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One thread. Multiple AIs. Deliberation, not polling. Most people use AI like this: 🤦 Ask one model → get one answer Ask multiple models → compare results That’s not thinking. That’s polling. Not side by side. Not isolated. But in sequence — where each one reads what the previous one said before responding. Manual Council is the simplest form of that idea. No backend. No orchestration. No
I recently had a requirement where, if a user opens the app in multiple browser tabs, only one tab can be active at a time, and the rest are locked. Here are the core requirements as I see them: Uniquely identify each tabs Store the active tab somewhere If a user opens a new tab, lock it if there is already an active tab If the active tab is closed, detect that and inform the other tabs Sounds sim
If you've ever built a form backend or an automation workflow, I built MultiValidator to fix that. One API call. Up to 50 fields. Send a batch of fields, get back validation results for all of them: import requests payload = { "fields": [ {"type": "email", "value": "[email protected]", "field_name": "email"}, {"type": "phone", "value": "+447911123456", "field_name": "mobile"}
Table of Contents Introduction Environment Requirements Core Features Core Design and Code Analysis Actual Execution Demo Architecture Overview How You Can Expand Future Plans & Conclusion What is this It is a basic debugger, running on Linux and implemented in C++, aiming to create a debugger that is easy to read and expand. In addition, Lavender's main function is to help users analyze the logic
I spent long hours debugging why Google couldn't index my React app. Lighthouse showed green scores. The app felt fast. But Search Console kept flagging LCP failures and CLS shifts I couldn't reproduce locally. The fix? Four lines of metadata and one misunderstood render strategy. If you've ever shipped a "fast" SPA and watched it flatline in search rankings, this Core Web Vitals SEO guide is for
I started where a lot of us do: a LangChain RAG walkthrough. You chunk some text, embed it, retrieve top‑k chunks, and wire an LLM to answer questions. It clicks quickly, which is exactly why it’s easy to walk away thinking you’ve “done RAG.” What bothered me was that the demo corpus is usually tiny and artificial. I write on DEV.to about things like NLP routing and CNN image classification. If I
I wanted to add live chat to my WordPress sites without loading a 500KB third-party script. So I built my own. GhostChat is an open source embeddable Widget: Vanilla JS, no framework, ~10KB Backend: Cloudflare Workers + Durable Objects for persistent WebSocket connections Payments: Stripe for the hosted tier Self-hostable: Bring your own Cloudflare account Durable Objects give you stateful serve