Agents
Don’t make the agent run every step
AI automation becomes more efficient when deterministic operations stay in code and agents appear only where the work requires judgement.
Episode 67 guest
Anthony
Anthony is an independent researcher who left the corporate world in 2024 to work in Bitcoin full time.
About
Anthony is an independent researcher who left the corporate world in 2024 to work in Bitcoin full time. He an alumni of Chaincode’s 2025 ₿OSS program and holds a PhD in computer science from the University of South Australia.
Conversations
Episode 67
Local AI Models
Local models can handle everyday personal tasks, but hardware costs and limited awareness leave hosted frontier AI as the easier consumer choice. For a small company in an unfamiliar category, larger rivals can bear the cost of teaching buyers before differentiation becomes decisive.
View episode ↗Episode 60Models or Harnesses
Declarative pipelines reduce AI agent drift by fixing the path through functions, steps and data while leaving people responsible for direction and course correction. A shared evidence search can fan out across many targets before separate agents pursue the most relevant lines in depth.
View episode ↗Ideas from the conversation
AI Models & Infrastructure
The bandwidth limit on local AI
A model may fit in a local AI machine yet still respond slowly if its memory cannot supply data quickly enough.
Agents
When agents need a shared layer
Flight Deck brings delegated work into one communications layer where tasks remain visible, missed activity can be recovered and access follows clear boundaries.
Business & Markets
When bigger competitors help
For a small company in an unfamiliar category, larger rivals can bear the cost of teaching buyers before differentiation becomes decisive.
Privacy & Security
When encrypted sync costs too much
Application-level encryption can impose more design complexity than it earns when an app already runs on its owner’s server.
Knowledge & Memory
When your news builds a memory
Pete’s personal newspaper turns accumulated coverage into a connected resource that his AI agents can consult for context without burdening him with another unread queue.
AI Models & Infrastructure
Why consumers reach for hosted AI
Local models can handle everyday personal tasks, but hardware costs and limited awareness leave hosted frontier AI as the easier consumer choice.
Agents
AI Agents Research Twenty Blockchains in Parallel
A shared evidence search can fan out across many targets before separate agents pursue the most relevant lines in depth.
AI Models & Infrastructure
Cheap Inference Squeezes Frontier AI Economics
Frontier labs must keep funding expensive model advances even as older capabilities become cheaper and harder to distinguish from the latest release.
Agents
Code-Directed Workflows Keep AI Agents on Track
Declarative pipelines reduce AI agent drift by fixing the path through functions, steps and data while leaving people responsible for direction and course correction.
Business & Markets
How AI Doomerism Could Shape Regulation
Warnings that AI could wipe out jobs can make the technology feel like an urgent political crisis, giving leading labs more influence over the rules that follow.
AI Models & Infrastructure
Mathematical AI Must Separate Discovery From Retrieval
A model can find an unexpected link between mathematical fields, but novelty depends on proving it hasn't merely recovered an overlooked result.
Business & Markets
Purpose-Built Robots Beat Humanoids at Home
The strongest case for household automation is a machine that reliably removes a defined chore, not a humanoid whose broad capabilities may never justify its cost.
Agents
Why AI Agents Still Need Human Direction
An AI agent can check its progress only against a destination it understands, and defining that destination may require much of the original work.