"mom, how did we get so poor?" "your father had Claude Max, ChatGPT Pro, Cursor Pro and shipped absolutely nothing"

Turns out black mirror was a very good source for startup ideas.

So an internal wiki?

Claude Code 4.7 is insane. i know literally NOTHING about coding. ZERO. and i just built 3 fully functioning web apps in 30 minutes. http://localhost:3000/ http://localhost:8000/ http://localhost:5000/ check it out.

you can outsource your thinking but you cannot outsource your understanding

In KalDB.com, indexing and serving are separate. You can rebuild indexes offline without impacting production. Worst case: slightly stale data. Best case: the system stays available. We don’t need faster reindexing. We need architectures where it doesn’t matter.

When it falls behind, you don’t degrade. You break. In 2026, this shouldn’t be acceptable. Indexing should be offline, versioned, swappable, and completely decoupled from serving.

GitHub has been down for over a day… because it’s reindexing. This is a failure mode we’ve normalized. We built systems where data is written once, indexed elsewhere, and the product depends on that index being perfectly up to date.

Honestly, this is the most accurate diagram I've seen. Waterfall: You plan for 18 months and deliver exactly what nobody needs anymore. Agile: You deliver something usable at every step, but the CEO keeps asking, "Where's the car?" AI: You get the car on day one. It has six wheels, the doors are on backwards, and it has a rocket launcher. You spend more time making it yours than actually "building"; it's shaping. owning. verifying. That's what the best AI developers do now. They don't build. They shape and own.

Truly insane hack.

Went to buy a new pair of shoes at the Nike store. Couldn’t find a single pair that was comfortable. Walked out empty handed. Seems like a very common experience.

I don't want to read this entire well written, photographed, and edited piece about journalism's grim AI future that took a ton of work and talent. Grok, can you summarize it for me in 280 characters so I can pretend to have it read it wsj.com/business/media/an-ai…

Anthropic's own study proves Vibe-Coding and AI coding assistants harm skill building. "AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average" Developers learning 1 new Python library scored 17% lower on tests when using AI. Delegating code generation to AI stops you from actually understanding the software. Using AI did not make the programmers statistically faster at completing tasks. Participants wasted time writing prompts instead of actually coding. Scores crashed below 40% when developers let AI write everything. Developers who only asked AI for simple concepts scored above 65%. Managers should not pressure engineers to use AI for endless productivity. Forcing top speed means workers lose the ability to debug systems later. ---- Paper Link – arxiv. org/abs/2601.20245 Paper Title: "How AI Impacts Skill Formation"

100%. If taxes in WA and CA are the same, moving to CA is a no brainer. The weather is nicer better job opportunities.

Your website should improve itself. Sherpa gives you an entire conversion optimization team with one line of code. Here's how 🧵

Maturing is realizing that Tony Stark was a vibe-coder.

Fascinating performance design direction: GenAI specializing your DB to your workload. This is fascinating to me due to some key parallels: * I already practice "eval-driven AI coding loops", and database performance is one of the happiest cases for this - clear conformance suites in correctness and clear goal to hill climb via optimization. * Moving this idea closer to the use case and runtime, while still staying safely offline, are both cool * In GFQL, our open-source GPU graph query language, we have started stacking specialization layers in the engine, where we take advantage of another phrasing of this: pay-as-you-go semantics. Simpler base layers can ignore complications of fancier language features so simpler queries can go faster, and fancier features get case-split so we optimize their different scenarios. Instead of one path straddling all cases, we have specialization layers and pockets. It used to be a LOT harder to identify the scenarios, refine the solutions, and build the safety & maintenance layers for this, while now we can easily adapt big conformance suites and run all sorts of test amplifiers whenever we add a new specialization. Overall, this paper leans into using AI to handle more complexity in, for now, narrow domains. It realizes databases are the happy case of easy for AI to test & verify, and the space of many optimizations that are well-understood: these make it a lot easier for eval-driven AI coding loops to hill climb up the performance charts for arbitrary workloads. Link:

x.com/i/status/1869608354034… me and claude code all day every day

Old world software estimates: 2 weeks (actually takes 4 weeks) New world software estimates: 2 weeks (actually takes 4 weeks, or 20 minutes)

The 20$ codex plan is worth more than the $200 Claude Code plan.