Le meilleur du dev et de l'IA, scoré chaque jour par un agent. Filtré, résumé, classé. Aucune couleur, aucun bruit — juste la matière.
Observability is crucial for AI-driven products; Day 3 covers seeing, debugging, and improving agents. You'll set up Langfuse, explore traces, and add custom trace metadata, then build a time-aware web scraper tool. By the end, your DeepSearch agent becomes understandable and deployable with confidence.
Ce workshop transforme un prototype de chatbot en expérience persistante: persistance des messages en streaming, création d’un chatId et routage vers /chat/:chatId, sauvegarde en DB et réhydratation de l’UI. Vous verrez comment router, revalider le sidebar et recharger les conversations de manière fiable.
That workshop reframes the agent as an evaluator-optimizer loop: one LLM call generates a response while another provides evaluation and feedback. It also covers showing sources in the frontend with a card-like UI (favicon, title, snippet) to surface provenance and improve traceability.
Day 3 focuses on observability for AI agents. It guides you to set up Langfuse, inspect traces, and add custom trace metadata to diagnose and improve your DeepSearch agent. By the end, you'll transform it from a mystery into a well-understood, diagnosable system.
In this workshop you'll tackle advanced challenges: an evaluator-optimizer loop and showing sources in the frontend. The description frames the system as an evaluator in a loop and proposes frontend source cards—favicon, title and snippet—similar to patterns in Anthropic's Building Effective Agents.
Utiliser useChat de Vercel AI SDK permet de lancer rapidement un chatbot. Cette session montre comment transformer un prototype en chat persistant: endpoint POST /api/chat, enregistrement des messages avec Drizzle, routing par chatId et réhydratation de l’UI à partir de l’historique stocké. Vous obtiendrez une expérience end-to-end avec streaming, sauvegarde et rechargement des conversations.
Observability is essential when building AI-driven products. Day 3 teaches you to see and debug agent behavior using Langfuse, explore traces, and add custom trace metadata, then to build a time-aware web scraper tool to improve your DeepSearch agent.
This workshop demonstrates turning a quick prototype with the Vercel AI SDK into a durable, persistent chat app. It covers streaming completions via a POST /api/chat endpoint, using Drizzle to create and return a chatId, saving messages as they stream, and rehydrating the UI while routing by chatId for seamless reloads.
This workshop reframes the typical LLM agent as an evaluator-optimizer loop: one call generates a response while another evaluates it, then feedback guides the next step. It emphasizes moving logic from a pure agent to an evaluative loop and optimizes around it. It also covers displaying sources in the frontend as card-like items (favicon, title, snippet) to mirror DeepResearch patterns.
Observability is essential when building AI-driven products, enabling you to see, debug, and improve agent behavior as conversations and calls grow. Day 3 guides you through setting up Langfuse, exploring traces with custom metadata, and building a time-aware web scraper tool to turn the DeepSearch agent into a well-understood system.
Showing Sources In The Frontend outlines an evaluator-optimizer loop for LLM workflows and recommends displaying sources in the UI as card-like entries (favicon, title and snippet). It reframes the agent as a decision loop—answer when you have enough information, otherwise search—and adapts frontend patterns to present sources clearly.
The article explains observability for AI agents using Langfuse, showing how to set up traces, view them, and attach custom trace metadata. It also covers implementing a time-aware web scraper tool to diagnose and improve a DeepSearch agent.
Observability is essential when AI agents drift in production. Day 3 guides you through setting up Langfuse, exploring initial traces, and adding custom trace metadata, culminating in a time-aware web scraper tool to diagnose and improve your DeepSearch agent.
Using Vercel AI SDK's useChat, you can prototype a chat quickly, but turning it into a durable, persistent product requires backend persistence, real-time updates, and solid routing. The workshop guides you through building a POST /api/chat endpoint that streams OpenAI completions, using Drizzle to create and return a chatId, saving messages as they stream, rehydrating the UI with past conversations, and keeping the sidebar in sync via route revalidation.
The article surveys additional paths toward code independence, examining how to reduce vendor lock-in and improve portability across runtimes and tooling. It highlights patterns and architectures that decouple software from single platforms, encouraging modularity and open standards.
A 2024 piece analyzing the computational aspects of transposing matrices, highlighting core algorithms and performance trade-offs. It discusses memory layout, cache locality, and parallel approaches, with practical notes for implementing efficient transposes in real-world workloads.
Zig enforces one of the strictest anti-LLM policies in OSS, prohibiting LLMs for issues, PRs, and even comments. The rationale, explained by Loris Cro, is that valuing contributors over immediately reviewable code preserves long-term contributor growth, since LLM-generated work doesn't help train new maintainers. The article also notes Bun's use of AI while Zig remains ban-focused, highlighting tensions between AI-assisted development and community governance.
The article argues that Clippy's configuration should be stricter to enforce better Rust code quality. It suggests adopting tighter lint rules and sensible defaults to catch bugs earlier in CI.
The post analyzes a preemption regression in Linux 7.0 that affected PostgreSQL performance. It explains the mechanics behind the latency impact and discusses potential mitigations.
Explores how goblin outputs propagate across AI models, tracing the timeline from early prompts to GPT-5 behavior quirks. It identifies root causes tied to alignment quirks and data contamination, and outlines fixes ranging from prompt scaffolding to model-side mitigations.