Open source
vLLMgithub.com/vllm-project/vllm
Correctness work across the engine, scheduler, and KV-cache paths. Found and fixed a
prefix-cache bug in the V1 scheduler: when a session truncates tokens, a hash covering a
discarded token stays in block_hashes, so a later request whose prefix matches
the old tokens can be served KV computed from the new ones. The fix is one line in
_update_request_as_session with a regression test that fails without it
(#49377, #52806). Review contributor on a KV-cache capacity fix, where the review surfaced a
decode-path livelock that became the fix's second commit (#52530,
writeup), and on a stop-string leak traced through the
detokenizer and reasoning parser (#52599).
Roofline study of five open models on an H100: measure the card's real ceilings,
predict each model's decode rate from its weights, report the gap. Qwen3-8B holds 81% of
measured bandwidth. A concurrency sweep against one server turned out to be running at a 98%
prefix cache hit rate, overstating throughput by 86%, filed upstream as vllm#52884.
Pydantic AIgithub.com/pydantic/pydantic-ai
Filed and fixed correctness issues in the agent framework's embeddings and AG-UI streaming
surfaces: EmbeddingSettings.dimensions claimed Bedrock support that the underlying Titan
models do not provide, and AGUIEventStream failed to emit agent-generated FileParts.
Agentic Workflowgithub.com/brianosaurus/agentic-workflow
A human-gated coding-agent pipeline: Claude builds, Codex audits in a read-only
sandbox, and a human approves at every gate. Approvals are SHA-256-pinned to the artifact
that was reviewed, and reviewer output is immutable, so an approval cannot be quietly
reused for different work.
Code
LLM agents · pipeline tooling · open source
Live systems
The operations dashboard for my automated Solana trading systems: running bots and their
positions, on-chain wallet balances, per-trade execution timing (build, submit, and on-chain
fill), slot-level latency, and live logs. It monitors the infrastructure the Solana research
below grew out of.
Machine learning research
Independent studies, each built on public or self-collected data with the
full pipeline, tests, and negative results published.
A causal study of block propagation latency and validator revenue, built from 216,000 mainnet
slots of public Xatu and MEV relay data. Identification comes from the protocol's 4-second
attestation deadline (a sharp regression discontinuity: correct head votes fall from 95.9% to
67.4% across it), and the value of a faster transport is priced through three channels, with
roughly 90% arriving via MEV timing rather than attestation rescue. Includes a 245-day analysis
of the vendor's only real deployment, which finds no adoption-shaped change in
propagation.
Quantifies how block completion timing relates to Solana validator earnings, using two days
of on-chain data collected from my own Frankfurt node. Within-leader-window fixed effects
separate timing from leader quality; leader fees turn out to be the larger channel, at
+0.00372 SOL per 100 ms of earlier completion. A modeled 6x transport projects to roughly
$794k per year for the largest operators. The confounds are stated up front: a single
vantage point, skip risk, and simultaneity between timing and demand.
A pre-registered ablation study of ASR training data value: how word error rate responds to
quantity, label noise, acoustic quality, and speaker diversity, using LibriSpeech and
Whisper-small across 35 planned fine-tuning runs and nine pre-registered hypotheses. The goal
is an exchange rate between noisy and clean training hours. The experimental pipeline is
complete and verified by 213 tests. The fine-tunes await GPU compute, so the page reports
no results yet.
Tests whether agent benchmark comparisons survive perturbations that should not matter:
prompt paraphrase, tool renaming, distractor tools, instance resampling, temperature, and task
order, across 156 episodes on three procedurally generated task families. All six axes shifted
scores, and detecting a 3-point gap between models turns out to require hundreds to thousands
of instances. The conclusion is a cost estimate: a comparison you can defend costs one to two
orders of magnitude more than these are usually given.