최근 업데이트된 채널

선택하면 해당 채널만 표시모든 채널 보기 →

필터 결과

4개
정렬
슬라이드 8장18:27EN
AI Engineer· 2026년 10월 26일Daksh Gupta on Building a Better AI Code Review AgentDaksh Gupta · GreptileGreptile co-founder Daksh Gupta explains why a webhook plus a coding agent is not enough for reliable pull-request review—and how targeted prompts, incident data, model inversion, codebase context, browser testing, and task-specific model routing can help.Code review & verification코딩 에이전트개발 워크플로와 테스트+2
슬라이드 8장15:27EN
AI Engineer· 2026년 10월 25일What AI Engineers Do Now: Build the Loop Around AgentsAparna Dhinakaran · Arize AIAparna Dhinakaran argues that AI engineers are moving from shipping individual features to building systems that let agent teams improve them. At Arize, that means tracing agent behavior, evaluating its work against human judgment, and feeding those signals back into an always-on agent.에이전트 엔지니어링평가(Evals)관측 가능성과 안정성+2
슬라이드 5장34:11EN
GOTO Conferences· 2026년 10월 23일Anatoliy Bilenko and Alberto Carretero: Making Database Protocols Reliable with Formal Methods and AIAnatoliy Bilenko, Alberto Carretero · CanonicalCanonical’s database engineers connect executable specifications, model-based tests, failure exploration, and reconstructed execution histories—and explain why none of these techniques proves a real system correct on its own.개발 워크플로와 테스트Code review & verification관측 가능성과 안정성+2
13:12EN
OpenAI· 2026년 10월 7일Alexandra Lato on Building Stack Overflow for Agents and Stack Internal with CodexAarti Bagul, Alexandra Lato · OpenAIStack Overflow’s VP of Product explains how a small team used Codex with opposite constraints on two products—and why agent-native design means letting agents contribute knowledge, not just read it.코딩 에이전트엔터프라이즈 AI에이전트+2