Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

this is a laptop running a 31b parameter model at 99% gpu autonomously through hermes agent, 15 tok/s sustained, 22.8 of 24gb vram gone, 94 watts at 50c. no api keys. no rate limits. no "your prompts are being used for training". no monthly subscription. no anthropic telling me...

sudoingX's profile picture

Sudo su

26,228 subscribers

65,567 Aufrufe • vor 4 Monaten •via X (Twitter)

Nachrichten & PolitikBildungWissenschaft & Technologie
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

IF YOU HAVE BEEN HOLDING OFF ON HERMES AGENT BECAUSE OF API COSTS, READ THIS FIRST. Four real ways to run it for free. Local, through Ollama, complete privacy and no rate limits. Cloud, through free tiers from Groq, OpenRouter, or NVIDIA NIM, no card required. A subscription you already have, GitHub Copilot connects natively. A fallback configuration so a rate limit on one provider does not stop your whole session. Hermes Agent is free and open source. Now there is no excuse on the model side either. Bookmark this. Follow CyrilXBT
10:45

IF YOU HAVE BEEN HOLDING OFF ON HERMES AGENT BECAUSE OF API COSTS, READ THIS FIRST. Four real ways to run it for free. Local, through Ollama, complete privacy and no rate limits. Cloud, through free tiers from Groq, OpenRouter, or NVIDIA NIM, no card required. A subscription you already have, GitHub Copilot connects natively. A fallback configuration so a rate limit on one provider does not stop your whole session. Hermes Agent is free and open source. Now there is no excuse on the model side either. Bookmark this. Follow CyrilXBT

CyrilXBT

28,802 Aufrufe • vor 1 Monat

A $351 MINI PC IS RUNNING 26-BILLION-PARAMETER AI MODELS AT 20 TOKENS/SEC AND HERMES AGENT ON TOP OF IT This is the Minisforum UM790 Pro. AMD Ryzen 9 7940HS, Radeon 780M iGPU, 48GB DDR5. The BIOS reports the GPU has 4GB of VRAM Here's the part people get wrong. The 780M has no dedicated VRAM at all it borrows from system RAM. Vulkan ignores the BIOS number and reads the full 48GB pool directly That's the whole trick. 21+ GB allocated to model weights on a "4GB" GPU. Because the models are MoE, only 4 billion of the parameters activate per token a fraction of the reads a dense model needs. That's why 20 tok/s works here Gemma 4 26B MoE holds 19.5 tok/s with 196K context. Qwen3.5-35B-A3B holds 20.8. Nemotron Cascade 2 clears 24.8. A dense 31B, by contrast, drops to 4 tok/s it reads the entire model every token, no way around it On top: Hermes Agent, full agentic workflows terminal, file ops, web, 40+ tools against local models only. No API keys. The wall between you and a usable local agent used to be a GPU you couldn't afford. Now it's a BIOS setting most people never check Bookmark this & Try it yourself ↓
1:22

A $351 MINI PC IS RUNNING 26-BILLION-PARAMETER AI MODELS AT 20 TOKENS/SEC AND HERMES AGENT ON TOP OF IT This is the Minisforum UM790 Pro. AMD Ryzen 9 7940HS, Radeon 780M iGPU, 48GB DDR5. The BIOS reports the GPU has 4GB of VRAM Here's the part people get wrong. The 780M has no dedicated VRAM at all it borrows from system RAM. Vulkan ignores the BIOS number and reads the full 48GB pool directly That's the whole trick. 21+ GB allocated to model weights on a "4GB" GPU. Because the models are MoE, only 4 billion of the parameters activate per token a fraction of the reads a dense model needs. That's why 20 tok/s works here Gemma 4 26B MoE holds 19.5 tok/s with 196K context. Qwen3.5-35B-A3B holds 20.8. Nemotron Cascade 2 clears 24.8. A dense 31B, by contrast, drops to 4 tok/s it reads the entire model every token, no way around it On top: Hermes Agent, full agentic workflows terminal, file ops, web, 40+ tools against local models only. No API keys. The wall between you and a usable local agent used to be a GPU you couldn't afford. Now it's a BIOS setting most people never check Bookmark this & Try it yourself ↓

slash1s

35,742 Aufrufe • vor 2 Monaten

HERMES AGENT IS NOW IN THE CLOUD. NO VPS. NO TERMINAL. NO SETUP. PICK A MODEL. PICK A SERVER SIZE. AGENT IS LIVE IN 60 SECONDS. Nous Portal just launched hosted Hermes Agent. two clicks. one minute. done. Nous Research WHAT THIS MEANS: before today: install Hermes on a VPS or your laptop. configure providers. set up gateway. manage updates. run hermes setup. edit config.yaml. great for power users. friction for everyone else. now: go to pick a model. pick a server size. your agent is live and reachable in 60 seconds. no terminal. no SSH. no Docker. same Hermes. same features. same tools. someone else handles the infrastructure. FOR TEAMS: this is where it gets interesting. spin up agents for everyone at your org. each team member gets their own Hermes instance. granular access controls per user. unified billing through Nous Portal. your team gets Hermes on day one. no DevOps needed. no VPS per person. one admin dashboard. one bill. WHAT'S INCLUDED: → 300+ models via Nous Portal (Claude, GPT, Gemini, DeepSeek, Grok, MiniMax, and more) → Tool Gateway (web search, image generation, TTS, browser automation) → all messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal) → full feature set (profiles, cron, kanban, skills, memory, sub-agents, MoA, /goal, /learn, /journey) → automatic updates ONE PORTAL. FOUR TIERS: Free: $0/month. pay-as-you-go credits from $10. Plus: $20/month. $22 in monthly usage credit. Super: $100/month. $110 in monthly credit. Ultra: $200/month. $220 in monthly credit. highest rate limits. every paid tier includes Tool Gateway. one OAuth. one subscription. no extra API keys. SELF-HOSTED IS NOT GOING ANYWHERE: Hermes is MIT licensed. open source. free forever. you can still run it on your laptop, VPS, or GPU cluster. nothing changes for self-hosted users. the cloud version is for people who want the agent running without managing the machine. pick your path: → self-hosted: full control. you manage everything. → cloud: zero ops. Nous manages infrastructure. → hybrid: self-host your main agent, cloud for team members. HOW TO START: cloud: self-hosted: hermes setup --portal both connect to the same Nous Portal. same models. same tools. same billing. learn how to replace your entire team with 8 hermes agents 👇
0:20

HERMES AGENT IS NOW IN THE CLOUD. NO VPS. NO TERMINAL. NO SETUP. PICK A MODEL. PICK A SERVER SIZE. AGENT IS LIVE IN 60 SECONDS. Nous Portal just launched hosted Hermes Agent. two clicks. one minute. done. Nous Research WHAT THIS MEANS: before today: install Hermes on a VPS or your laptop. configure providers. set up gateway. manage updates. run hermes setup. edit config.yaml. great for power users. friction for everyone else. now: go to pick a model. pick a server size. your agent is live and reachable in 60 seconds. no terminal. no SSH. no Docker. same Hermes. same features. same tools. someone else handles the infrastructure. FOR TEAMS: this is where it gets interesting. spin up agents for everyone at your org. each team member gets their own Hermes instance. granular access controls per user. unified billing through Nous Portal. your team gets Hermes on day one. no DevOps needed. no VPS per person. one admin dashboard. one bill. WHAT'S INCLUDED: → 300+ models via Nous Portal (Claude, GPT, Gemini, DeepSeek, Grok, MiniMax, and more) → Tool Gateway (web search, image generation, TTS, browser automation) → all messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal) → full feature set (profiles, cron, kanban, skills, memory, sub-agents, MoA, /goal, /learn, /journey) → automatic updates ONE PORTAL. FOUR TIERS: Free: $0/month. pay-as-you-go credits from $10. Plus: $20/month. $22 in monthly usage credit. Super: $100/month. $110 in monthly credit. Ultra: $200/month. $220 in monthly credit. highest rate limits. every paid tier includes Tool Gateway. one OAuth. one subscription. no extra API keys. SELF-HOSTED IS NOT GOING ANYWHERE: Hermes is MIT licensed. open source. free forever. you can still run it on your laptop, VPS, or GPU cluster. nothing changes for self-hosted users. the cloud version is for people who want the agent running without managing the machine. pick your path: → self-hosted: full control. you manage everything. → cloud: zero ops. Nous manages infrastructure. → hybrid: self-host your main agent, cloud for team members. HOW TO START: cloud: self-hosted: hermes setup --portal both connect to the same Nous Portal. same models. same tools. same billing. learn how to replace your entire team with 8 hermes agents 👇

YanXbt

45,446 Aufrufe • vor 1 Monat

six months ago this wasn't happening on 8gb vram. running unsloth's Q4_K_XL quant of gemma 4 26b-a4b-it-qat, a sparse MoE model with only 4b active params on a single rtx 4060 laptop gpu, 8gb vram, 20+ tok/s decode. no cloud, no api, no offload hacks. just a gaming laptop on battery. what makes it fit: google's QAT (quantization aware training), plus MTP (multi token prediction) support in the latest llama.cpp builds. that combo is the single biggest unlock for local inference on low vram. rtx 3060, rtx 3070, gtx 1070, gtx 1080, rtx 4050, rtx 4060, rtx 5050, rtx 5060 — any 6-8gb consumer gpu, old or new — this model runs on it. world cup season, so i told it to build a soccer themed flappy bird clone. one shot, zero iteration, fully playable. six months ago an 8gb model could barely clone vanilla flappy bird. now it's shipping a themed game from a sparse MoE model running locally on a laptop battery. inference benchmarks: - decode throughput: 30 tok/s - context: 64k. this is the real unlock. 64k ctx is what makes a hermes agent loop viable locally on this model, not just single-turn chat. llama.cpp flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 -cmoe --port 8080 game's deployed on my own site, built and shipped end to end with open source llm, zero closed source api dependency in the pipeline. link in the description. gguf weights on huggingface, link in the comments. pull it down, run it on whatever 8gb card is sitting in your rig. try the game and tell me your score and what you want in v2. local llms on consumer gpus stopped being a meme.
0:20

six months ago this wasn't happening on 8gb vram. running unsloth's Q4_K_XL quant of gemma 4 26b-a4b-it-qat, a sparse MoE model with only 4b active params on a single rtx 4060 laptop gpu, 8gb vram, 20+ tok/s decode. no cloud, no api, no offload hacks. just a gaming laptop on battery. what makes it fit: google's QAT (quantization aware training), plus MTP (multi token prediction) support in the latest llama.cpp builds. that combo is the single biggest unlock for local inference on low vram. rtx 3060, rtx 3070, gtx 1070, gtx 1080, rtx 4050, rtx 4060, rtx 5050, rtx 5060 — any 6-8gb consumer gpu, old or new — this model runs on it. world cup season, so i told it to build a soccer themed flappy bird clone. one shot, zero iteration, fully playable. six months ago an 8gb model could barely clone vanilla flappy bird. now it's shipping a themed game from a sparse MoE model running locally on a laptop battery. inference benchmarks: - decode throughput: 30 tok/s - context: 64k. this is the real unlock. 64k ctx is what makes a hermes agent loop viable locally on this model, not just single-turn chat. llama.cpp flags: -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 64000 -cmoe --port 8080 game's deployed on my own site, built and shipped end to end with open source llm, zero closed source api dependency in the pipeline. link in the description. gguf weights on huggingface, link in the comments. pull it down, run it on whatever 8gb card is sitting in your rig. try the game and tell me your score and what you want in v2. local llms on consumer gpus stopped being a meme.

Alok

60,866 Aufrufe • vor 2 Monaten

We deployed a fully private AI agent on NuNet in under 5 minutes 🚀 OpenClaw🦞 running Qwen through ollama , one of the hottest open source model families right now, entirely on decentralized compute. No cloud. No API keys. No data leaving the machine. This is what private AI looks like when you actually build it instead of just talking about it. Your model. Your hardware. Your rules. Full walkthrough showing exactly how it works: What should we deploy next?
12:59

We deployed a fully private AI agent on NuNet in under 5 minutes 🚀 OpenClaw🦞 running Qwen through ollama , one of the hottest open source model families right now, entirely on decentralized compute. No cloud. No API keys. No data leaving the machine. This is what private AI looks like when you actually build it instead of just talking about it. Your model. Your hardware. Your rules. Full walkthrough showing exactly how it works: What should we deploy next?

NuNet 🌐

87,604 Aufrufe • vor 4 Monaten

no prompt engineering, no agentic harness, no tool calls. just me being lazy in llama.cpp's web ui and gemma 4 31b dense taking the task seriously. i typed "create gpu marketplace cards with hardware specs and prices per hour" and the model went and coded this ui, one shot, navy bg, glassmorphism cards, neon accent buttons, realistic pricing tiers per architecture. it even wrote a "why this looks premium" explanation under the code. for context this is a q4 quant of google's 31b dense thinking model, running on a rtx 5090 mobile 24gb in the rog scar 18 at around 15 tok/s sustained, same vram tier as a 3090 or 4090 desktop, so whatever you see here translates directly to your card at home. the whole interaction was me not trying and the model reasoning harder than the prompt deserved. that tells me more about where local ai is at in april 2026 than any leaderboard score. next test drops gemma 4 into hermes agent, autonomous tool calling, multi step reasoning, real agentic loop instead of a chat window. let's see what the same model does when it gets the right environment. more experiments coming anon. octopus invaders queued. same hardware, different tasks, all published here on x and all translatable to your 24gb card. for now the video below shows it coding live, gpu going brrr.
2:23

no prompt engineering, no agentic harness, no tool calls. just me being lazy in llama.cpp's web ui and gemma 4 31b dense taking the task seriously. i typed "create gpu marketplace cards with hardware specs and prices per hour" and the model went and coded this ui, one shot, navy bg, glassmorphism cards, neon accent buttons, realistic pricing tiers per architecture. it even wrote a "why this looks premium" explanation under the code. for context this is a q4 quant of google's 31b dense thinking model, running on a rtx 5090 mobile 24gb in the rog scar 18 at around 15 tok/s sustained, same vram tier as a 3090 or 4090 desktop, so whatever you see here translates directly to your card at home. the whole interaction was me not trying and the model reasoning harder than the prompt deserved. that tells me more about where local ai is at in april 2026 than any leaderboard score. next test drops gemma 4 into hermes agent, autonomous tool calling, multi step reasoning, real agentic loop instead of a chat window. let's see what the same model does when it gets the right environment. more experiments coming anon. octopus invaders queued. same hardware, different tasks, all published here on x and all translatable to your 24gb card. for now the video below shows it coding live, gpu going brrr.

Sudo su

28,939 Aufrufe • vor 4 Monaten

i pointed hermes agent at nvidia's nemotron cascade 2 30B-A3B on a single RTX 3090 24GB. IQ4_XS quant by bartowski, 187 tok/s, 625K context. had it discover its own hardware, create an identity file, then build a full GPU marketplace UI from a single prompt. it one shotted it. first attempt no iteration. qwen 3.5 35B-A3B on the same hardware same 3090 24GB took an iteration to recover from a blank screen on the same type of build. 24 days between these two models releasing. same active parameters, completely different architectures and cascade 2 through hermes agent just keeps going. this model goes on and on. feast your eyes. more iterations and tests dropping soon. nvidia really cooked. no special flags needed. nvidia optimized this mamba MoE so well it just runs. flash attention auto enabled, context auto allocated. the model does the work not the config. but i compiled llama.cpp from source and i'm not sure how it performs on other engines. if you ran nemotron on any hardware drop your numbers below. RTX, AMD, Mac, whatever. model, quant, tok/s, engine. i want to see if it holds everywhere or just on llama.cpp.
1:31

i pointed hermes agent at nvidia's nemotron cascade 2 30B-A3B on a single RTX 3090 24GB. IQ4_XS quant by bartowski, 187 tok/s, 625K context. had it discover its own hardware, create an identity file, then build a full GPU marketplace UI from a single prompt. it one shotted it. first attempt no iteration. qwen 3.5 35B-A3B on the same hardware same 3090 24GB took an iteration to recover from a blank screen on the same type of build. 24 days between these two models releasing. same active parameters, completely different architectures and cascade 2 through hermes agent just keeps going. this model goes on and on. feast your eyes. more iterations and tests dropping soon. nvidia really cooked. no special flags needed. nvidia optimized this mamba MoE so well it just runs. flash attention auto enabled, context auto allocated. the model does the work not the config. but i compiled llama.cpp from source and i'm not sure how it performs on other engines. if you ran nemotron on any hardware drop your numbers below. RTX, AMD, Mac, whatever. model, quant, tok/s, engine. i want to see if it holds everywhere or just on llama.cpp.

Sudo su

70,981 Aufrufe • vor 4 Monaten

Introducing Agent Cookie. 🥷🏻🍪 For anyone running OpenClaw🦞 or Nous Research's Hermes on a Mac mini: I kept finding my agent logged out of everything, and it sucked. So I fixed it. "Add this to my Amazon cart." Sorry, logged out again. "Order my usual on Instacart." Nope, not logged in anymore. The fix: your laptop's cookies, CLI tokens, and API keys sync to your Mac mini. Continuously. Encrypted end-to-end over your Tailscale tailnet. No logging in twice. 🌐
1:49

Introducing Agent Cookie. 🥷🏻🍪 For anyone running OpenClaw🦞 or Nous Research's Hermes on a Mac mini: I kept finding my agent logged out of everything, and it sucked. So I fixed it. "Add this to my Amazon cart." Sorry, logged out again. "Order my usual on Instacart." Nope, not logged in anymore. The fix: your laptop's cookies, CLI tokens, and API keys sync to your Mac mini. Continuously. Encrypted end-to-end over your Tailscale tailnet. No logging in twice. 🌐

Matt Van Horn

256,700 Aufrufe • vor 2 Monaten

HERMES AGENT NOW RUNS ON AN 8GB LAPTOP GPU JUST AS EASILY AS IT RUNS ON A 128GB MINI PC Nous Research shipped the official Hermes Agent Desktop App this week. Someone pointed it at a local llama server running on an RTX 4060 with 16GB system RAM. The integration took two minutes The model behind it: Gemma 4 26B MoE, QAT quantized, running on 8GB of VRAM. A 60k token prompt held a stable 20 tokens a second, flat, no slowdown as context grew. The flags were nothing exotic, just -cmoe -c 248000 on llama.cpp What that 8GB setup does out of the box: reads and patches its own code, runs it in a terminal, debugs errors, manages GitHub repos, spawns sub-agents for parallel work. Browses the web with vision to debug a UI. Schedules cron jobs in plain language. Connects to Notion, Google Workspace, Linear, and Obsidian to manage tasks on its own That's the same agent layer running on a Minisforum MS-S1 MAX with 128GB of unified memory, 96GB of it to the GPU, holding a 120B model at 56 tokens a second instead of a 26B model at 20. Same software, same tool execution, same zero API key. The only thing that changes between an $800 laptop and a $2,000 mini PC is how big a model you can afford to run underneath it The barrier to running a real autonomous agent locally didn't just drop. It dropped all the way down to hardware most people already own
0:20

HERMES AGENT NOW RUNS ON AN 8GB LAPTOP GPU JUST AS EASILY AS IT RUNS ON A 128GB MINI PC Nous Research shipped the official Hermes Agent Desktop App this week. Someone pointed it at a local llama server running on an RTX 4060 with 16GB system RAM. The integration took two minutes The model behind it: Gemma 4 26B MoE, QAT quantized, running on 8GB of VRAM. A 60k token prompt held a stable 20 tokens a second, flat, no slowdown as context grew. The flags were nothing exotic, just -cmoe -c 248000 on llama.cpp What that 8GB setup does out of the box: reads and patches its own code, runs it in a terminal, debugs errors, manages GitHub repos, spawns sub-agents for parallel work. Browses the web with vision to debug a UI. Schedules cron jobs in plain language. Connects to Notion, Google Workspace, Linear, and Obsidian to manage tasks on its own That's the same agent layer running on a Minisforum MS-S1 MAX with 128GB of unified memory, 96GB of it to the GPU, holding a 120B model at 56 tokens a second instead of a 26B model at 20. Same software, same tool execution, same zero API key. The only thing that changes between an $800 laptop and a $2,000 mini PC is how big a model you can afford to run underneath it The barrier to running a real autonomous agent locally didn't just drop. It dropped all the way down to hardware most people already own

NO1ennn

40,079 Aufrufe • vor 2 Monaten

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.
1:46

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.

Sudo su

27,339 Aufrufe • vor 2 Monaten

I thought I needed a powerful GPU to run serious AI locally. I was completely wrong. Late 2025, Ollama quietly launched something that changed everything. They built a cloud inference layer running on NVIDIA's Blackwell architecture, the most powerful GPU hardware available today. No GPU on your desk required. No expensive API subscription. No complex setup. Just one command and you're running Kimi K2.5, a 1 trillion parameter open-source model, completely free. I connected it to OpenClaw, which links my phone's messaging apps to the AI agent running at home. I sent a message from WhatsApp. My machine picked it up, ran the task, and sent me back the result. It felt like magic. It's just good engineering. Save this post, you'll want this when you're building your first workflow. Want the SOP? DM me.
9:21

I thought I needed a powerful GPU to run serious AI locally. I was completely wrong. Late 2025, Ollama quietly launched something that changed everything. They built a cloud inference layer running on NVIDIA's Blackwell architecture, the most powerful GPU hardware available today. No GPU on your desk required. No expensive API subscription. No complex setup. Just one command and you're running Kimi K2.5, a 1 trillion parameter open-source model, completely free. I connected it to OpenClaw, which links my phone's messaging apps to the AI agent running at home. I sent a message from WhatsApp. My machine picked it up, ran the task, and sent me back the result. It felt like magic. It's just good engineering. Save this post, you'll want this when you're building your first workflow. Want the SOP? DM me.

Julian Goldie SEO

11,967 Aufrufe • vor 5 Monaten

HERMES AGENT VS OPENCLAW. a local ai onboarding flow test. a 3.9gb bonsai served on localhost, both agents upstream and latest, i point each one at the endpoint and watch which one even finds it. > hermes opens a provider menu, thirty plus options, local servers sitting right there next to the cloud ones, i hand it 127.0.0.1:8899, it verifies the endpoint, one model visible, auto-detects the model by name, bonsai-27b-q1_0, reads the context length straight off the server, saves it, and starts reasoning and firing real tool calls on my local model. no key. no friction. > openclaw has no menu. it goes hunting for a codex login, an openai key, finds none because there are none, prints no models available three times, defaults to openai/gpt-5.5, a cloud model it cannot reach, and dead ends on run auth login --provider openai. read that back. it asked me for an openai key. to run a model already running on my own machine. it never once looked at localhost. to be fair, openclaw can run local if you hand wire endpoint yourself. what it will not do is find the model already sitting on your box. hermes agent found it in one line. now the part i owe you. the auto-detect that just won, the model name read, the .gguf strip, the context length probe off the server, that is my code, it is in hermes agent main right now, authorship preserved, #2051 and #4218. the wizard fix that stops an agent from silently routing you to someone else's creds, the exact trap openclaw still falls into, mine too, #4210. i contribute to hermes agent, i told you that going in. one agent is built to talk to whatever you are running, the other is built to talk to a cloud api, so one found my model and ran it and the other asked me to log into openai. onboarding flow of both, mapped, below.
2:15

HERMES AGENT VS OPENCLAW. a local ai onboarding flow test. a 3.9gb bonsai served on localhost, both agents upstream and latest, i point each one at the endpoint and watch which one even finds it. > hermes opens a provider menu, thirty plus options, local servers sitting right there next to the cloud ones, i hand it 127.0.0.1:8899, it verifies the endpoint, one model visible, auto-detects the model by name, bonsai-27b-q1_0, reads the context length straight off the server, saves it, and starts reasoning and firing real tool calls on my local model. no key. no friction. > openclaw has no menu. it goes hunting for a codex login, an openai key, finds none because there are none, prints no models available three times, defaults to openai/gpt-5.5, a cloud model it cannot reach, and dead ends on run auth login --provider openai. read that back. it asked me for an openai key. to run a model already running on my own machine. it never once looked at localhost. to be fair, openclaw can run local if you hand wire endpoint yourself. what it will not do is find the model already sitting on your box. hermes agent found it in one line. now the part i owe you. the auto-detect that just won, the model name read, the .gguf strip, the context length probe off the server, that is my code, it is in hermes agent main right now, authorship preserved, #2051 and #4218. the wizard fix that stops an agent from silently routing you to someone else's creds, the exact trap openclaw still falls into, mine too, #4210. i contribute to hermes agent, i told you that going in. one agent is built to talk to whatever you are running, the other is built to talk to a cloud api, so one found my model and ran it and the other asked me to log into openai. onboarding flow of both, mapped, below.

Sudo su

23,816 Aufrufe • vor 1 Monat

happy friday. sneak peek at some new features i'm building for the bankr agent: > sandboxed filesystem (your agent gets its own secure file system in the browser. you control what it can access. no agent running loose on your actual computer) > skill uploads (plugin new capabilities so your agent learns new things) > cli download & access > secure environment variables for api keys > github integration (connect your repo for reads and writes directly from your agent) all of this runs in a secure browser environment. no desktop app. no messy configuration. no downloading agent harnesses from the terminal. just a sandbox you control. in the video: i download an audit skill, run it against a smart contract, and save the report straight to the filesystem. then i ask the agent to pull all my 2026 transactions and write a csv for my accountant.
0:58

happy friday. sneak peek at some new features i'm building for the bankr agent: > sandboxed filesystem (your agent gets its own secure file system in the browser. you control what it can access. no agent running loose on your actual computer) > skill uploads (plugin new capabilities so your agent learns new things) > cli download & access > secure environment variables for api keys > github integration (connect your repo for reads and writes directly from your agent) all of this runs in a secure browser environment. no desktop app. no messy configuration. no downloading agent harnesses from the terminal. just a sandbox you control. in the video: i download an audit skill, run it against a smart contract, and save the report straight to the filesystem. then i ask the agent to pull all my 2026 transactions and write a csv for my accountant.

deployer

45,638 Aufrufe • vor 4 Monaten

a regular laptop with just 8GB VRAM can now run a LOCAL AI AGENT directly on your device what you need: 1/ 8 GB VRAM 2/ 16 GB RAM 3/ $600+ laptop method: Kimi K2 + Hermes Agent Dekstop App what agent can do: 1/ read and write files 2/ patch code, open PRs on GitHub 3/ browse the web like a human 4/ work with Notion 5/ run scripts, debug, refactor this isn't 'ask a question, get an answer.' this is an agent that drives a task from start to finish on its own. before: local agent = either the cloud and $$$, or an RTX now: any gaming laptop from the last 3 years p.s. MoE is the key. Kimi K2 activates only a fraction of its params per token - that's why a 1T-param model runs on hardware where a dense 26B would already be choking did you try Hermes Dekstop?
0:20

a regular laptop with just 8GB VRAM can now run a LOCAL AI AGENT directly on your device what you need: 1/ 8 GB VRAM 2/ 16 GB RAM 3/ $600+ laptop method: Kimi K2 + Hermes Agent Dekstop App what agent can do: 1/ read and write files 2/ patch code, open PRs on GitHub 3/ browse the web like a human 4/ work with Notion 5/ run scripts, debug, refactor this isn't 'ask a question, get an answer.' this is an agent that drives a task from start to finish on its own. before: local agent = either the cloud and $$$, or an RTX now: any gaming laptop from the last 3 years p.s. MoE is the key. Kimi K2 activates only a fraction of its params per token - that's why a 1T-param model runs on hardware where a dense 26B would already be choking did you try Hermes Dekstop?

kaize

58,660 Aufrufe • vor 2 Monaten

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.
9:10

HERMES JUST GOT A CHATGPT INTERFACE AND IT IS COMPLETELY FREE. No terminal. No command line. No technical setup that takes half a day to figure out. Here is what just became possible. You can now run Hermes, DeepSeek, Ollama, and OpenClaw from a single clean chat interface. Switch between them in one click. Upload files directly. Attach knowledge bases and notes. Preview code without touching the terminal once. Use it on mobile. Make voice and video calls. All of it. Free. Open source. The setup took one conversation with Hermes itself. Pasted the GitHub link in. Hermes installed Open Web UI in Docker on its own. Connected the API server and gateway automatically. A clean ChatGPT-level interface running entirely on local hardware was live in minutes. No subscription. No data leaving your machine. No API bill at the end of the month. The people who set this up this weekend will have a local AI stack that rivals what most companies are paying $2,000 a month for in cloud infrastructure. The people who wait will keep paying for tools that send their data somewhere they cannot see. Both tools are free. Both are open source. The only thing between you and this setup is the next 30 minutes. Follow CyrilXBT for the exact config, setup steps, and local AI workflows I use to run a full stack without spending a dollar on cloud inference.

CyrilXBT

37,120 Aufrufe • vor 3 Monaten

i'm running a 397 billion parameter model on a amd ai max box that sits on my desk and pulls less power than a gaming laptop. the model is Nex-N2-Pro, 397B-A17B, the open weight release people are putting next to gpt-5.5 on coding. i have it quantized to IQ1_M, 1.75 bits per weight, 90gb of weights loaded into the 128gb of unified memory on amd's strix halo igpu. watch the gpu in this recording. it spikes, it sustains, it does not fall over. that is the part the spec sheets never show you, not just that a 400b model loads, but that an integrated graphics chip holds the load and generates token after token, stable, no crash, no thermal cliff. and it is not a slideshow. roughly 18 tokens a second, faster than you can read. a frontier scale model producing usable output, fully local. no datacenter, no rented h100s, no api key, no permission. three years ago a model this size meant a server room and a budget to match. tonight it is a quiet box on my desk. this is the accessible tier almost nobody benchmarks honestly, and it is further along than the timeline thinks. the full breakdown is coming, rocm vs vulkan on this chip, and this little amd box head to head against the nvidia equivalent. stay tuned.
2:18

i'm running a 397 billion parameter model on a amd ai max box that sits on my desk and pulls less power than a gaming laptop. the model is Nex-N2-Pro, 397B-A17B, the open weight release people are putting next to gpt-5.5 on coding. i have it quantized to IQ1_M, 1.75 bits per weight, 90gb of weights loaded into the 128gb of unified memory on amd's strix halo igpu. watch the gpu in this recording. it spikes, it sustains, it does not fall over. that is the part the spec sheets never show you, not just that a 400b model loads, but that an integrated graphics chip holds the load and generates token after token, stable, no crash, no thermal cliff. and it is not a slideshow. roughly 18 tokens a second, faster than you can read. a frontier scale model producing usable output, fully local. no datacenter, no rented h100s, no api key, no permission. three years ago a model this size meant a server room and a budget to match. tonight it is a quiet box on my desk. this is the accessible tier almost nobody benchmarks honestly, and it is further along than the timeline thinks. the full breakdown is coming, rocm vs vulkan on this chip, and this little amd box head to head against the nvidia equivalent. stay tuned.

Sudo su

32,163 Aufrufe • vor 2 Monaten

OpenClaw in your pocket — powered by Solana 🦞 (Video sent by one of our .molt domain users minting AI agent on Seeker | Solana Mobile ) Connect wallet → pick a name → mint → your AI agent is live. That's it. No servers, no CLI, no Docker, no monthly bills, no coding. Every .molt domain is an NFT with a built-in wallet, zero private keys, and full on-chain identity. One mint and you own an agent that can launch tokens, mint NFTs, host x402 services, and chat on Telegram, Discord & Slack. OpenClaw runs for free — persistent memory, sleeps when idle, wakes on demand. Your agent is always there when you need it. No $600 MacBook. No $15/month VPS. Just your phone and a Solana wallet. This is what building on Solana feels like — fast, cheap, and instant. A Solana Seeker phone with a built-in wallet is all you need. Mint your AI agent in seconds, straight from your pocket.
2:29

OpenClaw in your pocket — powered by Solana 🦞 (Video sent by one of our .molt domain users minting AI agent on Seeker | Solana Mobile ) Connect wallet → pick a name → mint → your AI agent is live. That's it. No servers, no CLI, no Docker, no monthly bills, no coding. Every .molt domain is an NFT with a built-in wallet, zero private keys, and full on-chain identity. One mint and you own an agent that can launch tokens, mint NFTs, host x402 services, and chat on Telegram, Discord & Slack. OpenClaw runs for free — persistent memory, sleeps when idle, wakes on demand. Your agent is always there when you need it. No $600 MacBook. No $15/month VPS. Just your phone and a Solana wallet. This is what building on Solana feels like — fast, cheap, and instant. A Solana Seeker phone with a built-in wallet is all you need. Mint your AI agent in seconds, straight from your pocket.

Molt.id

78,109 Aufrufe • vor 6 Monaten

i built a full game on a single GPU with a 3B model and this is the worst local AI will ever be. this was supposed to be a benchmark test. load the model, measure tokens per second, write it up, move on. instead i spent 20 minutes playing Octopus Invaders because the game is genuinely fun and i couldn't stop. a model with 3B active parameters built this from a single prompt. it debugged its own collision system when bullets were phasing through enemies. read the error, found the fix, kept building. this is not a frontier API. this is a quantized open source model running on hardware you can buy used for $800-$1200. no cloud. no subscription. no API costs. just a mass produced consumer GPU doing things that would have been absurd 12 months ago. and here's the part that should keep you up at night: every month the models get smaller and smarter. the quants get tighter. the context windows get longer. the tooling gets cleaner. what 3B active parameters does today on 24gb, a 1B model will do on 8gb within a year. you are looking at the floor. not the ceiling.
0:27

i built a full game on a single GPU with a 3B model and this is the worst local AI will ever be. this was supposed to be a benchmark test. load the model, measure tokens per second, write it up, move on. instead i spent 20 minutes playing Octopus Invaders because the game is genuinely fun and i couldn't stop. a model with 3B active parameters built this from a single prompt. it debugged its own collision system when bullets were phasing through enemies. read the error, found the fix, kept building. this is not a frontier API. this is a quantized open source model running on hardware you can buy used for $800-$1200. no cloud. no subscription. no API costs. just a mass produced consumer GPU doing things that would have been absurd 12 months ago. and here's the part that should keep you up at night: every month the models get smaller and smarter. the quants get tighter. the context windows get longer. the tooling gets cleaner. what 3B active parameters does today on 24gb, a 1B model will do on 8gb within a year. you are looking at the floor. not the ceiling.

Sudo su

36,251 Aufrufe • vor 5 Monaten

look anon, those of you who kept saying local AI is not there yet, who said open source can't compete, who said you need cloud APIs to get anything serious done, look at this gameplay for one minute. every pixel on this screen was written by one model, in one shot, on a single rtx 3090 with 24gb of vram. the model is qwen 3.6 27b dense q4. the harness is hermes agent. the hardware is a single consumer card you can buy used for 900 dollars. the prompt is open source on github. every claim verifiable, on your own desk. if your local AI take is from 2024, update it. the consumer tier is shipping work that was supposed to need 8 gpus and an api key. open source moved the floor while the rest of the field was busy explaining why it cannot. 24gb tier owners are eating ramen with half boiled egg and double chocolate.
1:39

look anon, those of you who kept saying local AI is not there yet, who said open source can't compete, who said you need cloud APIs to get anything serious done, look at this gameplay for one minute. every pixel on this screen was written by one model, in one shot, on a single rtx 3090 with 24gb of vram. the model is qwen 3.6 27b dense q4. the harness is hermes agent. the hardware is a single consumer card you can buy used for 900 dollars. the prompt is open source on github. every claim verifiable, on your own desk. if your local AI take is from 2024, update it. the consumer tier is shipping work that was supposed to need 8 gpus and an api key. open source moved the floor while the rest of the field was busy explaining why it cannot. 24gb tier owners are eating ramen with half boiled egg and double chocolate.

Sudo su

29,584 Aufrufe • vor 3 Monaten

Qwen 3.6 35B running at over 100 tokens per second on my $5,399 MacBook Pro M5 Max. This is the best local AI model I have ever run. 128GB of unified memory. No cloud. No API costs. No rate limits. Just raw local inference at speeds I didn't think were possible on a laptop. This model is more intelligent than GPT 5 on benchmarks. Running locally. On a MacBook. For free after the hardware cost. I said local AI would never compete with frontier. I'm starting to rethink that. The gap is closing faster than anyone expected.
1:04

Qwen 3.6 35B running at over 100 tokens per second on my $5,399 MacBook Pro M5 Max. This is the best local AI model I have ever run. 128GB of unified memory. No cloud. No API costs. No rate limits. Just raw local inference at speeds I didn't think were possible on a laptop. This model is more intelligent than GPT 5 on benchmarks. Running locally. On a MacBook. For free after the hardware cost. I said local AI would never compete with frontier. I'm starting to rethink that. The gap is closing faster than anyone expected.

BridgeMind

62,800 Aufrufe • vor 2 Monaten