Tootfinder

“Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity”
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/
50 page PDF:

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

I don't think that "give beginners LLMs to get them started so they can actually learn to do it later" checks out.
(Original title: But will they?)
https://tante.cc/2025/07/21/but-will-they/

But will they?
Armin Ronacher, who is quite a well-known open source developer (especially in the Python community) and who would probably consider himself a very rational, pragmatic person when it comes to “AI” usage posted an interesting post on Mastodon today: Post by @mitsuhiko View on Mastodon So his thought is that giving especially younger, less experienced […]

Please, this is israel we’re talking about. No need to be antisemitic. That was not a hand grenade attack, it was an explosive expression of love. https://todon.nl/@anarchiv/114714828424164798

I think this is related to the issue that some people have with static typing: it’s a series of micro-interruptions that serve to distract from the goal.
While I never found this to be a problem in somewhat-typed languages such as Java, and I no longer have this problem most of the time in Haskell, I still experience it in Rust, where the borrow checker might require me to totally rethink my approach.
I can absolutely see why some people prefer not to have a type system.

Series C, Episode 07 - Children of Auron
C.A. ONE: For what reason?
FRANTON: Conquest.
C.A. ONE: That's ridiculous.
FRANTON: Surely it would be safer to wait for the Liberator. At least we can trust them, and we know they're coming, Zelda heard.
https://blake.torpidity.net/m/307/223

Evaluating the Effectiveness of Cost-Efficient Large Language Models in Benchmark Biomedical Tasks
Israt Jahan, Md Tahmid Rahman Laskar, Chun Peng, Jimmy Huang
https://arxiv.org/abs/2507.14045

Evaluating the Effectiveness of Cost-Efficient Large Language Models in Benchmark Biomedical Tasks
This paper presents a comprehensive evaluation of cost-efficient Large Language Models (LLMs) for diverse biomedical tasks spanning both text and image modalities. We evaluated a range of closed-source and open-source LLMs on tasks such as biomedical text classification and generation, question answering, and multimodal image processing. Our experimental findings indicate that there is no single LLM that can consistently outperform others across all tasks. Instead, different LLMs excel in diffe…

Lawyers Just Discovered Something About Meta's AI That Could Cost Zuckerberg Untold Billions of Dollars https://futurism.com/lawyer-zuckerberg-ai?utm_source=dlvr.it&utm_content=topic/artificial…

Lawyers Just Discovered Something About Meta's AI That Could Cost Zuckerberg Untold Billions of Dollars
A legal expert found that Meta's AI is able to spit out entire portions of books verbatim — and if he's right, Meta is in huge trouble.

Zobrist Hash-based Duplicate Detection in Symbolic Regression
Bogdan Burlacu
https://arxiv.org/abs/2508.13859 https://arxiv.org/pdf/2508.13859

Zobrist Hash-based Duplicate Detection in Symbolic Regression
Symbolic regression encompasses a family of search algorithms that aim to discover the best fitting function for a set of data without requiring an a priori specification of the model structure. The most successful and commonly used technique for symbolic regression is Genetic Programming (GP), an evolutionary search method that evolves a population of mathematical expressions through the mechanism of natural selection. In this work we analyze the efficiency of the evolutionary search in GP and…

B09 - Countdown
ARRIAN: I don't believe that.
PROVINE: But it's true. It's out of our control now. Neither I nor anybody else can do anything about it. Now. You listen to me. You can give yourself a chance. You don't have to die here with the rest of them.
https://blake.torpidity.net/m/209/189

The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities
Xiancheng Li, Georgios D. Karampatakis, Helen E. Wood, Chris J. Griffiths, Borislava Mihaylova, Neil S. Coulson, Alessio Pasinato, Pietro Panzarasa, Marco Viviani, Anna De Simoni
https://arxiv.org/abs/2508.14032

The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities
Digital health analytics face critical challenges nowadays. The sophisticated analysis of patient-generated health content, which contains complex emotional and medical contexts, requires scarce domain expertise, while traditional ML approaches are constrained by data shortage and privacy limitations in healthcare settings. Online Health Communities (OHCs) exemplify these challenges with mixed-sentiment posts, clinical terminology, and implicit emotional expressions that demand specialised know…