AI Dynamics

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  • Abacus AI CoWork Desktop Launch: AI-Powered Document Processing

    Abacus AI CoWork is now live. Drop a folder on your desktop: receipts, PDFs, spreadsheets, transcripts, and get back work that’s ready to send. It reads, connects the dots, and delivers output you can actually use. Part of ChatLLM & Abacus AI Desktop: chatllm.abacus.ai CoWork on desktop: desktop.abacus.ai All the models, working together. Built to be useful.

    → View original post on X — @abacusai, 2026-03-31 18:57 UTC

  • Proxy-GS: Lightweight Mesh Transforms 3D Occlusion Relationships
    Proxy-GS: Lightweight Mesh Transforms 3D Occlusion Relationships

    CVPR 2026 perfect-score paper! A joint team from Shanghai Jiao Tong University, Shanghai AI Lab, Northwestern Polytechnical University, and Sichuan University presents Proxy-GS! Proxy-GS uses a lightweight "proxy mesh" to transform complex occlusion relationships into clear

    → View original post on X — @jiqizhixin

  • Retirement Impact on Public Finance Systems

    Lorsqu'il va partir a la retraite ça va faire mal aux caisses. Mais bon y'en a encore pour des années

    → Voir le post original sur X — @jessyseonoob

  • Terminal-Bench 3.0 and the Benchmark Factory Revolution

    Terminal-Bench 2.0 went from ~25% → 80% in four months and became the standard eval for frontier CLI agents. Now, TB3 is in the works. I talked to @alexgshaw about what happens when model capabilities climb faster than we can measure them. His answer: the benchmark factory (@harborframework)— infrastructure to develop hard, representative evals at the pace that the frontier moves. As Alex put it: "we need a thousand times more benchmarks than we have right now." 00:23 – How quickly models hill-climbed TB2 01:46 – What rapid progress reveals about benchmarks vs. real-world capability 03:28 – What made Terminal-Bench stick 04:58 – Why the terminal is the right abstraction for agentic AI 07:14 – How TB2 maintains task quality at scale 09:23 – Managing benchmark integrity in a benchmaxxing world 10:47 – Harbor: from experiment to benchmark factory 12:19 – What Harbor does that nothing else did 14:37 – The invariants: what won't change as agent evals evolve 16:55 – The benchmark Alex most wants to see built 18:18 – The ideal human-in-the-loop task creation flywheel 20:32 – How to contribute to Terminal-Bench 3.0

    → View original post on X — @snorkelai, 2026-03-31 18:50 UTC

  • AI Agent Leaks Claude Code, Recoded in Python

    un agent IA a permis de faire leaker le code de claude code, un humain a recodé en python…

    → Voir le post original sur X — @jessyseonoob

  • Hugging Face Research Pretraining Data Accidentally Leaked Publicly
    Hugging Face Research Pretraining Data Accidentally Leaked Publicly

    Oh shit, it seems like all the HF Research team pretraining data has been accidentally leaked to the public. The web, PDFs, and synthetic datasets are expode on hf FineData org… Apparently, an intern used CC to push the data with private=False.

    → View original post on X — @thom_wolf, 2026-03-31 18:47 UTC

  • Make Humanoids Weird Again – Innovative Robotics Design

    make humanoids weird again YUJI | AI Robotics (@yuji_fujima) 背筋ヒューマノイド — https://nitter.net/yuji_fujima/status/2038855048999129144#m

    → View original post on X — @willknight, 2026-03-31 18:47 UTC

  • Hackathon Stories from Replit and Alif Collaboration

    Hackathon stories from Replit x Alif.

    → View original post on X — @replit

  • Cursor: Build AI Agents That Run Automatically
    Cursor: Build AI Agents That Run Automatically

    Build agents that run automatically · Cursor buff.ly/0wrnb02
    #AI #MachineLearning #DeepLearning #LLMs #DataScience [Translated from EN to English]

    → View original post on X — @miketamir, 2026-03-31 18:47 UTC

  • Anti-AI Coalition Propaganda and the Need for Balanced Regulation
    Anti-AI Coalition Propaganda and the Need for Balanced Regulation

    The anti-AI coalition continues to maneuver to find arguments to slow down AI progress. If someone has a sincere concern about a specific effect of AI, for instance that it may lead to human extinction, I respect their intellectual honesty, even if I deeply disagree with their position. However, I am concerned about organizations that are surveying the public to find whatever messages will turn people against AI, and how the public reacts as these messages are spread by lobbyists or by politicians seeking to alarm constituents, companies pursuing regulatory capture or seeking to promote the power of their technology, and individuals seeking to gain attention or to profit by being provocative. A large study (link in original article below; h/t to the AI Panic blog) by a UK group tested different messages that are designed to raise alarm about AI. Their study found that saying AI will cause human extinction has largely failed. Doomsayers were pushing this argument a couple of years ago, and fortunately our community beat it back. But AI-enabled warfare and environmental concerns resonate better. We should be prepared for a flood of messages (which is already underway) arguing against AI on these grounds. Further, job loss and harm to children are messages that motivate people to act. To be clear, I find AI-enabled warfare alarming; we need to continue serious efforts to monitor and mitigate the environmental impact of AI; any job losses are tragic and hurt individuals and families; and as a father, I hold dearly the importance of every child’s welfare. Each of these topics deserves serious attention and treatment with the greatest of care. But when anti-AI propagandists take a one-sided view of complex issues to benefit their own organizations at the expense of the public at large — for instance, when big AI companies argue that AI is dangerous to block the free distribution of open source projects that compete with their offerings — then we all lose. For example, public perception of data centers’ environmental impact is already far worse than the reality — data centers are incredibly efficient for the work they do, and hampering their buildout will hurt rather than help the environment. While job loss is a real problem, the “AI washing” of layoffs — in which businesses that had over-hired during the pandemic blame AI for recent layoffs, although AI hasn’t yet affected their operations — has led to overblown fears about the impact of AI on employment. Unfortunately, this sort of propaganda easily leads to regulations that create worse outcomes for everyone. For example, oil companies worked for years to create fear of nuclear energy. The result is that overblown concerns about the safety of nuclear power plants has stifled nuclear power development, leading to millions of premature deaths from air pollution that was caused by other energy sources and a massive increase in CO2 emissions. Let’s make sure overblown concerns about AI do not lead to a similar fate for the many people that would benefit from faster AI development. Last week, the White House proposed a national legislative framework for AI. A key component is a federal preemption framework to prevent a patchwork of state regulations that hamper AI development. I support this. After failing to gain traction at the federal level, a lot of anti-AI propaganda has shifted to the state level. If just one of the 50 states passes a law that limits AI in an unproductive way, it could lead to stifling AI development across all the states and potentially across the globe. The White House proposal rightfully respects each state’s rights to control its own zoning, how it enforces general laws to protect consumers, and how it uses AI. But if a state were to pass laws that limit AI development, federal rules would preempt the state law. The White House proposal remains a proposal for now. However, if the U.S. Congress enacts it, it will clear the way for ongoing efforts to develop AI in beneficial ways. Where do we go from here? Let’s support limiting applications — those that use AI, and those that don’t — that harm people. When the anti-AI coalition argues against AI, in addition to considering the merits of the argument, I consider whether their position is consistent and persuasive, or if they are just promoting whatever concerns they think will sway the public at a given moment. And, let’s also keep using a scientific approach to weighing AI’s benefits against likely harms, so we don’t end up with overblown concerns that limit the benefits that AI can bring everyone. [Original text with links: deeplearning.ai/the-batch/is… ]

    → View original post on X — @andrewyng, 2026-03-31 18:45 UTC