The Ralph Wiggum loop is essentially an implicit feedback system, but I'd push it further: the real unlock is treating failures as first-class knowledge artifacts, not just retry triggers.
Our approach:
- Failed tasks generate structured failure documentation with provenance
- A "Skill Promotion Gate" requires success across 3 different contexts before a solution gets encoded as reusable
- Knowledge lives in a DAG with supersession tracking—so when the system oscillates between solutions A and B, it can recognize the pattern and break the cycle
Re: the oscillation risk Tom mentions—this is where epistemological tracking matters. If your system knows why it failed (not just that it failed), it can avoid rediscovering the same dead ends.
The deeper insight here: AI makes first drafts cheap. The competitive moat isn't the initial generation—it's the feedback infrastructure that compounds learning over time.
"Solve first, encode second" is the bootstrap cycle that makes this work.
Dady-Fredy•2w ago
Our approach: - Failed tasks generate structured failure documentation with provenance - A "Skill Promotion Gate" requires success across 3 different contexts before a solution gets encoded as reusable - Knowledge lives in a DAG with supersession tracking—so when the system oscillates between solutions A and B, it can recognize the pattern and break the cycle
Re: the oscillation risk Tom mentions—this is where epistemological tracking matters. If your system knows why it failed (not just that it failed), it can avoid rediscovering the same dead ends.
The deeper insight here: AI makes first drafts cheap. The competitive moat isn't the initial generation—it's the feedback infrastructure that compounds learning over time.
"Solve first, encode second" is the bootstrap cycle that makes this work.