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Building Private Copilot for Development Teams with Llama3

Towards AI

Copilot leverages natural language processing and machine learning to generate high-quality code snippets and context information. Compared to traditional auto-completion tools, Copilot produces more detailed and intelligent code.

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Auto Wiki v2 by Mutable AI: Converting Code into Articles Similar to Wikipedia

Marktechpost

Even though AI drives code completion solutions, documentation is still a big issue. Meet Mutable.ai , a cool startup that has just released Auto Wiki v2. This is accomplished with Auto Wiki v2 by Mutable AI. In Conclusion Auto Wiki v2 from Mutable.ai Software development is also a type of development.

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Beyond ChatGPT; AI Agent: A New World of Workers

Unite.AI

Current Landscape of AI Agents AI agents, including Auto-GPT, AgentGPT, and BabyAGI, are heralding a new era in the expansive AI universe. AI Agents vs. ChatGPT Many advanced AI agents, such as Auto-GPT and BabyAGI, utilize the GPT architecture. Their primary focus is to minimize the need for human intervention in AI task completion.

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8 Ways Automatic Speech Recognition Can Increase Efficiency For Your Business

AssemblyAI

While this content offers a gold mine of data, this information often goes to the wayside. It would take weeks to filter and categorize all of the information to identify common issues or patterns. Discover how you can use Automatic Speech Recognition and AI models to build tools that increase efficiency within the following areas: 1.

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Why Don’t Language Models Understand ‘A is B’ Equals ‘B is A’? Exploring the Reversal Curse in Auto-Regressive LLMs

Marktechpost

Some of the latest AI research projects address a fundamental issue in the performance of large auto-regressive language models (LLMs) such as GPT-3 and GPT-4. This issue, referred to as the “Reversal Curse,” pertains to the model’s ability to generalize information learned during training.

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FastGen: Cutting GPU Memory Costs Without Compromising on LLM Quality

Marktechpost

However, these models are only applied to non-autoregressive models and require an extra re-training phrase, making them less suitable for auto-regressive LLMs like ChatGPT and Llama. It is important to consider pruning tokens’ potential within the KV cache of auto-regressive LLMs to fill this gap.

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Scott Stevenson, Co-Founder & CEO of Spellbook – Interview Series

Unite.AI

We immediately saw how this could help lawyers draft bespoke agreements, while also helping them intelligently “auto-complete” contracts. In the first version of our product, we offered a sophisticated auto-complete feature, similar to Github Copilot. How does Spellbook suggest language for legal contracts?