sentinel · MCP conformance, and a server worth grading

On 28 July 2026 the Model Context Protocol removed sessions and the initialize handshake, made server/discover mandatory, replaced server-initiated requests with Multi Round-Trip Requests, and put Roots, Sampling, Logging, HTTP+SSE and OAuth Dynamic Client Registration on a twelve-month removal clock. Every server written before that date is now non-conformant in ways its authors have not enumerated. Sentinel is two halves: a Go broker built natively on the new revision, and a Python harness that scans any MCP endpoint, grades it rule by rule with a specification citation attached to each finding, and inventories the deprecated features still in use with the date each becomes removable.

The interesting part is what it refuses to say. Five of the normative MUST requirements cannot be settled from outside a server — whether a token's audience is really checked, whether an inbound token is forwarded to a downstream dependency, whether a retried request is idempotent at the effect rather than in the reply. The harness reports those as INDETERMINATE, excludes them from the gate, and reprints them on every scan. A scanner that graded them as passes would be lying, and the clean report it produced would be worse than no report. The broker implements all five and proves it with tests that have the access a scan does not — which is why both halves ship in one repository, and why the harness never imports server internals.

A rule ID also never changes meaning. Three rules turned out to demand more than the specification does; rather than being redefined in place — which would silently rewrite every report that had already cited them — they were deprecated and republished under new IDs, with the old ones still evaluable behind a flag so an earlier report can be reproduced and diffed.

Go Python Envoy PostgreSQL OpenTelemetry SARIF — 13k lines of Go and 5.7k of Python across 36 test files, golangci-lint, go test -race, mypy and ruff enforced in CI, and no model API key required anywhere, which is why the suite is fast and never flaky for a reason outside the repository.

meridian · agent evaluation harness

Answers one question: did this change make the agent better or worse, and can you reproduce that answer tomorrow. Every trial runs in its own container with its own workdir volume, and the suite ships a contamination probe with a deliberate failing direction — isolation is asserted rather than assumed, because a probe that cannot fail proves nothing. Scoring uses pass^k rather than a single accuracy number. A PR gate compares head against the merge base and blocks only when the drop clears both a tolerance and a paired significance test.

Two things it does that the tools it sits beside do not:

It never trusts the container. Assertions run on state extracted to the host, after the container is gone. In 2026 researchers broke several major agent benchmarks through exactly that hole — agents writing a conftest.py that rewrote every test result to passed, or replacing /usr/bin/curl to emit fake output. The same rule now applies to Meridian's own task files, since in a CI gate a pull request is the untrusted input: a security pass found a path traversal that could read a host file and print it into a public PR comment, and closed it along with four smaller siblings.

It reports its own blind spot. Every verdict carries the minimum detectable effect — "this run could only have caught a drop of 0.183 or larger; resolving your 0.030 tolerance would take about 262 tasks." A PASS from an underpowered suite is not evidence that nothing broke, and a gate that reports those identically teaches people to trust it exactly when it is least reliable.

It is also measured by itself, and publishes the result including the miss: 0/30 false regressions, 4/5 seeded regressions caught, 5/5 replay fidelity. The one it misses is explained with the arithmetic rather than tuned away.

Python 3.12+ Typer Docker Postgres + Alembic Starlette numpy — mypy --strict and ruff enforced in CI, 331 tests across unit, Docker-backed integration, and end-to-end layers.

serverless-rideshare-aws · serverless AWS backend

Six Lambda-backed services behind an HTTP API Gateway, a separate WebSocket API for live location, DynamoDB for state, Cognito for auth, and the environment declared in Terraform rather than clicked together in a console. Written as an infrastructure study, and the README says plainly which pieces are built and which are not.

Node.js Terraform Lambda DynamoDB API Gateway v2 Cognito EventBridge

cricket-shot-classification · video classification with physics constraints

Five model variants over the same seven-class problem, written to be compared: a CNN-GRU with key-frame selection, a MobileNet baseline, a 3D ResNet with Vision Transformer, and a physics-informed model that adds temporal continuity, energy, and momentum terms to the classification loss. A bat swing is a smooth trajectory, so a model whose per-frame beliefs jump around is wrong even when its final answer is right.

PyTorch ViT 3D ResNet Grad-CAM Weights & Biases

metaheuristic-llm-finetuning · fine-tuning by evolutionary search

How much of a pre-trained model should you freeze? Usually a guess. This searches for the answer instead: a genetic algorithm and the Whale Optimization Algorithm over freezing percentage and crossover operator for RoBERTa on SST-2. Two findings held across every configuration: SBX crossover wins, and less freezing wins. Best result 94.67% accuracy.

PyTorch Hugging Face RoBERTa genetic algorithms SST-2

EMG-Dumbbell-press · exercise form from surface EMG

A signal problem before it is a modelling problem. Neither the deltoid nor the pectoral channel says much alone, so the pipeline computes windowed RMS, integrated area, skewness and kurtosis per channel and classifies the balance between them.

Re-reading it recently turned up target leakage: the label is a threshold on a quantity that is also one of the model's input columns, so a three-line rule reproduces every label exactly and the accuracies in the notebooks measure nothing about EMG. The README now leads with that instead of the number, and says what would be needed to make the question answerable — labels from outside the signal, and a split that does not put 97%-overlapping windows on both sides. Left up rather than quietly corrected, because the paper is published and the reasoning is more useful than the figure was.

TensorFlow/Keras LSTM SciPy pandas

Three peer-reviewed conference papers, in computer vision, biosignal processing, and LLM optimization. Each has a public repository above. Citations and DOIs are on LinkedIn, or email me for the PDFs.

Building sentinel out from its MVP tier — the parts deliberately left out are named in its scope section, and the next ones in are consent-registry authorization and subscriptions/listen. Alongside it, taking meridian toward a release a team other than mine could adopt, and reading the 2026 literature on eval variance closely enough to argue with it. At SparkFX, agent-based issue triage in a Next.js and TypeScript codebase.

The longer version of all of this, with architecture diagrams, is at patsypppe.github.io — Astro and TypeScript, no client-side JavaScript, type-checked in CI before it can deploy.

Graduating May 2027, open to new-grad software, machine learning, and cloud engineering roles in the US.

patsypppe.github.io · ppattana@iu.edu · LinkedIn · Bloomington, Indiana

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