I build backend systems that people can actually run.
A Rust WebRTC server, a Rust CLI with release binaries on three platforms, and a Python package on PyPI, all built end to end.
- 🦀 Wrote a WebRTC SFU and an RTMP-to-HLS ingest server in Rust, part of an 8-service social platform.
- 📈 Built a data cleaner that raised model accuracy by +8.14 points on average across 15 benchmark runs and cut training time by ~49%.
- 📦 Ship things people can install: release binaries for Linux, macOS and Windows, and a package on PyPI.
- 🧪 Tested, documented and runnable by someone else, because code nobody can run isn't finished.
Each project has an architecture walkthrough and the design decisions behind it on my portfolio.
🎥 escld: real-time video calls and live streaming
A social platform with posts, follows and moderation. It also has WebRTC video calls (screen share, in-call chat, recording) and RTMP live streaming delivered as HLS.
- Hardest part: routing live media between callers. I wrote the SFU (Selective Forwarding Unit) in Rust rather than using a hosted service.
- 8 services: React SPA, Spring Boot API, two Rust services, three background workers, and 20 AWS CDK stacks as infrastructure code.
- Data: PostgreSQL, five DynamoDB single-table designs, Redis and Elasticsearch.
- Designed and tested locally. The whole system starts with one command:
docker compose up -d.
Rust Java / Spring Boot TypeScript / React WebRTC AWS CDK
📊 buffdata: better training data means better models
A Python CLI and SDK that validates, deduplicates, PII-redacts and LLM-scores datasets before training. It works with OpenAI, Anthropic, Gemini or a local model. For managed runs, a Rust worker claims jobs from the queue and supervises each run's process.
| Benchmark (40% duplicated, 10% empty rows) | Before | After |
|---|---|---|
| DBpedia-14 | 86.97% | 96.68% |
| Emotion | 71.23% | 83.77% |
| AG News, 100k rows | 83.39% | 89.42% |
| Training time, AG News 100k | 80.2 s | 42.3 s |
On already-clean data, accuracy stays within ±0.10 points, so it doesn't damage good datasets.
Python Rust LLM APIs Data quality
🔍 formwatch: finds out why people give up on government forms
A Rust CLI that drives a real browser through public-service forms (permits, benefits, license renewals). It catches broken submissions, accessibility failures, unusable mobile layouts and forms that lose what you typed.
formwatch test https://city.gov/apply
formwatch report --html
v0.1.0 released with binaries for Linux, macOS (Intel and Apple Silicon) and Windows.
Rust Chromium Accessibility CLI
I'm self-taught and learned by building real projects instead of tutorials. For every project, I:
- Start with the hard problem. I pick the part most people would outsource, like media routing or dataset contamination, and build it myself.
- Measure it. I don't claim a result until a benchmark or a test backs it up.
- Make it runnable. One-command setup, CI, release binaries and docs, so someone else can use it without asking me.
Hiring for a remote backend role? I'd like to hear about it.
📫 salihyilboga98@gmail.com · 🌐 portfolio.voidstackloop.com