Scout, understand any Enterprise Platform in minutes

Hacker News

Hi HN, I've been building Scout over the past few months. Point it at an OpenAPI spec and documentation, and it builds a grounded understanding of the platform: architecture, entities, workflows, starter code, a threaded AI chat, and an MCP server for coding agents. Everything runs locally. No accounts or hosted backend. One of the problems I kept running into was coding agents confidently inventing endpoints because they only knew APIs from training data. Scout uses the OpenAPI spec and docs as ground truth instead. It's MIT licensed and I'd love feedback from people building integrations or using coding agents heavily. GitHub: https://github.com/prabhuavula7/scout ... npm: npm i -g @dotapk7/scoutcli

Visit product View on Hacker NewsJul 27, 2026

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor

94%94% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

27%27% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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