Tootfinder

Can somebody point me towards a Myers diff algorithm in a C project?
I am looking for the following data as diff result:
1. (old) line number
2. (old) column number
3. (new) line number
4. (new) column number.
Need the above info so I can ask my text editor widget to highlight the line and column as to what was added/deleted.
Please reboost for reach :)
#cppdev

How Elon Musk's X is fueling the MAGA-Trump split (Aaron Mak/Politico)
https://www.politico.com/news/2025/07/15/elon-musk-x-maga-00455128
http://www.memeorandum.com/250715/p111#a250715p111

Mgmt: We don't have the resources to make our own LLM. Can we just monetize AI prompts?
Business School Student: Hold my beer 🍺
#blockchain

PromptChain: A Decentralized Web3 Architecture for Managing AI Prompts as Digital Assets
We present PromptChain, a decentralized Web3 architecture that establishes AI prompts as first-class digital assets with verifiable ownership, version control, and monetization capabilities. Current centralized platforms lack mechanisms for proper attribution, quality assurance, or fair compensation for prompt creators. PromptChain addresses these limitations through a novel integration of IPFS for immutable storage, smart contracts for governance, and token incentives for community curation. O…

MetaLint: Generalizable Idiomatic Code Quality Analysis through Instruction-Following and Easy-to-Hard Generalization
Atharva Naik, Lawanya Baghel, Dhakshin Govindarajan, Darsh Agrawal, Daniel Fried, Carolyn Rose
https://arxiv.org/abs/2507.11687

MetaLint: Generalizable Idiomatic Code Quality Analysis through Instruction-Following and Easy-to-Hard Generalization
Large Language Models, though successful in code generation, struggle with code quality analysis because they are limited by static training data and can't easily adapt to evolving best practices. We introduce MetaLint, a new instruction-following framework that formulates code quality analysis as the task of detecting and fixing problematic semantic code fragments or code idioms based on high-level specifications. Unlike conventional approaches that train models on static, rule-based data, Met…

HCOMC: A Hierarchical Cooperative On-Ramp Merging Control Framework in Mixed Traffic Environment on Two-Lane Highways
Tianyi Wang, Yangyang Wang, Jie Pan, Junfeng Jiao, Christian Claudel
https://arxiv.org/abs/2507.11621

HCOMC: A Hierarchical Cooperative On-Ramp Merging Control Framework in Mixed Traffic Environment on Two-Lane Highways
Highway on-ramp merging areas are common bottlenecks to traffic congestion and accidents. Currently, a cooperative control strategy based on connected and automated vehicles (CAVs) is a fundamental solution to this problem. While CAVs are not fully widespread, it is necessary to propose a hierarchical cooperative on-ramp merging control (HCOMC) framework for heterogeneous traffic flow on two-lane highways to address this gap. This paper extends longitudinal car-following models based on the int…

How Many Instructions Can LLMs Follow at Once?
Daniel Jaroslawicz, Brendan Whiting, Parth Shah, Karime Maamari
https://arxiv.org/abs/2507.11538 https://

How Many Instructions Can LLMs Follow at Once?
Production-grade LLM systems require robust adherence to dozens or even hundreds of instructions simultaneously. However, the instruction-following capabilities of LLMs at high instruction densities have not yet been characterized, as existing benchmarks only evaluate models on tasks with a single or few instructions. We introduce IFScale, a simple benchmark of 500 keyword-inclusion instructions for a business report writing task to measure how instruction-following performance degrades as inst…

Feeling Machines: Ethics, Culture, and the Rise of Emotional AI
Vivek Chavan, Arsen Cenaj, Shuyuan Shen, Ariane Bar, Srishti Binwani, Tommaso Del Becaro, Marius Funk, Lynn Greschner, Roberto Hung, Stina Klein, Romina Kleiner, Stefanie Krause, Sylwia Olbrych, Vishvapalsinhji Parmar, Jaleh Sarafraz, Daria Soroko, Daksitha Withanage Don, Chang Zhou, Hoang Thuy Duong Vu, Parastoo Semnani, Daniel Weinhardt, Elisabeth Andre, J\"org Kr\"uger, Xavier Fresquet

Feeling Machines: Ethics, Culture, and the Rise of Emotional AI
This paper explores the growing presence of emotionally responsive artificial intelligence through a critical and interdisciplinary lens. Bringing together the voices of early-career researchers from multiple fields, it explores how AI systems that simulate or interpret human emotions are reshaping our interactions in areas such as education, healthcare, mental health, caregiving, and digital life. The analysis is structured around four central themes: the ethical implications of emotional AI, …

Filings: Bengaluru-based fintech Cred, which offers rewards for paying credit card bills and more, raised ~$72M at a $3.5B valuation, down from $6.4B in 2022 (The Economic Times)
https://ec…

Cred raises fresh funds from GIC, others; valuation cut by 45% to $3.5 billion
The fintech major was valued at $6.4 billion in 2022 during its last major fund infusion. The current fund infusion is in the form of primary capital, which means that existing investors have not sold any shares in this round. The drop in valuation was in line with the company's plans to list in India over the next two years, people in the know said.

Emergent topology in thin films of nodal line semimetals
Faruk Abdulla
https://arxiv.org/abs/2506.13562 https://arxiv.org/pdf/2506.13…

Emergent topology in thin films of nodal line semimetals
We investigate finite-size topological phases in thin films of nodal line semimetals (co-dimension 2) in three dimensions. By analyzing the hybridization of drumhead surface states, we demonstrate that such systems can transition into either a lower-dimensional nodal line state (co-dimension 1) or a fully gapped trivial phase. Additionally, we explore the hybridization of bulk states along the nodal loop when the system is finite in directions parallel to the loop's plane. This generally result…

Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning
Na{\dj}a Terzimehi\'c, Babette B\"uhler, Enkelejda Kasneci
https://arxiv.org/abs/2506.11789

Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning
Large language models have not only captivated the public imagination but have also sparked a profound rethinking of how we learn. In the third year following the breakthrough launch of ChatGPT, everyday informal learning has been transformed as diverse user groups explore these novel tools. Who is embracing LLMs for self-directed learning, and who remains hesitant? What are their reasons for adoption or avoidance? What learning patterns emerge with this novel technological landscape? We presen…