Exploring the echoes reverberating through time left by the technology of yesterday as we embrace the technology of tomorrow.
MWC Barcelona pushes on-device AI back to the front of the stack, AMD and Nutanix sign a multi-year agentic-AI deal that makes the on-prem enterprise stack real, HyperNova 60B drops on Hugging Face, and the state-AI-law map keeps moving while the federal preemption clock ticks.
A year of using AI in daily work. Concrete cases where it earned its keep, concrete cases where it didn't, and the pattern that explains the difference. Worth being honest about both halves of the practitioner experience.
The playbook for a single-tenant to multi-tenant migration done well, additive schema, identity backfill, dual-path policy enforcement, row-level security, observability for both paths, traffic shift, deprecation. What I wish I'd known on day 1.
Privacy-by-design for personal AI in 2026 isn't a policy posture, it's an architecture. Local-first compute, redaction layers, scoped access, audit trails, deliberate sync. The patterns the principled-user community has converged on, written down concretely.
Gemini 3.1 Pro and Sonnet 4.6 land inside a week, Alibaba slips Qwen3.5 out hours before Lunar New Year, the EU stretches its AI Act timeline, and Baker McKenzie cuts 600-1,000 attorneys citing AI. The cadence is compressed; white-collar is now in scope.
Enterprise AI governance frameworks don't scale down to a solo operator, and yet governance is one of the four positions I keep coming back to. Here's the lightweight-but-real version I actually run, and why each piece earns its keep when nobody else is going to ask.
Most teams design types ad-hoc and apologize for the shape later. The periodic-table layout, an ordering property, vertical families, horizontal periods, turns type design into a discipline that lets you see the missing types before you build the wrong ones.
Apple Silicon is the most defensible inference platform a small shop can buy in 2026. Not because it beats H100s on absolute throughput, it doesn't, but because the unified-memory architecture, MLX maturity, and capex-vs-opex math all line up for the workloads small shops actually run.
Delhi hosts the first Global South AI summit, Chinese labs race to ship before Lunar New Year, OpenAI quietly retires GPT-4o and GPT-5 from ChatGPT, the Microsoft-OpenAI-AWS triangle gets rewired, and Block crosses the half-the-workforce mark on AI-cited cuts.
Three years after I sketched what a real training-data market would need, the structural pieces still aren't in place. The lawsuits stalled, the settlements happened, the tiny markets exist at the edges, and the actual marketplace at scale doesn't. Worth being honest about why.
A trace from request to response is half a trace. The other half is from outcome back to the rule that allowed it. Most platforms have one direction; few have both. Why bidirectional matters for debugging, for audit, and for AI agent decisions, and the pattern that makes it work.
A pragmatic playbook for small organizations adopting AI in 2026, where to start, what not to do, how to build governance early without grinding the work to a halt, and how to think about tools and data before vendor lock-in does it for you.
