BrowseBrawl
What if browser agents battled to generate training data?
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I remember watching the AlphaGo documentary in 2017. What stood out to me was that the model got drastically better when it started competing against itself. GANs clicked for me similarly: a generator and discriminator competing, and somehow the competition is what produces something remarkable.I've been curious whether this principle generalizes to today's agents.So mehulkalia and I built Browser Brawl at the YC / BrowserUse hackathon last weekend and won first place. It is a fun experiment in which an attacker agent tries to complete tasks on live websites while a defender agent injects JavaScript to sabotage it.The analogy isn't perfect, because browser tasks aren't zero-sum. But our hypothesis is that an agent faced with an adversary should produce more interesting training data than one navigating clean, static environments.Try it on: http://browser-brawl.comGitHub: https://github.com/RichardHruby/browser-brawlDemo Video: https://youtu.be/NIoFXv-JvBY(Skip to [0:55](https://www.youtube.com/watch?v=NIoFXv-JvBY&t=55s) to see the agents “brawling” in the arena :), [1:52](https://www.youtube.com/watch?v=NIoFXv-JvBY&t=1m52s) to see the browser traces generated)Would love to chat with anyone building or training browser agents. Happy to dive in below!
HN · launched Mar 4, 2026 · 30 upvotes (top 20%) · 0 views
Niche: ai browser agents · 105 launches, 9 in the last 7 days ▲ +50% · Explore this niche →
Category and tags
- Horizontal
- Agent / copilot
- Developer
- AI-native
- Hobby / open-source project
Visit website ↗ Source listing ↗
More like this: all 105 launches in ai browser agents →
Could you build this?
Partial Building the Next.js UI and running basic Playwright scripts is simple, but orchestrating concurrent adversarial browser agents with real-time DOM mutation and fine-tuning data pipelines is complex.
What it would actually take: The platform requires a distributed runner infrastructure (e.g., Docker containers running isolated Chromium instances via Playwright/Browserbase) coordinating real-time WebSocket communication between two LLM agents. The core difficulty is creating deterministic DOM mutation engines (saboteur) and structured trajectory-logging middleware to capture replayable step-action-observation datasets for RL/SFT. This requires expertise in browser automation at scale and reinforcement learning data pipelines.
Discussion
18 comments analyzed.
Concerns raised: Authenticity of comments (paid comments question)
Feature requests: Handicaps/abilities for attacker agents to increase difficulty
Competitors
Other products that read as similar to this one : 338 launches clear the similarity bar, closest 8 shown.
Attention rank: #78 of 339 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 121 days after the earliest competitor.
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- BrowserAct · ph · 2026-06-25 · 561 upvotes · similarity 0.44
- Open-source playground to red-team AI agents with exploits published · hn · 2026-03-15 · 30 upvotes · similarity 0.44
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