ContentsRecent large language models (LLMs) — such as OpenAI’s o1/o3, DeepSeek’s R1 and Anthropic’s Claude 3.7 — demonstrate that allowing the model to think deeper and longer at test time can significantly enhance model’s reasoning capability. The core approach underlying their deep thinking capability is called chain-of-thought (CoT), where the model iteratively generates intermediate reasoning …

Empowering LLMs to Think Deeper by Erasing Thoughts Read More »

I enjoyed reading this paper, not because I’ve met some of the authors before🫣, but because it felt necessary. Most of the papers I’ve written about so far have made waves in the broader ML community, which is great. This one, though, is unapologetically African (i.e. it solves a very African problem), and I think …

A Review of AccentFold: One of the Most Important Papers on African ASR Read More »

distributions are the most commonly used, a lot of real-world data unfortunately is not normal. When faced with extremely skewed data, it’s tempting for us to utilize log transformations to normalize the distribution and stabilize the variance. I recently worked on a project analyzing the energy consumption of training AI models, using data from Epoch …

Log Link vs Log Transformation in R — The Difference that Misleads Your Entire Data Analysis Read More »

GPT-powered fashion assistant, I expected runway looks—not memory loss, hallucinations, or semantic déjà vu. But what unfolded became a lesson in how prompting really works—and why LLMs are more like wild animals than tools. This article builds on my previous article on TDS, where I introduced Glitter as a proof-of-concept GPT stylist. Here, I explore …

What My GPT Stylist Taught Me About Prompting Better Read More »

Today we’re releasing early access to Gemini 2.5 Pro Preview (I/O edition), an updated version of 2.5 Pro that has significantly improved capabilities for coding, especially building compelling interactive web apps. We were going to release this update at Google I/O in a couple weeks, but based on the overwhelming enthusiasm for this model, we …

Coding, web apps with Gemini Read More »

ContentsEvery company I worked for until today, there it was: the resilient MS Excel. Excel was first released in 1985 and has remained strong until today. It has survived the rise of relational databases, the evolution of many programming languages, the Internet with its infinite number of online applications, and finally, it is also surviving …

Generating Data Dictionary for Excel Files Using OpenPyxl and AI Agents Read More »

We’ve seen developers doing amazing things with Gemini 2.5 Pro, so we decided to release an updated version a couple of weeks early to get into developers hands sooner. Today we’re excited to release Gemini 2.5 Pro Preview (I/O edition). This update features even stronger coding capabilities, for you to start building with before Google …

Gemini 2.5 Pro Preview: even better coding performance Read More »

posts, we explored Part I of the seminal book Reinforcement Learning by Sutton and Barto [1] (*). In that section, we delved into the three fundamental techniques underlying nearly every modern Reinforcement Learning (RL) algorithm: Dynamic Programming (DP), Monte Carlo methods (MC), and Temporal Difference Learning (TD). We not only discussed algorithms from each field …

Benchmarking Tabular Reinforcement Learning Algorithms Read More »

Research Published 31 August 2022 Authors Siqi Liu, Leonard Hasenclever, Steven Bohez, Guy Lever, Zhe Wang, S. M. Ali Eslami, Nicolas Heess Using human and animal motions to teach robots to dribble a ball, and simulated humanoid characters to carry boxes and play football Humanoid character learning to traverse an obstacle course through trial-and-error, which …

From motor control to embodied intelligence Read More »

Contentsyou should read this Our Agenda:A Brief Note on Mathematical AbstractionThe “Normal” Norms: L1 and L2L¹ vs. L² Loss — Similarities and DifferencesL¹ Regularization (Lasso)L2 Regularization (Ridge)L¹ Loss in Generative Adversarial Networks (GANs)Why tiny differences matterGeneralizing Distance to LᵖThe L∞ NormConclusion you should read this  As someone who did a Bachelors in Mathematics I was first introduced to L¹ and L² as a measure …

From a Point to L∞ | Towards Data Science Read More »