▲ ~/𝚛𝚊𝚞𝚌𝚑𝚐/𝚘𝚜𝚜-𝚐𝚛𝚊𝚗𝚝𝚜/ 𝚕𝚜▐ (v2) I'm announcing v2 of my open source grants of $1,000USD to 35 awesome contributors. The themes: ① Agent skills & tools Skills are now a valuable form of software, like Emil's world-class design eng work. Projects like 𝚙𝚐𝚋𝚘𝚝, 𝚔𝚗𝚒𝚙, 𝚞𝚗𝚕𝚒𝚐𝚑𝚝𝚑𝚘𝚞𝚜𝚎 create amazing AI engineering loops. ② Local AI Projects like 𝚜𝚕𝚘𝚝𝚜𝚝𝚛𝚎𝚊𝚖, 𝙻𝚘𝚌𝚊𝚕𝙰𝙸, and 𝚌𝚘𝚕𝚒𝚋𝚛𝚒 are making inference not just a demo but for many a real and growing utility. Exciting to watch local AI unfold. ③ Performance There's no excuse for software to be slow anymore. Projects like 𝚏𝚏𝚏 and 𝚝𝚊𝚔𝚞𝚖𝚒 leading with speed, compilers like 𝙿𝚎𝚛𝚛𝚢𝚃𝚂 and 𝚙𝚘𝚛𝚏𝚏𝚘𝚛 that can help us upgrade entire ecosystems to native, and 𝚐𝚙𝚞𝚒𝚡 pushing the GPU rendering speed frontier. ④ High quality foundations AI is still at its best when it can reuse great components than regenerating every time from first principles. Lots of love to folks working on @shadcn, Svelte(kit), Nuxt, Node.js and security foundations. ⑤ Cool experiments I couldn't resist donating to a 'Finance terminal in the terminal', or beautiful funky things like 𝚍𝚒𝚝𝚑𝚎𝚛-𝚔𝚒𝚝. 🔗 PS: These are personal, not affiliated with Vercel, and fully unconditional. If you are listed, I'll be DM'ing you or using your public contact info!

NeumanOS: A local-first personal OS that runs in the browser Combines notes, todos, calendar, spreadsheets, and an AI terminal in one place: • No account, no backend • Data stays in browser IndexedDB by default • Supports Ollama local models + multiple AI APIs • Spreadsheets with 400+ Excel formulas • PWA install & self-hosted deployment Worth checking if you like local tools and AI workflows. 👉 #OpenSource# #LocalAI# #Ollama#

Local AI tip: if you've got a big-memory Mac, you can run frontier open models at home now. antirez, the creator of Redis, built ds4, a lean C engine that runs a few top open models on your own machine. the setup: * models → DeepSeek V4 Flash, GLM 5.x and Qwen 3.8 Flash Next * hardware → high-memory Macs, NVIDIA and AMD GPUs * speed → about 39 tokens per second on a 128GB M5 Max * tools → a CLI, an OpenAI and Anthropic compatible API, and a coding agent why it's different: → built for a few models only, instead of everything at once → 2-bit quantization so big models fit in memory → 23,000+ GitHub stars, MIT licensed One dev. One engine. Frontier models on your desk. the repo:

Local AI will create so much chaos and don't hate me for this, I'm a big supporter of OSS but let's not pretend that it's okay that a teenager in his basement can download an open model on his gaming machine and hack into the whole neighborhood and NO i don't know how we can handle this dilemma.

Local AI has got to a point now i cannot imagine a life without it. I want it in every device i own. And as a result it has ruined how i shop or even plan for tech iphone or android? i'm checking the RAM Mac? Unified memory GPU? VRAM Every upgrade i'm considering now has to fit bigger models, low memory devices barely make the shortlist anymore. And now i'm checking how fast those models will run, too. Anyone else stuck in this rabbit hole? (image generated by Qwen-image-2.1 running locally on my 3090 🍻)

Local AI 101: open models, Hugging Face, and businesses to build (38 min masterclass) I still think cloud AI is the default for most things, and honestly it should be, the frontier models are the strongest and easiest to use. But something shifted in the last 4-5 months. You can now run genuinely good open models directly on your own laptop, or even your phone. And once you actually try it, it changes how you think about what AI is even for. LOCAL AI, CLEARLY EXPLAINED: 1. The model is the brain doing the thinking. Gemma, Llama, Mistral, and Qwen are the main families, and each is better at different things, some at reasoning, some at coding, some small enough to run on a phone. 2. Hugging Face is the warehouse where you find them. You go there to see what each model is good at, check the license, and grab the compressed versions that run on a normal computer. 3. The software is what runs the model on your machine. Start with LM Studio if you're not technical, it feels like a normal app where you search, download, and start chatting. Ollama is the one you reach for when you want to plug a model into your own apps. 4. The workflow is the actual product you build on top of it all. That's what I'm ideating around for some businesses to create. I think local AI just made a specific kind of business way easier to start. Find an industry that: 1. Sits on sensitive data they'd never paste into ChatGPT 2. Does the same review over and over 3. Runs on software from 2003 Then build a local AI tool that does that review on their own machine, so the data never leaves the building! Take home health agencies. Nurses write visit notes all day, and if a note is missing a detail, the billing gets denied or the audit flags it. Here's how I'd start: 1. Find 5 small agencies. Offer to review a batch of their notes for them. 2. Run the notes through Gemma locally (free, private, no cloud). Read every output yourself. 3. Write down the 20 issues that keep showing up: missing vitals, vague med changes, notes that don't support the billed level. 4. That list of 20 is your checklist. The checklist is the product. 5. Turn it into a local desktop app that flags those 20 things before a note gets submitted. You just went from a service anyone could offer to a product nobody else has, and you learned exactly what to build by doing the work by hand first. Same recipe works for restoration contractors (draft the damage report on-site before the tech leaves) and wealth advisors (catch the compliance landmine in a client email before it sends). Basically the framework is sensitive data, repeated review, ancient software. I think there are tons of businesses like this! Almost none of it clicked for me until I actually started using local AI. So if you take one thing from this, go run a model on your own machine once. Also a fun thing to try with your friends. Feel free to send this to a friend. The episode is live for free on @startupideaspod (thanks to Google for sponsoring today's episode and supporting local AI) I feel like local AI one of those things you need to try for it to really click. Run one model on your own machine and you'll see what I mean! I go way deeper in the full 38 minute masterclass, the models, the setup, and the businesses to build. Link below. LINK TO WATCH: OR WATCH BELOW ON X What do you think of local AI?