ML, Biotech, Hardware, and Coordination Problems. Sometimes I write about hard problems and how to solve them.
LLM Leaderboard 2026 — Compare Top AI Models
Compare the latest LLM benchmarks for GPT, Claude, Gemini and more. Updated rankings across reasoning, coding, math, and multilingual tasks with pricing and speed data.
How LLMs are and are not like the brain
Hi from buttondown! At the bottom of this newsletter is a bit of administrivia about the new platform How LLMs are and are not like the brain Beneath all the...
Solving a Million-Step LLM Task with Zero Errors
LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans,...
The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities (Version 1.0)
AI Model Leaderboards & Benchmarks
Explore leaderboards with expert-driven LLM benchmarks and updated AI model rankings across coding, reasoning and more.
LLM Rankings | OpenRouter
LLM rankings and AI leaderboard based on benchmarks and real usage data from millions of users. See which AI models developers actually use.
LLM Leaderboard - Comparison of over 100 AI models from OpenAI, Google, DeepSeek & others
Comparison and ranking the performance of over 100 AI models (LLMs) across key metrics including intelligence, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others.
Why do LLMs make stuff up? New research peers under the hood.
Claude's faulty "known entity" neurons sometimes override its "don't answer" circuitry.
AI Leaderboard 2026: Compare & Rank 300+ Top AI Models by Intelligence, Speed & Price
The AI Leaderboard — independent rankings of GPT, Claude, Gemini, Llama, DeepSeek and 300+ AI models by intelligence, speed and price. Composite LLM Stats Score updated continuously from public benchmarks and live API metrics.
Cognitive exponents and LLM leverage
I know a few people for whom LLMs have been a near-immediate multiplier of attention and effort. I know a lot for whom LLMs clearly make them worse at thinking and doing things. So: why?
Artificial
An LLM is a computer program. We should talk about it like a computer program.
Artificial
An LLM is a computer program. We should talk about it like a computer program.
Harnessing Frustration: Using LLMs to Overcome Activation Energy
One of my biggest weaknesses as a software engineer is procrastination when facing a new project. When the scope is unclear, I have a tendency to wait until I feel I’ve “felt out” the problem to start doing anything. I know I’ll feel better and work much faster when I get “stuck in” but I still struggle with that first step, overcoming the “activation energy” required to engage with the details. LLMs have been a game-changer for me in this respect: I can just throw a couple of sentences at them with the shape of the problem. This leads to one of two outcomes: The LLM comes up with a good solution, usually in a slightly different way than what I was thinking. I realize “oh wow the solution is much simpler than I thought”. Straight away I start thinking about the consequences of implementing and improving what the LLM suggested. The LLM comes up with a solution that I intuitively recognize as “wrong”. My immediate reaction is frustration (“How could it get it so wrong”) which leads me to go back and forth with the model, explaining to it why its solution could not possibly work. But in the process of arguing with the model, my brain is churning away and generating variations or different approaches that could work. After a while, even if the AI is still on the wrong track, the debate will trigger a moment of inspiration where suddenly the solution will come to me. I’ll excitedly start up a new conversation and start working through it with the model. The key is the emotional reaction I have immediately to the LLM’s response, either excitement or frustration. By harnessing this immediate feedback loop, I get my brain out of its passive, procrastination mode. It’s almost like a jolt: either I’m thrilled because it’s simpler than I thought, or I’m spurred to action by the urge to correct a perceived ‘wrong’ answer. This forces me to engage with the problem in a meaningful way.
Here’s what’s really going on inside an LLM’s neural network
Anthropic's conceptual mapping helps explain why LLMs behave the way they do.
