]) to personal memory ([[life-search|life search]]) to quality curation ([[quality-search|quality content search]]) to specific domains ([[foss-management|FOSS tools]]).
+
+the common insight: existing search (Google, LLM web search, deep research) all have gaps. the [[universal-agentic-searcher|universal searcher]] proposes a 10+ step research pipeline with adaptive feedback and real-time dashboards. the [[life-search|life search]] applies similar depth to personal data — "mgrep for life." the [[quality-search|quality filter]] adds a curation layer that rates content rather than just finding it.
\ No newline at end of file
new file mode 100644
index 0000000..1d66a90
@@ -0,0 +1,22 @@
+---
+ideas:
+- vibe-matcher
+- discord-connections-mapper
+- connection-hub
+- conversations-recorded
+- culture-fingerprint
+- info-exchanger
+tags:
+- social-networking
+title: social network and connections
+type: idea-cluster
+visibility: public
+---
+
+# social network and connections
+
+ideas about connecting people better — not building yet another social network, but fixing specific failure modes of existing social infrastructure. the recurring problem: online platforms are good at broadcasting but bad at actual connection. you accumulate followers, contacts, and Discord server members, but the depth of those relationships stays shallow. this cluster targets the connection quality problem from multiple angles.
+
+[[vibe-matcher|vibe matcher]] and [[discord-connections-mapper|discord connections mapper]] take the data-driven approach — ML-powered compatibility matching and relationship graph visualization to surface who you should know better. [[info-exchanger|info exchanger]] tackles the cold-start problem at conversation initiation: a quick paired Q&A before meeting someone to reduce awkwardness. [[conversations-recorded|conversations recorded]] addresses what happens after a conversation — capturing and sharing clips so the energy of a real in-person interaction can propagate further. [[culture-fingerprint|culture fingerprint]] is more diagnostic: a visual representation of a person's or community's actual values and norms.
+
+[[connection-hub|connection hub]] is the most built of these — a real Discord server for my school's community, which gives it ground truth about what community infrastructure actually looks like in practice. the insights from running a real community feed back into the more speculative ideas. this cluster has the closest relationship to [[cluster-habits-productivity|habits and productivity]] through consistency in maintaining relationships, and to [[cluster-memory-and-context|memory and context]] because rich connection requires remembering things about people — their history, interests, and context — which is exactly what [[axon|axon]] and [[me-model|me model]] are designed to store.
\ No newline at end of file
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index 0000000..9feae73
@@ -0,0 +1,25 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- software
+- education
+- ml
+- writing
+title: college essay grader
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# college essay grader
+
+a tool that grades college essays using vector embedding distance as a proxy for quality. the core technical insight: you can measure how close an essay's embedding is to high-quality exemplar essays (accepted essays, award-winning writing) versus low-quality exemplars, and use that distance as a quality signal. this is a more principled approach than rubric-based grading, because it captures holistic quality that is hard to reduce to discrete dimensions.
+
+the practical implementation would be: collect a labeled dataset of college essays (accepted/rejected, scored by counselors), embed them with a quality text model, and train a scoring model on top of those embeddings. the scores could be broken down by sub-dimensions — distinctiveness, voice, structure, relevance — each with their own embedding cluster. a key product decision: do you show a score or show "your essay sounds like these other essays" comparisons? the comparison view might be more actionable because it surfaces the fix, not just the diagnosis. connects to [[ultimate-describer|precision description engine]] for the making-language-more-precise angle and to [[writing-tools|writing tools suite]] for the broader writing quality product space.
+
+the market context: college counseling is expensive and access to good essay feedback is highly unequal. a cheap, good-enough automated grader could meaningfully democratize college prep. it also has a clear wedge into the [[cluster-learning-education|learning and education]] cluster — college prep is high-stakes and underserved by AI tools that are either too generic (ChatGPT) or too expensive (private counselors).
+
+related: [[youre-not-behind|you're not behind machine]], [[oncue|OnCue]]
\ No newline at end of file
new file mode 100644
index 0000000..cac6cde
@@ -0,0 +1,22 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- software
+- decision-making
+- productivity
+title: comparison engine
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# comparison engine
+
+a side-by-side decision tool that accepts any media — text, images, URLs, PDFs, audio — and structures it for comparison. the core use case: you are choosing between two things (laptops, job offers, apartments, frameworks, people) and you want to understand the tradeoffs clearly rather than going back and forth between tabs. the tool would ingest both items, extract the relevant dimensions, and render a clean side-by-side with the key differences highlighted.
+
+what makes this more interesting than a spreadsheet is the media-flexibility and the automatic dimension extraction. if you paste two LinkedIn profiles, it extracts relevant comparison dimensions. if you paste two research papers, it finds where they agree and disagree. if you upload two product spec sheets, it builds a feature comparison matrix. the LLM does the structuring work, you do the deciding. this is adjacent to [[decision-helper|decision helper]] (which is more therapist-style, helping you understand what you actually want) and [[choice-visualizer|choice visualizer]] (which maps the full possibility space). comparison engine is the most focused: two specific options, side by side, right now.
+
+related: [[cluster-hiring-work|hiring and work tools]], [[b2b-competitive-analysis|B2B competitive analysis]]
\ No newline at end of file
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index 0000000..78ce017
@@ -0,0 +1,25 @@
+---
+first_captured: 2026-01-01
+sources:
+- sources/ideaflow/2026-01-01_things-we-can-do.md
+status: built
+tags:
+- social
+- community
+- discord
+- education
+title: connection hub
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# connection hub
+
+connection hub is a Discord server built for my school's community — the first actually-built project in the [[cluster-social-networking|social networking cluster]]. the premise is that high school communities are fragmented: group chats by grade, activity silos, no neutral space where cross-grade or cross-interest connections can happen spontaneously. a well-structured Discord with intentional channel design can serve as community infrastructure — announcements, interest channels, random social content, resource sharing.
+
+being the person who builds and runs community infrastructure gives you insight that no amount of theorizing about social networks can. you see what channels go dead immediately (most of them), what keeps people coming back (ambient social presence, low-stakes posting norms), how moderation actually works, and what "connection" means in practice versus in design docs. these observations are ground truth for more ambitious ideas like [[vibe-matcher|vibe matcher]] (algorithmic matching), [[discord-connections-mapper|discord connections mapper]] (relationship graph visualization), and [[info-exchanger|info exchanger]] (conversation bootstrapping).
+
+the project also surfaces a real tension in community building: you can optimize for breadth (many members, low engagement) or depth (fewer members, high engagement), and the structure you choose encodes that choice implicitly. the school as a community is small enough that knowing everyone is theoretically possible — which makes it a useful petri dish for testing what community tools actually add versus what would happen organically anyway.
+
+related: [[conversations-recorded|conversations recorded]], [[culture-fingerprint|culture fingerprint]]
\ No newline at end of file
new file mode 100644
index 0000000..d50044e
@@ -0,0 +1,23 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- education
+- cognition
+- self-awareness
+- psychology
+title: student consciousness
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# student consciousness
+
+helping students become more self-aware — specifically, aware of their own cognitive and motivational patterns. the core diagnosis: most academic struggles are not about intelligence or even effort, but about metacognition. students do not know why they are procrastinating, when they are in a productive state versus burning out, what kind of tasks they actually do well on, or what is genuinely interesting to them versus what they have been told is interesting. making this visible is the intervention.
+
+the tooling could be as simple as structured reflection prompts that ask the right questions at the right moments: after finishing a study session, what happened? when did you lose focus and why? what did you actually find interesting today? over time, patterns emerge that the student can act on — not because someone told them to work harder, but because they actually understand their own patterns. this connects to [[cookedness-tracker|cookedness tracker]] (periodic self-checks for focus) and [[invoking-thoughts|invoking thoughts]] as more tactical implementations of the same metacognitive awareness goal.
+
+related: [[motivation-education|motivation in education]], [[consciousness-for-students|student consciousness]], [[intelligence-development|intelligence development startup]], [[intentionality-camp|intentionality camp]], [[cluster-learning-education|learning and education]]
\ No newline at end of file
new file mode 100644
index 0000000..d328ea9
@@ -0,0 +1,23 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- software
+- business
+- consulting
+- b2b
+title: consulting / custom software
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# consulting / custom software
+
+a Palantir-style audit/plan/make software business — you go into a company, audit what software they have and what they actually need, propose a plan, then build it. the model works because most companies are underserved by off-the-shelf software for their specific workflows, and the cost to build custom tooling has dropped dramatically with AI-assisted coding. what used to require a 6-month engagement can now be scoped, built, and deployed in weeks.
+
+the interesting business model tension is between the consulting side (high-margin, relationship-driven, hard to scale) and the productization side (lower-margin per client but recurring revenue, scalable). the Palantir reference is deliberate — they built a specific workflow tool for every client, then productized the patterns that emerged across clients. the play here might be: start with a handful of clients in a specific vertical (e.g. small biotech firms, local schools) to build deep workflow knowledge, then productize the patterns into a SaaS product. the [[overnight-app-grinder|overnight app grinder]] and [[spec-driven-dev|spec-driven dev kit]] are directly relevant infrastructure — the faster and more reliably you can build software, the more profitable the per-client engagement.
+
+related: [[b2b-competitive-analysis|B2B competitive analysis]], [[stress-testing-suite|stress testing suite]], [[idea-tester|idea tester]], [[cluster-hiring-work|hiring and work tools]]
\ No newline at end of file
new file mode 100644
index 0000000..f939bd3
@@ -0,0 +1,23 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- ai
+- agents
+- context-engineering
+- software
+title: context window optimizer
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# context window optimizer
+
+tooling for agent context management — the problem of deciding what goes into a limited context window when agents work with large, complex knowledge bases. most agents either stuff everything in (hitting the limit, degrading quality) or use naive retrieval (top-k semantic search, which misses structural dependencies). neither approach is good. the optimizer sits between the knowledge base and the agent, managing what gets surfaced, in what order, at what point in the task.
+
+the technical research here is genuinely interesting: context window management involves tradeoffs between recency, relevance, diversity, and dependency structure. a code editing agent needs the current file, related interfaces, recent change history, and relevant documentation — and those come from different retrieval strategies. the 40% context utilization target in [[spec-driven-dev|spec-driven dev kit]] is a practical heuristic: leave room for the agent's own output and reasoning, otherwise you get truncated context that causes errors mid-task. good tooling would make this principled: profile the task type, estimate context needs, and dynamically manage the budget across a multi-step workflow.
+
+related: [[axon|axon]], [[cluster-ai-tools|AI tooling]], [[hard-docs-writer|hard docs writer]]
\ No newline at end of file
new file mode 100644
index 0000000..e6c3cdc
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- software
+- social
+title: conversations recorded
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# conversations recorded
+
+a tool for capturing and sharing clips of actual conversations at events — conferences, parties, meetups. the insight is that the most valuable thing at any social event is the actual dialogue happening, but it evaporates the moment the event ends. you leave with vague impressions instead of the specific things that were said. a lightweight way to record and clip a 30-second exchange, tag the people involved, and share it would let you actually hold on to moments that matter.
+
+the UX would need to be dead simple — probably phone-native, one tap to start a clip, one tap to stop, auto-transcribes, and lets you share a link or add it to a shared event feed. the social layer is interesting: if two people at the same event both have the app, you could stitch clips from multiple perspectives together, or create a kind of ambient oral history of the event. privacy is the hard problem — people would need to consent to being recorded, which changes the dynamic.
+
+related: [[axon|axon]], [[always-on-ai-assistant|always-on AI assistant]], [[info-exchanger|info exchanger]], [[brain-rewinder|brain rewinder]]
\ No newline at end of file
new file mode 100644
index 0000000..7320127
@@ -0,0 +1,18 @@
+---
+status: built
+tags:
+- software
+- productivity
+title: convo-flow
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# convo-flow
+
+a conversation tracking tool with statuses and color highlighting — built and shipped. the problem it solves is managing multiple ongoing conversations across different threads and contexts without losing track of where each one is. each conversation gets a status (waiting, replied, needs follow-up, done) and a color that visually communicates state at a glance, so you can see the full landscape of your social/professional threads without having to open each one.
+
+the key design insight is treating conversations like tasks — giving them lifecycle states rather than just treating them as a flat stream of messages. most chat apps optimize for recency, which buries conversations you haven't replied to yet but care about. convo-flow inverts that: priority and status, not recency, drives what you see. highlighting adds another layer — you can mark specific things someone said that you want to remember or act on.
+
+related: [[info-exchanger|info exchanger]], [[discord-connections-mapper|discord connections mapper]], [[universal-habits|universal habits]], [[oncue|OnCue]]
\ No newline at end of file
new file mode 100644
index 0000000..3b76d7f
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- productivity
+- habits
+title: cookedness tracker
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# cookedness tracker
+
+a periodic self-check system for monitoring focus and cognitive state throughout a work session. "cooked" is slang for mentally fried — can't think clearly, making dumb mistakes, spinning wheels. the problem is that you often don't notice you're cooked until you've wasted 30 minutes. a tracker that prompts you every N minutes to rate your state (1-5, or just cooked/not cooked) could surface patterns: you're always cooked after lunch, cooked after 3 hours without a break, cooked when you haven't eaten.
+
+the intervention side is as important as the detection side. knowing you're cooked is useless without a clear action — take a walk, eat something, switch tasks, stop entirely. the tracker could learn what reliably un-cooks you and suggest the right recovery based on context. connects directly to [[universal-habits|universal habits]] which is the bigger vision for context-aware behavioral interventions. also overlaps with [[pause|Pause]] (screen break reminders) though Pause is time-based while cookedness tracker is state-based.
+
+related: [[consciousness-for-students|student consciousness]], [[invoking-thoughts|invoking thoughts]]
\ No newline at end of file
new file mode 100644
index 0000000..a7412b4
@@ -0,0 +1,30 @@
+---
+first_captured: 2026-03-21
+sources:
+- sources/ideaflow/2026-03-21_cross-platform-bots.md
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- automation
+- productivity
+- software
+title: cross-platform notification bots
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# cross-platform notification bots
+
+bots that bridge platforms you do not check often. examples: weekly summary of LinkedIn activity, ping every time someone DMs on Slack. the core problem is attention fragmentation across too many platforms.
+
+this is a specific instantiation of the [[ifttt-personal|personal IFTTT]] concept and relates to the [[build-in-public|build/learn in public]] crossposter idea (which goes in the other direction — posting outward rather than pulling inward).
+
+**spreadsheet evaluation:** tech depth 4/10, labeled [WRONG FIT] ("API wiring, not deep tech"). the "agent to agent communication" variant (LinkedIn/dating/negotiation) is listed separately with tech depth 6/10 but also [WRONG FIT] ("interesting multi-agent problem but scope unclear").
+
+---
+
+## timeline
+
+- [2026-03-21] captured — LinkedIn and Slack examples
+- [2026-04-10] google sheets evaluation — [WRONG FIT] tier, tech depth 4/10
\ No newline at end of file
new file mode 100644
index 0000000..a035843
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- software
+- ai
+- communication
+title: cultural translator
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# cultural translator
+
+a tool that translates across philosophical frameworks and cultural/intellectual spheres — not just language, but the way different communities conceptualize the same idea. the gap it addresses: a rationalist and a humanities person can say the same thing in totally different vocabularies and not realize they agree, or have a productive disagreement that gets derailed by surface-level conceptual mismatch. an effective translator would take a claim from one framework and express it in another without losing the substance.
+
+the hard part is that frameworks aren't just vocabulary — they carry different epistemic assumptions, different standards of evidence, different ideas about what a "good argument" looks like. a naive translation would just swap terms, but that often produces something that feels alien or wrong to the target audience. the deeper version would involve actually mapping the conceptual structure — what counts as a claim, what supports it, how you'd falsify it — across frameworks and then re-expressing the original content in those terms.
+
+related: [[idea-extraction-system|core idea extraction]], [[dense-info-generator|dense info generator]], [[ai-conversationalist|AI conversationalist]]
\ No newline at end of file
new file mode 100644
index 0000000..24e43ea
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- software
+- social
+- visualization
+title: culture fingerprint
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# culture fingerprint
+
+a visual graphic that represents the culture profile of a person or company — a "fingerprint" that you can look at and immediately understand what they value, how they work, what they're like. the goal is to make culture legible at a glance the same way a personality test does, but with more nuance and without the pseudoscience.
+
+the input could be a combination of surveys, behavioral signals, written artifacts (emails, docs, how people communicate), and self-assessment. the output would be a visual — probably a radar chart or something more distinctive — that shows dimensions like pace, autonomy, conflict tolerance, weirdness, formality, collaboration style. the company version could be used for hiring fit assessment, and the personal version for self-understanding or finding communities where you'd thrive.
+
+related: [[vibe-matcher|vibe matcher]], [[discord-connections-mapper|discord connections mapper]], [[referral-hiring|referral-based hiring]]
\ No newline at end of file
new file mode 100644
index 0000000..8827d2b
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- social
+- data
+- marketplace
+title: data selling platform
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# data selling platform
+
+a marketplace where people can sell their personal data directly to companies that want it — instead of having it harvested by platforms without consent or compensation. the framing is flipping the current ad-tech model: right now companies take your data implicitly as the price of using free services. this would make it an explicit, consensual, paid transaction.
+
+the hard problems are: (1) what data is actually valuable enough that companies would pay for it directly — probably behavioral patterns, purchase history, health signals, demographic + psychographic profiles; (2) pricing — how do you set a market price for a person's browsing history; (3) aggregation — individual data is worth little, but if you can aggregate across thousands of consenting users into a dataset, the value goes up dramatically. the platform would need to solve both the individual-facing UX and the buyer-facing data product side.
+
+related: [[personalized-medicine|personalized medicine]], [[universal-data-capturer|universal data capturer]]
\ No newline at end of file
new file mode 100644
index 0000000..32fb854
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- ai
+- productivity
+- cognition
+title: decision helper
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# decision helper
+
+an AI tool that helps you make decisions by acting more like a therapist than a calculator — instead of giving you a pros/cons list, it asks questions to diagnose what the actual decision is, what your real values are, and what's making it feel hard. most decisions feel hard not because the options are close in utility but because there's ambiguity about what you want, fear about commitment, or conflict between different versions of yourself.
+
+the product would walk you through a structured elicitation: what are the options, what does each future look like, what are you actually afraid of, what would you regret more. the output shouldn't be "pick option A" — it should be a clearer articulation of your own values and the crux of the decision, so you can actually decide. this is distinct from [[comparison-engine|comparison engine]] which is more about side-by-side feature analysis. decision helper goes deeper into the psychological and values layer.
+
+related: [[choice-visualizer|choice visualizer]], [[universal-habits|universal habits]], [[always-on-ai-assistant|always-on AI assistant]], [[axon|axon]]
\ No newline at end of file
new file mode 100644
index 0000000..9cc6f4f
@@ -0,0 +1,31 @@
+---
+first_captured: 2026-03-21
+sources:
+- sources/ideaflow/2026-03-21_have-a-generator-of-a-dense-page-of.md
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- learning
+- ai
+- search
+- software
+title: dense info page generator
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# dense info page generator
+
+generate a dense page of info to read up on any topic. "what should I know about networking?" — covers neuroscience, deploying apps, vibe coding, whatever the topic is. the output should have nuanced takes, valuable and actionable info, sources, high quality and stress-tested learnings from blog posts and YouTube, and strategies.
+
+a key feature: a calibration system for reading level / technicality so the output matches your background. connects to the [[learning-suite|learning suite]] for the broader learning optimization vision and to the [[quality-search|quality content search]] for finding the good source material.
+
+**spreadsheet evaluation:** tech depth 7/10, labeled [STRONG] ("RAG + quality scoring + calibrated output — interesting retrieval/ranking problem, could demo well").
+
+---
+
+## timeline
+
+- [2026-03-21] captured — dense info with calibrated technicality
+- [2026-04-10] google sheets evaluation — tech depth 7/10, [STRONG] tier
\ No newline at end of file
new file mode 100644
index 0000000..b73ec38
@@ -0,0 +1,19 @@
+---
+status: explored
+tags:
+- social
+- networking
+- software
+title: discord connections mapper
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# discord connections mapper
+
+a multi-platform relationship graph that maps who you know across Discord, Twitter/X, LinkedIn, GitHub, and other platforms — and then merges them into a single unified network view. the core insight is that your real social graph is fragmented across a dozen apps, each showing you a partial view. combining them would reveal connections you didn't know existed and make your actual network legible.
+
+the technical approach would involve API scraping where possible and manual import where not, followed by an entity resolution step — figuring out that the same person appears as "jsmith" on GitHub, "johnsmith_sf" on Twitter, and "John Smith" on LinkedIn. once you have the unified graph, you can run analyses: who are your connectors, where are your network gaps, which communities do you straddle, who has the most influence in a given cluster. the Discord-first name is probably too narrow — the real value is in the cross-platform merge.
+
+related: [[culture-fingerprint|culture fingerprint]], [[vibe-matcher|vibe matcher]], [[referral-hiring|referral-based hiring]], [[connection-hub|connection hub]]
\ No newline at end of file
new file mode 100644
index 0000000..12520ea
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- social
+- hardware
+- community
+title: eco-safe community
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# eco-safe community
+
+a climate-resilient luxury community — a small intentional community designed from the ground up to be durable against climate disruption while being a genuinely great place to live. the luxury framing isn't about exclusivity for its own sake, it's about proving that climate resilience doesn't require sacrifice. if you can make it aspirational, you change the incentive structure: instead of "bear hardship for the planet" it becomes "this is just the better way to live."
+
+the design would involve serious infrastructure choices: renewable energy, water independence, food production, robust communication infrastructure, physical durability against extreme weather. the social design is equally important — intentional communities often fail on governance and conflict resolution, so the setup would need clear structures for decision-making and exit. the "luxury" aspect means the amenities need to be genuinely excellent, not just functional.
+
+related: [[intentionality-camp|intentionality camp]], [[life-guide|life guide]], [[outdoor-work-setup|outdoor work setup]]
\ No newline at end of file
new file mode 100644
index 0000000..c4bb193
@@ -0,0 +1,22 @@
+---
+status: raw
+tags:
+- hardware
+- ml
+- neuroscience
+- bci
+title: EEG artifact rejection
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# EEG artifact rejection
+
+a self-supervised approach to cleaning neural signals — specifically removing artifacts (noise from eye movements, muscle activity, electrode movement, power line interference) from EEG data without requiring labeled training data. artifact rejection is one of the biggest pain points in EEG research and BCI applications; bad cleaning loses real signal, and manual cleaning is extremely time-consuming.
+
+the self-supervised framing is the interesting technical angle. instead of training a model to classify "artifact" vs "clean" with labeled examples (which require expert annotation), you use the structure of EEG itself — temporal consistency, spatial correlations between electrodes, known frequency properties of real neural signals — as a self-supervision signal. the model learns what "clean" looks like without being told, then can detect and remove deviations. similar approaches have worked well in other time-series domains.
+
+this sits at the intersection of several interests: ML for biosignals, hardware for sensing (→ [[sensor-capturer|sensor capturer]]), and BCI more broadly (→ [[emg-bracelet|EMG bracelet]], [[pupilometry-glasses|pupilometry glasses]]). the research angle would fit well as a paper or an open-source tool — there's a clear gap in the literature for robust self-supervised artifact rejection that generalizes across datasets and electrode configurations.
+
+related: [[symbolic-regression|symbolic regression]]
\ No newline at end of file
new file mode 100644
index 0000000..007478f
@@ -0,0 +1,20 @@
+---
+status: raw
+tags:
+- software
+- ml
+- nlp
+- writing
+title: embedding tone interpolation
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# embedding tone interpolation
+
+a research/tool direction for separating meaning from style in embedding space — so you can move a piece of writing along a tone axis while keeping the content constant, or vice versa. the core insight is that text embeddings conflate what is said with how it is said. if you can disentangle them, you get fine-grained control over writing style as a continuous parameter rather than a binary switch.
+
+the technical approach would involve learning a decomposed representation: one component that captures semantic content (what the text is about, what claims it makes) and another that captures stylistic/tonal dimensions (formality, emotional valence, voice, register). once you have this, interpolation becomes a geometric operation — you move along the style axis from "formal" to "casual" while holding the content vector fixed. this has obvious applications in writing assistance, but the interesting research question is whether such a clean decomposition actually exists in embedding space or has to be imposed.
+
+related: [[ai-conversationalist|AI conversationalist]], [[writing-tools|writing tools suite]], [[personalized-autocomplete|personalized autocomplete]], [[llm-behavior-improvement|LLM behavior improvement]]
\ No newline at end of file
new file mode 100644
index 0000000..116ee77
@@ -0,0 +1,27 @@
+---
+first_captured: 2026-03-28
+sources:
+- sources/ideaflow/2026-03-28_build-better-emg-gyro-bracelet-than-meta-smart.md
+status: raw
+tags:
+- hardware
+- hci
+- wearable
+- bci
+title: EMG + gyro bracelet
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# EMG + gyro bracelet
+
+build a better EMG + gyroscope bracelet than Meta's smart glasses wristband for hands-free computer control from far away — no-mouse cursor control, clicking, and shortcuts. the demo would be controlling a computer without touching it.
+
+this is the wrist-based version of the [[pupilometry-glasses|pupilometry glasses]] eye-tracking approach. both address the same problem: hands-free computer interaction. the bracelet approach may be more practical since it does not require glasses and EMG can detect fine hand gestures.
+
+---
+
+## timeline
+
+- [2026-03-28] captured — emphasis on building a "hella cool demo"
\ No newline at end of file
new file mode 100644
index 0000000..3c1eaeb
@@ -0,0 +1,31 @@
+---
+first_captured: 2026-02-16
+sources:
+- sources/ideaflow/2026-02-16_episodic-memory-builder.md
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- memory
+- cognition
+- app
+- software
+title: episodic memory builder
+type: idea
+updated: 2026-04-10
+visibility: public
+---
+
+# episodic memory builder
+
+an app that improves your episodic memory by prompting you to recall things — what did you eat for breakfast, what happened at that party, where did you hear that fact. the insight came from the experience of recalling a day and finding it both useful (surfacing follow-ups) and pleasant (exercising memory retrieval).
+
+the connection to memory competitions is noted as a related personal goal. this is also adjacent to the [[brain-rewinder|brain rewinder]] concept, but approaches the problem from the training side rather than the tooling side. the [[always-on-ai-assistant|always-on AI assistant]] could feed context into this to make prompts more specific and useful.
+
+**spreadsheet evaluation:** originality 8/10, excitement 5/10, MVP 1 weekend. competitive landscape: Recall (recallmem.com) quizzes on photo-based memories but is photo-centric not text-prompt, Lumosity/Elevate use abstract exercises not personal episodic recall — no app uses personal daily prompts specifically for episodic memory training. simple app concept — could be a daily push notification + text input. research-backed angle (spaced retrieval practice) adds credibility. tech depth 3/10, labeled [ARCHIVE] ("simple app").
+
+---
+
+## timeline
+
+- [2026-02-16] captured in ideaflow — noted simplicity of concept ("just like, what did you eat for breakfast?")
+- [2026-04-10] google sheets evaluation — 8/10 originality, 5/10 excitement, MVP 1 weekend, [ARCHIVE] tier
\ No newline at end of file
new file mode 100644
index 0000000..650d092
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- ai
+- research
+- ml
+title: flapping airplanes
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# flapping airplanes
+
+an exploration of AI training efficiency — the name comes from the analogy of early airplane designers who tried to build planes that flap their wings like birds. they were copying the surface behavior (flight) instead of the underlying principle (lift via airfoil). the question being asked: are current deep learning training methods "flapping" — imitating what works without understanding why — and are there fundamentally more efficient approaches waiting to be discovered?
+
+the specific research directions this points at include: sparse training (activate fewer parameters per forward pass), continual/online learning (learn from a stream of experience rather than a fixed dataset), and biologically-inspired learning rules that don't require backprop. the hypothesis is that gradient descent on massive static datasets is like flapping — it works, but it may be a local optimum in the space of learning algorithms, not the global one.
+
+related: [[llm-behavior-improvement|LLM behavior improvement]], [[llm-physical-intuition|LLM physical intuition]], [[consciousness-for-students|student consciousness]], [[intelligence-development|intelligence development]]
\ No newline at end of file
new file mode 100644
index 0000000..23cc8da
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- software
+- search
+- open-source
+title: FOSS management
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# FOSS management
+
+an open source software discovery and management tool that automatically tests and evaluates packages so you don't have to. the problem: there are thousands of FOSS tools for any given need, most are poorly documented or abandoned, and figuring out which one actually works requires hours of manual testing. a tool that continuously crawls GitHub/GitLab, installs and runs packages against standardized test suites, and surfaces the ones that actually work would save enormous time.
+
+the auto-testing layer is what makes this interesting rather than just another "awesome lists" aggregator. the system would need to define what "works" for a given category of tool — which requires either crowdsourced evaluation criteria or ML-based quality inference from signals like test coverage, commit activity, issue response time, and actual runtime behavior. the discovery side (finding what exists) is easier than the evaluation side (knowing if it's good).
+
+related: [[pain-point-builder-marketplace|pain point marketplace]], [[stress-testing-suite|stress testing suite]], [[b2b-competitive-analysis|B2B competitive analysis]], [[optimize-computers|computer optimizer]]
\ No newline at end of file
new file mode 100644
index 0000000..23ed0ce
@@ -0,0 +1,26 @@
+---
+first_captured: 2025-12-22
+sources:
+- sources/obsidian/home/ideas/disconnection_between_generations.md
+status: raw
+tags:
+- social
+- culture
+- youth
+title: bridging generational disconnect
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# bridging generational disconnect
+
+with airpods, memes, and internet content, younger generations are becoming more disconnected from older generations, especially between child and parent. as seen in Didi (2024), kids do not trust their parents for different needs due to disconnect, and instead seek out other sources of comfort or mentorship.
+
+how to bridge the gap? connects to the [[cultural-translator|cultural translator]] for cross-generational communication.
+
+---
+
+## timeline
+
+- [2025-12-22] captured in obsidian — Didi film as illustration
\ No newline at end of file
new file mode 100644
index 0000000..93ab5d2
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- ai
+- coding
+- developer-tools
+title: hard docs writer
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# hard docs writer
+
+a tool that automatically documents the confusing parts of a codebase, specifically optimized for making AI agents more effective. most codebases have sections that are genuinely hard to understand — complex state machines, subtle invariants, non-obvious initialization orders, implicit dependencies between modules. humans who built these parts hold the context in their heads; AI agents and new human engineers hit a wall.
+
+the approach would be to identify these "hard" sections automatically (high coupling, low comment density, frequent bug origins, high churn), then generate detailed prose explanations that capture the intent and gotchas, not just what the code does mechanically. the target reader is an AI agent starting a new task — so the docs need to be dense, precise, and structured in a way that fits efficiently into a context window.
+
+related: [[spec-driven-dev|spec-driven dev kit]], [[context-window-optimizer|context window optimizer]], [[agents-md-research|AGENTS.md research]], [[overnight-app-grinder|overnight app grinder]]
\ No newline at end of file
new file mode 100644
index 0000000..ee51db3
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- software
+- ai
+- cognition
+title: core idea extraction
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# core idea extraction
+
+a system for generalizing the core idea from any piece of content — text, image, audio, video — stripping away the surface framing to find the underlying concept. the motivation is that the same insight often appears in many different forms, and most people encounter it multiple times in different guises without recognizing the common thread. an extraction system would find that thread and make it explicit.
+
+the output would be something like: "the core idea in this paper / this conversation / this image is [X], which is an instance of [broader principle], related to [other ideas].". this requires understanding not just what content is about but what conceptual structure it exemplifies — which is much harder than summarization. the interesting research question is whether LLMs are good at this kind of abstraction across modalities, or whether they just rephrase surface content.
+
+related: [[cultural-translator|cultural translator]], [[idea-tester|idea tester]], [[dense-info-generator|dense info generator]]
\ No newline at end of file
new file mode 100644
index 0000000..14f4ed2
@@ -0,0 +1,21 @@
+---
+status: raw
+tags:
+- ai
+- research
+- product
+title: idea tester
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# idea tester
+
+an automated system for validating ideas and running market research — takes an idea as input and outputs a structured assessment: who has this problem, how bad is it, what exists already, what would differentiation look like, how big is the market, what would the MVP be. the goal is to compress the "should I build this" research phase from days to minutes.
+
+the pipeline would involve web research (competitors, similar products, market size estimates), synthesis of what problem the idea actually solves and for whom, and some model of what makes an idea worth pursuing (difficulty, differentiation, market size, personal fit). ideally it would be opinionated — not just returning information but giving a clear recommendation with reasoning. one challenge: market research that LLMs can do often surfaces known information; the genuinely useful signal is often in qualitative interviews and obscure niche forums, which are harder to automate.
+
+this is a meta-tool for the ideas wiki itself — every raw idea here could run through idea-tester. connects to [[b2b-competitive-analysis|B2B competitive analysis]] (competitive research is a subset), [[stress-testing-suite|stress testing suite]] (validation later in the cycle), and [[project-suggestor|project suggestor]] (which recommends ideas based on who you are). the [[overnight-app-grinder|overnight app grinder]] could use idea-tester as its first stage — validate before building.
+
+related: [[idea-extraction-system|core idea extraction]]
\ No newline at end of file
new file mode 100644
index 0000000..37e2f88
@@ -0,0 +1,32 @@
+---
+first_captured: 2026-02-16
+sources:
+- sources/ideaflow/2026-02-16_ifttt-for-me-eg-computer-scanning-for-text.md
+- sources/ideaflow/2026-02-19_linked-note-bfrfczyqoi.md
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- automation
+- productivity
+- software
+title: personal IFTTT
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# personal IFTTT
+
+IFTTT-style automation for personal use — computer scanning for text patterns, reminders, etc. example use case: "if finish data box, send something to a friend." current workaround is setting lots of reminders manually.
+
+this overlaps significantly with the [[universal-habits|universal habits]] concept (which described itself as "kinda like IFTTT but automatically create things"). the difference is that personal IFTTT focuses on task-triggered actions while universal habits focuses on contextual behavior change. both need the same infrastructure: event detection, context gathering, action triggering.
+
+**spreadsheet evaluation:** originality 7/10, excitement 8/10, MVP 2-4 weeks. competitive landscape: Hazel (Mac, $42) watches folders but is Mac-only and file-scoped, IFTTT/Zapier are cloud-service-to-service only, Keyboard Maestro does local triggers but is macro-focused — no cross-platform consumer tool scans broadly for local text patterns and triggers actions. scope creep risk noted as huge; MVP should focus on one trigger type (e.g. clipboard monitoring). tech depth ranked at moderate level.
+
+---
+
+## timeline
+
+- [2026-02-16] initial capture — computer scanning for patterns
+- [2026-02-19] follow-up — current solution is "hella reminders"
+- [2026-04-10] google sheets evaluation — 7/10 originality, 8/10 excitement, MVP 2-4 weeks
\ No newline at end of file
new file mode 100644
index 0000000..b2c4068
@@ -0,0 +1,30 @@
+---
+first_captured: 2026-01-06
+sources:
+- sources/ideaflow/2026-01-06_very-basic-info-exchanger-before-talking-just-like.md
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- social
+- networking
+- software
+title: info exchanger
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# info exchanger
+
+a very basic info exchanger before talking to someone. just send them a link, they open it, pair, and answer some super simple conversation-inducing questions. reduces cold-start anxiety in conversations by giving both parties some shared context.
+
+connects to the [[vibe-matcher|vibe matcher]] for the broader people-matching vision and to [[oncue|OnCue]] for the self-description component.
+
+**spreadsheet evaluation:** originality 9/10, excitement 5/10, MVP 1 weekend. competitive landscape: Donut matches randomly, dating apps provide profiles, Slido does in-meeting icebreakers — no product serves as a lightweight mutual pre-meeting questionnaire exchanged before the conversation happens. dead simple product — the hard part is getting adoption into people's meeting flow. tech depth 2/10, labeled [ARCHIVE] ("simple tool").
+
+---
+
+## timeline
+
+- [2026-01-06] captured — simple paired Q&A before conversations
+- [2026-04-10] google sheets evaluation — 9/10 originality, 5/10 excitement, [ARCHIVE] tier
\ No newline at end of file
new file mode 100644
index 0000000..2cff6aa
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- hardware
+- consumer
+title: instant warming blanket
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# instant warming blanket
+
+a cheap blanket designed for immediate, full-body warmth — solving the specific problem of being cold right now. the frustrating gap: electric blankets take 10 minutes to warm up and are expensive; regular blankets rely on body heat and take time to trap warmth; hand warmers help but are small. the idea is a blanket that is actively warm immediately when you wrap it around yourself, cheap enough to be disposable or at least widely accessible.
+
+the technical approaches: chemical heat packs scaled to blanket size (like hand warmers but larger — iron oxidation or supersaturated sodium acetate), resistive heating with fast warm-up and battery power, or novel insulation materials with ultra-low thermal conductivity that trap body heat almost instantly. each has trade-offs in cost, reusability, and safety. the sodium acetate approach (used in reusable hand warmers that you reset by boiling) is particularly interesting because it's safe, reusable, and produces heat on demand.
+
+related: [[sensor-capturer|sensor capturer]], [[pause|Pause]], [[cookedness-tracker|cookedness tracker]]
\ No newline at end of file
new file mode 100644
index 0000000..b0ee93a
@@ -0,0 +1,26 @@
+---
+first_captured: 2026-03-28
+sources:
+- sources/ideaflow/2026-03-28_interesting-post-from-here.md
+status: raw
+tags:
+- education
+- cognition
+- business
+title: intelligence development startup
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# intelligence development startup
+
+inspired by a LessWrong post on understanding your own cognitive edge. connected to debates about nature vs nurture, how to raise amazing children, how to develop intelligence, whether base intelligence exists.
+
+the idea: a startup that systematically develops people's intelligences and works with them to learn more skills. could be consumer, enterprise, education/schools, or elite-targeted. connects to the [[learning-suite|learning suite]] for the curriculum side and to the [[consciousness-for-students|student consciousness]] idea for the K-12 angle.
+
+---
+
+## timeline
+
+- [2026-03-28] captured — LessWrong inspiration, multiple market segments
\ No newline at end of file
new file mode 100644
index 0000000..bcbc8c8
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- social
+- education
+- community
+title: intentionality camp
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# intentionality camp
+
+an intensive program — camp format — designed to make participants more "cracked": highly intentional, driven, agentic, and clear about what they're doing and why. the theory of change is that most people who could be exceptional aren't because they've never been in an environment that pulled that out of them. the camp would be a concentrated dose of that environment — high-density intellectual engagement, deliberate practice in agency and self-direction, people who push each other.
+
+the programming would likely include: structured reflection (what do you actually want, why are you doing what you're doing), hard collaborative projects with real stakes, direct exposure to interesting people and ideas, and probably some element of physical challenge. the "intentionality" framing specifically is about combating unconscious drift — the tendency to do things out of inertia, social pressure, or default rather than genuine choice. you leave knowing what you're choosing and why.
+
+related: [[consciousness-for-students|student consciousness]], [[motivation-education|motivation in education]], [[eco-community|eco-safe community]]
\ No newline at end of file
new file mode 100644
index 0000000..84beef3
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- hardware
+- productivity
+title: invoking thoughts
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# invoking thoughts
+
+the idea is that specific sensory stimuli — smells, sounds, textures, ambient temperatures — can be used to reliably invoke mental and emotional states on demand. rather than relying on willpower or abstract behavioral cues to trigger habits or focus, you engineer the sensory environment so the nervous system does the work. the insight draws from the well-documented connection between olfaction and memory (the reason coffee shops feel productive, or why your childhood bedroom puts you in a different headspace instantly).
+
+one implementation angle: a small wearable or ambient device that emits specific scents or sounds tied to particular tasks or mental modes — deep work, relaxation, creativity. over time, through conditioning, those stimuli become reliable context switches. a softer version doesn't require hardware at all: just deliberately building sensory associations with specific activities (a particular playlist, a specific scent on your desk) so that the cue triggers the state rather than the other way around. the key difference from typical productivity advice is this is bottom-up (sensory → cognitive) rather than top-down (intention → behavior).
+
+related: [[brain-rewinder|brain rewinder]], [[smell-resetter|smell resetter]], [[universal-habits|universal habits]], [[sensor-capturer|sensor capturer]], [[motivation-education|motivation in education]]
\ No newline at end of file
new file mode 100644
index 0000000..4c4e372
@@ -0,0 +1,31 @@
+---
+first_captured: 2025-12-22
+sources:
+- sources/ideaflow/2025-12-22_job-application-tracking-platform-that-immediately-shows-i.md
+- sources/apple-notes/archived/potential-projects-archived.md
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- hiring
+- software
+- utility
+title: job application tracker
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# job application tracker
+
+a job application tracking platform that immediately shows important info and differentiates real people from fake applicants with high certainty. the problem: existing platforms (like Ryan's) only show messages, where patterns might hint at fakes but nothing is definitive. the solution should tell with 100% certainty whether applicants are genuine and whether they will be good fits.
+
+this is the employer-side complement to [[oncue|OnCue]] (which helps applicants) and connects to the [[referral-hiring|referral-based hiring platform]] (which approaches the same problem through trust networks rather than detection).
+
+**spreadsheet evaluation:** originality 6/10, excitement 6/10, MVP 4-8 weeks. competitive landscape: Lever, Greenhouse, Ashby are ATS platforms but don't auto-detect fake applicants or cross-reference external platforms, applicant AI fraud detection is emerging (HireVue, Paradox) but not as a standalone platform feature — the fake-applicant-filtering angle is timely. cold start problem — needs employer adoption. could MVP as a layer on top of existing ATS via API integration. tech depth 3/10, labeled [ARCHIVE] ("commodity product").
+
+---
+
+## timeline
+
+- [2025-12-22] captured — critique of existing platforms, need for better signal
+- [2026-04-10] google sheets evaluation — 6/10 originality, 6/10 excitement, [ARCHIVE] tier
\ No newline at end of file
new file mode 100644
index 0000000..ed81dd8
@@ -0,0 +1,17 @@
+---
+status: built
+tags:
+- software
+title: keystroke classical music
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# keystroke classical music
+
+a tool that turns keystrokes into classical piano performance — as you type, keyboardsounds (`kbs`) plays classical piano samples timed to your keystrokes, transforming mundane typing into something that sounds vaguely like a live piano performance. the experience of typing changes from utilitarian to pleasurable. the current version uses a `banana-split` profile with classical samples and is installed system-wide via uv.
+
+the motivation is partly aesthetic and partly about making work feel different. there is something real about the feedback loop: you type more confidently and rhythmically when each keystroke has acoustic weight. it is adjacent to the idea of making tools feel alive — same reason mechanical keyboards have a following, same reason some people prefer writing on typewriters. the difference here is that the result dynamically resembles a musical performance rather than just providing tactile/acoustic texture.
+
+related: [[surreal-sound-experiences|surreal sound experiences]], [[math-dopamine|math dopamine loops]], [[invoking-thoughts|invoking thoughts]]
\ No newline at end of file
new file mode 100644
index 0000000..0e9899a
@@ -0,0 +1,28 @@
+---
+first_captured: 2025-12-22
+sources:
+- sources/ideaflow/2025-12-22_life-guide-projectidea.md
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- advice
+- crowdsource
+- software
+title: life guide
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# life guide
+
+a guide for practical life skills — how to do taxes, apply to programs, etc. nuggets of wisdom that are super useful but scattered. acknowledged issue: "runs into advice issues" (liability, individual circumstances). could try crowdsourcing. "similar to some other forum things? e.g. reddit with r/lifeadvice."
+
+**spreadsheet evaluation:** originality 4/10, excitement 4/10, MVP 2-4 weeks. competitive landscape: WikiHow, Reddit (r/lifeprotips, r/personalfinance), Adulting.com, and various life-skills sites exist — advice quality problem is real but crowdsourcing makes it Reddit-like, hard to differentiate without strong curation or AI synthesis. content curation is the product not the tech. competes with ChatGPT/Claude for most queries. tech depth 2/10, labeled [ARCHIVE] ("content/community, not tech").
+
+---
+
+## timeline
+
+- [2025-12-22] captured — practical wisdom aggregation
+- [2026-04-10] google sheets evaluation — 4/10 originality, 4/10 excitement, [ARCHIVE] tier
\ No newline at end of file
new file mode 100644
index 0000000..94be57a
@@ -0,0 +1,17 @@
+---
+status: raw
+tags:
+- search
+title: life search
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# life search
+
+the `mgrep` for your entire life — a semantic search engine over all your personal data. you type a query like "what was that restaurant in tokyo" or "when did i talk to sarah about the internship" and it searches across notes, emails, messages, calendar events, photos, documents, and browser history to surface the actual memory. the gap it addresses: you know you experienced or captured something, but locating it requires remembering which app it lives in and which search syntax to use. that friction is high enough that most people just give up.
+
+the technical approach would be a unified ingestion pipeline that chunks and embeds data from all sources into a single vector store, then exposes a natural-language query interface. the indexing layer is the hard part — connecting to all data sources (Apple Messages, Gmail, Notes, Obsidian, browser history, photos via OCR/CLIP) and keeping embeddings fresh. the search layer itself is relatively straightforward once data is in a common format. a local-first design would be important for trust: this is maximally sensitive personal data and running it through a cloud service would be a non-starter for most people.
+
+related: [[axon|axon]], [[universal-data-capturer|universal data capturer]], [[me-model|me model]], [[always-on-ai-assistant|always-on AI assistant]]
\ No newline at end of file
new file mode 100644
index 0000000..78dba6c
@@ -0,0 +1,17 @@
+---
+status: raw
+tags:
+- ai
+title: llm behavior improvement
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# llm behavior improvement
+
+the observation that LLMs fail in predictable ways — sycophancy, context drift, hallucinated confidence, inconsistency across conversation turns — and that these failures are not just model problems but also prompt engineering and scaffolding problems. the idea is to systematically study and mitigate these failure modes through a combination of better prompting patterns, structured context management, and behavioral testing frameworks.
+
+one concrete direction: building a suite of tests that probe specific behavioral failure modes (does the model change its answer when the user pushes back? does it maintain consistency over a long conversation? does it respect negative constraints?). another direction: studying what kinds of AGENTS.md / system prompt patterns produce reliably better behavior, which overlaps with [[agents-md-research|AGENTS.md research]]. the meta-insight is that much of what people attribute to "bad AI" is actually addressable at the prompt and scaffolding layer without waiting for better base models.
+
+related: [[context-window-optimizer|context window optimizer]], [[spec-driven-dev|spec-driven dev kit]], [[llm-physical-intuition|LLM physical intuition]]
\ No newline at end of file
new file mode 100644
index 0000000..a50b612
@@ -0,0 +1,17 @@
+---
+status: raw
+tags:
+- ai
+title: llm physical intuition
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# llm physical intuition
+
+an empirical research question: can LLMs reason usefully about physical space and physical intuition? the question is motivated by the observation that models are trained predominantly on text, which means they have absorbed physics described in words but haven't developed the grounded spatial intuitions that come from actually interacting with the world. when you ask an LLM "if you drop a hammer off a table, where does it land relative to a feather dropped simultaneously?" the answer may be correct, but "picture a room with three doors — which is closest to you if you walk in from the north" starts to reveal gaps.
+
+the research direction would be: construct a benchmark of spatial/physical reasoning tasks that range from trivial to genuinely hard, evaluate current models, and analyze the failure modes. do models fail because they lack 3D spatial representation, because they have no sense of scale, or because physical reasoning requires composing multiple steps that compound errors? the findings could inform both how to prompt models for physical reasoning tasks (robotics control, architecture, simulation) and what data or training modifications would help.
+
+related: [[llm-behavior-improvement|LLM behavior improvement]], [[flapping-airplanes|flapping airplanes]], [[robotic-arm-assistant|robotic arm assistant]], [[emg-bracelet|EMG bracelet]], [[symbolic-regression|symbolic regression]]
\ No newline at end of file
new file mode 100644
index 0000000..b302fc5
@@ -0,0 +1,17 @@
+---
+status: raw
+tags:
+- education
+title: math dopamine loops
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# math dopamine loops
+
+the idea that math education fails because it has the wrong reward structure — you work hard for a long time before getting the satisfaction of understanding, and the feedback is mostly negative (wrong answer, red marks). video games figured out how to make hard things feel rewarding by engineering tight dopamine loops: small wins, visible progress, instant feedback, and escalating challenge that stays just above your current ability. math education could borrow these mechanics without trivializing the content.
+
+the implementation isn't about gamifying math with points and badges (that's been tried and mostly produces engagement without learning). it's about restructuring the curriculum and interface so that each step has a satisfying resolution. one approach: problems designed so partial progress is visible and rewarding, not just the final answer. another: framing each problem as a puzzle where the "aha moment" is engineered, not incidental. a third: adaptive difficulty that keeps you in flow — slightly hard but not frustrating. the goal is to make math feel like solving a good puzzle rather than passing a test.
+
+related: [[motivation-education|motivation in education]], [[intelligence-development|intelligence development]], [[task-optimization-game|task optimization game]], [[consciousness-for-students|student consciousness]]
\ No newline at end of file
new file mode 100644
index 0000000..16cfcad
@@ -0,0 +1,32 @@
+---
+first_captured: 2025-12-22
+sources:
+- sources/ideaflow/2025-12-22_me-model---data-sources-imessages-email-discord.md
+- sources/apple-notes/archived/potential-projects-archived.md
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- ai
+- personalization
+- data
+- finetuning
+title: me model
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# me model
+
+a fine-tuned language model trained on your personal data: iMessages, email, Discord, notes/Obsidian, GitHub. the goal is an AI that genuinely understands you — your communication style, your knowledge, your projects, your relationships.
+
+Fireworks AI was identified for the finetuning infrastructure. this is the foundation layer that several other ideas build on: [[oncue|OnCue]] needs your professional history, the [[always-on-ai-assistant|always-on AI assistant]] needs your context, the [[personalized-autocomplete|personalized autocomplete]] needs your writing patterns, and the [[ai-conversationalist|AI conversationalist]] needs your speaking style. the [[life-search|life search]] idea is essentially the query interface for a me model.
+
+**spreadsheet evaluation:** originality 7/10, excitement 5/10, MVP 4-8 weeks. competitive landscape: Character.AI lets others create chatbots, Delphi.ai trains on specific people, Personal.ai builds from your data, Inflection Pi learns over time — none pull from iMessages + email + Discord + Obsidian + GitHub as a unified training corpus. data extraction is the hard part (iMessage needs local SQLite access, Discord export is limited, email needs OAuth). privacy implications significant. tech depth 8/10, labeled [STRONG] ("fine-tuning, data pipelines from iMessage/Discord/GitHub, eval — technically meaty but demo is weird: 'look, it talks like me'").
+
+---
+
+## timeline
+
+- [2025-12-22] captured in ideaflow — data sources and finetuning approach
+- [2026-04-10] google sheets evaluation — 7/10 originality, 5/10 excitement, MVP 4-8 weeks, [STRONG] tier
\ No newline at end of file
new file mode 100644
index 0000000..1ad1f59
@@ -0,0 +1,17 @@
+---
+status: raw
+tags:
+- education
+title: motivation in education
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# motivation in education
+
+the thesis is that motivation is the actual bottleneck in education, and nearly all other educational reform is working around it. better curriculum, better teachers, better tools — none of it matters much if students aren't intrinsically motivated to engage. the observation that sparked this: a highly motivated student with mediocre resources will outperform an unmotivated student with excellent resources nearly every time, yet education systems spend enormous energy improving resources and almost none studying or fixing motivation.
+
+the analysis distinguishes between extrinsic motivation (grades, parental pressure, college admissions) and intrinsic motivation (genuine curiosity, personal relevance, the pleasure of understanding). extrinsic motivation is what most students operate on, and it produces fragile learning — optimized for tests, not retention or transfer. the interesting research question is what conditions reliably generate intrinsic motivation: autonomy (choosing what to learn), mastery (feeling progress), purpose (connecting material to goals you actually have), and belonging (learning in community). school as currently designed undermines all four.
+
+related: [[math-dopamine|math dopamine loops]], [[consciousness-for-students|student consciousness]], [[intelligence-development|intelligence development]], [[intentionality-camp|intentionality camp]]
\ No newline at end of file
new file mode 100644
index 0000000..d3bf982
@@ -0,0 +1,17 @@
+---
+status: raw
+tags:
+- hardware
+title: nose device
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# nose device
+
+a personal olfactory device designed specifically for someone with impaired or degraded smell — in this case for dad. the motivation is personal: someone who has lost or reduced their ability to smell food and environments is cut off from a major dimension of experience and safety (can't smell smoke, gas leaks, spoiled food). existing solutions are medical and expensive; consumer options barely exist.
+
+the device would do one or more of the following: amplify weak olfactory signals, classify smells and present them in an alternate modality (audio or haptic feedback describing what's present), or compensate for missing sensory input through a kind of olfactory prosthetic. the electronic nose direction (enose) is the most technically tractable — existing sensor arrays can classify a range of common smells, and paired with a lightweight haptic or audio output, could give someone back meaningful information about their environment even if they can't perceive the raw smell.
+
+related: [[smell-resetter|smell resetter]], [[sensor-capturer|sensor capturer]], [[acoustic-drone-detection|acoustic drone detection]]
\ No newline at end of file
new file mode 100644
index 0000000..d022bd9
@@ -0,0 +1,17 @@
+---
+status: built
+tags:
+- software
+title: oncue
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# oncue
+
+an interview prep tool that takes your scattered history — projects, experiences, accomplishments spread across a resume, LinkedIn, notes, and memory — and transforms them into perfectly-timed, well-structured answers for behavioral interviews. the pain point: most people have done real things but struggle to articulate them under pressure in STAR format. they either blank on relevant stories or give disorganized answers. OnCue preprocesses your history so the retrieval work is done in advance.
+
+the core mechanic: ingest your resume, notes, and freeform text about your experiences; parse out story units (individual projects, challenges, accomplishments); for each story, generate a structured STAR answer and tag it to the competencies it demonstrates (leadership, problem-solving, teamwork, etc.). during prep, you can browse by competency to see which stories fit which questions, and practice delivering them. the AI layer handles both the structuring (turning raw memories into coherent narratives) and the tagging (mapping stories to interview questions).
+
+related: [[info-exchanger|info exchanger]], [[axon|axon]], [[referral-hiring|referral-based hiring]], [[me-model|me model]]
\ No newline at end of file
new file mode 100644
index 0000000..dd5416f
@@ -0,0 +1,17 @@
+---
+status: raw
+tags:
+- software
+title: computer optimizer
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# computer optimizer
+
+a cross-platform tool that analyzes your computer's performance and applies targeted optimizations — startup items, background processes, disk usage, memory management, network settings — without requiring the user to have technical knowledge. the gap: existing tools like CleanMyMac are either bloatware or Mac-only; Linux has tools scattered across package managers and config files; Windows has its own dark arts. a unified tool that works intelligently across all three would be genuinely useful.
+
+the smarter version of this is not just a set of cleanup scripts but an AI-powered analysis layer: it observes your actual usage patterns over time, identifies which processes are consuming resources you could reclaim, and suggests or applies optimizations with explanations. it could also benchmark before and after, giving you concrete evidence that the optimization worked. the trust problem is real — users are rightly suspicious of tools that claim to speed up their computer, so transparency about what exactly is being changed and why is essential.
+
+related: [[foss-management|FOSS management]], [[spec-driven-dev|spec-driven dev kit]], [[task-scheduler|task scheduler]], [[pain-point-builder-marketplace|pain point marketplace]]
\ No newline at end of file
new file mode 100644
index 0000000..82d9ef8
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- hardware
+- productivity
+title: outdoor work setup
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# outdoor work setup
+
+the problem of wanting to work outside — fresh air, sunlight, a change of scene — but running into practical blockers: screen glare, no stable surface, wifi range, short battery life, and physical discomfort from improvised setups. everyone has tried it and ended up back inside. the idea is to actually solve this properly: design or curate a setup that makes outdoor work feel as capable and comfortable as indoor work.
+
+the core components: a monitor or laptop screen that is genuinely readable in direct sunlight (matte anti-glare or high-nit screens), a stable lightweight table and ergonomic chair that can move outside easily, a long-range wifi extender or portable hotspot, and either a large battery bank or a solar solution for power. the harder part is the organizational friction — making it easy to move the setup out and in without it feeling like a project. the ideal design is something that lives outside by default and gets brought in for bad weather, not the reverse.
+
+related: [[eco-community|eco-safe community]], [[pause|pause]], [[cookedness-tracker|cookedness tracker]], [[surreal-sound-experiences|surreal sound experiences]]
\ No newline at end of file
new file mode 100644
index 0000000..4265de1
@@ -0,0 +1,30 @@
+---
+first_captured: 2026-04-07
+sources:
+- sources/ideaflow/2026-04-07_a-overnight-app-grinder-for-coding-agents-so.md
+status: raw
+tags:
+- ai
+- agents
+- coding
+- automation
+- software
+title: overnight app grinder
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# overnight app grinder
+
+an autonomous app builder that runs coding agents overnight while you sleep. cycles through accounts (especially Codex which is free), maintains context, manages sessions, logs roadblocks (especially auth issues) one per line in a text file. when you wake up, fix the blockers and run it again.
+
+the pipeline: feed it an idea, it does research (possibly using Deep Research from GPT/Claude for validation), asks some questions, then starts full end-to-end implementation. because of the autonomous nature, there should always be a backbone/skeleton for whatever is being built, plus high insistence on quality.
+
+this is a meta-tool — it builds the other ideas. connects to the broader AI tooling interest including [[agents-md-research|AGENTS.md optimization research]] and the [[llm-behavior-improvement|LLM behavior improvement]] work.
+
+---
+
+## timeline
+
+- [2026-04-07] captured — detailed architecture for autonomous overnight coding
\ No newline at end of file
new file mode 100644
index 0000000..a0576cc
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- software
+- marketplace
+title: pain point marketplace
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# pain point marketplace
+
+a marketplace that matches user-reported pain points with FOSS builders who want real problems to solve. the two-sided problem it addresses: users have genuine frustrations with software but no clear channel to say "someone please build this"; FOSS developers have technical skills but often build things nobody uses because they chose the project based on what sounded interesting rather than what people actually need. the marketplace bridges them.
+
+the user side: a simple interface to describe a pain point, upvote existing ones, and optionally offer a bounty or commit to beta testing. the builder side: a feed of verified problems ranked by demand, with enough detail to assess feasibility. the verification layer matters — many posted "pain points" are really feature requests for existing tools, or too vague to act on, so some curation or structuring of submissions would be needed. the bounty mechanic doesn't have to be financial — committing to be a beta tester and give detailed feedback is valuable to FOSS builders too.
+
+related: [[foss-management|FOSS management]], [[idea-tester|idea tester]], [[stress-testing-suite|stress testing suite]], [[b2b-competitive-analysis|B2B competitive analysis]]
\ No newline at end of file
new file mode 100644
index 0000000..1e8b05b
@@ -0,0 +1,30 @@
+---
+first_captured: 2026-01-01
+sources:
+- sources/ideaflow/2026-01-02_a-nice-trio-to-work-on.md
+- sources/ideaflow/2026-01-01_things-we-can-do.md
+- sources/ideaflow/2025-12-22_invokingimprinting-thoughts.md
+status: built
+tags:
+- productivity
+- software
+- habits
+- macos
+title: pause
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# pause
+
+a screen break / reminder app that was actually built and shipped. part of a "nice trio" of software projects alongside convo-flow and ui-flow. features discussed include: hiding from command-tab, customizable scheduled reminders (locked or not locked, variable length, custom message), and an apple clock style system for specifying repeats with a default of instantly turning off afterwards.
+
+Pause became a real project. it is a subset of the broader [[universal-habits|universal habits]] vision — specifically addressing the digital side of habit interruption. the [[invoking-thoughts|invoking/imprinting thoughts]] idea noted that Pause's text reminders could be more emotionally impactful ("instead of 'diminishing returns', use emojis or anecdotes or specific feelings").
+
+---
+
+## timeline
+
+- [2026-01-01] listed as active software project in "things we can do"
+- [2026-01-02] detailed feature design in "nice trio" capture
\ No newline at end of file
new file mode 100644
index 0000000..1a976ec
@@ -0,0 +1,17 @@
+---
+status: raw
+tags:
+- software
+title: personalized autocomplete
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# personalized autocomplete
+
+autocomplete that actually knows you — your vocabulary, your writing style, your recurring phrases, your common recipients, your topics, your opinions. the gap with existing autocomplete (even AI-powered ones like Gmail's smart compose) is that they model generic English rather than your specific voice. the result is suggestions that are grammatically fine but feel wrong — too formal, too casual, using words you don't use, suggesting endings that don't reflect how you'd finish the thought.
+
+the technical approach: train or fine-tune a small language model on your corpus — emails sent, messages, documents, notes — so it learns your voice specifically. the model would autocomplete based on both semantic context (what makes sense to say here) and stylistic context (what sounds like you). a local-first approach is essential: your writing is maximally private data. the inference cost needs to be low enough to run on-device so suggestions appear instantly without a round trip to a server.
+
+related: [[me-model|me model]], [[axon|axon]], [[embedding-tone-interpolation|embedding tone interpolation]], [[life-search|life search]], [[writing-tools|writing tools suite]]
\ No newline at end of file
new file mode 100644
index 0000000..588b710
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- health
+- software
+title: personalized medicine
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# personalized medicine
+
+a unified testing framework for personal wellness — the idea that most health advice is population-average advice that may or may not apply to you specifically, and that the tools to figure out what actually works for you are now cheap enough to build a rigorous personal testing protocol. the structure is essentially n=1 RCTs: pick one variable (sleep timing, caffeine timing, exercise type, supplement), hold everything else as constant as possible, measure outcomes you care about (energy, mood, cognitive performance, sleep quality), and actually analyze the data.
+
+the implementation challenge is that most people's self-tracking is either non-existent or inconsistent. the framework needs to handle both the protocol design (what to test, what to measure, how long to run each arm) and the data capture (lightweight enough to be sustained, specific enough to be useful). wearable data (HRV, sleep stages, activity) provides the objective side; daily self-reports provide the subjective side. the AI layer helps with protocol design ("given your goals and current data, here's what to test next") and analysis (identifying signal in noisy n=1 data).
+
+related: [[eeg-artifact-rejection|EEG artifact rejection]], [[universal-data-capturer|universal data capturer]], [[axon|axon]], [[decision-helper|decision helper]]
\ No newline at end of file
new file mode 100644
index 0000000..2a7dc38
@@ -0,0 +1,27 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- defense
+- hardware
+- ml
+- embedded
+title: predictive maintenance sensors
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# predictive maintenance sensors
+
+embedded ML for anomaly detection and sensor fusion on equipment — predict when things will break before they do. similar to [[acoustic-drone-detection|acoustic drone detection]] but less flashy, applied to maintenance rather than detection.
+
+**spreadsheet evaluation:** tech depth 8/10, labeled [STRONG] ("embedded ML, anomaly detection, sensor fusion — similar to drone detection but less flashy").
+
+---
+
+## timeline
+
+- [2026-04-10] captured from google sheets — [STRONG] tier, 8/10 tech depth
\ No newline at end of file
new file mode 100644
index 0000000..a6ece0e
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- ai
+- education
+title: project designer
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# project designer
+
+an AI tool that designs curriculum-quality learning projects — given a topic, skill level, and goal, it generates a complete, well-scoped project spec with clear milestones, specific deliverables, and the kind of pedagogical structure that makes a project actually educational rather than just a to-do list. the difference between a good learning project and a bad one is usually in the design: good projects have just enough complexity to force learning, natural checkpoints, and a concrete artifact at the end.
+
+the key feature is that it doesn't just generate a project idea but engineers the learning path embedded in it. for a given topic, it knows which concepts need to be encountered in what order, where students typically get stuck, and what kinds of deliverables force understanding rather than surface-level familiarity. this is the kind of design that good curriculum designers and senior engineers do intuitively but that most people can't access. the AI layer makes that expertise broadly available.
+
+related: [[project-suggestor|project suggestor]], [[spec-driven-dev|spec-driven dev kit]], [[intentionality-camp|intentionality camp]], [[intelligence-development|intelligence development]]
\ No newline at end of file
new file mode 100644
index 0000000..9d73f5b
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- ai
+- education
+title: project suggestor
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# project suggestor
+
+an AI that recommends what to build based on who you are — your skills, your gaps, your goals, your interests, and your timeline. the observation is that one of the biggest sources of wasted effort for developers and learners is choosing the wrong project: too easy (nothing learned), too hard (abandoned after a week), wrong domain (doesn't move your goals forward), or too generic (the 100th todo app). a good mentor gives you project suggestions that fit your specific situation; this is that, automated.
+
+the input model: a lightweight profile capturing what you already know, what you want to learn, how much time you have, and what kinds of problems interest you. the AI matches this against a space of project templates and adapts them — it might suggest building a CLI tool in Rust if you're a Python dev who wants systems programming experience, or a data pipeline project if you want ML engineering practice. the suggestions should include reasoning (why this project for you, what you'll learn from it, potential blockers to watch for) not just a project name.
+
+related: [[project-designer|project designer]], [[axon|axon]], [[intelligence-development|intelligence development]], [[me-model|me model]]
\ No newline at end of file
new file mode 100644
index 0000000..24e357b
@@ -0,0 +1,30 @@
+---
+first_captured: 2025-12-22
+sources:
+- sources/ideaflow/2025-12-22_build-pupilometry-glasses-for-moving-mouse---some.md
+- sources/apple-notes/archived/potential-projects-archived.md
+status: raw
+tags:
+- hardware
+- cv
+- wearable
+- hci
+title: pupilometry glasses
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# pupilometry glasses
+
+3D-printed glasses with small cameras that track eye movement to control a mouse cursor, with blink or head movement for clicking. feasibility questions: datasets for CV, how existing pupilometry works (possibly reverse engineer), whether pupilometry can track at fine enough grain (if not, could click randomly in area or use ML to predict click target).
+
+pivot possibility: a face-watching camera that captures expressions (yawning, scratching face) and maps them to keyboard shortcuts, notifications, or "punishment" for bad habits.
+
+noted limitation: Apple Vision Pro basically solves eye tracking better, and Meta smart glasses solve it even more portably. this connects to the [[emg-bracelet|EMG bracelet]] idea as another hands-free computer control approach. cameras could come from monitor camera modules.
+
+---
+
+## timeline
+
+- [2025-12-22] detailed feasibility analysis and build plan
\ No newline at end of file
new file mode 100644
index 0000000..f36ac65
@@ -0,0 +1,17 @@
+---
+status: raw
+tags:
+- search
+title: quality content search
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# quality content search
+
+a search engine that filters specifically for high-quality content — not most popular, not most recent, but actually best. the motivation is that Google search results have degraded: SEO-optimized content dominates, real expertise gets buried under content farms, and you have to know in advance which sources to trust. the experience of searching for nuanced technical or intellectual topics and getting mostly mediocre results is now common enough to be a cultural complaint.
+
+the filtering approach: a combination of source reputation signals (which domains consistently produce substantive content), quality indicators that aren't easily gamed (genuine engagement in comments, citation by other quality sources, structural signals like depth and specificity), and potentially a community layer where people with demonstrated expertise in a domain can curate what's good. the last part is interesting — a small number of trusted curators per domain would dramatically improve results for that domain, and the curation work could be distributed rather than centralized.
+
+related: [[universal-agentic-searcher|universal agentic searcher]], [[dense-info-generator|dense info generator]], [[life-search|life search]], [[idea-tester|idea tester]]
\ No newline at end of file
new file mode 100644
index 0000000..92de78e
@@ -0,0 +1,18 @@
+---
+status: explored
+tags:
+- software
+- hiring
+title: referral-based hiring
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# referral-based hiring
+
+hiring is broken because resumes filter for credentials, not fit. referral-based hiring is a marketplace that flips this: instead of applying cold, candidates get vouched for by someone who actually knows them. the referrer attests to specific qualities — "she's the best debugger i've worked with" or "he ships fast and asks good questions" — not just "we worked together at X." the incentive structure is what makes this interesting: if referrers earn something when their referral succeeds and gets hired, they have skin in the game and won't refer indiscriminately. this creates a quality signal that resume screening can't fake.
+
+the fit-over-qualification framing is the core thesis. most hiring mistakes come from hiring someone technically qualified but wrong for the team, culture, or specific role. referrals by nature encode soft information — the referrer knows both the candidate and ideally has some sense of the role requirements, so their matching has higher bandwidth than a resume + JD scan. the platform could amplify this by having referrers answer structured questions about the candidate: their working style, biggest strengths, what kind of environment they'd thrive in. companies then search not just by skill but by team-fit signals.
+
+related: [[job-tracking|job tracking]], [[info-exchanger|info exchanger]], [[discord-connections-mapper|discord connections mapper]], [[vibe-matcher|vibe matcher]]
\ No newline at end of file
new file mode 100644
index 0000000..53f1531
@@ -0,0 +1,17 @@
+---
+status: raw
+tags:
+- hardware
+title: robotic arm assistant
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# robotic arm assistant
+
+an alexa with arms — a voice-controlled robotic arm that handles physical tasks around the house. the premise is that smart home has stalled at controlling switches and speakers; the actual bottleneck in domestic automation is physical manipulation. fetching something from another room, handing you a glass of water, picking up a dropped item — these require a physical effector that doesn't exist in any consumer product. the arm doesn't need to be anywhere near Boston Dynamics-level; even a limited-DOF arm that can reliably pick up and hand over objects in a fixed workspace (desk, kitchen counter) would be genuinely useful.
+
+the design space is more constrained than it sounds. modern hobby robotics (ROS, cheap servo arms, depth cameras) has made a basic manipulation platform buildable for a few hundred dollars. the hard part is the manipulation policy: getting reliable grasping on arbitrary household objects across lighting and position variation is an open ML research problem, but demos from RT-2, ACT, and similar work suggest it's becoming tractable. the simplest version would start with a fixed set of "known objects" the arm has been trained on, expanding the object vocabulary over time. voice interface via an LLM handles intent parsing, and a camera provides object detection and pose estimation.
+
+related: [[sensor-capturer|sensor capturer]], [[always-on-ai-assistant|always-on AI assistant]], [[emg-bracelet|EMG bracelet]], [[pupilometry-glasses|pupilometry glasses]]
\ No newline at end of file
new file mode 100644
index 0000000..8d331d7
@@ -0,0 +1,19 @@
+---
+status: explored
+tags:
+- hardware
+- memory
+- wearable
+title: sensor capturer
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# sensor capturer
+
+a device that captures sensory experiences beyond what audio and video recording covers — smell, touch, temperature, humidity, spatial orientation. the problem it's solving: you can video a concert, but you can't record how the room smelled or the physical feeling of the crowd. memory research shows these modalities are often the most emotionally salient; losing them means losing the texture of the experience. the device would act as a wearable multimodal logger, capturing a richer slice of lived experience for later recall.
+
+the hardware side is tractable. most of the relevant sensors are cheap and small: gas sensors for rough olfactory capture, temperature/humidity, barometric pressure, accelerometer/gyroscope for motion and orientation. the harder problem is making sense of the data — raw sensor streams aren't interpretable without calibration and context. the interesting approach is pairing the raw sensor data with an LLM-based annotation layer that infers "this was a forest, cold morning, post-rain" from the sensor fingerprint combined with location/time metadata. over time, the system could learn personal sensory associations (this temperature + smell pattern = the beach house).
+
+related: [[brain-rewinder|brain rewinder]], [[universal-data-capturer|universal data capturer]], [[smell-resetter|smell resetter]], [[always-on-ai-assistant|always-on AI assistant]], [[emg-bracelet|EMG bracelet]]
\ No newline at end of file
new file mode 100644
index 0000000..d4e4b31
@@ -0,0 +1,27 @@
+---
+first_captured: 2026-03-28
+sources:
+- sources/ideaflow/2026-03-28_simulink-for-signals-but-more-general-and-easier.md
+status: raw
+tags:
+- engineering
+- signals
+- software
+- gui
+title: simulink alternative
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+> stub — needs expansion
+
+# simulink alternative
+
+Simulink for signals but more general and easier to use, portable into Python etc. "kinda like generalized Orb in GUI." a visual programming environment for signal processing that is not locked into MATLAB.
+
+---
+
+## timeline
+
+- [2026-03-28] captured — open, general signal processing GUI
\ No newline at end of file
new file mode 100644
index 0000000..2f6bf82
@@ -0,0 +1,38 @@
+---
+first_captured: 2025-08-31
+sources:
+- sources/apple-notes/archived/smell-resetter.md
+- sources/apple-notes/archived/build-notes.md
+- sources/google-sheets-ideas.md
+status: explored
+tags:
+- hardware
+- research
+- olfactory
+- science
+title: smell resetter
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# smell resetter
+
+a device to combat olfactory fatigue — the phenomenon where you stop smelling things you have been exposed to for a while. the personal motivation: having a cat and wanting to know what the house smells like to visitors.
+
+extensive research was done on the mechanism: Ca2+ ions flood olfactory receptor neurons causing depolarization, and a balancing feedback loop prevents overstimulation. coffee beans as palate cleansers are disputed. key finding: retronasal delivery (through the mouth) shows almost no olfactory adaptation compared to orthonasal delivery (through the nose), which is a significant insight for device design.
+
+the BubblEat paper proposed retronasal delivery via bubbles that pop in the mouth. existing work covers molecule detection, smell mapping with GNNs, and electronic noses. conclusion from research: "people already know about all this — the reason its not widespread is that it was recent or too hard/annoying to actually make it."
+
+aromatherapy has mixed evidence. the strongest potential application would be continuous injection of alertness/focus-promoting scents, but this remains unproven. connects to the [[sensor-capturer|sensory capturer]] as part of the broader sensory technology interest.
+
+**spreadsheet evaluation:** listed in "best" picks as "build a necklace pendant or something that is really good at classifying different smells, describe it to people whose smelling gets bad." tech depth 7/10, labeled [WRONG FIT] for the olfactory fatigue device variant ("hardware project, interesting but niche"). the classification/necklace angle is different from the resetter angle — one classifies what is present, the other resets your perception of it.
+
+---
+
+## timeline
+
+- [2025-08-31] evaluated in build-notes — "will learn so much, some people will care (?)"
+- [2025-09-26] discussed with evan wang — "what problem are you solving? seems like something just because its cool"
+- [2025-09-26] deeper research on olfactory fatigue mechanisms, retronasal vs orthonasal delivery
+- [2026-04-10] google sheets evaluation — listed in "best" picks (smell classification necklace), [WRONG FIT] for olfactory fatigue device
\ No newline at end of file
new file mode 100644
index 0000000..ed87a4d
@@ -0,0 +1,32 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- ai
+- developer-tools
+- context-engineering
+- agents
+- software
+title: spec-driven dev kit
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# spec-driven dev kit
+
+"complete spec driven dev kit; code is literally just artifact." the HumanLayer talk describes exactly this: a Research → Plan → Implement pipeline with context management for coding agents. the philosophy: a bad line of research (misunderstanding how the system works) can cause thousands of bad lines of code, so you invest most time in specifying the right problem and understanding the system before launching the coding agent.
+
+key components: research prompts that output file names and line numbers so agents know exactly where to look, planning prompts that specify every change with files and snippets, implement prompts that maintain context under 40% utilization, intentional human review at each phase. the research file is a lot easier to review than a 2000-line PR. the workflow keeps mental alignment across the team.
+
+connects to the [[hard-docs-writer|hard software component docs writer]] for the codebase understanding layer, and to the [[overnight-app-grinder|overnight app grinder]] which would benefit from spec-first architecture.
+
+**spreadsheet evaluation:** tech depth 8/10, labeled [DO THIS] — "you know coding agents well, could build tooling others actually use." blue ocean analysis: RED OCEAN positioning with medium 2-month feasibility — the market is competitive but the approach is strong. listed as one of 5 [DO THIS] tier projects.
+
+---
+
+## timeline
+
+- [2026-04-10] captured from google sheets — [DO THIS] tier, RED OCEAN but strong technical fit
\ No newline at end of file
new file mode 100644
index 0000000..ec6ae12
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- ai
+- tooling
+title: stress testing suite
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# stress testing suite
+
+a platform that combines agent-based automated testing with targeted human beta testers to find the failure modes in a product before launch. the core insight is that automated testing finds the bugs you thought to look for, while human testers find the ones you didn't — the weird flows, the misunderstood UI, the use cases you never imagined. the stress testing suite tries to get both at scale: AI agents hammer the product with edge cases, adversarial inputs, and high-volume scenarios; a matched panel of human testers explores the same product with real intent and diverse mental models.
+
+the agent side is where it gets technically interesting. instead of scripted test cases, the agents would use something like a planning + memory architecture to explore the product state space autonomously — clicking through UIs, submitting forms, calling APIs, and building a coverage map of what they've touched. they'd also run known adversarial patterns: prompt injection for AI-powered apps, concurrency issues, boundary inputs, malformed data. the human side would be a lightweight marketplace where you can recruit testers matching a specific demographic or use-case profile, then collect structured feedback through a standardized reporting flow.
+
+related: [[idea-tester|idea tester]], [[b2b-competitive-analysis|B2B competitive analysis]], [[spec-driven-dev|spec-driven dev kit]], [[pain-point-builder-marketplace|pain point marketplace]]
\ No newline at end of file
new file mode 100644
index 0000000..060da18
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- hardware
+- audio
+- experience
+title: surreal sound experiences
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# surreal sound experiences
+
+using multiple devices in a physical space to create immersive, spatially-distributed audio experiences that would be impossible with a single speaker. the core premise: every phone, speaker, and computer in a room is an independent audio emitter, and if you synchronize them, you can do spatial audio without expensive hardware — sound that appears to move across the room, audio that layers differently depending on where you're standing, experiences designed to feel surreal or disorienting in an interesting way. it's less about hi-fi playback and more about using ubiquitous connected speakers as a canvas.
+
+the engineering challenge is latency. getting multiple devices to play audio synchronously over WiFi/Bluetooth is hard; even a few milliseconds of offset creates phase issues that make the spatial illusion collapse. solutions exist — Sonos does multi-room sync, and protocols like PTP (Precision Time Protocol) can achieve sub-millisecond sync over local networks — but consumer-accessible, open tooling for this is limited. the interesting design space is what experiences you'd build if sync were solved: a horror experience where whispers seem to surround you using everyone's phones, a music venue where different instruments play from different corners, or a guided meditation where sound moves with deliberate spatial choreography.
+
+related: [[keystroke-music|keystroke music]], [[sensor-capturer|sensor capturer]], [[agent-simulation|agent-based simulation]], [[acoustic-drone-detection|acoustic drone detection]], [[invoking-thoughts|invoking thoughts]]
\ No newline at end of file
new file mode 100644
index 0000000..31a8065
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- research
+- ml
+- math
+title: symbolic regression
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# symbolic regression
+
+symbolic regression is the problem of discovering a mathematical expression that fits a dataset — not just fitting parameters, but finding the functional form itself. instead of saying "the relationship is linear, fit the slope," it asks "what is the relationship?" and outputs something like `f(x) = 3x² + sin(x/2)`. this is scientifically interesting because it can recover interpretable physical laws from data, which is fundamentally different from a neural network that just approximates. PySR is the main modern tool; it uses evolutionary search over expression trees and is surprisingly good at recovering known physics equations.
+
+the research angle is about what kinds of hidden structure can be discovered this way. the interesting applications: finding simplified approximations for complex ML models (symbolic distillation), discovering conservation laws in physical simulations, and compressing expensive neural network components into cheap algebraic forms. there's also a connection to mechanistic interpretability — understanding what a neural network is computing by finding a symbolic approximation of its behavior. the challenge is that the search space is enormous and most symbolic regression methods scale poorly to high-dimensional inputs or complex expressions.
+
+related: [[llm-physical-intuition|LLM physical intuition]], [[eeg-artifact-rejection|EEG artifact rejection]], [[flapping-airplanes|flapping airplanes]], [[simulink-alternative|simulink alternative]], [[idea-extraction-system|core idea extraction]]
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@@ -0,0 +1,28 @@
+---
+first_captured: 2026-04-06
+sources:
+- sources/ideaflow/2026-04-06_task-optimization-game-ideas---google-docs-httpsdocs.md
+- sources/obsidian/home/ideas/game_ideas.md
+status: raw
+tags:
+- game
+- productivity
+- software
+title: task optimization game
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# task optimization game
+
+a game where you optimize task scheduling — referenced in a Google Doc with detailed game ideas. could use a CLI as skeleton so Claude can interact with it and one-shot implementation. the obsidian game ideas note also mentions: math clicker game, spelling bee variants, numbers-in-grid games like gridentify.com.
+
+connects to the [[task-scheduler|task scheduler / better morgen]] for the serious productivity variant.
+
+---
+
+## timeline
+
+- [2025-12-22] game ideas captured in obsidian — math and optimization games
+- [2026-04-06] task optimization game linked to Google Doc spec
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index 0000000..8d3a2d6
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- ai
+- productivity
+title: task scheduler
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# task scheduler
+
+a better version of Morgen (or any AI scheduling tool) that solves two problems the current crop misses: auto-planning and wait-time awareness. auto-planning means when you add a task, the scheduler doesn't just block time — it reasons about what the task actually requires, breaks it into steps, and slots them intelligently given your current calendar and energy patterns. wait-time awareness is the more unusual insight: many tasks have external dependencies that block you mid-execution (waiting for a build, waiting for a reply, waiting for an API to respond), and a good scheduler would interleave work around those gaps rather than treating each task as a monolithic block.
+
+the scheduling problem is harder than it looks because it requires a model of the task itself, not just its time cost. a task that takes 2 hours is very different from one that takes 20 minutes active + 100 minutes waiting. the system would need to learn, over time, which of your tasks have wait-time signatures and start predicting them. the auto-planning component would decompose a high-level task like "prep for meeting with X" into component steps, estimate each, and slot them in reverse from the deadline. this is essentially a planning agent with calendar access.
+
+related: [[universal-habits|universal habits]], [[ifttt-personal|personal IFTTT]], [[task-optimization-game|task optimization game]], [[overnight-app-grinder|overnight app grinder]]
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@@ -0,0 +1,31 @@
+---
+first_captured: 2026-02-16
+sources:
+- sources/ideaflow/2026-02-16_exceptional-tutor-finding-platform.md
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- education
+- mentorship
+- marketplace
+- software
+title: exceptional tutor platform
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# exceptional tutor platform
+
+a platform for finding truly exceptional tutors, validated on two dimensions: subject matter expertise (easier to validate) and teaching ability (harder — could use pattern-following validation, reviews, or a combination). the key differentiator would be an extremely high bar for quality, ensuring every tutor is genuinely excellent.
+
+this is the human complement to the [[learning-suite|learning suite]] — AI-driven learning optimization plus access to verified great teachers. the idea acknowledges: "this might be something that already exists, and it's just hard to achieve the ideal case. but if we do, it's better."
+
+**spreadsheet evaluation:** originality 5/10, excitement 8/10, MVP 3-6 weeks. competitive landscape: Gooroo uses in-person interview vetting and is closest, Wyzant and Varsity Tutors do background checks + reviews but don't enforce pedagogical standards — no platform structurally enforces a three-pillar vetting framework (technical experience, teaching ability, verified reviews). marketplace cold-start problem — vetting process is the moat but also the bottleneck, could start hyperlocal (Bay Area only). tech depth 3/10, labeled [WRONG FIT] ("marketplace, not a technical grind").
+
+---
+
+## timeline
+
+- [2026-02-16] captured in ideaflow — voice note exploring two-and-a-half dimensions of tutor quality
+- [2026-04-10] google sheets evaluation — 5/10 originality, 8/10 excitement, [WRONG FIT] tier
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@@ -0,0 +1,29 @@
+---
+first_captured: 2026-04-10
+sources:
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- productivity
+- ai
+- software
+- screen-recording
+title: UI flow — contextual software coach
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# UI flow — contextual software coach
+
+an app that watches your screen activity and proactively suggests UI tutorials, settings, and features you're not using in commonly known apps. like a contextual coach for software productivity — it notices you're doing something the hard way and shows you the shortcut or feature that would help.
+
+this was listed as part of a "nice trio" of software projects alongside convo-flow and [[pause|Pause]]. the cross-app screen-watching + contextual suggestion combo differentiates it from enterprise products like Whatfix and WalkMe, which are installed by the app maker for their own app. no consumer product watches your general screen usage and proactively suggests tips across all apps.
+
+**spreadsheet evaluation:** originality 8/10, excitement 9/10 (one of the highest-excitement ideas), MVP 4-8 weeks. privacy-heavy — needs screen recording permission. could use local LLM to avoid sending screenshots to cloud. useful but "screen watcher" products face trust barriers. could start with one app (e.g. just VS Code tips). tech depth 7/10, labeled [WRONG FIT] ("screen recording + activity recognition — interesting but massive scope for 2 months").
+
+---
+
+## timeline
+
+- [2026-04-10] captured from google sheets — 8/10 originality, 9/10 excitement, [WRONG FIT] for 2mo scope
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index 0000000..5988b40
@@ -0,0 +1,19 @@
+---
+status: built
+tags:
+- software
+- nlp
+- writing
+title: precision description engine
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# precision description engine
+
+an engine that takes a description and makes it more precise — cutting ambiguity, sharpening specificity, identifying where vague words are doing work that concrete ones should be doing. the motivation: most writing (product descriptions, essays, emails, technical specs) is precise enough to feel real but vague enough to mean nothing. "a comprehensive solution for modern teams" describes everything and nothing. the tool pushes back on this by flagging weasel words, suggesting more specific replacements, and scoring descriptions on how distinguishable they are from alternatives. this project won a hackathon.
+
+the technical core is essentially an adversarial evaluation: given a description, can an LLM distinguish it from descriptions of adjacent things? if "a powerful note-taking app" describes 50 different products equally well, it's not a good description of yours. the engine would generate "imposters" — things that the description could also apply to — and rate precision by how well the description excludes them. this is a clean formalization of the problem: a description is precise if and only if it rules out alternatives. improving the description means shrinking the set of things it could apply to while still accurately describing the target.
+
+related: [[writing-tools|writing tools suite]], [[embedding-tone-interpolation|embedding tone interpolation]], [[personalized-autocomplete|personalized autocomplete]], [[quality-search|quality content search]], [[idea-extraction-system|core idea extraction]]
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@@ -0,0 +1,28 @@
+---
+first_captured: 2026-01-24
+sources:
+- sources/ideaflow/2026-01-24_train-an-alg-to-predict-how-long-until.md
+- sources/google-sheets-ideas.md
+status: raw
+tags:
+- sports
+- ml
+- software
+title: ultimate point predictor
+type: idea
+updated: 2026-04-10
+visibility: public-edit
+---
+
+# ultimate point predictor
+
+train an algorithm to predict how long until an ultimate frisbee point ends, or classify states: playing, about to score, point over (not playing). a fun sports ML application.
+
+**spreadsheet evaluation:** originality 8/10, excitement 3/10, MVP 4-8 weeks. competitive landscape: sports analytics ML exists for basketball (Second Spectrum), soccer — ultimate frisbee analytics is extremely underdeveloped (Ultiworld does basic stats). novel sport + novel application. needs labeled training data (game footage with timestamps), could use AUDL or Ultiworld tournament footage. computer vision for player tracking + disc tracking is the hard part. tech depth 5/10, labeled [ARCHIVE] ("fun but niche and small").
+
+---
+
+## timeline
+
+- [2026-01-24] captured — ultimate frisbee point prediction
+- [2026-04-10] google sheets evaluation — 8/10 originality, 3/10 excitement, [ARCHIVE] tier
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index 0000000..94f1e97
@@ -0,0 +1,18 @@
+---
+status: explored
+tags:
+- ai
+- search
+title: universal agentic searcher
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# universal agentic searcher
+
+a deep research pipeline where an AI agent doesn't just retrieve results but actively iterates on its understanding — querying, synthesizing, identifying gaps, and querying again. the core insight is that good human research is a conversation with information: you search, find something, realize what you actually need to know, search differently, and gradually build a coherent picture. standard search is step-one-and-done. the agentic searcher loops: it generates an initial query, summarizes what it found, identifies what's missing or contradictory, generates follow-up queries, and repeats until it's confident it has a complete answer or until it surfaces its remaining uncertainty explicitly.
+
+the adaptive feedback piece is what distinguishes this from just chaining search calls. the agent maintains a model of what it knows and what it doesn't — essentially a knowledge graph of the research question — and uses that to drive the next query. it also asks the user targeted clarifying questions when the research direction is genuinely ambiguous, rather than picking an interpretation silently. the output isn't a list of links but a synthesized answer with explicit sourcing, confidence levels, and a statement of what couldn't be verified. this is closer to how a good research assistant operates than any current search product.
+
+related: [[life-search|life search]], [[dense-info-generator|dense info generator]], [[quality-search|quality content search]], [[spec-driven-dev|spec-driven dev kit]], [[b2b-competitive-analysis|B2B competitive analysis]]
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index 0000000..b6746c1
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- ai
+- memory
+- data
+title: universal data capturer
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# universal data capturer
+
+an unstructured data logger that accepts anything — text, voice, images, sensor readings, location, whatever you throw at it — and uses an LLM to generate dynamic schemas on the fly rather than requiring you to pick a category or fill a form. the problem with most logging tools is they impose structure upfront: here's a field for "mood," here's one for "food eaten," here's one for "notes." the structure forces you to fit your life into its buckets. universal data capturer inverts this: log freely, and let the system figure out what you're tracking and how to structure it. over time, the schema adapts to your actual behavior — if you keep mentioning sleep quality, it starts tracking that specifically.
+
+the technical core is an LLM that ingests each new entry, compares it to what's been logged before, and decides: does this fit an existing schema? should I update the schema? is this a new category entirely? this is surprisingly tractable with a modern LLM — the hard part is making the schema evolution feel stable rather than constantly shifting. the user-facing value is that it lowers the activation energy for logging to near zero: just dump whatever is on your mind, and the system handles the rest. the data becomes queryable and analyzable later because the schema inference has done the structuring work.
+
+related: [[axon|axon]], [[life-search|life search]], [[sensor-capturer|sensor capturer]], [[me-model|me model]], [[episodic-memory-builder|episodic memory builder]], [[ifttt-personal|personal IFTTT]]
\ No newline at end of file
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index 0000000..8282fdc
@@ -0,0 +1,19 @@
+---
+status: explored
+tags:
+- productivity
+- habits
+- ai
+title: universal habits
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# universal habits
+
+the biggest recurring idea in this wiki — a context-aware adaptive habit system that adjusts what it asks of you based on what's actually going on in your life. standard habit apps assume a stable, predictable day: same time, same trigger, same action. real life isn't like that. some weeks you're traveling, some days you're wrecked, some habits are only relevant under specific conditions (exercise only when not injured, journaling only when stressed). universal habits treats each habit not as a fixed daily action but as a conditional behavior: "when X, do Y" — and the system figures out when X is true.
+
+the context layer is what makes this hard and interesting. the system needs to know where you are, what you've been doing, how you slept, what's on your calendar, what your energy seems to be (inferred from behavior patterns) — and use all of that to decide which habits to surface, when, and with what priority. this is fundamentally a personalized recommendation problem, not a reminder problem. the UX would feel less like an alarm going off and more like a quiet prompt that arrives when the moment is actually right for the behavior you're trying to build. the system gets better over time by learning which contexts correlate with you actually doing the habit versus skipping it.
+
+related: [[ifttt-personal|personal IFTTT]], [[cookedness-tracker|cookedness tracker]], [[invoking-thoughts|invoking thoughts]], [[axon|axon]], [[task-scheduler|task scheduler]], [[pause|Pause]]
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index 0000000..3d02ad7
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- ai
+- social
+- ml
+title: vibe matcher
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# vibe matcher
+
+a social network where the matching algorithm tries to find people you'd actually like — not people who look similar on paper (same school, same job, same interests) but people whose energy, humor, communication style, and values are genuinely compatible with yours. the insight is that most social platforms match on surface features: LinkedIn matches by industry, dating apps by location + photos, Twitter by topic. "vibe" is the thing that determines whether you actually enjoy talking to someone, and it's almost entirely absent from current matching.
+
+the ML angle is interesting because vibes are implicit in behavior, not explicit in profiles. how you write, what you respond to enthusiastically, what you ignore, how you structure sentences — these are all signals. the system would build a profile from natural behavior (messages, reactions, post patterns) and use that to find people with compatible signatures. the obvious concern is that this kind of matching can create bubbles — only connecting you with people who already think like you. the counter-design would intentionally include some "interesting adjacent" matches: people who have a compatible vibe but different context or knowledge, which tends to produce generative conversations.
+
+related: [[referral-hiring|referral-based hiring]], [[discord-connections-mapper|discord connections mapper]], [[info-exchanger|info exchanger]], [[culture-fingerprint|culture fingerprint]], [[connection-hub|connection hub]]
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index 0000000..e61b2e3
@@ -0,0 +1,18 @@
+---
+status: raw
+tags:
+- software
+- networking
+title: wifi client
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# wifi client
+
+a better WiFi management app — one that handles auto-joining known networks intelligently, manages the annoyance of captive portals, and makes password sharing easy. the current state of WiFi UX on every OS is bad in predictable ways: the system joins the wrong network because you were near it once, captive portals interrupt everything, sharing a password requires either telling someone verbally or going through a convoluted OS share flow. these are individually small problems but they're daily friction.
+
+the more interesting version of this extends to network intelligence: tracking which networks are actually fast vs. just accessible, auto-switching when your current connection degrades below a threshold, and detecting when you're on a network where something is being throttled or intercepted. on mobile, the auto-join logic could be smarter about when to use WiFi vs. cellular — not just "which is connected" but "which will actually serve this task better." password sharing is the most socially useful feature: a simple, cross-platform flow where you can share a network credential with someone nearby via QR code or a short-lived link.
+
+related: [[ifttt-personal|personal IFTTT]], [[optimize-computers|computer optimizer]], [[outdoor-work-setup|outdoor work setup]]
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index 0000000..b819ee0
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- ai
+- writing
+- nlp
+title: writing tools suite
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# writing tools suite
+
+a suite of writing quality tools that go beyond grammar checking — covering AI detection, plagiarism detection, style analysis, and quality scoring. the framing is that these should be a coherent suite, not four separate products, because the problems are related: a piece of writing can be grammatically correct, AI-generated, subtly plagiarized, and still be bad writing. addressing each in isolation misses the forest. the suite would give you a unified quality picture — what's wrong, why, and how to fix it.
+
+the AI detection component is the most technically contested piece. current detectors have high false positive rates and can be fooled by minor paraphrasing, which makes them unreliable for high-stakes use (grading, publishing). the more honest approach is probabilistic scoring with explicit uncertainty rather than binary "AI/not AI" verdicts. plagiarism detection has the same spectrum issue: verbatim copying is easy to catch, but idea-level plagiarism (paraphrasing someone's argument without credit) requires semantic understanding of the content. the quality scoring component would build on [[ultimate-describer|precision description engine]] logic — evaluating specificity, distinctiveness, and clarity.
+
+related: [[ultimate-describer|precision description engine]], [[college-essay-grader|college essay grader]], [[personalized-autocomplete|personalized autocomplete]], [[embedding-tone-interpolation|embedding tone interpolation]], [[hard-docs-writer|hard docs writer]], [[ai-conversationalist|AI conversationalist]]
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index 0000000..da8886f
@@ -0,0 +1,19 @@
+---
+status: raw
+tags:
+- ai
+- mental-health
+- education
+title: you're not behind machine
+type: idea
+updated: 2026-04-11
+visibility: public
+---
+
+# you're not behind machine
+
+a tool for combating the college application anxiety loop — the experience of scrolling LinkedIn and feeling like everyone else has more internships, more awards, more everything. the core mechanic: you describe your situation, and the tool surfaces evidence that you're not actually behind relative to your peers, draws on base rates and realistic comparisons rather than the highlight-reel-biased sample you see online, and helps you articulate what you have done in a way that's accurate and not self-minimizing. the name is both the thesis and the pitch.
+
+the psychological mechanism matters here. anxiety of this type isn't usually resolved by reassurance ("you're doing great!"), which sounds hollow. it's resolved by recalibrating the reference class — comparing yourself to the actual distribution of people your age, not the visible-on-LinkedIn tail. the tool would need real data on what students with various profiles actually achieve, and use that to show where you sit relative to realistic peers, not impressive outliers. there's also a writing component: helping you see that what you've done is actually impressive, you're just describing it in a self-effacing way that makes it sound like nothing.
+
+related: [[consciousness-for-students|student consciousness]], [[motivation-education|motivation in education]], [[oncue|OnCue]], [[life-guide|life guide]], [[intentionality-camp|intentionality camp]]
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