Chester Corin

Engineering elegant solutions to everyday challenges.

About Me

I don't just write code or manage engineering teams. I build software that solves real business problems.

I'm a Senior Software Engineer with experience across the full SaaS lifecycle: taking ideas from early ambiguity to launch, scaling products as customer needs grow, and improving the systems, teams, and processes that support long-term product health.

Approach

My work has often sat at the intersection of engineering, product, and leadership. I like being close to the customer problem, understanding why something needs to be built, and then shaping the technical path to make it real.

That usually means front-loading clarity around requirements, architecture, risks, and tradeoffs before moving quickly through implementation. The goal is practical delivery: software that is small enough to ship, robust enough to trust, and clear enough for a team to own over time.

Experience

Throughout my career I've worked through different stages of product growth:

  • 0-1 product development: building new SaaS products from the ground up.
  • Product-minded engineering: translating customer feedback and business needs into practical software decisions.
  • Technical leadership: mentoring engineers, improving team practices, and creating clarity around delivery.
  • Engineering maturity: strengthening testing, CI/CD, code standards, architecture, and production reliability.
  • Data and scale: designing systems that can grow with increasing operational complexity.

Thoughts on AI

I'm investing heavily in agentic coding and the tooling around it, especially how agents can be used effectively and efficiently across the software delivery lifecycle.

For me, agentic development is not about replacing engineering judgment. It is about creating leverage while keeping a human responsible for ownership, context, and final decisions. Agents are most useful when work is front-loaded with clear specs, requirements, architecture, acceptance criteria, and review expectations.

Used well, agents can accelerate implementation, documentation, QA, operational support, and much of the review loop. The accountability still belongs with the engineer and the team using those tools.

Current Focus

I'm currently building software in the health-tech sector, where reliability, privacy, workflow design, and real-world usability matter deeply. Alongside that work, I'm focused on how agentic development can make software delivery faster and more rigorous: clearer planning, tighter feedback loops, automated review support, stronger QA, and better documentation while keeping a human engineer accountable for the outcome.

Recent Projects

Agentic Health

Challenge

Automating sensitive personal healthcare workflows without compromising data privacy through external cloud-based AI providers.

Solution

Architected a fully on-premise AI cluster. The decentralized setup leverages distributed inference across local hardware to securely process private health data and orchestrate intelligent automated workflows, completely removing cloud dependency.

Local AIDistributed InferenceAutomationHealthcare

Vantage

View Vantage
Challenge

Football outcomes are inherently difficult to forecast. Form fluctuates, context matters, and moving between competitions introduces new calibration problems that a single signal cannot handle reliably.

Solution

A football prediction engine that blends three independent probability models (XGBoost, ELO Ratings, and Market Consensus) into calibrated forecasts. The engine is used for making predictions for the Premier League season and the World Cup, with every forecast recorded in a versioned ledger and settled once results are available.

PythonMachine LearningXGBoostForecasting

Sports Vision

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Challenge

Raw broadcast video is unstructured and time-consuming to analyze manually. Coaches and analysts spend hours scrubbing through footage, tagging players, and estimating positions.

Solution

An automated pipeline that ingests standard broadcast video, detects and tracks every player and the ball, classifies players into teams, and renders annotated output with trajectories and stats overlays.

Computer VisionPythonSports Analytics

Purple Cadence

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Challenge

Combating the overwhelming urge to "do it all" at once, which often leads to burnout and a loss of focus on the daily process.

Solution

A privacy-first iOS application that acts as an intelligent life coach. By utilizing Apple's local Foundational Models (CoreML), it offers personalized guidance to help users find their rhythm, ensuring all sensitive reflections and data remain strictly on-device. The project is currently serving as an active testing ground for advanced agentic AI coding workflows.

React NativeExpoTypeScriptSwiftCoreMLApple IntelligenceMMKVAgentic AI

Flo

Challenge

Evolving the passive "rubber duck debugging" technique into an interactive experience that physically resides on the desktop.

Solution

A 3D-printed, Raspberry Pi-powered companion that has grown from a simple weather widget into an LLM-integrated assistant. Flo listens to spoken problem descriptions and replies with randomized or context-aware questions, making the debugging process a true conversation.

PythonRaspberry PiLLM3D PrintingIoT