The joy of meta programming with AI tools

One of the most joyful things for me is meta programming. And thanks to the new AI tools, it makes it super easy to just think in the meta space and not have to worry about the implementation. And as long as you have your meta functions layered correctly in the concept space, the actual implementation by AI doesn't even matter since it's forced to be correct at the input/output invariance layer. FUN!

  • Software architecture
  • Coding agents

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Plank

Sequoia, relentless execution and a golden era to build

Interesting to see Sequoia Capital continuing to innovate in Venture Capital. They have always set a high bar in how you operate with high integrity and rigor. They set a very high bar for themselves and come with a mindset of relentless execution. We are living in a golden era of innovation, it is the time to build and if you are someone who is excited to build your version of the future, now is the time. The mindset that is needed to win is that every week, every day, every minute is critical. What did you get done in the last 5 minutes?

Enterprise AI

Safe, sovereign AI deployment is the decade's top challenge

Safe secure sovereign deployment of AI will be the highest value challenge of the coming decade. There is an opportunity to rethink the security, deployment and fundamental business paradigms in this domain. The key is to get unshackled from the way of thinking of the past era. We have to start by accepting some new tenants like the cost of software is zero, cost of tokens in enterprise is near zero, model capabilities are already at AGI level when it comes to enterprise, and security and sovereignty of data is unsolved at the moment. It is an exciting space to innovate in.

Models and research

GPT-6 Astra edits Qwen's weights to make it better at math

Wow! This is like science fiction. This might be the clearest sign that we are on the verge of RSI (Recursive Self Improvement). In this case, GPT-6 Astra is able to modify the weights of an open-source Qwen 3.5 model to make it better at math by just editing 0.016% of the weights. Link to the paper and analysis in comments Paper: rikiyat.github.io/blog/weight-space-programming Analysis and reproduction of the result from Simon: simonwillison.net/2026/Nov/3/qwen-can-interpret-mod…