The Web Automation Platform

Joined December 2024
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Pinned Tweet
Anything API is live on Product Hunt! 🔥🚀 Most websites don't have public APIs. Anything API fills that gap. Describe the browser work you need. Our agent builds it, deploys it, and hands you a callable endpoint that you or Claude can invoke from everywhere. Any website. We deliver the API. producthunt.com/products/not…
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Notte retweeted
May 28
Agentic users of @NousResearch, @nanobot_project, @openclaw, and more are now welcome to automate behind login! Simply BYO Agent. Pre-packed: âś“ Hosted browser sessions âś“ Isolated credentials Vault for auth âś“ Agent swap? Flow stays the same The setup is one prompt. Enjoy!
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Notte retweeted
becoming a daily routine. not even looking at variations in latency just the UI 🌸
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Notte retweeted
May 28
Agentic users of @NousResearch, @openclaw, @nanobot_project, and more are now welcome to automate behind login! Simply BYO Agent. Pre-packed: - Hosted browser sessions - Isolated credentials Vault for auth - Agent swap? Flow stays the same The setup is one prompt. Enjoy!
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May 28
Browser Arena now runs daily 🌤️ Track every major cloud browser provider. Watch latency and reliability drift over time. • Same infrastructure • Full historical data included • Now automatically benchmarked Open-source. Reproduce, contribute, and run it yourself.
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May 28
Agentic users of @NousResearch, @nanobot_project, @openclaw, and more are now welcome to automate behind login! Simply BYO Agent. Pre-packed: âś“ Hosted browser sessions âś“ Isolated credentials Vault for auth âś“ Agent swap? Flow stays the same The setup is one prompt. Enjoy!
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May 28
▸ Console: console.notte.cc ▸ Get Setup: notte.cc/blog/notte-byo-agen…
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May 28
Browser Arena now runs daily 🌤️ Track every major cloud browser provider. Watch latency and reliability drift over time. • Same infrastructure • Full historical data included • Now automatically benchmarked Open-source. Reproduce, contribute, and run it yourself.
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Notte retweeted
Congrats on the $800M! 4x deserved for this team.
May 27
We just hit a major milestone — @FireworksAI_HQ passed $800M annualized run rate and reached 4x revenue growth, apart from Cursor, in Q1. We invite curious and courageous minds to join us and define new frontiers of specialized intelligence!
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Notte retweeted
May 20
We partnered with @FireworksAI_HQ and ran 720 browser agent tasks across 4 LLMs. The model that looks like a bargain on token price was 2.3x more expensive per successful task than the winner. Here's what was happening underneath.
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May 21
Two key takeaways: • Tail-latency consistency across heterogeneous architectures is a serving-layer property that @FireworksAI_HQ's infra makes visible. • Parse retry rate should be a first-class metric in every agent benchmark.
We ran 720 browser agent tasks with @nottecore across frontier models. One baseline model produced malformed outputs in ~1 out of every 5 calls, leading to retries inside multi-step workflows. Across Kimi K2.5, GLM-5, and MiniMax M2.5 served on Fireworks, retry rates were near zero and latency stayed stable even as tasks extended across multiple steps. Same workload. Same agent loop. Different execution behavior. That gap is what shows up as cost, latency, and reliability divergence in production agent systems. Read the report: fireworks.ai/blog/agent-exec…
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built a free site that uses real market data agentic research to tell you if your company is cooked. apologies in advance.
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May 20
We partnered with @FireworksAI_HQ and ran 720 browser agent tasks across 4 LLMs. The model that looks like a bargain on token price was 2.3x more expensive per successful task than the winner. Here's what was happening underneath.
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May 20
A note on method: we ran text-only to keep the four-model comparison fair (only 1 of 3 Fireworks models supports vision). That handicaps Gemini on its strongest dimension. However we ran a vision check anyway: it gains 6pp, and MiniMax M2.5 text-only still beats it.
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Across three models with very different architectures, p95/p50 latency stayed inside a tight 1.9-2.3x spread. Tail-latency consistency across heterogeneous architectures is a serving-layer property @FireworksAI_HQ's infra makes visible. ▸ Full report: fireworks.ai/blog/agent-exec…
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May 20
Reliability, not intelligence. Why? One model wasted nearly 1 in 5 LLM calls on malformed JSON it had to retry. Each retry burns a full inference call's worth of context and 2-3s of latency. Every static benchmark misses this because it happens inside the engine.
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Diana, a Slack-based AI assistant platform that connects employees to thousands of tools from a single interface, used Notte to give their AI agents: • Browser access to tools without APIs, using secure user-authenticated sessions • Live session visibility through a secure, shareable link, so users can watch what the agent is doing in real time "The live session viewer isn't just a technical feature but a transparency guarantee. This is a significant matter when you're asking someone to let an AI act on their behalf." — @upekabee, CEO of @getdiana
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