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AI agent that builds web automations through conversation

Hacker News

AI agent that builds web automations through conversation

Text: We previously posted Vectorly ( https://news.ycombinator.com/item?id=46209538 ), our open-source app that reverse-engineers websites into reusable APIs. The core idea: instead of clicking through UIs like a human, interact with websites through their underlying HTTP endpoints. Faster, cheaper, more reliable. Building routines by recording browser sessions worked well, but refining them was still manual. So we built Guide, a conversational agent that walks you through the whole process: - Describe your task and the agent helps scope it - Record in a cloud browser, just use the site normally - Agent analyzes the session, identifies backend endpoints, extracts parameters, builds a reusable routine - Refine through chat to propose edits, review diffs, iterate without hand-editing anything The prevailing approach to web automation is computer use agents that click and type like humans. We think this is backwards. Web app implementations are wildly diverse, but they all talk to structured backend APIs. LLMs are great at reading code and inferring structure, so we use them at build time to reverse-engineer those APIs, then the resulting routines run without AI in the loop. We'd love to hear what you think, and happy to answer any questions about the approach! :) - GitHub: https://github.com/VectorlyApp/bluebox-sdk - Blog post with more detail: https://vectorly.app/blog/introducing-guide-agent - Try it: https://console.vectorly.app/guide

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, computer · Missing: mac, macos, cursor
97%97% 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: para · Missing: supports, reddit linkedin, podcasting
79%79% 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
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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