Curated AI & Developer Learning Resources

In the realm of computer science and data analysis, various tools and libraries play a crucial role in enhancing productivity and efficiency. Python, in particular, offers a rich ecosystem of libraries that streamline code review, automate repetitive tasks, and facilitate data manipulation. Understanding the best practices and common pitfalls in using these libraries can significantly impact the performance of data analysis projects. Additionally, the integration of artificial intelligence in automating certain aspects of programming and data handling is becoming increasingly prevalent, raising questions about the future of software development and the role of human oversight in these processes.

You Don’t Need Deep Learning for Most Data Science Projects

Python in Plain English

There is a strange pressure in modern data science to reach for deep learning as soon as a problem looks remotely interesting. Continue reading on Python in Plain English

Quiz: How to Get Started With Ollama

Real Python

Check your understanding of installing Ollama, pulling local models, and calling the chat and generate functions from your Python code.

Applying recursive self-improvement to life

Pratik’s Pakodas 🍿

How to build a life that improves itself

The Week Three Real Security Incidents Happened to AI Agents, and What Each One Actually Teaches

Towards AI

None of them broke the model. All three broke the plumbing around it. I keep a folder of security writeups I tell myself I’ll “get to eventually,” and most weeks it grows by one or two links I never ...

Treating Prompt Templates as Hyperparameters in Scikit-LLM GridSearchCV

MachineLearningMastery.com

In this article, you will learn how to treat prompt templates as tunable hyperparameters for a language model, using scikit-learn's grid search to find the...

The Sequence Knowledge - Issue 933: When the Factory Starts Building Itself

TheSequence

Three frontier labs have now said out loud that their models help build their models. The interesting part is not the percentage. It is which half of the job got automated, and why.

The Self-Evolving Agent: How to Build Closed-Loop AI Systems That Write and Optimize Their Own Code

Python in Plain English

We have all been there. You spend hours meticulously crafting the perfect system prompt or tool description for your AI agent. It performs beautifully in your initial tests. But a week later, producti...

4 Python Libraries That Review Your Code Before Anyone Else Has To

Python in Plain English

Not a linter that argues about spacing. These four catch the things that actually matter, real security holes, dead code nobody noticed… Continue reading on Python in Plain English

19 Python Libraries That Quietly Automate the Work You Still Do by Hand

Python in Plain English

Writing Python taught me that the biggest productivity jumps rarely come from writing more code…. Continue reading on Python in Plain English

7 Pandas Habits That Quietly Make Your Data Analysis Slower

Python in Plain English

Your code can be correct, readable, and still be doing far more work than it needs to. Continue reading on Python in Plain English

I Compiled Python 3.15 : From Source Before It Was Even Released

Python in Plain English

It isn’t out yet. Official launch is October 1, 2026. I built the actual release candidate from scratch and ran real numbers nobody has… Continue reading on Python in Plain English

Stop Stalking Your Crush, Stalk Your Agents Instead: A LangSmith Deep Dive: Part -1

Towards AI

LangSmith is a monitoring and observability platform built by the creators of LangChain and LangGraph for tracing AI applications. But before diving into LangSmith, let’s first understand what observa...