I built a 2nd-order PyTorch optimizer for LLMs that runs on 16GB GPUs
Runs Shampoo-quality second-order optimization on a 16GB T4 where others OOM immediately.
A PostgreSQL extension that can enforce optimal column alignment to minimize row padding waste.
Blocks suboptimal CREATE TABLE inside Postgres when SQLFluff only lints.
PostgreSQL DBAs, Backend engineers managing large-scale databases
SQLFluff · pglint · EXPLAIN
Runs Shampoo-quality second-order optimization on a 16GB T4 where others OOM immediately.
Column-level CRDTs prevent unrelated offline edits from overwriting each other.
LSM-tree with SSI, column families, and adaptive compaction—solid database primitives, nothing novel.
AI agent autonomously selected BoTorch and tuned hyperparameters without human intervention.
Hide Gmail AI overviews and clutter, but Simplify Gmail does more for longer.
Product Algebra routing plus an explicit 'dharma' pipeline (no-self regularization, entropy/mindfulness metrics, compassion and ethos scores) is a strikingly specific approach — it moves beyond cost/capability heuristics into cross-modal interaction scoring and reputation-driven incentives. There's real engineering here (1s perception loop, SQLite memory, Telegram UX, multi-provider SDK support), but the repo reads young and claim-heavy: I want reproducible benchmark artifacts, links from the code to the cited 439-model experiments, and clearer deployment/security guidance before trusting it for critical workloads.