Hi, I'm Anthony.
Senior Staff Software Engineer · The Hartford
That means the shared tools and automation engineers rely on to ship software — faster, safer, and more consistently. Think of it as the factory, not the products that roll off it. Increasingly, with AI baked right in.
Anthony Brignano
Connecticut
I design and scale the systems big engineering teams build on — easy to operate, hard to break, and built to grow with the teams that use them.
In practice, that means taking the slow, repetitive, mistake-prone parts of building software and making them automatic and safe — the behind-the-scenes plumbing most people never see but every engineer relies on. Increasingly, that means building AI directly into those everyday tools.
Beyond the platform, I lead our enterprise hackathons, mentor emerging leaders as a rotation manager and career coach in the company's Leadership Development Program, and serve on the Central Connecticut State University (CCSU) Computer Science Industry Advisory Board.
You'll usually find me outside: climbing, snowboarding through the trees, hiking somewhere with a view, or out with my camera.
Climbing
Snowboarding
Hiking
Traveling
Photography
ExploringNew to the jargon? Tap any tag to see what it means in plain English.
One place for 4,000+ engineers to build
Before, everyone set up their tools differently — slow and easy to break. Now it's one shared, consistent setup.
1,000+ engineers chose it in the first 90 days
New tools inside big companies usually get ignored. This one caught on fast.
One command builds any project
The same result on every laptop, with no fiddly setup to do per project. Remote builds on platform compute are next.
The platform spots where engineers get stuck — live
So we fix the real slow-downs instead of guessing. AI assistants can read it too.
Now building
The problem, the constraints, what I built, and what changed. Nothing stated that isn't documented and current.
Case study · Developer platform
The Hartford's developer platform is a CLI, a desktop agent and IDE extensions that put onboarding, builds, policy, telemetry and AI tooling in one place for 4,000+ engineers. The next step is one build and pipeline system in place of seven.
Case study · Developer experience
A CEO-sponsored initiative to make a new engineer's setup at The Hartford self-service. A laptop now arrives with the platform on it and goes from unboxed to building in hours, against a 40+ day baseline from hire to first commit.
Case study · Source control
Every active repository at The Hartford moved from a self-hosted GitHub Enterprise Server to GitHub Enterprise Cloud, in waves ordered by impact, with thousands of engineers shipping the whole time.
Airbnb hosts upload their CSV exports and get occupancy, booking trends and earnings back as charts. Every file is parsed in the browser — no account, no upload, and no server that could leak a year of someone's bookings.
A versioned architecture model that lives in git and gets checked continuously against real infrastructure. When the model and reality diverge, the divergence arrives as a pull request instead of as a diagram nobody trusts. It also draws a Terraform plan on its pull request, before anything is applied.
Step-by-step help on any website, for anyone — a developer picking up a new tool, a team onboarding new hires, or someone learning a computer for the first time. A Chrome extension highlights each thing to click and says in plain words what to do. Record a guide by doing the task once, share it with a link, or browse the guide library. App teams can embed the same engine and check every guide in CI.
I'm a technical lead on The Hartford's developer platform — the one place 4,000+ engineers go to build, ship, and run their software, on its way to the full 8,000-engineer organization.
I make a lot of the day-to-day technical calls, and I keep the platform running, scaling, and getting better while new capabilities come online alongside it. The goal: automatic, reliable, and secure by default — so engineers hit fewer roadblocks and spend more time on the work that matters.
Under the hood: a real-time event pipeline (AWS, DynamoDB, Snowflake) with ML analysis of developer pain points; observability through Splunk and Dynatrace; and an end-to-end CI/CD and DevSecOps toolchain — Jenkins, GitHub Actions, Harness, AWS CodePipeline, Nexus, SonarQube, Checkmarx — supporting 95,000+ pipelines, with a unified pipeline model designed to consolidate them onto platform tooling. AI features ship as MCP-based skills.
Prior: migrated 10,000+ code repositories to GitHub Enterprise Cloud (case study); recognized with the 2023 Enterprise Tech Data & Cyber Award for the enterprise GitHub Actions implementation.
For my full career history, see my resume.
For more detailed information, see my coding activity.
Whether you want to talk shop about platforms, developer experience, or AI, swap climbing and snowboarding stories, or just say hi, I'd love to hear from you.
