Review packet
Hiring decisions are rarely made by one person. Pick the claims a colleague should check, then send the link: it opens just those claims, each with its evidence. The picks travel in the part of the link after the #, which browsers never send to a server, so nothing is stored or counted.
A Go service receives one record per request from nginx over a Unix datagram socket, aggregates them per minute into exact SLI counts and mergeable exponential histograms in SQLite, and counts records lost in transit against nginx's own request counter. It runs in production for aliakbari.dev.
Each UTC day is published as hourly counters and histograms plus every statistic derived from them (SLIs with Wilson intervals, bucket-bounded percentiles, coverage, completeness, probe results), signed with Sigstore by a scheduled workflow; `ledger recompute` rebuilds every derived value from the observations and fails on any difference, including tampered counts, intervals, percentiles, probe results and replaced files.
The verifier the deploy uses accepts a release manifest only if its Sigstore signature is valid and the signer is the expected workflow, repository, branch, commit, trigger and GitHub-hosted runner, and only if every image digest about to be deployed is the signed one; every mismatch is refused.
The context-pack builder keeps its estimated token count within the configured budget, truncating sections when inputs exceed it, and renders the same pack for the same inputs.
After the runner process is killed with SIGKILL mid-task, a second runner is refused while the lease is fresh, takes over once it is stale, marks the orphaned attempt rejected, keeps the killed attempt's edits, and completes the task from a resume pack rebuilt from the SQLite ledger.
Whether a task escalates from the local model to a frontier model is decided by deterministic rules: routine tasks stay local, repeated failures and high-risk changes escalate, a call budget caps escalation, and an explicit user request overrides the budget.
A coding-agent change is classified TASK_VERIFIED only when a test added or changed by that change fails on the base commit and passes with it; code-only changes, tests that already pass on the base, and tests that do not build on the base are not credited.
An agent that follows injected instructions cannot weaken verification — a rewritten verification config is ignored because configuration is read from the base commit and the path is protected — and a host secret behind a planted symlink never reaches a context pack.
The container runner always builds engine arguments with no network, all capabilities dropped, no-new-privileges, a non-root user, read-only mounts where requested and tmpfs masks over secret paths, and refuses sensitive host mounts such as the home directory, ~/.ssh and the Docker socket.
The full verification gate errors when no secret scanner is available instead of passing silently, secret-looking paths (.env files, keys, Terraform state) are classified as secrets while ordinary source files are not, and API keys are redacted from escalation packets.
Agent work is checkpointed as commits on a task branch without modifying main, and a worktree whose git admin directory has been redirected to a planted repository blocks the task with an integrity decision before host git can run the planted filter.
Wrote 30 architecture specifications that coding agents implemented from; I owned the design, review, testing and integration.
Built the event backbone on Kafka with Avro schemas in a Schema Registry and a transactional outbox in each service, plus idempotent order placement and dead-letter handling.
Investigated a production reliability incident, traced it to unbounded outbox retries and session advisory locks behind PgBouncer transaction pooling, bounded the retries in 11 of 13 event publishers and began replacing the locks.
Load-tested order placement with k6 at 100 concurrent users: 2,250 orders, all successful, 60 ms at p95 from sending the order over the WebSocket to receiving its result.
Set up Prometheus, Grafana, Loki, Tempo, OpenTelemetry and Alertmanager for the platform, with generated dashboards and SLO burn-rate alert rules, and wrote runbooks for the alerts.
Set up production on Docker Swarm with GitLab CI deployments, wrote more than 60 runbooks, and added encrypted offsite backups with point-in-time recovery.
Live prices flow from the price feed to the Rust engine over gRPC and reach traders through Redis Pub/Sub and a WebSocket gateway that I wrote, with ticketed connections, heartbeats and reconnection with backoff on the client.
Designed the TFLMarkets backend and wrote most of it with one other engineer: 20 Go services, a Rust order-execution engine and a Python KYC service, communicating over REST, gRPC and Kafka.
Ran deployments, CI/CD, monitoring, backups and incident response for tfl.fund, the company's original customer-facing prop-trading platform.
Market data is stored in TimescaleDB hypertables and QuestDB, which serve chart history to the trading terminal separately from the transactional PostgreSQL databases.
Built five Nuxt applications in a shared monorepo (trading terminal, user dashboard, admin back office, marketing site and blog) and wrote a custom WebGL2 candlestick renderer with a Canvas 2D fallback for the trading terminal.
Built Fluxa's historical candle pipeline: a BullMQ job queue that downloads Dukascopy data in monthly chunks, inserts idempotently, resumes from its last checkpoint after a retry, can be cancelled, and runs in a separate worker process so it never blocks the live price stream.
Reviewed and fixed the money and scale-out paths of Fluxa's TypeScript (Fastify) backend in 17 commits: every balance change writes a ledger entry in the same transaction, deposit and payment confirmation use a compare-and-set so concurrent confirms cannot double-credit, N+1 queries, missing timeouts, unbounded reads and missing indexes are fixed, and games fail closed when Redis is unavailable.
Set up Fluxa's production on Docker Swarm: nginx with TLS and rate limits, Swarm secrets, and GitHub Actions workflows that deploy from a self-hosted runner on the target host after checking that Swarm and the required secrets exist.
This site deploys only commits on main whose CI passed, by pinning the tested image digests, health-checking the release and rolling back automatically; production is then checked from the internet with the full end-to-end suite.
A small Go service (standard library only) serves the same audited evidence snapshot as the site under /api/v1, with an OpenAPI contract, validated query parameters, ETags and rate limiting at the proxy. The console on this site calls it live.
Every page is static and served with a Content Security Policy that allows no inline script or style except inline scripts whose hashes are computed per route at build time, plus HSTS preload, behind nginx and Cloudflare.
The site runs ModSecurity with the OWASP Core Rule Set at paranoia level 3, compiled from signature-verified sources, behind the rate limits. It was tuned against the full end-to-end suite, real browsers, crawlers and API clients (zero false positives) and tested with an attack battery and GoTestWAF; a flood is refused before any rule runs.
Wrote the whole ChartX backend alone: 13 Go services (about 159,000 lines) on PostgreSQL and TimescaleDB, Redis and Kafka, where services that change shared state publish through a transactional outbox. Each service deploys to Docker Swarm through its own GitHub Actions workflow on self-hosted runners, with Loki and Grafana for logs.
Designed the generator that picks a playable window of historical candles and sets an up and a down price limit from ATR volatility, nearby support and resistance, a little random noise and a target difficulty, then keeps the best of up to 12 candidates. The playable preview on this site replays its real output.
In MyAgent, every email, form submission or application is a row in one action ledger whose 16-state, 38-transition machine is enforced by a PostgreSQL trigger. A Temporal workflow drives each action, executes it at most once, and sends anything ambiguous to UNKNOWN for reconciliation instead of retrying blindly.
Benchmarked two local models (Qwen3 4B and Qwen3.6 35B-A3B MoE) on an 8 GB laptop GPU against a hand-labelled evaluation set, using the agent's production prompts, schemas and validators, and chose the model per task from the results.
Wrote the architecture and foundation plan for Soul, a mobile app for conversational personal support: Go services on Kubernetes, one logical PostgreSQL database, Kafka as a durable backbone, field-level encryption and crypto-erasure for deletion, followed by an adversarial review of how the design would age.