CodeAlmanac – Karpathy-style codebase wiki from your conversations
Hey HN! This is Divit from Almanac (YC S26). We built CodeAlmanac, a wiki for your coding agents that updates as you talk to them. It is open-source, local, and free.Here’s a demo: https://www.youtube.com/watch?v=XNQWV3TFBWMYour CC/Codex conversations contain a LOT of knowledge that is forgotten because it was never documented. People have their own methods of documenting their chats. We used to make Markdown files like MANUAL.md and DESIGN.md, and would prompt Claude to keep them updated. The problem is that these files quickly become outdated and messy, and there’s only so much you can put in a single file.So we set out to build CodeAlmanac. We wanted something that was 1) maintained automatically, 2) lived inside our repository, and 3) used our existing Codex/Claude Code subscriptions.CodeAlmanac maintains an almanac/ folder inside your repository. It contains connected Markdown pages that cover things not documented in the codebase, including decisions you have made and why the codebase is shaped this way.The pages are indexed in SQLite and are queryable through a CLI. We add instructions to AGENTS.md or CLAUDE.md so future sessions automatically search the wiki before they start coding.Every five hours, CodeAlmanac uses the Codex/CC SDK to spin up an agent that reads your new conversations and updates the relevant pages. We went with a time-based trigger instead of commits because we saw people commit very frequently, which would lead to high token costs.We originally made CodeAlmanac for individual developers, but the team use case has become much more obvious to us while using it ourselves. We are a team of three, and each of us works with our own coding agents. Before this, I would make some change after a lot of thinking and then later have to call my cofounders and explain why it looked this way. There is almost a sense of relief now knowing that the decisions I made are written down somewhere their agents will actually r
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Automatic documentation system that extracts and organizes knowledge from AI coding conversations into a searchable wiki that agents can reference.
The pain
The gap
Build angle
Strengths
- Solves a real problem for a growing user base (Claude Code subscribers are increasing)
- Open-source + local-first positioning removes vendor lock-in concerns
- Integrates into existing workflows (lives in repo, uses existing subscriptions)
- Team use case is stronger than solo, knowledge sharing across agents is defensible
- YC backing provides credibility and distribution channel
- Low friction to try (free, open-source, no new subscriptions)
Risks
- Market size unclear, how many developers actually use Claude Code enough to need this? Adoption depends on Claude Code growth
- Competitive threat from Anthropic building this natively into Claude Code or from other AI IDE vendors
- Quality of auto-generated documentation is unproven at scale, hallucinations or irrelevant updates could make wiki noise instead of signal
- Requires developers to change workflows (add AGENTS.md/CLAUDE.md instructions, trust the system)
- Monetization path unclear, open-source + free model limits revenue unless they upsell hosted version or premium features
- 5-hour batch window means stale context during active development; real-time indexing might be needed for teams
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Five lenses, one composite. How scoring works
The angle
No market research recorded for this idea yet.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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