Entrepreneurs Roundtable Accelerator’s Post

Have you ever imagined yourself where you are tipping to software or hardware? Well that is possible with Large Language Model and Prompt Engineering. You can literally say "I'm going to tip you $xxx for better solution!" or “You will be penalized” Explore the world of Prompt Engineering through the research paper linked below. It's packed with numerous innovative suggestions to amp up your Prompt Engineering ! https://lnkd.in/dkbtg9Ca #genai #llm #promptengineering #prompt

spelling out of plain signal processing flow in GPT from inputs to probabilities that is, forward path only, no back-propagation (and no programming) but it is as simple as it gets by Leon Petrou: https://lnkd.in/g_8Z2icS

🎯Exciting News: Math Solver Achieves Top 5 Ranking in GPT Store's Education Category Congratulations! Math Solver has achieved a position within the top 5 in the education category of the GPT Store's rankings：https://lnkd.in/gpwpRfXV 🎉🎉🎉 The Math Solver - a powerful and advanced computational tool designed to tackle complex math problems with ease. Explore Math Solver: https://lnkd.in/gMubzi_J What sets Math Solver apart? 🧐Unlike traditional chat GPTs, Math Solver combines various tools to provide detailed step-by-step solutions and seamless web browsing capabilities. Key Features: ✨Advanced Math Solver ✨Computational Tools ✨Step-by-Step Solutions ✨Web Browsing Integration If you don't have access to a GPT-4 account, you can explore Math Solver on the StudyX website (https://app.studyx.ai/) or through our discord server: https://lnkd.in/gPiPAXQz at ⁠📐math-solver.

🚀 Navigating Algorithm Complexity in the Age of AI and ChatGPT 🚀 In the ever-evolving realm of AI and the enchanting wonders of ChatGPT, it's crucial to anchor ourselves in the fundamentals, including the often-overlooked concept of Algorithm Complexity. 👥 Let me paint a vivid scenario for you: Picture two friends armed with distinct sets of computers—one boasting a mighty Intel i7 processor, adorned with generous RAM and a formidable GPU, and the other wielding a more modest dual-core setup with modest RAM (throw in some GPUs for flair). 💻 They both run the same program. Surprise: the i7 whizzes through the task, but the dual-core buddy manages it too, with its performance tied to how much RAM it has. 🔄 This tells us something important: when we judge how well a computer program works, it's not just about how fast the computer is. We need a smart way that looks at both the program itself (algorithms) and the computer's tools (CPU, RAM). 🔍 That's where Algorithm Complexity comes in. It's like the guide that helps our creations not only work well on super-fast computers but also handle things smoothly on simpler ones, using RAM wisely. It's like finding the right dance moves for every kind of dance floor. 💡 Amidst the AI marvels and ChatGPT brilliance, let's not forget the timeless wisdom encapsulated in understanding and optimizing Algorithm Complexity. It's the secret sauce ensuring our code dances gracefully everywhere. An old lecture link for the motivations for this post: https://lnkd.in/gCZi7WQb N.B: Made with help of ChatGPT providing the context. #SayefReyadhProgrammingMadeSimple #AlgorithmComplexity #AI #ChatGPT #ComputingPower #TechTales #SayefReyadh

As much as I love using GPTs for coding assistance, I’m always wary of how far a bad prompt can drop you from where you want to go. DevinAI and it’s predecessors like AutoGPT and smol AI are just chatGPT in clever while loops. I think this paradigm of using LLMs to code will fully mature when we can have IDEs that are built from the ground up to do what Devin is trying to do. Because it is imperative that we have the power to interrupt these gpt loops to course correct or backtrack. Much like Breakpoints in a debugger.

Small Language Models: Using 3.8B Phi-3 and 8B Llama-3 Models on a PC and Raspberry Pi Testing the models with LlamaCpp and ONNX

See how the Usable Math's Estella Explainer math chatbot functions - in a decision tree. More on our blog https://lnkd.in/gtzicAry #UsableMath #ChatGPT #mathchatbot #decisiontree #mathreadability #FleschReading

Custom GPTs allow users to automate repetitive tasks and enhance productivity without the need for coding skills, making this technology accessible. Creating a custom GPT is not just about incorporating technology; it's about transforming the way we interact with digital content, making it more personal, efficient, and creative. Through a step-by-step guide, I show the ease of setting up custom GPTs for various applications, such as generating thumbnails for blog posts, thereby opening new creative possibilities and efficiencies at work. No coding skills are required, just use your creativity. #CustomGPTs #ContentAutomation #DigitalCreativity

Have you seen the new ChatGPT desktop assistant? When it is released, you'll be able to share your screen and engage in a back-and-forth conversation with a natural, human-sounding voice about what you're looking at. In this video, watch as the assistant guides a geometry student through the step-by-step process of solving a problem. https://lnkd.in/gm-eFHU4

The new o1 is just mind-blowing! Yesterday, I started playing with it and quickly ran out of quota.... Unlike GPT-4o, which handles a wide range of general tasks, o1-preview is built with a laser focus on reasoning-heavy challenges. When I started asking about more complex questions requiring multi-step reasoning, it became evident that this model operates on an entirely different level. Its chain-of-thought methodology allows it to break down and tackle complex, multi-stage problems in a way GPT-4o cannot match. This isn't just another AI release; o1-preview marks a fundamental shift. Whether it's math, coding, or scientific hypothesis testing, the model engages deeply with the problems through reasoning tokens—a hidden, behind-the-scenes process that fuels its precision. These tokens don't appear in the output but allow the model to "think" before generating responses, making it remarkably accurate in solving complex challenges. Early benchmarks are just as impressive. In competitive programming on Codeforces, it scored in the 89th percentile, and in math, it ranked among the top 500 in the American Invitational Mathematics Examination (AIME). In areas that demand multi-layered reasoning—like data validation and scientific workflows—o1-preview showcases its strength, where clarity and logical structure are critical. While GPT-4o excels at broad-world knowledge, o1-preview dominates in reasoning tasks. Even in its preview phase, it's clear that o1-preview will transform how we approach tasks requiring advanced critical thinking. It's not just promising; it's a glimpse of the future of AI-assisted work. I can't wait to see what this model will do once it's fully rolled out and optimized.