evals

Read the Portuguese translation →

Mentioned in

• Agents That Write Their Own Tools at Runtime — Sandhya Subramani, AWS
• Build, Test, Deploy, Monitor: The Agent Development Lifecycle Explained
• Building the Safety Layer for AI Agents
• A IA parou de crescer? O limite é você
• The Loop Is the Product — Roland Gavrilescu, Introspection
• The Self-Driving Eval Trick No AI Benchmark Beats
• Agent Loop: entendendo o coração do agente
• What Does an AI Engineer Do Now? - Aparna Dhinakaran, Arize AI
• Hands on with Gemini 4 Argon: Amplitude
• Como avaliar corretamente qual o melhor modelo de I.A.?
• Livestream: Opening keynote and Product direction, at In The Loop 2026 (Oct 14)
• n8n In The Loop 2026: Inside n8n’s Next Chapter | David Roberts
• Evals in AI: A Deep Dive — Tejas Kumar, IBM
• The Boring Version of Continual Learning
• Agentic Search vs Vector Search for Coding Agents: We Ran the Eval
• Agents That Self Improve While You Sleep - Fuad Ali, Arize AI
• Jev para AGENTES: onde faz sentido eu usar esse novo modelo de I.A.?
• Meta’s New Models & Agent
• Why Frontier Models Fail as Buy-Side Analysts - Janie Gu, Google DeepMind & Suyeol Yun, LinqAlpha
• Claude Codes New REVIEW.md System, What is it?
• The 6 Pillars of an Agentic Harness for Production — Varun Krovvidi, Resolve AI
• Decision Models para Evals: Testei a API da OpenAI
• What a $900M Bank Error Teaches Us About AI Agents - John Feminella, Two Sigma
• Why We Deleted Our MCP Server and Rebuilt It — Abhi Arya, Reducto
• AI Engineer NYC: OpenAI, Blackstone, Ramp, Coinbase, Apollo, Citadel, Basis, Runway, Compute Trading
• We Mapped 115 Microservices for Our Coding Agents — Kamalakannan Nandagopal, Postman
• AI Writes the Code. So What’s Left for Engineers? - Laurie Voss, Arize AI
• Why Building an Eval Platform Is Harder Than It Looks — Braintrust
• Teach Your Agent to Prove It’s Done — Nick Nisi & Zack Proser, WorkOS
• I Monitored Crime Audio. Voice Agents Scare Me More. — Sumanyu Sharma, Hamming AI
• $100M ARR Founder: The Secret Behind Great AI Apps
• How to Build Agents That Improve Over Time: Model, Harness, Context | Interrupt London
• Stop Starting With Code. Anthropic’s New First Step

Related

Claude Code, MCP, Cursor, Codex, Anthropic, OpenAI, LLM as a judge, Opus 5.5, traces, OpenClaw, Harness, Arize AI