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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
TL;DR: You can test every agent individually and get clean results. Deploy them together and biased conventions emerge by round 15. The bias isn't in any agent — it's in the space between them. Published in Science Advances. Your unit tests won't catch this. Catch up: Part 1 your judge is biased. Pa
TL;DR: One adversarial agent. 2% of the population. That was enough to flip the entire swarm's behaviour. This is prompt injection at population scale — and your individual security audits can't see it. Catch up: Part 1 biased judge. Part 2 upgrading made it worse. Part 3 the population drifted on i
TL;DR: The EU AI Act's high-risk provisions take effect August 2026. Your multi-agent pipeline is covered. But the regulation doesn't define "bias," doesn't require population-level testing, and can't audit emergent behaviour. You can pass every compliance check and still have every problem from Par
TL;DR: Six parts of bad news. Here's what actually helps — with code. Cross-family judges reduce the core bias. Structured multi-dimensional evaluation cuts it by 31.5%. Chain-of-thought adds 1.5 to 13 accuracy points. Population monitoring catches drift before it locks in. Full implementation patte
That's the whole relay. The operator genuinely cannot read messages — not because of policy, but because the math doesn't let them. The honest tradeoff: the relay can see that a message was sent (metadata), even if it can't see what. For real anonymity you'd want onion-routing the fan-out. That's on
Stop Crashing the Browser Main Thread with Heavy Hashing Operations We have all been there. You need to verify a quick file checksum or compare asset footprints during build pipeline validation. You google a quick tool, drop a 500MB or 1GB file in, and bam—your browser tab freezes, the fan spins up
