There's a dangerous assumption most developers bring into Compact: "It's a privacy-first chain. My data is private unless I explicitly expose it." This is backwards. And it's where the serious mistakes happen. Compact doesn't give you automatic privacy. It gives you a hard boundary between two worlds, and a compiler that enforces it. World Where Who sees it Public On-chain, every network no
The DataFrame class (from Pandas) is a work of art. Even if you never "do data", priceless lessons can be gleaned by studying this class. It starts simple enough. Usually you will create a DataFrame by ingesting from a CSV file or database table or something. But you can whip up a small one like this: import pandas as pd df = pd.DataFrame({ 'A': [-137, 22, -3, 4, 5], 'B': [10, 11,
## INTRODUCTION Every blockchain application that handles value needs to answer the same question: how do you track who owns what? There are two dominant approaches, and choosing between them shapes your entire contract architecture. Contract-state accounting behaves like a bank ledger. A single smart contract holds a balance map, and transactions update entries in place. The UTXO model behaves li
When we talk about Data Visualization and Dashboards, enterprise tools like Tableau or PowerBI often dominate the conversation. However, for Data Scientists and Developers, these GUI-based tools can feel restrictive. What if you need complex machine learning integration, custom UI logic, or automated CI/CD deployments? Enter the holy trinity of Python visualization tools: Streamlit, Dash, and Boke
[05] When to Pull the Trigger on FIRE — Monte Carlo Says You're Already Free This is Part 5 of a 6-part series: Building Investment Systems with Python "You need 25x your annual expenses." That's the standard FIRE rule. For ¥9.6M annual expenses, that's ¥240M. Most people see that number and think: "I'll never get there." But the 25x rule assumes a fixed 4% withdrawal rate, zero income, zero ada