Code_puppy – a CLI vibe coder that I vibe coded using a vide coder

Hacker News

Hi HN! I've never posted anything on here before, but this project was too fun to pass up. I've been learning a lot about agents lately, and wanted to see if I could replicate some of the results of Windsurf and Cursor. Generally I love these tools, but when you're stuck behind an "enterprise" firewall, they don't work as well. This sad state of affairs motivated me to understand SWE agents more. Does it work? Yes. Is it as good as Cursor and Windsurf? Not quite, but it's pretty darn close, depending on the model you use. I'm lacking a few features like semantic indexing, MCP support, but I'll add them soon. Is it redundant with codex? Yeah, probably, but it was a super fun to build, and since I wrote it I have full control over any aspect of it to adapt to weird silly corporate environment network goofiness. Also, Codex doesn't give you a puppy emoji nor does it have "Always obey the Zen of Python, even when you're not writing Python." hardcoded into the prompt. Cute / nifty fun fact: At a certain point while I was building code_puppy, I swapped from Windsurf to using code_puppy to build code_puppy. The agent realized that it was working on itself, and made note of it while invoking the "thinking" tool. The puppy is unleashed!!!!

Visit product View on Hacker NewsMay 29, 2025

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

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.

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

70%70% 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 · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

48%48% 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

16%16% 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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