正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Introducing OpenVenice — an open-source, customizable frontend for Venice. Your API key stays in your browser, talks directly to Venice. No server, no backend. - Chat, image gen, audio, music, video, embeddings — everything venice offers in one clean UI - Visual workflows — chain models together (vision →... LLM → image gen -> video gen). Venice is multimodal so you can build some really powerful workflows - Full control over every model parameter - Runs locally, deploys anywhere — zero infrastructure I'm a $DIEM holder and heavy Venice user. The models are solid and website is great but I kept wanting more freedom with the UX — more control, custom tools, being able to give it access to my local computer, multimodal chaining -- something I could shape to how I actually work. Didn't exist, so I built it. Feel free to fork and make it yours. Open source, MIT licensed.show more

nikshep

6,512 subscribers

40,500 次观看 • 5 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Ansem shares his thesis on $VVV “Venice AI is essentially a private inference platform… you can access all of the top state-of-the-art models, all the best open source models but through Venice, it doesn’t store any of your information” “They’ve done really well. They have over 2 million users. I think they have 50k daily actives on their app and they’re making money from the subscription” “One of the cool features of Venice that I thought was interesting is they have a second token that’s called DIEM… if you have 1 DIEM, you get to use $1 of compute per day for that 1 DIEM that you have” “It’s a really interesting model. I think it’s honestly attracted a lot of people to stake $VVV. We don’t have a lot of real protocols there in the crypto and AI space that aren’t vapor and are actually doing well”

Ansem shares his thesis on $VVV “Venice AI is essentially a private inference platform… you can access all of the top state-of-the-art models, all the best open source models but through Venice, it doesn’t store any of your information” “They’ve done really well. They have over 2 million users. I think they have 50k daily actives on their app and they’re making money from the subscription” “One of the cool features of Venice that I thought was interesting is they have a second token that’s called DIEM… if you have 1 DIEM, you get to use $1 of compute per day for that 1 DIEM that you have” “It’s a really interesting model. I think it’s honestly attracted a lot of people to stake $VVV. We don’t have a lot of real protocols there in the crypto and AI space that aren’t vapor and are actually doing well”

Market Bubble

143,721 次观看 • 4 个月前

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Santiago

56,815 次观看 • 1 年前

Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:

Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:

Tom Greenwald

395,296 次观看 • 1 个月前

Venice recently added API & Credit purchases to the list of $VVV burn mechanisms, which 3x'd the rate of VVV burn overnight. Two thing are happening here: - More of the Venice business is directly connected to VVV burn. - VVV holders can now infer Venice API/Credit revenue by proxy of the VVV burn from this line of business I talk to Jon Venice about this: "The latest credit burns in the credit side of the business has been growing tremendously over the last couple of months. And that includes both people using the app, and buying credits to do things like use pay per use models, create videos, use professional image models...." "So all this this basically takes that whole side of the business that wasn't on chain yet and puts it on chain and ties it to the burn. So what you're seeing there with the credits is that we're averaging, somewhere in the range of $120,000+ credit purchases a day on Venice on top of subscriptions" "I expect to see this credit burn grow just because, that part of the business did not even exist, seven or eight months ago. So that's gone from nothing to almost, you know, in the range of like 50% of our business on an average day. So now that all flows directly back to the VVV token economy." "This just adds everything we've been saying, you know, since we launched VVV, since we've been talking about this over the last couple of years, we really do want to buy and burn as much VVV as we possibly can. We want Venice to grow into a large enough business to be able to afford and buy and burn very large amounts of VVV." "So we're just going to keep marching on that plan."

Venice recently added API & Credit purchases to the list of $VVV burn mechanisms, which 3x'd the rate of VVV burn overnight. Two thing are happening here: - More of the Venice business is directly connected to VVV burn. - VVV holders can now infer Venice API/Credit revenue by proxy of the VVV burn from this line of business I talk to Jon Venice about this: "The latest credit burns in the credit side of the business has been growing tremendously over the last couple of months. And that includes both people using the app, and buying credits to do things like use pay per use models, create videos, use professional image models...." "So all this this basically takes that whole side of the business that wasn't on chain yet and puts it on chain and ties it to the burn. So what you're seeing there with the credits is that we're averaging, somewhere in the range of $120,000+ credit purchases a day on Venice on top of subscriptions" "I expect to see this credit burn grow just because, that part of the business did not even exist, seven or eight months ago. So that's gone from nothing to almost, you know, in the range of like 50% of our business on an average day. So now that all flows directly back to the VVV token economy." "This just adds everything we've been saying, you know, since we launched VVV, since we've been talking about this over the last couple of years, we really do want to buy and burn as much VVV as we possibly can. We want Venice to grow into a large enough business to be able to afford and buy and burn very large amounts of VVV." "So we're just going to keep marching on that plan."

David Hoffman

10,461 次观看 • 1 个月前

✨ 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 use my MBP's M1 GPU! It runs maybe 2x-3x faster than ChatGPT4 and with about the same quality, no image gen of course cuz no DALL-E but with LCM (1 second img gen LoRa) that could happen in future If OpenAI implodes we might be able to continue after all thanks to open source! Nice to try this and see how accessible it is, here's how it looks: Thx for tips in replies 🐜 Max Rovensky MrDee@SOG🫡 Xenon John Shahawy @the_carlosdp Bilal KY @bigpt_dog

✨ 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 use my MBP's M1 GPU! It runs maybe 2x-3x faster than ChatGPT4 and with about the same quality, no image gen of course cuz no DALL-E but with LCM (1 second img gen LoRa) that could happen in future If OpenAI implodes we might be able to continue after all thanks to open source! Nice to try this and see how accessible it is, here's how it looks: Thx for tips in replies 🐜 Max Rovensky MrDee@SOG🫡 Xenon John Shahawy @the_carlosdp Bilal KY @bigpt_dog

@levelsio

745,229 次观看 • 2 年前

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 次观看 • 1 个月前

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!

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!

Brian Roemmele

85,565 次观看 • 5 个月前

I just built an open NotebookLM clone! Here's what it can do for you: - Process multi-modal data - Scrape websites and YouTube videos - Create a unified knowledge base - Lets you do RAG over it - Remember every conversation - Generate a podcast 🎙️ The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step! So by the end of this video, you'll learn how to: ↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs ↳ Store everything in a vector database for fast retrieval ↳ Add a memory layer that remembers conversations and preferences for a personalized experience ↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model The podcast generation feature is my favorite part! There's something powerful about turning written content into conversational audio that you can listen to while doing something else. The entire code is 100% open-source. I've shared a link in the replies! ____ Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this. Cheers! :)

I just built an open NotebookLM clone! Here's what it can do for you: - Process multi-modal data - Scrape websites and YouTube videos - Create a unified knowledge base - Lets you do RAG over it - Remember every conversation - Generate a podcast 🎙️ The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step! So by the end of this video, you'll learn how to: ↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs ↳ Store everything in a vector database for fast retrieval ↳ Add a memory layer that remembers conversations and preferences for a personalized experience ↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model The podcast generation feature is my favorite part! There's something powerful about turning written content into conversational audio that you can listen to while doing something else. The entire code is 100% open-source. I've shared a link in the replies! ____ Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this. Cheers! :)

Akshay 🚀

83,187 次观看 • 10 个月前

Ali's Wan 2.2 is absolutely insane 🤯 Map your voice and motion into a video clip featuring anyone with a single photo. In this video I shape-shift between an avatar woman, Matthew Mcconaughey, Anne Hathaway, Martin Luther King Jr., and Bane. Tools used: - Wan 2.2 for video gen - FreePik for image gen - ElevenLabs for voice cloning and voice changing Want the full prompting guide and a step-by-step tutorial video where I walk through exactly how I built it? Like, RT, and comment "REALITY", and I'll DM it to you. (must be following so I can dm you)

David Roberts

126,277 次观看 • 10 个月前

"Closed models are cheaper. The reason you need open models is that they give you control". - Nvidia CEO Jensen Huang "One of the things that people misunderstand about open models is this: yes, you can host them yourself, and you can build your own computer. But most people use computers in the cloud. Frankly, I think closed models are cheaper. If you do not build everything yourself, you avoid the significant cost and expertise required to train, fine-tune, maintain, guardrail, evaluate, and keep the model safe. You would also need to build or obtain the computers required to host it. So, there is nothing cheap about doing all of that yourself. The reason you need open models is that they give you control. You can adapt them for your own highly specialized use cases. I think there is a lot of misunderstanding about closed models versus open models. We felt it was important for people to understand that there is a place for both." ---- From "Bloomberg Podcasts" YouTube channel, (full video link in comment)

"Closed models are cheaper. The reason you need open models is that they give you control". - Nvidia CEO Jensen Huang "One of the things that people misunderstand about open models is this: yes, you can host them yourself, and you can build your own computer. But most people use computers in the cloud. Frankly, I think closed models are cheaper. If you do not build everything yourself, you avoid the significant cost and expertise required to train, fine-tune, maintain, guardrail, evaluate, and keep the model safe. You would also need to build or obtain the computers required to host it. So, there is nothing cheap about doing all of that yourself. The reason you need open models is that they give you control. You can adapt them for your own highly specialized use cases. I think there is a lot of misunderstanding about closed models versus open models. We felt it was important for people to understand that there is a place for both." ---- From "Bloomberg Podcasts" YouTube channel, (full video link in comment)

Rohan Paul

26,712 次观看 • 1 个月前

I'm now building Executor full time as a startup! The state of tool calling is a mess: - Everyone is using different agents - You have no idea what actions are going to run - Destructive actions are treated the same as normal Executor lets you set up everything once & use it with any agent. You're able to control what tools can be called, require approval on destructive actions, and invite your team to all use them. Under the hood, it's built on codemode to let you add thousands of tools with 0 context bloat. I've been using it for a month so far and it has completely changed how I interact with agents. There's so much to be built here, immediately next up on the roadmap is workspaces, skills, workflows, and generative ui. Everything is being built open source over at there's a lot more coming on an SDK to embed it into your own agents, a self hostable cloud app, and plugins There's an early beta version over at It's very early on - if you hit any bugs let me know and I'll get them fixed up, excited to share more as I build it!

I'm now building Executor full time as a startup! The state of tool calling is a mess: - Everyone is using different agents - You have no idea what actions are going to run - Destructive actions are treated the same as normal Executor lets you set up everything once & use it with any agent. You're able to control what tools can be called, require approval on destructive actions, and invite your team to all use them. Under the hood, it's built on codemode to let you add thousands of tools with 0 context bloat. I've been using it for a month so far and it has completely changed how I interact with agents. There's so much to be built here, immediately next up on the roadmap is workspaces, skills, workflows, and generative ui. Everything is being built open source over at there's a lot more coming on an SDK to embed it into your own agents, a self hostable cloud app, and plugins There's an early beta version over at It's very early on - if you hit any bugs let me know and I'll get them fixed up, excited to share more as I build it!

Rhys

220,559 次观看 • 4 个月前

An open source project turns any LLM into a 3D character that talks, moves, and reacts in the browser, no game engine, no local render setup. Characters have a memory system tracking emotional state and relationship history, so replies shift based on what's happened in the conversation so far, not just the last message. Fully customizable: swap the 3D model, the personality prompt, the LLM, or the text-to-speech provider. Try it free with zero setup, a hosted version includes complimentary API access to get started. Self-hosting needs your own API keys but gives full control to modify it. From the team behind Digital Life Project (CVPR 2024), presented at SIGGRAPH Asia 2025. 100% Free. Open Source.

An open source project turns any LLM into a 3D character that talks, moves, and reacts in the browser, no game engine, no local render setup. Characters have a memory system tracking emotional state and relationship history, so replies shift based on what's happened in the conversation so far, not just the last message. Fully customizable: swap the 3D model, the personality prompt, the LLM, or the text-to-speech provider. Try it free with zero setup, a hosted version includes complimentary API access to get started. Self-hosting needs your own API keys but gives full control to modify it. From the team behind Digital Life Project (CVPR 2024), presented at SIGGRAPH Asia 2025. 100% Free. Open Source.

Simplifying AI

60,897 次观看 • 9 天前

spent months on HumanPlane, a closed source Polymarket terminal. it did $250k+ in volume but i scrapped it anyways. why? cause every polymarket terminal is the same thing. closed source. a reskin of with marginal ux gains. polymarket already takes a 1–1.8% taker fee on every trade, so terminal fees are fees on top of fees you already pay. for a ui. the free ones are vc-funded — they won't stay free. so i rebuilt humanplane as the first free, open source, fully customizable polymarket terminal. run it locally, forever. don't like something? ask claude to modify it and make it yours. built w/ a performant rust backend and solidjs, supports market and trader exploration, live charts, orderbook and order execution (market + limit) -- intelligence, perps, trader scoring and more soon try it out:

spent months on HumanPlane, a closed source Polymarket terminal. it did $250k+ in volume but i scrapped it anyways. why? cause every polymarket terminal is the same thing. closed source. a reskin of with marginal ux gains. polymarket already takes a 1–1.8% taker fee on every trade, so terminal fees are fees on top of fees you already pay. for a ui. the free ones are vc-funded — they won't stay free. so i rebuilt humanplane as the first free, open source, fully customizable polymarket terminal. run it locally, forever. don't like something? ask claude to modify it and make it yours. built w/ a performant rust backend and solidjs, supports market and trader exploration, live charts, orderbook and order execution (market + limit) -- intelligence, perps, trader scoring and more soon try it out:

nikshep

23,197 次观看 • 4 个月前

Fck it. We no longer need Foundation. I built a free tool for every artist affected by the shutdown. Paste your wallet, see exactly what you created and what you collected - no wallet signature required. Two steps: 1. Paste your address. See every Foundation NFT you created or own, pulled straight from on-chain contract data. 2. Get a free Pinata account and pin your art to IPFS in one click. Your content, your storage, nobody can take it down. The tool also shows art you collected from other artists - so you can tell them before their work disappears. --- The Art Revolution starts now. So, I also built UNDERPIN to kick it off- a vision for a decentralized art marketplace with no company behind it. No gatekeepers, no single point of failure. AI dev tools exist now. Artists can run their own infrastructure. Foundation shutting down is not a loss - it's proof we need to own the stack. Everything is open source. Fork it, build on it, make it yours. links below.

Fck it. We no longer need Foundation. I built a free tool for every artist affected by the shutdown. Paste your wallet, see exactly what you created and what you collected - no wallet signature required. Two steps: 1. Paste your address. See every Foundation NFT you created or own, pulled straight from on-chain contract data. 2. Get a free Pinata account and pin your art to IPFS in one click. Your content, your storage, nobody can take it down. The tool also shows art you collected from other artists - so you can tell them before their work disappears. --- The Art Revolution starts now. So, I also built UNDERPIN to kick it off- a vision for a decentralized art marketplace with no company behind it. No gatekeepers, no single point of failure. AI dev tools exist now. Artists can run their own infrastructure. Foundation shutting down is not a loss - it's proof we need to own the stack. Everything is open source. Fork it, build on it, make it yours. links below.

tut™

32,319 次观看 • 4 个月前

Is Windsurf better than Cursor? impossible to say, but... it does one thing better: soooo... I spent an hour testing windsurf... and this is what i created... and app called "boop-boop" lol... one thing windsurf does is it searches the internet. If you've been watching my videos you know that i go to perplexity all the time to get code examples and docs. but with windsurf you can just say, "search the replicate top image gen models and add them to the chat we just created" It will search the internet, find the right model then write the code. This is great for replicate and openrouter. You can have one api key in your .env file then just tell windsurf to search for docs for [insert ai model] and it will just add it to your app. Impressed tbh.

Is Windsurf better than Cursor? impossible to say, but... it does one thing better: soooo... I spent an hour testing windsurf... and this is what i created... and app called "boop-boop" lol... one thing windsurf does is it searches the internet. If you've been watching my videos you know that i go to perplexity all the time to get code examples and docs. but with windsurf you can just say, "search the replicate top image gen models and add them to the chat we just created" It will search the internet, find the right model then write the code. This is great for replicate and openrouter. You can have one api key in your .env file then just tell windsurf to search for docs for [insert ai model] and it will just add it to your app. Impressed tbh.

Riley Brown

31,043 次观看 • 1 年前

To me, microduck looks like a droid. So I made it sound like one Microduck is open source and hackable. That means you can program it to do whatever you want! I trained a 1.3m parameter LLM to output droid like responses. It is running in the browser The model has learned to respond with an emotion, alongside English like Happy, Excited, Sad Then the emotion is fed into a Rust based synthesizer to generate R2D2 like sounds that are aligned with the emotion --- completely procedurally, no wav files, no copyright violation You can chat with it. You can also customize it, give it a name and set your own colors! All set in a spaceship hangar backdrop with other friendly ducks---simulated with realistic physics and microduck's actual walking and skating policies! All in the browser, with WASM!!! I can put literally the SAME code in the browser, and then compile it into an ARM binary and put it in the actual robot in real life 🤯 If you like this and want to see more of it, please like and share so that my boss puts me in the priority list for the first microduck batch 😎 All open source of course! Microquack! 🦆 Hugging Face Space:

To me, microduck looks like a droid. So I made it sound like one Microduck is open source and hackable. That means you can program it to do whatever you want! I trained a 1.3m parameter LLM to output droid like responses. It is running in the browser The model has learned to respond with an emotion, alongside English like Happy, Excited, Sad Then the emotion is fed into a Rust based synthesizer to generate R2D2 like sounds that are aligned with the emotion --- completely procedurally, no wav files, no copyright violation You can chat with it. You can also customize it, give it a name and set your own colors! All set in a spaceship hangar backdrop with other friendly ducks---simulated with realistic physics and microduck's actual walking and skating policies! All in the browser, with WASM!!! I can put literally the SAME code in the browser, and then compile it into an ARM binary and put it in the actual robot in real life 🤯 If you like this and want to see more of it, please like and share so that my boss puts me in the priority list for the first microduck batch 😎 All open source of course! Microquack! 🦆 Hugging Face Space:

Onur Solmaz

33,500 次观看 • 8 天前

I spent $4,699 on a DGX Spark and used it as a chatbot for six months. Not proud of it. But I think most Spark owners are doing the same thing, so let me tell you what changed. The moment was dumb. I had a recording of a client call, needed notes, and my first instinct was to open a browser tab and upload it to some transcription SaaS. Then I looked at the box on my desk. 128 GB of unified memory. Sitting there. Running a chat model I'd asked maybe 30 questions that day. And I thought: I have a supercomputer and I'm about to pay a stranger to listen to my client call. So I tried to do it locally. And here's where it got annoying: - my LLM can't hear audio - the ASR model I found is great but it's a CLI script - the transcript came out in two languages, need translation - summary needs the LLM again - now I want a mind map, that's another tool - oh and here's a scanned PDF from the same meeting, OCR, another tool Copy paste copy paste. Output saved to ~/Downloads/final_v2_REAL.txt. That's when it clicked: tokens per second was never the point of this box. A 24 GB gaming card forces you to pick ONE model, so everyone picks the biggest generalist and asks it to do everything. But 128 GB doesn't force that. You can keep six specialists loaded at once. A 1.7B speech model transcribes better than a 35B chat model ever will. A tiny translation model just translates and doesn't get creative. The big LLM does the thinking, and only the thinking. The hardware was never the problem. The missing piece was the thing that ties them together. So I built it. SparklingKit. (the name is a tribute to the Spark. also: a spark is one flash, sparkling is when it keeps going) What it actually is: → transcription, OCR, translation, image grounding, text-to-image, mind maps, chat. one panel. → six models hand-picked to fit together in 128 GB with room left over → queue-first: drop a 3-hour recording, go get coffee, check from your phone → nothing is a dead end: finish a transcript and "translate / map / chat with it" are already waiting → visual workflows: chain the steps once, run them forever → everything is a file. one folder. no database. copy it to back up. point Obsidian at it. → nothing leaves your machine unless you send it. The thing I care about most: if you stop using SparklingKit tomorrow, nothing happens to your data. It was already a folder of markdown and json. There's no lock-in to escape. Spark owners: you bought room, not a faster chatbot. This is how I'm using the room. No Spark? The panel runs anywhere Docker runs. Point it at your own endpoints. It's open source, and it's version one. Rough edges exist. Issues and PRs very welcome. Full details and a one-line install at If you've ever uploaded something private to a cloud AI and felt weird about it, this is for you.

I spent $4,699 on a DGX Spark and used it as a chatbot for six months. Not proud of it. But I think most Spark owners are doing the same thing, so let me tell you what changed. The moment was dumb. I had a recording of a client call, needed notes, and my first instinct was to open a browser tab and upload it to some transcription SaaS. Then I looked at the box on my desk. 128 GB of unified memory. Sitting there. Running a chat model I'd asked maybe 30 questions that day. And I thought: I have a supercomputer and I'm about to pay a stranger to listen to my client call. So I tried to do it locally. And here's where it got annoying: - my LLM can't hear audio - the ASR model I found is great but it's a CLI script - the transcript came out in two languages, need translation - summary needs the LLM again - now I want a mind map, that's another tool - oh and here's a scanned PDF from the same meeting, OCR, another tool Copy paste copy paste. Output saved to ~/Downloads/final_v2_REAL.txt. That's when it clicked: tokens per second was never the point of this box. A 24 GB gaming card forces you to pick ONE model, so everyone picks the biggest generalist and asks it to do everything. But 128 GB doesn't force that. You can keep six specialists loaded at once. A 1.7B speech model transcribes better than a 35B chat model ever will. A tiny translation model just translates and doesn't get creative. The big LLM does the thinking, and only the thinking. The hardware was never the problem. The missing piece was the thing that ties them together. So I built it. SparklingKit. (the name is a tribute to the Spark. also: a spark is one flash, sparkling is when it keeps going) What it actually is: → transcription, OCR, translation, image grounding, text-to-image, mind maps, chat. one panel. → six models hand-picked to fit together in 128 GB with room left over → queue-first: drop a 3-hour recording, go get coffee, check from your phone → nothing is a dead end: finish a transcript and "translate / map / chat with it" are already waiting → visual workflows: chain the steps once, run them forever → everything is a file. one folder. no database. copy it to back up. point Obsidian at it. → nothing leaves your machine unless you send it. The thing I care about most: if you stop using SparklingKit tomorrow, nothing happens to your data. It was already a folder of markdown and json. There's no lock-in to escape. Spark owners: you bought room, not a faster chatbot. This is how I'm using the room. No Spark? The panel runs anywhere Docker runs. Point it at your own endpoints. It's open source, and it's version one. Rough edges exist. Issues and PRs very welcome. Full details and a one-line install at If you've ever uploaded something private to a cloud AI and felt weird about it, this is for you.

stevibe

52,072 次观看 • 4 天前

My last open-source skill, /no-ai-slop, clearly hit a nerve with 4K GitHub stars. Today, I’m introducing /human-review, another free AI skill I think you’ll love. Let’s face it - giving feedback to AI in chat is painful. Asking it to “update the 3rd paragraph” or “update image size to X” is annoying when I just want to edit the text myself or give feedback to AI directly on the page. /human-review opens HTML and Markdown files in a visual editor that lets you: → Edit and format text directly → Resize images → Leave comments for AI like a Google Doc I've been using it to edit PRDs, landing pages, product copy, and much more. The whole review loop runs locally, so your data is safe and nothing is uploaded to the cloud. 📌 Get /human-review for free on GitHub: If you find it useful, please ⭐ the repo so more people can find it. 📌 Read my full post here for more on how I use /human-review:

My last open-source skill, /no-ai-slop, clearly hit a nerve with 4K GitHub stars. Today, I’m introducing /human-review, another free AI skill I think you’ll love. Let’s face it - giving feedback to AI in chat is painful. Asking it to “update the 3rd paragraph” or “update image size to X” is annoying when I just want to edit the text myself or give feedback to AI directly on the page. /human-review opens HTML and Markdown files in a visual editor that lets you: → Edit and format text directly → Resize images → Leave comments for AI like a Google Doc I've been using it to edit PRDs, landing pages, product copy, and much more. The whole review loop runs locally, so your data is safe and nothing is uploaded to the cloud. 📌 Get /human-review for free on GitHub: If you find it useful, please ⭐ the repo so more people can find it. 📌 Read my full post here for more on how I use /human-review:

Peter Yang

314,895 次观看 • 1 个月前

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Jerry Liu

20,434 次观看 • 1 年前

I built an open-source tool to fix something that drives me nuts. You ask ChatGPT or Gemini for a game asset and get an image with a background baked in. Ask for transparency and you get... a fake checkerboard. Then you hunt for a background remover, then slice each sprite out by hand. So I built Sprite Lab. Drop a spritesheet → it auto-detects the backdrop (checkerboard, white, magenta, green), removes it cleanly with no halo, and slices every frame for you. Export as PNGs + a strip + manifest. 100% in your browser. No uploads, no AI model, no sign-up. Free and open source. #gamedev #indiegame

I built an open-source tool to fix something that drives me nuts. You ask ChatGPT or Gemini for a game asset and get an image with a background baked in. Ask for transparency and you get... a fake checkerboard. Then you hunt for a background remover, then slice each sprite out by hand. So I built Sprite Lab. Drop a spritesheet → it auto-detects the backdrop (checkerboard, white, magenta, green), removes it cleanly with no halo, and slices every frame for you. Export as PNGs + a strip + manifest. 100% in your browser. No uploads, no AI model, no sign-up. Free and open source. #gamedev #indiegame

Ibrahim Boona

19,969 次观看 • 3 个月前