Articles for category: AI Tools

March 7, 2025

ikayaniaamirshahzad@gmail.com

Introduction to State Space Models as Natural Language Models

State Space Models (SSMs) use first-order differential equations to represent dynamic systems. The HiPPO framework provides a mathematical foundation for maintaining continuous representations of time-dependent data, enabling efficient approximation of long-range dependencies in sequence modeling. Discretization of continuous-time SSMs lays the groundwork for processing natural language and modeling long-range dependencies in a computationally efficient way. LSSL, S4, and S5 are increasingly sophisticated and efficient sequence-to-sequence state-space models that pave the way for viable SSM-based alternatives to transformer models. While transformer-based models are in the limelight of the NLP community, a quiet revolution in sequence modeling is underway. State Space Models

March 7, 2025

ikayaniaamirshahzad@gmail.com

Announcing General Availability of Lakehouse Federation for Google BigQuery and Public Preview for Teradata and Oracle

We’re excited to announce the General Availability of Lakehouse Federation for Google BigQuery and the Public Preview for Oracle and Teradata. Now, you can connect, discover, govern, and query data from these sources through Unity Catalog—without migration or ETL. This makes data access easier while ensuring an open, interoperable lakehouse architecture. Unify your data and governance across distributed platforms with the Lakehouse Federation Lakehouse Federation enables users to query and analyze data across disparate databases, data warehouses and catalogs without duplicating or transferring data. By integrating external data sources directly into the Unity Catalog, organizations can: Gain a unified view

March 7, 2025

ikayaniaamirshahzad@gmail.com

Wan2.1: generate videos with an API

If you’ve been following the AI video space lately, you’ve probably noticed that it’s exploding. New models are coming out every week with better outputs, higher resolution, and faster generation speeds. Wan2.1 is the newest and most capable open-source video model. It was released last week, and it’s topping the leaderboards. Your browser does not support the video tag. There’s a lot to like about Wan2.1: It’s fast on Replicate. A 5s video takes 39s at 480p, or 150s at 720p. It’s open source, both the model weights and the code. The community is already building tools to enhance it.

March 6, 2025

ikayaniaamirshahzad@gmail.com

Divide and Conquer: The Role of Warps in Parallel Processing

Introduction GPUs are described as parallel processors for their ability to execute work in parallel. Tasks are divided into smaller sub-tasks, executed simultaneously by multiple processing units, and combined to produce the final result. These processing units (threads, warps, thread blocks, cores, multiprocessors) share resources, such as memory, facilitating collaboration between them and enhancing overall GPU efficiency. Note: it may be helpful to read this "CUDA refresher" before proceeding One unit in particular, warps, are a cornerstone of parallel processing. By grouping threads together into a single execution unit, warps allow for the simplification of thread management, the sharing of

March 6, 2025

ikayaniaamirshahzad@gmail.com

How Build.inc used LangGraph to launch a Multi-Agent Architecture for automating critical CRE workflows for Data Center Development.

Editor’s note: This is a guest blog post from our friends at Build.inc. They built one of the more complex multi-agent workflows we’ve seen – with over 25 sub agents. Check out the screenshot of their graph for an idea of the complexity. They also share practical lessons learned from building agent that we think will be helpful for other agent builders. Build.inc is pushing the boundaries of agentic systems to automate manual and labour intensive workflows in the built world. Our first “worker”, is now in production for a range of industry titans in Commercial Real Estate (CRE) aimed

March 6, 2025

ikayaniaamirshahzad@gmail.com

LLMs Turn Every Question Into an Answer

The world has changed considerably since our last ”think week” five months ago—and so has Every. We’ve added new business units, launched new products, and brought on new teammates. So we’ve been taking this week to come up with new ideas and products that can help us improve how we do our work and, more importantly, your experience as a member of our community. In the meantime, we’re re-upping four pieces by Dan Shipper that cover basic, powerful questions about AI. (Dan hasn’t been publishing at his regular cadence because he’s working on a longer piece. Look out for that

March 6, 2025

ikayaniaamirshahzad@gmail.com

Chinese Leadership On Cost, True Training Cost, Closed Model Margin Impacts – SemiAnalysis

The DeepSeek Narrative Takes the World by Storm DeepSeek took the world by storm. For the last week, DeepSeek has been the only topic that anyone in the world wants to talk about. As it currently stands, DeepSeek daily traffic is now much higher than Claude, Perplexity, and even Gemini. But to close watchers of the space, this is not exactly “new” news. We have been talking about DeepSeek for months (each link is an example). The company is not new, but the obsessive hype is. SemiAnalysis has long maintained that DeepSeek is extremely talented and the broader public in

March 6, 2025

ikayaniaamirshahzad@gmail.com

Will the future of software development run on vibes?

Will the future of software development run on vibes? I got a few quotes in this piece by Benj Edwards about vibe coding, the term Andrej Karpathy coined for when you prompt an LLM to write code, accept all changes and keep feeding it prompts and error messages and see what you can get it to build. Here’s what I originally sent to Benj: I really enjoy vibe coding – it’s a fun way to play with the limits of these models. It’s also useful for prototyping, where the aim of the exercise is to try out an idea and

March 6, 2025

ikayaniaamirshahzad@gmail.com

Combined aggregations for efficient analysis

Dealing with massive amounts of data is not always straightforward. Often, a data scientist needs to trim down a large data table into a handful of key components that can be used for further analysis. For example, we may want to condense a big table of stock quotes from a specific day to show only the highest, lowest, opening, and closing prices for each stock symbol. Streaming data like this could scale up to hundreds of millions of rows across a full year. Your computer doesn’t like this, so you must find a way to trim it down if you