Bootstrap a team of coding agents from a template, OSS

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I have spent the last few months working on infrastructure and tools to give agents global ids, and the ability to communicate. That is up and running now, but actually structuring their work together has been a real pain: I still have to give them roles and responsibilities, and start the agents in the right directories with the right id so that the actually get things done. I have automated that part now: a team can be bootstrapped from a template with one command: aw team bootstrap https://github.com/awebai/aweb-team-dev-review.git \ --username <name> \ --work-directory /path/to/your/repo So far I have published three templates that I find useful (linked from the submitted github page). I am looking for feedback, please let me know what you think and how to make it more useful. One direction I am considering is to replace the markdown-based structure with an actual ontology.

Visit product View on Hacker NewsMay 28, 2026

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

80%80% 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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% predicted probability of success on TrustMRR, 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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