Python frozenset() is an immutable version of the built-in set. Once created, a frozenset cannot be modified — elements cannot be added or removed. This immutability makes frozenset hashable, which means it can be used as a dictionary key, stored inside another set, or used anywhere Python expects a hashable object. Regular sets are mutable. They have methods like add(), remove(), and update() that change the set in place. frozenset has none of these mutation methods. The tradeoff is that frozenset gains hashability — the one thing regular sets lack. Understanding when to use each one comes down to whether…
Author: drweb
Pandas makes moving data around straightforward, and ORC has become a reliable choice for analytical workloads. It sits in the same space as Parquet, but ORC offers better compression and solid type preservation. Writing a Pandas DataFrame to ORC takes about three lines of code, and the round-trip is fast enough that it never slows things down. Exactly how to write a DataFrame to ORC, practical examples that go beyond the basics, and the things that trip people up the first few times. At the end, the difference between ORC, Parquet, and CSV is clear, and getting data into ORC…
I have used linked lists in Python projects for years, and I keep coming back to them when I need fast insertions or deletions in the middle of a collection. Arrays give you O(1) random access, but inserting at position 0 means shifting everything. A linked list sidesteps that entirely. Let me show you how they work and when they actually make sense. In this article, I walk through what linked lists are, how to build one from scratch in Python, and the operations that make them worth using. I have tested each code example against Python 3.12. TLDR Linked…
Microsoft has unveiled plans to incorporate Anthropic’s Claude Mythos Preview model and other AI models into its Security Development Lifecycle, embedding AI directly into the stages where code is written and tested. Rather than relying primarily on static analysis tools, Microsoft is adopting AI models capable of analyzing code dynamically and identifying complex vulnerabilities that might otherwise go undetected until later stages of development. Released on April 7, Anthropic’s Mythos model has already demonstrated a previously unmatched ability to uncover critical flaws across operating systems and widely used software. Anthropic claimed that the model’s ability to find security vulnerabilities is…
Most B2B applications collect incomplete data by design. A lead form captures a name and company. A recruiting tool surfaces a LinkedIn profile. An event registration system logs an email address and job title. The record enters your system and sits there, half-formed, waiting for someone to manually fill in the gaps before it can be acted on. This is an architectural problem, not a workflow problem, and solving it at the architecture layer is what separates applications that create operational leverage from ones that just digitize manual work. Understanding how to build contact enrichment into your application using professional…
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I do believe that Redgate has been very customer focused since it’s inception. I’ve worked with them in some capacity since 2002 and I’ve felt this along the way:The next page has this statement:We believe that if we do what is right for our customers then we will thrive.I think that’s been true when we keep this in mind. The (relatively) few times we’ve started to do things for ourselves rather than thinking about customers, things haven’t worked out as well.I think this sentiment is one that guides a lot of my life. Certainly inside Redgate, but also in the…
A lot is going on at Anthropic. Access to the almost-fabled Mythos model remains restricted (despite some reports of unauthorized access), and nobody knows quite what is likely to happen or when in terms of its final rollout. Developers, meanwhile, are left with their own challenges; last week’s “upgrade” to Opus 4.7 has left some software engineers already longing for a return to 4.6 with its less literal instruction interpretation and its perhaps less cautious use of safeguards and controls. Then there’s the Claude quality conundrum in and of itself. Root of the Problem? Anthropic says it recognizes the fact…
hyperfine is a command-line benchmarking tool that runs your commands repeatedly, collects timing data across multiple runs, and gives you statistically reliable results with mean, min, max, and standard deviation, making it far more accurate than a one-shot time measurement. You’ve been timing commands with time for years, and it’s been lying to you, not because time is broken, but because a single run captures one data point that can spike or dip based on cache state, CPU load, or kernel scheduling. If you’re choosing between two scripts, two compression tools, or two database queries, you need the average across…
I am staying relevant. Are you?I know that is a heavy way to start. But I needed to say it first, because I have been carrying that question for over a year, and I think some of you are carrying it too. You just have not said it out loud yet. Maybe to a spouse late at night. Maybe to yourself in the car after a long call. Maybe you have not even said it to yourself, because saying it makes it real.So let me be the one who says it first. And let me say the part underneath it…