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.
nazgulsenpai•44m ago
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!
majorchord•27m ago
Opt-out telemetry is a hard no for me, sorry.
stronglikedan•18m ago
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.
niutech•7m ago
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?
swrrt•1h ago
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.
stronglikedan•21m ago
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.
throw_m239339•15m ago
> 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.
bigfishrunning•14m ago
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?
NitpickLawyer•12m ago
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.
_pdp_•10m ago
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?
jhgik798•9m ago
no source code
perching_aix•14m ago
Same idea that sends people down needless game engine development / procrastination rabbit holes.
jatora•10m ago
This is such a horrible cringe and bad faith take.
ubermon•1h ago
- 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.
nazgulsenpai•44m ago
majorchord•27m ago
stronglikedan•18m ago
niutech•7m ago