We have been publishing about practical architecture frameworks for teams building agentic AI systems in production.
The collection covers recurring decision points, such as understanding the real cost structure of AI labor
Full collection here: machine-wisdom.ai/writing/
Research seems to point towards three possible futures:
- We all work less
- A small class of hyper-leveraged operators absorbs massive economic value while everyone else struggles
- We simply enter a new productivity arms race instead of slowing down
open.substack.com/pub/machinewis…
Shipped fava-trails.org v0.5.5: Use it with codevos.ai so that development artifacts (specs, plans, reviews) can get domain-aware quality validation — at the storage layer before they enter shared agent memory! Full write up about my multi-agent setup below.
Hacking on a @karpathy style research agent this weekend? Keep a close eye on agents memory. We learned from hundreds of thousands of dollars in token burn that memory pollution is the killer of agent reliability. We built FAVA Trails to solve this! Try it fava-trails.org
We brought three research-backed Context Engineering protocols into FAVA Trails so that quality holds up as memory scales. It keeps your agents' minds clear, so they can stay focused:
open.substack.com/pub/machinewis…
We don’t let unvalidated code reach production, but our AI agents believe anything they find in memory.
Today we're open-sourcing 🫛👣FAVA Trails, agentic memory that actually curates itself:
📖Docs & case study: fava-trails.org
🐙GitHub: github.com/MachineWisdomA…
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