HN monitor and notification via slack for subjects of interest

Hacker News

Code: Single click deploy https://github.com/DevScoreInc/samples/tree/master/hacker-news-monitor Youtube demo: https://www.youtube.com/watch?v=5nEab7ALc3c&feature= The above is a fun example of what users can do with https://devscore.com Devscore is a flexible low code service to automate workflows and execute different types of custom functions. Our goal is to help developers deploy functions (micro application) that helps them become more productive by building integrations, flows and lifecycles between and within their tools. We integrate with many of the most popular tools developers and DevOps teams use every day. For a full list please visit the connection hub at https://app.devscore.dev/connection-hub One simple and specific example of such micro apps is a GitHub file monitor to notify devs when files in specific GitHub repos have been modified. https://github.com/DevScoreInc/samples/tree/master/github-file-monitor The service architecture is an event based model which supports functions, function connectors, webhooks (GET & POST) and cron jobs. Additionally there is a file storage utility and a database to persist and access application data. It currently supports node.js and has an embedded library of functions in the runtime, that enables programatic access for much of the above. We would love to hear the community feedback on how they would use the service and what micro apps (use cases) they’d want to built with a service like this. [1] Docs: https://github.com/DevScoreInc/docs [2] Examples: https://github.com/DevScoreInc/samples [3] Youtube Demos: https://www.youtube.com/channel/UClgE-uFdQIJ2GWhaNjz6WRw/videos [4] Site: https://devscore.com

Visit product View on Hacker NewsApr 13, 2020

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

80%80% 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

75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% 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

17%17% 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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