I recently switched over to using primarily Bullet for my projects and the speed of it makes it very nice to get projects off the ground quickly and work properly. I also found that Adi and Alex make updates very regularly based on some of the feedback i've submitted to their feedback tab. Good work on this product guys! I'm excited to see how it develops in the future.
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!
The breakdown with which model, per-task cost, and methodology is in the "Full results and methodology" link in the post, not omitted. Definitely check it out if you haven't.
On the saturation point, we agree that a 95.8% result on a mature benchmark isn't the main proof, which is why we're currently running against harder, less saturated benchmarks like Terminal-Bench, CursorBench, and SlopCodeBench (going to publish results on these hard benchmarks shortly). Apart from current user experiences, that will show the value of our harness.
I did read "Full results and methodology". It doesn't seem to show which agents you are routing to. Am I missing something? And how do those agents score on SWE-Bench Verified?
The same thing holds for speed. I could build a system that speeds up Fable on GPQA Diamond ~50%, while improving score, by literally randomly selecting between Fable and Gemini 3.7 Flash. (Solve time for Flash is 0.1min and 0.8min for Fable, with Flash having a better score.)
Hell, I could publish better score at 87.5% time reduction by having the router always pick Flash!
Congrats on the launch.
I can't find which model providers are supported?
Are you calling the claude-code CLI directly and make bullet usable with anthropic subscriptions like orca or herdr?
I guess however this is a harness and it needs to connect to the API?
Thanks for bringing this up and trying it out! We've disabled Cmd+Option+I in the new version and also have a message during signup about sharing chats with Bullet, let us know if you run into any other issues!
Are you freaking kidding me with YC throwing money at something like this? I guess I can fund raise just by having built https://maki.sh, and months ahead of other founders too...
I recently switched over to using primarily Bullet for my projects and the speed of it makes it very nice to get projects off the ground quickly and work properly. I also found that Adi and Alex make updates very regularly based on some of the feedback i've submitted to their feedback tab. Good work on this product guys! I'm excited to see how it develops in the future.
Your landing page is very hard to read. The font size is literally 10px for some content, while animations distract the reader.
Dear YC, please force the startups to dedicate some of the 500k funding for standard web design.
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!
The breakdown with which model, per-task cost, and methodology is in the "Full results and methodology" link in the post, not omitted. Definitely check it out if you haven't.
On the saturation point, we agree that a 95.8% result on a mature benchmark isn't the main proof, which is why we're currently running against harder, less saturated benchmarks like Terminal-Bench, CursorBench, and SlopCodeBench (going to publish results on these hard benchmarks shortly). Apart from current user experiences, that will show the value of our harness.
I did read "Full results and methodology". It doesn't seem to show which agents you are routing to. Am I missing something? And how do those agents score on SWE-Bench Verified?
The same thing holds for speed. I could build a system that speeds up Fable on GPQA Diamond ~50%, while improving score, by literally randomly selecting between Fable and Gemini 3.7 Flash. (Solve time for Flash is 0.1min and 0.8min for Fable, with Flash having a better score.)
Hell, I could publish better score at 87.5% time reduction by having the router always pick Flash!
Congrats on the launch. I can't find which model providers are supported? Are you calling the claude-code CLI directly and make bullet usable with anthropic subscriptions like orca or herdr?
I guess however this is a harness and it needs to connect to the API?
Thanks! We support a variety of model providers (OpenCode/Codex/Grok/Claude) via their subscriptions and API keys. This can be seen in the app.
> P.S: we hid a code on the website, see if you can unlock the secret page at the footer, all built with Bullet
Is it even hidden when your AI ends up tagging it with `aria-label="Hidden secret code"`? Lol. Fun mini game.
haha fair enough, surprisingly people still struggle to find it, glad you found it and liked the game!
Focusing on execution speed for coding agents is the right bottleneck to tackle. Exciting launch.
Thank you!
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.
Thanks for bringing this up and trying it out! We've disabled Cmd+Option+I in the new version and also have a message during signup about sharing chats with Bullet, let us know if you run into any other issues!
> We've disabled Cmd+Option+I in the new version
Bulletproof.
Truly built on vibes.
Are you freaking kidding me with YC throwing money at something like this? I guess I can fund raise just by having built https://maki.sh, and months ahead of other founders too...
I'm not sure why anyone would fund "creating an agent". OpenCode is competing with Codex and Claude already.
"The bullets" are not any different than other new agent projects. New agent projects pop up and die constantly and it's not from a lack of funding.
YCombinator's investments are unfortunately very questionable nowadays.