AI is powerful, but it is not an oracle. It does not have a magic wand to create what you want exactly. You own the possibilities, and leverage AI to help at many different altitudes. Here's a concrete example of how I wrote the blog about space data centers. AI is the MATLAB, the Wikipedia, the Excel, the search engine, all packaged into a single tool that you can customize and extend. The capability is there; the human/AI interaction is still being sorted out.
With SpaceX IPO looming, let's take a look at the case of space data center, how realistic it is. As I poked around, I realize it's a matter of time before we run data centers in space, and that we really DO need Starship to pull it off economically. That is, if we look at the problem in the time frame of decades.
In the era of AI, your personal data can be a lot more valuable to you, as an individual.
Saturday morning, before exercise, I asked Claude one stray question. By afternoon there was a new repo, a pensive, four issues, and a working tool I'm using to write this post.
What is an AI Native Company?
Two weeks of building the substrate underneath an AI-native personal workflow: a base agentic development environment, a personal data access layer, a credential gateway, and a private canvas where ideas form, collide, and grow. Not ready for release yet — here's a sneak peek.
Adding tool calls to Parley on a flight, watching Claude make a mess, then drawing the architecture boundary for Claude and watching it execute flawlessly within it. The eureka: Claude Code is the execution harness, Parley is the interface for human to inspect and influence — both running on the same shared filesystem state.
Reflecting on a year of tinkering and adapting to the ever changing world of AI. After a year and 400+ commits on Parley.nvim, I'm convinced the future is humans setting up the environment, with full automation of the coding inside it. Here's my journey so far.
When agentic coding lets a vim user write a custom mouse-aware floating picker because Telescope couldn't do that. The implication for SaaS: programmability stops being a nice-to-have, it becomes the moat.
An iterative spec flow built on a small Makefile and a handful of markdown files — issue, todo, lessons, specs — cuts through the opaque magic of workflow tools. The trick is treating specs as sketches that bound the agent's search space, not exact behavior to confine it. Let AI bringing in world's knowledge!
The week of February 23, 2026: Dorsey's layoffs, Cloudflare's one-engineer Next.js rewrite, Karpathy's tweet. Picking up Conductor, OpenCode, and OpenSpec over a weekend and shipping 15K lines on Parley — agentic coding is no longer the toy it was ten months ago.
LLMs are fundamentally stochastic, but that's not new — the whole ML field always was, and it worked. The interesting question is how to ground stochastic computation in deterministic domains. Two paths: build a deterministic shell around the model (coding), or apply it directly where the domain itself tolerates noise (recruiting, marketing, design).
After more time pair-coding with Claude Code, the awe wears off. It's an overly eager junior with raw smarts and no taste — fast, useful for onboarding, but prone to ridiculous code that you'd reject from a new grad.
Five days, ten thousand lines of Lua, no prior experience with the language. Claude Code is not auto-complete — it's a giant pattern-matching machine that needs grounding through tests and architecture. The 25x figure is cheeky, but the shift is real.
