// posts
The ideas I share on LinkedIn — abstraction layers, business cases, debugging black boxes, and the discipline of building on non-deterministic systems. Published here as a durable archive.
// 6 posts
Every abstraction layer in computing let you drop down and look when things broke. AI is the first layer where you can't. The bugs are non-deterministic by design.
Everyone keeps saying AI will reduce the number of developers. After months of working with these tools, I think they're looking at it wrong. AI reduces the cost of shipping a feature with the same team.
If AI is a black box you can't debug, how do you trust it in production? Honest answer: you don't. Not for everything. Use AI where the output is inspectable.
I asked Claude directly: 'Why should I use you?' And it gave me the clearest framing I've heard. The history of computing is a story of rising abstraction layers.
Like AI models, our biological neural networks require constant fine-tuning. When we skip the struggle, our synaptic connections don't strengthen.
I ran 1,500+ tests across 7 small open-source models. Forcing JSON Mode can backfire. A 2B model beat one 4× its size with the right guidance.