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 »
Image by Editor Contents# Introduction# 1. JIT Compilation# 2. Intermediate Caching# 3. Schema Validation# 4. Lazy Parallelization# 5. Memory Profiling# Wrapping Up # Introduction Data pipelines in data science and machine learning projects are a very practical and versatile way to automate data processing workflows. But sometimes our code may add extra complexity to the core logic. Python decorators can overcome this …
5 Powerful Python Decorators for High-Performance Data Pipelines Read More »
has handled 2.3 million customer conversations in a single month. That’s the workload of 700 full-time human agents. Resolution time dropped from 11 minutes to under 2. Repeat inquiries fell 25%. Customer satisfaction scores climbed 47%. Cost per service transaction: $0.32 down to $0.19. Total savings through late 2025: roughly $60 million. The system runs on a …
Image by Author Contents# Introduction# Setting Up# Discovering the First Surprise: Inflated Results From Multiple Testing# Comparing 5 Methods on 1 Dataset# Discovering the Real Difference: They Identify Different Things# Checking Sanity: Do Outliers Correlate With Wine Quality?# Making Three Decisions That Shaped Our Results# Determining Which Method Performs Best For This Wine Dataset# Understanding What All This Means For Your Own Projects# Concluding …
