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In this tutorial, we build a fully functional event-driven workflow using Kombu, treating messaging as a core architectural capability. We walk through step by step the setup of exchanges, routing keys, background workers, and concurrent producers, allowing us to observe a real distributed system. As we implement each component, we see how clean message flow, […]
Google has released T5Gemma 2, a family of open encoder-decoder Transformer checkpoints built by adapting Gemma 3 pretrained weights into an encoder-decoder layout, then continuing pretraining with the UL2 objective. The release is pretrained only, intended for developers to post-train for specific tasks, and Google explicitly notes it is not releasing post-trained or IT checkpoints
In this tutorial, we shift from traditional prompt crafting to a more systematic, programmable approach by treating prompts as tunable parameters rather than static text. Instead of guessing which instruction or example works best, we build an optimization loop around Gemini 2.0 Flash that experiments, evaluates, and automatically selects the strongest prompt configuration. In this
Fine-tune popular AI models faster with Unsloth on NVIDIA RTX AI PCs such as GeForce RTX desktops and laptops to RTX PRO workstations and the new DGX Spark to build personalized assistants for coding, creative work, and complex agentic workflows. The landscape of modern AI is shifting. We are moving away from a total reliance
Meta has released SAM Audio, a prompt driven audio separation model that targets a common editing bottleneck, isolating one sound from a real world mix without building a custom model per sound class. Meta released 3 main sizes, sam-audio-small, sam-audio-base, and sam-audio-large. The model is available to download and to try in the Segment Anything
In this tutorial, we implement how we build a small but powerful two-agent CrewAI system that collaborates using the Gemini Flash model. We set up our environment, authenticate securely, define specialized agents, and orchestrate tasks that flow from research to structured writing. As we run the crew, we observe how each component works together in
Thinking Machines Lab has moved its Tinker training API into general availability and added 3 major capabilities, support for the Kimi K2 Thinking reasoning model, OpenAI compatible sampling, and image input through Qwen3-VL vision language models. For AI engineers, this turns Tinker into a practical way to fine tune frontier models without building distributed training
In this tutorial, we explore how we design and run a full agentic AI orchestration pipeline powered by semantic routing, symbolic guardrails, and self-correction loops using Gemini. We walk through how we structure agents, dispatch tasks, enforce constraints, and refine outputs using a clean, modular architecture. As we progress through each snippet, we see how
OpenAI team has released their openai/circuit-sparsity model on Hugging Face and the openai/circuit_sparsity toolkit on GitHub. The release packages the models and circuits from the paper ‘Weight-sparse transformers have interpretable circuits‘. https://arxiv.org/pdf/2511.13653 What is a weight sparse transformer? The models are GPT-2 style decoder only transformers trained on Python code. Sparsity is not added after
Mistral AI has introduced Devstral 2, a next generation coding model family for software engineering agents, together with Mistral Vibe CLI, an open source command line coding assistant that runs inside the terminal or IDEs that support the Agent Communication Protocol. https://mistral.ai/news/devstral-2-vibe-cli Devstral 2 and Devstral Small 2, model sizes, context and benchmarks Devstral 2
