Journal
Posts and long-form series on AI, startups, venture capital, and more.
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TRL 6-9: From Prototype to Operational System
Explore TRLs 6-9: from prototype demonstration in relevant environments (TRL 6) through qualification (TRL 8) to proven operational deployment (TRL 9).
PyGeoHash 2.1.0: Modernizing a Geospatial Python Library
A look at the latest updates to PyGeoHash, a lightweight Python library for working with geohashes, and how modern AI tools helped revitalize the project.
China's AI Regulatory Framework: A Comprehensive Analysis
Explore China's comprehensive approach to AI regulation, covering key measures for generative AI, algorithmic recommendations, and deep synthesis technology.
Geohash: When Clever Isn't Always Smart
A deep dive into the limitations of the Geohash algorithm, including boundary issues, non-uniform cell sizes, and common implementation mistakes to avoid.
Where Did All the RAM Go? Memory Profiling with Memray
High CPU isn''t the only performance issue. Learn how Memray helps track memory leaks and excessive allocation in your Python library to optimize usage.
TRL 4-5: Laboratory and Relevant Environment Validation
Deep dive into TRL 4-5. Understand laboratory validation (TRL 4) and the crucial transition to relevant environment testing (TRL 5) for tech maturity.
How to Get a Crosswalk Installed in Atlanta: A Practical Guide
A step-by-step guide for Atlanta residents on how to advocate for and get a new crosswalk installed in their neighborhood
Claude 3.7 and new Cursor: first impressions
First impressions of Anthropic's Claude 3.7 model and the latest Cursor AI coding assistant update. Exploring improved reasoning, speed, and new features.
Experiential Learning: Lessons from Battle-Tested Leaders
How hands-on experience and real-world challenges shape effective leaders, drawing insights from military leadership development approaches
Finding the Slowdown: Profiling Python Code with Pyinstrument
Your benchmark says a function is slow, but why? Profilers like Pyinstrument help you pinpoint exactly where your Python code is spending its time.