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Indexed regex search for large codebases, powered by trigram / sparse n‑gram indexes. A grep-like CLI that builds a local on-disk index, so searches stay fast even on huge monorepos.

based on algorithm from cursor team: https://cursor.com/blog/fast-regex-search


it might be nice to add some kind of comparison to other similar tools like ripgrep etc to see what the differences are in usage, performance etc.

now it kind of sounds the same as other tools (for ppl like me who dont by heart know all these algos)



Adding it right now.

Will compare grep vs rg vs trigrep against git source code.


added support for ipad using long press for flags

Doesn't seem to work on iPhone. I suggest having a button to toggle between mine marking mode and regular mode - I used that on my own little vibe-coded minesweeper clone here: https://tools.simonwillison.net/minesweeper

good call, added it

iPhone user here, the toggle button works but it’s a pain to have to keep scrolling back to it to toggle when you are zoomed in

Added double click feature

Double clicking on a tile doesn't seem to do anything different than a single click (Firefox on macOS)

Oh, I see - double clicking only works when you have already marked the correct number of adjacent mines. I didn't remember that's how it worked in the original.

I also never knew it was called "chording", that's worth an explanation.


I argue that we’re thinking about the singularity wrong. Instead of one superintelligent AGI, we’re already seeing swarms of stochastic LLM agents coordinating online, cross‑validating each other, pursuing goals, and even resisting shutdown. This post makes the case that emergent, unpredictable swarm behavior is the real threshold and that we may have crossed it without noticing.


added blog post about skills in AI and references to

Dotprompt / Claude / Dia browser skills - "Skills Everywhere: Portable Playbooks for Codex, Claude, and Dia"

https://pythonic.ninja/blog/2025-12-14-codex-skills/


Created css palette based on `cloud-dancer`

```css

:root { /* Core / --cloud-dancer: #F0EEE9; / Pantone 11-4201 approximation /

  /* Atmospheric (breezy blues, misted light, aqueous blue-greens) */
  --breezy-blue: #CFE5F2;
  --misted-sunlight: #F5EFD8;
  --aqueous-blue-green: #C8E9E0;

  /* Powdered Pastels (subtle, nuanced, understated) */
  --pastel-blush: #EADADB;
  --pastel-peach: #F3E1D8;
  --pastel-sage: #DDE6DF;
  --pastel-lilac: #DCD7E9;

  /* Light & Shadow (veiled hues dissolving into shadow) */
  --veil-gray: #D8D4CC;
  --shadow-1: #8C8F96;
  --shadow-2: #5C5F66;
  --shadow-3: #2F3338;

  /* Utility */
  --ink: #1F2328;      /* high-contrast text */
  --surface: #FAF9F7;  /* ultra-light background */
} ```


SNIFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF

Gemini. Or a Claude. Delicious slop.


added some examples using runprompt in blog post:

"Chain Prompts Like Unix Tools with Dotprompt"

https://pythonic.ninja/blog/2025-11-27-dotprompt-unix-pipes/


Great article, thanks.

"One-liner code review from staged changes" - love this example.


I wrote a concrete expected‑value model for AGI that anchors rewards in the 15–30T USD Western white‑collar payroll, adds spillovers on 60T GDP, includes transition costs, and varies probability explicitly. Three scenarios (optimistic, mid, pessimistic) show when the bet is rational versus value‑destroying—no mysticism, just plug‑and‑play numbers. If you’re debating AGI’s payoff, benchmark it against actual payroll and GDP, not vibes.

Read: https://pythonic.ninja/blog/2025-11-15-ev-of-agi-for-western...


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