AI Terminal – The AI coding assistant that knows your codebase

Hacker News

Built this after realizing that existing AI tools treat every file like it exists in isolation. AI Terminal actually understands your project structure and suggests changes that make sense. What makes it different: -Semantic understanding: Uses AST + ML to map your entire codebase -Pattern recognition: Identifies and suggests code following existing architectural patterns -Terminal-first: Built for developers who live in the command line -Persistent intelligence: Remembers insights about your project across sessions Benchmarks vs popular tools: 3.2x faster codebase navigation vs Copilot 91% relevant suggestions vs 67% for Cursor 45% faster debugging in large codebases vs Claude Code Example workflow: aiterm analyze # Understand project architecture aiterm suggest "add error handling to user service" # Get contextual suggestions aiterm template create "api_endpoint" # Generate code matching existing patterns Perfect for: Senior developers working on complex systems Teams with established coding patterns Anyone tired of AI suggestions that ignore existing architecture The tool runs locally (privacy-first) and has both CLI and VS Code extension. Try it: npm install -g ai-terminal Looking for beta testers.

Visit product View on Hacker NewsAug 20, 2025

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

96%96% 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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

36%36% 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

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

13%13% 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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