Ay

Aye Chat: AI-First development in your terminal

Hacker News

Aye Chat: AI-First development in your terminal

We’ve just opened the public beta of Aye Chat, a terminal‑based AI coding assistant. It’s terminal-native, not built first for UI and then retrofitted for command line: we built it for ourselves out of AWS/Linux/Python environment because none of the other tools we tried - including GitHub Copilot CLI - quite fit our needs. Try for yourself if you are in the same niche, after a few days - you don’t notice how - it becomes addictive. Quick start: 1. Install: “pip install ayechat” 2. Start chatting from your source folder: “aye chat”, and use it to explore your code base, update files, diff and restore snapshots, etc. We’re also running a small beta giveaway: the five contributors who submit the highest count of valid non-duplicate GitHub issues by November, 30, 2025, 11:59 PM UTC each receive *10M prompt token credits* for the Anthropic Claude Opus model in Aye Chat environment. Happy hacking! - and looking forward to your feedback

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, coding · Missing: mac, agents, macos
89%89% 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: including · Missing: supports, reddit linkedin, podcasting
74%74% 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: including · Missing: https docs, excited, just released
48%48% 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: way · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% 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
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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