AI Dynamics

Global AI News Aggregator

About

AUTOMATION

  • Enterprise AI ROI: Focus on Existing Systems and Measurable Outcomes

    By

    @theahmadosman

    –

    They all know I LOVE opensource winning

    → View original post on X — @theahmadosman

  • Multi-Agent Architecture: CC Hands Off Tasks to Codex

    By

    @reach_vb

    –

    they are both still used in their own harnesses, that’s the whole reason why this is effective. CC can hand off an issue to codex to work on in its own harness!

    → View original post on X — @reach_vb

  • Robot intelligent nettoie et ramasse les objets avec un bras

    By

    @ronald_vanloon

    –

    This Smart Cleaning #Robot Picks Up Clutter and Scrubs Floors with a #Robotic Arm
    by @XRoboHub #EmergingTech #Technology #Innovation

    → View original post on X — @ronald_vanloon

  • Claude’s Secret Pomodoro Mastery Mode Revealed
    Claude’s Secret Pomodoro Mastery Mode Revealed

    By

    @godofprompt

    –

    BREAKING: Claude has a secret mode called "Francesco Cirillo's Pomodoro Mastery Coach." It goes way beyond setting a 25-minute timer. It assesses where you're stuck, builds a full daily execution plan in Pomodoro units, teaches you to track and crush every interruption, and

    → View original post on X — @godofprompt

  • SquareMind Raises $18M for AI Skin Cancer Detection Robotic Platform
    SquareMind Raises $18M for AI Skin Cancer Detection Robotic Platform

    By

    @theahmadosman

    –

    This goal hasn’t changed https://
    x.com/theahmadosman/
    status/1978790002835267707?s=46
    …

    → View original post on X — @theahmadosman

  • Top Robotics News: Airport Humanoids, SoftBank’s New Company, Harvard’s Ant Bots

    By

    @theahmadosman

    –

    Information will be shared in the coming weeks, and it will be open for anyone and anywhere

    → View original post on X — @theahmadosman

  • 5 Tips for Onboarding AI Agents Effectively

    By

    @saboo_shubham_

    –

    Stop dumping 10 folders of context on your AI agent and wondering why it’s confused. Instead, on board your agent like you would a new hire. Tell it who you are, what the job is, and point it to the right links. Always be SPECIFIC about what you want, and let the agent figure out the rest for itself. More of Google AI PM @Saboo_Shubham_’s ultimate OpenClaw starter guide on today’s new TWiST… plus demos of @AgentMail and @MoltworldIO. Chapters : 0:00 Intro 1:24 Plaud: If your work depends on conversations — interviews, meetings, calls — you need a Plaud NotePin. You can check it out at Plaud.ai/twist and use code TWIST for 10% off! 3:05 We're Claw-pilled once again; it's an all AI Agent showcase 6:15 Google AI PM Shubham Saboo's Top 5 OpenClaw tips 10:14 Quo (formerly OpenPhone) gives you a clean, modern way to handle every customer call, text, and thread all in one place. Try it free at quo.com/TWiST. 13:26 Tip #1 — Onboard your agent like a new hire 17:31 Tip #2 — Talk to your agents constantly 19:02 Tip #3 — Put your agents on a schedule 19:51 LinkedIn Jobs – Hire right, the first time. Post your first job and get $100 off towards your job post at LinkedIn.com/twist. 23:41 Tip #4 — Add cross-agent memory 29:33 Tip #5 — Let your agents self-improve 29:51 Iru unifies identity, endpoint security, and compliance into one platform. TWiST listeners get 20% off when they book a demo at iru.com/twist! 34:03 Why Jason says founders should avoid journalists (cc: @Jason, @Lons, @Haakamaujla) 🎥 Watch the full episode here 👇

    → View original post on X — @saboo_shubham_, 2026-03-31 00:11 UTC

  • Must-Read AI Research of the Week: LLM Agents and Optimization
    Must-Read AI Research of the Week: LLM Agents and Optimization

    By

    @debashis_dutta

    –

    Must-read AI research of the week: ▪️ Learning to Commit: Generating Organic Pull Requests via Online Repository Memory ▪️ Effective Strategies for Asynchronous Software Engineering Agents ▪️ Composer 2 ▪️ From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents ▪️ Scalable Prompt Routing via Fine-Grained Latent Task Discovery ▪️ MSFT: Addressing Dataset Mixtures Overfitting Heterogeneously in Multi-task SFT ▪️ On the Direction of RLVR Updates for LLM Reasoning: Identification and Exploitation ▪️ Sparse but Critical: A Token-Level Analysis of Distributional Shifts in RLVR Fine-Tuning of LLMs ▪️ Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs? ▪️ RL for Distributional Reasoning in LMs ▪️ Rethinking Token-Level Policy Optimization for Multimodal Chain-of-Thought ▪️ EVA: Efficient Reinforcement Learning for End-to-End Agent Find the full list and the main AI news here: turingpost.com/p/fod146

    → View original post on X — @debashis_dutta, 2026-03-30 23:41 UTC

  • Natural Language Agent Harnesses: From Code to AI-Defined Control Logic
    Natural Language Agent Harnesses: From Code to AI-Defined Control Logic

    By

    @debashis_dutta

    –

    We’re trying to build intelligent systems… using control frameworks designed by humans. That’s the core limitation of today’s agent harnesses. A new paper from Tsinghua University and Shenzhen proposes something radically different: 👉 What if the harness itself is not code—but natural language? Instead of hardcoding orchestration logic, they introduce Natural-Language Agent Harnesses (NLAH): – The control logic is written as an editable natural language SOP – The LLM interprets and executes that SOP dynamically – A shared runtime enforces structure via contracts, artifacts, and adapters Even more interesting: ➡️ The SOP itself can be generated and adapted by AI depending on the task So instead of: > Humans define → Agents execute We get: > AI defines → AI executes → AI evolves 🧠 Technical takeaway This shifts agent design from: – Static orchestration graphs – Hardcoded tool pipelines – Rigid planner-executor loops To: – Executable natural language control logic – Runtime-interpreted orchestration – Portable, composable harness artifacts The harness is no longer buried in code—it becomes a first-class abstraction. 🏗️ Architecture implications – Decouple control logic from implementation – Treat orchestration as data, not code – Use LLMs as meta-execution engines – Design systems that scale with tokens, not constraints 💡 Bigger question If agents can define and execute their own control logic… What else in AI system design should stop being code—and start being language? 📄 Paper: arxiv.org/abs/2603.25723 🔗 Follow my communities and personal initiatives: • Amazing AI, Data, Quantum Computing & Emerging Technologies — drdebashisdutta.com/ • Research & Innovation – Quantum, AI & Advanced Systems — researchedge.org

    → View original post on X — @debashis_dutta, 2026-03-30 21:25 UTC

  • OpenClaw: The Next AI Breakthrough Beyond ChatGPT?

    By

    @catherineadenle

    –

    🦞👀👏🏾 Could OpenClaw be the next ChatGPT? In an interview with Jim Cramer, Jensen Huang says OpenClaw could unlock a whole new wave of AI. ➡️ Not just chat. ➡️ AI that acts. We’ve moved from: Search ➝ Generate ➝ Execute It’s a big shift and I am here for it. 💬 What’s your take, real breakthrough or overhyped? 📊 source: madmoneyoncbc | IG 📌 Tags: #AI #NVIDIA #ChatGPT #Tech #OpenClaw #TechNews #Innovation #GenAI #FutureOfWork #ArtificialIntelligence #Technology #MachineLearning #Startups

    → View original post on X — @catherineadenle, 2026-03-30 21:19 UTC