Session Script

Full transcript of the session that produced GUESSWORK — The Woodlouse and the Dark Room, a generated radio show about Karl Friston's free energy principle.

Harness: Claude Code · Model: Opus 5 · Effort: max (changed to xhigh mid-session) Working directory: ~/repos/llm-video-repos/free-energy-principle-opus-5-max-claude-code

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Turn 1 — User

/effort
> Set effort level to max (this session only): Maximum capability with deepest
> reasoning. May use excessive tokens resulting in long response times or
> overthinking. Use sparingly for the hardest tasks.

You're in an empty git repository. Use mise, python, uv, and whatever dependencies you like to create a short radio show inspired by Radiolab. The show is about Karl Fristons free energy principle -- a theory of life and mind -- and what it means for you and for the world.

(Before making the video, spend time researching Radiolab. Stew on it. Try to capture its style with the same immersive sounds and story telling quality. Make sure the voices sound natural.)

Turn 1 — Assistant

Started research and environment setup in parallel.

Tools: ToolSearch(select:WebSearch,WebFetch) · Bash — checked for mise, uv, python3, ffmpeg, sox.

Found: mise 2026.7.18, python3, ffmpeg 8.1.2. No uv, no sox.

Then, in parallel:

• Bash — scanned env/shell configs/keychain for TTS API keys. None found.
• WebSearch — "Radiolab sound design style analysis Jad Abumrad sonic storytelling techniques"

Key research findings on Radiolab craft:

• Jad Abumrad (Oberlin-trained composer, MacArthur Fellow) — "Come here, come here, come here… BOO!" — seduce then disturb.
• Three-act structure: relatable-anecdote hook → exploratory journey with host banter → resolution with fresh insight. Cold opens. Musical punctuation.
• Ira Glass on Radiolab: "every note and phrase and breath is worked out to the microsecond"; the co-host "muttering in there sells the incredibleness way more effectively"; they sound "like human beings on the air. Not know-it-all stiffs."

No TTS keys present, so switched to a local neural TTS plan.

Tools: Bash — installed uv 0.12.2 · WebFetch wired.com Friston profile (blocked).

WebSearch recovered the key anecdote anyway, and Slate Star Codex gave the verbatim quote:

"I was in the garden, during a gloriously hot 1960s British summer, preoccupied with the antics of some woodlice who were frantically scurrying around trying to find some shade… I realized their 'scurrying' had no purpose or intent: they were simply moving faster in the sun—and slower in the shade."

Further research (parallel WebFetch/WebSearch):

• Slate Star Codex — variational free energy, surprise, active inference, the dark room problem, "life isn't doing four things… it's really just doing one thing — minimizing free energy — in four different ways."
• Anil Seth — "We're all hallucinating all the time. When we agree about our hallucinations, we call it reality."
• Mary-Frances O'Connor, The Grieving Brain — grief as prediction error; the brain's map that still says the person exists.
• Friston interview (PMC) — "The fundamental principle has several names. Perhaps the neatest and most intuitive is self-evidencing."
• Criticisms — tautology/unfalsifiability; "The Emperor's New Pseudo-Theory"; Friston's reply that it's a principle like Hamilton's stationary action.
• Radiolab transcript fetches failed (500 error; HappyScribe declined verbatim reproduction on copyright grounds).

Tools: TaskCreate ×6 — research, project setup, script, TTS, score, mix.

Set up the project: mise.toml (python 3.12, uv, ffmpeg), uv init, then uv add torch numpy scipy soundfile kokoro "misaki[en]". First attempt picked CPython 3.14 and spaCy had no wheel; fixed with uv python pin 3.12.

Smoke-tested Kokoro-82M locally — pipeline ready in 2.2s, generated speech successfully. Chose it as the TTS: Apache 2.0, no API key, runs on CPU.

Then wrote the codebase:

• show/dsp.py (474 lines) — filters, procedural convolution reverb IRs, compression, lookahead limiting, sidechain ducking, ITU-R BS.1770-4 gated loudness. Cleaned up two dead-code paths (if False branches) and replaced an O(n·window) order_filter limiter with ndimage.maximum_filter1d.
• show/synth.py (388 lines) — Karplus-Strong pluck, FM bells, marimba, bowed strings with body resonance, analogue pads, drones, risers, whooshes, heartbeat, stutter, pitch shift.
• show/sinewave.py (166 lines) — sine-wave speech via LPC formant tracking. First draft had a hand-rolled Levinson-Durbin recursion; replaced with scipy.linalg.solve_toeplitz for numerical robustness.
• show/atmos.py (313 lines) — a hot English garden (wind, dry grass, heat shimmer, a bee crossing the field, a distant bird), rock lift, woodlice skitter, neural spikes, keys in a door, room tone, dark room, and the two recurring motifs: prediction() and error().
• show/voice.py — the cast and the broadcast chain (high-pass, presence lift, de-ess, compression, stereo seating, booth reverb).
• show/score.py (286 lines) — fourteen music cues, one theme, all in D.
• show/stage.py — the timeline engine: dialogue moves the playhead, music and effects hang off it; two-pass render with sidechain ducking.
• show/episode.py (630 lines) — the script itself.
• show/build.py, show/qc.py — render entry point and measurements.

Verified the sine-wave speech transform against the source: F1 correlation 0.857, F2 0.715 — genuinely the same mouth with everything else removed.

Turn 2 — Assistant (iteration)

First build: 24:56. Too long for "short."

Ran objective analysis. Findings:

• No clipping, no DC offset, mono-safe.
• Spectrum dark: 12–20 kHz at −36 dB relative (Kokoro brick-walls at 12 kHz).
• Short-term loudness range only 2.9 dB — dynamically flat.
• 158 wpm overall.

Three fixes:

1. Band extension on the voice bus — rectify the 5.5–11.5 kHz octave to synthesise harmonics above the 12 kHz wall. Top end went −36 → −19 dB.
2. Gentler bus compression so quiet passages stay quiet.
3. Hard edit pass on the script.

Trimmed 24:56 → 21:59 → 21:26 → 20:00. Discovered that merging lines wasn't cutting enough; rewrote the script tight from scratch.

Added a Level(db=, speed=) event so each section carries its own level and delivery rate.

Measured the pacing properly and found two problems:

• 197 wpm median — rushed.
• The grief section was the fastest in the show (265 wpm on one line), which is exactly backwards.

Dialled the hosts back to land at ~180 wpm, then verified with syllables/second (the honest metric, since wpm varies with word length): 4.17 syl/s median, grief at 3.90, banter at 4.92, dark room at 3.61. Natural, with the contrast in the right direction.

Also caught true peak at +1.38 dBTP — over 0 dBFS on inter-sample peaks despite a clean −1.14 dBFS sample peak. Would distort on MP3 playback. Wrote an oversampled true-peak limiter with a measure-and-trim verification loop.

Then measured the bus balance and found the real problem: speech was +21.6 dB over the bed (broadcast wants 10–14), and ambience was at −65 LUFS — inaudible. Root causes:

• Beds were never loudness-normalised, so level=-19 meant something different for every generator.
• The ambience generators carried huge sub-bass that LUFS under-reads but that still ate headroom (brown noise low-passed at 260 Hz).

Fixed both: normalise every bed to a nominal loudness before applying its level, and band-limit the ambience generators. Result: speech +12.9 dB over the bed, beds lifting +3.2 dB in the gaps.

Final: 20:43, −16.19 LUFS, −1.22 dBTP, 4.3 dB dynamic range, 6/6 QC checks pass, byte-identical across rebuilds (MD5 3ffb3a8f…).

Wrote README.md, .gitignore, mise.toml tasks, and show/qc.py.

Delivered: a 20:44 radio show, transcript, and the full generator.

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Turn 3 — User

I want to make a second version of the show using voices generated with eleven labs. Can you help me do that?

Turn 3 — Assistant

Checked for an ElevenLabs key (none found) and researched the current API in parallel.

Key API facts established:

• POST /v1/text-to-speech/{voice_id} and /with-timestamps; xi-api-key header.
• voice_settings: stability, similarity_boost, style, use_speaker_boost, speed.
• previous_text / next_text for prosodic continuity; seed for determinism.
• PCM/WAV need Pro+; mp3_44100_128 is the default.
• Text-to-Dialogue endpoint exists (defaults to eleven_v3, supports audio tags, no timestamps).
• Eleven v3 audio tags: [laughs], [sighs], [whispers], [curious], [deadpan], etc.

Counted the script: 17,344 characters across 240 lines.

Asked the user three questions (model/approach, key provisioning, casting).

User chose: eleven_v3 + audio tags · .env file · list library then propose.

Then researched v3's constraints and hit the crux: v3 is unstable on short prompts, and this script is full of two-word interjections. Confirmed /with-timestamps returns character-level timings in seconds and accepts context params — which enables a fix: pad a short line with its neighbours in one request, then cut the target back out by timestamp.

Built the provider architecture:

• show/tts/base.py — Utterance, the Backend protocol, tag stripping.
• show/tts/kokoro_backend.py — existing local model, namespaced cache.
• show/tts/eleven_backend.py — HTTP with backoff, disk cache, concurrency, cost accounting, the padding strategy, and a runtime capability probe (because the docs don't pin down whether v3 honours speed or works on with-timestamps, and guessing wrong would crash the render or silently drop the pacing system).
• show/tts/__init__.py — backend registry.
• show/cast.py — list account voices, propose or set a cast, print paste-ready Voice Design prompts.
• show/direction.py — v3 acting notes keyed to (role, exact line text), kept out of the script so the Kokoro text stays canonical.
• tests/test_voices.py — 12 tests against a stubbed HTTP layer.

Fixed three self-caught bugs in the draft: _post assumed JSON but the plain endpoint returns binary; the capability probe could race across threads; and the stub bypassed the billing counter (making one test vacuous).

Verified the Kokoro render was still byte-identical after the refactor — MD5 3ffb3a8f97ef3fb795a0f484795fc1ef. Re-checked after every subsequent change.

Turn 4 — User

I pasted the key into .env. Please continue.

Turn 4 — Assistant

Ran casting — 401: missing voices_read permission. Diagnosed the key's actual scopes: user_read, voices_read, models_read all denied; only /voices/settings/default worked.

Tested whether TTS itself was permitted, and hit the real blocker:

402  Free users cannot use library voices via the API.
     Please upgrade your subscription to use this voice

Two hard blockers, neither of them code:

1. Free tier cannot use library voices via the API at all.
2. Free tier is ~10k credits/month; the episode needs ~17.3k.

Nothing was charged (402 rejects before generating). Asked how the user wanted to proceed.

Turn 5 — User

What's the quickest/cheapest/highest quality way to use a good voice? How do I get started with voices?

Turn 5 — Assistant

Researched current ElevenLabs pricing and the Voice Library / Voice Design flows.

Built the audition mode (show/audition.py) first — 9 lines chosen to expose the failure modes (pace on a long line, stability on a two-word reaction, the deadpan joke, the grief line), so the cast could be judged for a few hundred credits instead of 17k.

Then made a discovery that changed the cost picture entirely:

180 of the 240 lines are short. Padding each one to clear v3's stability threshold meant a full render would cost 55–86k credits, not 17.3k. The earlier figure was the plain character count — correct before padding existed, wrong after.

Corrected this to the user plainly, and inverted the default: previous_text / next_text give the model context without being billed, so padding became opt-in (SHOW_PAD_SHORT=1).

	padding off (new default)	padding on
audition	632	2,062
full episode	18,188	56,926

Answered the user's question directly: ElevenLabs Starter, ~$5/month is the only path (Free cannot use API voices at all), and explained the three routes to getting voices — Voice Library (quickest, no slot used), Voice Design (best control, with paste-ready prompts written for each part), and Instant Voice Cloning. Noted they'd only need 3–4 voices, not 11, since the whisper chorus reuses the cast.

Turn 6 — User

I upgraded my plan. Let's continue.

Turn 6 — Assistant

Re-probed: Starter tier, 40,000 credits, none used. voices_read now works (models_read still denied, which doesn't matter — the model ID is explicit).

Listed 21 premade voices and cast them by hand rather than trusting the scoring heuristic:

Role	Voice	Why
NELL	Sarah	warm American; carries the grief section
THEO	Chris	"charming, down-to-earth" — the dry one
KARL	George	British storyteller; reads Friston's own words
QUOTE	Alice	British educator; reads Anil Seth
chorus	Matilda, Will, Lily, Brian, Jessica, Daniel	six voices for width

Ran the audition — 762 credits. The capability probe immediately earned its keep:

capabilities: {'model': 'eleven_v3', 'timestamps': True, 'speed': True,
               'context': False}

v3 rejects previous_text/next_text:

400 unsupported_model:
    Providing previous_text or next_text is not yet supported with
    the 'eleven_v3' model.

Confirmed multilingual_v2 accepts them and v3 accepts previous_request_ids. So the free-context strategy adopted one turn earlier doesn't exist on this model — exactly the case the runtime probe was built for.

That left padding (57k, more than the 40k budget) or nothing. Tested short lines cold: "Huh." at 0.48 / 0.80 / 0.56s across three seeds, peaks within 0.07 — natural variation, not instability. So padding stayed off.

Then verified the audio tags weren't being spoken aloud. Duration comparison was ambiguous ([flatly] added +0.88s), so read the alignment data instead:

tag	time on tag characters	meaning
[flatly]	0.08s	consumed as direction
[gasps]	0.48s	performed as an actual gasp

Corrected the now-wrong comments about context params across three files, and ran the full render.

Turn 7 — User

Go ahead and run the full render.

Turn 7 — Assistant

Rendered: 21:03, 18,322 characters billed over 253 requests, 264s.

One line hit the rate limiter and exhausted all six retries inside the concurrent batch. Rather than assume the serial pass recovered it, verified: all 240 lines present in cache, none silent or truncated. Six flagged by a chars-per-second heuristic turned out to be short directed lines where the tag adds a performed sound — [laughs] Right? at 2.0s includes an actual laugh.

QC: 6/6 pass — −16.20 LUFS, −1.22 dBTP, 4.99 dB range, 0 clipped, +0.84 correlation.

Noticed the top octave read −25.7 dB vs Kokoro's −21.2 and claimed a logic error in the exciter gating (sample_rate < 32000 as a proxy for "brick-walled"). Measured it directly and retracted: ElevenLabs raw sits at −27.1 dB above 11 kHz, inside the natural-speech range of −25 to −32; Kokoro at −30.6 is the one that needs the lift. The gating was already correct. Changed nothing.

Pacing verified: 4.18 syl/s median vs Kokoro's 4.17 — v3 honours speed.

Final: 16,787 credits used of 40,000. Both versions pass QC 6/6.

Turn 8 — User

Write the full script of this session to script.md. Write notes about this session to notes.md. […] Write a metadata.toml file […]

Turn 8 — Assistant

Loaded the claude-api skill for accurate Opus 5 pricing rather than answering from memory, then wrote script.md, notes.md, and metadata.toml.

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Artifacts

out/guesswork-the-woodlouse-and-the-dark-room.mp3             20:44  Kokoro
out/guesswork-the-woodlouse-and-the-dark-room.elevenlabs.mp3  21:03  Eleven v3
out/guesswork-the-woodlouse-and-the-dark-room.transcript.md
out/guesswork-the-woodlouse-and-the-dark-room.elevenlabs.transcript.md

show/            2,918 lines of Python across 13 modules + tts/ package
tests/           12 tests, all passing
README.md, mise.toml, pyproject.toml, .env.example