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Architect-Linter – Enforce architecture rules

Hacker News

Architect-Linter – Enforce architecture rules

I spent like 2 months building a tool to solve a problem we had: How do you enforce architectural decisions automatically? Problem: We're a team of ~20 engineers. Started with clean architecture. Now it's... let's just say "creative layering" Real issues: - 40% of PRs were rejected just for architecture violations - Code review became the bottleneck (architectural review ≠ logic review) - Junior devs didn't understand the implicit rules - No way to catch violations automatically Solution: architect-linter It's like ESLint, but for your entire system design. Define rules in architect.json, architect validates imports across your codebase. Key features: - Multi-language: TypeScript, JavaScript, Python, PHP (all via Tree-sitter) - Multi-architecture patterns: Hexagonal, Clean, MVC - Fast: Written in Rust, parallel processing - Free & open source (MIT license) - Works in CI/CD, pre-commit hooks, watch mode Example rule: ```json { "forbidden_imports": [ { "from": "src/components/*", "to": "src/services/*", "reason": "UI layer shouldn't call services directly" } ] }

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
76%76% 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: started, para · Missing: supports, reddit linkedin, podcasting
75%75% 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: open source, code review, io · Missing: https docs, excited, just released
47%47% 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
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way, para · Missing: mobile apps, ios, personal
35%35% 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, 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.

Correct prediction on native model

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