Sofian Hamiti

Model training on TPUs just got way easier! A completely reimagined vLLM TPU for LLM inference 👉 for PyTorch and JAX developers, this means more flexibility to run PyTorch model definitions performantly on TPU without any additional code changes, while also extending native support to JAX. read: https://lnkd.in/gW7476Hs #GoogleCloud #TPUs #LLMs

14

Did you hear it? … the song of the AI Agents dominating #awsreinvent2025 Nearly every other booth you walk to is demoing an #aiagent platform. In a nutshell, we’re talking about simple ai agents. I’d call them baby agents. A prompt, a RAG, and a few MCPs here and there to collect data, all governed by an agent studio to design agent workflows. Most platforms are promoting an open model framework where you can pick and choose your preferred LLM. Bedrock is one of the key options available. What’s missing? The business application. A lot of those platforms are in their very early stages, many are not even GA yet. What’s common: build from scratch mentality, and my ai agent platform can do everything, everywhere, all at once. IT buyers? Torn down between all of these platforms not knowing if it’s the right time to start choosing one or many. The potential? It’s going to be insanely huge, with businesses and organizations reaching incredible levels of automation, while also providing superior quality and experience to their customers. Get ready as 2026 will be exciting! [sounds surreal to say so, nearly have to apologize that my post did not use any single AI, not even for brainstorming or improving my style. WDYT: Better with or without AI?]

16

4 Comments

Late last week, I had the pleasure of sitting down with Michael Nuñez from VentureBeat to discuss my team's latest work on building and open sourcing SWE-PolyBench, the first industry benchmark to evaluate AI coding agents' ability to navigate and understand complex codebases, introducing rich metrics to advance AI performance in real-world scenarios. In the interview I discuss the importance of building fine grained metrics to track, measure and improve upon agents' reasoning, decision making and their ability to understand (very) large context spaces. https://lnkd.in/gVJKaxt7

81

3 Comments

Redis 🤝 Weights & Biases Weights & Biases with Redis enables devs to build smarter, more efficient, RAG systems by combining Redis' fast vector search with W&B's tools for optimizing and monitoring AI models. Check out the tutorial below: https://lnkd.in/g6faUFs7 Tyler Hutcherson Jeremy Plichta Katie Dunn Talon Miller Jim Allen Wallace Cody Henshaw Sasha Manuilova

49

2 Comments

🆕 The Ultimate Guide to Open-Source RL Libraries for LLMs: 9 Frameworks Compared 🎯 Three primary RL applications for LLMs: 1️⃣ RLHF for human preference alignment 2️⃣ reasoning models for problem-solving 3️⃣ multi-turn agentic interactions [via Philipp Moritz of Anyscale ] https://lnkd.in/gcF2dNGq

57

1 Comment

Today, Microsoft joined Google's developed Agent2Agent (A2A) framework ecosystem to contribute to, and advance the protocol, including interoperability with Azure AI Foundry and Copilot Studio. This is good news for the entire ecosystem! Next to MSFT, we have already welcomed over 50 major Tech players who, together, are shaping the world of AI Agents. Onwards and upwards 🚀

562

13 Comments

I’ve been experimenting with structured knowledge graphs, optimizing semantic search, and distilling reasoning frameworks that sit on top of LLM outputs. Despite all the progress, no one has yet solved how large language models can offer meaningful guidance about the future. The real breakthrough may come not from bigger models, but from architectures that build persistent agentic memory, systems that learn, recall, and reason more like the human brain. DM me if you see something worth a deeper conversation.

51

New from O’Reilly + Redis: Managing Memory for AI Agents 📘 AI agents are only as good as their memory. This new report explores how short-term, long-term, and persistent memory shape agent performance—and how Redis provides real-time, scalable architectures for production-ready AI. 📖 Read the full report: https://lnkd.in/g7t--57p

50

1 Comment

Introducing "Building with Llama 4." This short course is created with Meta, and taught by Amit Sangani, Director of Partner Engineering for Meta’s AI team. Meta’s new Llama 4 has added three new models and introduced a Mixture-of-Experts (MoE) architecture to its family of open-weight models, making them more efficient to serve. In this course, you’ll work with two of the three new models introduced in Llama 4. First is Maverick, a 400B parameter model, with 128 experts and 17B active parameters. Second is Scout, a 109B parameter model with 16 experts and 17B active parameters. Maverick and Scout support long context windows of up to a million tokens and 10M tokens, respectively. The latter is enough to support directly inputting even fairly large GitHub repos for analysis! In hands-on lessons, you’ll build apps using Llama 4’s new multimodal capabilities including reasoning across multiple images and image grounding, in which you can identify elements in images. You’ll also use the official Llama API, work with Llama 4’s long-context abilities, and learn about Llama’s newest open-source tools: its prompt optimization tool that automatically improves system prompts and synthetic data kit that generates high-quality datasets for fine-tuning. If you need an open model, Llama is a great option, and the Llama 4 family is an important part of any GenAI developer's toolkit. Through this course, you’ll learn to call Llama 4 via API, use its optimization tools, and build features that span text, images, and large context. Please sign up here: https://lnkd.in/gXKeipht

2,832

111 Comments

Super excited to launch a new AI course! 🚀 Fine-Tuning & Reinforcement Learning for LLMs: Intro to Post-Training.  🎓 Learn for free: https://lnkd.in/gxyqfxsF Created by the teams at AMD 🤝 Andrew Ng’s DeepLearning.AI. What you’ll learn: ▫️ How post-training works in frontier labs ▫️ How to use fine-tuning and RL to solve real business problems - hands-on! ▫️ How it all works: the intuition, the math, and how it enables reasoning and agentic behavior All powered by AMD GPUs – compute designed to support you, whether you’re just tinkering or scaling your next AI breakthrough. The future of AI belongs to everyone who builds it. Our mission is to make things open, so we can bring that future to life together: open knowledge, open software, open compute access. Let us know what else you want to learn! ❤️

814

34 Comments

Just getting started with LLMs? Don’t let the jargon slow you down. This 5-part LLM Glossary is your shortcut to mastering the fundamentals👇 1️⃣ Model Types • Foundation Model: Pretrained on large-scale, diverse datasets to learn general capabilities. • Instruction-Tuned Model: Further trained to follow user instructions precisely. • Multi-modal Model: Handles multiple input/output formats like text, images, and audio. • Reasoning Model: Optimized for logic, problem-solving, and step-by-step thinking. • Small Language Model (SLM): Lightweight models for fast, efficient, on-device tasks. 2️⃣ Training & Fine-Tuning • Pretraining: Initial learning phase using massive, diverse data sources. • RLHF (Reinforcement Learning with Human Feedback): Aligns model behavior with human preferences. • DPO (Direct Preference Optimization): Uses ranked preferences instead of rewards for training. • Synthetic Data: AI-generated data used to supplement real datasets. • Fine-Tuning: Targeted retraining on specific domains or tasks. • LoRA / QLoRA: Techniques for efficient low-resource fine-tuning. • Guardrails: Rules or filters applied to enforce safety, ethics, and compliance. 3️⃣ Prompt Engineering • System/User Prompts: Define model behavior and user context in a session. • Chain-of-Thought (CoT): Prompts that guide the model to reason step-by-step. • Few-Shot / Zero-Shot Learning: Demonstrating tasks with few or no examples. • Prompt Tuning: Optimizing prompts via training for specific outcomes. • Context Window: The total number of tokens the model can "remember" per interaction. 4️⃣ Inference & Generation • Temperature: Adjusts output randomness; lower values are more deterministic. • Max Tokens: Limits the number of tokens in a generated response. • Seed: Controls reproducibility of outputs. • Latency: Time taken by the model to respond. • Hallucination: When the model generates factually incorrect but plausible-sounding information. 5️⃣ Retrieval-Augmented Generation (RAG) • Retrieval: Fetching relevant data from external sources before generation. • Semantic Search: Search based on meaning, not just keywords. • Embeddings: Vector representations of text used for similarity matching. • Chunks: Dividing documents into smaller segments for better retrieval. • Vector Databases (VectorDBs): Stores embeddings for fast and accurate search. • Reranking: Rescoring retrieved documents to prioritize relevance. • Indexing: Structuring data for efficient retrieval during generation. 🌟 Follow the AIKaDoctor (Free AI & Data Science Resources) channel on WhatsApp: https://lnkd.in/dCTCEKKc 📌Follow Habib Shaikh For more such content.

232

36 Comments

I'm thrilled to share that we've just launched nova.amazon.com - a new gateway to Amazon's frontier AI capabilities! This platform gives developers and tech enthusiasts direct access to Amazon Nova foundation models for text, code, image generation, and document analysis using simple natural language prompts. Equally exciting is the introduction of Amazon Nova Act - our specialized AI model trained to perform actions within web browsers. This represents a fundamental shift in how AI agents operate in digital environments, enabling reliable execution of complex web-based tasks from form submissions to calendar management with unprecedented accuracy. This is just the beginning of what's possible with agentic AI. I can't wait to see what the community builds with these new capabilities! https://lnkd.in/gPhwNnBv https://lnkd.in/gWWRSNwM #AmazonNova #AI #AgenticAI #AmazonWeb #DeveloperTools

318

7 Comments

How does Low-Rank Adaptation (LoRA) for large language models work? Fine-tuning large language models for every new task could create a significant operational challenge, requiring massive computational and storage resources. Low-Rank Adaptation (LoRA) offers an elegant solution. It freezes the original model's weights and injects small, trainable matrices to dramatically reduce the number of parameters that need updating. In the accompanying article, I use a simple, step-by-step example to provide an intuitive understanding of LoRA's forward and backward passes. Learn how this technique makes fine-tuning more accessible, minimizes storage overhead, and adds no extra latency during inference.

215

6 Comments