Gauge – Open-Source CLI to recursively improve your docs
Hi HN, I’m Evan from Gauge. We’re open-sourcing our CLI to recursively improve your docs for coding agents. The CLI includes a local execution backend, so you don't need an account to try it. Explainer video here: https://youtu.be/zhLUlIKB1MgThe CLI supports a hillclimbing loop:- set up tasks for coding agents that would need your docs, like 'set up Gauge CI checks'- run the tasks- analyze session transcripts for friction or failures- create variants of your docs to resolve the issues- re-run the tasks with the doc variants injected (transparently, at the same hostname)- repeat until you have a variant that resolves the issuesThe same loop supports hillclimbing your Skills, CLI, and other agent-facing surfaces.We started out 2 years ago building open-source developer tools (like Tach https://github.com/tach-org/tach), then pivoted to AI Search optimization, and are now focused on the intersection of the two spaces. We were already working with awesome devtool companies, and they were the first ones to ask us how to optimize for Claude Code and Codex. We realized this would be something that eventually every company needs to do. Our CLI provides primitives for docs/devrel/eng teams to measure, analyze, and improve how coding agents use their product.The design boils down to two components: VMs and a network gateway.The VMs are straightforward, we want the agents to be highly privileged within their environment so that they are representative of developers' coding agents. Each VM generally comes with the agent harness itself, language toolchains, and a git repository.The network gateway is where we terminate TLS and swap in variants of your docs transparently. The gateway also handles credential replacement, so agents see placeholders and can't exfiltrate keys.The CLI comes with a 'local' execution backend (inference is still remote). For VMs, it uses Docker containers, and the network gat
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Gauge is an open-source CLI tool that iteratively improves developer documentation by simulating coding agents to identify and fix friction points.
The pain
The gap
Build angle
Strengths
- Clear and specific problem in a growing AI developer tooling niche
- Open-source approach lowers adoption friction
- Technical design leverages VMs and network gateways for realistic agent simulation
- Early interest from devtool companies validates market need
Risks
- Uncertain willingness to pay beyond early adopters
- Complexity of integrating with diverse documentation and agent environments
- Dependence on remote inference infrastructure
- Potential competition from larger AI tooling platforms
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 85
- Solution gap
- 75
- Willingness to pay
- 70
- Buildability
- 80
The problem of improving documentation for coding agents is clear and real, the solution addresses a gap in tooling, there is moderate willingness to pay among dev teams, and the product is technically feasible for a small team.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea targets a niche but growing market with a clear pain point, the value proposition is strong but monetization and scaling remain uncertain.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The product fits a specific developer tooling need with a defensible technical approach, though market size and customer acquisition could be challenging.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The concept aligns with current AI and developer trends, but the complexity of adoption and integration may slow growth.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The startup idea solves a real developer pain with a technical moat, but requires validation on demand and business model.
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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