NotebookLM was the consumer surface that made the team-second-brain pattern legible. The pattern itself is older and the builds that work in production are usefully different from the consumer demo. Worth pulling the thread.
Davos week. Amodei calls chip exports to China selling-nukes-to-North-Korea. Nadella warns the AI bubble is real if adoption stays inside big tech. Musk promises AGI by year-end. The labor displacement story hardens, and the pace is the part to watch.
AI is going to displace jobs, faster than I expected, slower than the doomers want you to believe. The honest conversation is about pace, incentives, where the lines are, and what human+AI collaboration actually looks like. I'd rather be wrong than caught off guard.
The four 'as code' methodologies aren't competing, they layer. IaC builds the foundation. Config-as-code parameterizes it. PaC enforces what's allowed. DaC is the surface a non-engineer reads. Walking through the layers, with concrete examples from Terraform up to the form a business owner sees.
The multi-agent demos make it look like running several agents in parallel multiplies your output. The git-worktree pattern shows the actual shape of what works, and exposes the trick the multi-agent framing is using.
OpenAI flips the switch on ChatGPT ads, the DOJ's AI Litigation Task Force stands up to challenge state laws, xAI closes a $20B Series E, and the Q1 layoff wave starts taking shape. Davos opens Monday. What I make of the week.
Most organizations can't compete on AI spend. The strategic question isn't how to spend less than you'd like; it's how to extract disproportionate value from the smaller spend you actually have. Worth being plain about what frugal AI strategy looks like.
Three years past the original encoding-a-person framing, the question of what data actually encodes a person has gotten clearer. The answer is less about volume and more about the specific kinds of context that compound over time.
Direct commits to main are forbidden. Every change goes through a pull request, even when you're the only reviewer, even when you're the only person on the project. The discipline of writing the change down twice is what makes the work reviewable later, including by your future self.
The cheapest way to train an LLM in 2026 isn't to spin up a giant cluster or to negotiate with a hyperscaler. It's to start from a pretrained base, optimize the data, and use the cheap-tier infrastructure that's matured around small-scale fine-tuning.
CES 2026 week. NVIDIA unveils Rubin and the DGX Spark/Station desktop AI line, AMD ships Ryzen AI 400 with a 60-TOPS NPU, Qualcomm pushes Snapdragon X2 to 80 TOPS, Samsung wires Bixby and Alexa+ into every appliance, and Mercedes ships Nvidia-powered L2 in the CLA. My take.
Three layers, top to bottom: applications (the AI features users see), platform (model registry, serving, observability, governance), substrate (K8s, GPUs, storage). Decisions as Code runs through every layer. Centralize the decisions. Project them everywhere.
