We've built a coding agent that runs entirely on your own machine. No account. No API key. Your code never leaves the laptop. llmsforall.com/blog/millie-c… Our ternary 35B-A3B model resolves 55.8% of SWE-bench Verified in 9GB — on a 16GB Mac or a gaming PC with 4GB of VRAM. macOS + Linux

used Krea 2 raw to generate these, very impressive, closer to hand drawn than openAI or Gemini

I’m curious, does anyone use Muse to cold call your clients ?

Okay, yes I know you guys are traumatized by the subscription plans - your Dev

今天全世界所有的猫咪都会感谢一位姓程的女孩

Digging into Jev today, TypeSafe's new "System One" model that skips text generation entirely. You give it app state + a question, it hands back typed decisions & probabilities, not prose. It literally can't hallucinate, because it only ever picks from labels you define upfront, no free text, no room to invent stuff. Also apparently 40-200x faster than frontier LLMs. Here's a table comparing Jev with traditional LLMs:

So me and @benwkrause open sourced a little project called AutoGRAMS to years ago. The AutoGRAMS classifier is Jev. In AutoGRAMS, we implemented a whole load of cool functionalities that are probably unicorns on their own: github.com/autograms/autogra…

A full-size AI model running entirely on an iPhone. No internet, no cloud, no data leaving your phone. It beat Bonsai 1-bit on every test we ran, and answers noticeably faster while using less memory. Free on the App Store if you want to try it. Try Millie by LLMs for All on AppStore

We built the best local coding model you can run fast on a 16GB MacBook 40+ tokens/s, 35B parameters with a ternary quantization, 8.3 GB, 56% on SWE-bench Verified (GPT-4.1 level), fully offline. Tell us what beats it. llmsforall.com/blog/millie-c…

𝗟𝗟𝗠 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴：Adaptation, distillation, and alignment（《大模型定制与微调》） Manning新书，Amit Bahree（G42 Americas CTO，此前在微软Core AI负责Applied AI Engineering）和Weehyong Tok合著。 全书用一个虚构企业的IT help desk贯穿始终，同一个问题在prompting、RAG、LoRA/QLoRA、全量SFT、知识蒸馏、DPO偏好对齐这几种方案里各做一遍，方便直接横向比较。所有实验都能在单张GPU上复现，书里刻意用小模型跑通，作者的说法是技术本身和模型规模无关，企业上大模型时换的只是算力和显存，方法不变。 内容不止有训练，还包括训练数据管道的质量门控和血缘追踪、蒸馏出更小的student模型、以及上线后的漂移检测、金丝雀prompt、回滚流程和安全监控。目标读者是需要把开源大模型落地到具体业务场景、并且要在生产环境里稳定跑起来的ML工程师和MLOps从业者。

Wow, thanks to everyone who pulled a copy, and especially to those who sent feedback. If you're running Millie locally, we'd love to hear what hardware you're on and how it's holding up. I can run it comfortably on my M2 Pro with just 32GB of memory.

Ever wonder how a 7B model fits on a laptop? Quantization — storing each weight in fewer bits. 16-bit: 14 GB 8-bit: 7 GB 4-bit: 3.5 GB Same model, same parameter count, smaller payload. Made a cheat sheet on what that does If you're into running AI locally, let's #connect, I'd like to hear how you're solving this.

Get your first 100 real customers on Reddit and X. Product Hunt mostly rewards founders who pay for visibility, the interest you get there rarely converts.

The most important skills for using AI coding agents effectively. Presenting the AI Engineering Skills Map for using coding agents.

If training small LLM is your thing, let’s #connect !

What are you guys building today ? I’m building a local LLM application. This is my second product. Let’s #connect

Looking to #connect with founders in Tech & AI: I grew Psyfy app from 2k users to 30k users in 6 months with organic traffic. Many people ask me what the most effective channel for growth and marketing is. There isn’t one channel that works best for every product, it really depends on your product. If your product is an AI tool for building software, X, Reddit, and Threads may be the place, since that’s where you can connect with a lot of potential customers. If your product is visually appealing to young people, TikTok and Instagram may be the channel. But what matters more is what content you create on whichever platform you use to drive traffic to your product. It has to be very specific. For example, if you have an exercise app, you can film yourself working out every day and post it on TikTok, or you can go to r/running and r/walking on Reddit and show how your app helps people, just don’t over-promote. What matters is your content: you need an interesting script and good product taste. If you can’t do it, you need to hire someone to do it. (Leave more questions in the comments and follow me if you found my experience helpful to you)

Hi guys 👋 Looking to #connect with people who are: Founders Tech & AI Creators with marketing minds Yesterday, many people asked me how we brought 30k users to Psyfy in just a few months with MAINLY organic traffic. The key is: 1. Your product needs to have a market. Many people try hard to promote an immature product that can barely attract users, whether it's the UI or the features. 2. You need to create content that brings users to your product. The content needs to be highly tailored to the platform you're using. That's not something I can explain in a single paragraph, but keep asking me specific questions and I'll answer them in future posts.