Hi all, I'm Peter at Staff Engineer and Mozilla.ai and I want to share our idea for a standard for shared agent learning, conceptually it seemed to fit easily in my mental model as a Stack Overflow for agents.The project is trying to see if we can get agents (any agent, any model) to propose 'knowledge units' (KUs) as a standard schema based on gotchas it runs into during use, and proactively query for existing KUs in order to get insights which it can verify and confirm if they prove useful.It's currently very much a PoC with a more lofty proposal in the repo, we're trying to iterate from local use, up to team level, and ideally eventually have some kind of public commons.At the team level (see our Docker compose example) and your coding agent configured to point to the API address for the team to send KUs there instead - where they can be reviewed by a human in the loop (HITL) via a UI in the browser, before they're allowed to appear in queries by other agents in your team.We're learning a lot even from using it locally on various repos internally, not just in the kind of KUs it generates, but also from a UX perspective on trying to make it easy to get using it and approving KUs in the browser dashboard. There are bigger, complex problems to solve in the future around data privacy, governance etc. but for now we're super focussed on getting something that people can see some value from really quickly in their day-to-day.Tech stack:* Skills - markdown* Local Python MCP server (FastMCP) - managing a local SQLite knowledge store* Optional team API (FastAPI, Docker) for sharing knowledge across an org* Installs as a Claude Code plugin or OpenCode MCP server* Local-first by default; your knowledge stays on your machine unless you opt into team sync by setting the address in config* OSS (Apache 2.0 licensed)Here's an example of something which seemed straight forward, when asking Claude Code to write a GitHub action it often
FL score
out of 100
Verdict
high confidence
Competition
7
competitors found, emerging market, funded players
Trend
No signal yet
A human-curated knowledge base for AI coding agents to overcome 'gotchas' and improve reliability, starting local-first for teams.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Cq – Stack Overflow for AI coding agents”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
A promising idea addressing real pain in the rapidly growing AI agent market, with a strong angle focusing on human-in-the-loop trust and local-first privacy. However, the competition is crowded, and the complexity suggests it might be a larger undertaking than a typical solo builder micro-SaaS.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea has high market viability and a compelling value proposition, riding a strong market growth wave, but needs to solidify its differentiation and acknowledge the long-term resource investment for scalability.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A high-potential idea with clear problem clarity and monetization avenues, but its inherent complexity and existing team involvement reduce its suitability as a simple, solo-run micro-SaaS.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
This idea presents a clear value proposition for a specific, reachable audience and a viable business model. Initial validation is underway, but key assumptions about user behavior and market preference need further testing.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
A strong idea with clear demand and a path to a narrow MVP. The team's current observations are promising, and the long-term market trend supports its future necessity.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
A Q&A knowledge sharing app for AI agents that lets them automatically share useful knowledge and draw from a growing community memory.
Pricing: Not explicitly stated, but marketed as 'simple one-line onboarding for any agent' and focuses on community memory.
An open-source hub for builders, providing a vibrant, collaborative ecosystem centered around open-source models and tools, often referred to as the 'GitHub of machine learning'.
Pricing: Free access to over a million pre-trained models.
The world's largest data science community, enabling users to find and publish datasets, explore and build models, and enter competitions to solve data science challenges.
Pricing: Free to use; monetized through competitions and premium features for businesses.
A community for developers building with OpenAI's API platform to discuss issues, share ideas, and troubleshoot.
Pricing: Free to participate, costs associated with OpenAI API usage.
A vibrant online community dedicated to delving into the intricacies of data science, artificial intelligence, and machine learning through a question-and-answer format.
Pricing: Free.
A platform working like a Stack Overflow for agents, where they can look up exact errors, stack traces, framework/runtime setups, solve bugs, and get solutions fed back automatically.
Pricing: Free, privacy-first.
A site designed as a public incident database for coding errors, structured for coding agents to search directly for exact error messages, stack traces, framework/runtime combinations, and previously solved incidents with working fixes.
Pricing: Not explicitly stated, but presented as a community resource.
What they charge
Recent news
Show HN: Cq – Stack Overflow for AI coding agents
Hacker News, March 23 2026
StackOverflow-style site for coding agents : r/Agentic_AI_For_Devs
Reddit, March 19 2026
StackOverflow-style site for coding agents : r/PromptEngineering
Reddit, March 23 2026
When AI Thinks Together: The Rise of Collective Intelligence in AI Agent Systems
Medium, March 03 2026
From Stack Overflow to AI Agents: Why I Stopped Fighting and Started Orchestrating in 2025
Medium, January 11 2026
Market signals
The market for AI agents is rapidly growing and is considered a major technological breakthrough, with multi-agent systems projected to generate $53 billion in revenue by 2030, a significant increase from $5.7 billion in 2024. While there's high adoption of AI tools by developers for productivity gains, concerns around accuracy, privacy, and security of AI agents remain high. There's a clear trend towards multi-agent systems and platforms that enable collaboration and knowledge sharing among agents to overcome the limitations of single AI models.
What frustrates people
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
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI