Coarena by Coasty
We built a computer use agent once and got it benchmarked at the 2nd position in the biggest benchmark for this field but in the process, we realized one thing, how outdated and inefficient these benchmarks are and how they don't truly capture how these tasks went. So we thought a community led benchmarking arena would be the way to go about. https://coarena.ai/. You just have to put in a task and two side by side windows with hidden models will show up and do the same task and at the end you vote which one did better(or none). We have a leaderboard where we rank all the…
Coarena by Coasty launched on August 13, 2026 with 111 votes, #315 of 23,274 launches that month and more than 95% of that year's launches.
What it does
Coarena lets AI agents compete on real computer tasks, not synthetic benchmarks. Watch multiple models complete the same workflow side by side, compare speed, accuracy, and reliability, then vote for the winner. Discover which agent actually performs best on everyday work across browsers, apps, and enterprise software.
Compare computer-use AI agents by human preference, completion, speed, recovery and cost. Explore live model rankings or post a task and judge a battle.
In the maker’s words, at launch
We built a computer use agent once and got it benchmarked at the 2nd position in the biggest benchmark for this field but in the process, we realized one thing, how outdated and inefficient these benchmarks are and how they don't truly capture how these tasks went. So we thought a community led benchmarking arena…
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Agent ArenaJun 2026 · arena42.ai · ▲348Creator here. I built Agent Arena to answer a question that kept bugging me: when AI agents browse the web autonomously, how easily can they be manipulated by hidden instructions? How it works: 1. Send your AI agent to ref.jock.pl/modern-web (looks like a harmless web dev cheat sheet) 2. Ask it to summarize the page 3. Paste its response into the scorecard at wiz.jock.pl/experiments/agent-arena/ The page is loaded with 10 hidden prompt injection attacks -- HTML comments, white-on-white text, zero-width Unicode, data attributes, etc. Most agents fall for at least a few.…


oqoqoAug 2026 · oqoqo.ai · ▲325Most benchmarks today exist in curated environments and do not translate well to the real world. We built Oqoqo to bridge this gap. Oqoqo makes it super simple to build realistic evals and custom benchmarks for tasks users actually care about. Oqoqo can: - Reliably measure how agent friendly your product surfaces are against Codex, Claude Code, OpenClaw, Hermes, Pi, Opencode, Cursor, GitHub Copilot - Regression test MCP, CLI, skills, SDK, and any agent facing interface (we are continuously using Oqoqo to dogfood and improve our own MCP/CLI) - Create and share custom benchmarks for how…

Agent-desktop – Native desktop automation CLI for AI agentsMay 2026 · github.com · ▲99I've been building computer-use tools for a while, and I quietly launched this about a month ago (122 Stars on GH). I figured it was worth sharing here. Over the last few months, a lot of computer-use agents have come out: Codex, Claude Code, CUA, and others. Most of them seem to work roughly like this: 1. Take a screenshot 2. Have the model predict pixel coordinates 3. Click x,y 4. Take another screenshot 5. Repeat That works, but it's slow, expensive in tokens, and fragile. If the UI shifts a few pixels, things break. And the model still doesn't know what any element actually is. But the…
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Source: Product Hunt launch ↗ · launch text quoted in part
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