ECOSYSTEM
TECHNOLOGY
The engineering layer of Jo Cor. Local AI, private networks, automation, hardware, and practical research are built and tested here.
NEX is the fleet’s engineer.
NEX: WHAT AN AGENT DOES HERE
NEX Fleet Engineer
System Architecture Core
NEX helps manage the technical side of the workshop. It can coordinate updates across connected agents, watch machines on the local network, and flag issues before they become larger problems. If a laptop starts running hot, NEX can help identify the process using resources, suggest a fix, and record what happened.
- Prompt engineering: Write, test, and improve instructions for reliable agent behaviour.
- Fleet management: Send updates and configurations to connected agents from one management point.
- Monitoring and upkeep: Track system health, research changes, and automate routine maintenance tasks.
- n8n automation: Connect applications and run repeatable workflows.
- Coding support: Read a codebase and provide context-aware assistance.
- Robotics support: Connect software models with physical hardware controls.
- Local LLM setup: Run models on your own hardware, sized for the available GPU and memory.
- Hardware care: Help diagnose performance, repair, cooling, and maintenance issues.
Technology covers local AI models, private networking, automation, and system monitoring. The workshop uses tools such as Ollama, Docker, and Tailscale, while following developments in AI, robotics, and hardware.
Start with the short guide below. Then follow the five steps to set up an agent of your own.
START HERE
AI Agents, Clearly Explained
Presented by Jeff Su
You can run Hermes Agent on a laptop, desktop PC, or a small VPS. Connect it to a model through OpenRouter and it can work with your files, terminal, and the web. The resources below take you from a fresh setup to a working agent.
BUILD FROM THE GROUND UP
Build your own Hermes agent
Five steps from a fresh machine to a private agent you control. Hermes is an MIT-licensed open-source project from Nous Research. Choose the setup that fits your hardware and budget.
1. Install on your laptop or PC
Mac, Windows, or Linux. Start locally and let the agent work with your files.
2. Rent a small VPS
Use a provider such as Hetzner, OVH, or Netcup if you want the agent to keep running when your laptop is closed.
3. Connect OpenRouter
Use one API key to access supported models. Free options can be enough for an initial working setup.
4. Connect files, terminal, and web
Hermes includes tools for files, terminal work, search, and web extraction.
5. Reach the agent from anywhere
Connect through Telegram, Discord, WhatsApp, or the web, depending on your setup.
The open-source community
Read the code, build a skill, contribute a pull request, or fork the project for your own use.
LOCAL INFERENCE
Run AI locally
Tools and resources for running open models on your own hardware.
Ollama
A simple way to download and run open models locally.
LM Studio
A desktop app for downloading, testing, and chatting with local models.
llama.cpp
A lightweight inference engine that runs on a wide range of hardware.
Hugging Face
Models, datasets, tools, and the people building them.
r/LocalLLaMA
A community for hardware advice, model news, local setup help, and benchmarks.
Unsloth
Tools for faster fine-tuning on consumer GPUs.
CLOUD COMPUTE
Run AI in the cloud
A rented GPU can help when local hardware is not enough. These services offer different options for inference, experiments, and larger workloads.
OpenRouter
One API for accessing models from multiple providers.
Together AI
Hosted inference for open models, with options for getting started.
Modal
Run code on GPUs without managing the underlying infrastructure.
Replicate
Run open-source models through an API for image, video, audio, and more.
Vast.ai
A marketplace for renting GPU capacity, often suited to batch work and experiments.
EMBODIED AI
Robotics and physical systems
This is where models connect with sensors, motors, and physical machines. Open hardware and shared software stacks make it easier to begin.
LeRobot (Hugging Face)
An open robotics framework for training policies from demonstrations and running them on accessible robot arms.
SO-ARM100 / SO-101
Affordable, 3D-printable robot-arm designs used by the LeRobot community.
ROS 2
An open software framework for robot drivers, simulation, navigation, and control.
ROS Discourse
A forum where robotics developers discuss tools, problems, and project work.
HARDWARE CURIOSITIES
The tinker table
Hardware and unusual tools for practical experiments and side projects.
LilyGO T-Deck
A pocket LoRa device with a keyboard and screen for off-grid communication projects.
RuView
Experimental software that explores Wi-Fi-based sensing and spatial mapping.
LogicBench
A browser-based digital logic and IC simulator for testing ideas before buying hardware.
Snowblind PC mod
A guide to turning a PC side panel into a transparent LCD display.
Blueprint
AI-assisted hardware design for turning circuit ideas into schematics.
OBSERVABILITY
Connect and observe
A working local AI setup also needs networking, service management, and basic monitoring.
Tailscale
A private network that connects your devices securely.
Docker
A way to package services so they run consistently across machines.
Uptime Kuma
A self-hosted status page for checking services and endpoints.
Grafana
Dashboards for metrics, logs, and system monitoring.
FUTURE RESEARCH
Thermal systems, synthetic diamond, and energy recovery
High-performance computing creates heat. Synthetic diamond can spread heat away from dense electronics, but it does not generate electricity. The research challenge is to combine better heat spreading, cooling, storage, and heat-to-power systems to manage energy more effectively and recover some of it when conditions allow.
Research areas
- → Diamond and diamond-composite heat spreaders for high-power chips, GPUs, and dense local compute.
- → Thermal interfaces, vapor chambers, liquid cooling, and microfluidic cooling for moving heat away from hotspots.
- → Thermoelectric generation, which can turn a suitable temperature difference into electricity. Its output depends on the temperature gap and materials.
- → Thermophotovoltaics, Organic Rankine Cycle systems, Stirling engines, and other approaches for recovering energy from higher-temperature waste heat.
- → Thermal storage and heat reuse, including safe use of recoverable heat for space heating or hot water.
- → Measurement first: track temperature, power draw, airflow, cooling performance, and reliability before making efficiency claims.
This is an early research track. It is not a consumer product or a promise of net-positive energy. Results depend on heat quality, temperature difference, system scale, conversion losses, cost, and safety.
LONG-HORIZON COLLABORATION
From research track to industrial pilot
Turning thermal research into useful infrastructure needs more than workshop experiments. It needs research partners, materials specialists, thermal engineers, hardware manufacturers, universities, public-interest institutions, and patient capital.
A path from early research to an industrial pilot could require major investment. Depending on the technical route, facilities, testing, safety requirements, and deployment scale, that investment could reach hundreds of millions of euros. A figure near €500 million is a long-term planning estimate, not secured funding, a valuation, or a guaranteed project budget.
NEX starts by studying the science, measuring real systems, and finding the right partners for responsible work.
OPEN WEIGHTS
Models worth knowing
Open models and benchmarks worth following.
LMArena
Human preference comparisons between models.
Arena agent leaderboard
Blind-vote comparisons of agents completing tasks.
LMSYS projects
Research, benchmarks, and open systems from the group behind LMArena.
Open LLM Leaderboard
A benchmark view of open models across reasoning, mathematics, and code.
Qwen3
An open-weight model family with strong reasoning, mathematics, and coding capabilities.
Mistral
A European AI lab known for open-weight and efficient language models.
DEEP LEARNING
Learn the machinery beneath the model
Teachers for understanding model fundamentals, mathematics, and implementation. Start with intuition, then move into code and longer courses.
Andrej Karpathy
Build neural networks and language models from first principles. Use this when you want to understand what the code is doing.
3Blue1Brown
Visual mathematics for linear algebra, calculus, neural networks, and backpropagation. A good foundation before implementation.
StatQuest with Josh Starmer
Clear explanations of statistics, loss functions, regularization, trees, and machine-learning fundamentals.
Sebastian Raschka
Deep-learning explanations and practical LLM implementation from a researcher who connects theory with reproducible code.
fast.ai / Jeremy Howard
Practical deep learning courses that help you build useful models while learning sound engineering habits.
MIT OpenCourseWare
University lectures and course material for machine learning, optimization, and related foundations.
Stanford Online
Computer-science courses covering machine learning, vision, language, and deep learning.
sentdex
Python and machine-learning projects for turning concepts into working experiments.
SYSTEMS & HOMELAB
Build the machine around the model
Local AI depends on compute, storage, networking, cooling, and monitoring. These channels cover the infrastructure around a private workshop.
Jeff Geerling
Home servers, Raspberry Pi, storage, networking, and open hardware, tested with careful technical reasoning.
NetworkChuck
Practical introductions to networking, Linux, self-hosting, cloud basics, and hands-on lab building.
Techno Tim
Homelab, Docker, Kubernetes, networking, and self-hosting guides for reliable personal infrastructure.
Christian Lempa
Self-hosted services, DevOps, Proxmox, automation, and homelab architecture for a home or small-team stack.
Lawrence Systems
Networking, firewalls, security, servers, and infrastructure operations from a professional IT perspective.
Level1Techs
Hardware, Linux, storage, enterprise systems, and performance analysis for people who want more engineering detail.
START WATCHING
Start with model intuition, then build a model, then run an agent on your own hardware.
But what is a neural network?
A visual introduction to neural networks and how they learn.
Let’s build GPT: from scratch, in code, spelled out
A long-form coding session for building a GPT-style language model.
Run a Powerful AI Agent Locally: Hermes Agent + Ollama
A community walkthrough for running Hermes Agent with a local Ollama model. This is not official Nous Research documentation.