Show HN: A Karpathy-style LLM wiki your agents maintain (Markdown and Git)

Markdown, Git, and Durability

• Markdown is praised as open, simple, widely supported, and likely to remain readable long term.
• Git versioning is seen as a natural fit for tracking evolving agent-written artifacts.
• Some question whether markdown itself improves LLM performance or is mainly about distribution and tooling.

Obsidian vs Dedicated Agent Wiki

• Several suggest “just use an Obsidian vault + plugins.”
• Project authors argue Obsidian is single‑user–centric, lacks promotion workflows and machine-facing APIs (MCP tools), but can still act as a read-only or parallel editor on the same markdown tree.

Retrieval Strategy: BM25, Vectors, Indexing

• Many approve the “BM25-first” design over defaulting to vector databases.
• Discussion on routing: text length/shape is a weak signal; using the agent’s task context may be better for choosing between exact-match vs narrative retrieval.
• Others propose simple indices or TOCs; counterpoint is that cascaded filtering reduces context noise and makes reranking feasible.

Quality of Agent-Generated Wikis

• Strong skepticism that “teams of agents” mostly produce low-quality “slop.”
• Others report positive experiences when agents operate over a curated, git-based knowledge base, improving coordination across tools and repos.
• Cited research suggests fully LLM-maintained docs can degrade quality vs human-maintained; hybrid setups with human curation work better.

Note-Taking Philosophy and Noise

• Some reject automated note-taking entirely: the value is in humans building their own mental models.
• Others use agents for structuring, tagging, and refactoring notes, while keeping humans responsible for actual understanding.
• Concern that AI makes it too easy to generate mountains of text nobody reads.

Governance, Promotion, and Knowledge Decay

• Multiple comments stress separating “capture” from “promotion”: agents can draft freely, but trusted entries need human review or multi-agent agreement.
• Worries about confidently wrong entries compounding over time and being re-cited.
• Questions raised about missing features like temporal vs atemporal memory, snapshots, rollbacks, and explicit handling of business rules.

Deployment, Privacy, and Provider Support

• Current model is local use with git, without pushing to public hosts; some want easy self-hosted, multi-user setups.
• OpenAI-compatible endpoints (including local or alternative providers) are reportedly supported via an intermediate runtime.

Ecosystem, Overlap, and Hype

• Noted that multiple LLM-wiki systems hit the front page in a day; some see duplication and wish for collaboration.
• Mixed reactions to the product’s playful branding: some find it slick; others see it as satire or fad-chasing, while maintainers emphasize prior serious CRM/context-infra work underpinning it.