Every time you prompt an LLM, it doesn’t generate a complete answer all at once — it builds the response one word (or token) at a time. At each step, the model predicts the probability of what the next token could be based on everything written so far. But knowing probabilities alone isn’t enough —…
How can speech editing become as direct and controllable as simply rewriting a line of text? StepFun AI has open sourced Step-Audio-EditX, a 3B parameter LLM based audio model that turns expressive speech editing into a token level text like operation, instead of a waveform level signal processing task. https://arxiv.org/pdf/2511.03601 Why developers care about controllable…
In this tutorial, we explore how to build an Agentic Voice AI Assistant capable of understanding, reasoning, and responding through natural speech in real time. We begin by setting up a self-contained voice intelligence pipeline that integrates speech recognition, intent detection, multi-step reasoning, and text-to-speech synthesis. Along the way, we design an agent that listens…
How can we build AI systems that keep learning new information over time without forgetting what they learned before or retraining from scratch? Google Researchers has introduced Nested Learning, a machine learning approach that treats a model as a collection of smaller nested optimization problems, instead of a single network trained by one outer loop….
Agents that use the Model Context Protocol MCP have a scaling problem. Every tool definition and every intermediate result is pushed through the context window, which means large workflows burn tokens and hit latency and cost limits fast. Anthropic’s new ‘code execution with MCP’ pattern restructures this pipeline by turning MCP tools into code level…
Tabular data is still where many important models run in production. Finance, healthcare, energy and industry teams work with tables of rows and columns, not images or long text. Prior Labs now extends this space with TabPFN-2.5, a new tabular foundation model that scales in context learning to 50,000 samples and 2,000 features while keeping…
In this tutorial, we build an advanced Reflex web application entirely in Python that runs seamlessly inside Colab. We design the app to demonstrate how Reflex enables full-stack development with no JavaScript, just reactive Python code. We create a complete notes-management dashboard featuring two pages, real-time database interactions, filtering, sorting, analytics, and user personalization. We…
How do you build reliable AI agents that plug into your existing Go services without bolting on a separate language stack? Google has just released Agent Development Kit for Go. Go developers can now build AI agents with the same framework that already supports Python and Java, while keeping everything inside a familiar Go toolchain…
Even strong ‘long-context’ AI models fail badly when they must track objects and counts over long, messy video streams, so the next competitive edge will come from models that predict what comes next and selectively remember only surprising, important events, not from just buying more compute and bigger context windows. A team of researchers from…
Large language models are now limited less by training and more by how fast and cheaply we can serve tokens under real traffic. That comes down to three implementation details: how the runtime batches requests, how it overlaps prefill and decode, and how it stores and reuses the KV cache. Different engines make different tradeoffs…
