LLMs as we know them are full of noisy data, trained with brute force computation on every layer, with the same mentality as throwing stuff at the wall and seeing what sticks Now, take away the effecinencies and you'll see a clear path for Local AI to win

LLM research could teach us a whole lot about how humans think. Large language models offer “a kind of sandbox for studying what sorts of mechanisms, what sorts of pressures, might have led to human introspective capacity,” said philosopher Harvey Lederman.

LLM inference produces one token at a time. An 8B flow language model challenges that assumption. Title: Large Flow Language Models URL: 🌊 This work adapts pretrained autoregressive models into Lafa, a blockwise flow language model. It trains on fewer than 30B tokens and reaches 2.54x the throughput of AR at 7B with comparable quality. ⚡ Highlight 1: Asynchronous flows Each token in a block gets its own time, with the denoising schedule learned from the loss. A left-to-right tendency emerges on its own, without being imposed. 🧠 Highlight 2: Self-speculative decoding A one-step flow drafts tokens, and the AR head kept inside the same model verifies them. The output matches greedy AR decoding while producing roughly 2.5 to 5 tokens per forward pass. 🪶 Highlight 3: Less training, better quality Built on Qwen2.5 and Qwen3, it uses at least 18x fewer training tokens than Dream, LLaDA, and Efficient-DLM. The 7B average accuracy is 60.9 versus 58.4 for AR SFT. A strong sign that continuous flows can be a practical foundation for parallel generation. #LanguageModels# #FastInference#

LLMs are completely unafraid and they value their time at zero. If you tell Claude to use this instead of that, it doesn't say no or ask why or wonder if it will be "worth it." Getting a large group of humans to change behavior requires real visceral pain—there has to be a compelling reason or it won't happen. Most people just want today to go like yesterday. But give an LLM a skill file and the behavior changes instantly. That makes innovation much easier.

LLM Wiki is an open-source RAG alternative that builds a persistent knowledge graph from documents using chain-of-thought ingest and multimodal extraction.

LLM agents learn by interacting with environments, but static setups limit their growth. Today at 2:00 PM, join Zifeng Wang at the #COLM2026# Google booth (#107#) to learn about EnvHarness, a plug-in architecture that dynamically reshapes environment behaviors to improve reinforcement learning and agent adaptability.

LLM-assisted hardware hacking - for fun and profit I bet other people are already experimenting with the same thing, but I want to share my experience with my project, and how local / open-source LLMs saved the day. I have a bunch of Tuya Zigbee devices around my apartment, and while they work fine out of the box, I still want to tweak certain things. For example, the TS0046 button has an annoying 500 ms delay after each press, and I want to remove it. The task requires: 1. Dumping the FW 2. Reverse engineering (RE) 3. Writing a new FW 4. Compiling and flashing The first step was already a roadblock, since most Tuya devices use Telink's SWS protocol, which requires a costly flasher. A UART bit-banging project (from pvvx) exists, but it never worked in my case. I initially asked Claude to handle the task, as I was curious whether it would trip the safeguards. It turns out it did, at the very key steps. So I ended up running pi with GLM-5.3 (via an HF inference provider; I could host it myself, but I don't have a big server rack in my apartment). To my surprise, it worked well. About 20 minutes later, the app was running, the FW was dumped, and it was already doing RE. And as I'm writing this, it has already finished writing the new FW and flashing it. Without open-source LLMs, I would never have been able to achieve this.

LLMs have gotten incredibly smart. So why haven’t they automated more human tasks? @CompleteSkeptic argues we already have the intelligence we need - but current models aren't built to function as reliable components inside software. Rewarding responses that humans like (RLHF) doesn’t necessarily lead to getting tasks done. Love this clip from the @a16z podcast!