ContentsIntroductionThe issueThe solutionConclusionReferences Introduction This piece is a follow-up to my last article, about building the Like-for-Like (L4L) solution based on Power Query: The solution works as expected for the most part. I showed it to my peers and to some clients. The feedback was positive, but I’ve got some questions, and the results of …
Following Up on Like-for-Like for Stores: Handling PY Read More »
Sponsored Content Abacus AI’s ChatLLM takes a different approach. Instead of competing with individual tools, it bundles them into a single workspace that handles writing, coding, image generation, video creation, and even autonomous workflows. Built on top of a production-grade AI platform, ChatLLM isn’t just another chatbot—it’s designed to function as a …
ChatLLM Review: Tired of Multiple AI Tools? Here’s a Smarter All-in-One Alternative Read More »
Healthcare is increasingly embracing AI to improve workflow management, patient communication, and diagnostic and treatment support. It’s critical that these AI-based systems are not only high-performing, but also efficient and privacy-preserving. It’s with these considerations in mind that we built and recently released Health AI Developer Foundations (HAI-DEF). HAI-DEF is a collection of lightweight open …
Our most capable open models for health AI development Read More »
In my previous post, Prompt Caching — what it is, how it works, and how it can save you a lot of money and time when running AI-powered apps with high traffic. In today’s post, I walk you through implementing Prompt Caching specifically using OpenAI’s API, and we discuss some common pitfalls. ContentsA brief reminder …
Prompt Caching with the OpenAI API: A Full Hands-On Python tutorial Read More »
Image by Author Contents# Introduction# What Is SynthID?# Text Media# Images And Video Media# Audio Media# Watermark Detection And Verification# Strengths And Limitations Of SynthID# Applications And Broader Impact# Conclusion # Introduction As AI-generated media becomes increasingly powerful and common, distinguishing AI-generated content from human-made content has become more challenging. In response to risks such as misinformation, deepfakes, and the misuse of synthetic media, …
To understand AI capabilities across these cognitive abilities, we propose a three-stage evaluation protocol that benchmarks system performance in relation to human capabilities: Evaluate AI systems across a broad suite of cognitive tasks covering each ability, using held-out test sets to prevent data contamination Collect human baselines for the same tasks from a demographically representative …
Measuring Progress Towards AGI: A Cognitive Framework Read More »
on LinkedIn a few days ago saying that a lot of the top engineers are now just using AI to code. It reached thousands and got quite a few heated opinions. The space is clearly split on this, and the people against it mostly think of it as outsourcing an entire project to a system …
Image by Author Contents# Introduction# Why Data Structure Changes Your Coding Style# What We Measure: Code Structure Characteristics# Which Constructs Are Most Common# SQL Frequency Highlights# Pandas Method Highlights# Why These Patterns Keep Appearing# Practical Takeaways For Faster, Cleaner Solutions# Conclusion # Introduction When you solve enough interview-style data problems, you start noticing a funny effect: the dataset “shape” quietly dictates your coding style. …
Visualizing Patterns in Solutions: How Data Structure Affects Coding Style Read More »
In the rapidly evolving landscape of large language models (LLMs), the spotlight has largely focused on the decoder-only architecture. While these models have shown impressive capabilities across a wide range of generation tasks, the classic encoder-decoder architecture, such as T5 (The Text-to-Text Transfer Transformer), remains a popular choice for many real-world applications. Encoder-decoder models often …
T5Gemma: A new collection of encoder-decoder Gemma models Read More »
is not a data quality problem. It is not a training problem. It is not a problem you can solve with more RLHF, better filtering, or a larger context window. It is a structural property of what these systems are optimized to do. I have held this position for months, and the reaction is predictable: researchers working …
Hallucinations in LLMs Are Not a Bug in the Data Read More »
