All tags
Topic: "code-editing"
Canvas: OpenAI's answer to Claude Artifacts
gpt-4o claude-artifacts openai cursor_ai daily inline-suggestions collaborative-editing code-editing model-training model-integration feature-detection accuracy-evaluation voice-ai hackathon open-source-libraries marijn-haverbeke karina-nguyen vicente-silveira swyx
OpenAI released Canvas, an enhanced writing and coding tool based on GPT-4o, featuring inline suggestions, seamless editing, and a collaborative environment. Early feedback compares it to Cursor and Claude Artifacts, noting strengths and some execution issues. OpenAI also sponsors Marijn Haverbeke, creator of ProseMirror and CodeMirror, which are used in Canvas. The integration involved training a detector to trigger Canvas appropriately, achieving 83% accuracy in correct triggers. Unlike Claude Artifacts, Canvas currently lacks Mermaid Diagrams and HTML preview support. Additionally, Daily is sponsoring a $20,000 voice AI hackathon in San Francisco, highlighting voice AI as a key emerging skill.
Learnings from o1 AMA
o1-preview o1-mini claude-3.5-sonnet gpt-4o openai weights-biases cohere weaviate reinforcement-learning chain-of-thought reasoning model-performance prompting code-editing rag hybrid-search sama rohanpaul_ai gdb andrew-mayne
OpenAI released the o1 model series, touted as their "most capable and aligned models yet," trained with reinforcement learning to enhance reasoning. The o1-preview model scored 21% on ARC-AGI, ~80% on aider code editing (surpassing Claude 3.5 Sonnet's 77%), and ~52% on Cognition-Golden, showcasing a shift from memorizing answers to memorizing reasoning. The model employs a unique chain-of-thought approach enabling "System II thinking" for better problem-solving. Experts like Andrew Mayne advise framing o1 as a smart friend providing thoughtful explanations. Additionally, an advanced RAG course sponsored by Weights & Biases, Cohere, and Weaviate offers strategies for hybrid search and prompting to optimize AI solutions.
Cerebras Inference: Faster, Better, AND Cheaper
llama-3.1-8b llama-3.1-70b gemini-1.5-flash gemini-1.5-pro cogvideox-5b mamba-2 rene-1.3b llama-3.1 gemini-1.5 claude groq cerebras cursor google-deepmind anthropic inference-speed wafer-scale-chips prompt-caching model-merging benchmarking open-source-models code-editing model-optimization jeremyphoward sam-altman nat-friedman daniel-gross swyx
Groq led early 2024 with superfast LLM inference speeds, achieving ~450 tokens/sec for Mixtral 8x7B and 240 tokens/sec for Llama 2 70B. Cursor introduced a specialized code edit model hitting 1000 tokens/sec. Now, Cerebras claims the fastest inference with their wafer-scale chips, running Llama3.1-8b at 1800 tokens/sec and Llama3.1-70B at 450 tokens/sec at full precision, with competitive pricing and a generous free tier. Google's Gemini 1.5 models showed significant benchmark improvements, especially Gemini-1.5-Flash and Gemini-1.5-Pro. New open-source models like CogVideoX-5B and Mamba-2 (Rene 1.3B) were released, optimized for consumer hardware. Anthropic's Claude now supports prompt caching, improving speed and cost efficiency. "Cerebras Inference runs Llama3.1 20x faster than GPU solutions at 1/5 the price."