`u
`uvx ptn` and expose any system to agents (dangerously)
Hacker News`uvx ptn` and expose any system to agents (dangerously)
I made this dangerously easy and convenient: Step 0: install uv (with one command) Step 1: uvx ptn Step 2: press c Step 3: paste to your Codex, Claude Code or virtual girlfriend The agent gets full access to any Linux, PC, Mac, etc. There is 0 registration, rate limit, SSH keys, Docker, port forwarding, or config. You can monitor, copilot or kill switch on your phone with a QR scan. pypi: https://pypi.org/project/ptn/ repo: https://github.com/lyehe/porterminal
Share cardActual performance
3points
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, model87%87% 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, podcasting71%71% 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: io · Missing: https docs, excited, just released44%44% 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, intuitive36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue24%24% 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, crypto7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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