Aaron Birnbaum and Matthew Makansi

GenAI systems affect how we work. This general notion is well known. However, we are still unaware of the exact impact of GenAI. For example, how much do these tools affect our work? Do they have a larger impact on certain tasks? What does this mean for us in our daily work? To answer these questions, Anthropic released a study based on millions of anonymized conversations on Claude.ai. The study provides data on how GenAI is incorporated

Creating efficient prompts for large language models often starts as a simple task… but it doesn’t always stay that way. Initially, following basic best practices seems sufficient: adopt the persona of a specialist, write clear instructions, require a specific response format, and include a few relevant examples. But as requirements multiply, contradictions emerge, and even minor modifications can introduce unexpected failures. What was working perfectly in one prompt version suddenly breaks in another. If you

This is Part 2 of an exploration into the unexpected quirks of programming the Raspberry Pi Pico PIO with Micropython. If you missed Part 1, we uncovered four Wats that challenge assumptions about register count, instruction slots, the behavior of pull noblock, and smart yet cheap hardware. Now, we continue our journey toward crafting a theremin-like musical instrument — a project that reveals some of the quirks and perplexities of PIO programming. Prepare to challenge your understanding

Ten years ago, everybody was fascinated by the cloud. It was the new thing, and companies that adopted it rapidly saw tremendous growth. Salesforce, for example, positioned itself as a pioneer of this technology and saw great wins. The tides are turning though. As much as cloud providers still proclaim that they’re the most cost-effective and efficient solution for businesses of all sizes, this is increasingly clashing with the day-to-day experience. Cloud Computing was touted

In recent years, Parquet has become a standard format for data storage in Big Data ecosystems. Its column-oriented format offers several advantages: Faster query execution when only a subset of columns is being processed Quick calculation of statistics across all data Reduced storage volume thanks to efficient compression When combined with storage frameworks like Delta Lake or Apache Iceberg, it seamlessly integrates with query engines (e.g., Trino) and data warehouse compute clusters (e.g., Snowflake, BigQuery).

Now that we’ve explored Hadoop’s role and relevance, it’s time to show you how it works under the hood and how you can start working with it. To start, we are breaking down Hadoop’s core components — HDFS for storage, MapReduce for processing, YARN for resource management, and more. Then, we’ll guide you through installing Hadoop (both locally and in the cloud) and introduce some essential commands to help you navigate and operate your first

LoRA (Low Rank Adaptation – arxiv.org/abs/2106.09685) is a popular technique for fine-tuning Large Language Models (LLMs) on the cheap. But 2024 has seen an explosion of new parameter-efficient fine-tuning techniques, an alphabet soup of LoRA alternatives: SVF, SVFT, MiLoRA, PiSSA, LoRA-XS 🤯… And most are based on a matrix technique I like a lot: the SVD (Singular Value Decomposition). Let’s dive in. LoRA The original Lora insight is that fine-tuning all the weights of a