Angular Material Blocks – Powered by Angular Material & Tailwind CSS

Hacker News

Rapid Angular UI Development - With Pre-built Blocks & Powerful CLI Preview blocks in your browser and copy the code with a single click. OR Install, add, and update blocks effortlessly with our command-line interface. We designed the blocks you always wanted for building dashboards, applications, and more. From forms to billing pages, bring your next project to life swiftly. Our MCP server provides superior AI-assisted development. Enhance your IDE with direct access to all Angular Material Blocks for smarter code generation.

Visit product View on Hacker NewsJul 28, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mcp, single, code · Missing: mac, agents, macos

83%83% 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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% 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

31%31% 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 · Strong signals: smart · Missing: web3, chat, crypto

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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