164 related articles

last30days-skill is a GitHub AI Agent skill with 50K+ Stars, enabling cross-platform research across Reddit, X, YouTube, Hacker News, and Polymarket to generate grounded 30-day summary reports.

Explore how ASD-STE100 Simplified Technical English from aviation can be applied to LLM prompt engineering. STE's disambiguation principles—controlled vocabulary, short sentences, active voice—can improve LLM output accuracy and consistency.

An open-source GitHub repo curates 30+ legally free AI/ML classic books covering deep learning, RL, NLP, computer vision & more, with automated link checking.

Claude Opus 5's high+ reasoning mode has been jokingly called an "anxious overthinker." This article analyzes why LLMs over-reason, the diminishing returns tipping point, and how to balance reasoning intensity.

last30days-skill is a 50K+ Star AI Agent skill on GitHub that performs cross-platform research across Reddit, X, YouTube, Hacker News, and Polymarket to generate grounded 30-day summary reports.

A maker builds a DIY companion robot with NVIDIA Jetson Orin and 4S LiPo battery. Explore the full development journey from first power-up to AI interaction, including edge computing, power design, and companion robot trends.

Redis author antirez built the pure-C inference engine DS4 "Dwarf Star," compressing DeepSeek V4 Flash from 500GB to 80.8GB via asymmetric quantization for local deployment on 128GB unified memory at 26.7 tokens/sec.

Redis creator antirez built DS4 "Dwarf Star," a pure-C inference engine, using asymmetric quantization to compress DeepSeek V4 Flash from 500GB to 80.8GB for local 128GB unified-memory deployment at 26.7 tokens/sec.

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.

New to AI test development? This article breaks down the differences between machine learning and traditional programming, the origins of AI hallucinations, and the core principles of NLP/NLU/NLG to help test engineers build a solid AI knowledge framework.
Getting Legal AI Right: Why the Coding…
Most legal AI products are just general-purpose models wrapped in RAG and prompt engineering scaffolding. Learn why the coding agent paradigm fails in law and what real legal AI requires.
Designing APIs for AI Agents: A Paradi…
When AI Agents become the primary API callers, traditional interface design assumptions break down. This article explores agent-friendly API design principles and how MCP is driving this paradigm shift.

Google and Yale propose RLMF, using metacognitive feedback to train LLMs for honest uncertainty expression, achieving 63% calibration improvement in benchmarks.
Why Is GPT's Conversational Style So A…
Why do users find GPT so addictive to talk to? This deep dive explores how OpenAI uses RLHF, tone design, and interaction quality to build a conversational moat.

How do AI agents predict the World Cup winner? This article uses a real conversation to explore AI's use of real-time search, odds analysis, and probabilistic reasoning — and what it reveals about generative AI design.
How DSLs Make LLM Outputs More Reliabl…
LLM output instability is a core production challenge. This article analyzes how DSLs improve LLM reliability through verifiability, semantic convergence, and structural constraints.

Introducing an LLM Gateway in LangChain production brings unified APIs and auto-fallback, but also quality drift, cost spikes, and debug black boxes. This article breaks down the five key engineering concerns and what it takes to earn trust.

LLM JSON output unstable in your Agent? This guide covers 6 engineering layers: constrained decoding, validation retry, fake tool calls, Logit Masking, Schema contracts, and anti-pattern locking.
LLM Juries: How Multi-Model Voting Bui…
Single LLMs risk hallucinations and bias in metadata generation. This article breaks down the LLM Jury mechanism — using multi-model voting and consensus to boost annotation accuracy, with real engineering insights for food, medical, and e-commerce use cases.

AgentScope 2.0 by Alibaba's Tongyi Lab delivers six major upgrades: typed event streaming, dangerous instruction interception, human-in-the-loop, concurrent execution, workspace system, and agent-as-a-service for production-grade multi-agent development.