beamnxw ./ (@beamnxw)

CLOUD AGENT WITH A BUILT-IN SELF-EVOLUTION SYSTEM Hermes operates continuously in the cloud and actively improves itself based on real usage The standout feature is the self-improving loop: > background processes analyze conversations > extract facts and procedural knowledge > update memory files > create or edit skills > and a curator periodically maintains quality by archiving, merging, and cleaning the skill library The guide covers everything in depth ➔ easy installation ➔ model routing to avoid inconsistencies ➔ different terminal environments (Local, Docker, SSH, Modal, Daytona) ➔ slash commands for goals and complex orchestration ➔ context management options ➔ memory systems ➔ webhooks ➔ cron jobs ➔ and smart usage principles to keep human oversight where it matters This architecture makes the agent more capable and reliable over time instead of degrading Read the complete article quoted below. It’s one of the clearest and most comprehensive explanations of modern agent design available Bookmark it for future reference

I FOUND THE EASIEST AI FACELESS NICHE TO START WITH. VIRAL BACKYARD VIDEO CREATION, 5 MINS INSTALLATION Those viral restoration timelapses dominating your feed are entirely AI-generated, and here's how to have the same ones: - Firstly, reverse-engineer a reference take a screenshot of an aesthetic yard design and feed it into ChatGPT to extract the exact prompt architecture. - Secondly, run that data through the "Restoration Timelapse" custom GPT. This tool instantly builds out the step-by-step construction sequence. - Finally, plug those prompts into an image generator to create the stages of the build, and use an AI video generator to animate the transition from dirt to the finished design. This format requires zero 3D modeling or complex editing skills. Bookmark this to build your automated content engine

Claude Fable 5 was tested against Opus 4.8 on physics simulation tasks Both models received the same prompts and were tasked with generating standalone HTML5 simulations without using third-party libraries: → Chaotic double pendulum → Galton board → Water in a rotating drum (WCSPH) Generation costs: • Fable 5 - $3.35, 68.7k tokens, 14 min 47 sec • Opus 4.8 - $0.93, 38.9k tokens, 8 min 10 sec Fable demonstrated its most notable advantage in the water simulation. The model created a more cohesive and stable fluid volume Opus exhibited large gaps near the walls, with individual particles scattering across the scene, and the fluid itself was less stable

While you’re writing prompts, Google’s agents built Windows from zero > They took Gemini 3.5 Flash + 93 sub-agents and built a fully working operating system from absolute scratch in 12 hours > Less than $1000 > 15,000+ model requests > 2.6 billion tokens And yes ➜ they even ran Doom on it This is what happens when you stop using AI as a fancy chatbot and start using it as an army of agents that actually builds real shit While most people are still writing single prompts and complaining the output is mid… Big tech is already deploying hundreds of agents in parallel that ship entire systems The game isn’t “better prompts” The game is agentic systems Wake up. The ones who figure out how to orchestrate agents right now will eat everyone else alive in the next 12–18 months This TikTok is not clickbait This is the new baseline

NVIDIA might have just declared war on the cloud GPU business For years, AI builders had one option Rent compute Pay every month Watch the bill grow every time usage increased Now NVIDIA is putting serious AI hardware directly on people's desks Small enough to fit next to a monitor Powerful enough to run workloads that used to require expensive cloud infrastructure That's why this launch is getting so much attention The real story isn't the hardware specs It's the business model shift Every month, developers send money to cloud providers for inference, testing, fine-tuning and AI applications The question nobody can answer yet is what happens if enough developers decide they'd rather buy infrastructure once than rent it forever Because if local AI hardware keeps getting more powerful, the economics start changing very quickly Cloud providers built empires on renting access to compute NVIDIA is betting more people will eventually want to own it And that's a much bigger story than a new piece of hardware sitting on a desk

«YOUR COMPANY IS NOT GOING TO GO OUT OF BUSINESS BECAUSE OF AI. YOUR COMPANY IS GOING TO GO OUT OF BUSINESS BECAUSE ANOTHER COMPANY USED AI» — Jensen Huang Today at Computex, @nvidia made it very clear: > The era of Local AI has officially begun They announced RTX Spark - a powerful new chip for thin laptops with up to 128GB unified memory Combined with their new Vera CPU, it’s designed specifically for agentic AI workloads ➜ It delivers an 80% speedup over x86 on agent-based tasks. . . > You can now run strong 70B–200B models locally > No more paying $1k–2k/month on cloud APIs (rapid ROI within a couple of months of purchase) > No rate limits and no data leaving your machine > Persistent agents and multi-agent systems become practical and cheap Local AI gives you three big advantages: - much lower costs - full privacy - and the ability to run agents 24/7 Cloud was convenient Local is the new competitive edge The shift is happening now. Those who build their AI stack locally will have a real advantage in speed and cost over the next 12–24 months My bet: The future belongs to those who own their compute Full podcast below ⇩ ⇩ ⇩