now available for @eleven_ty users!
TOOLS
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Rewind AI: Find Anything You’ve Seen, Heard or Said
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Rewind AI lets you find anything you've seen, heard or said. https://
futurepedia.io/tool/rewind-ai #AI #ChatGPT #Productivity -
Post-Copilot Directions from Anshul and Codeium Team
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see also the proposed post-Copilot directions from @_anshulr and the @codeiumdev team on last week's post
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Beyond Copilot: The Next Evolution in AI Developer Experience
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The Big Question in AI-driven developer experience: What's after Copilot? Everyone is convinced this is "just" the beginning but can't articulate what the next killer form factor will be. Increasingly convinced next step change won't be from optimal stuffing context windows
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Automating AI Video Generation with Avatar and Audio Processing
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Will automate AI process once holidays end, it will send the selected avatar to @d_id_ with an audio msg or text script. Processing time 1 minute and customer gets an MP4 video!
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LangChain Improves Memory Documentation Organization
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Improved memory docs In order to keep up with all these changes to memory, we also revamped the memory docs slightly to be more organized https://
langchain.readthedocs.io/en/latest/exam
ples/memory.html
… As always, let us know if you have feedback! -
Weaviate Vectorstore: New add_texts Method Implementation
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add_texts for @weaviate_io The @weaviate_io vectorstore implementation was previously missing an `add_texts` method @CShorten30 added one! This allows you to easily insert embeddings (eg, of examples for the example selector)
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Map-Reduce and Refine Chains Now Return Intermediate Steps
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Return intermediate steps for map-reduce & refine chains @johnjnay had the great idea to allow for returning of intermediate steps of these chains to the user This makes it easy to inspect things like Q&A over each document, before combining https://
langchain.readthedocs.io/en/latest/exam
ples/data_augmented_generation/question_answering.html#The-map_reduce-Chain
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Mixed Precision Training: Complexity Beyond High Level Concepts
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the high level picture is easy enough but keeping track of the mixed precision around the whole network, the dynamical behavior of the values and ranges, the support for them and their conversions across all the various kernels and library versions everywhere, is the nightmare
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Splitting minGPT into educational and efficient versions
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Context I realized I have to split up minGPT because I can't properly simultaneously satisfy both 1) educational and 2) efficient in one repo. So I'm separately writing 1) the maximally educational minGPT (+video etc.) and 2) a more efficient (still ~clean) version that has teeth