Linking to a github repo for a binary release (no source code related to the agent that I could see) is a bit iffy IMO. You should clarify your intentions or link to something else. Might confuse folks.
This is a coding agent implementation I am working on, which delivers what this promises (at least on the "lean" part), except it's actually fully open source, and even more lean (few MBs of runtime memory usage).
MIT-licensed, written in C, multi-provider / multi-model, minimalist approach to system prompt and tools (think kinda like pi, but with a bit more "batteries included", like subagents and background tasks out of the box), polished presentation, inspectable (usable transcript view), etc.
That's like saying you aren't building a house if you use a hammer to drive nails instead of your hand. AI is just a tool like any other, and you use it to build things like you would any other tool.
I wouldn't even compare it to a tool. People frequently "build their own house" where they sub out 75% of the skilled labor and act as a General Contractor/glorified gopher.
And you won't get purity tests from the layperson: in the end, you're responsible for the build quality so if you tirelessly labor/oversee those teams you're considered capable; if it ends sub-par, then you're a stooge.
> That's like saying you aren't building a house if you use a hammer to drive nails instead of your hand. AI is just a tool like any other, and you use it to build things like you would any other tool.
A coding agent is more like a carpenter, a mason, an electrician,... rather than a hammer in that case.
Considering that ripgrep, git, and, you know, other dev tools are part of the toolbox, then why ship them inside this executable? And, furthermore, if you ship them, then why stop there?
Hi HN, I'm Mohan from Antigma Labs. Ante is a coding agent that ships as one self-contained ~15MB binary: the TUI, an embedded ripgrep, local PDF/OCR, and a natively managed llama.cpp engine are all inside. No runtime dependencies, no node_modules, no account.
- Ante installs a pinned, checksum-verified official llama.cpp build matched to your machine (Metal on Apple silicon; CUDA, Vulkan, or CPU on Linux) and handles upgrades when the pin changes.
- It discovers GGUF files already on disk (~/.ante/models, the llama.cpp and Hugging Face caches), attaches to llama servers already running on local ports, and estimates RAM/VRAM from model size and context window before anything loads.
- `ante --offline-model /path/to/model.gguf "prompt"` boots the server, runs the session, and shuts it down. `/offline-mode` does the same interactively; `ante serve --offline-model` loads a model once for many clients.
- No API key, no account. Once the model is on disk, inference needs no network at all; set ANTE_TELEMETRY=off and no telemetry is exported either.
On capability, we'd rather publish the number than oversell: we benchmark local models with the same harness and auditable runs as frontier ones, and Qwen3.6 27B (a 17 GB download) scores 56.2% on Terminal-Bench 2.1 across 445 trials (live results: https://antigma.ai/eval). That's a real gap from frontier models. The design bet is that you mix: hosted providers and local live in the same catalog, `/providers` switches mid-session, so sensitive repos or high-volume work go local and hard problems go frontier.
Hosted models work with your own keys or subscription. But nothing about trying Ante requires signing up for anything: download the binary, point it at a GGUF.
Offline mode is under active development and has rough edges with the overview at https://ante.run/local/overview. I'll be in the comments.
Why not make it Actually Portable Executable using Cosmopolitan Libc, like Llamafile, to make it run on Windows/Linux/MacOS? Why don't you support Windows with CUDA?
Right? How hard is it to just ask a single opt-in question during installation. Opt-out just seems lazy, especially if I have to dig though configs to get to it.
Linking to a github repo for a binary release (no source code related to the agent that I could see) is a bit iffy IMO. You should clarify your intentions or link to something else. Might confuse folks.
I will leave this here: https://usehax.dev/ GitHub repo: https://github.com/OleksandrChekhovskyi/hax
This is a coding agent implementation I am working on, which delivers what this promises (at least on the "lean" part), except it's actually fully open source, and even more lean (few MBs of runtime memory usage).
MIT-licensed, written in C, multi-provider / multi-model, minimalist approach to system prompt and tools (think kinda like pi, but with a bit more "batteries included", like subagents and background tasks out of the box), polished presentation, inspectable (usable transcript view), etc.
Linking to a binary is iffy from a security perspective. Linking to a GitHub repository is exactly what HN should do.
this would go very hard with a lightweight gui
How good is it to work on building games, compared to existing agents? I am building my own game?
You aren't building your own game if you have a chatbot do it for you.
That's like saying you aren't building a house if you use a hammer to drive nails instead of your hand. AI is just a tool like any other, and you use it to build things like you would any other tool.
I'd say it's closer to a Roomba then a hammer -- if you just let a Roomba run around your living room, can you say you vacuumed?
Well, you tell your Roomba "clean the floor" but you don't ask an AI "make a game". You give it very specific instructions.
Specific compared to writing procedural code> Barely even by analogy, IMO
One could even argue what defines AI instructability is heuristics as opposed to specifics
I wouldn't even compare it to a tool. People frequently "build their own house" where they sub out 75% of the skilled labor and act as a General Contractor/glorified gopher.
And you won't get purity tests from the layperson: in the end, you're responsible for the build quality so if you tirelessly labor/oversee those teams you're considered capable; if it ends sub-par, then you're a stooge.
[delayed]
> That's like saying you aren't building a house if you use a hammer to drive nails instead of your hand. AI is just a tool like any other, and you use it to build things like you would any other tool.
A coding agent is more like a carpenter, a mason, an electrician,... rather than a hammer in that case.
This is such a horrible cringe and bad faith take.
Same idea that sends people down needless game engine development / procrastination rabbit holes.
we have a detailed launch thread explaining and show case exactly this! https://x.com/NoCommas/status/2086835536598351955
no source code
Considering that ripgrep, git, and, you know, other dev tools are part of the toolbox, then why ship them inside this executable? And, furthermore, if you ship them, then why stop there?
Hi HN, I'm Mohan from Antigma Labs. Ante is a coding agent that ships as one self-contained ~15MB binary: the TUI, an embedded ripgrep, local PDF/OCR, and a natively managed llama.cpp engine are all inside. No runtime dependencies, no node_modules, no account.
- Ante installs a pinned, checksum-verified official llama.cpp build matched to your machine (Metal on Apple silicon; CUDA, Vulkan, or CPU on Linux) and handles upgrades when the pin changes. - It discovers GGUF files already on disk (~/.ante/models, the llama.cpp and Hugging Face caches), attaches to llama servers already running on local ports, and estimates RAM/VRAM from model size and context window before anything loads. - `ante --offline-model /path/to/model.gguf "prompt"` boots the server, runs the session, and shuts it down. `/offline-mode` does the same interactively; `ante serve --offline-model` loads a model once for many clients. - No API key, no account. Once the model is on disk, inference needs no network at all; set ANTE_TELEMETRY=off and no telemetry is exported either.
On capability, we'd rather publish the number than oversell: we benchmark local models with the same harness and auditable runs as frontier ones, and Qwen3.6 27B (a 17 GB download) scores 56.2% on Terminal-Bench 2.1 across 445 trials (live results: https://antigma.ai/eval). That's a real gap from frontier models. The design bet is that you mix: hosted providers and local live in the same catalog, `/providers` switches mid-session, so sensitive repos or high-volume work go local and hard problems go frontier.
Hosted models work with your own keys or subscription. But nothing about trying Ante requires signing up for anything: download the binary, point it at a GGUF.
Offline mode is under active development and has rough edges with the overview at https://ante.run/local/overview. I'll be in the comments.
Where is the source code?
Not sure why this was dead but I vouched. It would be nice if telemetry was opt-in, otherwise this looks awesome and can't wait to try it!
Why not make it Actually Portable Executable using Cosmopolitan Libc, like Llamafile, to make it run on Windows/Linux/MacOS? Why don't you support Windows with CUDA?
Opt-out telemetry is a hard no for me, sorry.
Right? How hard is it to just ask a single opt-in question during installation. Opt-out just seems lazy, especially if I have to dig though configs to get to it.