I built Axe because I got tired of every AI tool trying to be a chatbot.Most frameworks want a long-lived session with a massive context window doing everything at once. That's expensive, slow, and fragile. Good software is small, focused, and composable... AI agents should be too.Axe treats LLM agents like Unix programs. Each agent is a TOML config with a focused job. Such as code reviewer, log analyzer, commit message writer. You can run them from the CLI, pipe data in, get results out. You can use pipes to chain them together. Or trigger from cron, git hooks, CI.What Axe is:- 12MB binary, two dependencies. no framework, no Python, no Docker (unless you want it)- Stdin piping, something like `git diff | axe run reviewer` just works- Sub-agent delegation. Where agents call other agents via tool use, depth-limited- Persistent memory. If you want, agents can remember across runs without you managing state- MCP support. Axe can connect any MCP server to your agents- Built-in tools. Such as web_search and url_fetch out of the box- Multi-provider. Bring what you love to use.. Anthropic, OpenAI, Ollama, or anything in models.dev format- Path-sandboxed file ops. Keeps agents locked to a working directoryWritten in Go. No daemon, no GUI.What would you automate first?
FL score
out of 100
Verdict
high confidence
Competition
8
competitors found, emerging market, funded players
Trend
No signal yet
A 12MB Go binary that re-envisions AI agents as composable Unix programs, solving developer pain with bloated frameworks.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Axe – A 12MB binary that replaces your AI framework”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Strong problem-solution fit for developers tired of bloated AI frameworks, with a clear technical angle and good market timing, but faces competition in the broader 'lightweight AI' space.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Excellent market timing and a compelling value proposition for developers, offering a unique approach to AI agent development with strong growth potential.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
An excellent idea for a solo builder with a clear problem, strong creator fit, and a highly differentiated, simple solution for a reachable developer audience.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A highly specific, valuable solution for a reachable developer audience, with good early validation but requiring a clear monetization strategy.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Axe addresses a deeply felt, specific pain point for developers with a minimalist, composable approach that is highly relevant for the future of AI automation.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
An open-source application that allows users to run large language models (LLMs) locally on their own hardware.
Pricing: Free, open-source. Costs are associated with API keys for external LLM providers or hardware for local models.
A Python-based AI agent framework providing essentials for a personal AI assistant with a minimal footprint.
Pricing: Not explicitly stated, but described as open-source and lightweight, implying a free or low-cost usage model.
Google's lightweight library for on-device machine learning inference, supporting mobile and embedded devices.
Pricing: Free, open-source.
Apple's machine learning framework for integrating trained models into applications across Apple platforms.
Pricing: Free, included with Apple's developer tools.
A cross-platform inference engine for ONNX models, providing high performance for machine learning models.
Pricing: Free, open-source.
An SDK for high-performance deep learning inference, optimizing models for NVIDIA GPUs.
Pricing: Free for developers; commercial use may involve NVIDIA hardware costs.
Offers SmolAgents, a lightweight framework for building and deploying AI agents with minimal setup.
Pricing: Hugging Face offers free and paid tiers. Specific pricing for SmolAgents is not detailed, but the platform generally operates on a freemium model with paid options for enterprise features and dedicated compute.
Various libraries like SmartCore and Linfa offer algorithms for classification, regression, and clustering, while Candle is a PyTorch-like deep learning framework in Rust.
Pricing: Free, open-source.
What they charge
Recent news
Axe: A 12MB Binary That Replaces Your AI Framework
Agent Wars, March 12 2026
Show HN: Axe – A 12MB binary that replaces your AI framework
AI Navigate, March 12 2026
Axe: A 12MB Go Binary That Treats AI Agents Like Unix Programs
Top AI Product, March 13 2026
Show HN: Axe – A 12MB binary that replaces your AI framework
Hacker News, March 12 2026
Show HN: Axe – A 12MB binary that replaces your AI framework
ZeroMiss AI, March 13 2026
Market signals
The edge AI market, which focuses on processing AI close to data generation, is experiencing significant growth. It was valued at USD 12.5 billion in 2024 and is projected to reach around USD 109.4 billion by 2034, growing at a CAGR of 24.8% between 2025 and 2034. Other estimates place the market at USD 23.3 billion in 2024, growing to USD 196.6 billion by 2034 with a CAGR of 23.8%. This growth is driven by the expansion of IoT and connected devices, demand for real-time and low-latency data processing, and increasing adoption of AI automation across industries. North America holds a dominant market position, with the U.S. expected to reach USD 20 billion by 2034.
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