Tootfinder

Mixpanel founder Suhail Doshi calls out Soham Parekh, who he says "works at 3-4 startups at the same time" and has "been preying on YC companies and more" (Bhavya Sukheja/NDTV)
https://www.ndtv.c…

AI Founder Accuses Indian Techie Soham Parekh Of Scamming Multiple Startups: "Fired This Guy In First Week"
A US-based AI founder has accused an Indian techie of working at multiple startups simultaneously under false pretences.

Geometric Duality Between Constraints and Gauge Fields: Mirror Symmetry and Spencer Isomorphisms of Compatible Pairs on Principal Bundles
Dongzhe Zheng
https://arxiv.org/abs/2506.00728

Geometric Duality Between Constraints and Gauge Fields: Mirror Symmetry and Spencer Isomorphisms of Compatible Pairs on Principal Bundles
This paper develops a mirror symmetry theory of Spencer cohomology within the geometric framework of constrained systems on principal bundles, revealing deep symmetric structures in constraint geometry. Based on compatible pairs $(D,λ)$ under strong transversality conditions, we construct a systematic family of mirror transformations: from basic sign mirrors $λ\mapsto -λ$ to general automorphism-induced mirrors $λ\mapsto (dϕ)^*(λ)$. Our core result proves that these transformations preser…

Just read this post by @… on an optimistic AGI future, and while it had some interesting and worthwhile ideas, it's also in my opinion dangerously misguided, and plays into the current AGI hype in a harmful way.
https://social.coop/@eloquence/114940607434005478
My criticisms include:
- Current LLM technology has many layers, but the biggest most capable models are all tied to corporate datacenters and require inordinate amounts of every and water use to run. Trying to use these tools to bring about a post-scarcity economy will burn up the planet. We urgently need more-capable but also vastly more efficient AI technologies if we want to use AI for a post-scarcity economy, and we are *not* nearly on the verge of this despite what the big companies pushing LLMs want us to think.
- I can see that permacommons.org claims a small level of expenses on AI equates to low climate impact. However, given current deep subsidies on place by the big companies to attract users, that isn't a great assumption. The fact that their FAQ dodges the question about which AI systems they use isn't a great look.
- These systems are not free in the same way that Wikipedia or open-source software is. To run your own model you need a data harvesting & cleaning operation that costs millions of dollars minimum, and then you need millions of dollars worth of storage & compute to train & host the models. Right now, big corporations are trying to compete for market share by heavily subsidizing these things, but it you go along with that, you become dependent on them, and you'll be screwed when they jack up the price to a profitable level later. I'd love to see open dataset initiatives SBD the like, and there are some of these things, but not enough yet, and many of the initiatives focus on one problem while ignoring others (fine for research but not the basis for a society yet).
- Between the environmental impacts, the horrible labor conditions and undercompensation of data workers who filter the big datasets, and the impacts of both AI scrapers and AI commons pollution, the developers of the most popular & effective LLMs have a lot of answer for. This project only really mentions environmental impacts, which makes me think that they're not serious about ethics, which in turn makes me distrustful of the whole enterprise.
- Their language also ends up encouraging AI use broadly while totally ignoring several entire classes of harm, so they're effectively contributing to AI hype, especially with such casual talk of AGI and robotics as if embodied AGI were just around the corner. To be clear about this point: we are several breakthroughs away from AGI under the most optimistic assumptions, and giving the impression that those will happen soon plays directly into the hands of the Sam Altmans of the world who are trying to make money off the impression of impending huge advances in AI capabilities. Adding to the AI hype is irresponsible.
- I've got a more philosophical criticism that I'll post about separately.
I do think that the idea of using AI & other software tools, possibly along with robotics and funded by many local cooperatives, in order to make businesses obsolete before they can do the same to all workers, is a good one. Get your local library to buy a knitting machine alongside their 3D printer.
Lately I've felt too busy criticizing AI to really sit down and think about what I do want the future to look like, even though I'm a big proponent of positive visions for the future as a force multiplier for criticism, and this article is inspiring to me in that regard, even if the specific project doesn't seem like a good one.

Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
Miguel Romero-Arjona, Jos\'e A. Parejo, Juan C. Alonso, Ana B. S\'anchez, Aitor Arrieta, Sergio Segura
https://arxiv.org/abs/2507.02533

Meta-Fair: AI-Assisted Fairness Testing of Large Language Models
Fairness--the absence of unjustified bias--is a core principle in the development of Artificial Intelligence (AI) systems, yet it remains difficult to assess and enforce. Current approaches to fairness testing in large language models (LLMs) often rely on manual evaluation, fixed templates, deterministic heuristics, and curated datasets, making them resource-intensive and difficult to scale. This work aims to lay the groundwork for a novel, automated method for testing fairness in LLMs, reducin…

CiteEval: Principle-Driven Citation Evaluation for Source Attribution
Yumo Xu, Peng Qi, Jifan Chen, Kunlun Liu, Rujun Han, Lan Liu, Bonan Min, Vittorio Castelli, Arshit Gupta, Zhiguo Wang
https://arxiv.org/abs/2506.01829

CiteEval: Principle-Driven Citation Evaluation for Source Attribution
Citation quality is crucial in information-seeking systems, directly influencing trust and the effectiveness of information access. Current evaluation frameworks, both human and automatic, mainly rely on Natural Language Inference (NLI) to assess binary or ternary supportiveness from cited sources, which we argue is a suboptimal proxy for citation evaluation. In this work we introduce CiteEval, a citation evaluation framework driven by principles focusing on fine-grained citation assessment wit…

Y salió el cartel oficial del festival mexicano Corona Capital 2025. Foo Fighters, Queens of the Stone Age, Garbage, Alabama Shakes, Weezer, Linkin Park y Deftones estšn entre los principales artistas.
#CoronaCapital #CoronaCapital2025

Sodium-Decorated P-C3N: A Porous 2D Framework for High-Capacity and Reversible Hydrogen Storage
Jose A. S. Laranjeira, Nicolas F. Martins, Kleuton A. L. Lima, Lingtao Xiao, Xihao Chen, Luiz A. Ribeiro Junior, Julio R. Sambrano
https://arxiv.org/abs/2506.02374

Sodium-Decorated P-C3N: A Porous 2D Framework for High-Capacity and Reversible Hydrogen Storage
The development of reversible hydrogen storage materials has become crucial for enabling carbon-neutral energy systems. Based on this, the present work investigates the hydrogen storage on the sodium-decorated P-C$_3$N (Na@P-C$_3$N), a porous carbon nitride monolayer recently proposed as a stable semiconductor. First-principles calculations reveal that Na atoms preferentially adsorb with an adsorption energy of -4.48~eV, effectively suppressing clusterization effects. Upon decoration, the syste…

Vendor of AI tech comes to Australia and promises to grow our GDP by 4% if only we give tax incentives to increase use of AI tech. Stenographers in the press report the amazing "windfall" but thankfully we have Crikey to call BS:
https://www.crikey.com.au/2…

OpenAI’s ‘productivity’ garbage is an age-old scam that’s long crippled Australia
Touting the economic benefits of AI is straight from business lobbyists' playbook, one used over and over again.

News Sentiment Embeddings for Stock Price Forecasting
Ayaan Qayyum
https://arxiv.org/abs/2507.01970 https://arxiv.org/pdf/2507.01970

News Sentiment Embeddings for Stock Price Forecasting
This paper will discuss how headline data can be used to predict stock prices. The stock price in question is the SPDR S&P 500 ETF Trust, also known as SPY that tracks the performance of the largest 500 publicly traded corporations in the United States. A key focus is to use news headlines from the Wall Street Journal (WSJ) to predict the movement of stock prices on a daily timescale with OpenAI-based text embedding models used to create vector encodings of each headline with principal componen…

Constructing Two Metrics for Spencer Cohomology: Hodge Decomposition of Constrained Bundles
Dongzhe Zheng
https://arxiv.org/abs/2506.00752 https://

Constructing Two Metrics for Spencer Cohomology: Hodge Decomposition of Constrained Bundles
This paper establishes a metric framework for Spencer complexes based on the geometric theory of compatible pairs $(D,λ)$ in principal bundle constraint systems, solving fundamental technical problems in computing Spencer cohomology of constraint systems. We develop two complementary and geometrically natural metric schemes: a tensor metric based on constraint strength weighting and an induced metric arising from principal bundle curvature geometry, both maintaining deep compatibility with the…