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Report abuseI build systems that have to be right when nobody is watching: trading research that refuses to flatter itself, tooling that a non-technical person can run without help, and products that act on what they observe instead of just reporting it.
Most of what I enjoy is measurement. It is easy to build something that looks like it works. The interesting engineering is in the apparatus that would tell you if it did not.
Resume and full record: csnyder256.github.io
| Project | What it does |
|---|---|
| shadow-options-trading-lab | Runs about 20 options strategies against live market data and never places an order. Records every hypothetical fill under worst, base, and optimistic cost assumptions, then grades each strategy with anytime-valid e-processes so nightly checking stays statistically legitimate. |
| option-contract-grader | The valuation half of the same problem, standalone. Solves implied volatility and Greeks from Black-Scholes-Merton rather than trusting vendor numbers, then scores and letter-grades every contract in a chain. |
| ux-struggle-detector | Maps a customer's web app, watches real users through a drop-in script tag, detects 40 named struggle patterns server-side, and returns help in the same HTTP response the events arrived in. |
| grain-bids-to-excel | Built for one person doing one tedious job. Scrapes grain elevator cash-bid pages that share no common format, normalizes them into one schema, and produces the Excel workbook they used to retype by hand. |
| gba-rom-hack-ide | A local web IDE for Game Boy Advance ROM hacking. Scans a decompilation project into one typed manifest, edits it visually or in plain English, and builds a playable ROM. Tooling only, no game data included. |
| kafka-wire | A message broker that speaks the Kafka wire protocol, in one Go binary with no ZooKeeper, no JVM and no cluster. I wrote it as ClarusStream to replace a managed Kafka bill that had grown larger than the rest of the infrastructure, and it has run that platform's production traffic since May 2026. This is the vendor-neutral rebuild: cold storage behind one interface so it works with a directory or any S3-compatible store rather than only AWS, and configuration that assumes nothing about where you deploy. |
| RAG-OS | A blueprint, not an application. It describes how to build a self-hosted personal AI operating system: a zero-context kernel that stays running, a git-Markdown knowledge base you can audit, and dispatch of coding tasks across your own repositories. You paste it into a coding agent and it builds the system with you, stopping to ask at every design fork. Ships a runnable stdlib-only starter for the first two milestones. |
| harness-tuner | Measures the scaffolding around a model rather than the model: where a harness re-reads what it already read, loops without erroring, misses its own prompt cache, or grows context until the last step of a task costs more than the first. It reads any harness through an adapter that your own coding agent writes, so nobody's product is named in the code. Then it does the part that makes it worth running twice. It proposes a change, and after you apply it, it re-runs the identical task set and tells you whether that helped, using an anytime-valid test so watching the running comparison does not quietly invalidate it. It publishes no numbers about anybody's harness and has no leaderboard, by design. |
| org-memory-os | The organizational sibling of RAG-OS: one shared, permission-aware, auditable AI memory that any number of employees use through their own agents, with the same compaction and degradation-avoidance discipline underneath. It keeps the parts of the personal design that survive a crowd and replaces the parts that do not, the single writer, the one trusted operator, and never-delete, which is right for knowledge and illegal for personal data the moment erasure applies. Thirteen pillars, seventy decision forks left open, and no starter by design. |
The first two are a pair. One decides what a contract is worth; the other runs and grades strategies that act on that kind of judgment.
RAG-OS and org-memory-os are the odd pair, and they are meant to be. The rest of this list is software I built; those two are the architecture written down, with the decision forks left open instead of resolved for you. One is the always-on agent I run on my own hardware; the other is what it has to become before a whole organization can share it without leaking it, poisoning it, or letting it rot. Both came out of collecting the failure modes worth designing against rather than guessing at them.
The two AI memory systems above are the work I am proudest of, for the same reason as everything else here: what makes them worth anything is the apparatus that would catch them lying.
- In RAG-OS, security enforcement that is proven to fire rather than trusted. The agent framework's own permission callback silently never ran, so a fenced read leaked; enforcement moved to a hook that fires on every single tool call, and a nightly canary re-proves the crown-jewel paths still deny by reading a real denial back out of the audit log.
- In org-memory-os, a cross-tenant isolation canary that is a hard release gate. Every night it plants a unique marker in each team's memory and tries to read another team's marker back through the real retrieval path, and it fails the build if that ever succeeds. A shared index is a design claim until that probe turns it into a measured fact.
- A test in the trading repo that spawns a clean subprocess and asserts no order-placement code is even reachable from the live decision path. Inside a shared test process the same check would pass vacuously, and the test says so in a comment.
- A scraper that decides whether a column is basis or net change by testing it against the arithmetic identity basis == cash - futures across the rows, instead of guessing from the header text.
- An implied volatility solver that returns nothing when there is no root, rather than a plausible-looking lie.
- A struggle detector that hydrates prior session history before running its rules, because the patterns worth catching play out across several requests and a batch-local detector would quietly only ever fire the easy ones.
- In harness-tuner, a check that every configuration setting is read by something. The first time it ran it found twelve of twenty-nine settings that were accepted, type-checked, validated against their legal values, documented, written into the run artifacts, and read by absolutely nothing. Each one looked finished from every angle except the one that mattered. The same project reports an unobserved value as unavailable rather than as zero, because a harness whose adapter cannot see caching would otherwise look identical to one that caches nothing, and the tool would have invented a finding out of a gap in its own instrumentation.
- A consumer-group test in the broker that asserts an exact record count rather than a minimum. Generalizing that broker from one in-house consumer to anybody's turned up a coordinator that settled each rebalance on the first member to arrive, so every member of a group was assigned every partition and received every record twice. Nothing errored and nothing hung. A test written as "at least twenty" would have passed, which is roughly how the bug survived in the first place.
Every repo here is MIT licensed and has a short landing page under its About link. Where a repo has a test suite, the count in its badge comes from CI rather than from me. Where something is unfinished or unproven, the README says so.
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