Simplex: Automate browser workflows using code and natural language

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Simplex is an SDK that provides low-level functions you can use to create reliable web automations using code and natural language. Here's a quick video to show you how it works: https://www.loom.com/share/8d27d0f9e0f34c77a81b0ac770151c12 A couple weeks ago, we needed a way to automatically find 3D assets from the internet for one of our work contracts. We didn’t need an AI to choose all the steps for us autonomously — we knew generally what the flow to find items should be and just needed some intelligence to handle different webpage formats. Playwright couldn’t easily generalize to the different sites we wanted to search over and Claude computer use was tough to use — it was slow, expensive, struggled to click on the correct web elements, and there was no way to correct its actions if it failed at step 20 of our workflow. So we built a vision-based solution using vision language models that sits on top of Playwright. In our SDK, building a function that can universally search websites is as easy as this: from simplex import Simplex simplex = Simplex(api_key="api_key") def search(website, search_query): simplex.goto(website) simplex.click("the search bar") simplex.type(search_query) simplex.press_enter() search("mit.edu", "alyssa p hacker") You can play around with what we’ve built and see a few more examples at https://simplex.sh/playground . We'd love feedback!

Visit product View on Hacker NewsJan 14, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, computer · Missing: mac, agents, macos

93%93% 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

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

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: video, way · Missing: mobile apps, ios, personal

45%45% 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

40%40% 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

14%14% 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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