135 related articles

In-depth comparison of Fable 5 vs GPT-5.6 (Sol) for AI coding. Covering token efficiency, code quality, design, cost, and safety based on $10K+ real usage data.

Deep dive into building a YOLO26n object detection inference engine from scratch using ARM64 assembly and C, covering NEON SIMD, Winograd convolution, GEMM micro-kernels, and cache tiling optimizations.

GitHub Dependabot now defaults to a three-day cooldown for version update PRs to guard against supply chain attacks and improve dependency stability.

Deep analysis of building layered tunnel architectures using VLESS+Reality to evade DPI and domestic relays with whitelisted IPs to bypass geographic blocking in extreme censorship environments.

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.

A deep dive into enterprise Agent engineering: long-running execution, HITL safety approvals, and event sourcing — with two real-world commercial projects for content ops and SRE.

A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.

From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.

A data-deletion disaster reveals the biggest AI Agent risk: the problem isn't the model, it's Harness design. Learn context management, process standards, and permission isolation.

OpenAI's upgraded voice assistant can speak dialects, do real-time simultaneous interpretation, teach English, and even get flustered. Here's what changed.

Set up an AI coding workstation on NVIDIA Jetson using VS Code + Claude Code. Covers JetPack NVIDIA Skills, token budget management, headless SSH dev, and coding agent best practices.
Frontier AI Models Keep Making Element…
Why do frontier AI models like GPT-5 still make basic errors? This deep dive explores the reliability crisis in advanced LLMs, benchmark gaps, and what developers should do.

Most AI agents never make it past the demo stage. This guide covers four production-grade agent patterns—workflow orchestration, policy-constrained execution, anomaly handling, and load routing—to help teams build reliable agent systems.

A comprehensive breakdown of the OWASP Agentic Security Top 10 framework, covering ASI01–ASI10 risks including goal hijacking, tool misuse, identity abuse, supply chain vulnerabilities, and cascading failures — with practical mitigations for AI agent systems.

A developer's hands-on account of building a brief-to-storyboard video Agent: JSON errors, missing fields, pacing issues — and how JSON Schema, retry loops, and MCP tools solved them.

Deep dive into langgraph-agent-stack: per-run dollar budget control, canary traffic routing, Mock testing mode, and 800+ test cases to safely deploy AI Agents from demo to production.

A deep dive into DeepAgents' core mechanisms, with a hands-on guide to building a HarmonyOS automated testing Agent — covering create_deep_agent, LangChain comparison, and long-chain task planning.

OpenAI's GPT-Live voice model tackles the cocktail party problem through Background Robustness — enabling precise speaker focus in noisy, multi-person environments with natural multi-turn dialogue.

A deep dive into the three-layer AI Agent evaluation framework — outcome, process, and system layers — covering trajectory evaluation, tool call accuracy, automated testing, and key engineering challenges.

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.