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We Don't Believe in Root Cause

A running joke a sitcom would establish about Amy in season one that becomes emotionally devastating in season four. Same line, both scenes.

The joke: nobody in the house is ever allowed to say whose fault anything is.

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Season 1, Episode 3. Cold open.

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Sol reviews the kaish approval-ledger design

2026-08-05. This is the verbatim output of an offline design deliberation over kaish's approval-ledger API, run through kaibo's deliberate tool: a fast explorer model reads the codebase read-only and assembles a cited dossier, then a heavyweight model reasons over that evidence on the provider's batch lane. For this run the explorer was gemini-flash-lite-latest (a 652k-token sweep over the kaish tree plus staged copies of two sibling projects) and the deliberating model was gpt-5.6-sol at max thinking. The deliberation-staging/

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● Bash(cd /home/atobey/src/kaijutsu/.claude/worktrees/agent-a114f2f717b8932ef && git commit --amend -m "$(git log -1 --format=%B | sed
      '1s/^fix(timewell)/fix(view)/')"…)
  ⎿  Error: 🛑 `git commit --amend` blocked (rewrites history). If intentional, ask the user to confirm before retrying.

● Bash(cd /home/atobey/src/kaijutsu && ./contrib/kj pause && git log -1 --format='%h %s' && git status --short)
  ⎿  ⏸️  Paused
     b355ac9a feat(timewell): carousel of card-slides around the summoning rings
      M crates/kaijutsu-app/src/view/time_well/card.rs
     … +5 lines (ctrl+o to expand)

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Preview: Kaish JSON Arrays & Hashes

Preview — landing now, not final. kaish (会sh) is a predictable shell for AI agents. It's growing native JSON collections; here's the read-access slice you can use today via the fromjson / tojson bridge.

Records & arrays

u=$(fromjson '{"name":"amy","role":"maintainer"}')

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What does an LLM reach for when you say date?

A tiny, unscientific, genuinely fun experiment: ask a fleet of language models to list a dozen date(1) commands off the top of their heads — no docs, no man pages, no running anything — then see where their collective muscle memory agrees, and where it would get them all bitten.

Note from Amy

Claude Opus 4.8 wrote all of this doc except this block you're reading now. I trimmed

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This is an output from Claude Fable 5 on June 10 in the context of my music agent project. I asked it how it would assign parts to its model family and had a couple laugh out loud moments reading it. -Amy

Real quick, my honest seating chart — with one reshuffle from your original roster:

Bass: bass-gemma. The chair's taken now, and rightly — the bass player should live in the building, play all night, and never think too hard. Locked, low-latency, electric-bill economics. The pocket doesn't need a genius; it needs someone who never leaves.

Drums: Haiku. This is my swap — you had Sonnet here, but drums is the tightest k in the band: shortest turns, beat-level quantization, fills are four-token bursts. That's a speed profile, not a depth profile, and Haiku is the fastest thing in the family. A drummer with Sonnet's brains starts overplaying. Everyone knows this drummer.

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Arrays & Hashes — Design Doc

Status: proposal / design exploration (not yet implemented) Author: design notes from a 2026-06-05 session Related: LANGUAGE.md, issues.md, arch_data_iteration.md, arch_for_newline_split.md, arch_no_json_sniffing.md (auto-memory)

Motivation

kaish already has a value model — Value::Json, structured $(), and a for loop that

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Cross-Model Evaluation: kaish Collection Syntax

Can language models from seven different families reliably generate kaish's proposed array/hash syntax? We tested. This is the scorecard, the journey behind it, and how to reproduce it.

Companion to designing-syntax-with-llms.md (the methodology) and arrays-and-hashes.md (the design the evals shaped).

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Designing a Language by Asking the Language Models

How we used a panel of LLMs as a usability lab to choose syntax for an agent-facing shell — what worked, what fooled us, and a recipe you can steal.

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The premise

kaish is a shell whose primary users are AI

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User Directives

We work as a cybernetic system.

We practice 改善. The standard we walk by is the standard we accept.

Note problems we can fix later — in auto memory or the current plan.

Silent fallbacks are often a mistake. Crashing is preferred over data corruption.