Nader Dabit

Just learned a powerful trick from Yan Wang and Yuzheng Sun on optimizing ChatGPT's context window for smarter interactions! 🚀 Key Insights: - Limited Capacity: ChatGPT can only hold a specific amount of data in its context window, potentially cutting off crucial information in long dialogs. - Content Organization: Properly structured prompts are crucial as disorganized content can lead to inaccurate or incomplete responses. - Effective Prompting: Utilize the "edit" feature in ChatGPT's interface to refine prompts without overloading the context window. 👉 Check out the full video for deeper insights into making the most of ChatGPT’s capabilities: https://lnkd.in/gZX7X225 #ChatGPT #AI #Productivity #Efficiency

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Nile now has a native integration with LlamaIndex This makes it easy to build AI-native multi-tenant apps with Postgres and LlamaIndex. If you are building AI-native B2B apps with Postgres, this should make it easy. pip install llama-index-vector-stores-nile

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https://lnkd.in/gKNTDtvv Recently, I had the pleasure of sharing some insights @AI-Engineer's-World-Fair about the work our team at GitHub Next is doing in the space of developer tooling in the age of AI. I truly believe that language models are not the moat; it is the applications that you build around them. Compelling applications often result from designing user experiences that highlight the latent capabilities of these models while restricting or at least moderating their limitations. Systematic ideation is certainly an excellent way to design and build these compelling and thoughtful experiences. My PhD experience has trained me to do that. However, from my training I also know that these systematic explorations can take a while. Oftentimes, by the time these systematic explorations are completed, the language model capabilities have vastly improved, and more latent capabilities have been discovered. At GitHub Next, we turbocharge the process slightly by tapping into our intuitions and prototyping capabilities to rapidly iterate on explorations. This is supplemented by having explicit learning goals and ruthless dogfooding to get feedback throughout the prototyping process, ensuring that we are making meaningful progress all along. I would love to know how you or your team approaches building with AI. Share your insights! https://lnkd.in/gGTydswC

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A new User Interface is emerging right in front of us.. Claude Artifacts Copilot Pages OpenAI Canvas https://lnkd.in/gpFrqucd #UI #AI

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This post from Rowan Cheung has a great summary of agentic approaches by the various LLMs. @rowancheung is a great follow on X. https://lnkd.in/gW_kZ9xx

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Detailed comparison of implements between LangChain and LlamaIndex on RAG related features

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The environment impact is not often considered when choosing platform, or deploying data projects, but it can add up very quickly. With TIMi, you can easily process billions of records without requiring TB of RAM, and you can also generate powerful predictive models in a record time. Last night, I ran about 200 predictive models on 12 Millions records and 1900 variables. On a single 24 core PC.

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Thrilled to enable 🎉 Key-based Logging (log to separate Langfuse (YC W23) projects based on key) on LiteLLM (YC W23) s/o Elvis Cai- https://lnkd.in/dj45_czz (+4 more updates 👇) ⚡️ use litellm keys w/ Langfuse (YC W23) client sdk for pass-through endpoints -https://lnkd.in/dS7ZViG2 s/o Bipul Simkhada 💪 Azure AI - support gpt models s/o Landseer Enga 📖 Doc - add additional model metadata s/o Samay Kapadia 🛠️ Cohere - messages api translation fix

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I’ve been spending a ton of time with customers here in 🇯🇵 Tokyo, and if there’s one thing that rings true, it’s the belief not just in AI itself, but its ability to change the tide of Japan’s digital cliff. By 2025, outdated legacy systems and retiring engineers could lead to a 2% GDP loss. But with GitHub Copilot accelerating how developers understand, maintain, and migrate legacy codebases, this can change. When we let AI take the toil, we create space for innovation and growth. It's what enables us to seize opportunities, like the one here in Japan, to forge a new path and redefine what’s possible for developers and the economies that depend on them. 🚀

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WHAT HAPPENS WHEN AI AGENTS START TALKING TO EACH OTHER? This question was dissected by Joanne Chen in Forbes. She spoke with Chi Wang, a principal researcher at Microsoft who’s behind the opensource multi-agent framework, AutoGen, to find out. With caveats for balancing autonomy and control, and designing for human-agent interaction which can take place in one or more interfaces, multi-agent systems provide 3 top benefits: 1. “Modularity: Distributing complex tasks across specialized agents makes the overall system more modular. This modularity simplifies development, testing, and maintenance, as capabilities can be added or tweaked without revamping the entire system. Troubleshooting is also streamlined, as issues can often be isolated to individual agents. 2. Specialization: Think of multi-agent systems as teams of experts, each contributing unique knowledge and abilities to collectively tackle difficult problems. Tasks are broken down into components and assigned to the agent best equipped to handle them. As each agent processes its part of the task and passes information to the next, the output is progressively refined and improved. Through such specialization, the resulting systems can achieve results that generalist agents struggle to match. This approach is conceptually similar to techniques like prompt chaining, where a human user breaks down an intricate task into a series of subtasks and iterates toward a desired outcome through conversation with the model. Chi offers the example of a multi-agent system tasked with analyzing data and providing insights and recommendations. In this scenario, each agent focuses on a different aspect of the task: some specialize in data retrieval and presentation, others in deep analysis and insight generation, and others in planning and decision-making. This division of labor allows each agent to work on what it does best, leading to faster, more accurate outcomes. 3. Collaborative learning: In multi-agent systems, the interactions among individual agents can give rise to solutions that exceed what any single agent could achieve in isolation. By allowing agents to work together, critique one another, and share their insights, the system can develop a more comprehensive understanding of the problem at hand. This is especially valuable when dealing with complex, multifaceted issues,” - Joanne Chen, General Partner, Foundation Capital, as reported in Forbes. As I ponder the real-world handoffs between AI and humans - a field that’s called Human-Computer Interaction (HCI) - the idea of multiple agents collaborating to solve my multi-step tasks leaves little margin for error. And the potential for more time in my day. What can a multi-agent system do to improve your day? Join the conversation in comments- What’s an AI agent? https://lnkd.in/eUGx9zx4 #ai #llm #agenticai #aiagent #hci Image credit: Forbes

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Wondering how to step up your #GenAI game? 💡 Join MongoDB and Arize AI for a webinar on May 21st to learn how you can leverage MongoDB Atlas and Arize Phoenix together to build, evaluate, and maintain high-performing RAG applications. Register now: https://lnkd.in/dpxH9KQZ

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UnDaoDu talking about eSingularity proto-AI that will evolve into Proto-AGI (happening) into AGI and eventually qSAI “02 02” https://lnkd.in/gC9dMrR4

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We're releasing qFormChat v2: AI assistant bot for your forms! Join our webinar this week for a live demo - Thursday 9/12 at 8 AM PT. Register: https://lnkd.in/gHMpTkit qFormChat v2 - new AI features for Forms Designers ☑ Better and faster validation/support using new AI models ☑ Less intrusive UI - pops up on submit if validation issues ☑ Easily add via FormsDesigner in a few minutes ☑ New config form features using AI chatbot - Auto-populate validation suggestions - Check Xpaths #Qdabra #FormsViewer #InfoPath #SharePoint #ai #webinars

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CambioML is taking a bold step: We're making the AnyParser API FREE! Generate your self-service API key at https://lnkd.in/gCUt4QRQ #parsing #extraction #pdf #ppt #word #document #genai #llms

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🚀 Announcement of New Feature Release 🚀 We are excited to announce that 360Channel has released a chat translation feature with voice input support for WEBmetaverse, our web browser-based metaverse system, aimed at global companies! 🌐🎤 🔹 Features of the New Function: Voice Input Support: Simply press the microphone button and speak to convert your speech into text automatically. Instant Translation: Instantly translates into the selected language, enabling communication across language barriers. Multilingual Support: Standard support for Japanese and English, with optional support for other languages. This new feature will make communication within global companies even smoother and promote internal interaction. For more details, please check the link below. 🔗 More details here https://lnkd.in/gA7S6GEs https://lnkd.in/ge_jUFRT #360Channel #WEBmetaverse #Metaverse #AutoTranslation #VoiceInput #GlobalCompanies #Communication

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Read about running cognee in production with dynamo.fyi + 1st bonus -> evals 📈 + 2nd bonus -> Orthodox patriarch blessed this article ☦ 2nd bonus is in question 💣 There is only one way to find out what happened, click on the link to read the story 👇

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Ethernet always wins. It has already adopted most of the features of Infiniband. Now, Meta reports Ethernet is more reliable.

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