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- Good to see more harnesses coming out. I think the initial set of "features" that made into harnesses like tool calling, multi-turn chat, MCP, skills and so on can all be optimized. And then much more can be done on top.
I am trying out a two-model approach where small model has access to tools, large model does not. Small model shapes prompts from the repo graph. And repo graph is the only tool that small model has when reading. The small model is already given a set of context from git log, codebase and Markdown/text files (generally design files) depending on the user's prompt.
I do not want to use use multi-turn chat. Small model would instead create fresh prompts for the larger model feeding context and reshaping the original ask every time.
Also, reference repositories can be added for small model to help ask right questions. Once a plan is made by large model, execution is mostly task-by-task, done by small model. Lots of deterministic code doing all this orchestration.
by brainless - > Bullet started as an AI hedge fund, a browser-use agent, synthetic financial data (oof), a mobile IDE, and a bunch of other things.
Is there another pivot coming? This would make me nervous.
- Skip signup:
Cmd+Option+I > Console > 'allow pasting'
const onboarding = document.querySelector('#onboarding'); const app = document.querySelector('#app'); onboarding.style.setProperty('display', 'none', 'important'); app.inert = false; app.removeAttribute('aria-hidden'); document.querySelector('#prompt')?.focus();
Also, warning:
# Share chats with Bullet — helps us improve model routing and answer quality
Enabled by default.
by apimade - Since I can't get this thing not to annotate its commits with itself as an author, I can't use it.
That should be an option, not forced.
by etchalon - 35% faster than swe-mini-agent, nice. You say this is due to somehow parallelizing operations?
I'm using a custom harness based on swe-mini-agent (actually its little brother, their tutorial [0]) and found it way faster than codex (for small tasks) despite being "just bash" in a while loop.
The main difference is that I do the opposite of what you said, i.e. I do dump the context in the prompt. You don't need to grep for what's right in front of you :)
But my repos are small (often smaller than Claude's system prompt!), and I have a script that dumps an "outline" (e.g. function headers and top level vars).
I had an even smaller harness for surgical edits but it was a bit too fiddly and I missed the "let it poke around and figure stuff out" mode of full agentic ones.
What I do miss from that old one though is that it could edit several files simultaneously, in one LLM call. Maybe someday I'll add that back :)
That being said, within a few months everyone who cares about speed will be on Cerebras etc., which will make even the slow harnesses way faster than mine and yours! (I've tested them already and it's insane how fast it is...)
by andai - I think this adds no value. I would stick to OpenCode.
Things I would value: high-fidelity visualization (bonus points for Figma-like visual edits), good tool use (don't force me to tell the model), token efficiency, resource efficiency. Things that are not problems:
* Routing: OpenCode predefines subagents that you can set to appropriate models. * Search: there are AST and embedding-based search MCPs. I use https://github.com/DeusData/codebase-memory-mcp
by esafak - So I hit download, and I got a .deb file.
Installing a .deb requires root. Okay, a coding agent that wants root access. I know almost no one is good at supply chain security unless it is their core business, 2500 companies hacked this week for using LiteLLM, etc. So I look around for source code links and come up empty.
So, I am supposed to just give a random binary on a website that may or may not be malware depending on what NPM dependencies are in play, and give it root access, and trust whatever unknown CI/CD system that builds it to be able to execute any commands on my system with root privileges, and I am not even allowed to review the code?
That is an amount of trust I would not even extend to family, and you are asking for it from every single user.
So I asked a local LLM to decompile it for me, the 198 dependencies, and license compliance.
From here I could have it write an extensive test suite, delete the code, have LLM re-write the code to pass all the tests, then open source the result myself.
I am probably not actually going to do this, but anyone could. Proprietary software is no longer a moat, and for something like this it just makes your software very hard to trust. Clean room code laundering is so easy now you might as well open source it yourself.
by lrvick - This is a promising direction! Unfortunately, I think the benchmark result here is essentially meaningless.
I recently discovered this same lesson the hard way. I was trying to get a multi-agent system I was building to improve upon GPQA Diamond scores (system here: http://pellmell.ai). No matter how hard I tried, I could not get any lift. When Fable 5 dropped, it also did not improve upon Opus, and I realized my mistake. The benchmark was saturated!
Now, looking at the result here, I see a similar pattern. Fable is not better than Opus, and the score is ~95%. Notably, this post omits which subagent is being used. Why? An intellectually honest way to tell if this thing really works would be to run that agent and report its score and cost as well.
Going back to my GPQA Diamond lesson, you can see here how a saturated leaderboard behaves https://artificialanalysis.ai/evaluations/gpqa-diamond. Fable gets 92.6% for $0.22 per task while several models score higher for $0.01. I could easily publish a router that “enhances Fable on GPQA Diamond” showing improved score for lower cost, just by implementing a router that picks the model at random!