AI

AI spelling and grammar checker for docs

Hacker News

AI spelling and grammar checker for docs

Hey HN! Spellcaster is a Python-based CLI tool for running spelling and grammar checks across documentation files. LLMs are great proofreaders, and while rule-based spellcheckers exist, we noticed there wasn’t a good CLI tool that leveraged AI agents. So we built one! Spellcaster came together over a weekend at the Mintlify AI DevTools hackathon in San Francisco[0], where it won the Best Demo Award. We ran Spellcaster against several large open source projects and identified hundreds of readability issues in their docs. Some popular ones: * Cursor: 116 errors * Mintlify 245 * Vercel: 1053 (My favorite was Vercel spelling "Lorem ipsum" wrong in one of their pages). Spellcaster uses LiteLLM for easy model swapping and AgentOps for cost and error tracking. [0] https://x.com/AlexReibman/status/1834524984183767276

Visit product View on Hacker NewsSep 18, 2024

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3points

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

82%82% 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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, ide · 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 · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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