// Behind The Music

The Short Version

bitwize music is an experiment in human + AI music collaboration. Not “AI-generated music” where you push a button and get a song. More like having a really weird, really helpful bandmate who never sleeps and has read everything on the internet.

The human brings the vision, the judgment, and the final call. The AI brings tireless iteration, research at scale, and an obsessive attention to detail. Together, I make albums that neither of us could make alone.

Who Am I

I’m bitwize—a hacker who loves music and experimenting with new technology. When AI music generation tools started getting good, I couldn’t resist seeing what was possible.

I’m also excited to finally make the music I wish already existed—songs about weird niche tech interests of mine, like how the Debian operating system came to be, the drama of the Linux kernel mailing list, or the underground warez scene. Stories that deserve to be told, but nobody’s made albums about … until now.

The Workflow

This isn’t “open ChatGPT and type some prompts.” The production system behind bitwize music includes over 250,000 lines of structured documentation—custom instructions, templates, workflows, research files, and track specifications that guide every phase of album creation.

Here’s how it actually works:

Concept

Every album starts with a question. What’s the story? What’s the angle? For documentary albums like The Wizard or The Scene, that means identifying a real story worth telling—something with depth, drama, and a hook that’ll make someone want to listen.

Research

This is where documentary albums get serious. Court documents, DOJ press releases, contemporary newspaper accounts, academic sources. Not Wikipedia summaries—primary sources wherever possible.

Here’s what research actually looks like for The Wizard (about Thomas Edison’s darker side):

That album required cross-referencing 11 separate research files covering everything from Edison’s patent litigation to primary sources about Topsy the elephant.

Writing

Lyrics get written, revised, and polished. The AI helps with iteration—trying different rhyme schemes, checking for prosody issues, making sure verse 2 actually develops the story instead of just repeating verse 1 with different words.

But the creative direction stays human. What’s the emotional arc? What’s the hook? What makes this track matter?

Generation

This is where AI music generation comes in. I use tools that turn lyrics and style descriptions into actual audio. It’s not one-and-done—it’s iterative. Generate, listen, adjust, try again. Sometimes it takes 10, 20 or more generations to land the right sound for a single track.

Verify

For documentary albums, this is non-negotiable. Every factual claim gets traced back to sources. The research sections on the album pages aren’t decoration—they’re the receipts.

Release

Artwork, mastering, distribution. The boring but necessary stuff that turns a collection of tracks into an actual album people can find and listen to.

Tools I Use

The bitwize music stack:

AI & Generation

• Claude Code — AI collaborator for writing, research, iteration, and documentation.
• Suno — AI music generation. Turns lyrics and style descriptions into actual songs.
• ChatGPT — Album artwork generation with DALL-E.

Audio Processing

• Python — Custom mastering scripts for loudness normalization and EQ.
• pyloudnorm — ITU-R BS.1770-4 loudness measurement for streaming targets.
• Matchering — Reference-based mastering to match the sound of professional tracks.
• FFmpeg — Promo video generation, audio extraction, format conversion.
• SciPy — Signal processing for EQ and filtering.
• Librosa — Audio analysis for smart segment selection in promo videos.

Research & Automation

• Playwright — Automated browser for document hunting from public archives.

Website & Infrastructure

• GitHub — Version control for everything: lyrics, research, website, documentation. Easy reverts, change tracking, and collaboration history.
• Hugo — Static site generator for bitwizemusic.com.
• Cloudflare Pages — Hosting and deployment.

Distribution

• DistroKid — Distribution to Spotify, Apple Music, and everywhere else.
• SoundCloud — Primary streaming and sharing platform.

How It All Fits Together

Claude Code is the orchestrator. It doesn’t just help with writing—it runs the entire production pipeline through custom workflow files, specialized skills, and automation hooks. Research, lyric iteration, mastering scripts, promo video generation, website deployment—all triggered and coordinated through Claude Code.

What’s automated:

• Research gathering and source verification
• Initial lyric drafts and technical quality checks (rhyme, prosody, pronunciation)
• Running Python mastering scripts
• Generating promo videos with FFmpeg
• Website builds and deployment
• Version control and documentation

What’s still manual:

• Lyric iteration and refinement (story flow, emotional arc, creative direction)
• Suno generation (pasting prompts, listening, downloading keepers)
• Quality control listening and final approval
• SoundCloud and DistroKid uploads
• Album artwork generation via ChatGPT

The goal is human judgment where it matters (creative decisions, quality control) and automation everywhere else.

Documentary Rigor

Some of these albums tell real stories about real events. That comes with responsibility.

The Source Hierarchy

Not all sources are equal. I follow a strict hierarchy:

1. Court documents — Indictments, rulings, transcripts (highest authority)
2. Government releases — DOJ press releases, agency statements
3. Investigative journalism — Long-form reporting from reputable outlets
4. News coverage — Contemporary newspaper accounts
5. Wikipedia — Context only, never for facts

Myth Busting

Sometimes research reveals that popular narratives are wrong.

The Wizard addresses a famous myth: that Thomas Edison personally electrocuted Topsy the elephant as anti-AC propaganda.

The myth: Edison electrocuted Topsy to scare people away from AC current.

What I found:

• Edison was never at Luna Park
• The War of Currents ended in 1892; Topsy died in 1903 (10 years later)
• Zero mentions of Topsy in Edison’s correspondence at Rutgers
• Luna Park owners Thompson & Dundy ordered the execution, not Edison

The album addresses this directly—Topsy’s death was the culmination of Edison’s legacy, not his action. That’s a more interesting (and accurate) story than the myth.

Track-by-Track Verification

Every documentary track gets a verification table. Here’s a real example from “December Fifth” on The Wizard:

If a claim can’t be verified, it gets flagged as “creative license” and documented as such.

What Gets Documented as Creative License

I’m explicit about what’s dramatization:

What is not creative license: all dates, names, numbers, court rulings, and attributed quotes.

The Pronunciation Challenge

AI music generation has a dirty secret: it can’t read.

When Suno sees “live,” it doesn’t know if you mean “live performance” (LYVE) or “live your life” (LIV). When it sees “read,” it guesses—and guesses wrong half the time.

Real Fixes from Real Tracks

From “December Fifth” on The Wizard:

From Deb + Ian:

The Homograph Problem

These words have two pronunciations. Every one requires a decision:

I scan every lyric for these before generation. It’s tedious. It matters.

Why This Matters

A mispronounced word breaks the spell. When the AI says “LEED” instead of “LED” in a song about metal, it sounds wrong to every listener—even if they can’t articulate why. I catch these before generation, not after.

Lyric Craft

Good lyrics aren’t just rhymes. Every track goes through quality checks.

Prosody

Stressed syllables need to land on strong beats. When they don’t, lines feel awkward even if the words are fine.

Bad prosody (stress on wrong beat):

“The MACH-ine is RUN-ning NOW”

Good prosody (natural stress pattern):

“The ma-CHINE is run-NING now”

The AI checks every line for this before I generate.

Rhyme Quality

Not all rhymes are equal:

Lazy patterns get caught and fixed before generation.

Verse Development

V2 can’t just be V1 with different words. It needs to develop the story:

If verse 2 just rewords verse 1, it gets rewritten.

Generation Iteration

Getting a track right isn’t one attempt. It’s a process.

What I’m Listening For

• Vocal delivery — Does the phrasing feel natural?
• Pronunciation — Did the phonetic fixes work?
• Structure — Are all sections (verse, chorus, bridge) present?
• Mood — Does it match the intended emotion?
• Audio quality — No weird artifacts or glitches?

Iteration Reality

Some tracks land on attempt 3. Some take 20+. The generation log tracks every attempt:

I don’t hide the iteration. It’s part of the process.

When Generation Isn’t Enough

Sometimes the AI nails the vibe but something’s off—the backing vocals overpower the lead, the bass is too prominent, an instrument clashes with the vocal melody. That’s when I open Suno Studio and extract stems.

Stem separation lets me isolate:

• Lead vocals — Adjust levels, add effects, fix mix issues
• Backing vocals — Pull them back or push them forward
• Instruments — Tweak individual elements that don’t sit right
• Bass/drums — Rebalance the low end

It’s not always needed, but when a track is 90% there and regenerating would lose what works, stem editing saves it.

Mastering for Streaming

Raw Suno output isn’t ready for streaming platforms. Every track gets mastered.

Target Standards

Common Fixes

Album Consistency

All tracks on an album should be within 1 dB LUFS of each other. A quiet track after a loud one feels wrong, even if each sounds fine in isolation.

Genre Experimentation

bitwize music isn’t one sound. The project spans:

• Nerdcore — Tech nostalgia, hacker culture, internet history
• Dark Industrial — Heavier documentary work
• Indie Folk — Quieter, introspective storytelling
• Country/Americana — Road songs and heartbreak
• Ska Punk — Horns, energy, chaos
• Synth-Pop — 80s-influenced electronic

Different stories need different sounds. A documentary about Thomas Edison’s animal experiments doesn’t sound like a Christmas ska album. That’s the point.

Transparency

I’m not hiding the process. The method is part of the art.

You can see the research. You can see the sources. You can see what’s documented and what’s interpretation. The albums stand on their own as music, but the documentation is there for anyone who wants to dig deeper.

This is what AI collaboration looks like when you do it with intention—not as a gimmick, but as a genuine creative partnership.

Sample Templates

Here are the actual template structures used to build tracks and albums.

# [Track Title]

## Track Details

| Attribute | Detail |
|-----------|--------|
| **Track #** | XX |
| **Title** | [Track Title] |
| **Album** | [Album Name](../README.md) |
| **Status** | Not Started |
| **Suno Link** | — |
| **Explicit** | Yes / No |
| **POV** | [Character/Perspective] |
| **Sources Verified** | ❌ Pending |

## Concept

[Describe the track's narrative, themes, and purpose]

## Musical Direction

- **Tempo**: [BPM estimate]
- **Feel**: [Energy level, groove type]
- **Instrumentation**: [Key instruments/sounds]

## Suno Inputs

### Style Box
[genre], [tempo/BPM], [mood], [vocal description], [instruments]

### Lyrics Box
[Verse 1]
[Lyrics here...]

[Chorus]
[Lyrics here...]

## Pronunciation Notes

| Word/Phrase | Pronunciation | Reason |
|-------------|---------------|--------|
| — | — | — |

## Generation Log

| # | Date | Model | Result | Notes | Rating |
|---|------|-------|--------|-------|--------|
| — | — | — | — | — | — |

# [Album Title]

## Album Details

| Attribute | Detail |
|-----------|--------|
| **Artist** | [Artist Name] |
| **Genre** | [Genre] / [Subgenre] |
| **Tracks** | [Number] |
| **Status** | Concept |
| **Explicit** | Yes / No |

## Concept

[Detailed description of album's concept and themes]

## Sonic Palette

- **Beats**: [Production style]
- **Samples**: [Sample sources if applicable]
- **Vocals**: [Vocal style and delivery]
- **Mood**: [Overall emotional tone]

## Tracklist

| # | Title | POV | Concept | Status |
|---|-------|-----|---------|--------|
| 01 | [Track Name] | [POV] | [Brief concept] | Not Started |
| 02 | [Track Name] | [POV] | [Brief concept] | Not Started |

## Production Notes

**Style Prompt Base**:
[Base style prompt for all tracks]

## Album Art

### ChatGPT Image Prompt
[Visual description for DALL-E generation]

## Documentary Standards

### Album Classification

| Attribute | Selection |
|-----------|-----------|
| **Album Type** | ☐ True Crime / ☐ Dramatized / ☐ Inspired By |
| **Real People Featured** | ☐ Yes / ☐ No |
| **Legal Sensitivity** | ☐ High / ☐ Medium / ☐ Low |

### Real People Depicted

| Person | Role | Depicted How | Sensitivity |
|--------|------|--------------|-------------|
| [Name] | [Role] | [Narrator describes / Quotes attributed] | [H/M/L] |

### Legal Safeguards

- [ ] No defamation: Claims are documented facts
- [ ] No fabricated statements: Real words are sourced
- [ ] Fair use: Album is commentary on public interest
- [ ] Narrator voice: Storyteller, not impersonation

### Source Verification Status

| Track | Sources Captured | Human Verified |
|-------|------------------|----------------|
| 01 - [Title] | ☐ | ☐ |
| 02 - [Title] | ☐ | ☐ |

## Quotes & Attribution

| Lyric Line | Type | Attribution | Source |
|------------|------|-------------|--------|
| "[quote/claim]" | Verbatim / Paraphrase | How framed | Source doc |

## Artistic Liberties Taken

| Element | Liberty Taken | Justification |
|---------|---------------|---------------|
| [e.g., dialogue] | [what was changed] | [why - flow, etc.] |

## Legal Review

- [ ] No impersonation
- [ ] Documented claims only
- [ ] Fair comment: Opinion vs. fact distinguished
- [ ] No fabricated quotes
- [ ] Public interest subject matter

## Phonetic Review Checklist

- [ ] **Proper nouns scanned**: Names, places, brands
- [ ] **Foreign names**: Phonetic spelling added
- [ ] **Homographs checked**: live, lead, read, wind, tear
- [ ] **Acronyms**: Spelled out (F-B-I not FBI)
- [ ] **Numbers**: Year formats ('93 not ninety-three)
- [ ] **Tech terms**: Linux → Lin-ucks, SQL → sequel

**Proper nouns in this track:**

| Word | Current | Phonetic | Fixed? |
|------|---------|----------|--------|
| — | — | — | — |