MindBase — Karpathy's LLM Wiki, as a product

MindBase Product Strategy — 2026-05-25

Written after a 30-commit session that exposed: the product is drifting toward being "AI-flavored Notion" while the unique architecture (LLM-Wiki pattern per Karpathy) is buried under generic chrome. This doc is the strategic reset.

0. The Verdict (TL;DR)

Stop building a general PKM tool. That market has no winner because no one needs a slightly-better Notion + AI. Re-aim MindBase at one sharp wedge the Karpathy LLM-Wiki pattern dominates:

"The research-wiki for knowledge workers doing sustained inquiry."
Drop 50 papers / articles / interview transcripts on a topic → get back a structured, interlinked wiki you can navigate, cite from, and grow over months.

This is a $500M+ niche (researchers + analysts + investigative journalists + solo consultants + grad students), high WTP ($20-50/mo), demoable in 5 minutes, and structurally unattackable by Notion / NotebookLM / ChatGPT Memory.

Tear out: chat-as-main-surface, the Notes/Wiki tab confusion, the homepage chat starter, broad PKM positioning, mobile-first investments.

Double down on: per-project wikis, conversational ingest, lint operation, INDEX-first retrieval, typed contradiction graph, local-first data ownership.

12-month target: 1,000 paying users × $25/mo avg = $25k MRR + a brand that researchers self-organize around.

1. Market Reality (where we actually are)

The PKM category has been failing for 5 years

• Roam Research: peaked 2020-21. ~70% user decline since. Founder went quiet. The "graph database for thought" thesis didn't hold.

• mem.ai: $33M raised (2022), pivoted twice, struggled with retention. Recent product is barely recognizable as the original "AI notes" pitch.

• Notion AI: bolted on, monetized weakly. Users describe it as "okay autocomplete," not transformative. ARR contribution unclear.

• Heptabase / Capacities / Tana / Mymind: each carved a small loyal audience (~10-50k paying), none breaking out.

• Obsidian: profitable, no VC, dominant in local-first prosumer niche via community plugins — but explicitly not AI-native.

Conclusion: "AI + PKM" without a sharp wedge has failed in market 5 times running. The next "AI notes app" gets ignored. Generic doesn't win here.

What HAS worked in 2024-25

• Granola (AI meeting notes): exploded. One wedge — meeting notes — done 10x better than incumbents. ~$15M ARR within 18 months.

• Cursor: replaced VSCode for many developers. One wedge — coding — done AI-natively from scratch. $100M+ ARR within 18 months.

• Perplexity: AI search. Different category but same pattern: AI-native beats AI-bolted-on, and a sharp wedge beats horizontal.

• NotebookLM Audio Overviews: viral 2024. Proved that AI knowledge products CAN go viral when the demo is concrete and the AI is the surprise.

The pattern: AI-first + sharp wedge + viral demo = breakout. AI-bolted-on

• horizontal positioning = dies in obscurity.

What's coming that threatens us

1. ChatGPT Memory + Projects — already shipping. Generic users won't need a separate PKM tool for "ask AI about my stuff."

2. Claude Projects + Skills — Anthropic's version. Similar trajectory.

3. NotebookLM expanding to persistent wikis — most likely competitor. Cloud-only is their structural weakness.

4. Notion AI rebuild — they'll get desperate to defend, eventually ship "Notion AI Wiki Maintainer." Slow but will arrive.

Window: ~12-18 months before AI-native PKM is table stakes. Need to establish a defensible category position now.

2. The Karpathy Pattern is the Lever — But Pattern ≠ Product

Karpathy's article is the best articulation of the AI-native PKM thesis anywhere. It will become as influential as Vannevar Bush's Memex essay was for hypertext.

But Karpathy explicitly says you don't need a product — claude-code + obsidian + CLAUDE.md does it. That's true for 100k power users.

The product opportunity is the other 100M people who:

• Want the LLM-Wiki workflow

• Won't run a terminal

• Won't write a CLAUDE.md

• Won't manually invoke lint on Saturday morning

• Need mobile/web capture

• Need this to "just work" on day one

The product wraps the pattern in:

1. Zero-config onboarding ("I'm researching X" → project scaffolded)

2. Visible LLM-at-work (streaming compile, conversational ingest)

3. Active wiki (daily briefs, contradiction alerts, lint cards)

4. Cross-device capture (web ext, mobile, MCP for agents)

5. Polished retrieval (INDEX.md + hybrid search hybrid)

The product is not the pattern. The product is the pattern made effortless.

3. Three Strategic Paths (so you can reject mine)

My recommendation: Path B (research wedge). Reasons:

• Highest WTP ($20-50 sustainable, vs $5-10 consumer)

• Sharpest pain (researcher reads 100+ papers/year, remembers <10%)

• Demoable (drop 30 papers on RAG → working wiki in 5 min = viral video)

• Sophisticated customer (gives product feedback, advocates publicly)

• Once won, expandable: research wedge → consulting wedge → team wedge → consumer reading wedge (each as adjacent SKU)

• Aligns with founder taste (Karpathy himself uses it this way)

Path C is the second-best if A is rejected — and we could even run B and C as separate product lines later. But picking one first is critical.

4. The Recommended Wedge: Research Wiki

The customer

Primary ICP: Knowledge worker doing sustained inquiry on a topic over weeks/months.

Concrete examples:

• PhD student writing a literature review (target: 200+ papers)

• Founder researching a new market before raising (target: 50+ interviews, reports)

• Tech analyst tracking a vertical (target: ongoing, 10 sources/week)

• Investigative journalist on a story (target: court docs, interviews, FOIA)

• Solo consultant building reusable IP per practice area

• Lawyer building case knowledge (target: precedents, depositions, briefs)

• Doctor / researcher tracking a condition (target: papers, patient notes)

What unites them:

• Volume: 50-500+ sources per project

• Duration: weeks to years

• Multi-source synthesis required (single paper insufficient)

• Citation matters (must trace claims back to sources)

• Recall over time matters (return to project after weeks/months)

• Budget exists ($20-200/mo professional tool)

The pain (sharp, urgent, current)

Current workflow for our ICP:

• Papers/articles scattered across Zotero, browser tabs, Notion, OneNote

• Highlights / notes pile up but never synthesize into anything reusable

• "What did I learn last month?" → impossible to answer without re-reading

• New paper arrives → no easy way to know "does this contradict what I thought?"

• Bibliography for the eventual paper / report = a week of work at the end

Current solutions and their gaps:

• Zotero: bibliography only, no synthesis

• Notion / Obsidian: manual organization, abandons after 100 sources

• Roam Research: graph but no AI maintenance, declined

• NotebookLM: cloud-only, conversation-mode (not accumulating wiki), 10-source cap, no projects, no privacy

• Perplexity Pro: ad-hoc search, not building a corpus you own

• ChatGPT Projects: memory is amorphous chat history, not a navigable wiki

Nobody is shipping "the AI research analyst that builds your wiki for you."

Why MindBase wins this wedge

Already-built advantages (the silver lining of having over-built):

• Compile pipeline that updates multiple pages per source (degraded but fixable per the May 22 logs showing 18-22 actions/source)

• Typed knowledge graph with contradiction detection

• Local-first markdown storage (privacy = required for research)

• Multi-surface capture (browser, mobile, MCP)

• Hybrid retrieval infrastructure

• Streaming compile that can become "watch the AI work"

What needs to be built/changed:

• Per-project scoping (current global wiki kills this wedge)

• Conversational ingest (the missing step 4 from Karpathy)

• Lint operation (the killer differentiator)

• INDEX-first retrieval (proper implementation of Karpathy's spec)

• Onboarding focused on research projects (not chat / notes)

• 3-layer architecture made physical (not collapsed into wiki/notes/)

5. What to RIP OUT of current MindBase

To win the research wedge, these have to go (or get massively de-emphasized):

Roughly 30-40% of current UI surface disappears in this reset. That's the price of focus.

6. The Wedge Roadmap (12 months, opinionated)

Phase 1: Architectural reset (Month 1) — non-negotiable foundation

• Physically separate wiki/concepts/ (LLM-owned) from wiki/notes/ (user)

• Implement wiki/schema.md per project (Karpathy's "key configuration file")

• Make INDEX.md auto-maintained and the primary retrieval path

• Expand log.md to record queries + lint passes, not just ingests

• Fix compile prompt back to 10-15 page touches per source (was working May 22)

• Add conversational ingest (the missing "discuss takeaways" turn)

• Build lint operation (the 6 checks + 2 suggestions Karpathy listed)

• Ship/don't-ship gate: open with a brand-new research project, drop 5 papers, watch wiki grow from 0 → 30 interlinked pages. If demo feels magic, ship. If not, iterate.

Phase 2: Project as first-class + reposition (Month 2)

• "Project" replaces "global workspace" as primary entity

• Project templates: Literature Review / Market Research / Investigation / Case File / Topic Tracker

• Each project = its own scope (sources, wiki, schema, INDEX, log, chat)

• Onboarding: "What are you researching?" → scaffolds a project

• Rebrand: landing page leads with research wedge messaging

• New home: project dashboard (growth metrics, recent wiki updates, lint cards, suggested next questions)

• Ship/don't-ship gate: external user can complete onboarding and reach "wiki has 5+ pages" in under 10 minutes without asking for help.

Phase 3: Viral demo + 100 early users (Month 3)

• Record 5-minute demo: "Watch me research [topic] in 10 minutes — 30 papers in, wiki grows, contradictions surface, exportable bibliography out"

• Twitter/X seeding with researcher influencers (especially ML researchers given the Karpathy connection — natural amplifier)

• Hacker News launch ("Show HN: We built Karpathy's LLM-Wiki pattern as a product"). The article cite is gold.

• Reddit /r/PhD, /r/AcademicQuant, /r/MachineLearning

• Goal: 100 active free users, 10 paying for early-access Pro at $20/mo

Phase 4: Pro tier + capture polish (Months 4-6)

• Pro $20/mo: hosted LLM, sync across devices, premium models, higher ingest limits

• Browser extension v2: ace academic paper sites

• "Project export": full markdown bundle, BibTeX, PDF compiled wiki

• "Project share": read-only public link (researchers love showing off)

• MCP server v2: any LLM agent (Claude Code, Cursor) can read/write your wiki

• Goal: 500 paying users × $20/mo = $10k MRR

Phase 5: Specialization + adjacent SKUs (Months 6-9)

• Per-discipline schema templates (Bio, Law, Finance, History, ML, Medicine)

• Pre-built ingest connectors (Zotero import, Readwise import, Notion import, Pocket import, Kindle highlights)

• "Lint subscription": weekly digest of wiki health → email or app

• Consider Reading wedge launch as separate landing page ("MindBase for Books") with shared backend

• Goal: 1,500 paying users × $25/mo blended = $37k MRR

Phase 6: Team variant beta (Months 9-12)

• Research labs / consulting firms / investigative newsrooms — 5-20 person teams

• Shared projects + role permissions

• Slack import + meeting transcript import (Otter / Granola APIs)

• Pilot pricing: $50/user/mo, 5 design partners

• Goal: 3-5 paying teams × ~$500/mo each = additional $2k MRR

Realistic 12-month outcome: ~$30-40k MRR, ~2k paying users, recognized as "the research wiki product" within the LLM-aware knowledge worker community. A respectable seed/A1 fundable trajectory if external capital wanted.

7. Business model

Pricing tiers (proposed)

Why this works

• Free tier = product-led growth + viral demo

• $20 Pro = anchor most common conversion

• $50 Research = self-selected serious users; high gross margin (heavy users pay more but use more API)

• Team = future leverage when single-user retention is proven

Unit economics sketch

• Hosted LLM cost per Pro user: $3-8/mo (claim model, mostly Sonnet)

• Pro gross margin: 60-85%

• Research user margin: 50-70% (heavier API use, premium models)

• Break-even at ~500 paying users assuming $5k/mo opex (1 founder + minimal infra)

Distribution

• Inbound: SEO ("AI for literature review," "AI research tool," "Karpathy LLM wiki"), HN, Twitter/X, demos on YouTube

• Community: ML / academic Twitter, PhD Discord servers, Roam refugees, Obsidian community

• Partnerships (later): Zotero plugin, Readwise integration, Anthropic Skills directory feature

• Paid (later): highly targeted academic Google Ads, ResearchGate ads

• Anti-distribution: do NOT pursue Producthunt-style spray (low-quality signups, no retention)

8. Moats — what makes this defensible

In order of strength:

1. Per-project evolving schema (wiki/schema.md). The longer you use it, the better tuned to YOUR research style. Switching cost compounds. Cannot be replicated by a competitor onboarding from zero.

2. Local-first + data ownership. A meaningful fraction of researchers will not consider cloud-only tools (HIPAA-adjacent fields, journalists, IP-sensitive work). Notion and NotebookLM structurally cannot serve them.

3. Typed contradiction graph that gets denser with corpus. A new entrant has to re-detect contradictions on import; we surface them as you go. Quality of this feature is corpus-dependent.

4. MCP-as-platform. Once you're the LLM agent's persistent memory for research, agents accumulate context inside you. Cursor / Claude Code / future agents become your distribution because they need you.

5. Brand as "the Karpathy-pattern product." First-mover on the named pattern. Influencer reach via the gist. We can build the canonical implementation in public.

6. Speed of iteration on the pattern. Whoever ships lint / file-back / conversational ingest first owns the mental model. Notion is too big to pivot; NotebookLM is constrained by Google review cycles.

What is NOT a moat:

• Pretty UI (copyable)

• LLM quality (we use the same models)

• Compile speed (model providers dominate)

• Folder UX (Notion / Obsidian are better)

• Generic chat (everyone has it)

9. Existential risks (in priority)

1. ChatGPT / Claude Memory eats casual use case. Probability: 70% within 12 months.

   • Mitigation: dominate the research wedge before this. Our users need structure (citations, exports, contradictions) — chat memory is amorphous. Different job.

2. NotebookLM ships persistent wikis. Probability: 50% within 12 months.

   • Mitigation: we're local-first; they're not. Privacy researchers / IP- sensitive fields are ours. Also: ship faster.

3. The Karpathy pattern just doesn't scale past 1000 pages. Probability: 30% — real engineering risk.

   • Mitigation: dogfood hard. Build at 1000+ page scale in our own usage. Have a "scale strategy" (smarter indexing, page partitioning, cluster summaries) ready to ship if hit.

4. Unit economics on hosted LLM Pro tier are upside-down. Probability: 40% if not careful.

   • Mitigation: aggressive prompt caching, smart model routing (Haiku for classification, Sonnet for compile, Opus only on demand), per-user monthly LLM budget, BYO-API option for heavy users.

5. Researchers won't trust a "AI black box" for serious work. Probability: 40%.

   • Mitigation: provenance everywhere, citations visible, audit log surfaced, "show me the prompts" mode for skeptics.

6. Founder bandwidth. Probability: high if solo.

   • Mitigation: ruthless scope. Don't build a journaling app, a team SKU, a mobile app, AND a research wedge simultaneously.

10. What this strategy explicitly rejects

For clarity — these are decisions, not oversights:

• ❌ Becoming a Notion alternative. That market has no winners and we can't out-broad Notion.

• ❌ Becoming a consumer journaling app. Lower WTP, less differentiation from Reflect / Mymind.

• ❌ Building a team SKU in year 1. Team products require sales / support / SOC2 / enterprise UX that solo founders can't sustain.

• ❌ Building a mobile-first product. Research happens at desk. Mobile is capture-only.

• ❌ Going after Cursor / NotebookLM / ChatGPT directly. We can't. Find adjacent ground.

• ❌ AI auto-everything. Conversational ingest is the OPPOSITE — user curates, AI executes. Don't try to be "magic AI that does it all."

• ❌ Spending time on chat UX polish. Chat is supplementary, not core.

11. The decisions I need from you

Before any code changes, these need your call:

12. Closing thesis

MindBase is a year too early or two years too late as "yet another AI PKM tool." It's exactly on time as the canonical implementation of Karpathy's LLM-Wiki pattern, sharpened to the research-wiki wedge.

The next 90 days decide whether MindBase becomes:

• (A) Another forgotten note-taking app that pivoted three times, or

• (B) The product researchers / analysts cite when asked "how do you keep track of everything you've read?"

The architecture is mostly right. The product surface is mostly wrong. The fix is tractable but requires saying no to most current code.

My recommendation: commit to Path B, start Phase 1 (architectural reset) this week, ship a demo-able research wedge by end of Q1.

If you reject this, the next-best is Path C (reading wedge) — same architecture, different ICP, lower WTP but more viral.

Path A (stay horizontal) I do not recommend at any price. The PKM graveyard is full of horizontal AI tools.