# Steve Rawlinson: Independent Systems Lab

Source: https://rawlinson.ca

"I build AI systems, agents, and cloud infrastructure. I take them apart, measure what matters, and publish what I learn."

Steve Rawlinson is a software engineer, technical lead, and independent AI systems researcher in Ottawa, Ontario, Canada, with 20+ years in software. Steve currently works at Amazon Web Services as a Senior Delivery Consultant / Technical Lead.

## Pages

- [About](https://rawlinson.ca/about.md): background, experience, and contact details
- [Open source](https://rawlinson.ca/open-source.md): projects and benchmark code
- [Writing](https://rawlinson.ca/articles.md): all articles
- [Contact](https://rawlinson.ca/contact.md)

## Open-source projects

- [Offload](https://github.com/srawlin/offload): Runs coding agents' builds and tests on a warm remote Linux node. It ships only the files that changed, keeps one warm workspace per agent, and streams the output back as if the command had run locally.
- [Changeward](https://changeward.com/): A change-control layer between AI agents and your databases, clusters and infrastructure. Every change is dry-run against the real system, checked against policy, applied exactly as approved, and kept undoable.
- [Overclock](https://github.com/srawlin/overclock): A minimal coding-agent harness built on pi, around Cerebras inference. Exploring the cost of context, tools, and orchestration when generating tokens is no longer the slow part.

## Latest writing

- [Introducing Offload: run your coding agents' builds and tests on a warm remote machine](https://rawlinson.ca/articles/introducing-offload.md) (2026-10-25, Release / Open source): When three agents run their tests at once on a laptop, every run takes three times as long. Offload sends each build and test to a warm remote node and streams the output back, up to 12x faster.
- [Introducing Changeward: let AI agents change real systems, safely](https://rawlinson.ca/articles/introducing-changeward.md) (2026-10-25, Release / Open source): An open-source change-control layer between AI agents and your databases, clusters and infrastructure. Every change is previewed exactly, checked against policy, and kept undoable.
- [Web search for LLM agents: 16 options benchmarked on latency, cost, and accuracy](https://rawlinson.ca/articles/search-api-bakeoff.md) (2026-10-12, Experiment / Search APIs): Which search is best when someone is waiting for you to speak? Search APIs plus your own model vs. the built-in search tools from Anthropic, OpenAI, Google and Perplexity: 320 calls timed, priced and rated blind.
- [What I Learned Using Coding Agents](https://rawlinson.ca/articles/what-i-learned-coding-agents.md) (2026-10-02, Field notes / Coding agents): What real work with Claude Code, Codex, Devin, Muse, pi, and OpenCode taught me about the harness around the model.
- [Systematic Framework for vLLM Inference Optimization](https://rawlinson.ca/articles/vLLM-inference-optimization.md) (2025-12-14, Field guide / Inference): Large language model inference gets expensive fast and it's not always clear where or how to optimize without losing accuracy. This guide lays out a systematic, high-ROI framework for vLLM inference optimization, starting with the biggest cost and throughput wins before diving into lower-level tuning. It's written for engineers running vLLM in production who care about latency, throughput, and $/token.
- [vLLM vs SGLang: Performance Benchmark on Dual H100 GPUs](https://rawlinson.ca/articles/vllm-vs-sglang-performance-benchmark-h100.md) (2025-12-05, Experiment / Benchmark): Comprehensive performance comparison of vLLM and SGLang on 2× NVIDIA H100 GPUs. Testing latency, throughput, cache efficiency, and production workloads with 5,980 requests across 80 benchmarks. Results show SGLang dominates low concurrency (3.7× faster), while vLLM excels at scale.
- [The Evolving Landscape of Software Engineering Teams](https://rawlinson.ca/articles/evolving-landscape-of-software-engineering-teams.md) (2025-05-14, Essay / Engineering teams): As AI tools automate coding tasks, the role of software engineers is shifting toward product vision and strategic thinking. Here's how to build effective teams for this new era.
- [Nine Things That Surprise Me About LLMs](https://rawlinson.ca/articles/nine-surprising-things-about-llms.md) (2025-05-14, Observations / Language models): From emergent behaviors to rapidly growing context windows, discover surprising insights about large language models that might change your perspective.

## Contact

- Website: https://rawlinson.ca
- Email: steve@rawlinson.ca
- GitHub: https://github.com/srawlin
- LinkedIn: https://linkedin.com/in/srawlinson
- Résumé (PDF): https://rawlinson.ca/steve-rawlinson-resume.pdf