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 understand that claude-code takes a lot of memory and that's bad. However, harneses are simple loops, in theory should take very little memory even if written in python or typescript. See for e.g. pi agent

> “while taking the time to figure out how open source should work in the agentic era”

I can’t even guess what this means

> We care about the harness, not the model or the prompts.

I wonder if this is a viable approach; after all frontier model providers are betting on the opposite.

Then again, they bundle their harness and offer subsidiary pricing - so maybe they themselves aren’t sure if models are as important.

How good is it to work on building games, compared to existing agents? I am building my own game?

called out the most asked questions - where is the source - telemetry opt-in/opt-out in README of https://github.com/AntigmaLabs/ante

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?

this would go very hard with a lightweight gui

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.

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