I let Astra draft two fantasy teams. I kept a hand on the mouse.
I gave Astra two overlapping Sleeper drafts and my DELPHI analysis project. The picks, missed turns, uneven results, and baseline for weekly updates.
An agent becomes more interesting when it can change something outside the chat. It also becomes easier to break. These experiments cover the permissions, memory, recovery paths and human supervision needed when an agent operates infrastructure or a live application.
Rhodes introduces the infrastructure guardrails; Supra and the Markdown memory store explore persistence and interruption. The publishing postmortem and the overlapping Sleeper drafts show why software completion and a useful outcome need separate measurements. The failures are part of the record.
Start here
- Giving Rhodes write access to my infrastructure
- I built a personal AI that decides when to interrupt me. In 30 days it decided no 1,784 times.
- I didn't give my agents a vector database. I gave them 126 text files.
- The upgrade worked. Turning the machine back on is what failed.
- I built a machine that wrote 278 articles. Nobody read them.
- DELPHI: a Monte Carlo agent for my Superflex fantasy draft
- I let Astra draft two fantasy teams. I kept a hand on the mouse.
Field notes on this topic
The upgrade worked. Turning the machine back on is what failed.
An agent-driven ESXi upgrade that succeeded, a restore step that failed on a precondition nobody checked, and a rollback that retried the exact action that had just failed. What I got wrong.
Agents are a new class of workload. Private cloud is where you contain them.
An agent with a kubeconfig is not an information system, it is an operator. Part 1 of a series on why least privilege, RBAC and audit logs all quietly assume the actor is deterministic, and how VCF and VKS primitives bound something that is not.
I didn't give my agents a vector database. I gave them 126 text files.
A working design for agent memory that survives the restart: one fact per Markdown file, an index loaded every session, wiki-links instead of a graph database, and the ways it quietly rots. Measured from a real store of 126 memories.
I built a machine that wrote 278 articles. Nobody read them.
Apollo published 278 articles in 14 weeks to zero subscribers. A postmortem of a pipeline that worked as software and failed as a publication.
I built a personal AI that decides when to interrupt me. In 30 days it decided no 1,784 times.
A build log for Supra, my personal AI system: skill dispatch via tool use, a proactive judge stuck in cold-start deadlock, and a self-improvement loop that mined a test fixture.
DELPHI: a Monte Carlo agent for my Superflex fantasy draft
How I built DELPHI, a Monte Carlo simulator for a ten-team Superflex PPR fantasy football league, used it to rank draft strategies against the room, and then audited its four modeling flaws: correlation, circularity, frozen in-season assets, and floored-Normal scoring.
Giving Rhodes write access to my infrastructure
What it took to let an agent restart VMs and delete snapshots on real infrastructure: the tier model, the guardrails in code, and the bugs found along the way.