Latest insights on AI, DX, and global business
Benchmarking local LLMs on a single DGX Spark-equivalent: Ollama, llama-server, SGLang with speculative decoding and 3 quantizations on real coding-agent tasks, plus KV cache fixes.
From detecting N+1 problems to solving them (JOIN, batch processing, caching), use this implementation-level checklist to choose the best optimization method.
Should you build an AI red team in-house or outsource it? Compare cost, talent, and operational load, with selection criteria and steps by organization size.
Compare 3 ways to run LLMs on multi-GPU: tensor parallel (TP), pipeline parallel (PP), and layer splitting—communication, PCIe vs NVLink fit, speed, throughput, and vLLM vs Ollama choices.
Explains the roles of Strix, Cairn, and Hermes from primary sources, compares vulnerability discovery, goal-based execution, and memory management, plus AI misuse data and defenses.
GLM-5.3 is the AI NIST rated as the most cyber-capable open-weight model yet. Based on Anthropic's findings and real attacks, learn what changed and defenses to review first.
How companies adopting LLMs can build in-house security testing: role assignment, process design, tool selection, and tips for ongoing operations.
From defining Graph Engineering to production operation: entity design, edge quality, RAG integration, and monitoring metrics explained from a practical standpoint.
Learn how to automatically measure output quality of generative AI models in production. Explore BLEU, ROUGE, BERTScore metrics, monitoring tools, and degradation detection with examples.
Jev is a judgment-only model that returns probabilities for each option instead of generating text. We fine-tuned Laya, a Jev-compatible OSS, in Japanese, Thai, English, and Lao, and compared accuracy, response speed, and cost against LLMs.
A step-by-step guide to rolling out AI coding agents like Claude Code to dev teams in Thailand/Laos in 90 days: CLAUDE.md setup, review standards, language barriers, and KPIs.
Checklist of operational rules to prevent AI agent incidents in production: permissions, human review, CI/testing, and kill switches—use alongside Harness design.
