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Notte – Full-stack web-agent framework (open-source)

Hacker News

Notte – Full-stack web-agent framework (open-source)

Hi HN, This is Lucas, one of the creators of Notte. Notte is an open-source full-stack framework for web agents, designed to be fast and reliable in production. Our tech revolves around a perception layer that translates the DOM to natural language, allowing LLMs to reason and act upon it reliably. This allows observation of website states and execution of actions using intuitive natural language commands. The goal with Notte is to ultimately be production-ready for a wide range of workflows. Notte can be used via a unified API, and includes unified session management, secure vault (payment, creds, keys, cookies, OTPs), built-in stealth, async agent/browser execution, stateful sessions, agent personas (email, phone, identity vault), as well as built-in retry logic and session replay. We also have captcha pause + HITL integration, as well as an MCP package, plus some cool features for scrapers (JSON schema-driven scraping helpers). All via Python SDK & API. Apache-2.0 licensed: https://github.com/nottelabs/notte Docs: https://docs.notte.cc/ We’d love to get feedback on: Is there anything missing before you’d try or trust it on certain workflows? Any edge cases we might have missed? ...or just any general feedback from the HN community. Let us know what you think! — Lucas/Andrea

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Actual performance

3points
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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, intuitive · Missing: platform, reviews, host
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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