My AI Setup
Most people's AI stack looks like a messy browser tab graveyard — ChatGPT, Midjourney, ElevenLabs, Python scripts, and Notion docs trying to tie it all together. It works but barely scales.
An AI workspace fixes this by providing a unified environment where tools share context and communicate through standardized APIs. Agents can orchestrate tasks without manual copy-pasting between tabs.
What's an AI Workspace?
- Unified I/O: Files, models, and outputs live in shared dirs.
- Agent-accessible APIs: Each module exposes functions for orchestration.
- Composable pipelines: Workflows chain seamlessly.
- State persistence: Long-running tasks survive session restarts.
The Problem with Point Solutions
Using standalone tools adds hidden friction:
- Context re-entry
- Format conversion errors
- Rate limit juggling
- No automation path
The NEPA AI Workspace Stack
NEPA builds modular workspaces for specific mediums:
| Workspace | Function |
|---|---|
| Video | Transcribe, scene detect, extract clips |
| Audio | Stem separation, effects, mixing |
| Music | Text-to-music via MusicGen |
| Image | Upscale, background removal, style transfer |
| Code IDE | Multi-file generation, project analysis |
| TTS | OpenAI, ElevenLabs, Google Cloud, Azure Speech |
| Vector | SVG generation, vectorization |
| 3D Viewport | Text-to-3D (Shap-E), mesh optimization |
| Animation | Keyframe generation, RIFE interpolation |
| Design Studio | UI mockups, logo gen, brand assets |
| Game Scene | Asset gen, level layout |
| AR/VR | Hand tracking, SLAM, spatial audio |
Every workspace is agent-controlled. An orchestrating agent can call functions like music_workspace.generate(prompt="lo-fi hip hop") and get back paths to outputs.
How a Real Workflow Uses Multiple Workspaces
async def content_pipeline(footage_path: str, topic: str):
# 1. Video transcribe + extract clips
transcript = await video_workspace.transcribe(footage_path)
highlights = await video_workspace.extract_viral_clips(footage_path, transcript)
# 2. Audio clean up
cleaned_audio = await audio_workspace.denoise(highlights[0].audio)
# 3. Music generate background track
bg_music = await music_workspace.generate(
prompt=f"upbeat background for {topic} video"
)
# 4. Video mix and export
final = await video_workspace.compose(
video=highlights[0].video,
voiceover=cleaned_audio,
background_music=bg_music
)
# 5. Image generate thumbnail
thumbnail = await image_workspace.generate_thumbnail(
frame=final.keyframe,
style="youtube_thumbnail",
text=topic
)
This workflow runs headlessly once configured.
Setting Up Your First Workspace
Start with a bottleneck task. For most, video transcription and clip extraction is it. Steps:
# Install NEPA AI workspace CLI
npm install -g @nepa-ai/workspace-cli
# Initialize a workspace in your project folder
nepa-workspace init --type video
# Run a task
nepa-workspace run transcribe --input ./footage/raw.mp4 --output ./output/
The CLI handles model downloads and output dirs.
Why Workspaces Beat Standalone APIs
Workspaces handle auth, error handling, rate limits, local fallbacks transparently. Example:
result = await tts_workspace.speak(
text="Hello world",
voice="nova",
# Falls back to local piper-tts if OpenAI API unavailable
fallback_to_local=True
)
The Business Case
Save time — 2 hours/day pays off in weeks. For content creators, solo founders, and agencies.
→ Get the Full AI Workspace Suite at mnemelabs.nepa-ai.com
