ubermon

8 ms·

1.
by ubermon 1mo ago
the point is you can redirect all the telemetry to your own exporter with nothing leaves your machine and all the benefits.
2.
by ubermon 1mo ago
it can be opt-out, opt-in, but the telemetry needs to be there to actually improve the agent.
3.
by ubermon 1mo ago
it is more like an actual harness with a managed and pinned llama the control is inverse. Harness is compute and Model is data
4.
by ubermon 1mo ago
there are so much content and valuable stuff in the github repo you can pretty much recreated with your own agent.
5.
by ubermon 1mo ago
yes. Offline mode is choice, should be able to run frontier one first and then figure out how do incorporate local models as real workhorse
6.
by ubermon 1mo ago
there are still many bad players in the industry, i wouldn't mind sharing it with trusted group. But I am not yet strong enough with deal and handle all those yet.
7.
by ubermon 1mo ago
generally the challenge now is that 1. how to deal with PR spams by AI bots 2. how to make the project sustainable especially when one has no distribution. when anyone can insta remix and re-package and re-sell your hard work. For knowledg
8.
by ubermon 1mo ago
called out the most asked questions - where is the source - telemetry opt-in/opt-out in README of https://github.com/AntigmaLabs/ante
9.
by ubermon 1mo ago
I wonder my self, was notified by a friend, but I am grateful.. Probably because of the Meta's open model release?
10.
by ubermon 1mo ago
my view on harness is that it is to capturing the structural mechanism with llm interacting the world. They are a dynamic duo evolving together. The technical depth will continue to grow (e.g. /goal being the new primitive, multi-agent
11.
by ubermon 1mo ago
I love pi and share many vision and value with it. But my view on harness is that it is to capturing the structural mechanism with llm interacting the world. They are a dynamic duo evolving together. The technical depth will continue to gro
12.
by ubermon 1mo ago
Oh no, definitely not Internet Explorer. Will figure out a way to ship with source first to address the security and concern and then figure out how to do the open source development in agentic era later, I was too carried away by the compl
13.
by ubermon 1mo ago
the public repo README is updated.
14.
by ubermon 1mo ago
there is one toy version https://github.com/AntigmaLabs/nanochat-rs README updated.
15.
by ubermon 1mo ago
even with power of AI, we are mere human and still slow. Adding this to backlog.
16.
by ubermon 1mo ago
feedback received, it was carry over from the preview dev build.
17.
by ubermon 1mo ago
for now only some of core crates is migrated, will do so progressively
18.
by ubermon 1mo ago
so it is more for being self contained and works out of box if being deployed in a bare linux environment. we tried shell out to `rg` it didn't work very well and instead spending time handling the args parsing and jugging string outp
19.
by ubermon 1mo ago
nice, good to see more contributor in this space
20.
by ubermon 1mo ago
we put it in the repo README, will add migrate more into public repo as soon as possible.
21.
by ubermon 1mo ago
yes, the goal is to perfect the `ante serve` so it is easy to build gui. We are building one internally to test the protocol version
22.
by ubermon 1mo ago
will add those soon!
23.
by ubermon 1mo ago
we have a detailed launch thread explaining and show case exactly this! https://x.com/NoCommas/status/2086835536598351955
24.
by ubermon 1mo ago
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
25.
Show HN: Ante, a coding agent in a single binary that runs offline(github.com)
169 points by ubermon 1mo ago|93 comments
26.
by ubermon 1mo ago
yeah, that bench has a lot of room to improve, but it is the best we can find now
27.
DeepSeek V4 Flash 0731: 82.7% on Terminal-Bench 2.1 with a public harness(antigma.ai)
37 points by ubermon 1mo ago|6 comments
28.
by ubermon 2mo ago
it is indeed surprising how much a small model can do! As you said, for specific tasks like coding, the training data quality and new architecture of newer models probably beats model size
29.
by ubermon 2mo ago
yes. I think there is a threshold of some sort where if a model pass it can tolerate less system prompts. For example, same short prompt could work with deepseek v4 flash but not so much with Qwen 3.6 27B.
30.
Claude Code Cut Their System Prompt by 80%. Does That Work for Small Models Too?(antigma.ai)
7 points by ubermon 2mo ago|4 comments

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