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herdr 0.7.0 is out, and it's a major one: it introduces plugins! the idea is simple: herdr stays lean, and everything custom gets extended through plugins. shareable, scoped, built however you want, to fit your own flow. with this release we're also shipping a few examples of what the...

104,793 просмотров • 3 месяцев назад •via X (Twitter)

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Hermes Desktop does not have to stay exactly the way it shipped. You can build the tools you wish were already inside it. A project dashboard. A Bot control panel. A research tracker. A queue for work that needs your attention. A status board for scheduled jobs. A set of quick actions you use every day. That is what the Hermes Desktop Plugin SDK opens up. SDK is the technical name, but the idea is pretty simple: It gives you a way to add your own tools and interfaces directly inside Hermes Desktop. A plugin can add its own page, pane, sidebar item, status-bar element, keyboard shortcut, command-palette action, or composer extension. And it can work with live Hermes data, including things like sessions, config, Skills, and Cron. So if you keep asking Hermes to recreate the same dashboard, show the same information, or give you the same controls... you may be at the point where that workflow deserves to become an actual part of your Desktop. The starting point can be surprisingly lightweight too. A basic local plugin can be a single `plugin.js` file under: `$HERMES_HOME/desktop-plugins/ /` No Hermes repo clone. No separate build step. The bigger idea here is not “turn every prompt into an app.” It is that Hermes Desktop can become much more personal than the interface you downloaded. When a workflow proves useful enough, you can give it its own permanent place. Video via Brooklyn! — a good look at what this can actually become.

Hermes Release Watch

15,411 просмотров • 21 дней назад

🧃 Introducing stereOS: a Linux based operating system hardened and purpose built for AI agents. It's clear that agents need an ACTUAL operating system (not what people are calling an "OS") to witness the full breadth and depth of their capabilities while mitigating the blast radius of autonomous, untrusted actors. But there are so many problems with AI sandboxes today: * Going out to the apple store and buying a mac mini will never scale and is way too expensive (obviously) * Running in Docker is too restrictive (agents can't stand up their own container infrastructure, no sub virtualization, docker-in-docker is very broken) * Firecracker strips all the hardware so GPU PCIe passthrough, secure boot, FIPs, etc. is out of the question. * Native VMs are too fat and the overhead of 1 agent per VM is too much. stereOS takes a different approach: it's a full NixOS system that you boot and then kick off agent sandboxes inside with gVisor + /nix/store namespace mounting. Each agent gets their own kernel and the /nix/store is read only by nature. Even if the agent was somehow able to escape the gVisor virtual kernel, they'd land on the NixOS system as the "agent" user! Not your actual hardware!! If you want to take a defense-in-depth approach, we support "native" agents that run at the system level kicked off by our `agentd` utility. These agents, on their own, can manage and kick off other sub agents using the internal sandboxing mechanisms. Today, we're open sourcing all of this: * stereOS: our purpose built Linux OS - * masterblaster: client utility to launch, manage, and orchestrate agents - * stereosd: the stereOS system control plane daemon - * agentd: the stereOS system agent management daemon - Give it a try, throw us a star, and let me know what you think 🧃⭐️

John McBride

150,844 просмотров • 6 месяцев назад

whoever leaked this has bigger balls than sense Google Research and MIT ran the same agent jobs 260 different ways for Nature last month: they held the prompts, the tools and the compute budget identical and moved nothing but the wiring between the agents, and the same work swung from 70% worse than a single agent to 80.8% better, averaging out at 0.0% i ran my own single agent against the task list first and it cleared 6 of 10 alone, already past the line where a crew starts subtracting this is Graph Engineering, the layer that decides whether a crew is worth 80% more or 70% less, and it installs into the agent you already pay for: - score your solo agent on the real task first: above roughly 45% success that study predicts zero to negative returns from any crew you put around it - under that line, put one supervisor over the fan out: crews with no correction step amplified their own errors to 17.2x the single agent rate, supervised aggregation held it to 4.4x - give every worker one output and let none of them read a peer's draft, so a wrong step reaches the supervisor instead of four other agents - run the comparison again after every model upgrade, because a better model raises your baseline and a higher baseline is what makes a crew stop paying - keep the single agent alive as the control, the only number that says the wiring is earning its calls turns out the shape does not travel: the biggest win came off a finance task under one supervisor and the worst collapse off a planning task with independent agents my position, and it is the arguable one: a crew is a bet on your own diagram, and the model you pick moves that bet less than one arrow does bookmark this, the three moves that draw those arrows before you pay for one extra call are in the post below ↓

Argona

891,346 просмотров • 1 месяц назад