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Autotab – Programmable AI browser for turning web tasks into APIs

Hacker News

Autotab – Programmable AI browser for turning web tasks into APIs

Hey HN, we're Alexi and Jonas the co-founders of Autotab ( https://autotab.com ). Autotab is a chrome-based browser you can teach to do complex tasks, with a simple API for running them from your app or backend. Here is a walkthrough of how it works: https://youtu.be/63co74JHy1k , and you can try it for free at https://autotab.com by downloading the app. Why a dedicated editor? The number one blocker we've found in building more flexible, agentic automations is performance quality BY FAR ( https://www.langchain.com/stateofaiagents#barriers-and-chall... ). For all the talk of cost, latency, and safety, the fact is most people are still just struggling to get agents to work. The keys to solving reliability are better models, yes, but also intent specification. Even humans don't zero-shot these tasks from a prompt. They need to be shown how to perform them, and then refined with question-asking + feedback over time. It is also quite difficult to formulate complete requirements on the spot from memory. The editor makes it easy to build the specification up as you step through your workflow, while generating successful task trajectories for the model. This is the only way we've been able to get the reliability we need for production use cases. But why build a browser? Autotab started as a Chrome extension (with a Show HN post! https://news.ycombinator.com/item?id=37943931 ). As we iterated with users, we realized that we needed to focus on creating the control surface for intent specification, and that being stuck in a chrome sidepanel wasn't going to work. We also knew that we needed a level of control for the model that we couldn't get without owning the browser. In Autotab, the browser becomes a canvas on which the user and the model are taking turns showing and explaining the task. Key features: 1. Self-healing automations that don't break when sites change 2. Dedicated authoring tool that builds memory for the model while defining steps for the automation 3. Control flows and deep configurability to keep automations on track, even when navigating complex reasoning tasks 4. Works with any website (no site-specific APIs needed) 5. Runs securely in the cloud or locally 6. Simple REST API + client libraries for Python, Node We'd love to get any early feedback from the HN community, ideas for where you'd like the product to go, or experiences in this space. We will be in the comments for the next few hours to respond!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
98%98% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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.

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