I in my previous article, “From Code to Insights: Software Engineering Best Practices for Data Analysts”, that engineering skills and best practices can be incredibly useful for analysts and other data professionals. This is even more true now in the AI era, when we have far more opportunities to build our own analytical tools: from …
Contents# Introduction# Setting a Complexity Budget with Textstat# Implementing the LangChain Pipeline# Wrapping Up # Introduction Large language models (LLMs) have a taste for using “flowery”, sometimes overly verbose language in their responses. Ask a simple question, and chances are you may get flooded with paragraphs of overly detailed, enthusiastic, and complex prose. This usual behavior is rooted in …
Guardrails for LLMs: Measuring AI ‘Hallucination’ and Verbosity Read More »
any time in the data engineering world, you’ve likely encountered this debate at least once. Maybe twice. Ok, probably a dozen times😉 “Should we process our data in batches or in real-time?” And if you’re anything like me, you’ve noticed that the answer usually starts with: “Well, it depends…” Which is true. It does depend. But “it …
Batch or Stream? The Eternal Data Processing Dilemma Read More »
Contents# Introduction# Why JSON Wastes Tokens in LLM Pipelines# What TOON Actually Is and When It Is Worth Using# Getting Started with TOON# Final Thoughts # Introduction JSON is great for APIs, storage, and application logic. But inside large language model (LLM) pipelines, it often carries a lot of token overhead that does not add much value to the model: …
Stop Wasting Tokens: A Smarter Alternative to JSON for LLM Pipelines Read More »
(not that long ago) when being a data scientist meant living in a notebook, tweaking hyperparameters as if your life depended on it, and in a lot of cases, the whole project did, indeed, depend on it. Do you remember those overnight grid searches? Or building feature engineering pipelines that felt more like art than …
Contents# Introduction# Initial Setup# Checking Univariate Normality# Checking Multivariate Normality# Checking Homoscedasticity# Checking Sphericity# Checking Multicollinearity# Wrapping Up # Introduction Anyone who has spent a fair amount of time doing data science may sooner or later learn something: the golden rule of downstream machine learning modeling, known as garbage in, garbage out (GIGO). For example, feeding a linear regression model with highly collinear …
Across 64 English authorities and six 2026 scenarios, even the strongest scenario shock was only 13% of the median uncertainty band. In plain English: the model’s assumptions moved the result less than historical forecast error did. The most aggressive challenger surge I could parameterise sits inside the noise the model has produced in past elections. …
Contents# Introduction# Bootleggers and Baptists# Why the Coalition Works So Well# Learning to Tell Them Apart# Final Thoughts # Introduction Every organization loves to call itself “data-driven.” It’s become the gold standard of credibility, the thing you say to shut down dissent in a meeting. But here’s something worth sitting with for a second: the phrase “according to data analytics” …
Baptists and Bootleggers: The Hidden Coalition Behind ‘Data-Driven’ Decisions Read More »
ContentsAI AgentsComponents of an AI AgentReAct (Reasoning + Acting) in AgentsA basic ReAct workflow in an AI agent usually looks like this:Structure of AI AgentsSingle Agent vs Multi-AgentWhen to Build A Multi-Agent SystemWalkthrough of A Multi-Agent ProjectMulti-Agent ArchitectureMemoryOrchestratorProject SetupDemo Video of the Multi Agent Agent RAG ResearcherNotesConclusionReach to me via:References AI Agents When building an …
Single Agent vs Multi-Agent: When to Build a Multi-Agent System Read More »
Image by Editor Contents# Introduction# What Is Voxtral TTS?# Cloning a Voice from Three Seconds of Audio# Latency Performance: Built for Real-Time Conversations# How Voxtral TTS Works# Getting Started: Installation and Setup# Voice Cloning with a Custom Voice: A Practical Example# Use Cases# Licensing and Deployment Considerations# Conclusion # Introduction Voice-enabled applications are everywhere, from virtual assistants to customer service chatbots. But for developers, building …
