Most of the AI conversation centers on the largest models. The interesting work in 2025 happens at the small end of the spectrum, where a handful of 1-8B-class models are doing more useful daily work than the marketing acknowledges.
OpenAI surprise-launches GPT-5.1 with a tone-picker. Anthropic reportedly commits $200B to Google Cloud. Moonshot's Kimi K2 Thinking, a trillion-parameter open-weight model that beats GPT-5. Microsoft Ignite warming up. My take on the week.
The legal framework for treating accumulated organizational knowledge as a real asset is older than the AI conversation and incomplete in ways the AI conversation makes more visible. Worth pulling on the thread.
The NVIDIA device plugin, node taints and tolerations, the multi-tenancy reality of one GPU per pod vs MIG vs vGPU. The scheduling primitives that work at homelab scale on a single 1080 Ti and the ones that change shape entirely at fleet scale.
DSPy lets you optimize prompts the way you'd optimize a model, programmatically, against an objective, with structured evaluation. The default DSPy workflow sends a lot of prompts to a hosted optimizer. The local-first version is doable, faster than expected, and worth the setup.
OpenAI-AWS $38B makes multi-cloud official. Moonshot's Kimi K2 Thinking is the first open-source model to beat GPT-5 on agentic benchmarks. Apple reportedly pays Google $1B/yr for Siri. Ironwood TPUs ship. The concentration story cracked.
Every AI team I've talked to has the same problem: private information moves through their tools, and nobody on the team owns it. The gap is real, it's getting wider, and something will force the issue. Here's the breakdown, and who should own it.
GitHub Universe ships Agent HQ. Big-tech earnings push 2025 AI capex past a quarter trillion. Amazon cuts 14k jobs the same week. OpenAI completes its PBC restructuring. Cursor ships its own model. The week that made the AI capex-vs-jobs trade explicit.
Latency, token throughput, cost-per-request, queue depth, drift. Prometheus for the metrics, Loki for the prompts and responses with PII discipline, Tempo for the tracing across agent calls. The dashboard JSON itself as a Decisions as Code surface, projected per-environment.
The keynote pitch for agent orchestration is engineering-shaped, pipelines, queues, message buses. The framing that holds up better in production is workforce-shaped, roles, teams, supervisors, performance review. Worth being explicit about why.
What happens when every tool the agent uses is exposed via MCP, every memory access is via MCP, every observability hook is via MCP. The architecture gets cleaner, the lock-in shrinks, and a few specific design decisions become obvious.
OpenAI ships Atlas, an AI-native browser that watches everything you read. Anthropic locks in a million Google TPUs. 850 luminaries call for a superintelligence ban. The week the centralization story got load-bearing at both ends of the stack.
