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Building a no-code browser automation system for OSINT

Hacker News

Building a no-code browser automation system for OSINT

I have been working on a browser automation system for OSINT analysts. I took this up because the tools that exist today have a few problems: 1. They require a little more precise notions of what needs to be done by the system (like, go here, click here, then do this, then do that etc.) 2. To perform complex tasks, the few tools that exist, they need some knowledge of the system itself (like telling it to call a specific function or a class etc.) My goal with this project (I call it `pyba`) was to abstract everything from the user's side. I have two "exploratory" modes built into it, these are for Depth First Search (wherein, it will pick one train of thought, and execute that in full, then revert), or Breadth First Search (where it executes multiple tangential plans in parallel). All an analyst has to do is type out in words, everything they already know and can share, and what all they'd like to find out more. To ensure that there are no roadblocks in this journey, I also have hardcoded logins, which read your credentials from the environment and log you in to websites like gmail, facebook and instagram (so your credentials never go to the LLM). And of course, you can use it for normal stuff as well (like tell it to scroll reels for you if you want)! Test it out: 1. Install using `pip install py-browser-automation` 2. A simple sample code you can just plug and run: ```py from pyba import Engine eng = Engine(openai_api_key="", use_logger=True, handle_dependencies=True) output = eng.sync_run("go to hackernews and tell me what is the most upvoted post") print(output) ```

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, openai · Missing: mac, agents, macos
87%87% 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
83%83% 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: exist, ide, io · Missing: https docs, excited, just released
49%49% 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 · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
36%36% 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
15%15% 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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