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Training Modpack version 6.0 is now released! Finally, we have Input Recording with tons of options, FGC-style Input Display, an auto-updater, customizable button configurations, and SO much more in bugfixes and QoL improvements. Thanks as always for sharing!

218,204 görüntüleme • 3 yıl önce •via X (Twitter)

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jugeeya3 yıl önce

Download can be found here, along with the full changelog: Thanks to: @_CPU_1 , @Scythe_SSBU, @xhudaman, GH/austintraver: dev team @jam1garner: Backend framework @Maroof_64: video clips @KingOfWizards: video editor Discord Members: feedback and testing

Scythe profil fotoğrafı
Scythe3 yıl önce

Super proud to have contributed to a lot of the new features here, and have loved having the chance to work with the team. Wonderful to see all of this in a stable build - I hope everyone enjoys!

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Jahzz03 yıl önce

Peak.

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Kami3 yıl önce

Great! So we can finally have that on Mac and it’s not mandatory to go through Windows again? (I’m kidding you do a great work keep it up I just hope that one day we mac users can have it as well 🥲)

jugeeya profil fotoğrafı
jugeeya3 yıl önce

There is nothing that blocks Mac users from using the mod. Neither for booting a modded Switch with it nor for using emulation, I believe at least one of the emulators supports MacOS now.

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🦔 • ODG | Ace★3 yıl önce

YOU GUYS ARE JUST SO AMAZING!!!! THANK YOU FOR THE UPDATE!

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Shinta3 yıl önce

WAY TOO TALENTED

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PlayAid.app3 yıl önce

Incredible!!!

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Phenom3 yıl önce

@TheDakpo This is sick af, makes me want to play and lab again. Props to everyone who has been working on this and continues to do so! Also, is this compatible with hdr?

jugeeya profil fotoğrafı
jugeeya3 yıl önce

@TheDakpo Apparently it is!

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New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

201,127 görüntüleme • 1 yıl önce