Philosophically, I am a proponent of AI Vegetarianism. In that light, this blog is changing. All posts from now on will be written by me with no AI assistance, even for the complicated faff that is things like proper LaTeX-style mathematical notation. Use AI cautiously, or ideally, not at all. You can thank me later.
How I decide when AI belongs in my writing and coding workflow, what I measure when I use agents, and where I refuse the tool so learning and quality stay mine.
I grew up thinking reading meant screenshotting pages with my eyes, built rigid coping systems to manage ADHD, dyslexia, and possible ASD, and that same obsession with how minds process information eventually drew me toward building models from first principles.
A practical guide to choosing hardware for running local LLMs. Covers GPU rankings, pre-built vs DIY tradeoffs, and what you actually need for different model sizes. Includes an open-source tool for hardware comparison and benchmarking.
A day of holiday book shopping leads to a reflection on where intelligence comes from. After conversations with family, the author lands on curiosity, skepticism, and attention as the pillars of how intelligence is proffered, with motivation as a multiplier.
Introduces the author, a self-taught engineer turned startup founder in San Francisco, and his philosophy on AI tools as productivity amplifiers that need clear attribution. He built attest.97115104.com, a free badge generation service for marking content as human-generated or AI-assisted.