Sunday, July 19, 2026

China housing market -= $18T

important story and situation, that could have global ripple effects. danger!

so much about benefit and effectivenes of central "planning and control"... 

BREAKING: China’s ENTIRE Housing Market Just Collapsed - YouTube

"China's housing market has now erased roughly 20 years of gains,
with home prices falling for 35 consecutive months
and an estimated $18 trillion in household wealth disappearing. 
In a country where housing represented nearly 70% of household wealth, 
the impact has been enormous and is reshaping the entire economy."


Here is a summary and the key points from Graham Stephan’s video

The video analyzes the severe downturn in China’s housing market as of mid-2026. It highlights that the market has erased roughly 20 years of real estate gains, driven by a massive shift away from speculative real estate investment toward housing as a utility, leading to widespread developer defaults and plummeting property values.

Key Points

  • Erasing Decades of Gains: The Chinese real estate market has experienced a correction so severe that it has effectively wiped out two decades of accumulated growth and property valuation gains.

  • The "Housing is for Living" Shift: The collapse is heavily tied to the Chinese government's policy enforcement of Xi Jinping’s doctrine: "Housing is for living, not for speculation." Unlike previous Western financial crises, the state has actively chosen to punish speculative buyers and developers rather than bailing them out with taxpayer funds.

  • Overbuilding and "Ghost Cities": A major catalyst for the systemic unwinding was over a decade of aggressive overproduction, which resulted in entire uninhabited municipal developments, skyscrapers, and infrastructure projects built purely on speculative demand.

  • Fundamental Market Differences: Unlike the U.S. housing market, which is structurally supported and valued based on rental cash flow, China's property market operated heavily as a speculative vehicle without matching rental yields.

  • Global Visuals and Indicators: The broader macroeconomic landscape shows that other major global real estate hubs (like Australia, where the housing market is noted as being 400% of GDP, or Canada) are seeing similar long-term listings and stagnating prices, indicating that the era of hyper-speculative real estate is facing massive global headwinds.


scale: max was 115, now is 85, not "0" as it may appear;



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AI book: Build a Large Language Model (From Scratch) by Sebastian Raschka, PhD

 Books | Sebastian Raschka, PhD

In Build a Large Language Model (from Scratch), you’ll discover how LLMs work from the inside out. In this book, will guide you step by step through creating your own LLM, explaining each stage with clear text, diagrams, and examples.





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The Story of Java Programming Language

Java (programming language) - Wikipedia


 The Java Story | The Official Documentary - YouTube

"From its humble beginnings as a project code-named "Oak" at Sun Microsystems to becoming a global standard for enterprise software and billions of devices, Java's journey is one of radical innovation, strategic pivots, and enduring community strength."


Based on the provided YouTube page for "The Java Story | The Official Documentary" by CultRepo and Java, here is a summary and the key points from the video page and its audience reception:

Summary

This 1-hour and 14-minute documentary details the history and massive global impact of the Java programming language. It tracks Java's journey from its early, humble beginnings as a secret project code-named "Oak" at Sun Microsystems to its evolution into an enterprise-grade global standard that runs much of the modern world's infrastructure.

Key Points

  • The Evolution of "Oak": The film covers Java’s origins at Sun Microsystems, starting under the code-name "Oak" before being rebranded and launched as Java.

  • The "Write Once, Run Anywhere" Revolution: A major focus is placed on the groundbreaking concept of the Java Virtual Machine (JVM), which allowed code to run across different platforms seamlessly—a feature that revolutionized software development in the late 1990s.

  • Key Pioneers Featured: The documentary includes firsthand insights and appearances from Java creator James Gosling, the co-authors of the JVM specifications, and other early team members like Brian, Arthur Van Hoff, and Lee.

  • Massive Industry Impact: Comments from viewers and engineers highlight how Java has served as the backbone for critical global infrastructure, major banking systems (dating back to integration with Netscape in 1998), and enterprise software for over 25 years.

  • Emotional Community Resonance: The reception from the developer community is profoundly grateful, with many long-time engineers noting that learning Java completely shaped their careers, provided their livelihood, and serves as a historical time capsule for the software industry as a whole.

Read more »
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AI + DB: Relational Graph Transformers: Kumo.ai

interesting, informative podcast:

Foundation Models for Structured Data - Software Engineering Daily

Relational deep learning proposes a new approach. It treats databases as graphs and applies transformer-style attention mechanisms directly over structured relational data. Researchers are now building foundation models for tabular data that aim to generalize across predictive tasks without painstaking feature engineering.

Jure Leskovec is a Professor of Computer Science at Stanford University and he previously served as Chief Scientist at Pinterest and was an investigator at the Chan Zuckerberg Biohub. Most recently, he co-founded the machine learning startup, Kumo.AI.







recent advances in ML offer a suite of tools to build graph neural networks (GNN) tailored for graph-structured data, where industry-relevant tasks can be framed as node classification (or regression) or graph-level predictions.

Relational tables as graphs

...leveraging the connectivity structure between tables is key for effective ML algorithms and better downstream performance, even when tabular feature data (e.g., price, size, category) is sparse or noisy. To this end, the only data preparation step consists of transforming a collection of tables into a single heterogeneous graph.


Relational Graph Transformers Explained: Multi-Table Attention | Kumo.ai

A relational graph transformer applies type-aware attention to heterogeneous graphs built from relational databases. Each table is a node type, each foreign key is an edge type, and the transformer learns cross-table patterns that flat-table ML cannot see.
  • A relational graph transformer converts relational databases into heterogeneous temporal graphs and applies type-aware attention. Tables become node types, foreign keys become edge types.
  • It handles heterogeneous types (different columns per table), temporal ordering (causal masking from timestamps), and scale (neighbor sampling for millions of rows).
  • On RelBench (7 databases, 30 tasks), it achieves 81.14 AUROC fine-tuned vs 62.44 for LightGBM. Much of the gain comes from automatic cross-table pattern discovery.
  • This replaces months of manual feature engineering. SQL aggregations like 'average order amount in 30 days' are discovered automatically from the raw relational structure.
  • KumoRFM is a pre-trained relational graph transformer. Zero-shot predictions on unseen databases (76.71 AUROC) without any training on the target data.


Kumo AI: Instant Predictive Analytics for Relational Data | Kumo.ai




Relational Graph Transformer @emergentmind.com









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