An offline static archive reading roadmap of stng.substack.com by Adam Chlipala
The foundational principles tying correctness, efficiency, and holistic design together.
Start here to understand the core thesis of the blog. This outline frames the rest of the articles as a full-stack journey from system optimization to societal structures.
Why we should simplify hard engineering problems by redesigning the environments around them.
Explore how we can circumvent complex AI challenges (like computer vision or natural language parsing) by building dedication into our physical layouts, programming interfaces, and linguistic structures.
And how should we think about whether we want AI systems to have it?
Preparing to design an AI-powered substitute
The benefits of designing the parts of a system together
And how can we protect our AI systems from those challenges?
A clear case where the power of simplification is in our hands
Sometimes all it takes is changing the world.
Encapsulating intelligence away from avoidable complexity
Ensuring mathematical safety to unlock recursive improvement and rapid iteration.
Learn how formal mathematical verification shifts quality feedback loops earlier, allowing compilers to self-improve safely and enabling distrusting AI agents to instantly share verified code modules.
Thinking about it as a distributed protocol
Watch out for hard problems and expensive solutions
How to accelerate evolutionary search while building confidence in its results
First musings on differences in "hiring" AI to develop better ideas
A just-right challenge problem in Al alignment
Enabling safe code-sharing among agents
Why deep neural networks fall short and how symbolic methods solve trust and latency.
An analysis of deep learning's limitations—specifically its lack of correctness guarantees and high latency—and why combining symbolic verification with search processes is the key to trustworthy AI.
The value of finding prior art
Multiplicative compounding of latency with generative AI
Deep learning's disadvantage compared to an unpopular cousin
A counterintuitive advantage for the good guys
Why we don't need to worry about enumerating possible attacks
How shifting from writing code to writing specifications reshapes our tools and economics.
Investigate the practical outcomes of this paradigm shift: how requirements simplify, how user interfaces transform, and how low-level systems languages must adapt to compile AI-synthesized code.
Abstraction boundaries pay off
Why AI will have an easier time keeping itself happy
AI coding tools are facing a major handicap in using popular languages.
Dissecting the benefits of different kinds of decentralization