I put down my trials in wrangling my tasks and choosing a to-do application #to_do_planning on my blog. It’s roughly inspired by David Allen’s Getting Things Done.
| My blog | https://ah-journal.com |
aha1225
@ahasan
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IT consultant focussing on long-term & short-term Data Management, distribution and access
Wrote a short note on how I landed on my preferred note-taking application (Obsidian). It took me a bit of time to get there. Obsidian allows me to write notes without getting in the way. My setup is quite simple (only use the calendar plugin) #note_taking_app
I managed to get my blog (https://ah-journal.com) up and running. I looked around at the various frameworks for building blogs from markdown, but I found them complicated to use. I also found that some of the simple templates that I would have liked to use would have required me to understand the engine in order to update the template to work. In the end I chose plain, simple HTML and tailwindcss. It requires me to edit more files, but I think it is simpler. #blog_framework
Finally managed to get my blog deployed! It took a bit of effort. Now, I need to populate it with some useful content (at least what I consider to be useful)
Terence TaoA new paper with Bogdan Georgiev, Javier Gomez-Serrano, and Adam Zsolt Wagner: "Mathematical exploration and discovery at scale" https://arxiv.org/abs/2511.02864 , in which we record our experiments using the LLM-powered optimization tool #AlphaEvolve to attack 67 different math problems (both solved and unsolved), improving upon the state of the art in some cases and matching preivous literature in others. The data for these experiments can be found at https://github.com/google-deepmind/alphaevolve_repository_of_problems and further discussion is at https://terrytao.wordpress.com/2025/11/05/mathematical-exploration-and-discovery-at-scale/
Mathematical exploration and discovery at scale
AlphaEvolve (Novikov et al., 2025) is a generic evolutionary coding agent that combines the generative capabilities of LLMs with automated evaluation in an iterative evolutionary framework that proposes, tests, and refines algorithmic solutions to challenging scientific and practical problems. In this paper we showcase AlphaEvolve as a tool for autonomously discovering novel mathematical constructions and advancing our understanding of long-standing open problems. To demonstrate its breadth, we considered a list of 67 problems spanning mathematical analysis, combinatorics, geometry, and number theory. The system rediscovered the best known solutions in most of the cases and discovered improved solutions in several. In some instances, AlphaEvolve is also able to generalize results for a finite number of input values into a formula valid for all input values. Furthermore, we are able to combine this methodology with Deep Think and AlphaProof in a broader framework where the additional proof-assistants and reasoning systems provide automated proof generation and further mathematical insights. These results demonstrate that large language model-guided evolutionary search can autonomously discover mathematical constructions that complement human intuition, at times matching or even improving the best known results, highlighting the potential for significant new ways of interaction between mathematicians and AI systems. We present AlphaEvolve as a powerful new tool for mathematical discovery, capable of exploring vast search spaces to solve complex optimization problems at scale, often with significantly reduced requirements on preparation and computation time.