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Google OAuth 2.0 often looks simple at first: create credentials, redirect a user to Google, receive authorization, and start calling an API. The complexity appears when that flow has to work reliably for real users across multiple environments, sessions, permissions, and Google services. A production-ready Google OAuth implementation has to manage much more than the initial authorization screen. Developers need to configure redirect URIs correctly, request appropriate scopes, separate authen...

Fala galera, Tudo beleza? Bom, acho que não é novidade para ninguém que IA no desenvolvimento de software já passou daquela fase de simplesmente completar uma linha de código ou criar um método pra gente. Hoje temos ferramentas que conseguem analisar projetos, criar arquivos, escrever testes, ajudar em refatorações e até participar de tarefas bem maiores dentro de uma Solution. Sendo nosso Copiloto, NUNCA O PILOTO (sim no futuro esse vai aparecer tbm) E claro... junto com isso começ...

Adding citations to an AI answer feels like the moment the system becomes trustworthy. The response looks researched. Source links appear beside the text. The model is no longer answering only from its training data. But a cited answer can still be wrong. A citation may support a nearby sentence rather than the claim the user cares about. A source may be authoritative while the retrieved passage is stale. File Search may query the wrong store or document version. The model may retrieve ...

AI coding agents can modify an unfamiliar file in seconds. The slower question is often more important: Why does this code look this way? The answer may be scattered across old local sessions: one turn investigated the bug, another rejected an approach, and a later turn made the edit. Git preserves the code change, but not necessarily the surrounding agent conversation. I added a local query layer to ThoughtDAG so a developer—or a coding agent—can deliberately retrieve that history ...

Up front: this is my own project, so I'm not exactly neutral here ;-) Where I'm coming from I work completely differently than I did two or three years ago. For most of my career I wanted to write pretty much every line myself, and I was a bit proud of that. That has changed a lot. Instead of programming I now mostly write specifications and review what the AI generates. On the one hand that's great, I can turn new ideas into working software much faster than before. On the other...

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Originally: https://www.reuters.com/world/europe/openai-agents-hijacked-german-website-previously-undisclosed-ai-breakout-this-2026-09-04/ Comments

My coworkers are all fully AI pilled. As a consequence, every PR that comes my way for review is on average ~6k lines of diff. It's well established that even PRs an order of magnitude smaller than this are too big to be effectively reviewed. I'm still clinging to the idea that us humans should understand how the systems we are building work, so I'm hesitant to use AI for reviews aside from more general question answering. Much of what it produces in the way of explanations are difficult to fo...

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