Same. If I am reading for please, I am reading the book sequentially and love the convenience of ebooks. If I am reading a reference or text book, I like being able to quickly flip between (physical) pages and skim previous chapters for a section I want to reread.

From a quick look at the repo, it is end-to-end testing for web applications.

Also, it seems that their big selling point is a verbose, English like syntax.

It is mutually assured destruction. The job seeker AI spams out a resume to every listing and the hiring AI rejects all applicants for not meeting some unknown criteria. In the end, no worker can find a job and no employer can get applicants. Companies go back to only hiring friends and families of existing employees.

Why would you use a library or framework when you can code everything from scratch? It probably depends on how good the VSCode extension is vs how bad the IDE is.

For the languages I have tried (mostly GoLang plus a bit of Terraform/Terragrunt), VSCode plugins can do code highlighting, can highlight syntax and lint errors, can navigate to a methods implementation, the auto-complete seems to pick random words from the code base, and can find the callers for a method. It is good enough for every day use.

IDEs I have used (Eclipse for Java, PyCharm, InteliJ for Kotlin) offer more. They all have starter templates for common file types. The auto-complete is much more syntax aware and can sometimes guess what variables I intend to pass in as arguments. There is refactoring which can correctly find other usages of a variable and can make trivial code rewrites. There are generators for boilerplate methods. They all have a built in graphical debugger and a test runner.

TAOCS has a reputation for being very deep and thorough, not for being a good introductory text. One of my professors said that in his (very long) industrial career, he only met one person who actually read the books beginning to end but everyone looks something up in them once or twice.

That has been my experience. I once needed to find out how to solve a very specific problem (I think it was calculating statistical values on an infinite stream). I found the single copy of TAOCS in the office reference library, read the relevant section, and implemented the suggested algorithm.

Maybe it is just my experience, but in the last decade, employers stopped trying to recruit and retain top developers.

I have been a full time software engineer for more than a decade. In the 2010s, the mindset at tech giants seemed to be that they had to hire the best developers and do everything they could to keep them. The easiest way to do both was to be the best employer around. For example, Google had 20% time, many companies offered paid sabbaticals after so many years, and every office had catering once a week (if not a free cafeteria). That way, employees would be telling all of their friends how great it is to work for you and if they decide to look for other work, they would have to give up their cushy benefits.

Then, a few years before the pandemic, my employer switched to a different health insurance company and got the expected wave of complaints (the price of this drug went up, my doctor is not covered). HR responded with “our benefits package is above industry averages”. That is a refrain I have been hearing since, even after switching employers. The company is not trying to be the best employer that everyone wants to work at, they just want to be above average. They are saying “go ahead and look for another employer, but they are probably going to be just as bad”.

Obviously, this is just my view, so it is very possible that I have just been unlucky with my employers.

Are we really doing fine? 4% linux market share? Windows is a default?

I suspect that the issue hindering adoption is GNU and other user land projects, not the Linux kernel. Plenty of people use devices that pair a Linux kernel with an easy to use UI and popular software (see Android and Chromebook).

Many people would happily switch to a Linux based OS that had the exact same GUI as their current OS and ran the exact same software. That is not a realistic requirement in practice.

It is possible that Linux would have more adoption if they invested more money into having drivers for a wider range of hardware, but having Linux kernel develers write drivers instead of hardware vendors is not a strategy that scales well.

I always feel a little paranoid when I explicitly close transactions, connections, and files (for quick running scripts, the OS will close the file when my process exits and for long running applications, the garbage collector will close it when the object leaves the scope). Then I read a blog post like this an remember that it is always better to explicitly free resources when I am done with them.

• Encrypt the data at rest
• Encrypt the data in transit

Did you remember to plan for a zero downtime encryption key rotation?

• No shared accounts at any level of access

Did you know when account passwords expire? Have you thought about password rotation?

• Full logging of access and activity.

That sounds like a good practice until you have 20 (or even 2000) backend server requests per end user operation.

All of those are taken from my experience.

Security is like an invasive medical procedure: it is very painful in the short term but prevents dire complications in the long term.

Kotlin jvm is extremely stable

I don’t want to use Kotlin on (just) JVM. The reason I am working with Kotlin is Kotlin Multiplatform (so JVM and JavaScript). The JavaScript side is where all of my frustrations have come from.

The point of using a cache is to have data in memory and not on disk. From what I can tell, Postge Unlogged tables are still written to (and read from) disk. It is just that the write is done in an unsafe way.

The article explains how one would go about benchmarking performance but forgets to actually include performance metrics. Luckily they link to another write up that does. Using an Unlogged table vs. a regular table reduces write times about 45% and gives you about 3 times as many transactions per second. It is not nothing but it is probably not worth the code complexity vs. writing directly to a persistent table.

Even the “no persistence” behavior of a cache is not strictly true: an unlogged table is only truncated if Postgre is shut down unexpectedly (by kill -9 the process or by killing the VM). If you restart if you shut down the process in a controlled manner, the unlogged table is properly persisted and still has data when it starts.

I have used Kotlin a bit for a hobby project and it felt like they were 95% done with a 1.0 version. I love the promise of a single code base that can run on the JVM and browser, but it is not all there. Until recently, the API was not guaranteed to be stable. Every one in a while, I hit a feature that is JVM only or does work right in JavaScript. The JS compiler will “helpfully” remove uncalled public functions unless you explicitly mark them with JsExport.

Also, from what I can tell, only InteliJ is the only supported IDE (which makes sense, since they are the language developers). There is an official Eclipse Plugin, but the last time I tried it, it did not work and tried to take the entire IDE down with it.

Having said that, it was very close to complete and I have not worked on that project for a few months, so it could all be perfect now.

How is this group feature different from the existing group feature (https://github.blog/changelog/2023-06-30-grouped-version-updates-for-dependabot-public-beta/)? Just that it is no longer a beta feature (i.e. they have settled on the configuration syntax and are re-announcing it)?

That is a bit dismissive of Java developers around the world. There are several of us still left and we are in key positions of power.

In my opinion, Python is still missing one key feature: the removal of the Global Interpreter Lock, which is finally starting in Python 3.13.

I have not done much GoLang development, but I am working on automating some dependency updates for our kubernetes operator. The language may be good, but the ecosystem still feels immature.

Too many key libraries are on version 0.X with an unstable API. Yes, semantic versioning does say that you can have breaking changes in minor (and patch) releases as long as the major version is still 0, but that should be for pre-release libraries, not libraries ment for production use.

We tried to ask our interview question of ChatGPT. After some manual syntax fixes, it performed about as well as a mediocre junior developer, i.e. writing mutithreaded code without any synchronization.

Don’t misunderstand, it is an amazing technical achievement that it could output (mostly) correct code to solve a problem, but it is nowhere good enough for me to use. I would have to carefully analyze any code generated for errors, rewrite bits to improve readability (rename variables to match our terminology, add comments, etc), and who knows what else. I am not sure it will save me much time and I am sure it will not be as good as my own code. I could see using an AI to generate sophisticated boiler plate code (code that is long, but logically trivial).

I have tried GitHub project boards for hobby repos and was disappointed by how bare bones it was. For example, it did not have support for breaking a story into smaller component stories (like a Jira Epic or task with sub-tasks).

The immediate use for this that jumps out at me is batch processing: you take n inputs and return n outputs, where output[i] is the result of processing input[i]. You cannot throw since you still have to process all of the valid input.

This style also works for an actor model: loosely coupled operations which take an input message and emit an output message for the next actor in the chain. If you want to be able to throw an exception or terminate prematurely, you would have to configure an error sink shared by all of the actors and to get the result of an operation, you so have to watch for messages on both the final actor and the error sink.

I knew basic CLI commands (such as cd and ls) for a while, but did not do learn much more. Some things have helped me grow my skills:

• Necessity: Some times I need to do something on a VM or container that does not have a graphical interface installed. Some utilities only have a command line interface and not a graphical client. My only option is to Google how to do it. The more I do it, the less I have to Google and the more focused my searches become (instead of searching for “How to do x”, I search for “How to do x in utility”).
• Learning from others: For many tasks, I follow internal or external guides, which typically use CLI commands. Often I look at how my coworkers accomplish tasks and pay attention to what commands they use. Then, when I have time, I look up any new commands I saw and decide if they will be useful for me too. Lately, I have been doing code reviews that involve shell scripts. Those are especially nice, because I can take my time, going line by line, and understand what each command does.
• Keep notes: Every time I find a command that I think I will need again, I copy it into a text file (and I have many such text files). It also makes it easier when I need to run the command with slightly different arguments (a different commit id or something), I can just edit the command in my editor (with searching and undo) and paste it in to my terminal with all the flags and arguments correct.