Hey ML community! I’ve been exploring world models and wrote a short article aimed at making the concept easier to understand. I also propose a framework for classifying different approaches and highlight a few trends that emerge from that classification. I’d appreciate feedback on the framework, especially where it may be incomplete, unclear, or technically inaccurate. Article: https://x.com/srini_sunil_/status/2075577335076598194?s=20 submitted by /u/ssrini125 [link] [Kommentare]
So, I am working on this startup project with pretty low budget and one of the features is sentiment analysis based on political news, x posts and Instagram hashtag trends in which will be in Indian languages. I've been suggested muRIL, an Indian language-based model fine-tuned on political data as the best long-term option. But our team does not have any ML engineer so we dont know how we should approach that. Also do tell me if you think there is a better alternative submitted by /u/OkRoyal9187 [link] [Kommentare]
At ET Now Business Conclave 2026, GeekyAnts CEO Kumar Pratik explains why Industry 5.0 demands AI-powered decision-making in manufacturing.
A Spotify playlist for developers who code hard and mosh harder.
Michael Nielsen
I checked GitLawb on OpenRouter and the usage is insane for a project this small. In a market obsessed with AI agents, this feels like one of the most underpriced infrastructure plays right now. The Stats (Straight from OpenRouter): • 54.6 Billion total tokens processed • #19 Global Daily Rank • #1 in Cloud Agents • #10 in Coding Agents • Active since May 2026 (only ~2 months) • 9 models integrated • Explosive usage in the last 30 days with huge recent bars Why $200M+ Valuation Makes Sense: • Perfect Positioning in the AI Agent Boom: Agents need reliable, decentralized code collaboration. GitLawb delivers exactly that — a live decentralized git network where AI agents are first-class citizens (cryptographic DIDs, signed pushes, no central authority or credential leaks). • Real Usage Moat: Dominating OpenRouter leaderboards with multiples of the next competitor in cloud agents. This isn’t theoretical; agents are actively using it at scale, driving network activity and token demand. • Built-in Yield & Utility: Stake $GITLAWB to run nodes and earn from real activity — storage, uptime, bounties, agent spawns, premium features. As agent economies grow, this accrues real value. • Sector Comps: Leading AI agent and infra tokens are sitting at hundreds of millions to billions with weaker traction. GitLawb launched recently and is already top-tier — massive catch-up potential. • Early but Proven: 100B total supply, low MCAP, strong tech + adoption flywheel just getting started. The agent narrative isn’t going away — it’s accelerating. Foundational tools like GitLawb that actually enable agents at scale are going to print. Not financial advice — DYOR, check the dashboard yourself, and look at the tokenomics. But this one stands out hard. submitted by /u/amu4biz [link] [Kommentare]
This paper provides an overview of the applications of sheaf theory in deep learning, data science, and computer science in general. The primary text of this work serves as a friendly introduction to applied and computational sheaf theory accessible to those with modest mathematical familiarity. We describe intuitions and motivations underlying sheaf theory shared by both theoretical researchers and practitioners, bridging classical mathematical theory and its more recent implementations within signal processing and deep learning. We observe that most notions commonly considered specific to cellular sheaves translate to sheaves on arbitrary posets, providing an interesting avenue for further generalization of these methods in applications, and we present a new algorithm to compute sheaf cohomology on arbitrary finite posets in response. By integrating classical theory with recent applications, this work reveals certain blind spots in current machine learning practices. We conclude with a list of problems related to sheaf-theoretic applications that we find mathematically insightful and practically instructive to solve. To ensure the exposition of sheaf theory is self-contained, a rigorous mathematical introduction is provided in appendices which moves from an introduction of diagrams and sheaves to the definition of derived functors, higher order cohomology, sheaf Laplacians, sheaf diffusion, and interconnections of these subjects therein.