Articles for category: AI Tools

Building The Most Scalable Experiment Tracker For Foundation Models

At a large-scale model training (in huge models), anomalies are not rare events but problematic patterns that drive failure. Detecting anomalies early in the process saves days of work and training. ML model training observability is not just about tracking metrics. It requires proactive monitoring to catch issues early and ensure model success, given the high cost of training on large GPU clusters. If you are an enterprise or a team operating a model, focus on three key areas: fine-tune your prompts to get the most effective outputs (prompt engineering), ensure that your model behaves safely and predictably, and implement

Publish to Multiple Catalogs and Schemas from a Single DLT Pipeline

DLT offers a robust platform for building reliable, maintainable, and testable data processing pipelines within Databricks. By leveraging its declarative framework and automatically provisioning optimal serverless compute, DLT simplifies the complexities of streaming, data transformation, and management, delivering scalability and efficiency for modern data workflows. We’re excited to announce a much-anticipated enhancement: the ability to publish tables to multiple schemas and catalogs within a single DLT pipeline. This capability reduces operational complexity, lowers costs, and simplifies data management by allowing you to consolidate your medallion architecture (Bronze, Silver, Gold) into a single pipeline while maintaining organizational and governance best practices.

“Focus on the basics. If you have a solid foundation, basic technology…

This course introduces people to the patterns and approaches for writing effective prompts for large language models. Will start with basic prompts and build towards writing sophisticated prompts to solve problems in any domain. By the end of the course, you will have strong prompt engineering skills and be capable of using large language models for a wide range of tasks in their job, business, personal life, and education, such as writing, summarization, game play, planning, simulation, and programming. Source link

Ideogram v2 is an outstanding new inpainting model

Today Ideogram are launching their new inpainting feature for Ideogram v2. We’re thrilled to be partnering with Ideogram, to bring Ideogram v2 to Replicate’s API. We’ve been blown away by the quality of this model. It’s really good. Ideogram v2 comes in two flavors: For example, here is a herd of dinosaurs grazing on the Bucolic Green Hills: Ideogram v2 is not just for inpainting: you can use it to generate any type of image. In our tests, we found it to be particularly good at generating text. Run Ideogram v2 with an API on Replicate To inpaint an image

SAM 2: Meta’s Next-Gen Model for Video and Image Segmentation

The era has arrived where your phone or computer can understand the objects of an image, thanks to technologies like YOLO and SAM. Meta’s Segment Anything Model (SAM) can instantly identify objects in images and separate them without needing to be trained on specific images. It’s like a digital magician, able to understand each object in an image with just a wave of its virtual wand. After the successful release of llama 3.1, Meta announced SAM 2 on July 29th, a unified model for real-time object segmentation in images and videos, which has achieved state-of-the-art performance. SAM 2 offers numerous

How Vizient empowers healthcare providers with reliable GenAI insights using LangGraph and LangSmith

Vizient, a leader in healthcare performance improvement, is revolutionizing how healthcare providers access and analyze data. Today, many healthcare providers rely on disparate data sources, needing to mine for data to produce actionable insights on patient care — a long, drawn-out process. Vizient’s GenAI platform empowers systems of all sizes to query and unify siloed datasets, driving better decisions in supply chain management and clinical outcomes. Vizient’s GenAI platform helps answer questions like: “Are my ambulatory investments effective?” or “Are we delivering the most cost-effective care?” and get immediate, data-backed answers. The goal is to improve operational efficiency and democratize

How to Figure Out What People Want

In my piece last week about five new thinking styles for working with thinking machines, I wrote about why it is important, in the age of AI, to think in terms of sequences rather than essences: learning to cope with dynamic contexts rather than trying to locate fixed, discoverable truths. This week I’m covering a practical application of this thinking style.  Was this newsletter forwarded to you? Sign up to get it in your inbox. A common piece of startup advice from the last 10-15 years is: “Make something people want.” It resonates because it effectively distills the core job

Intel on the Brink of Death

Intel’s board is incompetent and its horrible decisions over the decades are going to push it towards death. The decision to fire Pat Gelsinger, put in charge a CFO + career sales and marketing leader, and cut spending on fabs in favor of a renewed focus on x86 is an example of the incompetence that will end Intel. Fabricated Knowledge wrote The Death of Intel: When Boards Fail recently explaining how board issues around leadership and planning have failed the company. Simply put, the Intel board has escaped blame for over a decade of failures. This decade of failure culminates

Adding AI-generated descriptions to my tools collection

Adding AI-generated descriptions to my tools collection 13th March 2025 The /colophon page on my tools.simonwillison.net site lists all 78 of the HTML+JavaScript tools I’ve built (with AI assistance) along with their commit histories, including links to prompting transcripts. I wrote about how I built that colophon the other day. It now also includes a description of each tool, generated using Claude 3.7 Sonnet. This is the single largest chunk of AI-generated text I’ve ever published on the indexable web. I share lots of LLM transcripts but I usually put them in a secret Gist such that they’ll be kept

Major Deephaven Interactive Broker Updates

Deephaven recently introduced major updates to deephaven-ib, our Python package that allows users to interact with Interactive Brokers (IB) using Deephaven. This update includes a new build and deployment process, bug fixes, and updates to the Java, Deephaven, and Interactive Brokers versions used by deephaven-ib. Deephaven-IB now uses the latest versions of Java, Deephaven, and Interactive Brokers, and can be run without Docker. Updated examples that demonstrate what deephaven-ib can do. deephaven-ib now uses the latest versions of Java, Deephaven, and Interactive Brokers: Java has been updated from version 11 to 17. Interactive Brokers has been updated from version 10.19.01