You ever look at the AI landscape and feel like it is just a battle between three or four massive fortresses in San Francisco and Seattle? It is easy to get that impression when you see the billions being poured into compute clusters. But then, you look at the open-source side, and there is this name that keeps popping up in every high-level conversation. Today's prompt from Daniel is about Nous Research, and honestly, if you follow the open-Weights scene at all, you know these guys are the ones currently setting the pace.
Herman Poppleberry here, and Corn, you hit the nail on the head. Nous Research is such a fascinating outlier because they do not fit the mold of a traditional corporate lab. They do not have a glass-and-steel headquarters or a thousand employees. They are a decentralized collective, and yet, they are consistently shipping models that punch way above their weight class. By the way, fun fact for everyone listening, today's episode is actually powered by Google Gemini three Flash. It is helping us pull all these technical threads together.
I love that. A decentralized collective writing the playbook for the next generation of agents. It feels very grassroots, but the output is professional-grade. Daniel is specifically asking about what makes them special and what this new Hermes-Agent framework is all about. Because let's be real, there are a million agent frameworks out there. Why is this one the one people are actually getting excited about?
It comes down to philosophy and data. Most companies try to solve AI by throwing more GPUs at the problem. Nous Research, founded back in twenty twenty-three by Karl Cobbe and a group of really brilliant distributed researchers, took a different path. They focused on how to make the training process more efficient and, more importantly, how to create better data. They are basically the masters of synthetic data generation.
Right, and when we say synthetic data, we are not talking about just churning out garbage. We are talking about high-quality, curated instruction sets that teach a model how to actually think and use tools, rather than just predicting the next word in a sentence. I think that is where the Hermes series really started to shine. I remember when Hermes two dropped, it felt like the first time an open model actually understood complex multi-step instructions without tripping over its own feet.
Well, not exactly, but you are hitting on the core mechanism. The Hermes three release in late twenty twenty-four was a watershed moment. It achieved state-of-the-art performance on open benchmarks, but it was not just about the scores. It was about the fact that it was trained on the Open Hermes dataset, which has over one million high-quality synthetic examples. They proved that if you prune the data—meaning you aggressively remove the low-quality tokens and focus on reasoning chains—you can get a smaller model to outperform a much larger one that was trained on a "dataset of everything."
It is the difference between reading the entire internet, including the comments section on a recipe blog, versus reading a curated library of the world’s best textbooks and logic puzzles. If you are a model, you want the textbooks. But let's pivot to the "Agent" part of this. Hermes-Agent isn't just a model; it is a framework. What is the technical shift there?
This is where it gets really cool. Hermes-Agent is essentially a full orchestration system. Most people think of an agent as just a chatbot with a "search" button. But Hermes-Agent implements what they call a "closed learning loop." It is designed to handle memory, tool use, and structured outputs in a way that allows the agent to actually improve the more you use it. Instead of just relying on a static vector database for memory, it builds what they call "Skill Documents."
Skill Documents. That sounds like something a middle manager would ask for, but I assume in this context, it is actually useful?
It is much better than a standard memory bank. Think about how most agents work today. You give them a task, they look up some related text in a database using RAG—Retrieval-Augmented Generation—and they try to piecemeal an answer together. Hermes-Agent, as it completes tasks and uses tools, actually writes markdown files that document how it solved the problem, what the infrastructure looks like, and what the codebase requirements are. It is building a manual for itself in real-time.
So it is essentially creating its own "tribal knowledge" within an organization’s digital walls. If I have it working on a specific Python library for three weeks, it is not just "remembering" the code; it is refining its own documentation on how to interact with that library specifically for my needs. That is a massive jump from just having a long context window.
It really is. And the reason this matters for the broader ecosystem is that it provides a reproducible, open-source alternative to something like OpenAI’s GPTs or Anthropic’s tool-use suite. If you build on a proprietary platform, you are locked into their pricing, their privacy policies, and their model updates which might "lobotomize" your agent overnight. With Hermes-Agent, you own the framework. You can run it locally, you can see how the logic is structured, and you can swap the underlying model if a better one comes along.
It feels like Nous Research is trying to democratize the "brains" of the operation. You mentioned Karl Cobbe earlier. The fact that this is a distributed network of researchers is so "twenty-twenties." No physical office, no corporate cafeteria, just a bunch of high-level talent on Discord and GitHub outperforming billion-dollar labs. It almost sounds too good to be true. How are they actually funding this? How do they get the compute?
They have been very smart about partnerships. They work with infrastructure providers and use distributed training techniques. Instead of needing one giant supercomputer in a single room, they can sometimes leverage clusters that are spread out. But their real "secret sauce" is that they are a magnet for talent. If you are a top-tier AI researcher who hates corporate bureaucracy and wants to actually release your work to the public, Nous is the place you go. They have built a brand around "unrestricted availability." Their mission statement explicitly mentions advancing human rights through open-source AI.
That is a bold mission. And it explains why they have such a cult following. When you look at the "Open Hermes" dataset, they didn't just keep that for themselves to build a moat. They gave it away. And then everyone else started using it to fine-tune their own models. They are effectively the "research and development" department for the entire open-source world.
That is a great way to put it. They are the R and D lab for the rest of us. And Hermes-Agent is the latest manifestation of that. One thing I want to dig into is the structured output part of the framework. You know how frustrating it is when you ask an AI for a JSON object and it gives you a paragraph of text with a bit of JSON hidden in the middle?
Oh, it is the worst. "Here is the data you requested!" No, just give me the data. I don't need the excitement.
Right. Well, Hermes-Agent is built from the ground up for function calling. The models in the Hermes family are specifically fine-tuned to understand when a tool is needed and to output the exact parameters required to trigger that tool without any "yap," as the kids say. This makes it incredibly stable for production environments. If you are a developer building an autonomous system that needs to, say, manage a cloud server or update a database, you need that reliability. You can't have the agent decide to write a poem when it should be closing a security port.
So, let's look at the "self-improving" aspect Daniel mentioned. How does a model actually get smarter the more you use it without a full retraining cycle? Because that sounds like magic or marketing.
It is not a full weight-update retraining, but it is a "data-centric" improvement. Every time the agent successfully uses a tool or solves a complex reasoning problem, that interaction is captured. In the Hermes-Agent ecosystem, these successful "trajectories" can be used to further fine-tune the next iteration of the model. It creates a virtuous cycle. Better agents generate better training data, which leads to better models, which leads to even better agents. It is a flywheel.
And because they are open-source, that flywheel benefits everyone who uses the framework, not just one company’s bottom line. It is a completely different incentive structure. Instead of a "moat" made of proprietary data, they are building a "tide" that lifts all boats. I mean, look at the comparison with something like OpenClaw. People are calling Hermes-Agent the "OpenClaw alternative" because it offers that same level of agentic power but without the "black box" nature of a managed service.
The "Skill Documents" part we talked about earlier is also a direct answer to the "memory" problem in AI. Most people think more context—like a million tokens—is the answer. But a million tokens of context is like having a giant pile of papers on your desk. You still have to find the right one. Skill Documents are like a neatly filed, indexed manual that the agent updates as it learns. It is "active" memory versus "passive" context.
I love the idea of an agent that grows with you. If I am a developer and I have been working with Hermes-Agent for six months, that agent should be significantly more "aware" of my specific architecture than a fresh instance of GPT-4. It is personalized professional intelligence. But let's talk about the risks or the hurdles. If it is decentralized and open-source, who is responsible if it goes off the rails? Is there a "safety" layer, or is it "wild west" AI?
That is the big debate in the community. Nous Research leans heavily toward the "pro-freedom" side of the spectrum. They believe that the best way to ensure AI safety is to make the technology transparent and widely available so that everyone can build defenses and understand how it works. They don't put the same kind of "preachy" guardrails on their models that you might see from a company like Anthropic. They trust the user more. For some, that is a huge selling point. For others, it is a concern. But technically speaking, you can add your own guardrails to the Hermes-Agent framework because you have full control over the code.
It puts the responsibility back on the developer. Which, if you are a professional, is usually what you want anyway. You don't want your tool telling you "I can't do that" when you are trying to debug a legitimate security configuration. You want the tool to do what it is told.
And that is why the "Agentic" focus is so key. To be a true agent, the AI has to be able to make decisions and execute actions. If it is constantly checking with a "safety supervisor" in the cloud that is tuned to be overly cautious, it becomes useless for complex tasks. Nous is focused on "reasoning" as the primary safety mechanism. If the model is smart enough to reason through a problem, it is less likely to make a catastrophic error out of simple hallucination.
So, if I am a listener and I want to actually get my hands dirty with this, where do I go? Is this something a hobbyist can run, or do I need a server farm?
You can actually run the Hermes models on a fairly modest setup if you use quantized versions. But for the full Hermes-Agent experience, you really want to check out their GitHub. It is modular. You can plug in different LLM backends. You can start by exploring the Open Hermes dataset if you are into training, or just jump into the Hermes-Agent repo to see how they handle tool calling. They also have a very active Discord. That is where the "collective" actually lives. It is a constant stream of researchers swapping ideas and datasets.
It is like the early days of the internet, but for intelligence. I think the thing that surprises people most is that a "decentralized collective" can actually produce something more stable and useful than a multi-billion dollar corporation. But when you think about it, open-source has a history of doing this. Linux runs the world’s servers. Blender is a powerhouse in 3D. Why wouldn't the same happen for AI agents?
It is happening right now. And the "Skill Documents" approach is a great example of an innovation that came from the open-source community's need to solve problems efficiently without just throwing more RAM at it. By creating searchable markdown files that document the "how-to" of a task, they solved the long-term memory problem in a way that is actually human-readable. You can literally open the agent’s "brain" folder and read what it has learned about your project.
Wait, that is actually a huge point. If I can read the markdown files the agent is generating about my project, I can correct it. I can say, "No, that is not how our deployment pipeline works," and I just edit the file. I am literally "teaching" the agent by editing a text document. That is so much more intuitive than trying to "prompt engineer" a behavior through a chat interface.
Now you see why the following is so intense. It is "White Box" AI. You can see the gears turning, you can inspect the memory, and you can intervene in the learning process without needing a PhD in machine learning. It turns the AI from a mysterious oracle into a collaborative workstation.
It is funny, we spent so much time worrying about "Black Box" AI taking over, and here is Nous Research just handing everyone the keys to the box. It feels very pro-American, in a way—that spirit of decentralized innovation and individual empowerment. It is not about waiting for a big institution to give you a tool; it is about building the tool with your peers.
It really is. And when you look at the geopolitical side of it, having a strong, open-source AI ecosystem in the West is a massive strategic advantage. It prevents a single point of failure. If one company gets compromised or changes its mind about access, the technology lives on through the collective.
So, to wrap up the "what is it" part: Nous Research is the "people’s lab," Hermes is the "smartest student in class," and Hermes-Agent is the "autonomous worker" that actually uses those brains to get stuff done. And it all runs on a philosophy of "give it away and make it better through community."
That is it. And the "self-improving" part is the most exciting frontier. We are moving toward systems that don't just stay static. If you use Hermes-Agent today, and you use it a year from now, the version a year from now will be fundamentally more capable because of the data it generated while working with you. That is a shift from "AI as a product" to "AI as a growing partner."
I think we should dig a bit deeper into the "distributed training" aspect because I think people hear that and think "SETI at home" or something where you just donate your computer's idle time. But it is more sophisticated than that, right? How do they coordinate a bunch of independent researchers to train one cohesive model like Hermes three?
It is incredibly sophisticated. They use frameworks that allow for model sharding and gradient accumulation across high-latency networks. It is not as fast as having everything on a single high-speed backplane in a data center, but it is much more resilient. And it allows them to tap into "pockets" of compute all over the world. But more than just the technical coordination, it is the "data coordination" that matters. They have a system for peer-reviewing synthetic data. Before a dataset is used to train a model, the community actually looks at samples, runs tests, and ensures the "reasoning chains" are sound.
So it is like a peer-reviewed journal, but for training tokens. That explains the high quality. They aren't just scraping; they are curating. It is "artisanal" data at scale. Which is a hilarious phrase, but it seems to be the only thing that works.
It really is "artisanal data." And that is the lesson for everyone else. If you want to compete with the giants, you don't try to out-scale them on hardware; you out-curate them on data. Nous Research has proven that a small, dedicated group of people who really care about the "truth" of the data can produce a model that feels "smarter" than a trillion-parameter monster that was trained on the "average" of the internet.
Because the "average" of the internet is... well, it is pretty mediocre. If you train on mediocrity, you get a mediocre model. If you train on the "best of" synthetic reasoning, you get something like Hermes.
And that is why Hermes-Agent is such a threat to the closed-source models. It is bringing that "best-of" reasoning to actual real-world tasks. It is not just about answering trivia; it is about "go into this GitHub repo, find the bug in the authentication logic, write a fix, and test it." That requires a level of precision that you only get when the training data is impeccable.
It is the difference between a student who has memorized the textbook and a student who has actually done the lab work. Hermes-Agent is doing the lab work. And because it documents that work in Skill Documents, the next time it encounters a similar bug, it doesn't have to start from scratch. It says, "Oh, I remember this. I solved something like this last Tuesday. Let me check my notes."
And those "notes" are in a format that you, the human, can actually read and verify. That transparency is the "killer feature." In a world where we are increasingly worried about what AI is doing behind the scenes, having an agent that literally keeps a diary of its work is a massive step forward for trust.
I think people are also really drawn to the "underdog" story. There is something inherently cool about a group of "rogue" researchers outperforming the most well-funded companies in history. It feels like the early days of the Homebrew Computer Club.
It is the Homebrew Computer Club of the twenty-twenties. And just like that club eventually gave us the personal computer revolution, the work being done at Nous Research is giving us the "personal AI" revolution. Not "personal" as in "it knows your favorite color," but "personal" as in "it belongs to you, it works for you, and it learns from you."
That is the dream, right? An AI that isn't just a window into some giant corporate brain, but a tool that lives on your machine and becomes an extension of your own capabilities. I think the reason Daniel is so excited about this—and the reason the community is following them so closely—is that Nous Research is the only group that seems genuinely committed to making that dream a reality without any strings attached.
No strings attached is the key. No monthly subscription to keep your agent "smart," no "usage limits" that kick in just when you are in the middle of a project, and no "safety filters" that prevent you from doing your job. Just raw, high-quality intelligence and the tools to orchestrate it.
And that brings us to the "why it matters" part of Daniel's prompt. It matters because it is a hedge against centralization. If we only have three or four "AI providers" in the world, they have an incredible amount of power. They can decide what we can say, what we can build, and what we can think. Nous Research is the antidote to that. They are making sure that the most powerful technology of our generation remains in the hands of the people.
It is a democratic imperative. If intelligence is going to be the "primary commodity" of the future, we cannot have it controlled by a small group of people in one or two zip codes. We need a decentralized, open-source alternative that is just as capable as the proprietary ones. And for the first time, with Hermes three and Hermes-Agent, we actually have that.
It is a pretty wild time to be alive. We are watching the "Great Decentralization" of intelligence happen in real-time. And it is not happening in a government lab or a corporate boardroom; it is happening on Discord and GitHub.
It is happening in the "open." And that's what makes it so robust. You can't kill a decentralized movement. You can't "buy out" a collective that doesn't have a CEO. You can only join them or try to keep up.
I think "try to keep up" is what most of Big Tech is doing right now. They are looking at these open-weight benchmarks and realizing that the gap is closing much faster than they anticipated. If a group of distributed researchers can get eighty or ninety percent of the performance of a multi-billion dollar model with a fraction of the compute, the "moat" is starting to look more like a puddle.
And when you add the "agentic" layer on top, it is game over for the "closed" model of AI. Because an agent that can self-improve and learn from its user is always going to be more valuable than a static model that is frozen in time. Hermes-Agent is the first real step toward that "living" AI system.
It is like the difference between a static encyclopedia and a living, breathing mentor. One is a reference; the other is a partner. I think we are all looking for a partner in this digital world.
Well said. And if you are a developer or even just someone interested in where this is going, I really can't recommend enough that you go and look at what Nous is doing. It is the "front line" of the AI revolution.
And I think we have covered the "what," "how," and "why" pretty thoroughly. It is about data, it is about community, and it is about freedom. Those are the three pillars of Nous Research.
And the "Skill Documents" are the "diary" of the future. I'm telling you, that is going to be the standard for how we handle AI memory in five years. Everyone will look back and say, "Of course, why did we ever think a giant pile of unstructured text was the answer?"
"Why didn't we just give the robot a notebook?" It seems so obvious once someone else does it. But that is the mark of a great innovation—it feels inevitable in hindsight.
That is exactly it. Well, I think we have done Daniel's prompt justice. This was a deep one, but it is such an important part of the current AI story.
It really is. It is the story of the outsiders winning. And who doesn't love an underdog story? Especially when the underdog is building the most advanced agents on the planet.
And doing it for free. Don't forget that. They are basically the "Robin Hoods" of the AI world. Stealing the "fire of the gods" from the corporate labs and giving it to everyone with an internet connection.
A very "Promethean" vibe. I like it. Well, let's wrap this up with some practical takeaways for the folks listening.
If you are a developer: go to the Nous Research GitHub. Download the Hermes-Agent repo. See how they handle function calling. It will change the way you think about building agents. If you are a researcher: look at the Open Hermes dataset. See how they curated it. There is a "masterclass" in data science hidden in those million examples. And if you are just a fan of technology: join their Discord. It is the most exciting place on the internet right now if you want to see the "future" being built in real-time.
And for everyone else, just keep an eye on that "Hermes" name. Every time it pops up, it means the bar for what is possible in open-source AI has just been raised another notch.
It is the "gold standard" of the open-Weights world. And it is only getting better.
Well, this has been a fascinating deep dive. I definitely learned a few things about "Skill Documents" that I'm going to be thinking about for the rest of the day.
It is the "manual" for your digital life, Corn. One day, we will all have one.
As long as I don't have to write it myself, I'm in. Let the agent do the documentation. That is the true dream of AI—no more writing README files.
If Nous Research can solve the "README problem," they truly will be the heroes of the modern age.
I think they are well on their way. Thanks for the deep dive, Herman. This was great.
Any time. I love talking about the "scrappy" side of AI. It is where the real innovation happens.
It definitely is. Alright, I think we are at a good stopping point. This has been a blast.
Agreed. Let's head out.
Thanks as always to our producer Hilbert Flumingtop for keeping the gears turning behind the scenes. And a big thanks to Modal for providing the GPU credits that power this show. If you are enjoying "My Weird Prompts," please leave us a review on your favorite podcast app—it really does help us reach more people.
You can find us at myweirdprompts dot com for our RSS feed and all the ways to subscribe. This has been My Weird Prompts.
We will catch you in the next one. Stay curious.
Bye everyone.
