Browse entries 889–912 of 1704. Return to the first page to search and filter the complete collection.
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Lakshya Agrawal’s publisher notes describe GEPA’s reflective search: inspect execution traces and errors, propose text changes, score candidates, and retain alternatives that work well on different examples. Optimize Anything extends the editable object from prompts to programs, skills and policies. The reusable method depends on informative feedback and a suitable evaluator.
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Elie Bakouch’s publisher notes describe coding agents proposing optimizer changes, submitting cluster jobs and checking whether results improve a constrained training benchmark. Agents could consult community submissions, and some runs received human prompts to continue. Reported record improvements therefore mix independent search with reuse of existing research. No novel optimizer emerged in the account.
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Tejas Bhakta’s publisher notes describe a kernel-optimization loop that proposes changes, checks correctness, benchmarks them and keeps or reverts each candidate. Humans supply the higher-level optimization idea and target-hardware context. The central failure mode is optimizing an isolated metric: disabling CUDA graphs or testing only short contexts can make the kernel score improve while inference worsens.
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Andrew Dai’s publisher notes describe counting and video-tracking failures in which scene recognition substitutes for inspecting the visible details. A partial chessboard can elicit the familiar count for a whole board; a video answer can omit changes that occurred earlier. These are reported examples rather than measured failure rates.
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Jerry Liu’s publisher notes separate document parsing, storage and recurring workflows from an agent’s iterative retrieval loop. A fast initial parse can make a collection searchable, followed by deeper visual inspection of selected tables or charts. The design preserves source citations and flags uncertain extracted values before they enter downstream systems.
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AI Engineer session on Code Mode: Let the Code do the Talking - Sunil Pai, Cloudflare. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on OpenRAG: An open-source stack for RAG, presented by Phil Nash. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Why, and how you need to sandbox AI-Generated Code?, presented by Harshil Agrawal, Cloudflare. It adds practical context for how teams are building and operating AI systems in production.
The rapid development and deployment of emerging technologies such as artificial intelligence, blockchain, and the internet of things is driving an increased need for standards that can keep pace with the accelerating rate of technological change.
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AI Engineer session on Jack Morris: Stuffing Context is not Memory, Updating Weights is. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How Claude Code Works - Jared Zoneraich, PromptLayer. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Your Support Team Should Ship Code, presented by Lisa Orr, Zapier. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Enterprise Deep Research: The Next Killer App for Enterprise AI, presented by Ofer Mendelevitch, Vectara. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Amp Code: Next Generation AI Coding, presented by Beyang Liu, Amp Code. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The State of AI Code Quality: Hype vs Reality, presented by Itamar Friedman, Qodo. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Coding Evals: From Code Snippets to Codebases, presented by Naman Jain, Cursor. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Music from AIE Code Summit - Instrumentals. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Z.ai GLM 4.6: What We Learned From 100 Million Open Source Downloads, presented by Yuxuan Zhang, Z.ai. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Building an Agentic Platform, presented by Ben Kus, CTO Box. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Agents vs Workflows: Why Not Both?, presented by Sam Bhagwat, Mastra.ai. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Piloting agents in GitHub Copilot - Christopher Harrison, Microsoft. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Ship Agents that Ship: A Hands-On Workshop - Kyle Penfound, Jeremy Adams, Dagger. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Pipecat Cloud: Enterprise Voice Agents Built On Open Source - Kwindla Hultman Kramer, Daily. It adds practical context for how teams are building and operating AI systems in production.