AI Dynamics

Global AI News Aggregator

About

GENERATIVE AI

  • Veo 3: AI-powered audio generation for video creations

    By

    @theahmadosman

    –

    NVIDIA AI pulled me in for an interview at GTC this week x.com/TheAhmadOsman/…

    → Voir le post original sur X — @theahmadosman

  • Model Improvements Reduce Need for Complex Workflows

    By

    @steipete

    –

    That was last year’s workflow, not doing that much anymore, models got better.

    → View original post on X — @steipete

  • Training vs Context: How to Actually Give AI Your Company Data
    Training vs Context: How to Actually Give AI Your Company Data

    By

    @whats_ai

    –

    If you paste your company data into ChatGPT, you did NOT just train it. ❌ I keep getting different versions of this same question: → Can I inject knowledge directly into the model? → Does adding data through RAG actually change how the model thinks? Let's understand the answer with the example of a small company that sells climbing gear. 🧗 They have a return policy, a product catalog, and internal guidelines. They want AI to handle customer questions. If they paste their return policy into ChatGPT – did they train the model? No. They gave it temporary context. The model's brain didn't change at all. If they build a RAG system that retrieves relevant documents when a question comes in – did they train the model? Still no. They built an external bookshelf the model can read from. But the model itself is exactly the same. If they fine-tune the model on their climbing gear data – now they actually changed the brain. But even then, they didn't insert a clean fact into a specific location. The knowledge gets distributed across millions of parameters. There's no single neuron labeled "climbing shoe return policy." 🧠 So what should they actually do? If the goal is for the model to know a specific fact, don't retrain it. Give it through context or external memory. It's cheaper and more controllable. Save fine-tuning for changing behavior like tone, style, reasoning patterns, not for injecting knowledge. I covered all of this and more in a video: → How embeddings work (without the math) → What the latent space actually is → Why reasoning models aren't fundamentally different → When to choose prompting vs RAG vs fine-tuning The mental model I want you to keep: 👉 Parameters = the brain 👉 Training = changes the brain 👉 Embeddings = coordinates for searching meaning 👉 RAG = a bookshelf the brain reads from 👉 Latent space = the internal geometry created by the brain Full video 👇

    → View original post on X — @whats_ai, 2026-03-29 12:00 UTC

  • Autonomous AI Agent Implements Voice Transcription and Response Workflow

    By

    @kimmonismus

    –

    This is nuts: Clawdbot figured out how to transcribe and respond to a voice message on its own, detecting the Opus format, converting it via FFmpeg, calling OpenAI’s Whisper with a found API key, and replying as if voice support had always existed.

    → View original post on X — @kimmonismus

  • LLM token prices decreased 99% since 2022 launch

    By

    @waitin4agi_

    –

    To give you an idea since we launched godinabox in 2022 till today token prices for top of the line LLM is something like 1% of what it used to be.

    → View original post on X — @waitin4agi_, 2026-03-29 11:30 UTC

  • Kyutai and Moshi: Strategic positioning in AI solutions

    By

    @jessyseonoob

    –

    c'est parcqu'ils veulent mettre en avant l'autre solution Moshi avec kyutai

    → Voir le post original sur X — @jessyseonoob

  • KV Cache Optimization and DDR5 Memory Pricing Misconception

    By

    @kimmonismus

    –

    The claim mixes up memory types: KV cache optimization (like TurboQuant) reduces GPU VRAM usage during inference, not system memory like DDR5 RAM. DDR5 prices are driven by broader semiconductor supply-demand cycles, so there’s no direct link between KV cache compression and

    → View original post on X — @kimmonismus

  • Google TurboQuant GPU VRAM Optimization Not System Memory

    By

    @kimmonismus

    –

    That claim is incorrect because Google’s TurboQuant reduces GPU VRAM usage for AI inference, not system memory like DDR5 RAM. DDR5 prices are driven by semiconductor supply-demand cycles, not by a single AI optimization.

    → View original post on X — @kimmonismus

  • Claude Skills: Self-Contained Workflow Packages for Efficient AI

    By

    @akshay_pachaar

    –

    What are Claude Skills? 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 was never meant to hold entire workflows. But that's exactly where they end up. General rules, coding conventions, 20-step security review processes, deployment checklists. All in one file that loads into every single session, eating context even when Claude is just renaming a variable. 𝗦𝗸𝗶𝗹𝗹𝘀 fix this by turning workflows into self-contained packages that Claude loads only when the task demands it. Here's the idea. A skill is a folder inside .𝗰𝗹𝗮𝘂𝗱𝗲/𝘀𝗸𝗶𝗹𝗹𝘀/. Each folder contains a 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱 file with two things: a 𝗱𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 that tells Claude when to activate it, and the workflow instructions that tell Claude what to do. The description is the trigger. Claude reads all available skill descriptions, watches the conversation, and when your request matches, it pulls in that skill automatically. You don't paste the steps. You don't type a command. Claude recognizes the intent and invokes the right skill on its own. You can also trigger any skill explicitly with a slash command like /𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆-𝗿𝗲𝘃𝗶𝗲𝘄 when you want manual control. I recorded a deep dive on skills when they were first released, and everything in it is even more relevant today. The video below walks through exactly how this works. But auto-invocation is just the surface. The real power is what skills can carry with them. Skills are full packages, not just instruction files. A 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱 can reference supporting files that live right next to it using the @ symbol. A detailed security standards document. A release notes template. A compliance checklist. Whatever the workflow needs, the skill bundles it together. Inside 𝗦𝗞𝗜𝗟𝗟.𝗺𝗱, YAML frontmatter defines the name, description, and which tools the skill is allowed to use. The 𝗮𝗹𝗹𝗼𝘄𝗲𝗱-𝘁𝗼𝗼𝗹𝘀 field is worth paying attention to. A security review skill only needs 𝗥𝗲𝗮𝗱, 𝗚𝗿𝗲𝗽, and 𝗚𝗹𝗼𝗯. It has no business writing files. Restricting tool access makes the skill safer and far more predictable. Skills live at two levels. Project skills go in .𝗰𝗹𝗮𝘂𝗱𝗲/𝘀𝗸𝗶𝗹𝗹𝘀/ and get committed to git so the whole team shares them. Personal skills go in ~/.𝗰𝗹𝗮𝘂𝗱𝗲/𝘀𝗸𝗶𝗹𝗹𝘀/ and follow you across every project. A 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 with a 20-step security process baked in is dead weight in 90% of your sessions. A 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆-𝗿𝗲𝘃𝗶𝗲𝘄 skill that activates only when security is on the table is precision. 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 tells Claude what rules to follow. Skills tell Claude what workflows to execute. The article below is a complete guide to 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱, hooks, skills, agents, and permissions, and how to set them up properly. Akshay 🚀 (@akshay_pachaar) x.com/i/article/203496196714… — https://nitter.net/akshay_pachaar/status/2035341800739877091#m

    → View original post on X — @akshay_pachaar, 2026-03-29 10:13 UTC

  • Next Major Frontier Model Releases Expected in April

    By

    @kimmonismus

    –

    Andrew's article is very well written, and I'd like to add just a few minor things. First of all, we'll probably see the next major frontier model releases in April. The Information wrote that "Spud" will be released "in a few weeks," so April is a very logical timeframe.

    → View original post on X — @kimmonismus