Self-Improving AI
6 essays on this topic.
What the Research Says: Mapping RIAI Against the Field
RIAI Lab Notes #6: I read four research fields against my own results. What others built, where RIAI stands, and the experiments I'll run next.
Watching JEPA Write Code Live: What It Got Right, and the Check It Fooled
RIAI Lab Notes #5: a live test of the JEPA code writer. Fast, readable, no LLM, and correct for its examples. But the examples didn't say what was meant.
Next: Teaching an Agent to Imagine Code With JEPA
RIAI Lab Notes #4: why exact-value prediction hit a wall, how a JEPA plans code in vector space, and first results: 10 of 12 when grounded.
Writing Its Own Code: From a Teacher Model to Code Built by Search
RIAI Lab Notes #3: moving the 'write the code' step from Claude Haiku into the agent, with program search, execution traces and honest gaps.
A Terminal That Learns What Commands Do
RIAI Lab Notes #2: an agent that learns what sort, grep and its own Python tools do by experimenting, then plans in its imagination.
RIAI: Toward a Terminal Agent That Writes Its Own Skills
Lab notes from RIAI: my goal is an agent that does everything from the terminal and writes its own Python skills to keep growing. Where it stands today.
