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AI Engineering
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AI Engineering

Focused on cutting‑edge product and technology information and practical experience sharing in the AI field (large models, MLOps/LLMOps, AI application development, AI infrastructure).

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Latest from AI Engineering

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AI Engineering
AI Engineering
Jul 13, 2026 · Artificial Intelligence

Satya Nadella’s New Essay Introduces the ‘Reverse Information Paradox’ – Is It a New ‘Convenience Theory’?

The article explains how AI flips Arrow’s classic information paradox, turning enterprises into both customers and unwitting data suppliers, and outlines five strategic steps—Control, Capability, Choice, Cost, and Compound—to protect proprietary knowledge while still leveraging large models.

AIdata ownershipenterprise strategy
0 likes · 8 min read
Satya Nadella’s New Essay Introduces the ‘Reverse Information Paradox’ – Is It a New ‘Convenience Theory’?
AI Engineering
AI Engineering
Jul 11, 2026 · Artificial Intelligence

Why Logs Should Be the Agent Itself, Not Just a Byproduct

The article analyzes Yohei Nakajima’s "The Log is the Agent" paper, showing how ActiveGraph unifies goals, rules, tool calls, LLM responses, and artifacts into a single append‑only event log, enabling deterministic replay, cheap forking, and full provenance for LLM‑driven agents.

ActiveGraphAgent ArchitectureEvent Sourcing
0 likes · 13 min read
Why Logs Should Be the Agent Itself, Not Just a Byproduct
AI Engineering
AI Engineering
Jul 9, 2026 · Artificial Intelligence

OpenAI Launches ChatGPT Work, Echoing Anthropic's Claude Cowork

OpenAI’s new ChatGPT Work adds agent‑driven, multi‑step task execution to the familiar ChatGPT interface, letting users give a goal and have the system break it into steps, invoke tools, and produce deliverables, while the article compares it to Codex and Anthropic’s Claude Cowork, notes reliability concerns, and highlights desktop app and plugin integrations.

AI agentsChatGPT WorkClaude
0 likes · 5 min read
OpenAI Launches ChatGPT Work, Echoing Anthropic's Claude Cowork
AI Engineering
AI Engineering
Jul 8, 2026 · Artificial Intelligence

How AI Can Achieve Recursive Self‑Improvement: Lilian Weng Says Build a Robust Harness First

The article examines recursive self‑improvement in AI, arguing that a well‑designed harness—responsible for workflow orchestration, context management, and tool integration—is as crucial as model intelligence, and outlines design patterns, meta‑engineering approaches, evolutionary search methods, and the remaining challenges for truly autonomous AI systems.

AI self‑improvementAgent designEvolutionary Search
0 likes · 17 min read
How AI Can Achieve Recursive Self‑Improvement: Lilian Weng Says Build a Robust Harness First
AI Engineering
AI Engineering
Jul 7, 2026 · Artificial Intelligence

Inside Claude: Uncovering the Global Workspace that Reveals Unspoken Model Thoughts

Anthropic’s new study reveals a spontaneously emergent “J‑space” inside Claude that acts as a global workspace, allowing researchers to read and even manipulate the model’s internal, unspoken thoughts across tasks such as error detection, protein function inference, and multi‑step reasoning.

AI interpretabilityAnthropicClaude
0 likes · 15 min read
Inside Claude: Uncovering the Global Workspace that Reveals Unspoken Model Thoughts
AI Engineering
AI Engineering
Jul 6, 2026 · Artificial Intelligence

How to Use Claude to Uncover Your Unknown Unknowns

The article explains how Claude can be prompted to identify unknown unknowns in a codebase, outlines a four‑quadrant framework for categorizing knowledge gaps, and provides concrete techniques—blind‑spot scans, brainstorming, interviews, references, implementation plans, notes, pitches, and quizzes—to turn hidden uncertainties into actionable insights.

AIClaudeagentic coding
0 likes · 8 min read
How to Use Claude to Uncover Your Unknown Unknowns
AI Engineering
AI Engineering
Jul 4, 2026 · Backend Development

How SGLang Encoded Engineering Experience into Agents and Achieved Up to 2.75× Kernel Speedups

The SGLang team turned their benchmarking, profiling, CUDA kernel tuning, and production‑issue triage know‑how into reusable agent skills, merging three KDA‑Pilot PRs that delivered up to 2.75× kernel acceleration, a 71.4% throughput boost for Qwen3‑Next and a TTFT reduction from 456 ms to 168 ms, while outlining a repeatable workflow and practical rules for large‑scale performance engineering.

Agent AutomationCUDA optimizationLLM serving
0 likes · 16 min read
How SGLang Encoded Engineering Experience into Agents and Achieved Up to 2.75× Kernel Speedups