#code-complexity

#code-complexity

Legacy software, often described as 'big balls of mud,' has accumulated over decades, becoming difficult to maintain and understand. These systems rely on extensive teams to function, despite their outdated technology.

Contracts are a means of setting preconditions and postconditions on function declarations, and adding assertion statements within functions. The feature is intended to help make C++ code safer and more reliable.

COBOL, short for Common Business-Oriented Language, is the most widely adopted computer language in history. Of the 300 billion lines of code that had been written by the year 2000, 80 percent of them were in COBOL. It's still in widespread use and supports a large number of government systems, such as motor vehicle records and unemployment insurance; on any given day, it can handle something on the order of 3 trillion dollars' worth of financial transactions.

Structural pattern matching excels at... matching the structure of your objects! For the two examples in this article, we'll be using a number of dataclasses that you can use to build abstract Boolean expressions: from dataclasses import dataclass class Expr: pass @dataclass class And(Expr): exprs: list[Expr] @dataclass class Or(Expr): exprs: list[Expr] @dataclass class Not(Expr): expr: Expr @dataclass class Var(Expr): name: str

Which Algorithm Is This? If you step back, this maps almost perfectly to the Top K Frequent Elements problem.We usually solve it for integers in a list. Here, the "elements" are audience profiles age and body-type combinations. First, define what an audience profile looks like: case class Profile(age: Int, height: Int, weight: Int) What we want is a function like this:

Anthropic has launched Claude Sonnet 4.6, an update to the company's hybrid reasoning model that brings improvements in coding consistency and instruction following, Anthropic said. Introduced February 17, Claude Sonnet 4.6 is a full upgrade of the model's skills across coding, computer use, long-context reasoning, agent planning, design, and knowledge work, according to Anthropic. the model also features a 1M token context window in beta.

This extends to the software development community, which is seeing a near-ubiquitous presence of AI-coding assistants as teams face pressures to generate more output in less time. While the huge spike in efficiencies greatly helps them, these teams too often fail to incorporate adequate safety controls and practices into AI deployments. The resulting risks leave their organizations exposed, and developers will struggle to backtrack in tracing and identifying where - and how - a security gap occurred.

One thing I always do when I prompt a coding agent is to tell it to ask me any questions that it might have about what I've asked it to do. (I need to add this to my default system prompt...) And, holy mackerel, if it doesn't ask good questions. It almost always asks me things that I should have thought of myself.