LAI #82: MCP, Byte-Level LLMs, Vision Transformers, and the Week Backprop Finally Clicked

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts,

This week’s issue zooms in on what happens when you go one layer deeper, whether it’s understanding MCP for smarter tool integrations, or hand-coding backprop to finally grasp what your model’s doing under the hood.

In What’s AI, we break down the Model Context Protocol (MCP), a new standard that might save you hours of repetitive integration work across tools and orgs. Then we go hands-on: building Vision Transformers in PyTorch, testing byte-level LLMs without tokenization, comparing neural optimizers, and evaluating when open-source alternatives are actually good enough to use.

Also in the mix: metadata enrichment tools for Rust, new Discord collabs, and a meme that may hit a little too close for anyone who’s wrangled gradient descent this week.

Let’s get into it.

What’s AI Weekly

This week in What’s AI, I will dive into Anthropic’s Model Context Protocol. How many times have you started a new AI project and found yourself re-wiring the same GitHub, Slack, or SQL integration? That annoying code copy-paste repetition and lack of norms between organizations and individuals is exactly why Model Context Protocol, or MCP, exists. Read about what it is and why it matters in your day-to-day life, or watch the video on YouTube.

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

Learn AI Together Community Section!

Featured Community post from the Discord

Superuser666_30897 has developed a system for gathering, enriching, and analyzing metadata for Rust crates, utilizing AI-powered insights, web scraping, and dependency analysis. It combines web scraping, AI-powered analysis, and cargo testing to provide comprehensive insights into Rust ecosystem packages. Check it out on GitHub and support a fellow community member. If you have any questions or suggestions, reach out to him in the thread!

AI poll of the week!

Over half of this community entered AI after ChatGPT, and that’s not a bad thing. It marks a clear generational shift: from research-first to product-first, from academic papers to API calls. If you joined before ChatGPT, what mindset do you think the newer crowd misses? And if you joined after, what’s one thing you think the earlier crowd underestimates? Tell us in the thread!

Collaboration Opportunities

The Learn AI Together Discord community is flooded with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, we share cool opportunities every week!

1. Skaggsllc is building VERA AI, an AI-driven system for predictive vehicle maintenance and fleet diagnostics, and is looking for developers who may be interested in contributing to the development of this platform. If this sounds like your niche, connect in the thread!

2. Vergil727 is looking for someone to help integrate an Advanced Planning & Scheduling (APS) system into their ERP/MES environment. You’ll handle data mapping, scheduling config, and system integration (SQL, ERP, MES). If this falls within your skill set, please reach out in the thread!

Meme of the week!

Meme shared by bigbuxchungus

TAI Curated Section

Article of the week

From Pixels to Predictions: Building a Transformer for Images By Vicki Y Mu

As Generative AI continues to advance, understanding foundational models like the Vision Transformer is essential. This walkthrough details the process of building a Vision Transformer (ViT) from scratch in PyTorch, explaining the theory behind converting images into patch sequences and processing them with multi-head self-attention. The author implements the model, trains it on the CIFAR-10 dataset, and analyzes the results, achieving an accuracy of 60%. It also covers ViT limitations and mentions more advanced architectures.

Our must-read articles

1. The Week I Spent Hand-Coding Neural Networks to Finally Understand Backpropagation By Abduldattijo

The author details their experience building a neural network from scratch using only NumPy, despite having years of experience with frameworks like PyTorch. Prompted by a gap in understanding backpropagation, the process covered the challenges of manual gradient calculation, implementing the backward pass, and building an optimizer. This exercise yielded a deeper, practical understanding of how neural networks function, resulting in improved debugging skills and an enhanced ability to comprehend complex architectures.

2. From Bytes to Ideas: LLMs Without Tokenization By MKWriteshere

The author examines Meta’s Autoregressive U-Net (AU-Net), an architecture designed to overcome the limitations of traditional tokenization in language models. Instead of using predefined tokens, AU-Net processes raw text at the byte level, learning to build understanding from letters up to concepts. This method improves the handling of typos and new languages. Performance benchmarks show AU-Net is competitive with standard models, demonstrating particular strength in multilingual translation and character-level tasks. However, the current version is primarily optimized for Latin-script languages.

3. Understanding Model Context Protocol (MCP): The Future of AI Tool Integration By Mahendramedapati

This blog explains the Model Context Protocol (MCP), a standardized method for connecting AI models to external systems, such as databases, APIs, and files. It functions as a universal translator, eliminating the need for complex, custom integrations for each tool. Using a hotel concierge analogy, the author illustrates how MCP securely fetches and formats data for the AI. The text outlines benefits such as reduced costs and improved security, and provides guides with code examples for implementation.

4. The Best Optimization Algorithm for Your Neural Network By Riccardo Andreoni

The author presented a guide to neural network optimization algorithms designed to reduce training time. It reviews foundational methods, such as Batch and Mini-Batch Gradient Descent, before explaining more advanced techniques. It covered Momentum, which uses past gradients for faster convergence, and RMSprop, which adapts learning rates for each parameter. The discussion then progressed to Adam, an algorithm combining both approaches, whose superior performance was demonstrated in a practical comparison on the Fashion MNIST dataset. The summary also noted learning rate decay as a complementary technique to refine the training process.

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