From 4 Hours to 5 Minutes: Automating Club Registration with AI

🚀 Did you know that the name of a kernel could significantly boost its performance? ⚡ Here's a fascinating insight from a recent discussion on the Triton GitHub repository: - Fp8 performance can skyrocket by ~100 tflops with the right kernel naming, particularly when the name includes "cutlass." This insight isn't just a quirky detail—it's a reminder of the nuanced optimizations possible in AI development and how seemingly minor changes can lead to substantial performance gains. 🔍 In the fast-paced world of AI, leveraging such insights can differentiate between good and great performance. It's a call to all AI developers and engineers to pay attention to every detail, from algorithmic efficiency to naming conventions. 💬 What naming conventions or coding practices have you found to make a significant impact on your projects? Share your experiences below! 📢 If you found this interesting, consider sharing it with others who might benefit from this insight. ✨ Embrace the small changes that lead to big results. As we refine our tools and techniques, let's push the boundaries of what's possible in AI. #AI #MachineLearning #PerformanceOptimization #CodingBestPractices 🔗 Check out the full discussion: https://lnkd.in/gDUX-C4Q

I just found what is hands-down one of the 𝗯𝗲𝘀𝘁 𝗶𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗟𝗟𝗠 𝗻𝗲𝘄𝗯𝗶𝗲𝘀 by 100xdevs. It offers a phenomenal level overview that covers all the fundamental mechanics without sacrificing clarity, making it the perfect starting point for your AI journey! Why this video is a game-changer: 𝟭. 𝗡𝗼 𝗠𝗮𝘁𝗵 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱: It focuses on conceptual understanding, which is perfect for beginners who want the 'why' and 'how' without diving deep into the calculus. 𝟮. 𝗕𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝘁 𝗔𝗻𝗮𝗹𝗼𝗴𝗶𝗲𝘀: It uses powerful analogies to explain concepts like local and global context, giving you an intuitive grasp of how every word is generated inside the model. 𝟯. 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗖𝗼𝘃𝗲𝗿𝗮𝗴𝗲: It clearly explains the core components, including: ◦ The Transformer Model ◦ Tokenization & Embeddings ◦ Positional Encodings ◦ Attention Mechanism & MLP ◦ Training vs. Inference ◦ The Effect of Scale & Current Trends If you've been looking for a resource to bridge the gap between "LLMs are magic" and "LLMs are fascinating engineering," this is it. Stop postponing your learning! 𝗪𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘃𝗶𝗱𝗲𝗼 𝗵𝗲𝗿𝗲: https://lnkd.in/g2H_8YWf Day 3/30 of posting AI content. Follow for more! #LLM #Transformer #AI #MachineLearning #DeepLearning #ArtificialIntelligence #TechEducation

Building multi-agent AI systems is now as easy as creating a flowchart. That's the power of OpenAI's new AgentKit, and it's a monumental leap from the code-heavy SDKs of the past. I just wrote a hands-on guide exploring how its intuitive, no-code, drag-and-drop interface makes building sophisticated agents accessible to everyone. It's genuinely mind-blowing to witness this level of simplification for such powerful technology. You have to wonder, where is this all heading! Check out the full guide here: https://lnkd.in/gd8563gb #OpenAI #AgentKit #AI #ArtificialIntelligence #NoCode #LowCode #GenerativeAI #FutureOfTech #Automation

AI that codes for 30 hours straight? Claude 4.5 just did exactly that pushing the limits of autonomous coding and challenging what “developer productivity” means.   Anthony Sapountzis breaks it down in this week’s AI Roundup, exploring: 1️⃣ Claude 4.5 vs GPT-5 Codex 2️⃣ What continuous coding really looks like 3️⃣ Why humans still matter in the loop 👉 Watch the full breakdown linked in comments! #AI #TechRoundup #Claude4 #GPT5 #DevReady #AerionTechnologies

AI#2 Skilling approach (Thriving & Leading) * GenZs -> Specialization in a field + Applied AI skills * Non Genz-> Unlearn & Relearn AI ways of working. ( given already specialized in a field by now).

OpenAI has launched AgentKit, a toolkit designed to help developers build and deploy AI agents. Sam Altman, CEO of OpenAI, said that AgentKit provides a complete set of building blocks to take agents from prototype to production with less friction. AgentKit includes features like Agent Builder, ChatKit, and Evals for Agents, which aim to simplify the process of creating and optimizing AI agents. Read more: https://lnkd.in/euVqmT7k 📰 Subscribe to the weekly AI Programming Weekly: https://lnkd.in/eUQ-KTc2 #ai #artificialintelligence #ainews

🚩 From Red Flag to Useful Convenience: Overfitting in AI In classical machine learning, overfitting was a clear warning sign. With small datasets and models built for extrapolation, even minor noise could derail predictions. Avoiding overfitting wasn’t optional—it was survival. Today, the story looks different. Modern deep learning models are trained on massive datasets and mostly operate in the regime of interpolation, not extrapolation. At this scale, data acts as a stabilizer: noise is diluted, and local errors often cancel out. When a model struggles to extrapolate, the fix is usually simple—just scale up the data. We now live in the era of overparameterized models. These enormous architectures—long assumed to be guaranteed overfitters—often defy that expectation. Paradoxically, greater model capacity can lead to better generalization, reshaping how we think about risk in high-dimensional learning. But in modern AI, treating overfitting lightly is like drinking poison to quench thirst: it may seem harmless in the moment, but the long-term risks are real—especially in low-data, safety-critical, or shifting environments. Scaling is ultimately under the curse of dimensionality—making it often infeasible and unsustainable as dimensionality grows. #AI #MachineLearning #DeepLearning #Overfitting #ModelCapacity #Interpolation #Extrapolation #Scaling #CurseOfDimensionality

In an exciting advancement, OpenAI preps for the launch of its latest marvel, GPT-5, this August 🚀. The AI model brags about merging language capabilities with o3-style reasoning, making all our AI queries simpler and us feel, well, a bit "useless" 💡. Meanwhile, researchers from Denmark have developed an AI platform set to revolutionize personalized medicine by creating custom proteins in record time 🧬. In other news, Perplexity's Comet AI browser turns into your very own productivity assistant, saving hours of admin work. And Microsoft's recent study on Bing Copilot conversations suggests a potential AI disruption in occupations ranging from computer science to media roles. Keeping an eye on these developments, at UPlytix AI, we innovate to leverage the best of AI and automation for businesses cutting across industries, making 'busy' a thing of the past. Ready to supercharge productivity with AI? 🚀 #AI #Automation #Productivity #OpenAI #FutureOfWork

Understanding Cross-Validation in Machine Learning: A Step-by-Step Guide Train on Train, tune on Validation, and test on Holdout. Here’s how different cross-validation techniques work: 1️⃣ Leave-One-Out (LOOV) Each data point is used once as a validation set. Simple but computationally expensive for large datasets. 2️⃣ K-Fold Cross-Validation Split your dataset into K equal folds. Train on K–1 folds, validate on the remaining one. Repeat K times and average the results for stability. 3️⃣ Rolling Cross-Validation (for Time-Series) Train on past data, validate on the next chunk, and test on future data. Maintains time order, which is crucial for forecasting models. 4️⃣ Blocked Cross-Validation Use a fixed-size training window that moves forward. Ensures stable training performance in evolving time-series environments. #MachineLearning #DataScience #CrossValidation #AI #ModelEvaluation #MLTips #LearningJourney #TimeSeries

Wired writes "Mira Murati’s Stealth AI Lab Launches Its First Product - Thinking Machines Lab, led by a group of prominent former OpenAI researchers, is betting that fine-tuning cutting-edge models will be the next frontier in AI." https://lnkd.in/ej_RYsjf. #artificialintelligence #miramurati #stealthailab #firstproduct #thinkingmachineslab #finetuning #cuttingedgemodels #wired #generativeai