AI Dynamics

Global AI News Aggregator

AI Dynamics

  • API Coming Soon, Appreciation for Deep Dive Analysis

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    fun deep dive, and good job on finding some of our more interesting capabilities 🙂 we'll get you an API soon!

    → View original post on X — @alexandr_wang,

  • Open to Feedback on Model Weaknesses and Performance Limitations

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    We are always open to feedback and welcome any perspective on weaknesses you've noticed in the model from using it. We are quite upfront that our model does not perform well on ARC AGI 2 for example, and published those results for the community to understand. That might reflect

    → View original post on X — @alexandr_wang,

  • AI Skill Tree for 2026: Machine Learning and Deep Learning

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    The #AI Skill Tree for 2026 by @Khulood_Almani #MachineLearning #ArtificialIntelligence #ML #DL

    → View original post on X — @ronald_vanloon,

  • AI Struggles to Spread in Companies: Organizational Obstacles

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    Steven Sinofsky on why it's hard for AI to diffuse through firms: "Algorithmic thinking is really, really, really hard for the vast majority of people who have jobs… If you were to go into any person and ask them to create a flow chart for a particular thing that they have to go do, they would probably fail at producing that flow chart." "So within any organization, say doing a marketing plan… one person probably understands and could document the flow chart. So if you put one of these agents or this coworking tool in front of people… their ability to explain to it what to do is really, really limited." "You're basically just developing the next abstraction layer for how people interact… at each level of the abstraction layer, [it's] been a highly skilled, very specific individual within an organization… and then the little parts they build become little toollets… and some people can stitch together and some can't." @stevesi Box CEO Aaron Levie on the AI Adoption Gap Aaron Levie joins Steven Sinofsky, Martin Casado, and Erik Torenberg to discuss how AI agents will revolutionize work, the growing pains of building software for the agent economy, what Wall Street gets wrong about AI, and more. 00:00 Intro
    00:51 Building software for agents vs. humans
    02:10 Can non-technical workers actually use AI agents?
    14:31 CFO/CIO pushback: the real fear of agents doing integration
    18:39 Treating agents like employees and why it breaks down
    27:35 Diffusion gap: startups vs. enterprises
    42:53 What Wall Street gets wrong

    → View original post on X — @scobleizer,

  • Live Q&A with CTO on Hermes AI Tomorrow at 4pm

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    Happening in 15 minutes! Come meet our CTO, @theemozilla Robert Scoble (@Scobleizer) You have heard of @openclaw competitor from @NousResearch called “Hermes.” Tomorrow at 4 pm we will get nerdy with @theemozilla. Live. I will get people up who asks questions here first. nitter.net/i/spaces/1DGleEpaXLrJL — https://nitter.net/Scobleizer/status/2041660785110479314#m

    → View original post on X — @scobleizer,

  • OpenClaw Revolution Continues with Nous Research and Hermes

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    AI Dynamics

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    The revolution that @openclaw started continues. Learn about @NousResearch and its Hermes competitor. Starts at 4 p.m. Pacific Time (in about 20 minutes). nitter.net/i/spaces/1DGleEpaXLrJL

    → View original post on X — @scobleizer,

  • Abacus Claw: Ultra Efficient with Small Models

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    Abacus Claw Is Now Ultra Efficient – runs on small models
    – super easy to connect to WhatsApp, Telegram and Discord
    – will automatically sleep when not being used The best OPEN CLAW implementation on the cloud

    → View original post on X — @abacusai,

  • Hermes CTO Space: Five Key Advantages Over OpenClaw

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    Probably a good idea to join @Scobleizer space in about 45 minutes to hear from the Hermes CTO, I'd expect them to address the differences between Hermes and OpenClaw. But from my own perspective here are my top five observations: 1. Easier to setup initially 2. Easier to upgrade 3. Lower token usage over time due to less bloated system prompts and less "rework" on assigned tasks 4. Better skill system out of the box 5. Self improving without having to setup a ton of "pointers" like with OpenClaw The only thing I can say that's negative about Hermes is that it is still mostly for the bleeding edge tech crowd and non technical people will still struggle and still need to learn new concepts. But hey, that's why every Tom, Dick and Harry out there is building a custom agent platform that's GUI driven 100 percent…

    → View original post on X — @scobleizer,

  • Namanopedia: AI-Generated Personal Wikipedia from Just a Name

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    AI Dynamics

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    This is Namanopedia. I built lifewiki [mylife.wiki]. Paste a name, get their entire Wikipedia. An AI agent researches the web and compiles 40-50+ articles with infoboxes, wikilinks, citations, and categories. Takes about 3 minutes. Inspired by @karpathy's LLM Wiki pattern and @FarzaTV's Farzapedia. Except this one works for anyone, from just a name. mylife.wiki/naman-ambavi Andrej Karpathy (@karpathy) LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts. — https://nitter.net/karpathy/status/2039805659525644595#m

    → View original post on X — @scobleizer,

  • Roadrunner: Innovative 15kg Robot with Walking and Rolling Capabilities

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    Meet Roadrunner: The 15kg #Robot That Walks, Rolls, and Adapts by @rai_inst #Robotics #AI #EmergingTech #Innovation #Technology

    → View original post on X — @ronald_vanloon,