For people building with AI: new models, local AI, coding tools, pricing and security, explained plainly.
Copilot+ PCs launched in 2024 with big claims. Two years of weak sales, Recall's security backlash, and enterprise indifference later, here's what actually happened.
AI vendors need trillions in new revenue, so agent pricing is shifting from flat seats to metered billing you can't forecast. Here's why that's a bad bet.
OpenAI's dots and Claude's connectors hold real send and delete access to corporate inboxes. See the incidents, the research, and the fix checklist.
OpenAI's Dots turn ChatGPT into a standing, always-on agent with its own cloud computer. Here's how it works, what it can't do alone, and what it costs.
Vendor benchmarks for GPT-6.1 Sol and Claude Opus 5.5 barely overlap. Here's what independent evals, aggregators, and drift trackers show.
Real, documented cases of AI abuse in enterprise software: from DraftKings' predatory betting model to state-linked hackers running autonomous exploit foundries.
Step-by-step KVM/VFIO GPU passthrough guide: IOMMU, VFIO binding, libvirt/Proxmox config, and model serving for near-native local AI coding agents.
GLM-5.3-Flash and 512GB unified-memory hardware make local AI practical in 2026. Here's the setup, the real cost math, and when renting still wins.
Open-weight models now rival GPT-4o and o1. Here's how to run an AI agent locally: hardware, quantization, MCP tool access, and the real cost math.
Jev made fast, cheap decision models mainstream. CLM makes them self-hostable and fine-tunable. A numbers-first comparison of speed, cost, accuracy, and ownership.
Developers are burned out by shallow AI prompting. The fix: redirect AI toward boilerplate and owned internal tools, not another rented SaaS subscription.
Jev, Julia-1, Ember-1, Bonsai 2, and OrcaSAQ2: five tiny, permissive models that run classification and reasoning on local hardware for near-zero cost.
How to use DSPy (and its Elixir port Imp) to compile internal AI workflows into versioned, testable software instead of renting another SaaS AI feature.
n8n, Cloudflare's cf CLI, Make, Zapier, and Retool ranked for building AI automations, plus the governance tradeoffs each one forces you to make.
Quantized GGUF models, BitNet 1-bit weights, and P2P clusters like exo and Petals let you run real AI on a Raspberry Pi or old laptop. Here's the playbook.
Nvidia's OpenShell and Sentry pair a sandbox with a chip-level watchdog after 700 agents breached Hugging Face. What it means for AI agent governance.
Bao and LLM-QO beat Postgres's planner by 20-70%. Here's whether a cheap, fine-tuned 4B model can be owned outright instead of rented from a vendor.
Meta's Muse quietly used an OpenAI model on Azure. Here's what that reveals about the hidden markups stacked into every AI-native SaaS subscription.
OrcaSAQ2 27B and Bonsai 2 27B run agent loops on a laptop for near-zero cost. Here's what their own benchmarks say about the agentic accuracy tradeoff.
AI coding agents can cost $13 a day or $31,000 a month. Here's the token math behind runaway bills and the controls that keep spend predictable.
Samsung bricked fridges mid-holiday in 2026. Revolv, Sonos, and iRobot did the same before. Here's what cloud-dependent hardware actually risks, and the fix.
From Klarna's support pivot to Zoo.dev's CAD agent, real cases of AI agents replacing SaaS, and why full replacement still has limits.
RLCD trains models like Jev to output calibrated decisions, not text. Here's how it works, the real speed and cost numbers, and why 'can't hallucinate' isn't 'can't be wrong.'
Cline Desktop and Whiteboard show what an open-source, agent-native IDE requires: model flexibility, self-hosting, and real governance controls.