Mastodawn

Developers on AI coding: many show enthusiasm and now feel more like architects than construction workers, some think software jobs might actually grow, more (Clive Thompson/New York Times)

https://www.nytimes.com/2026/03/12/magazine/ai-coding-programming-jobs-claude-chatgpt.html?unlocked_article_code=1.SlA.iAbQ.SHJOG4M1qGu0&smid=url-share
http://www.techmeme.com/260312/p61#a260312p61

Coding After Coders: The End of Computer Programming as We Know It

In the era of A.I. agents, many Silicon Valley programmers are now barely programming. Instead, what they’re doing is deeply, deeply weird.

The New York Times

Bartolomeo Stellato Feb 27

ISMP 2027 Amsterdam Feb 27

📢 #ISMP2027 comes to Amsterdam!

The 26th International Symposium on Mathematical Programming will be held July 25–30, 2027.

Join researchers from around the world to discuss advances, challenges and opportunities in the theory and practice of mathematical optimization.

🔗 https://ismp2027.mathopt.nl
#MOS

ISMP 2027 | Home

Bartolomeo Stellato Feb 18

Honored to receive a 2026 Sloan Research Fellowship in Mathematics. This wouldn't be possible without my entire research group at Princeton, and I'm grateful to the colleagues who supported my research. #SloanFellow

https://sloan.org/fellowships/

Bartolomeo Stellato Feb 12

Anders Eknert Feb 12

AI agent "contributes" PR to matplotlib.
PR gets rejected.
AI agent *writes and publishes blog to shame the maintainer*.

What a time to be alive.

https://github.com/matplotlib/matplotlib/pull/31132

Bartolomeo Stellato Feb 11

Terence Tao Feb 11

Rado Kirov has been one of several people working through the exercises in my Lean formalization of my Analysis I textbook at https://github.com/teorth/analysis . For several months he proceeded by hand, teaching himself Lean; see https://rkirov.github.io/posts/lean3/ and https://rkirov.github.io/posts/lean4/ . But recently, he switched to using Claude Code, significantly accelerating the process, to the point where all the exercises in three section could be formalized in a weekend, with only a few hours of active intervention: https://rkirov.github.io/posts/lean5/ . This is already notable, but I found Rado's description of his precise workflow, and the carefully curated prompt at https://github.com/rkirov/analysis/blob/main/CLAUDE.md he used, to be particularly interesting, with an emphasis not on just solving the exercises, but aligning it to his desired writing style, and identifying "pitfalls" in the formalizing process (which were recorded separately at https://github.com/rkirov/analysis/blob/main/TACTICS.md ). These sorts of experiments suggest that using these automated tools to take over tedious tasks such as formalization may not necessarily reduce our own capability to achieve these tasks; with the proper workflows, they could actually enhance our understanding of the process.

GitHub - teorth/analysis: A Lean companion to Analysis I

A Lean companion to Analysis I. Contribute to teorth/analysis development by creating an account on GitHub.

GitHub

Bartolomeo Stellato Jan 18

Stephen Turner Jan 18

How scientists are using Claude to accelerate research and discovery www.anthropic.com/news/acceler... 🧬🖥️🧪

Bartolomeo Stellato Dec 31

Proud to celebrate the graduation of my PhD student Vinit Ranjan, who defended his thesis this month: "Beyond the Worst Case: Verification of First-Order Methods for Parametric Optimization Problems" 🎉 Congratulations Dr. Ranjan!

Bartolomeo Stellato Dec 24

Wishing everyone happy holidays! 🎄 Feeling lucky to work with such a fantastic group of students and postdocs. Here's to good research, great company, and Neapolitan pizza 🍕

Bartolomeo Stellato Dec 18

New preprint! 📄 We combine PEP with Wasserstein DRO to get data-driven convergence guarantees for first-order methods.

Use observed algorithm trajectories to derive tighter probabilistic rates that reflect how your solver actually behaves. We recover known average-case rates (O(K⁻¹·⁵) for GD, O(K⁻³ log K) for FGM) without knowing the underlying distribution. 🎯

📎 arxiv.org/abs/2511.17834
💻 github.com/stellatogrp/dro_pep

Joint work with Jisun Park and Vinit Ranjan. #optimization #dro

Bartolomeo Stellato Nov 26

arXiv cs.SE bot Jul 23, 2025

AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?

Ori Press, Brandon Amos, Haoyu Zhao, Yikai Wu, Samuel K. Ainsworth, Dominik Krupke, Patrick Kidger, Touqir Sajed, Bartolomeo Stellato, Jisun Park, Nathanael Bosch, Eli Meril, Albert Steppi, Arman Zharmagambetov, Fangzhao Zhang, David Perez-Pineiro, Alberto Mercurio, Ni Zhan, Talor Abramovich, Kilian Lieret, Hanlin Zhang, Shirley Huang, Matthias Bethge, Ofir Press
https://arxiv.org/abs/2507.15887 https://arxiv.org/pdf/2507.15887 https://arxiv.org/html/2507.15887

arXiv:2507.15887v1 Announce Type: new
Abstract: Despite progress in language model (LM) capabilities, evaluations have thus far focused on models' performance on tasks that humans have previously solved, including in programming (Jimenez et al., 2024) and mathematics (Glazer et al., 2024). We therefore propose testing models' ability to design and implement algorithms in an open-ended benchmark: We task LMs with writing code that efficiently solves computationally challenging problems in computer science, physics, and mathematics. Our AlgoTune benchmark consists of 155 coding tasks collected from domain experts and a framework for validating and timing LM-synthesized solution code, which is compared to reference implementations from popular open-source packages. In addition, we develop a baseline LM agent, AlgoTuner, and evaluate its performance across a suite of frontier models. AlgoTuner achieves an average 1.72x speedup against our reference solvers, which use libraries such as SciPy, sk-learn and CVXPY. However, we find that current models fail to discover algorithmic innovations, instead preferring surface-level optimizations. We hope that AlgoTune catalyzes the development of LM agents exhibiting creative problem solving beyond state-of-the-art human performance.

toXiv_bot_toot

AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?

Despite progress in language model (LM) capabilities, evaluations have thus far focused on models' performance on tasks that humans have previously solved, including in programming (Jimenez et al., 2024) and mathematics (Glazer et al., 2024). We therefore propose testing models' ability to design and implement algorithms in an open-ended benchmark: We task LMs with writing code that efficiently solves computationally challenging problems in computer science, physics, and mathematics. Our AlgoTune benchmark consists of 155 coding tasks collected from domain experts and a framework for validating and timing LM-synthesized solution code, which is compared to reference implementations from popular open-source packages. In addition, we develop a baseline LM agent, AlgoTuner, and evaluate its performance across a suite of frontier models. AlgoTuner achieves an average 1.72x speedup against our reference solvers, which use libraries such as SciPy, sk-learn and CVXPY. However, we find that current models fail to discover algorithmic innovations, instead preferring surface-level optimizations. We hope that AlgoTune catalyzes the development of LM agents exhibiting creative problem solving beyond state-of-the-art human performance.

arXiv.org