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In this tutorial, we explore OpenMythos by building an advanced recurrent-depth transformer workflow that runs end-to-end in Google Colab. We create both MLA and GQA model variants, compare their parameter counts, and check the stability of the recurrent injection matrix through its spectral radius. We then move from simple forward and generation tests into a […]
For years, AI inside software meant a chat widget bolted onto the corner of an application. You typed, the model responded with text, and you manually translated that output into whatever you actually needed it to do. It was useful the way a calculator is useful: functional, but fundamentally passive. CopilotKit, a Seattle-based startup co-founded
How CopilotKit Is Redefining the Agentic AI Stack in 2026 Read More »
Most AI models today are not designed for sustained, multi-step autonomous execution. Tasks like running hundreds of iterative code modifications, or chaining tool calls across hours without human intervention, require a different kind of model architecture and training focus. Alibaba’s Qwen team formally announced Qwen3.7-Max at the 2026 Alibaba Cloud Summit on May 20. Although,
Qwen Introduces Qwen3.7-Max: A Reasoning Agent Model With a 1M-Token Context Window Read More »
Cohere just released Command A+, as an open-source model targeting enterprise agentic workflows. Available under an Apache 2.0 license, Command A+ is a mixture-of-experts (MoE) model built for high-performance agentic tasks with minimal compute overhead. The model is optimized for reasoning, agentic workflows, RAG, multilingual, and multimodal document processing. It unifies capabilities from four prior
What is a Forward Deployed Engineer? The term ‘Forward Deployed Engineer’ (FDE) sounds military. That is intentional. A Forward Deployed Engineer is a software engineer who works embedded with the customer’s technical and operational environment on-site, hybrid, remote, or inside a customer cloud or VPC, depending on the engagement. The FDE does not sit at
Vector search underpins most retrieval-augmented generation (RAG) pipelines. At scale, it gets expensive. Storing 10 million document embeddings in float32 consumes 31 GB of RAM. For dev teams running local or on-premise inference, that number creates real constraints. A new open-source library called turbovec addresses this directly. It is a vector index written in Rust
In this tutorial, we will generate knowledge graphs from plain text, conversations, and multiple source documents using kg-gen. We start by setting up the required dependencies and configuring an LLM through LiteLLM, then we extract entities, predicates, and relationships from simple text. As we move forward, we work with longer passages using chunking and clustering,
NVIDIA researchers have released Nemotron-Labs-Diffusion, a language model family that unifies three decoding modes in one architecture. The model supports autoregressive (AR) decoding, diffusion-based parallel decoding, and self-speculation decoding. It is available in 3B, 8B, and 14B parameter sizes. The family includes base, instruct, and vision-language variants. Sequential Decoding Limits Throughput Standard autoregressive (AR) language
Simultaneous interpretation is one of the harder problems in applied AI. You’re asking a model to translate speech before the speaker has finished a sentence. Every extra second of delay breaks the illusion of real-time communication. Alibaba’s Qwen team has been chipping away at this with each release. Their latest model, Qwen3.5-LiveTranslate-Flash, brings that latency
Google just released Gemini 3.5 Flash at Google I/O May, 2026. It is the first Gemini 3.5 model. The series combines frontier intelligence with action. Google calls it a major leap for intelligent agents. The Flash tier has historically been faster and cheaper. 3.5 Flash outperforms Gemini 3.1 Pro on challenging benchmarks. The previous premium
