74 related articles

Deep analysis of Moonshot AI's Kimi-K3 technical report covering long context processing, MoE architecture, reasoning capabilities, and China's position in the global AI competition.

Deep dive into Moonshot AI's Kimi-K3 technical report, analyzing its long-context processing, MoE architecture, reasoning improvements, and its position in global AI competition.

How Pinterest engineers built Medic for Apache Spark — a multi-agent auto-diagnosis tool — covering the evolution from a single ReAct agent, observability, log denoising, and end-to-end testing.

Alibaba's Qwen releases a 2.4T parameter MoE model claiming to be 'second only to Gemini 2.5.' We break down what's real—and what's just hype.

A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.

A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.

This AI Daily covers five sectors: DeepSeek V4 reportedly enters grayscale testing with improved chain-of-thought; Musk previews Grok 4.6 and 5.0 with 2T parameters; Intern-S2 open-source model beats Claude Opus 4.8; xAI builds 2000MW Colossus cluster; NVIDIA RoboTTT breaks long-horizon robotics barriers.

Head-to-head test of Codex vs Fable AI Agents autonomously auditing a business with zero instructions. Codex executes reliably but self-limits; Fable shows deeper strategic vision. Includes optimal combo strategy and reusable automation skill framework.

Codex vs Fable in an open-ended problem space: Codex delivers flawless execution but plays it safe; Fable shows sharp strategic vision but lands too narrow. Here's how to combine both.

A structured zero-to-one roadmap for AI Agent development: Phase 1 covers Python & LLM basics, Phase 2 tackles five core Agent capabilities and LangChain/LangGraph, Phase 3 delivers hands-on RAG projects.
AI Exposes Workplace Authoritarianism:…
AI is exposing the hidden authoritarian structures in modern workplaces — and the education systems that feed them. Here's why we must shift from training compliance to cultivating autonomous thinking.

A complete beginner's guide to AI large language models: principles, the Transformer architecture, strengths, weaknesses, and practical tips for testers.

A complete four-stage AI Agent development roadmap: from LLM fundamentals and core modules, to ReAct/CoT paradigms, multi-agent collaboration, and real-world projects.
Using Claude for Constrained Optimizat…
How Claude and LLMs assist constrained optimization research — from problem modeling to solver integration. An honest look at AI's real capabilities and limits in automated science.

An in-depth look at Databricks MLOps core features: MLFlow experiment tracking, Unity Catalog governance, Agent Bricks agent development, and Genie natural language queries—plus real deployment challenges and practical advice.

Based on Fireship's review, an in-depth look at GPT-5.6 Sol's Ultra Mode multi-agent parallelism, its 91.9% Terminal Bench score, and how it differs from Claude Fable in cost, speed, and precision.

APA (Agentic Process Automation) merges LLM agents into RPA, supporting natural language, operation manuals, and video recording to generate scripts—paired with financial-grade security and three-layer protection for enterprise automation.

A Reddit user tested GPT-5.6 Sol with a single prompt. In the Row-Bot framework, the model built a 3D interactive London website in 5 minutes—complete with browser verification and vision analysis. A deep dive into AI coding Agents.

OpenAI launches the GPT-5.6 model family (Sol/Terra/Luna) and ChatGPT Work, enabling automated financial analysis, local file operations, Codex coding, and cross-app workflows—AI officially becomes a real work partner.

OpenAI's model completely solved all 5 problems in the AtCoder World Tour Finals Algorithm Contest, while no human competitor solved more than 3. A deep dive into this milestone: AI surpasses top programmers in both symbolic reasoning and heuristic optimization.