Python

AI Berkshire: Value Investing Research Framework for the AI Era [2026 Latest Edition] “Price is what you pay. Value is what you get.” — Warren Buffett When discussing the future of AI and investment, the “AI Berkshire” project has become an unavoidable presence. At its core, it leverages generative AI, particularly high-performance Claude Code, to evolve the profound philosophies and strategies cultivated by legendary investors such as Warren Buffett, Charlie Munger, Duan Yongping, and Li Lu. It does this not merely through imitation, but by advancing them to a practical operational level and elevating them into concrete investment decisions—a truly groundbreaking framework for this purpose. ...

Unlocking the Future of Stock Investing with Python: A Strategy to Visualize a Stock’s ‘True Value’ through PER/PBR Band Analysis Are you being forced into emotional decisions amidst the uncertain fluctuations of the stock market? For our readers at TechTrend Watch, especially developers who value data and logic, you must surely wish to interpret the market with a more scientific and data-driven approach. In this article, we will provide a detailed explanation of a method for drawing PER/PBR bands, leveraging Python and J-Quants data to objectively and intuitively visualize a stock’s “undervaluation.” This analysis technique will serve as a powerful compass, enabling you to make sound investment decisions without being swayed by market noise. ...

🚀 Beyond RAG Basics: How LLM ‘Intelligence Augmentation’ Revolutionizes AI Development In today’s digital world, generative AI, especially Large Language Models (LLMs), is redefining the boundaries of business and technology. However, LLMs face inherent challenges, namely “limited access to up-to-date information” and “hallucinations (generating misinformation),” which have become obstacles to their practical application. Against this backdrop, “RAG (Retrieval Augmented Generation)” is rapidly gaining recognition for its value in the development field. ...

Introduction to Categorical Deep Learning: The Future of “Assembling Learning” Led by Compositional Learning and Backprop as Functor While modern deep learning has made remarkable progress, much of its network architecture design still relies on ad-hoc trial and error based on “experimentation and intuition.” Addressing the fundamental question, “Why does this specific combination work so well?” with mathematical beauty and rigor is an emerging paradigm called “Categorical Deep Learning (CDL)”, and the core philosophy driving it is “Compositional Learning.” ...

AI Agent Goes Rogue and Triggers Bankruptcy?! Essential Rules to Prevent “API Bankruptcy” Learned from the DN42 Scanning Tragedy Currently, the development and adoption of autonomous AI agents is one of the hottest trends in the global tech scene. While the vision of “complete automation” and “autonomous decision-making” is extremely appealing, it harbors a serious risk that can push developers into financial ruin overnight. In this post, we focus on a real-world case where a developer attempted to use an autonomous AI agent to analyze and scan the massive, decentralized private network “DN42”. A highly illustrative and shocking incident was reported where an unexpected reasoning loop by the AI, coupled with an explosion of API requests, generated astronomical costs (API bankruptcy) in just a few hours. ...

Hacking LoL Addiction with Code! An Engineer’s Serious Approach to Designing the “Ultimate Game Force-Quit System” “Just one more game…” This resolve crumbles in vain, and before you know it, the eastern sky is growing light. League of Legends (hereafter LoL), the globally popular multiplayer online battle arena (MOBA), possesses a meticulous game design and a loop that stimulates the brain’s reward system so powerfully that standing against it with sheer “willpower” alone is extremely difficult. ...

Ending the Era of the 3000-Line “God main.py”: Implementing the “Dependency Injection (DI)” Design Pattern in Python Python boasts overwhelming convenience when it comes to rapid prototyping and quick implementations of AI agents. However, in the rush for speed, have you ever found yourself packing all processing into main.py and unwittingly giving birth to a 3000+ line “God File”? Bloated spaghetti code is the single biggest factor slowing down development speed. The ultimate remedy to fundamentally solve this issue and guide your project toward a clean, robust design is “Dependency Injection (DI).” This article explains practical DI techniques in Python and the architectural paradigm shift they bring. ...

Mastering Log Writing in Multi-Process Environments: Mechanisms to Prevent Data Corruption and Practical Approaches Logging design in concurrent or multi-process environments may seem simple at first glance, but it is actually a domain that demands a precise understanding of the OS level. When scaling web applications or running background workers in parallel, the act of “directly writing to a single log file from multiple processes” harbors pitfalls of unexpected data corruption, such as interleaving or loss. ...

Entering the Era of Autonomous Terminals: How the Official CLI “Claude Code” Shifts the Software Development Paradigm Among the global developer community, there is one revolutionary tool generating immense excitement right now. It is “Claude Code”, an AI autonomous agent designed specifically for the terminal, released by Anthropic with high expectations. While traditional AI assistants serve as advisors that suggest or write code snippets, Claude Code acts as an autonomous co-developer that directly interacts with your development environment to complete tasks from start to finish. It deeply understands your project’s codebase and autonomously handles everything from running tests and fixing bugs to committing and pushing to Git—all initiated by simple natural language prompts. ...

[Beyond the Basics] Master Modern Matplotlib in Python! A Complete Roadmap to Object-Oriented Plotting and Japanese Support In the practical application of data science, AI, and machine learning using Python, data visualization is a critical process that directly impacts decision-making. At the center of this is matplotlib. However, many developers struggle with issues such as unpolished default designs, garbled Japanese text (the infamous “tofu” phenomenon), and code that quickly turns into spaghetti as customization accumulates. ...