Latest insights on AI, DX, and global business
Benchmarking PFN's PLaMo 3 Translate LLM (4 sizes) on DGX Spark against Claude Haiku/Sonnet using chrF & COMET-22 for EN-JA, JA-EN, and Thai translation accuracy.
How safe is AI pentesting? We ran ScopeWeaver v0.9 in an isolated lab to check authorization-flaw detection, network limits, kill/restore, and AI report errors—covering local Qwen and cloud GLM findings and limits.
Learn how to estimate token consumption from expected processing volume before deploying AI agents or chatbots, and calculate monthly costs in advance—useful for PoC cost planning.
Why LLMs handle functional requirements well in AI coding, but humans still own non-functional needs like security, performance, cost, and maintainability—plus hooks and CI tips.
Benchmark of 6 open-weight models on a DGX Spark (128GB), tested on 3 file-reading, listing, and extraction tasks. Qwen 27B passed all tasks; only Nemotron Bolt had 0% hallucination.
Based on public case studies from OpenAI, LangChain, Anthropic, and Uber, see how harness changes (instructions, verification, permissions, execution environment) affect performance, quality, and cost.
Write business AI prompts in 5 elements: tag your materials, fix the output format, and compare versions on the same inputs. Includes tips for Thai output.
Explains the critical PHP vulnerability "PHP2Shell": causes, impact, fixes, and workarounds. ARTEX×GLM RCE tests on PHP apps found issues in 104s and 305s.
ARTEX verifies Juice Shop: NoSQL challenge solved in 10–15 min, ★6 Multiple Likes in ~40 min. Covers goal-setting tips, Docker constraints, and AI pentest results.
Learn how to design a feature store to streamline AI model feature management, plus common pitfalls to avoid during implementation.
We explain practical ChatGPT use for Thailand-based operations in Japanese and Thai, covering emails, quote inquiries, data aggregation, personal data handling, and trial-based evaluation.
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.
