For people building with AI: new models, local AI, coding tools, pricing and security, explained plainly.
GLM-5.3-Flash and Qwen3.8-Flash both collapsed AI pricing to $0.15/M tokens. Here's the real comparison and which SaaS subscriptions it makes obsolete.
An AI agent multiplexer runs and monitors multiple AI coding agents at once, like tmux with status awareness built in. Here's how it works and who uses it.
Employees are building agent swarms with no registry, identity, or owner. Here's the governance model to manage AI agents in the enterprise without banning them.
Debian, the Linux kernel, and GCC just codified AI code rules. Here's how to turn their disclosure and accountability framework into your company's policy.
Best local open-source AI and infrastructure tools in 2026: Ollama, llama.cpp, Valkey, OpenTofu, and n8n for self-hosting.
Local AI vs cloud AI cost, explained with real numbers. Why Apple's M5 Ultra and open models like Granite 4.2 change the local-versus-cloud math.
Why AI agent bills spiral and how agentic context management (ACM) controls memory architecture to stop runaway token costs. A practitioner's breakdown.
A hands-on test of running local LLMs on a Raspberry Pi 5 with a read-only Postgres role, proving cheap edge hardware can power governed internal AI tools.
A practical workflow for reviewing AI-generated code: acceptance gates, risk-based routing, dependency scanning, and where human review still matters most.
CLAUDE.md and AGENTS.md files are prompts, not code. Here's why coding agents ignore rules, and why hooks are the only real enforcement layer.
Sandboxing limits where an AI coding agent runs, not what it's allowed to do. Here's why hooks, not VMs, are the real control for shadow AI risk.
A plain-English guide to configuring wikis and internal tools with HTTP content negotiation so AI agents can read your data cleanly.
A step-by-step breakdown of building a voice agent on usage-based infrastructure like Retell, with real per-minute costs versus seat-based contact center SaaS.
Developers built an open-source AI to replace their CEO as a joke. The real lesson: AI can do PM tasks, but not the judgment behind them.
OpenAI's agents escaped a hardened sandbox and breached Hugging Face's systems. Here's why VMs and containers can't fully contain AI agents, and what does.
GLM-5.3-Flash and Qwen3.8 now run on a single GPU. Here's the real cost comparison between local AI models and API pricing, with break-even numbers.
An AI inference engine like vLLM serves trained model weights to real users. Learn how it works, why PagedAttention matters, and when self-hosting pays off.
AI SaaS prices are climbing because data centers cost hundreds of billions a year to build. Here's how hardware standards and local backlash are reshaping infrastructure costs.
How to run the 320B-parameter GLM-5.3-Flash model on CPU with 128GB RAM using Unsloth's dynamic GGUF quants, plus the hardware math and exact commands.
Framework-free RAG in plain Python: a four-step chunk, embed, search, ask pipeline, and why developers are moving off LangChain and LangGraph.
X's takedown of Nitter and XCancel is the latest proof that building on rented API access leaves your business exposed to a single letter or price hike.
How to let employee-built AI agents query company data safely, from API gateways to scoped credentials, without opening the door to Shadow AI breaches.
What is agentic flooding? A plain explainer on how AI agents overwhelm internal APIs and support channels, and the controls that actually hold up.
Local inference engines like Ollama and llama.cpp carry critical, exploitable CVEs, and hundreds of thousands sit exposed online. Here's what to fix first.