#code linter

[kaiasky]

my religious beliefs are that docker is ritually unpure. like it's not a sin but u should probably wash your hands or whatever before shaking my hand

[random-random-things]

And the current iteration of the snake game. Printing the game grid is fucked. But the basic game logic is working.

[primepaginequotidiani]

PRIMA PAGINA Leggo di Oggi lunedì, 19 maggio 2025

[kali-official]

pros of using rust:

very helpful compiler messages

very helpful linter messages

code will almost always compile and work on first try because of the above

cargo manages packages better than pip, with about as many handy packages to make your life easier

cons of using rust

rust-analyzer *will* eat 2-3 gigs of ram

the compiler will also eat 2-3 gigs of ram when running

that is almost half my system ram

what does it need all of it for

please give it back

[eightyonekilograms]

I like having linters tuned to my idiosyncratic code style preferences, but I hate writing config files for them, especially when my preferences are more complicated than "opening brace on new line, y/n". Is there a tool which will do the programming style equivalent of an eye exam, and then spit out the settings for you? Like, it would ask you a bunch of questions of the form

Do you prefer left or right?

and when you were done you'd have a file you can just drop in to your repo or something.

Does that exist or do I have to start (ugh) vibe coding one up?

[learn-ai-free]

OpenAI Releases Codex: A Software Agent that Operates in the Cloud and Can Do Many Tasks in Parallel

OpenAI has released a research preview of Codex, a cloud-based software engineering agent that's not just another code completion tool. Codex is a cloud-based software-engineering agent that turns on isolated sandboxes, pulls your repo, and chips away at features, bug fixes, test suites, and even pull-request boilerplates—often in parallel.

What is OpenAI Codex? 📌

→ Cloud-based software engineering agent

→ Can write features, answer codebase questions, run tests, and propose Pull Requests for review

→ Each task runs in its own isolated cloud environment

→ Provides detailed terminal logs, test outputs, and citations

→ Users can create AGENTS.MD files in their repository to instruct Codex on project-specific commands, testing procedures, and coding standards

→ Powered by codex-1

How to use Codex: 📌

→ Users can access Codex through the ChatGPT sidebar

→ Assign coding tasks by typing a prompt

→ Each request is handled independently

→ Codex can read and edit files and run commands like test suites, linters, and type checkers

→ Task completion generally takes between one and thirty minutes

Once done, Codex runs its changes within its sandboxed environment, which users can then review, ask for more changes, open a GitHub PR, or pull the changes into their local setup.

↗️ Full Read: https://aiagent.marktechpost.com/post/openai-releases-codex-a-software-agent-that-operates-in-the-cloud-and-can-do-many-tasks-in-parallel

Codex: Availability 📌

Codex is currently rolling out to ChatGPT Pro, Enterprise, and Team users, with access for Plus and Edu users planned to come soon.

[la-principessa-nuova]

i don’t get why every library seems to want to separate linting for formatting and semantic issues into separate tools. it’s so exhausting keeping up with the tools constantly changing when we used to have one linter that handled all of it just fine and autocorrected most issues

literally just spent all day adding back in the old tool we used to use because the new one we’re using has all formatting listed as deprecated and says to use the tool we used to use for that, only to find out that that tool also recommends against using it for formatting and recommends a different tool, which i personally hate bc it can’t be configured and disagrees completely with how my team has always formatted code.

i don’t even care about the super advanced rules, i’m just trying to stop people from formatting everything awfully and inconsistently and it’s so much harder than it was a few years ago (mostly due to certain tools needing to be approved by the company i work for before i can access them, which takes long enough that they’re no longer supported by the time they get approved and we get a chance to implement them).

[mistakenot4892]

Nebula vledbog May '24

This post was crossposted from cohost.

It has been a busy month on the dev branch. A lot of forward progress has been made on the Shaded Hills fantasy map, and several big rewrites have gone in and are under active adjustment and review.

Nebula SS13 is an open source project based on the Baystation 12 version of Space Station 13. SS13 is a topdown multiplayer simulation game where you play the crew of a ship, station, colony, etc. depending on your fork and map, with the Nebula and Bay forks having a focus on roleplaying and simulation interactions.

Notable changes

A big chunk of the work this month has been adding subtypes and variants for the fantasy map. You can now override things like loadout options, backpack types, survival box types, and starting cash options on a per-map basis. The fantasy map has also had a lot of aesthetic adjustments made to lighting and turf colouring to make things look really nice.

Penny has finished removing a billion unused procs and vars, cleaning up the code significantly.

Lots of clothing has been generalized. The /accessory subtype has been removed, and combining clothing items has been generalized. Shirts can be freely matched with pants, suit jackets no longer come with full business casual attached, and more.

Penny has also gotten the dev branch compiling on OpenDream, an open-source BYOND alternative with a lot of potential. The OD compiler is also included in the continuous integration testing for each merge to the dev branch, identifying problems that would be missed by the linter or the base BYOND DM compiler.

Penny has also resolved a really annoying issue with map edge lighting causing weird artifacting, making edge transitions much nicer visually.

The daycycle system has been gutted and reworked to take advantage of our much faster ambient lighting. Instead of crawling left to right across a single level, daycycles are now applied to entire z-level chunks, and can be customize with individual periods, lighting changes, and temperature. Dawn is bloody red, evening is dim.

Storage has been datumized! This is a bit of an arcane technical change, but in essence it means anything can have the ability to act as storage and hold items inside itself. Previously this was restricted to a single specific item subtype.

Supporting code for quadrupedal (or hexapodal, or n-podal) species has been merged, and immediately caused all simple animals to believe they had no legs and fall over. Working as intended. Grafadreka (reworked from the Polaris version) will be going in soon...

Bugs of note

C4 immediately melts at room temperature when spawned, making it very hard to attach it to doors or walls.

Taking the 'synthetic brain' aspect to fulfil your beep boop robot roleplay needs appeared to work fine, but five minutes after you spawned it would delete your brain and kill you.

You can use a lighter or welding torch to 'carefully heat' yourself, a mechanic intended for chemistry, but one that results in your hands becoming white-hot beacons that instantly melt anything you pick up.

Due to hairstyles defaulting to a human buzzcut when hidden by gear, neo-avians and yinglets manifested a floating buzzcut two feet above their head anytime they wore a hat.

Current priorities

r5 is very close to being ready to go. Once the robot bugs are sorted, we can finish off this staging period and start on r6.

The MVP TODO list for Shaded Hills is down to 10 or so items - getting through that before r6 staging would be ideal, since Shaded Hills being incomplete on staging/stable would suck.

I really want to completely dismantle the /under subtype and make all the various uniforms into component clothing items. It's much nicer for customizing your character and making outfits for jobs etc. than the current arrangement. As of my current PR, there's only ~200-odd uses of /under in the codebase, so we are closing on victory (act now).

[girlwith15cents]

Modern versions of Python include py, which is a launcher that lets you run different versions of Python. If you install virtualenv as well then you can make virtual environments for with different versions of Python, which makes using others' code wildly easier!

If you want to get write your own code then I'd also suggest installing hatch and using it to setup your project. It can automate a bunch of the environment stuff for you and also useful tools like linters or doc generators.

😅 sorry if this wasn't what you were looking for. I hope you're having a good day!

Yo they made technobabble from scifi real?

[notarealwelder]

I wonder if there's a textbook that summarizes, attempts to teach, or at least names all the ~low- and middle-level skills that comprise the ~craft of programming.

....I want to mean a particular subset of skills that has nothing to do with CS theory, and have no idea how to single them out, that might be a problem.

If trying to gesture: not data structures, algorithms, complexity; not low-level ~description of how a computer works (bytes, memory, instructions, etc).

But rather: that, in the course of programming, you'll need to know how to get (and manage) libraries, setup projects, generate executables. Use language server and a linter and an ide and maybe other nice things.

How to debug, i.e., know how to sprinkle debug output, form models of what's going on and test them, ~binary-search the code for the line that violates your assumptions.

How to manage imports/export sensibly, and why; how to structure modules and packages (and why); how to design good interfaces; maybe some more general architecture.

Know how exactly your code will (may) be executed, where to read up on that, and how to check.

(Might be none of this can be taught in general, because specific skills are perfectly language-specific. Still, maybe a ~sketch of what you'll want to know if you don't have a good idea yet of how to learn a language.)

[transienturl]

wait a second. the gpl requires you to a) license derivative works the same, and b) share source code when distributing. if you distribute an interpreted language like javascript with no build step and don't minify... you are, inherently, distributing the source, right? there's nothing that says you need to link a public git repository or whatever. so if you just, like, email someone a zip, or publish to npm...

okay, let's see:

The “source code” for a work means the preferred form of the work for making modifications to it. “Object code” means any non-source form of a work. The “Corresponding Source” for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities.

You may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways...

I guess it hinges upon whether what you are distributing is the "preferred form of the work for making modifications to it." do things like linter config files count as things that must be distributed?

(I guess in the case of a web extension, which is most likely what I would be using gpl code in thanks to the xkit projects, this is all irrelevant. while an xpi file is in fact a zip archive of the potentially entirely unmodified source of your extension, suggesting the recipient rename the .xpi to .zip if they want to extract it and read the source is almost certainly too arcane to count.)

[kaplooie]

Writing code so bad even the linter is mad at me

[govindhtech]

CodeRabbit GitHub builts AI code review agent with Cloud Run

CodeRabbit GitHub

CodeRabbit, a fast-growing AI code review platform, uses Google Cloud execute to safely execute untrusted code and cut code review time and mistakes in half.

CodeRabbit automates code reviews and improves code quality by comparing changes to the whole codebase and creating scripts for deeper analysis. Code hosting integration handles pull requests automatically.

To securely execute untrusted code, CodeRabbit needed a scalable, inexpensive, and secure execution environment to evaluate and run its clients' code.

You'll see how CodeRabbit utilised Google Cloud Run to construct an AI code review agent that can scale dynamically and safely manage massive amounts.

CodeRabbit integrates directly to GitHub and GitLab to automate pull request-triggered code reviews. Its interface with fundamental models analyses the whole change's impact, not just the updated files. This requires a sophisticated system that:

Clone the user's repository.

Install build environment requirements (npm install, go mod download, etc.).

Static analysis with 20+ linters and security scanners.

Run AI programs. Now things get really interesting. CodeRabbit's AI agent develops shell scripts to read the code, find patterns (using cat, grep, and ast-grep), and extract relevant information. Python code can be generated for analysis.

Use outside services. CodeRabbit generates and runs curl instructions to interact with Slack, Jira, and Linear.

Any solution must be safe, inexpensive, and scalable. By definition, analysed and run code is unreliable. It may have problems, be incomplete, or be dangerous.

The answer: Cloud Run

CodeRabbit seamlessly integrates many technologies to create a reliable and isolated execution environment:

Cloud Run services underpin CodeRabbit. First, a lightweight Cloud Run service validates subscriptions and invoicing and handles GitHub, GitLab, etc. webhook events. This service pushes a task to Google Cloud Tasks.

Google Cloud tasks: Serving as a queue isolates webhook handling from code execution. CodeRabbit now handles pull request surges without overloading.

The core of the system is Cloud Run execution service. Another Cloud Run service pulls tasks from Cloud Tasks. Every job requests code review. A 3600-second request timeout and 8 requests per instance allow this service to grow with CPU use. This setup is necessary since code reviews take 10–20 minutes. The Execution Service's in-memory volume mount holds the repository, build artefacts, and temporary files.

Sandboxing: A separate service identity lets you give all Cloud Run instances minimum IAM privileges. Both sandboxing levels are applied to all instances. CodeRabbit employs Cloud Run's second-generation Linux cgroup-capable microVM. CodeRabbit uses cgroups to restrict jailed processes and Jailkit to isolate processes within Cloud Run instances.

CodeRabbit prioritises sandboxing while running untrusted code, such as:

Rubocop and ESLint accept unique, unstable plugins.

LLM verification programs for codebase-wide analysis.

LLM CLI tasks like Jira or GitHub problem creation.

Python-based advanced analysis.

CodeRabbit scales dynamically using Cloud Run. During peak hours, over 200 Cloud Run computers submit 10 queries per second to CodeRabbit's Agentic PR Reviewer. Each big Cloud Run instance utilises 32GiB RAM and 8vCPUs. CodeRabbit's PR reviewer service on Cloud Run uses a lot of CPU, memory, and network traffic (downloading repositories and dependencies).

Try it yourself

CodeRabbit's use of Google Cloud Run shows how to build a secure, scalable, and affordable AI-powered code analysis platform. Their experience shows serverless technologies' promise, and their design can help developers solve similar difficulties. Cloud Run constantly adding features.

[appdevelopmentgurgaon]

QicApp’s Proven Approach to Delivering Clean, Reliable Code

At QicApp, we believe that code quality isn’t just a technical metric—it’s the very foundation of a successful digital product. As a trusted iOS-app-development-company-in-Gurgaon, we know that high-quality code directly impacts performance, scalability, security, and long-term maintainability. Our goal is never just to deliver working solutions—we aim to build clean, efficient, and future-proof codebases that clients can rely on for years to come.

Whether it's a mobile app for a fast-growing startup or a complex backend system for a large enterprise, we follow rigorous development practices to ensure our code is robust, adaptable, and maintainable. Here’s how we maintain top-tier quality across all our projects:

Structured Architecture With MVVM

At QicApp, we adopt the MVVM (Model-View-ViewModel) design pattern in both our iOS and Android projects. This architectural approach separates UI logic, data management, and business rules, leading to cleaner and more testable codebases. This structure not only enhances code clarity but also improves scalability and facilitates faster feature rollouts with fewer bugs.

Continuous Performance Profiling

User experience is everything, and performance plays a huge role. Our team regularly uses tools like Instruments for iOS and Android Profiler to detect and fix issues like memory leaks, thread blocks, and UI slowdowns. By identifying bottlenecks early, we ensure our apps deliver smooth performance, even in demanding environments.

Enforced Coding Standards With Linters

Code consistency drives collaboration. We use linters like SwiftLint (iOS), Ktlint/Detekt (Android), and ESLint/Prettier (web/backend) to enforce coding standards. Paired with CI pipeline checks and pre-commit hooks, this ensures that all developers follow best practices and deliver clean, readable code every time.

Peer Code Reviews for Every Feature

Every new feature goes through a peer review before it’s merged. These sessions aren’t just about catching bugs—they foster team-wide accountability, spark ideas for improvements, and ensure our solutions stay aligned with architectural, performance, and security standards.

Security by Design: JWT & AES

As an iOS-app-development-company-in-Gurgaon, security is baked into everything we do. We use JWT (JSON Web Tokens) for secure API communication and AES encryption for sensitive data storage. Our secure development lifecycle includes HTTPS enforcement, input validation, endpoint protection, and adherence to best coding practices.

Clear Documentation and Transparent Workflows

We prioritize documentation at every level—from code and APIs to Git workflows and setup guides. Well-documented projects reduce onboarding time, improve developer productivity, and give our clients full visibility into the development process.

Automated Testing & CI/CD Integration

We champion frequent, reliable deployments. Our teams integrate automated testing (unit and integration) and CI/CD pipelines to validate code quality, catch issues early, and ensure smooth, continuous delivery. This automation helps us push stable updates faster while minimizing bugs in production.

Code Quality: A Culture, Not a Task

At QicApp, code quality is more than a checklist—it’s embedded into our culture. From MVVM architecture and secure-by-design coding to peer reviews and test automation, every aspect of our workflow is designed to deliver products that are stable, scalable, and secure.

If you're looking for an iOS-app-development-company-in-Gurgaon that’s committed to long-term product excellence, QicApp is your trusted partner.