Latest insights on AI, DX, and global business
Synthetic Test evaluates AI using synthetic data. We explain its role in LLM & AI agent quality assurance, differences from LLM-as-a-Judge, and 4 implementation steps.
Based on 2026 cases like Hugging Face model poisoning and SaaS breaches, this implementation guide covers AI BOM, least-privilege OAuth, and sensitive secret management for AI developers.
Learn how "automation bias" from blindly trusting AI output distorts business decisions, and explore implementation patterns using confidence level display design, HITL, and audit logs to prevent blind trust.
A sector-by-sector guide for Thai B2B companies choosing AI investments. Compare manufacturing, logistics, tourism, healthcare & retail by ROI, complexity, and talent needs.
AI agents create new attack surfaces via MCP / Skills. An enterprise implementation guide covering MCP by-design vulnerabilities, malicious skill delivery, and SSRF mitigation.
Learn how Test-Time Compute scaling works and how to optimize the tradeoff between inference cost and accuracy. A practical guide to LLM operations and cost design in the age of reasoning models.
Bangkok B2B trade shows (May 2026–May 2027) by industry & month. Venues: BITEC / IMPACT / QSNCC. Includes exhibitor & visitor checklists.
Explore hybrid AI design combining cloud LLM and on-device SLM, with strategies for routing tasks by cost, latency, and compliance.
Protect LLM apps from prompt injection & hallucinations. Learn guardrail design basics, input/output guards, evaluation, and multi-tenant operations from an implementation perspective.
Learn the basics of edge AI & on-device LLMs, and how to design workflows requiring low latency, data privacy, or unstable connectivity—where cloud LLMs fall short.
Anthropic's Claude Mythos Preview & Project Glasswing redefine AI-driven vulnerability discovery. From OpenBSD's 27-yr bug to FreeBSD CVE-2026-4747—5 DevSecOps actions you can take now.
Context Engineering is a design approach for dynamically assembling optimal information for LLM apps. Covers differences from prompt engineering, context window components, and RAG/agent implementation patterns.
