AI editor for websites (Next.JS)

Hacker News

Hi all, I've playing with an idea to plug LLM into a website editor and see how I make that work. I used mostly Claude Code and a little bit of Codex to build it in my evenings and on weekends. The result is this open source project you can try out on your machine - all you needed is an LLM API key - recommended to use Anthropic models (others are supported but not so well tested). https://github.com/avocadostudio-ai/avocado It works for now specifically on Next.js websites but can be easily adapted to any web framework, modern ones especially. I do wonder if this is something of interest for anyone and do want to get your feedback. And of course, Copilot/Assistants are now being added to many products but many of those are expensive SaaS solutions like Adobe's AEM. Thanks, Yury

Visit product View on Hacker NewsMay 20, 2026

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2points

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Launch Intel predictions

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

96%96% 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

82%82% 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, ide, io · Missing: https docs, excited, just released

49%49% 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

45%45% 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

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

20%20% 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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