In our report, we find that people delegate complete tasks to Claude 39% of the time—up from 27% just eight months ago. Meanwhile, DC and Utah lead the US in Claude use per capita. Read more:
LLMS
-
Meta MobileLLM-R1-140M: Local Browser Chat App
By
–
Meta MobileLLM-R1-140M, which can run 100% locally in your browser, no server inference required vibe coded a chat app powered by transformers.js in anycoder
-
Own Your GPU: Securing AI Compute Independence From Corporations
By
–
friendly reminder to buy a GPU and secure your compute on this wonderful day in LLMs not your weights means eventually not the same model – they have all the knobs and you're at their mercy your AI cannot be controlled by a self-serving corporate
-
Reinforcement Learning for Reasoning: A Category Error
By
–
Using reinforcement learning for reasoning is a category error.
-
GLM 4.5 Outperforms Opus 4 in Real-World Task Comparison
By
–
it's what I have been using since late July literally ran the same tasks side-by-side during daytime and Opus 4 was STRUGGLING while GLM 4.5 COOKED and at least GLM 4.5 is opensource and runs from my basement lol
-
ParserGPT: Smart Web Scraping with LLMs and LangGraph
By
–
ParserGPT: Smart Web Scraping Transform messy websites into clean CSV data using LLMs and deterministic rules. Powered by LangChain and LangGraph, ParserGPT learns website structures once to enable efficient, repeated data extraction. Learn more about ParserGPT:
-
Eight Years of Consumer AI Deployment Timeline Overview
By
–
Eight Years of Consumer AI Deployment in One Giant Timeline In recent years, the LLM ecosystem has exploded into a vast, interwoven web of models and labs. Public-facing names like OpenAI, Meta’s Llama and Google’s Gemini stand prominent, but a throng of other players keeps the
-
AI Capability Expansion: Variable Performance Across Tasks
By
–
You can find examples of both on X every day. The existence of graduate-level performance does not mean they are equally good at everything & the existence of idiotic failures does not mean they are not capable of leaps. So far, the trend had been fast expansion of abilities.
-
GSM8K Paper Insights on Mathematical Reasoning in AI
By
–
reminded of this paragraph from gsm8k paper, 2021 🙂
-
Self-Taught LLM Mastery: From Zero to Understanding Attention
By
–
> be you
> random CS grad with 0 clue how LLMs work
> get tired of people gatekeeping with big words and tiny GPUs
> decide to go full monk mode
> 2 years later i can explain attention mechanisms at parties and ruin them > here’s the forbidden knowledge map
> top to bottom, how