No-code benchmarking, collaborative AI builds, new RAG evaluation techniques, and community tools worth trying.

5 min readJust now

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Good morning, AI enthusiasts,

Choosing the right model is becoming just as important as knowing how to prompt it. In What’s AI, I explain why treating LLMs as interchangeable is a mistake and how a no-code benchmark tool can help you separate hype from fit.

From there, we dig into the details: a multi-agent system that now runs multiple models in parallel, new methods for evaluating RAG pipelines without guesswork, and a practical guide to prompt optimization you can use right away.

We also surface some bigger questions, like what it means when embeddings hit mathematical limits, or how to build offline AI apps that keep your data private.

Let’s get into it!

What’s AI Weekly

This week, in What’s AI, I dive into an important topic: why are all LLMs not equal? In fact, treating them all the same can lead to missed opportunities. I will break down how to choose the best model for the job, and then, in our practical section, use an awesome, easy-to-use, no-code benchmark tool built by Integrail to compare LLMs. Additionally, we will use the multi-agent system that we built in the previous article, which draws from multiple models in parallel. Read the complete article here 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

Lukylab has built PromptKit, an AI prompt generator & library that helps users create, organize, and test prompts with popular AI models. It also includes a curated library of high-quality prompts designed for various AI tools. One of the most interesting features is Preview Results, which allows users to see expected outcomes from each prompt before using it with their AI tool of choice. Check the tool out here and support a fellow community member. If you have questions or feature requests, reach out to him in the thread!

AI poll of the week!

It’s telling that nearly a third of you said only 10–25% of AI news feels relevant. That means the majority of what we see is noise: funding rounds, flashy demos, or “yet another model release” with little real-world impact. But here’s the paradox: buried in that 10–25% are often the breakthroughs that truly matter: new benchmarks, infrastructure shifts, or use cases that actually change workflows. Currently, how do you filter the signal from the noise? Do you rely on benchmarks, trusted voices, hands-on testing, or something else? Share in the thread and help other members find the most relevant sources.

Collaboration Opportunities

The Learn AI Together Discord community is flooding 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, too — we share cool opportunities every week!

1. Superuser666_sigil is a member of RLAgentBot, a crypto trading agent. Their team is seeking individuals with spare time to contribute their financial/crypto trading knowledge to our team. If this is your domain, connect with him in the thread!

2. Cpnk75m is working on an AR + AI app for Devpost Hackathon and needs help with picking the right ML models, some quick hacks to get it running, AR objects/modals for overlays, and solid testing. If you are interested in working on this project and potentially scaling it further, reach out in the thread!

Meme of the week!

Meme shared by bin4ry_d3struct0r

TAI Curated Section

Article of the week

Comparing Four Time Series Forecasting Methods: Prophet, DeepAR, TFP-STS, and Adaptive AR By Shenggang Li

Selecting an appropriate time series forecasting model is a practical challenge that involves trade-offs between accuracy and complexity. This piece presents a comparative analysis of four distinct methods using a U.S. transportation dataset. It assesses industry-standard models, including Meta’s Prophet, Amazon’s DeepAR, and Google’s TFP-STS, alongside a proposed Adaptive Decay-Weighted AR model. The findings suggest that while Prophet excels in interpretability and DeepAR handles complex patterns effectively, the author’s adaptive model performed competitively by prioritizing recent data.

Our must-read articles

1. Reducing Hallucinations in VLMs using REVERSE By Youssef Farag

To address hallucinations in Vision Language Models (VLMs), researchers have developed REVERSE, a framework for real-time error detection and self-correction. The model is trained on a curated dataset containing tagged examples of both factual and hallucinated content, teaching it to recognize its own potential inaccuracies. During generation, if the likelihood of a hallucination exceeds a set threshold, the system automatically uses rejection sampling or query rewriting to produce a more grounded response. This approach has demonstrated improved performance over existing methods on captioning and question-answering benchmarks, while also highlighting the trade-off between factual accuracy and descriptive expressiveness.

2. XAI: Graph Neural Networks By Kalpan Dharamshi

This article addresses the interpretability of Graph Neural Networks (GNNs) through Explainable AI (XAI). It outlines how GNNs function by aggregating data from neighboring nodes to perform tasks like node classification. Using Zachary’s Karate Club dataset, a Graph Attention Network is trained to predict group memberships. It then applies the GNNExplainer tool to analyze a specific prediction, visually identifying the most influential neighboring nodes and input features. This demonstrates how XAI provides insight into a GNN’s decision-making process, which is valuable for debugging, model validation, and building trust in the results.

3. Beginner’s Visual Guide to Quantisation Methods for LLMs By Parth Chokhra

Optimizing large language models for efficient deployment is a growing necessity. This blog offers a visual guide to quantization, the process of reducing numerical precision to create smaller, more efficient models. It outlines two primary approaches: Quantization-Aware Training (QAT), which is integrated during the training phase, and Post-Training Quantization (PTQ), applied to a completed model. The summary further details various PTQ methods, including calibration-based (GPTQ, AWQ), weight-only, and dynamic techniques. Finally, it covers essential deployment formats, such as GGUF and ONNX, demonstrating how the optimal strategy depends on balancing accuracy, efficiency, and hardware requirements.

4. On-Device AI Chat & Translate on Android (Qualcomm GENIE, MLC, WebLLM): Your Phone, Your LLM By Tarun Singh

This article presents a practical approach to on-device AI for Android, focusing on implementing the MLC-LLM framework for an offline chat and translation application. It provides a complete Kotlin module designed for immediate use via a fallback mechanism. The module is built to seamlessly upgrade to full MLC inference once the official libraries are added, using reflection to detect the runtime. It also covers module configuration, model packaging, and performance considerations on Snapdragon chipsets.

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