Modal and Hugging Face's Text Embedding Inference server can embed the entire English Wikipedia in about 15 minutes for just over $15, using Modal's serverless GPU scaling. The post argues open-source embedding models beat API-based ones like OpenAI's text-embedding-ada-002 for production RAG, since they can be fine-tuned on user data and avoid rate limits and per-token costs.
Cursor published a list of problems it is working on, including training a custom fast reranker that filters 500k tokens from all sources down to the most relevant 8k per request. The list also covers an edit-oriented copilot, constrained in-flow agents for large codebases, bug finding, and larger-scale edits. As of Oct 12, 2023, Cursor had indexed 1.4B vectors and 150k codebases.
Modal announced that its cloud function platform is now generally available, letting anyone sign up and run Python code in the cloud within minutes, after opening a waitlist in 2022. The platform offers serverless execution billed per second, autoscaling GPU inference endpoints on A100, A10G, T4, L4 and H100, and large-scale batch jobs across thousands of containers with no YAML configuration. Modal also raised a $16M Series A led by Redpoint Ventures, with Amplify Partners, which led the seed round, and plans to use the capital to accelerate its product roadmap.
Modal Labs has raised a $16 million Series A led by Redpoint Ventures, with existing investor Amplify Partners and new participants including Lux Capital and Definition Capital, bringing total funding to $23 million. The company also officially launched its cloud platform, previously in beta with early customers such as Ramp, Substack and SphinxBio, and will use the funds to accelerate product development and hire engineers at its New York City headquarters.
Cursor measured Llama-2-70B inference on 2 80-GB A100 GPUs and found it over 3x cheaper than GPT-3.5 for prompt tokens but far more expensive for generated tokens at small batch sizes. Prompt processing costs about $0.00042 per 1K tokens versus GPT-3.5's $0.0015, while generation reaches $0.066 per 1K tokens at batch size 1 and only approaches GPT-3.5 pricing at large batches that push time to first token to several seconds. Cursor recommends open-source models for prompt-heavy tasks like classification and reranking, and closed-source APIs for generation-heavy workloads.
Why it matters: Cursor's measured cost and latency data shows when self-hosting Llama-2-70B beats GPT-3.5 and when it does not, offering a reusable framework for open-source model serving decisions.
Cursor researcher Arvid Lunnemark argues that prompting should be called "prompt design" and compared to web design, since both pursue clear communication with dynamic inputs and must fit content to different "sizes" — screen sizes for the web, context windows for prompts. He released Priompt v0.1, a React-like JSX-based prompt design library already used inside Cursor, with a preview site for rendering prompts against real request data.