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Buildex – Interactive system design practice with AI feedback

Hacker News

Buildex – Interactive system design practice with AI feedback

Hey HN, I built Buildex (https://buildex.dev) to help engineers practice system design interviews interactively. The problem: System design prep usually means reading blogs or watching videos passively. There's no way to actually practice designing systems and get feedback. How it works: - Pick a challenge (URL shortener, rate limiter, chat system, etc.) - Drag-and-drop components onto a canvas to design your architecture - Connect them to show data flow - Submit and get scored on efficiency, cost, and reliability - AI evaluates your design and gives specific feedback Tech stack: - Frontend: React + Vite - Backend: Go - Database: PostgreSQL - AI: Claude API for design evaluation - Payments: Razorpay Free tier gives you 2 AI evaluations/day. I'm a solo dev and built this over the past few months while prepping for interviews myself. Would love feedback on: - Challenge difficulty/variety - Scoring system fairness - UI/UX issues - What's missing? Happy to answer any questions about the implementation.

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: claude · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, month, way · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
30%30% 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
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
21%21% 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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