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Tutorial Time: Run any open-source LLM locally. Now we will run an LLM on your M1/2 Mac. And its fast. All you need is moved to: @lmstudio let's get started. Good to be back. A thread

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Linus ✦ Ekenstam

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915,786 görüntüleme • 2 yıl önce •via X (Twitter)

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Our AI financial agent now shows its thinking Much like how Perplexity shows its tasks Here is how it works: • we have two LLMs • LLM #1 receives raw user query • LLM #1 creates tasks to answer query • LLM #2 receives these tasks • LLM #2 uses tools to execute tasks • LLM #2 streams answer to you We can add a 3rd LLM to do the last step. This would let LLM #2 focus only on task execution. I will add this in a follow-up. The code is open source so you can see how everything is done.
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Our AI financial agent now shows its thinking Much like how Perplexity shows its tasks Here is how it works: • we have two LLMs • LLM #1 receives raw user query • LLM #1 creates tasks to answer query • LLM #2 receives these tasks • LLM #2 uses tools to execute tasks • LLM #2 streams answer to you We can add a 3rd LLM to do the last step. This would let LLM #2 focus only on task execution. I will add this in a follow-up. The code is open source so you can see how everything is done.

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⚡️Introducing Open LLM Server⚡️ Looking to run models like GPT4All or LLaMa locally, but tired of python-based UIs that are impossibly slow/clunky to install? Now with a single command you can be up and running in seconds! (Windows/Mac/Linux) Here's why this is a big deal:
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⚡️Introducing Open LLM Server⚡️ Looking to run models like GPT4All or LLaMa locally, but tired of python-based UIs that are impossibly slow/clunky to install? Now with a single command you can be up and running in seconds! (Windows/Mac/Linux) Here's why this is a big deal:

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Chat now has an LLM tool to run notebook cells, which allows the agent to perform updates based on cell run results or perform its own data exploration.
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Watch out for these LLM run accounts that will comment on your posts and then block you I block them back so they can’t comment again, but it’s impossible to block all the spambots
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LM Studio 0.3.4 ships with Apple MLX 🚢🍎 Run on-device LLMs super fast, 100% locally and offline on your Apple Silicon Mac! Includes: > run Llama 3.2 1B at ~250 tok/sec (!) on M3 > enforce structured JSON responses > use via chat UI, or from your own code > run multiple models simultaneously > download any model from Hugging Face Video at 1x speed.
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LM Studio 0.3.4 ships with Apple MLX 🚢🍎 Run on-device LLMs super fast, 100% locally and offline on your Apple Silicon Mac! Includes: > run Llama 3.2 1B at ~250 tok/sec (!) on M3 > enforce structured JSON responses > use via chat UI, or from your own code > run multiple models simultaneously > download any model from Hugging Face Video at 1x speed.

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Iterating... Evals take time and are boring: but are a fundamental validation step of sane LLM inference. Let's try to make them as easy and fun to run as possible.
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Iterating... Evals take time and are boring: but are a fundamental validation step of sane LLM inference. Let's try to make them as easy and fun to run as possible.

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every time i get an LLM on a phone call i try this, this is the first time its worked :3
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every time i get an LLM on a phone call i try this, this is the first time its worked :3

maeve ~

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Now you can run a swarm of droids, or claudes, or codexes in parallel using the open source emdash app congrats on shipping fast Arne Strickmann (and you didn't need to raise $200m to do it 😉)
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"It's on my todo to build my own stack of apple minis and run Llama on my own little cluster on my desk." - Garry Tan Quick AI homelab tutorial: - Install EXO Labs on each mini (open source, steps in README) - Make sure all the minis are on the same WiFi network or for faster/more reliable results, connected over Thunderbolt/Ethernet. Devices will auto-discover each other and auto-shard the LLMs. - Now you can chat to your cluster with your preferred LLM GUI (the tiny corp tinychat ships with exo)
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An 100% open-source rival to n8n just launched. Sim is a drag-and-drop platform for building agentic workflows that runs entirely on your local machine. → Visual, Figma-like canvas interface → Works with any local LLM via Ollama → Native MCP support to connect 100+ tools
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An 100% open-source rival to n8n just launched. Sim is a drag-and-drop platform for building agentic workflows that runs entirely on your local machine. → Visual, Figma-like canvas interface → Works with any local LLM via Ollama → Native MCP support to connect 100+ tools

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Unpopular opinion: Most agent evals are theatre. You run them once before the deployment. It'll take 800ms+ as another LLM would be judging your LLM. Most annoying part - no one tells where in the chain things went wrong. I wasted a lot of time in this loop. And then I came across Future AGI bringing 5 different tools under one umbrella, best part - the platform is completely open source. They open sourced their entire platform and the eval layer is noticeably different. It is multimodal - works on everything text, image, audio, pdf. Not an LLM-as-judge adding latency but an agent with memory and tools. The biggest win are learned classifiers trained on actual production failure patterns to run evals at low cost. It also runs across the full reasoning chain, not just the final response. Check out → Try it here →
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Microsoft killed the GPU mafia 🤯 They finally open-sourced their 1-bit LLM inference framework called bitnet.cpp. It lets you run 100B parameter models on your local CPU without GPUs. - 6.17x faster inference - 82.2% less energy on CPUs 100% Open Source.
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Does LLM RL post-training need to be on-policy?

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You can now run Cursor on any machine and control it from anywhere. Kick off agents from your phone to run on your devbox.
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