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Same prompt tested across Replit, Bolt, v0, Lovable and Raq.com

Hacker News

Same prompt tested across Replit, Bolt, v0, Lovable and Raq.com

Hi HN, I built Raq.com – a platform that uses Claude Code to build working internal tools directly in the browser. Claude Code is great at self correcting when given the right tools. I've found that the popular web-based AI coding tools look great in demos but fail on real API integrations or require a lot of error back and forth. They don't appear to do much research or self-correcting, likely to reduce spend. I wanted to see the current state of these tools, so I ran the same prompt on five platforms (Replit, Bolt, v0, Lovable, and Raq.com) to build a tool that requires 3 different APIs (Companies House, FinUK and OpenRouter) working together. Four platforms produced broken prototypes or needed manual fixes. Raq.com delivered a complete working solution from a single prompt (that can be deployed to live with one click). Full test with videos: https://raq.com/real-world-test We're in early access (requires Claude Pro/Max for free usage) - we're looking non-coders who would like to build internal tools for their team. Some technical info: - Raq.com provisions isolated dev and prod Docker environments for each company (companyname.raq.com and companyname-dev.raq.com). - The dev site includes a persistent terminal streamed to the browser, so the session continues even while tab is closed. - CLAUDE.md file provides best practices, known pitfalls, and coding patterns for the Laravel + Filament stack. - Self-Correction Loop: Claude can test and debug its own work. It has direct shell access to a custom script that bundles PHPUnit, syntax checks, and cache clearing. Plus a Playwright wrapper to check for errors and take screenshots. - A single click runs a script that rsync's the dev workspace to the prod container, runs migrations, and clears caches.

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Actual performance

6points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, lovable, dock · Missing: mac, agents, macos
97%97% 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
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: plus, platform · Missing: intuitive, reviews, host
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% 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: video · 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.

Incorrect prediction on native model

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