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This article is a continuation of the article I wrote on the RLS policy issue I solved with Supabase. After I had fixed the RLS policy issue, I discovered that the way I structured my code was wrong. Semantically, it looked sort of correct but it was logically wrong. Here's a live preview of the pro
I was tired of seeing boring GitHub profiles (including my own), so I built ReadmeForge — a CLI tool that ## Demo ## Quick Start bash npx github:hitaozhou/readmeforge Choose your language, answer 8 questions, and done! What You Get ┌────────────────────┬──────────────────────────────────────────────
Last week, Toronto hosted Tech Week. A city-wide celebration filled with events and workshops revolving around the Technology sector. I had such a great time and wanted to share with you my experience and reasons to attend Tech Week in your city or nearest city hosting it. Additionally I want to sha
Agriculture IoT is often discussed through the lens of field monitoring: soil sensors, weather stations, irrigation control, drones, and crop analytics. But many operational problems in agriculture happen outside the field. Greenhouses, crop storage facilities, processing lines, shared machinery, an
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If you've tried to integrate Paddle Billing with Next.js I spent an embarrassing amount of time getting this right. Why Paddle instead of Stripe? Stripe is excellent if you're in the US or EU. For founders The tradeoff is a steeper integration. Let's go through it. Mistake 1: Using passthrough inste
To provide SRE as a service, a team built a center of excellence, introducing Federated SREs and roles like production manager and technical tribe lead. They created a culture of data-driven conversations where SLOs and SLAs were democratised. Surviving growing cognitive load meant continuously simp
📌 This article was originally published on Sherdil E-Learning. I'm republishing it here so the dev.to community can benefit too. The Kubernetes vs Docker question is one of the most common sources of confusion for developers entering DevOps. People hear both names constantly, see them used together
A few weeks of watching agents fail in slow, expensive ways has pushed me toward a single test for whether a system is actually verified, and it is narrower than I expected: could the thing being checked have produced the check? That sounds glib, but it cuts through a lot. "Is this verified?" usuall
Kenya's M-PESA processes $314 billion annually. 35 million users. And until recently, there was no way for an AI agent to interact with it natively. That changed when I built mpesa-mcp — a Model Context Protocol server that wraps the Daraja API and makes it directly available to Claude, GPT-4, and G
Why I am writing this This is the third piece in an accidental series about convergent evolution in agent tooling, and I think it is the most useful one, because this time the two systems being compared are not merely neighbours in the same field, they are the same species of thing: governance syste
Originally published at moday.me. Building MODAY in public. Une grande partie de MODAY a été construite avec Claude Code. Ajustements du theme Shopify, integration Gelato, distribution du blog en plusieurs langues, J'ai deja ecrit sur ma facon d'utiliser Claude Code. Et pourtant, j'essaie maintenant
I tried Vibe Coding with Claude Code for about three weeks on a side project, and the result is messier than I expected. Not unusable, just messier. The pitch on Juejin lately is that you can hand Cursor or Claude Code a one-line description and walk away with a working app. In my case I asked Claud
Originally published at moday.me. Building MODAY in public. Una parte consistente di MODAY e stata costruita con Claude Code. Modifiche al tema Shopify, integrazione con Gelato, distribuzione del blog in piu lingue, Ho gia scritto di come uso Claude Code. Eppure ora sto provando a spostare l'ambient
Originally published at moday.me. Building MODAY in public. MODAY의 꽤 큰 부분은 Claude Code로 만들었다. Shopify 테마 수정, Gelato 연동, 다국어 블로그 배포, 챗봇, 이미 Claude Code를 어떻게 쓰는지도 글로 쓴 적이 있다. 그런데 지금은 MODAY의 작업 환경을 Codex로 옮기려고 하고 있다. Claude Code가 별로였기 때문은 아니다. 가장 큰 이유는 이것이다. 언제든 옮길 수 있는 상태로 있고 싶다. AI 도구의 변화 속도는 아직 너무 빠
Originally published at moday.me. Building MODAY in public. Uma parte grande da MODAY foi construída com Claude Code. Ajustes no tema da Shopify, integração com a Gelato, distribuição do blog em vários idiomas, Eu já escrevi sobre como uso o Claude Code. E mesmo assim, agora estou tentando mover o a
The speakers discuss the architectural challenges of executing safe data deletion across distributed datastores. Balancing durability, availability & correctness, they explain how to orchestrate multi-system deletion propagation without impacting live traffic. They share lessons on controlling tombs
Stop paying to evaluate your LLM outputs. Stop tolerating non-deterministic quality gates. LongTracer is the MIT-licensed Python library that catches RAG hallucinations at inference time — no API calls, no cloud dependency, no per-verification cost. The Hallucination Problem Is Now a Production Engi
TL;DR: Researchers tested 20 AI models as judges. 17 out of 20 were statistically biased. True negative rate: 42.5% — your judge misses bad output more than half the time. If you have an LLM checking another LLM's work, this is your problem. You probably have this in production right now. response =
TL;DR: Smarter models are better judges — unless they're judging their own output. Then they defend wrong answers 86% of the time. Capability makes the bias worse, not better. The only structural fix: generator and judge from different model families. Part 1: Your judge is biased. 17 out of 20 model
