An

An API for human-powered browser tasks

Hacker News

An API for human-powered browser tasks

At APM Help, we have a large team that performs repetitive, browser-based tasks. Years ago, to manage this work securely and get a clear audit trail, we built an internal platform we call "Hub." It's essentially a locked-down environment where our team works that records their sessions, tracks every interaction, and prevents data from being copied or shared. It's been our internal source of truth for years. More recently, like many companies, we've been building more automation. And like everyone else, we've seen our automations fail on edge cases—a weirdly formatted invoice our parser can't read, a website layout change that breaks a scraper, etc. Our team would have to manually step in to fix these. We realized other developers must have this exact same problem, but without a 250-person team on standby. So we connected our old, battle-tested Hub to a new, modern front door: a Human-in-the-Loop (HITL) API. We're calling it browser-work.com. The idea is simple: when you hit a task that needs a human, you can send it to our team through the API. Here's how it works: - You POST a request to our endpoint. The payload contains the context for the task (like a URL) and a set of instructions for the human on what to do. - The task appears in the Hub, where one of our trained operators can claim it. - They perform the task exactly as instructed, all within the secure Hub environment. - When they're done, we send a webhook to your system. The return payload includes the task's output, any notes left by the human, and a detailed log of their actions (e.g., DOM elements they interacted with). For example, if your automation for paying a utility bill fails, you can pass the task to us. A person will log in, navigate the portal, make the payment, and return a confirmation number. The product is live and we're looking for people with interesting use cases. I'm Robert, the CIO. If this sounds useful to you, send me a brief email about your use case at robert@apmhelp.com and we can get you started right away. Happy to answer any questions here.

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, email, context · Missing: mac, agents, macos
90%90% 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
84%84% 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
54%54% 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: way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
39%39% 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
16%16% 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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