VA

VAEN – Package and import portable AI coding-agent Harnesses

Hacker News

VAEN – Package and import portable AI coding-agent Harnesses

Hi HN, I built VAEN (an open source CLI) because I kept running into a boring problem with AI coding-agent workflows: the setup becomes useful, but then it is hard to move. A good, useful agentic harness consists of more than just instructions: skills, mcp servers and more. There should be a better way to share those than just .MD files, and that is why I created VAEN. What works: create a yaml, run the CLI commands as per the repo, and get a .agent file that you can share and extract. Think of how awesome it could be if anyone could use a very useful agentic harness, and share it with one CLI command.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, agentic, mcp · Missing: mac, agents, macos
97%97% 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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting
35%35% 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: open source, io · Missing: https docs, excited, just released
30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% 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
19%19% 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.

Incorrect prediction on native model

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