AI on Leafw's Blog

OpenAI and Microsoft Officially “Break Up”: The Most Important Alliance of the AI Era Finally Unbundles

wyr95626@gmail.com (Leafw) — Tue, 28 Apr 2026 00:00:00 +0000

On April 27, 2026, OpenAI and Microsoft released statements at the same time: the two companies had revised their partnership agreement.

You could call it a “breakup.” The reason is not hard to understand. Microsoft no longer has exclusive licensing rights to OpenAI’s models and products. OpenAI can offer products on any cloud platform. Microsoft will also stop paying revenue share to OpenAI. For the past few years, these two companies were almost treated as one AI camp. Now there is suddenly a line between them, and that is big news.

I Used AI to Write a DeepSeekV4 Paper Review, but I Do Not Think I Should Publish It

wyr95626@gmail.com (Leafw) — Sat, 25 Apr 2026 00:00:00 +0000

I just used AI to write a paper review of DeepSeek-V4.

The workflow was smooth. I dropped the PDF in, asked AI to extract the content, organize the structure, generate illustration ideas, and then turn everything into a technical blog post that looked fairly complete. The key innovations and the interpretation of evaluation results were both pretty decent. I put the outline below. It looks quite good, doesn’t it?

The Collective Retreat of AI Coding Subscriptions: From Carnival to Tightening in One Quarter

wyr95626@gmail.com (Leafw) — Wed, 22 Apr 2026 00:00:00 +0000

A Shutdown Notice

On April 20, GitHub published a blog post with a restrained title: “Changes to GitHub Copilot Individual Plans.” The content was much less restrained:

New signups for Pro, Pro+, and Student plans were paused
Usage limits were tightened through both session limits and weekly limits
Claude Opus was removed from the Pro plan

The reason was direct: agentic workflows were consuming far more compute than expected, and “a small number of requests can now cost more than the subscription price itself.”

I Paused Vibecoding for a Month

wyr95626@gmail.com (Leafw) — Sun, 19 Apr 2026 00:00:00 +0000

How I Stopped

Over the past few months, I had been using vibecoding almost obsessively. The term has become common enough by now: using AI to quickly build products, sometimes taking an idea from scratch to launch in just a few days, or even a few hours. I enjoyed that pace. I could have an idea at midnight, start coding the next day, and publish it over the weekend.

During that period I built two apps and a client somewhat like a combination of Lobehub and local Claude Code. The number was not huge, but the last one gave me the strongest sense of frustration.

In the LLM Era, the Programmer's Virtue of Laziness Is Disappearing

wyr95626@gmail.com (Leafw) — Tue, 14 Apr 2026 00:00:00 +0000

Introduction

Have you had this experience too: you use an LLM to write code, produce thousands of lines in a day, and then look back only to realize that the truly valuable part may be no more than a few hundred lines?

It sounds absurd, but it is becoming normal. Bryan Cantrill, CTO of Oxide and co-creator of DTrace, recently wrote The Peril of Laziness Lost, aiming directly at the heart of the problem: LLMs are killing one of the programmer’s most important virtues: laziness.

Case Studies on Deep Convolutional Neural Networks

wyr95626@gmail.com (Leafw) — Fri, 08 Sep 2023 00:00:00 +0000

In the rapidly evolving field of deep learning, innovative neural network architectures are constantly emerging. Keeping pace with these developments necessitates the study of these case studies. This blog is based on the content from the second week of the fourth course in Professor Andrew Ng’s deep learning specialization, focusing on some case studies of convolutional neural networks.

Significance of Case Studies

Firstly, consider why we need to study these cases.

Introduction to Convolutional Neural Networks

wyr95626@gmail.com (Leafw) — Mon, 04 Sep 2023 00:00:00 +0000

Convolutional Neural Networks (CNNs) are a type of deep neural network designed for image processing. Inspired by the structure of biological visual systems, CNNs utilize convolution operations to extract spatial features from images and combine these with fully connected layers for classification or prediction tasks. The integration of convolution operations allows CNNs to excel in image processing, making them widely applicable in tasks such as image classification, object detection, and semantic segmentation. This blog will provide a brief introduction to the basics of convolutional neural networks, based on the first week of Professor Andrew Ng’s deep learning specialization, course four.

Detailed Explanation of Machine Learning Strategies

wyr95626@gmail.com (Leafw) — Mon, 17 Jul 2023 00:00:00 +0000

Machine learning is a key driver of technological advancement today. Establishing a systematic machine learning strategy is essential for efficiently advancing projects and achieving desired outcomes. This requires careful consideration of several critical steps, including goal setting, model selection, data processing, and results evaluation.

In this blog, we will explore these steps in detail. We will particularly focus on effective strategies and methods for setting machine learning goals, evaluating model performance, and optimizing models. By the end of this blog, you should have a deeper understanding of the machine learning project lifecycle and be able to apply these methods to enhance your project’s performance.

Hyperparameter Tuning, Batch Normalization, and Deep Learning Frameworks

wyr95626@gmail.com (Leafw) — Wed, 12 Jul 2023 00:00:00 +0000

The primary focus of this blog is on hyperparameter tuning, batch normalization, and common deep learning frameworks. This is also the final week of the second course in the specialized deep learning curriculum. Let’s dive in!

Hyperparameter Tuning

Hyperparameter tuning is a crucial process in deep learning. Properly setting hyperparameters will directly impact the performance of deep learning models. This section will explore the significance of hyperparameter tuning, the key hyperparameters that affect model performance, and methods and strategies for selecting these hyperparameters.

Optimize Algorithms

wyr95626@gmail.com (Leafw) — Tue, 04 Jul 2023 00:00:00 +0000

This week’s content focuses on optimization algorithms, which can significantly enhance and expedite the training of deep learning models. Let’s dive in!

1. Importance of Optimization Algorithms

Optimization algorithms are crucial in the fields of machine learning and deep learning, particularly when training deep neural networks. These algorithms are methods used to minimize (or maximize) functions, typically the loss function in deep learning, with the goal of finding the optimal parameters that minimize this function.

Foundations of Practical Deep Learning

wyr95626@gmail.com (Leafw) — Sun, 02 Jul 2023 00:00:00 +0000

In the journey of learning deep learning, we encounter extensive theoretical knowledge, including gradient descent, backpropagation, and loss functions. A true understanding and application of these theories allow us to solve practical problems with ease. This blog, drawing from Week 1 of Course 2 in Professor Andrew Ng’s Deep Learning Specialization, explores critical concepts and methods from a practical standpoint. Key topics include how to divide training, development, and test sets, understanding and managing bias and variance, when and how to use regularization, and properly setting up optimization problems.

Introduction to Deep Learning and Neural Networks

wyr95626@gmail.com (Leafw) — Sat, 24 Jun 2023 00:00:00 +0000

The explosive popularity of ChatGPT and the recent flurry of large-scale model developments have thrust the field of artificial intelligence into the spotlight. As a tech enthusiast keen on exploring various technologies, understanding the principles behind these advancements is a natural inclination. Starting with deep learning is a logical step in delving deeper into AI, especially since I’ve already studied Professor Andrew Ng’s Machine Learning course. Now, through his Deep Learning Specialization, I am furthering my knowledge in this domain. This article aims to demystify deep learning, drawing insights from the first course of the specialization.