Fantastic video by Hannah Fry, my favourite mathematician and broadcaster, who along with software engineer Brendan built an AI agent called Cass, gave it a bank card, and let it loose for two weeks to see what it would do.

I used SolveIt to get me through trying to understand the latest 3blue1brown video Reinventing Entropy | Compression is Intelligence Part 1. This is my "raw" dialog of close reading/watching/listening.

I find SolveIt as valuable for writing and clarifying my thoughts as it is for coding. Just as I find SolveIt makes coding more enjoyable, it does the same for writing. I often learn along the way or make connections between concepts I would not have otherwise.

For me, writing with SolveIt is not about asking an LLM to generate text for you. It is about using the dialog to work together to question your drafts, spot inconsistencies and keep your own voice. This creates a piece of writing I believe is better than you would produce on your own.

Recently, I have been drafting short, dystopian science fiction stories in collaboration with AI, mainly to share as thought experiments rather than being literary pieces. I wanted to explore and highlight potential scenarios in years to come where we face a silent cognitive decline, driven by our over-reliance on AI agents that we no longer fully understand how to build or audit. Also, scenarios such as no novel research, even no novel architectures. As I drafted these stories, I kept noticing a strange recurring pattern. No matter which model I used to write, I kept meeting a character named Elara.

I have been using SolveIt since late 2024 as part of the first cohort of students, and honestly could not recommend it more. After spending more time with the platform, its growing functionality and tools, certain features stand out to me as genuinely unique in AI tooling.

Fantastic short interview by Dr Károly Zsolnai-Fehér with Jeff Dean, who amongst many other things co-founded Google Brain and co-created both TensorFlow and MapReduce!

Running a large language model is not just a compute problem. It is increasingly a memory problem, and the KV cache sits at the centre of it.

I asked an LLM to sum some hours from a text string. It got it wrong. I should not have been surprised.

Are agentic coding tools that good?