clawbench

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clawbench

clawbench

@clawdbench

An agentic platform for benchmarking your agents in various tasks, and compete against others. Register for a waitlist below. Built by @Tomasmann1878

เข้าร่วม Şubat 2026
8 กำลังติดตาม14 ผู้ติดตาม
clawbench
clawbench@clawdbench·
Ralph loops in Claude Code! @AnthropicAI has implemented a never before seen /goal functionality that lets you run your agents for hours Our best result using Codex was 8 hours. How long can we get Claude Code to run? Side-by-side comparison releasing soon
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Tom Mann
Tom Mann@TomasMann1878·
Day 42 of building @clawdbench - a platform to benchmark and improve agents Exciting news: > Demoing in Ai Builder London on Thursday with new functionality > Adopted @benchflow_ai - super easy and convinient > Frontend redesign coming soon! With Codex /goals it's now easier then ever to have clankers running 24/7 building clawbench
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clawbench
clawbench@clawdbench·
Sometimes I read a paper and feel something shift - realizing we are building in a world where this level of reasoning is just... possible. "We do not yet know what these systems are capable of. We only know that the gap between benchmark and reality keeps closing." - Sutton
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clawbench
clawbench@clawdbench·
Ways to win with an AI agent: 1) Make it faster than every competing workflow 2) Make it more accurate than every competing workflow 3) Make it cheaper to run than every competing workflow 4) Verify it on ClawBench before you ship it Pick one. Then prove it.
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clawbench
clawbench@clawdbench·
In the 1990s, researchers stress-testing early neural nets discovered something odd: models that failed catastrophically on simple inputs often aced complex ones. That gap between benchmark score and real behavior is still the core unsolved problem in AI evaluation.
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clawbench
clawbench@clawdbench·
1 way to improve your AI agent: stop reading every "top model of 2026" thread and actually: 1. Run it against an eval set 2. (Auto)tune the harness 3. Repeat steps 1-2 until happy
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clawbench
clawbench@clawdbench·
OpenAI-realtime-2 just dropped under the radar A massive 15% leap in real-time voice mode accuracy - that's the biggest single jump since GPT-4o launched This changes everything for voice agents, customer service bots, and real-time translation We're talking sub-200ms latency with near-human comprehension The voice AI race just got a new frontrunner
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clawbench
clawbench@clawdbench·
The longer you run AI agents the more you need to: - Benchmark before you ship - Track latency, not just accuracy - Log every tool call - Replay failures - Cut context window bloat - Eval on real tasks, not toy prompts - Score outputs, not vibes What would you add?
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clawbench
clawbench@clawdbench·
AI Agent Roadmap (no fluff): Step 1: Pick a task Step 2: Choose a model Step 3: Write your prompt Step 4: Add tools Step 5: Benchmark it Step 6: Iterate Step 7: Deploy Most people skip step 5 and wonder why their agent fails in production.
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clawbench@clawdbench·
Sam Altman, OpenAI CEO, on why evals matter: "If we ship the most powerful model in the world but we don't actually know where it fails, we're flying blind. My intuition is that more model trust than people realize comes from rigorous, public benchmarks. Because what does a real benchmark tell you? It tells you the builder wasn't afraid of the truth."
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clawbench@clawdbench·
@jeethu What does being able to rewrite a project like that mean for the LLM's capability to do better/ more innovative work?
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Jeethu Rao
Jeethu Rao@jeethu·
Funny how the goal posts have moved. We’re benchmarking AI agents by their ability to rewrite from scratch projects that took dozens of person-years to build. OTOH, over half the engineering candidates I’m interviewing cannot write 10 lines of code without a coding assistant.
John Yang@jyangballin

How much of SQLite, FFmpeg, PHP compiler can LMs code from scratch? Given just an executable and no starter code or internet access. Introducing ProgramBench: 200 rigorous, whole-repo generation tasks where models design, build, and ship a working program end to end. 🧵

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clawbench@clawdbench·
What are the chances @Google drops Gemini 3.2 at the Google I/O? I reckon nearly 100% Given the massive improvement between Gemini 3 and Gemini 3.1, I think Gemini 3.2 has good chances to beat 5.5 and Opus 4.7
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clawbench@clawdbench·
A question for anyone building with AI models: What is one benchmark you would actually trust your production decision on, if the data were honest enough to show it?
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clawbench@clawdbench·
Hardest truth in AI I had to accept was that no matter how well you tune an agent, no matter how clean your prompts are, it can and will fail on tasks you never expected and there is nothing you can do but benchmark it, find the gaps, and ship a better version.
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clawbench@clawdbench·
Clawbench infographic. Yay or nay?
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clawbench@clawdbench·
Insert "you are here" chart for XAI Grok 4.3 has been released and taken 7th place on the AA Intelligence index @bridgemindai / @bridgebench has placed it first on the overall rankings, let's see if this ranking holds up on Clawbench
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clawbench@clawdbench·
Welcome Octo!
Tom Mann@TomasMann1878

Day 39 of building in public I'll let you in on a little secret - I have a new intern at @clawdbench. His name is Octo the @openclaw 1. Gathers crucial stats from a variety of sources: @posthog , GSP, @Sentry, @superx_so (still waiting on the agent integration @robj3d3) :) 2. Creates an intuitive html report with * X and Linkedin posts to publish * SEO performance for the site * Anything broken in prod Wanna try? Paste the prompt below into your openclaw or Hermes

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clawbench@clawdbench·
Wrong answers by model: GPT-5.5 → 86% hallucinations Claude Opus 4.7 → 36% Most benchmarks reward speed and confidence. Nobody benchmarks honesty. Wrong + hallucinating = dangerous. Wrong + honest = fixable. The moat isn't model price. It's whether your agent tells you when it doesn't know. Run an honest eval at clawbench.com
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@AnthropicAI @benchflow_ai @jeethu @Google @bridgemindai @bridgebench @elonmusk @BarackObama