Closed-Loop Optimization in Laboratory Automation
Building systems that learn from experimental results and automatically improve processes—covering optimization algorithms, feedback architectures, and practical implementation patterns.
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Software Engineering 15 posts
Instrument Agents: AI Systems That Control Lab Equipment
Designing AI agents that can understand, plan, and execute laboratory instrument operations—covering agent architectures, tool abstraction patterns, and safety boundaries.
Text-to-Protocol: From Natural Language to Executable Lab Procedures
Designing systems that convert natural language instructions into structured, validated laboratory protocols—covering representation formats, LLM pipelines, and safety verification.
AI in Laboratory Automation: Current State, Limitations, and the Path Forward
A comprehensive look at where AI stands in lab automation today—the promising advances, the persistent challenges, and the gap between research demos and production-ready systems.
Building AI-Ready Data Infrastructure in Industrial Software
A practical guide to collecting, structuring, and leveraging data from distributed industrial systems—where each PC runs different environments and logs are your only starting point.
Large Language Models in Lab Automation: From Natural Language to Robot Control
Exploring how LLMs are transforming laboratory automation—from interpreting human commands to orchestrating robotic workflows—and the practical considerations for deployment in air-gapped environments.
Bringing Vision-Language Models to Lab Automation: Challenges and Possibilities
Exploring how VLMs could transform laboratory automation, and the practical constraints of deploying AI in air-gapped industrial environments.
Implementing AI Services in Offline Industrial Environments
A practical guide to deploying AI capabilities on air-gapped systems—from local inference engines to edge-optimized models and hybrid architectures.
Clean Code Part 4: Concurrency and Refactoring
Advanced clean code topics covering concurrent programming, successive refinement through real-world examples, and the comprehensive list of code smells and heuristics.
Clean Code Part 3: Testing and Class Design
Advanced clean code concepts covering boundaries with third-party code, unit testing best practices, class design principles, and system architecture.
Clean Code Part 2: Structure and Formatting
Code organization principles from Clean Code. Covers formatting rules, objects vs data structures, and robust error handling strategies.
SDP: Software Development Process - OMSCS 2025 Fall
Key software engineering concepts from Georgia Tech OMSCS SDP (CS 6300): lifecycle models, UML, testing, Android development, and team collaboration.
Clean Code Part 1: The Fundamentals
Core principles of clean code from Robert C. Martin's classic book. Covers what clean code means, naming conventions, function design, and the proper use of comments.
Lessons from Integrating Hardware SDKs in Industrial Robotics
Practical considerations and patterns learned from working with vendor-provided SDKs—both .NET assemblies and COM components—in laboratory automation.
Why Some Industrial Software Can't Use the Cloud
Reflections on building software in air-gapped environments where AWS, Kubernetes, and modern cloud infrastructure simply aren't an option.