One session, one piano app

1. 1 10 Nov 19:53
   Method Request

   I really want to get very good at piano, and I am ready to spend the next 10 years of my life on it. I want you to do deep research into learning methods from a neurobiological perspective that can help me do that. I want to teach myself and I am ready to put in concentrated effort to achieve harder things, but overall I want to hack my learning by using this knowledge from neurobiology. The outcome of the research should be a knowledge base in OKF (https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md) as well as a website hosted at https://piano.zengarden.space, with its state attached to a Google account if possible, stored in the browser or on the web. But first let's discuss, methodically, the variety of approaches to learning, the stages, how those stages vary, the possible branches of progression, and so on. As a result I would like a training app that is addictive enough. I am also thinking of buying a real physical piano like the Kawai CA401, to get the touch and feel of a really good instrument instead of my Arturia KeyLab Essential 61 mk3 MIDI keyboard. You can assume I am near zero: I understand just the very basics.

   What it led to Nothing was built yet. The reply laid out approaches, stages and branches for discussion, asked four questions back, and started four research agents on motor learning, music cognition, how practice apps hold attention, and piano pedagogy.

   Work it set off

   3 model calls · 3 tool calls · 170 k tokens · under $1 at list prices

   Fable 5.2: 3 calls, 170 k tokens

   More

   4

   research agents

   ~330

   concepts in the knowledge base

   • The research overturned several things the assistant had first said from memory: sleep "enhancing" a skill, micro-rests, external focus, interleaving and the 85% rule all turned out weaker or narrower than claimed. The corrections were reported and the method was built on what survived.
   • The main principle that came out: measure learning cold, on the first attempt of a different day, and not by how a passage sounds at the end of a practice session.
   • The knowledge base grew to about 330 concepts in OKF format, each with its sources and a rating of how strong the evidence is.
2. 2 10 Nov 20:02
   Method Answer

   1. I want all the branches. 2. Honestly, my motivation comes and goes. I hate it when apps push me with streaks, reminders and daily goals; the moment something feels like an obligation, I drop it. My phone and YouTube also fight for my attention. Still, I feel that I want to learn. 3. I have a spare Android tablet. 4. That may make sense.

   What it led to All five branches stayed in: classical, jazz, pop, improvisation and composition. The answer about motivation changed the app more than any other message in the session.

   Work it set off

   4 agents · 264 model calls · 876 tool calls · 69.2 M tokens · about $72 at list prices

   Fable 5.2: 264 calls, 69.2 M tokens

   More

   • No streaks, quotas, reminders or calendars anywhere in the app. Nothing accumulates while the learner is away.
   • The app opens straight to live keys with no decision to make. Suggestions sit behind a handle and are worded as offers.
   • The tablet became the device at the piano. Occasional check-ins with a teacher stayed optional.
3. 3 10 Nov 20:15
   Method Answer

   I listen to a lot of podcasts lately, though I doubt that is what you are asking about. I started my "music hobby" two years ago, with varying degrees of success, and I am not rushing. I was more focused on Ableton and synthesis, without deep understanding. As a result I produced around 10 "beginner" tracks on YouTube when inspiration came, so probably not everything is so bad. It's just that I do things in bursts: a few intense weeks, then nothing for a month or two, then back again.

   What it led to The starting point was redrawn: near zero at the keys, but with two years of producing tracks. Creation became a first-class track, and because practice comes in bursts, the app was designed to behave the same after seven weeks away as after one day.

   Work it set off

   4 agents · 51 model calls · 149 tool calls · 6.1 M tokens · about $21 at list prices

   Fable 5.2: 51 calls, 6.1 M tokens

   More

   • Free play can be kept as a sketch: phrases are detected, a groove is taken from the playing, and a loop can be cut after the fact.
   • A companion layout sits beside a DAW, follows its clock and stays silent.
   • After an absence nothing changes colour, nothing is moved back, and time away is never mentioned.
4. 4 10 Nov 20:48
   Method Answer

   My tablet is a Samsung Galaxy Tab A 10.1 (T510/T515).

   What it led to A slow tablet became the performance target, which fixed several technical choices early.

   Work it set off

   18 model calls · 57 tool calls · 3.8 M tokens · about $4 at list prices

   Fable 5.2: 18 calls, 3.8 M tokens

   More

   • Scores are engraved ahead of time by the library pipeline and shipped as SVG; the tablet never engraves.
   • Budgets: 150 KB of initial script, live keys within 1.5 s of a warm start, 16 ms frames while the cursor moves.
   • Tests run under a six-fold processor slowdown at 1280 by 800 as a stand-in for the device.
5. 5 shortly after
   Method Answer

   I am also thinking I could try to make it work with the Arturia MIDI keyboard.

   What it led to The app was planned to work on the 61-key controller from the first day, through Web MIDI, with the app making the sound.

   More

   • A later change of instrument keeps everything that is known and starts a settling period only for touch.
6. 6 10 Nov 21:06
   Library Request

   Can you scan useful sources like https://www.musictheory.net/ and collect MIDI files or sheet music that might be useful?

   What it led to A collection effort began: each source was read for its licence first, then catalogued as bundle, personal use, link only or avoid.

   Work it set off

   9 agents · 862 model calls · 1,132 tool calls · 177 M tokens · about $118 at list prices

   Fable 5.2: 862 calls, 177 M tokens

7. 7 shortly after
   Library Request

   You can search broadly: hundreds of sources, using subagents.

   What it led to The search widened to many agents working in parallel across teaching repertoire, exercises, jazz and pop charts, folk tunes, datasets and partimento.

   More

   642

   sources catalogued

   50,149

   files collected

   ~1 GB

   on disk

   • 642 sources catalogued: 263 to bundle, 111 for personal use, 242 to link to, 26 to avoid.
   • Lapses were reported, not hidden: one collector sent a browser-like client name after being refused, then corrected itself and deleted what it had fetched; another overwrote two manifests, which were regenerated and checked.
8. 8 shortly after
   Library Request

   Maybe there is something on GitHub.

   What it led to Open repositories of scores and corpora were added: typeset editions, chorale sets, tune books and chord-progression datasets.

9. 9 10 Nov 21:35
   Architecture Request

   Generate 20 innovative ideas for the application, each with a fallback (and possible to turn off in settings). Do it in a subagent.

   What it led to Twenty feature ideas, each written with what happens when it cannot work and a switch to turn it off.

   Work it set off

   1 agent · 30 model calls · 52 tool calls · 5.4 M tokens · about $10 at list prices

   Fable 5.2: 30 calls, 5.4 M tokens

   More

   • The rule that every optional feature has a fallback and an off switch carried through to the specification, where each feature is a self-contained folder the app can run without.
10. 10 10 Nov 21:38
    Architecture Request

    Think about the core engine, sheet-music visualisation and maybe a dynamic timeline, various modes, tools, scale trainers (and maybe other kinds of trainers) and similar things I may need over the next 10 years.

    What it led to The architecture of the app itself: a capture layer that records every key press, a matcher that follows the score, analysis, a transport, interchangeable views of the music, and a catalogue of trainers.

    Work it set off

    12 model calls · 28 tool calls · 4.0 M tokens · about $3 at list prices

    Fable 5.2: 12 calls, 4.0 M tokens

    More

    • Raw playing is the source of truth. Everything derived from it can be rebuilt years later with better analysis.
    • Views ("lenses"): staff, piano roll, keyboard, chords, structure, scanned pages.
    • Trainers cover technique, reading, rhythm, ear, harmony, repertoire and creation.
11. 11 shortly after
    Architecture Answer

    If a database is needed, I am thinking of either Firebase or a backend on my Raspberry Pi 5 (with the static site on Cloudflare Pages).

    What it led to The two were compared. The app was made local-first: it works with no server at all, and a server only adds sync between devices.

12. 12 10 Nov 21:49
    Architecture Correction

    Let's make it slightly more generic by having users, and let's store the data in PostgreSQL. You can find out how my Raspberry Pi 5 homelab is organised by searching earlier Claude sessions for "server ssh".

    What it led to The design moved from one person's tool to accounts, profiles and PostgreSQL on the home server. Notes from earlier sessions gave the conventions of that server, and the deployment was written to match them.

    Work it set off

    20 model calls · 42 tool calls · 7.6 M tokens · about $4 at list prices

    Fable 5.2: 20 calls, 7.6 M tokens

    More

    • One stack per app, a proxy with a rule set in front, its own tunnel, images built in CI and never on the server, backups to a separate bucket.
    • A second person can use the tablet as a Guest without touching the first profile.
13. 13 shortly after
    Architecture Request

    Still, let's include personal-use content that is available only to o***o@gmail.com.

    What it led to Content tiers: open, copyleft, restricted and private. Restricted collections have an access list, and nothing restricted ever appears in the public site.

14. 14 shortly after
    Library Request

    Let's include Ukrainian folk songs for personal use.

    What it led to A corpus of Ukrainian folk songs was collected and placed in the restricted tier, visible to the owner only.

15. 15 shortly after
    Architecture Correction

    The backend needs to be in Go.

    What it led to The server was specified in Go: the standard HTTP library, pgx for PostgreSQL, plain SQL migrations.

16. 16 10 Nov 22:00
    Design Request

    Now let's design the application itself: its UX/UI, style, features and catalogues. Let's "virtually play" several scenarios and improve the initial design. We need to achieve really great usability and learning efficiency, specifically for me.

    What it led to Fourteen screens were designed, then three independent agents each "played" through scenarios against them and reported where the design contradicted its own principles.

    Work it set off

    5 agents · 194 model calls · 376 tool calls · 53.0 M tokens · about $54 at list prices

    Fable 5.2: 194 calls, 53.0 M tokens

    More

    14

    screens

    3

    simulated playtests

    36

    revision decisions

    • Found by the playtests: a panel that switched by itself, a "carry on" offer at the hardest spot, running tallies of mistakes shown to the learner, cues that moved during an attempt.
    • 36 decisions revised the screens and the knowledge base.
    • The look: dark ground, brass accent, paper-coloured score, a blue ramp for the states New, Met, Coming and Held.
17. 17 shortly after
    Architecture Request

    Meanwhile, let's generate 20 more ideas.

    What it led to The idea list grew to 41, each still with a fallback and an off switch.

18. 18 shortly after
    Architecture Question

    Maybe it makes sense to store the library in Cloudflare's bucket service, or is it not that big?

    What it led to The library was sized (a few gigabytes once engraved) and a bucket was proposed for it.

19. 19 10 Nov 22:34
    Architecture Correction

    I don't think I am talking about a public bucket. Is a private bucket on Cloudflare possible for free?

    What it led to A correction: the proposal had assumed a public bucket for open material. The answer was yes, and the design changed to one private bucket for everything.

    Work it set off

    3 model calls · 3 tool calls · 1.9 M tokens · under $1 at list prices

    Fable 5.2: 3 calls, 1.9 M tokens

20. 20 10 Nov 22:36
    Architecture Answer

    Yes, I think we can put private material there as well and use signed links.

    What it led to The storage design was settled: the whole library in one private bucket, tiers as prefixes, and the server handing out signed links that last five minutes.

    Work it set off

    4 model calls · 3 tool calls · 2.6 M tokens · under $1 at list prices

    Fable 5.2: 4 calls, 2.6 M tokens

    More

    • A small starter pack ships inside the static site so the app is useful with no account and no network.
21. 21 10 Nov 22:40
    Design Question

    Is it possible to somehow find images for the songs and display them?

    What it led to Options for pictures were looked at, with their licence problems.

    Work it set off

    1 model call · 0 tool calls · 667 k tokens · under $1 at list prices

    Fable 5.2: 1 call, 667 k tokens

22. 22 10 Nov 22:40
    Design Question

    Maybe there is some metadata service, or additional sources?

    What it led to Open metadata services were named as possible sources.

23. 23 10 Nov 22:40
    Design Correction

    I mean as logos.

    What it led to The idea narrowed from cover pictures to a small mark per piece.

    Work it set off

    8 model calls · 14 tool calls · 5.5 M tokens · about $2 at list prices

    Fable 5.2: 8 calls, 5.5 M tokens

    More

    • Every item gets a generated mark drawn from its opening notes, so no outside picture is needed.
24. 24 shortly after
    Design Answer

    OK, let's make it a feature that is off by default.

    What it led to Fetched cover art became an optional feature that is off unless switched on.

25. 25 shortly after
    Design Answer

    And keep the cover art small.

    What it led to Covers were capped at 96 by 96 pixels.

26. 26 10 Nov 22:55
    Specification Request

    I need to build this app end to end, without any phases, using Opus and Sonnet agents. I want you to provide specs: spec/FRONTEND.md, spec/BACKEND.md and spec/LIBRARY.md, which should be enough on their own to build it fully end to end. Later I will launch everything in parallel (not necessarily 3 agents), so you also need some kind of spec/EXECUTION.md: a graph of the things to do to complete everything fully. You will also need to use feedback loops somehow, maybe by building an emulator or simulator, so that things can be tested early. LIBRARY should suggest how to collect everything in one bucket in a properly organised manner, with metadata, potentially adding cover art or converting things. There should be a verification of the library that confirms there are no broken files. If the markdown files become too big, you can subdivide them semantically.

    What it led to The specification: shared contracts first, then frontend, backend and library documents split by topic, a simulator that stands in for a person, a keyboard and weeks of time, and an execution graph.

    Work it set off

    34 agents · 2,687 model calls · 3,175 tool calls · 962 M tokens · about $992 at list prices

    Fable 5.2: 2,687 calls, 962 M tokens

    More

    146

    spec files

    43,900

    lines of specification

    4,180

    numbered requirements

    619

    tasks, 2,181 dependencies

    • Contracts fix what the three parts agree on: record formats, the API, the bucket layout, configuration names.
    • The simulator has a virtual learner that makes mistakes and hesitates, a fake MIDI device, a virtual clock (eight weeks of practice run in seconds), and 19 required scenarios such as "back after seven weeks" and "two devices".
    • Library verification loads every published item with the web app's own loader; a generation is published only with zero broken objects.
27. 1 h 22 min later
28. 27 11 Nov 00:17
    Build Request

    Once the full specification is finished, start an unattended run of agents to build, test and iterate on everything. Bias towards Sonnet, as the specifications are very detailed and were worked through by Fable. Use Opus, Sonnet and Haiku wisely. I am going to sleep.

    What it led to A model policy was written into the plan: Sonnet builds by default, Opus takes the hard or cross-cutting tasks and later repairs, Haiku runs the independent checks.

    Work it set off

    1,063 agents · 17,383 model calls · 18,206 tool calls · 2.00 B tokens · about $750 at list prices

    Sonnet 5.5: 12,591 calls, 1.34 B tokens · Opus 5.5: 3,499 calls, 599 M tokens · Fable 5.2: 40 calls, 8.5 M tokens · Haiku 5.5: 1,253 calls, 54.5 M tokens

    More

    • Overnight the frontend specification agents ran into the usage limit of the most capable model and all 32 stopped mid-revision. The build could not start until the specification was whole.
29. 5 h 16 min later
30. 28 11 Nov 05:33
    Specification Request

    Continue from the points where it failed.

    What it led to A plan to finish the interrupted frontend work, at first with a different model.

    Work it set off

    3 model calls · 4 tool calls · 2.5 M tokens · about $14 at list prices

    Fable 5.2: 3 calls, 2.5 M tokens

31. 29 11 Nov 05:37
    Specification Correction

    Use Fable.

    What it led to The plan was interrupted and redirected to the most capable model.

    Work it set off

    2 model calls · 1 tool call · 1.7 M tokens · about $1 at list prices

    Fable 5.2: 2 calls, 1.7 M tokens

32. 30 11 Nov 05:39
    Specification Correction

    For finishing the specs.

    What it led to The scope of that instruction was made clear: the strongest model finishes the specification, the build stays biased to Sonnet. A 40-agent workflow audited the 78 frontend files, assembled the entry document and settled the open questions between the three specifications.

    Work it set off

    43 agents · 3,489 model calls · 4,124 tool calls · 973 M tokens · about $582 at list prices

    Fable 5.2: 3,489 calls, 973 M tokens

    More

    40

    agents

    16M

    tokens

    3 h 44 min

    run time

    • 183 contract change requests adopted, 13 decided differently, with each decision written down.
    • The limit was reached again with three agents left.
33. 4 h 19 min later
34. 31 11 Nov 09:58
    Build Request

    The Fable limit has been reset; please continue from where you stopped.

    What it led to The last three agents ran, the execution graph was generated, and the build started: first the scaffold and contracts as code, then lanes of agents side by side.

    Work it set off

    9 model calls · 13 tool calls · 1.1 M tokens · about $4 at list prices

    Fable 5.2: 9 calls, 1.1 M tokens

    More

    14

    lanes run so far

    483

    tasks passed their check

    6

    failed after three repairs

    • Each task goes through a build agent, then an independent agent that only runs the task's verification command, then up to three repair rounds.
    • The graph is split into lanes that can run at the same time; a lane holds only tasks whose unfinished dependencies are in the same lane.
    • A usage limit stops a lane without marking its tasks as failed, so the run can resume where it stopped.
35. 32 11 Nov 10:20
    About the session Question

    Are you sure you need those Python scripts? Previously Claude was able to succeed without them.

    What it led to A straight answer about which helper scripts were needed and which were convenience.

    Work it set off

    1 model call · 0 tool calls · 146 k tokens · under $1 at list prices

    Fable 5.2: 1 call, 146 k tokens

    More

    • Needed: something that writes the task list into the workflow script, because a script cannot read files, and a record of finished tasks between runs, because one workflow may start at most 1,000 agents and the build needs more.
    • Convenience: a script that prints one task for its agent, one that runs a task's check, and the splitting into parallel lanes.
    • They were kept, with the simpler alternative offered.
36. 5 h 55 min later
37. 33 11 Nov 16:15
    Build Question

    Can you show some intermediate results?

    What it led to The whole app could not be opened yet, so screenshots were taken of the parts that already render alone.

    Work it set off

    14 model calls · 16 tool calls · 2.9 M tokens · about $4 at list prices

    Fable 5.2: 14 calls, 2.9 M tokens

    More

    • Three scores engraved by the library pipeline, each with its generated mark.
    • The piano-roll view with fingering and the cursor.
    • The staff view was shown too, with the caveat that its test fixture is a hand-made schematic and it had not yet been tried on a real engraved score.

    

    

38. 1 h 30 min later
39. 34 11 Nov 17:45
    About the session Question

    What are the subagents doing? It looks like they are stuck.

    What it led to A check of every agent in flight. All four had written to their logs within four minutes; the run only looked idle because a few slow tasks were holding back everything else.

    Work it set off

    3 model calls · 2 tool calls · 751 k tokens · under $1 at list prices

    Fable 5.2: 3 calls, 751 k tokens

    More

    • The bottleneck was the one task that wires the whole web app together, with about 97 scenario and browser tasks waiting on it.
    • A change followed: tasks in a lane that is still running are released as soon as their own check has passed, so other lanes need not wait for the lane's slowest task.
40. 2 h 35 min later
41. 35 11 Nov 20:20
    About the session Question

    Can you share this session?

    What it led to The honest answer was no: a terminal session has no share link. The practical routes are an exported transcript or a page like this one.

    Work it set off

    1 agent · 10 model calls · 12 tool calls · 1.4 M tokens · about $4 at list prices

    Fable 5.2: 4 calls, 1.1 M tokens · Haiku 5.5: 6 calls, 297 k tokens

42. 36 11 Nov 20:23
    About the session Request

    Build an interactive website about this session, with my prompts and a summary of what each prompt caused. Also add a simple SKILL.md for a similar Node.js website-building skill. After the website about the session is built, publish it to piano-session.zengarden.space. Use Cloudflare Pages, and log in via Google in the browser if needed.

    What it led to This page. It is a small Node project with no framework: one data file, one build script, static output on Cloudflare Pages.

    Work it set off

    28 model calls · 29 tool calls · 8.9 M tokens · about $5 at list prices

    Fable 5.2: 28 calls, 8.9 M tokens

    More

    • The build checks its own output and refuses to finish if an email address appears in it.
    • A short skill file describes how to make a site like this again.
43. 37 11 Nov 20:34
    About the session Request

    Also measure things: costs, tokens, agents launched, and so on.

    What it led to A script that reads every transcript of the session and counts agents, model calls, tool calls and tokens, then prices the tokens at API list rates. Its figures are in the section below.

    Work it set off

    16 agents · 397 model calls · 426 tool calls · 39.3 M tokens · about $14 at list prices

    Sonnet 5.5: 343 calls, 26.3 M tokens · Fable 5.2: 24 calls, 10.2 M tokens · Opus 5.5: 30 calls, 2.8 M tokens

    More

    • Model calls are counted once per message, tool calls once per call, across the conversation and all agent transcripts.
    • The cost is an estimate at list prices, not a bill.

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