This is awesome from @github
, helping developers make more informed decisions about the Copilot code they use. >> Introducing code referencing for GitHub Copilot https://
github.blog/2023-08-03-int
roducing-code-referencing-for-github-copilot/
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CODE
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GitHub Copilot Code Referencing Helps Developers Make Informed Decisions
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Transformers Can Be Applied to Tabular Non-Time Series Data
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Yup there are transformers for tabular non-time series data
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Annotate Datasets with CVAT and Train YOLOv8 Models
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Hey, you can annotate your dataset using CVAT and then train it using Ultralytics YOLOv8. Checkout https://
learnopencv.com/a-closer-look-
at-cvat-perfecting-your-annotations/
… and https://
learnopencv.com/train-yolov8-o
n-custom-dataset/
… for more info. -
AI-Focused Investments: Pre-Seed, New Products, Dev Tools
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Ai focused, pre/seed, net new things vs existing tool+ai, dev tools, infra
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Replicate Integration with Vercel AI SDK for Llama 2 Chatbot
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Replicate is now supported by the @vercel AI SDK. `npm ai replicate` Here's how to build your own Llama 2 Chatbot, with streaming: https://
sdk.vercel.ai/docs/guides/pr
oviders/replicate
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File Upload Beta: Ask Questions Directly to Your Documents
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👩🔬 Now in Beta: File Upload
— Perplexity (@perplexity_ai) 3 août 2023
Try it now: https://t.co/pICSQrcaRY
Start asking your questions directly to your documents, code, or research papers. Dive into information faster and get insights quicker. It's in beta, and we'd love your feedback. pic.twitter.com/GNbQU0D0nRNow in Beta: File Upload Try it now: https://
pplx.ai/file-upload-be
ta
… Start asking your questions directly to your documents, code, or research papers. Dive into information faster and get insights quicker. It's in beta, and we'd love your feedback. -
AI Primers and Guides on Diverse Machine Learning Topics
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Really like these primers/guides on nearly any topic you can think of in AI. From model architectures, training techniques, speech, vision, NLP, multimodal, evaluation, etc… https://
aman.ai/primers/ai/ -
Llama 2: Responsible AI Development Guide for Developers
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With Llama 2 we’re continuing to invest in responsible AI efforts, including a new guide to support devs with best practices and considerations for building products powered by large language models in a responsible manner. Download the full guide https://
bit.ly/3YjrNhC -
Loading Model Weights into GPU Memory with Ray Object Store
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How did we do it? By loading the model weights into memory once before training begins and inserting them as numpy arrays into the #Ray object store, we can then zero-copy read the weights directly from shared memory into each GPU worker process.
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Ludwig simplifies distributed computing with Ray backend integration
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Doing this yourself would normally be a fair bit of cumbersome code, but in Ludwig you get it for free just by running with Ray as the backend runtime. Try it out for yourself: https://
pbase.ai/3qfet19