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✨ Managed to load my custom GPT script into an open source LLM called OpenHermes 2.5 Mistral 7B I used which makes it super easy to run open source LLMs on your laptop without any cloud servers or OpenAI or ChatGPT, just your own GPU in your MacBook! I...

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745,285 görüntüleme • 2 yıl önce •via X (Twitter)

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@levelsio2 yıl önce

Sometimes I get this, it breaks into ### Response: inside the sentence and it all gets rekt, any fixes? @MaxRovensky

Rodrigo Rocco 👨‍💻📈📗 from JobBoardSearch 🔎 profil fotoğrafı
Rodrigo Rocco 👨‍💻📈📗 from JobBoardSearch 🔎2 yıl önce

I can't believe all this stuff, I installed on my windows laptop and is faster than GPT4, the OpenHermes model is just mind blowing, he also said they are building the Hermes 2.5 vision model

𝑨𝒓𝒕𝒊𝒇𝒊𝒄𝒊𝒂𝒍 𝑮𝒖𝒚 profil fotoğrafı
𝑨𝒓𝒕𝒊𝒇𝒊𝒄𝒊𝒂𝒍 𝑮𝒖𝒚2 yıl önce

Hey @Teknium1 talking about u

@levelsio profil fotoğrafı
@levelsio2 yıl önce

@Teknium1 Oh damn the guy that made it follows me W T F

Alvin De Cruz profil fotoğrafı
Alvin De Cruz2 yıl önce

Have you tried using LM Studio as a server with your own frontend. Then you can access it via localhost then connect to OpenAI’s DALL-E 3 to create images.

Brandon Braner profil fotoğrafı
Brandon Braner2 yıl önce

Any chance you could make a tutorial on how you trained it on your data. I’ve been looking into Lora but still not fully following it. Doesn’t help I’m an Ilm newb

Duncan — e/zucc profil fotoğrafı
Duncan — e/zucc2 yıl önce

Great success! Now all you need is custom Actions support (HTTP endpoints with auth) and embedded image generation can be done Image generation serving model could be hosted on the same laptop (slow) or some minimal maintenance cheap private cloud

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Atlas3D2 yıl önce

If you don't know about @Teknium1 you two teaming up would be legendary.

codejake 🇺🇸 profil fotoğrafı
codejake 🇺🇸2 yıl önce

I’ve been using on my M3 Max and like it a lot. Modelfiles are really useful as GPTs-like. Any clue how compares?

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Dr. Daniel Bender2 yıl önce

The good old text adventure is back and is smarter than ever. Love the idea!

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There's a free, open-source music generator that claims to land between Suno v4.5 and Suno v5 in quality, and it'll run locally on a GPU with under 4GB of VRAM. It's called ACE-Step 1.5. → Free to try instantly, no GPU, no install → Generates a full song in under 10 seconds on a regular RTX 3090 → Train it on your own sound: feed it about 8 of your own songs and it learns your style as a LoRA in about an hour Paste a genre and mood, get back a finished song with vocals, in any of 50+ languages.

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nikshep

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I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

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Agentic coding puts a kind of stress on GPU clusters that providers are struggling to handle. SemiAnalysis runs it on the clusters it rates anyway, with an open-source CLI in front. "A lot of our ClusterMAX testing, we lead with our CLI, cmax. It's open source. Feel free to try it out on your managed cluster." "We do use agentic coding for testing all these clusters, and the workload is very different. The workload is very stressful on these systems." "That's why we have cmax CLI as a front end, and the agent is just a wrap around it. If there are any issues cmax CLI faces, the agent debugs into it and understands what's going on."

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Databricks CEO Ali Ghodsi describes the difficulties of building a business around open source: "You have to hit two home runs, consecutively." "Your first home run - and you're banking on hitting that home run - is that your open source project takes over the world. Everybody wants to download it." If you don't hit that home run, Ali says, you just shot yourself in the foot. You just gave away your IP and got nothing back for it. But even if you hit this home run, you still don't have a monetizable business. You still can't make any money. So you have to hit the second home run: "You need a 10x innovation on top of the open source. That's the proprietary value you'll provide companies. That's why they'll come to you. Otherwise, they can get the open source anywhere." If you don't hit the second home run, the hyperscalers pick up your open source model, and they offer it for almost-free. "Getting into open source and monetizing is not an easy sport. Preferably don't get into it, because you might get hurt."
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Databricks CEO Ali Ghodsi describes the difficulties of building a business around open source: "You have to hit two home runs, consecutively." "Your first home run - and you're banking on hitting that home run - is that your open source project takes over the world. Everybody wants to download it." If you don't hit that home run, Ali says, you just shot yourself in the foot. You just gave away your IP and got nothing back for it. But even if you hit this home run, you still don't have a monetizable business. You still can't make any money. So you have to hit the second home run: "You need a 10x innovation on top of the open source. That's the proprietary value you'll provide companies. That's why they'll come to you. Otherwise, they can get the open source anywhere." If you don't hit the second home run, the hyperscalers pick up your open source model, and they offer it for almost-free. "Getting into open source and monetizing is not an easy sport. Preferably don't get into it, because you might get hurt."

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45,393 görüntüleme • 9 ay önce

built a dead simple chrome extension for my wife and myself. sharing here as free open source as usual Simple Words we find ourselves spending too much time wordsmithing our email replies, just to make sure we sound friendly and respectful this chrome extension is really simple and quite literally a gpt wrapper that only does one thing - one click turning what you mean to what's ready to send it runs on your own api key, subscription or local LLM. the extension itself is free and open source. if you tried it out, would be keen to hear how it works for you! extension: repo:
0:31

built a dead simple chrome extension for my wife and myself. sharing here as free open source as usual Simple Words we find ourselves spending too much time wordsmithing our email replies, just to make sure we sound friendly and respectful this chrome extension is really simple and quite literally a gpt wrapper that only does one thing - one click turning what you mean to what's ready to send it runs on your own api key, subscription or local LLM. the extension itself is free and open source. if you tried it out, would be keen to hear how it works for you! extension: repo:

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Weave is launching the number 1 prompt router in the world. It enables you to get 70% more efficient use of your tokens. We analyzed millions of prompts and found that the vast majority don't need a frontier model. Weave Router fixes that. It analyzes your prompt and routes it to the highest quality model with the lowest cost (across open and closed source models). This all happens in your current workflow on Claude, Cursor or Codex so you don't have to change a thing. Early customers have seen an ~70% reduction in costs without any slowdowns. Source code available.
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Weave is launching the number 1 prompt router in the world. It enables you to get 70% more efficient use of your tokens. We analyzed millions of prompts and found that the vast majority don't need a frontier model. Weave Router fixes that. It analyzes your prompt and routes it to the highest quality model with the lowest cost (across open and closed source models). This all happens in your current workflow on Claude, Cursor or Codex so you don't have to change a thing. Early customers have seen an ~70% reduction in costs without any slowdowns. Source code available.

Adam Cohen

28,338 görüntüleme • 4 ay önce

Andrej Karpathy (co-founder of OpenAI, Eureka Labs): "The future of AI is not all in the cloud. The most important models will run locally on your hardware under your control." June 2026 is the first time that the future is actually accessible to everyone! Here is what the local LLM landscape looks like right now: → raspberry pi 5: coherent chatbot, no GPU required → macbook air: matches GPT-3.5 quality on most tasks natively → used RTX 3090 at $700: something close to GPT-4 running locally → ollama: one command to an openAI-compatible endpoint, every major toolchain integrates with it → LM studio: best GUI, native MLX acceleration on apple silicon, MCP support in 0.4.0 → llama.cpp: the engine everything else runs on, 90MB footprint, runs on raspberry pi to android → the honest case: 80% of daily work is genuinely good enough locally right now Your prompts never leave your machine. no per-token billing. no rate limits. no refusals. The playbook for every hardware tier, from Raspberry Pi to RTX 5090, is here. Bookmark it so you do not lose it! Follow NeilXbt for more local AI and open source intelligence that tracks the hardware and tooling most people discover six months late.
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Andrej Karpathy (co-founder of OpenAI, Eureka Labs): "The future of AI is not all in the cloud. The most important models will run locally on your hardware under your control." June 2026 is the first time that the future is actually accessible to everyone! Here is what the local LLM landscape looks like right now: → raspberry pi 5: coherent chatbot, no GPU required → macbook air: matches GPT-3.5 quality on most tasks natively → used RTX 3090 at $700: something close to GPT-4 running locally → ollama: one command to an openAI-compatible endpoint, every major toolchain integrates with it → LM studio: best GUI, native MLX acceleration on apple silicon, MCP support in 0.4.0 → llama.cpp: the engine everything else runs on, 90MB footprint, runs on raspberry pi to android → the honest case: 80% of daily work is genuinely good enough locally right now Your prompts never leave your machine. no per-token billing. no rate limits. no refusals. The playbook for every hardware tier, from Raspberry Pi to RTX 5090, is here. Bookmark it so you do not lose it! Follow NeilXbt for more local AI and open source intelligence that tracks the hardware and tooling most people discover six months late.

NeilXbt

19,405 görüntüleme • 3 ay önce

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Announcing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry. Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential. OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose. rohit and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think! Try it out: (requires your own API key) Source code:

Andrew Ng

1,167,202 görüntüleme • 2 ay önce

J-Cal: Nvidia is taking the gloves off with its open source model. They will own the whole stack. “You will not be able to tell the difference between Jensen Huang's open source LLM and Claude for 95% of your searches, I guarantee you. Now, why has Nvidia and Jensen downplayed their open source model until this moment? Why would he do that? Why would he never bring it up in the All-In interview, never bring it up? Because his top customers (OpenAI, Anthropic, etc.) were very concerned, from what I understand, about the fact that Nvidia had made so much progress on their open source model. But suddenly, after OpenAI announced their Jalapeno chips, after Anthropic started making chips, after AMD did successful projects with both of these companies, after Elon said he's going to do his own fab, Nvidia's taking the gloves off. They are going to own the whole stack. You get the hardware from them, and you're going to get a model that's competitive with OpenAI's for free.”
0:54

J-Cal: Nvidia is taking the gloves off with its open source model. They will own the whole stack. “You will not be able to tell the difference between Jensen Huang's open source LLM and Claude for 95% of your searches, I guarantee you. Now, why has Nvidia and Jensen downplayed their open source model until this moment? Why would he do that? Why would he never bring it up in the All-In interview, never bring it up? Because his top customers (OpenAI, Anthropic, etc.) were very concerned, from what I understand, about the fact that Nvidia had made so much progress on their open source model. But suddenly, after OpenAI announced their Jalapeno chips, after Anthropic started making chips, after AMD did successful projects with both of these companies, after Elon said he's going to do his own fab, Nvidia's taking the gloves off. They are going to own the whole stack. You get the hardware from them, and you're going to get a model that's competitive with OpenAI's for free.”

The All-In Podcast

227,849 görüntüleme • 3 ay önce

🚨 Founders: free, private, 100% local AI is finally here I downloaded Qwen 3.6 and ran it on my MacBook inside Strawberry browser. I couldn't believe it. We now have 100% free and private AI that is on par with the best models from OpenAI, Anthropic and Google. (see video!) The CTO of Hugging Face called this "the second revolution of AI." He's right. If you are a building an AI product, you HAVE to start thinking about how you can leverage open source models in your product. It's only a matter of time until local models are mainstream and can run on most people's devices. LLMs are commoditizing. The human brain only requires a AAA battery's worth of energy to run. If biology figured out efficient intelligence, software can too. Maybe we won't need artificial super intelligence and thousands of data centres to do most economically valuable work. Maybe there's a future where AI agents just run on your laptop, fine-tuned to you, fully owned by you, costing only what your energy bill costs. In this future, it's all about building the most delightful, intuitive experience. We've been building for exactly this moment. Stay tuned.
0:41

🚨 Founders: free, private, 100% local AI is finally here I downloaded Qwen 3.6 and ran it on my MacBook inside Strawberry browser. I couldn't believe it. We now have 100% free and private AI that is on par with the best models from OpenAI, Anthropic and Google. (see video!) The CTO of Hugging Face called this "the second revolution of AI." He's right. If you are a building an AI product, you HAVE to start thinking about how you can leverage open source models in your product. It's only a matter of time until local models are mainstream and can run on most people's devices. LLMs are commoditizing. The human brain only requires a AAA battery's worth of energy to run. If biology figured out efficient intelligence, software can too. Maybe we won't need artificial super intelligence and thousands of data centres to do most economically valuable work. Maybe there's a future where AI agents just run on your laptop, fine-tuned to you, fully owned by you, costing only what your energy bill costs. In this future, it's all about building the most delightful, intuitive experience. We've been building for exactly this moment. Stay tuned.

Charles Maddock

12,731 görüntüleme • 5 ay önce

Here is how you can install an open-source, enterprise-grade RAG system on your server (with the best document understanding I've seen.) First, something obvious to anyone trying to sell RAG in the market: You are crazy if you think companies will let their data travel to a hosted model. No one wants to send their data anywhere (those who do haven't found an alternative.) Every single company would rather have an air-gapped system with no internet access. GroundX is an open-source RAG system that you can run on your servers (or any cloud provider, as long as you have access to GPUs) and works without a network. (If the military wants to do RAG, this is precisely what they will be looking for.) I installed GroundX on my AWS account and recorded a video to show you how to use it. There are two services you can use: 1. Ingest: This service uses a pretrained vision model to ingest and understand your knowledge base. 2. Search: This service combines text and vector search with a fine-tuned re-ranker model to retrieve information from your knowledge base. A quick note about the Ingest service: 99% of people think they need better "retrieval" mechanisms. I think they need better "ingestion." That's where this service comes in! Ingest "understands" your documents in a way I haven't seen before. After you try it, you'll realize why showing your LLM your raw documents is a bad idea. In the video, I use a free tool called X-Ray to test a document and understand how the Ingest service breaks it down. You can access this tool by signing up for a free GroundX cloud account and uploading your documents. You'll see a bit more about this in the video.
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Here is how you can install an open-source, enterprise-grade RAG system on your server (with the best document understanding I've seen.) First, something obvious to anyone trying to sell RAG in the market: You are crazy if you think companies will let their data travel to a hosted model. No one wants to send their data anywhere (those who do haven't found an alternative.) Every single company would rather have an air-gapped system with no internet access. GroundX is an open-source RAG system that you can run on your servers (or any cloud provider, as long as you have access to GPUs) and works without a network. (If the military wants to do RAG, this is precisely what they will be looking for.) I installed GroundX on my AWS account and recorded a video to show you how to use it. There are two services you can use: 1. Ingest: This service uses a pretrained vision model to ingest and understand your knowledge base. 2. Search: This service combines text and vector search with a fine-tuned re-ranker model to retrieve information from your knowledge base. A quick note about the Ingest service: 99% of people think they need better "retrieval" mechanisms. I think they need better "ingestion." That's where this service comes in! Ingest "understands" your documents in a way I haven't seen before. After you try it, you'll realize why showing your LLM your raw documents is a bad idea. In the video, I use a free tool called X-Ray to test a document and understand how the Ingest service breaks it down. You can access this tool by signing up for a free GroundX cloud account and uploading your documents. You'll see a bit more about this in the video.

Santiago

89,664 görüntüleme • 1 yıl önce