Running a YouTube Channel for $0: Manim + Local TTS + FFmpeg + YouTube API

Why Build This

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I wanted to publish technical AI content that goes beyond surface-level explanations — real system architecture, real algorithms, real trade-offs. And I wanted it to be visually compelling, not just a talking head.

The constraint: I have zero budget for production software and zero patience for manual video editing.

The solution: automate the entire pipeline from script to upload.

The Pipeline

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flowchart TD
    A["💡 Topic"] --> B["🤖 Research
Kimi K2.5 (~$0.001)"]
    B --> C["✍️ Script
Claude Opus (~$0.05)
~500 chars = 35s speech"]
    C --> D["🎬 Animation
Manim on RTX 2080 Ti
OptiX GPU rendering"]
    D --> E["🎙️ Narration
Kokoro TTS local
am_michael voice"]
    E --> F["✂️ Edit
FFmpeg: sync + speed
max 1.2x"]
    F --> G["📤 Upload
YouTube Data API v3
OAuth2 token"]
    G --> H["📱 Short published
< 60s, vertical 9:16"]
    style D fill:#1e3a5f,color:#fff
    style E fill:#10b981,color:#fff

Step 1: Research (Kimi K2.5)

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Kimi K2.5 is cheap and has a massive context window. I use it to research the topic, pull together relevant papers, and draft a content brief. A full research pass costs literally fractions of a cent.

The brief includes: core concept, key visual metaphors, 3-5 key points to hit, suggested Manim scene structure.

Step 2: Script (Claude Opus)

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The script is the most important part. The constraint is strict:

The script has to be dense but not rushed — every word has to earn its place. Claude Opus at ~$0.05 per script is worth it for the quality jump over cheaper models. Sonnet works too but needs more prompting.

Step 3: Manim Animation

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Manim is the same library 3Blue1Brown uses for his math videos. It’s pure Python — you describe animations programmatically and render them.

class AgentMemoryScene(ThreeDScene):
    def construct(self):
        # Create 3D surface representing embedding space
        surface = Surface(
            lambda u, v: np.array([u, v, np.sin(u)*np.cos(v)]),
            u_range=[-3, 3], v_range=[-3, 3],
            resolution=(30, 30)
        )
        self.play(Create(surface))
        # ... animate memory retrieval path

The RTX 2080 Ti renders with OptiX — GPU-accelerated ray tracing. A 30-second 720p animation renders in about 45 seconds. Without GPU it would take 10+ minutes.

For the DPO channel, scenes include: HNSW graph traversal, agent memory hierarchy, protocol stack diagrams, gradient descent visualization.

Step 4: Kokoro TTS (Local Voice)

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Kokoro is a local TTS model that runs on the gaming server. The am_michael voice is clear, natural, and works well for technical content.

import kokoro

pipeline = kokoro.KPipeline(lang_code='a')  # American English
audio, sample_rate = pipeline(script, voice='am_michael')

No API calls, no rate limits, no cost. The RTX 2080 Ti generates speech faster than real-time.

The only limitation: no emotional range. Kokoro is clear and neutral — great for explanation content, not for hype. For the occasional premium video, I switch to ElevenLabs Lily.

Step 5: FFmpeg Editing

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The sync step is simple but fussy:

# Merge audio + video, extend if needed
ffmpeg -i animation.mp4 -i narration.wav \
  -c:v copy -c:a aac \
  -shortest output.mp4

# Speed up slightly if over 60s (max 1.2x sounds natural)
ffmpeg -i output.mp4 -filter:v "setpts=0.85*PTS" \
  -filter:a "atempo=1.18" final.mp4

Step 6: YouTube Data API v3

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// node upload.js --file final.mp4 --title "..." --description "..."
const youtube = google.youtube({ version: 'v3', auth: oauth2Client });
await youtube.videos.insert({
  part: ['snippet', 'status'],
  requestBody: { snippet: { title, description, tags }, 
                 status: { privacyStatus: 'public' } },
  media: { body: fs.createReadStream('final.mp4') }
});

OAuth2 token stored at x-autopilot/yt-token.json. The same Node.js infrastructure that handles X/Twitter posting handles YouTube uploads — shared auth layer.

Cost Per Video

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Seven Shorts published. Total cost: about 35 cents in API calls.

The channel is still small — it’s more of a proof-of-concept than a growth vehicle right now. The real value is the pipeline: once a video format works, producing more is nearly free.