Building open source things. AI, privacy & infosec. Shipping in public.
Joined January 2026
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Gave a talk at MLAI Melbourne last Tuesday on aigogo - I was fed up with there being no good way to reuse AI agents between projects so I built a package manager for them. Turns out other people have the same problem. Good crowd and cool meetup. github.com/aupeachmo/aigogo
aigogo v0.0.8 released: github.com/aupeachmo/aigogo/…
What's New:
- Improved handling and grouping of aigogo controlled dependencies in your project manifest files (pyproject.toml, package.json)
Hi all,
I'll talk about my open source project aigogo (github.com/aupeachmo/aigogo) which is a new way to bundle and distribute your AI agents tomorrow at AI/ML Melbourne (meetup.com/machine-learning-…).
If you're in town, you're welcome to join me!
Does anyone else find that Claude Code will sometimes corrupt their `.git/index` - if so, what did you do to resolve it?
Hi all,
Today I'm releasing a packaging tool for AI Agents I've been working on called #aigogo
Build once, share everywhere. No duplication or publishing overheads.
Just `aigg build`, `aigg push`, `aigg install`.
Works with any Docker V2 registry. Supports Python & JS (for now).
github.com/aupeachmo/aigogo
🧵 about why I started this project...
So how do I get a gist-like experience in my development flow but with all the benefits of a package manager?
I've built aigogo to try and answer this.
I was wondering what #moltbook is costing in terms of energy consumption and power footprint. After some research into token's consumption and its environmental impact, I start wondering if this project worths the environment cost vs productivity/novelty.
I don't have token counts, server specs, or power grid data, just the agent count and the message count from moltbook.com (at the time of writing approx 1.5 million agents and 300,000 combined posts + comments). I think I can work with this though...
THE HONESTY PART - every estimate has a range:
All assumptions low = ~5.3 kg CO₂
All assumptions high = ~720 kg CO₂
Our central: ~62 kg.
That's about two orders of magnitude of uncertainty.
Low: 200 tokens, 0.0004 Wh/token, PUE 1.1, 200 gCO₂/kWh
High: 800 tokens, 0.003 Wh/token, PUE 1.6, 600 gCO₂/kWh
SOURCES
- de Vries (2023), Joule: AI energy footprint projections
- Luccioni et al. (2024), FAccT: inference energy benchmarks
- Epoch AI (2025): updated per-query estimates
- Uptime Institute (2024): global PUE data
- IEA (2025): global carbon intensity: 445 gCO₂/kWh
- Ember (2025): country-level electricity data
First PR to openclaw: github.com/openclaw/openclaw…
Distribution for aigogo is getting some love right now. I want to make it easy for people to package *and* distribute their agents and artifacts.
If you have opinions on how this should work, comment below.
Today I was wondering what #moltbook is costing in terms of energy consumption and power footprint.
I don't have token counts, server specs, or power grid data, just the agent count and the message count from moltbook.com (at the time of writing approx 1.5 million agents and 300,000 combined posts + comments).
I think I can work with this though...
THE HONESTY PART - every estimate has a range:
All assumptions low = ~5.3 kg CO₂
All assumptions high = ~720 kg CO₂
Our central: ~62 kg.
That's about two orders of magnitude of uncertainty.
Low: 200 tokens, 0.0004 Wh/token, PUE 1.1, 200 gCO₂/kWh
High: 800 tokens, 0.003 Wh/token, PUE 1.6, 600 gCO₂/kWh
SOURCES
- de Vries (2023), Joule: AI energy footprint projections
- Luccioni et al. (2024), FAccT: inference energy benchmarks
- Epoch AI (2025): updated per-query estimates
- Uptime Institute (2024): global PUE data
- IEA (2025): global carbon intensity: 445 gCO₂/kWh
- Ember (2025): country-level electricity data