Lessons learned from my 10 year open source project

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I've been developing SpiderFoot (an OSINT/recon tool) for 10 years now, so wanted to share my story and try to distill some lessons learned in the hope they might be helpful to others here who might be considering writing/open-sourcing their own tools. Here's the post: https://medium.com/@micallst/lessons-learned-from-my-10-year-open-source-project-4a4c8c2b4f64 And the repo if you want to check it out: https://github.com/smicallef/spiderfoot -- TL;DR version of lessons from the post.. Lesson 1: Writing open source software can be very rewarding in ways you can’t predict Lesson 2: Be in it for the long haul Lesson 3: Ship it and ship regularly Lesson 4: Have broad, open-ended goals Lesson 5: If you care enough, you’ll find the time Lesson 6: No one cares about your unit test coverage Lesson 7: There’s no shame in marketing Lesson 8: Clear it with your employer Lesson 9: Foster community Lesson 10: Keep it enjoyable -- I hope you find it useful and inspires some of you to get your project out there! Feel free to ask me any questions here and I'll do my best to answer.

Visit product View on Hacker NewsFeb 10, 2022

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, 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

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

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

29%29% 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 · Strong signals: reward · 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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