Edit: Included more details in my workflow I was working on in the Context section. I just really wanted to say thank you to all of you folks in here who have been so helpful and patient and amazing regardless of anyone’s knowledge level. This sub is VERY different from “big reddit” in that most …
Holiday season 2025 is introducing a new set of norms for retailers amid ongoing tariff pressures. In the face of increased costs of materials, sourcing shifts, and ever-more cautious spenders, marketers are adjusting their Black Friday strategies and use of language — and doubling down on certain incentives — to court and convert consumers this …
Read more “How Tariffs Are Forcing Brands to Pivot Marketing Campaigns This Holiday Season”
From November 6 to November 21, 2025 (starting at 8:00 a.
Every large language model (LLM) application that retrieves information faces a simple problem: how do you break down a 50-page document into pieces that a model can actually use? So when you’re building a retrieval-augmented generation (RAG) app, before your vector database retrieves anything and your LLM generates responses, your documents need to be …
Read more “Essential Chunking Techniques for Building Better LLM Applications”
TL;DR: Artificial Intelligence learns patterns from data and uses them to make predictions, generate content, or solve problems. Generative AI, such as ChatGPT or image and video generators, takes this a step further by creating new things, text, art, music, and more, that have never existed before. People often ask: “How does AI actually work?” …
Text Normalization (TN) is a key preprocessing step in Text-to-Speech (TTS) systems, converting written forms into their canonical spoken equivalents. Traditional TN systems can exhibit high accuracy, but involve substantial engineering effort, are difficult to scale, and pose challenges to language coverage, particularly in low-resource settings. We propose PolyNorm, a prompt-based approach to TN using …
Read more “PolyNorm: Few-Shot LLM-Based Text Normalization for Text-to-Speech”
As AI agents are adopted at scale, developer teams can create dozens to hundreds of specialized Model Context Protocol (MCP) servers, tailored for specific agent use case and domain, organization functions or teams. Organizations also need to integrate their own existing MCP servers or open source MCP servers for their AI workflows. There is a …
Read more “Transform your MCP architecture: Unite MCP servers through AgentCore Gateway”
As machine learning models continue to scale, a specialized, co-designed hardware and software stack is no longer optional, it’s critical. Ironwood, our latest generation Tensor Processing Unit (TPU), is the cutting-edge hardware behind advanced models like Gemini and Nano Banana, from massive-scale training to high-throughput, low-latency inference. This blog details the core components of Google’s …
Read more “From silicon to softmax: Inside the Ironwood AI stack”
Even as concern and skepticism grows over U.S. AI startup OpenAI’s buildout strategy and high spending commitments, Chinese open source AI providers are escalating their competition and one has even caught up to OpenAI’s flagship, paid proprietary model GPT-5 in key third-party performance benchmarks with a new, free model. The Chinese AI startup Moonshot AI’s …
The unprecedented payday will go into full effect by 2035—as long as Tesla hits ambitious financial and production targets.