242 related articles
sqlite-utils 4.1 Released: The --code …
sqlite-utils 4.1 brings practical new features: the --code option for generating rows with Python, field type overrides fixing leading-zero ZIP codes, STRICT table mode switching, drop-index, and more. A deep dive into each design and the AI-assisted dev workflow.

Reddit users report Gemini feeling "worse over time." This deep dive explains the real causes—silent updates, safety over-alignment, and compute throttling—plus how to rationally evaluate AI model performance.
Kronos Financial Foundation Model: Usi…
Kronos is the first open-source foundation model treating candlestick data as the "language of financial markets," using an autoregressive Transformer and earning 32K GitHub Stars. A deep dive into its principles, applications, and limits.

Alibaba's Qwen3.8 challenges larger models with a 2.4T-parameter MoE architecture, claiming second only to Gemini. A deep dive into MoE mechanics, continuous updates, two-speed release strategy, and real local deployment requirements.

Did Claude drop ~10 benchmark points after redeployment? We dig into the safety classifier routing mechanism, Arena voting data, and developer feedback to reveal the truth.

T-Head open-sources AI software stack T-Head SAIL at WAIC to lower the barrier for domestic chip development; Kimi K3 tops the WebDev leaderboard; Qwen 3.8 Max Preview cuts prices aggressively; Moonshot prepares a Hong Kong IPO; and Oracle switches its data center to a fuel cell microgrid.

Alibaba Qwen 4, DeepSeek V4, and Zhipu GLM's next-gen models are all nearing release. A deep dive into the latest leaks, capability improvements, and timelines for these three Chinese AI flagships.

Anthropic's Claude Sonnet 5 claims near-OPUS 4.8 performance at lower cost. Real-world tests reveal hidden tokenizer costs, weak creative output, and only 13th place on Cursor rankings.

Anthropic engineers reveal Claude Code's 18-month evolution: system prompt cut by 80%, 65% of PRs shipped automatically by AI, Claude Tag collaboration, and the safety logic behind auto mode.

AI Agents in production face systemic dependency drift risks — model updates silently change outputs and silent failures are hard to detect. Learn detection strategies, hidden costs, and engineering practices.

A user spotted a suspected Gemini 3.5 Pro model identifier in Google AI Studio, triggering a 'Model not recognized' error. We break down the leak, naming strategy, and what it means for developers.

Cosine AI founder reveals how the UK's first sovereign LLM is being built — from government compute grants and RL credit attribution to multi-agent orchestration and synthetic data pipelines.

A RAG pipeline crashed three times due to inter-stage data format mismatches. Learn how JSON Schema and Pydantic explicit contracts turn fragile AI pipelines into maintainable systems.

A practical guide to Claude Code covering installation, Chinese LLM switching, project analysis, key commands, and conversational Git workflow automation for developers.

Ornith 1.0 by Deep Reinforce reinforces Qwen 3.5 for code agents. We test Ornith 9B & 35B MoE on Chinese writing, logic, and invoice OCR, with full llama.cpp deployment guide.

A practical Claude Code handbook built for QA engineers — covering environment setup, spec-driven development, Skill encapsulation, MCP integration, and AI test sub-agents across 10 core modules.

A creator tests GPT-5.6 Codex on two real projects: an Android MAUI app and a tower defense game prototype. Zero code written for the app; the game demo surfaces multiple bugs. Honest look at AI coding's real limits.

Google DeepMind engineer Philip reveals: almost everyone uses coding agent Skills, yet almost no one writes evals for them. A deep dive into Skill evaluation methods, 8 actionable tips, and a real Gemini API case study.

Explore core AI Agent debugging challenges in production: non-determinism, multi-step error propagation, and observability gaps — with practical guidance on LangSmith and tracing tools.

Should you implement ML algorithms from scratch or just use sklearn? This guide breaks down the optimal learning path for ML engineers by career stage and company type.