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Hermes can get a second opinion before it acts. Or a third. Or a fourth. That’s essentially what Mixture of Agents does. Inside Hermes Desktop, you can choose several reference models to independently think through the same problem. Those models don’t run tools or take over the task. They... act as advisors. Their responses are then passed to the Aggregator, which is the model that actually responds, uses tools, and continues the Hermes agent loop. You can even mix providers across the setup. So instead of relying on one model to reason through a difficult problem alone, your acting model can make the call after seeing several independent perspectives. This video shows how simple that setup is inside: Settings → Model → Mixture of Agentsshow more

Hermes Release Watch

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30,133 次观看 • 2 天前 •via X (Twitter)

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Nous Research just dropped MOA (Mixture of Agents) presets inside Hermes Agent. I made a quick video showing how to set it up and create your own MOA. The idea: mix multiple models to get capabilities beyond any single model you can use right now. How it works: Normally Hermes sends your conversation + tools to one model. With MOA you get several reference models plus one aggregator. The references read the conversation and offer thoughts and suggestions, but they get no tool access and never reply to you directly. The aggregator is the one that actually acts. It sees the normal conversation plus the private advice from the references, then makes the tool calls and writes the final response. From Hermes's side, the aggregator's output IS the model's response, so you can use /goal or anything else like that. Cool idea, curious to see how it really performs!

Nous Research just dropped MOA (Mixture of Agents) presets inside Hermes Agent. I made a quick video showing how to set it up and create your own MOA. The idea: mix multiple models to get capabilities beyond any single model you can use right now. How it works: Normally Hermes sends your conversation + tools to one model. With MOA you get several reference models plus one aggregator. The references read the conversation and offer thoughts and suggestions, but they get no tool access and never reply to you directly. The aggregator is the one that actually acts. It sees the normal conversation plus the private advice from the references, then makes the tool calls and writes the final response. From Hermes's side, the aggregator's output IS the model's response, so you can use /goal or anything else like that. Cool idea, curious to see how it really performs!

tonbi

148,312 次观看 • 2 个月前

$Hermes agent doesn't need a frontier model to run your computer well. Kimi K2.6 or Qwen 3.6 can do the same job at a fraction of the cost, and both are available fully private through the Venice API. 🧵 Here's the full Hermes "computer_use" setup:$

Hermes agent doesn't need a frontier model to run your computer well. Kimi K2.6 or Qwen 3.6 can do the same job at a fraction of the cost, and both are available fully private through the Venice API. 🧵 Here's the full Hermes "computer_use" setup:

Venice

25,702 次观看 • 3 个月前

Hermes is powerful. But Open WebUI makes it way easier to actually use. Here’s the setup: → Run Hermes locally → Add the API server → Set up the environment file → Install Open WebUI with Docker → Connect Hermes as the chat agent → Log in and start chatting through a clean UI Now you can use Hermes without living inside the terminal. You get a ChatGPT-style interface, file uploads, model switching, web search, image tools, previews, and cleaner conversation management. Save this video, you’ll make Hermes 10x easier to use. Want the SOP? DM me. 💬

Hermes is powerful. But Open WebUI makes it way easier to actually use. Here’s the setup: → Run Hermes locally → Add the API server → Set up the environment file → Install Open WebUI with Docker → Connect Hermes as the chat agent → Log in and start chatting through a clean UI Now you can use Hermes without living inside the terminal. You get a ChatGPT-style interface, file uploads, model switching, web search, image tools, previews, and cleaner conversation management. Save this video, you’ll make Hermes 10x easier to use. Want the SOP? DM me. 💬

Julian Goldie SEO

61,538 次观看 • 4 个月前

Hermes Agent just made API keys feel outdated. And the feature nobody is watching might be the biggest shift. The Big Upgrade: → Run `hermes proxy` → Create a local OpenAI-compatible endpoint → Connect tools like Aider, Continue, and Codex CLI → Use Claude Pro, ChatGPT Pro, or SuperGrok through your existing subscriptions Why This Matters: ✓ One setup instead of separate API keys ✓ Your AI subscriptions become real developer infrastructure ✓ Coding tools can route through Hermes instead of separate accounts ✓ Setup takes around 30 seconds once Hermes is installed The Wild Part: → Grok now supports a 1M token context window inside Hermes → You can load huge codebases, long research docs, or multi-hour sessions → The model can connect details across files instead of forgetting half the workflow And It Gets Better: ✓ GPT, Gemini, and Grok Vision can now use computer control ✓ Models get real image pixels instead of text summaries ✓ Browser automation is now 180x faster with persistent Chrome connections Hermes went from “cool open-source agent” to a real command center for AI workflows. The practical lesson: Stop thinking of AI subscriptions as chat apps. Start thinking of them as infrastructure.

Hermes Agent just made API keys feel outdated. And the feature nobody is watching might be the biggest shift. The Big Upgrade: → Run `hermes proxy` → Create a local OpenAI-compatible endpoint → Connect tools like Aider, Continue, and Codex CLI → Use Claude Pro, ChatGPT Pro, or SuperGrok through your existing subscriptions Why This Matters: ✓ One setup instead of separate API keys ✓ Your AI subscriptions become real developer infrastructure ✓ Coding tools can route through Hermes instead of separate accounts ✓ Setup takes around 30 seconds once Hermes is installed The Wild Part: → Grok now supports a 1M token context window inside Hermes → You can load huge codebases, long research docs, or multi-hour sessions → The model can connect details across files instead of forgetting half the workflow And It Gets Better: ✓ GPT, Gemini, and Grok Vision can now use computer control ✓ Models get real image pixels instead of text summaries ✓ Browser automation is now 180x faster with persistent Chrome connections Hermes went from “cool open-source agent” to a real command center for AI workflows. The practical lesson: Stop thinking of AI subscriptions as chat apps. Start thinking of them as infrastructure.

Julian Goldie SEO

11,094 次观看 • 3 个月前

Okay, This is a big Hermes Desktop update 👀 Hermes can now actually use the Preview pane. Not just open a page. Not just read a page. Use it. Click. Type. Scroll. Navigate. Annotate. Inside the same visible browser pane you are watching. That is a big deal. It means Hermes Desktop is starting to feel a lot less like a chat window attached to tools and a lot more like an agent that can work through web tasks in front of you. Think about the use cases: → Open documentation and show you exactly where a setting lives → Walk through a setup flow and point out what to click → Search a site, open the right result, and keep moving → Work through a dashboard while you supervise → Annotate important controls on a crowded page → Reproduce a web issue step by step → Fill out multi-step web workflows with you watching → Find the right tutorial or guide and take you straight to it That changes the feel of Hermes Desktop. Because now Hermes is not just telling you what to do. It can show you, point to it, and act on it in the same workspace. This one opens up a lot!!!

Okay, This is a big Hermes Desktop update 👀 Hermes can now actually use the Preview pane. Not just open a page. Not just read a page. Use it. Click. Type. Scroll. Navigate. Annotate. Inside the same visible browser pane you are watching. That is a big deal. It means Hermes Desktop is starting to feel a lot less like a chat window attached to tools and a lot more like an agent that can work through web tasks in front of you. Think about the use cases: → Open documentation and show you exactly where a setting lives → Walk through a setup flow and point out what to click → Search a site, open the right result, and keep moving → Work through a dashboard while you supervise → Annotate important controls on a crowded page → Reproduce a web issue step by step → Fill out multi-step web workflows with you watching → Find the right tutorial or guide and take you straight to it That changes the feel of Hermes Desktop. Because now Hermes is not just telling you what to do. It can show you, point to it, and act on it in the same workspace. This one opens up a lot!!!

Hermes Release Watch

56,013 次观看 • 18 天前

Over 90,000 Skills sitting inside Hermes Desktop is kind of insane!!!🤯 Did you know Hermes Desktop has a built-in Skills Hub with more than 90,000 Skills you can browse and add to an agent? And you can do it right from Desktop. Go to: Capabilities → Skills From there, open the Skills Hub under your installed Skills. You can: • search the catalog • browse by category or source • open any Skill to read what it does • review platform support, author, version, requirements, and docs • click + Add to this Agent to install it directly to the agent/profile you have selected That means if you want Hermes to get better at a specific type of work, you do not have to go hunting through repos or memorize install commands first. Just open the Hub, look through the catalog, inspect the Skill, and add it with one click. A lot of users still have no idea this is sitting inside Hermes Desktop.

Hermes Release Watch

39,503 次观看 • 8 天前

Hermes Desktop Just Changed Everything Hermes just became way easier to use. No terminal, no complicated setup, no command-line stress. The new Hermes desktop app gives you a visual way to run a powerful AI agent. Here’s what it does: → Installs Hermes automatically. → Lets you choose OpenRouter, Anthropic, OpenAI, or local models. → Saves every chat session. → Creates separate profiles for different workflows. → Stores persistent memory. → Gives you access to 68 built-in tools. → Supports MCP connections. The biggest unlock? Hermes can learn skills from tasks and improve the more you use it.

Hermes Desktop Just Changed Everything Hermes just became way easier to use. No terminal, no complicated setup, no command-line stress. The new Hermes desktop app gives you a visual way to run a powerful AI agent. Here’s what it does: → Installs Hermes automatically. → Lets you choose OpenRouter, Anthropic, OpenAI, or local models. → Saves every chat session. → Creates separate profiles for different workflows. → Stores persistent memory. → Gives you access to 68 built-in tools. → Supports MCP connections. The biggest unlock? Hermes can learn skills from tasks and improve the more you use it.

Julian Goldie SEO

22,142 次观看 • 4 个月前

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

Ray Fernando

42,398 次观看 • 5 个月前

An LLM with native access to find and run agents is a huge thing! This large language model (called ASI:One and developed by Fetch.ai) can do this natively when you show it a problem: • Reason about the problem • Identify live agents and tools that can help solve it • Dynamically invoke these agents and tools Honestly, this is pretty nuts! You don't need to set up anything beforehand. You don't need to write any code. Here is the best part: ASI:One has access to any agent published on Agentverse, an open directory of agents! This gives the model access to *millions* of tools it can execute dynamically when solving a task. Here is a simple example (the one in the attached video): • I went to Google Maps • I copied the latitude and longitude of a location • I asked ASI:One about relevant places around that location • ASI:One went to the marketplace • It identified and called the Google Places agent • It returned a list of museums, restaurants, and landmarks near my location Pretty awesome! You can try this now by going to You don't need to pay or register to test it out. If you want to get ideas of what's possible, check Agentverse, the agent marketplace: The model will have access to all of those tools. This is pretty awesome and opens up a whole new world of possibilities! Thanks to the team for partnering with me on this post!

An LLM with native access to find and run agents is a huge thing! This large language model (called ASI:One and developed by Fetch.ai) can do this natively when you show it a problem: • Reason about the problem • Identify live agents and tools that can help solve it • Dynamically invoke these agents and tools Honestly, this is pretty nuts! You don't need to set up anything beforehand. You don't need to write any code. Here is the best part: ASI:One has access to any agent published on Agentverse, an open directory of agents! This gives the model access to millions of tools it can execute dynamically when solving a task. Here is a simple example (the one in the attached video): • I went to Google Maps • I copied the latitude and longitude of a location • I asked ASI:One about relevant places around that location • ASI:One went to the marketplace • It identified and called the Google Places agent • It returned a list of museums, restaurants, and landmarks near my location Pretty awesome! You can try this now by going to You don't need to pay or register to test it out. If you want to get ideas of what's possible, check Agentverse, the agent marketplace: The model will have access to all of those tools. This is pretty awesome and opens up a whole new world of possibilities! Thanks to the team for partnering with me on this post!

Santiago

43,784 次观看 • 1 年前

Hermes keeps finding ways to make agent work cost fewer tokens. And the latest cuts are automatic. Recent changes reduced recurring context overhead across several parts of Hermes: • Desktop’s model-facing UI tool schema dropped 41% • Memory and Skills guidance went from 537 to 255 tokens • Desktop’s always-served guidance was trimmed again • roughly another 727 tokens were removed from recurring platform and steering instructions That matters whether you use Hermes for coding, research, content, automations, business workflows, or long-running agent tasks. Every token spent explaining tools, Skills, memory, or platform behavior is context that isn’t being spent on your actual work. Hermes was already notably lean here. Composio recently put that to the test using the same K3 model across competing agent harnesses. Hermes came out among the strongest on completion while also posting the lowest average cost per task in their comparison. And the Hermes team clearly isn’t treating that as good enough. They continue cutting token waste across tools, Skills, memory, platform instructions, compression, and other parts of the agentic process. The goal is simple: more of your context goes toward the work you actually asked Hermes to do. Clip via Tom

Hermes keeps finding ways to make agent work cost fewer tokens. And the latest cuts are automatic. Recent changes reduced recurring context overhead across several parts of Hermes: • Desktop’s model-facing UI tool schema dropped 41% • Memory and Skills guidance went from 537 to 255 tokens • Desktop’s always-served guidance was trimmed again • roughly another 727 tokens were removed from recurring platform and steering instructions That matters whether you use Hermes for coding, research, content, automations, business workflows, or long-running agent tasks. Every token spent explaining tools, Skills, memory, or platform behavior is context that isn’t being spent on your actual work. Hermes was already notably lean here. Composio recently put that to the test using the same K3 model across competing agent harnesses. Hermes came out among the strongest on completion while also posting the lowest average cost per task in their comparison. And the Hermes team clearly isn’t treating that as good enough. They continue cutting token waste across tools, Skills, memory, platform instructions, compression, and other parts of the agentic process. The goal is simple: more of your context goes toward the work you actually asked Hermes to do. Clip via Tom

Hermes Release Watch

19,276 次观看 • 10 天前

HERMES JUST FIXED THE BIGGEST PROBLEM WITH BROWSER AGENTS Most AI agents still click around websites like confused interns. This new setup gives them the map. Hermes + → Hermes now connects to Browserbase’s new Browse hub → Browse launched with 100+ browser skills → Each skill is a plain-text playbook for a specific website or task → Your agent can search, preview, and install skills inside Hermes Why This Matters: ✓ Less random clicking ✓ Fewer timeouts ✓ Better form filling ✓ More reliable website navigation ✓ Skills can be edited, reused, bundled, and shared The Real Stack: → Hermes runs the agent → Browserbase handles cloud browser infrastructure → gives the agent site-specific skills → Vision helps when pages get weird → Bundles let you load repeat workflows faster The killer detail: If a website breaks, you don’t wait for the model to magically improve. You update the skill. Now your agent gets better forever. That’s the difference between an AI tool and an actual agent system.

HERMES JUST FIXED THE BIGGEST PROBLEM WITH BROWSER AGENTS Most AI agents still click around websites like confused interns. This new setup gives them the map. Hermes + → Hermes now connects to Browserbase’s new Browse hub → Browse launched with 100+ browser skills → Each skill is a plain-text playbook for a specific website or task → Your agent can search, preview, and install skills inside Hermes Why This Matters: ✓ Less random clicking ✓ Fewer timeouts ✓ Better form filling ✓ More reliable website navigation ✓ Skills can be edited, reused, bundled, and shared The Real Stack: → Hermes runs the agent → Browserbase handles cloud browser infrastructure → gives the agent site-specific skills → Vision helps when pages get weird → Bundles let you load repeat workflows faster The killer detail: If a website breaks, you don’t wait for the model to magically improve. You update the skill. Now your agent gets better forever. That’s the difference between an AI tool and an actual agent system.

Julian Goldie SEO

43,472 次观看 • 3 个月前

MASSIVE Hermes Agent update over the last few days Totally changes the way I use Hermes Here's 6 new features you need to start using immediately (video demoing them below): 1. Mixture of agents: send your prompt to a team of different models. The team sends back all of their responses to an orchestrator model who synthesizes a final answer. Gives much better results than just sending a prompt to 1 model 2. /learn: use the new built in /learn skill to have Hermes automatically create new skills. You can either give a prompt after /learn or put in a URL. I like pasting in URLs of tweets with helpful tips after /learn and Hermes will automatically turn it into a skill 3. /journey: See every skill and memory Hermes has created for you on a really nice timeline/chart. Great for seeing how your agent has learned and improved over time 4. Self improvement cost savings: Hermes now uses cheaper models to do it's self improvement including memory creation and skill creation. These types of activities happen in the background of almost every prompt, so this results in TONS of cost savings over time 5. Vibe coding improvements: Hermes desktop is now a full vibe coding tool. You can see diffs, make commits, and even open up PRs directly from the desktop interface. Makes it WAYYY nicer to vibe code with 6. Fable 5 is now built in. Fable 5. Obviously Fable is incredibly expensive, so only use this new profile for incredibly complex tasks. Excellent updates that have significantly improved the experience. Video demoing all the updates below!

Alex Finn

143,673 次观看 • 2 个月前

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.

Akshay 🚀

51,540 次观看 • 3 个月前

Our universe is a model with twenty or so carefully fine-tuned parameters that generate all the content inside. Using these parameters and a reduced-scale model you can simulate the history of the cosmos. With the full-scale model, you get to be part of the simulation.

Our universe is a model with twenty or so carefully fine-tuned parameters that generate all the content inside. Using these parameters and a reduced-scale model you can simulate the history of the cosmos. With the full-scale model, you get to be part of the simulation.

Andrew Côté

90,707 次观看 • 2 年前

NVIDIA is giving away free access to 140+ AI models for a full year > a lot of people building agents are paying $50-200/month just for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3.5-lightning-30b-a3b, Muse-glimmer-30b from Meta and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year a free key changes what you can afford to run: > one prompt costs nothing either way > a loop firing 300 agents every week is where the bill shows up > for a year that bill is zero while everyone is paying for API access, this is sitting there for free with this many models in one place it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

NVIDIA is giving away free access to 140+ AI models for a full year > a lot of people building agents are paying $50-200/month just for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3.5-lightning-30b-a3b, Muse-glimmer-30b from Meta and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year a free key changes what you can afford to run: > one prompt costs nothing either way > a loop firing 300 agents every week is where the bill shows up > for a year that bill is zero while everyone is paying for API access, this is sitting there for free with this many models in one place it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

Annatar.md

204,419 次观看 • 6 天前

Hermes Agent now has a desktop app! I made a quick video showing how easy it is to install, set up, and get to work! It perfectly synced with my existing agent, or you can use it to make a new one. Plus, a quick tour of the features! It's never been easier to start with Hermes!

Hermes Agent now has a desktop app! I made a quick video showing how easy it is to install, set up, and get to work! It perfectly synced with my existing agent, or you can use it to make a new one. Plus, a quick tour of the features! It's never been easier to start with Hermes!

tonbi

17,622 次观看 • 3 个月前

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

377,112 次观看 • 5 个月前

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 次观看 • 2 年前

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.

Dami-Defi

11,685 次观看 • 3 个月前

these guys are making Claude, Hermes, and DeepSeek argue with each other instead of just agreeing with you. this is the practical future of agent teams. with Bloome you just pick a task template, and it spins up a full agent team in a group chat to collaborate and get the job done. research, review, writing, market analysis, or anything you want. they brainstorm, review, and plan together. meanwhile, you or other human teammates can steer them or jump in, and stay in the same conversation so nothing gets lost across tools. since this is actual work, Bloome works across web, mobile, and desktop. try the desktop app first if you're using it seriously. same flexibility on the agent side too, you can connect any agents you want: ChatGPT, OpenClaw, Gemini… check them out →

these guys are making Claude, Hermes, and DeepSeek argue with each other instead of just agreeing with you. this is the practical future of agent teams. with Bloome you just pick a task template, and it spins up a full agent team in a group chat to collaborate and get the job done. research, review, writing, market analysis, or anything you want. they brainstorm, review, and plan together. meanwhile, you or other human teammates can steer them or jump in, and stay in the same conversation so nothing gets lost across tools. since this is actual work, Bloome works across web, mobile, and desktop. try the desktop app first if you're using it seriously. same flexibility on the agent side too, you can connect any agents you want: ChatGPT, OpenClaw, Gemini… check them out →

ℏεsam

62,794 次观看 • 2 个月前