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Shoaku – Your Coding Navigator

Hacker News

Shoaku – Your Coding Navigator

AI Agents like Codex and Claude are incredibly powerful and have drastically sped up implementation. However, I noticed a strange side effect: I began to lose confidence in my own coding. I submitted a pull request after the AI completed the implementation. It looked good, but during code review, I was asked, "Why did you implement it this way?" I grew increasingly embarrassed because I couldn't immediately explain the complex code. Previously, I built confidence by understanding the design through reading and writing code, which was enjoyable, but now I've lost that. This coding assistant helps users understand their thought process and coding progress, with the AI acting solely as a guide and not writing code on its own. To still leverage the AI agent's autonomous implementation capabilities, a separate AI thread runs in the background in a temporary working directory, providing highly accurate navigation assistance to the user. The AI's responses are still a bit strange at times, but if this concept resonates with you, please share your feedback. As a solo creator, I find that very encouraging. Thank you.

Visit productView on Hacker NewsJun 30, 2026
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Actual performance

4points
4comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
94%94% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
93%93% 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: code review, ide, io · Missing: https docs, excited, just released
42%42% 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: users, way, para · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · 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 · Strong signals: arr · Missing: mrr, revenue, profit
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.

Correct prediction on native model

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