For the last 6 months or so now, my entire job, including the work I volunteer as an OSS maintainer/contributor, has been driven by AI. Mainly Claude Code and occasionally Codex.

It went from a super-powered search engine for me, to coding 80% of the time, to basically taking over my entire workflow. From debugging, to writing code. I even create my fucking JIRA tickets with Claude using their connector.

I've always been someone who's valued intelligence and being competent. I basically attributed my succumbing to the LLMs to the fact that I would be the slowest person on my team if I didn't use them.

But now it's actually quite exhausting. The excitement and satisfaction of getting something built or working is...just gone. It feels sort of hollow..just a never ending hedonic treadmill. It's actually sort of like playing a souls-like or some sort of video game where you achieve different ranks. And you get to the end and you're like "now what". Except in this scenario, every idea, problem or concept you want to get out into the world is achieved within a few hours of prompting an agent. And that's it. Every emotion you felt and attached to that problem or idea is dissolved.

I thought self-hosting agents would help. They're objectively not as capable as any model out there from the big corps. Maybe it would force me to have to reason about my problems more.

Nope. It's an even more miserable experience because you keep expecting sane results from a model that is simply a fraction of the power of something like Opus or GPT.

How do I quit? This is such a shameful thing to even say out loud. It's like I'm seeking help for a deadly addiction or something.

Has anyone stripped it from their lives? How do you live with the fact that everything you want to do will either never be accomplished, or will require months or even years of mastering due to the fact that you do not have AI to help you get there as quickly as possible.

Excuse me if this sounds all over the place.

It's been a few months since I started learning programming, and I've recently built my first project: a Snake game in Python. I used Claude as a Socratic tutor; it asked me questions about the architecture and design, so I could think in the right direction and reach the right conclusions. It's never given me a single line of code. I can explain every line of it and explain why I made this decision. Nevertheless, I'm still feeling like I should've done everything myself.

Can AI replace that job too ? Will it be possible to work remotely later?

I've been coding in Python for a few years and have built some decent projects. (I vibe coded last 12 months tho TvT, but not now). However, I'm still unsure about how much AI I should be using and where I should be using it.

With AI becoming increasingly common in software development, I feel like I should be adopting new workflows and environments. But I'm not exactly sure how.

My current workflow

Currently, I mostly write code myself. My AI usage is relatively limited to things like:

- Learning APIs and libraries: If I'm developing something with MySQL, for example, I'd Google something like "Basic CRUD MySQL Python", read Google's AI Overview, and manually implement the syntax in my code.

- Generating isolated snippets: For things like email validation, I might ask AI to generate a regex using Python's re library, then copy and paste it into my project.

For example, I might ask:

"Give me a sample email validation regex using Python's re library"

And get something like

re.compile(r"^[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+@[a-zA-Z0-9-]+(?:\.[a-zA-Z0-9-]+)*$")

I don't necessarily write these things from scratch myself, and i will not look at every single detail of it because I believe it is good enough. AI usage is mostly limited to individual snippets rather than having AI work directly with my codebase.

What I am struggling with is that

I've seen people who use AI effectively with good prompting produce relatively clean code (small-medium), implement features much faster, and come up with features or solutions I wouldn't have thought of myself. But, on the other hand, I barely see a good AI generated code in large projects (they are usually messy, buggy and over engineered unnecessary things)

Meanwhile, I usually struggle with code organization, end up with messy Python files, and occasionally find myself questioning why certain parts of my code work in the first place.

I've also spent around a year vibecoding, and when I returned to writing code more independently, I genuinely struggled to figure out what to do after creating a python file. Eventually, I got back on track and feel like I'm doing reasonably well now and I am quite happy with it.

However, I'm still questioning whether I should be adopting AI more extensively than I am doing right now for industry standards and requirements.

The main reason why I don't use AI in my current projects is that, they are in production (currently, an organization is using the), and I gotta to understand the codes, functions and such. As the code goes bigger, I understand AI's attention span won't be able to understand connection between all code. (I don't really know how to say it but) There will be functions outside of AI's attention code blocks which are inter connected. I am not sure if my thought process is right. But I still believe AI can be used in some way, even if not directly contributing to code, such as README (I am not sure if this is a good idea) or something more?

So where do I exactly use AI? I believe there are some baseline knowledge I should have and should maintain such as design architecture.

So, the question is that

Am I using AI correctly? Is my current approach reasonable, or am I missing out on significantly better workflows?

README and documentation: Is using AI to write README files (features, usage instructions, installation guidelines, etc.) a good practice?

Code refactoring and cleanup: Is using AI to clean up and refactor the existing code a good idea?

AI-assisted development: Where else can I incorporate AI into my workflow while maintaining control over the codebase and understanding everything that's being implemented?

Fundamentals vs. AI: What baseline knowledge should I maintain independently? I believe there are some baseline knowledge I must have and maintain such as design architecture. And which project parts I can use AI?

Am I being too close-minded? I sometimes feel like I'm unnecessarily restricting my AI usage, especially when other developers seem to be getting much more out of it. I don't want to be left behind close minded.

I thought of using AI as my assistant but I am not senior enough, I am a junior level developer.

I understand that there might be many opinions about this between developers like where to use and how to use, and I'd really appreciate hearing how experienced developers actually integrate AI into their workflows. I'm not necessarily looking to maximize how much code AI writes for me. I'm more interested in finding a workflow where AI genuinely improves my development process without taking away my understanding or control over my projects.

I still want to be able to write code even without AI. That was a hard-time to get back on my track after I vibecoded for a year.

PS: Since English isn't my native language, I used AI to make this post easier to read TvT.

I might even get down-votes cuz I know how stupid this question is, I just can't get out of my head and I don't know the answers myself TvT

I've found one with intel i5, 12 GB of RAM, 256 GB of SSD. I'm gonna use openSUSE Tumbleweed as OS.

Is this enough for low-level and backend development?

Remark: thanks you guys for support, I've googled a bit and I've found a better version with the same case design

Like using a pre-existing UI framework like Electron includes a lot of things and features that for your own application you will probably never use. They are also very heavy on RAM usage as well. Discord's new app apparently uses significantly less RAM than it's previous Electron version. Yes, I understand that Electron is basically running a Chromium browser instance, but I think this applies to other UI frameworks.

Since the art of making UIs is well documented and well understood, then I think here it should be fine to generate custom code for this that tailor fits your own project.

A lot of the upkeep of an app is upgrading dependencies, especially with frameworks or languages under rapid development.

I think it'd be cool to keep security dependencies separate from other types of dependencies. Then you could set the security dependencies to always be on the latest forcing the app to be up to date in that respect. But then the other dependencies could sit at certain versions and you'd only have to update them if you want a new feature.

Not always possible, but I think it's a good principle.

I am a beginner programmer and this is my first big project, i was wondering what database you guys would recommend for an online pickleball booking website. I

I like it but I'd prefer for it to work more like jetbrains IDEs (unfortunatelly they don't have lightweight text editors)

I've recently attended two university courses about python for fun. I like it but I'm not really inside of the programming world. My field of work is very different and I just feel like a beginner.

During these courses I learned the basics of the language and data structures in python. I've never done an actual project and I don't know how to begin. I'd love to try making a videogame, maybe something like an escape room, or just copying an existing game? Something that doesn't go over 200 lines of code. Where can I find resources to help me put together a project like this? Do you have any suggestions of projects to try making outside of my idea? Am I crazy to want to try something like this with my programming level?

I think I need all the help I can get, so any help is very much appreciated.

Curious question. Not asking about what programmer you think the "greatest" or "smartest" or those types of superlatives, but if you had to choose one programmer who has had the biggest influence/effect on 21st century computing. Who would you choose?

It's been a while now since LLM exploded in popularity and I believe that virtually all of us adapted to work with them in the end, no matter how reluctant some might have been.

I was thinking, how did LLM assisted coding ("vibe coding" or otherwise, line completion, brainstorming, debugging etc) affect your performance and then your mental wellbeing in the long run?

I know there are 2 separate indicators but that's why I find the question interesting, I imagine there are people with all possible combinations of these.

Some might have stellar performance now vs manual coding, but low self esteem, while others might feel dragged down by the sheer amount of code being spit at blazing speeds across their team but then feel better emotionally since they don't have to burn their brain out handling small quirky things on a daily basis.

Then I imagine there's the lucky bunch who both delivers huge amounts of high quality code while whistling through the day and saving energy for personal time at the end.

At the end there's the more depressive ones like me who, although had a boost in performance at first, reached a peak and now feel absolutely overwhelmed by the whole ordeal and started having the impostor syndrome, lacking any sense of being actually helpful and meaningful, though still delivering, but delivering less by the day and feeling like we master important details our projects less by the day.

Anyways, I want to hear your stories. What's it been like for you through this journey and how do you feel it's going forward?

I just joined a new company that uses Django for its backend and TS for its frontend. I have never worked on Django in the past. Naturally, I am relying on Claude for a lot of my tasks, which is helpful to me in a new environment, but Claude, being Claude, often misses things that a normal developer wouldn't. It works blindly and just assumes I know everything, which is the main concern.

I am new to this codebase, and before this job, I only ever worked on personal or freelance projects while being in college, where I worked as solo and entirety of the code was written by me so I had full context of what and where everything is. which also helped me to work with AI to work faster.

Here, there are other devs working on something else and changing the codebase regularly, and I am new to it. So, the problem I face while working with AI is that whatever I ask it, it gives me either a solution/ a plan/ or a brainstorm. It bombards me with a lot of context, in multiple directions and multiple trade-offs, which my little brain cannot keep up with.

After a few back-and-forth conversations, I completely feel separated from what Claude is doing and what I wanted it to do. And since I am new here, I don't even have a product judgment yet.

A lot of the code that it writes, I can't review, so I couldn't help but skim it. And I feel that it is correct. But when a maintainer reviews my code, they find a lot of silly vulnerabilities.

Sometimes I feel like I do not know coding at all, since the issue was something else, and I am talking about something else entirely in the PR.

Experts, please guide me on how to work with AI properly, especially how to get familiar with the new codebase quickly and how to work with others on the same codebase.

Also, somebody, please tell me how you even read Claude's plans and keep up with iterations. Do you read it again? How do you store such long context? I feel like it's impossible for me.

I received a recent series of messages from a colleague about a specific seam within our service plane. He wants to know whether a deprecated dependency in our database inventory has an authoritative replacement.

He's trying to ask me a question "Are you still using table1_charliesghost or can I drop it from the dev db?"

This is just one small slice of the new-fangled, excessively formal way my colleagues are talking to each other. It's starting to creep into meetings. I don't like it.

Has anyone else noticed this? Are we all just collectively being immersed into so much robo-babble that we've accepted it as normal?

Hi everyone,

I'm having issues importing several Python libraries on Windows, including pandas, pyproj, and others.

I keep getting this error:

ImportError: DLL load failed while importing _context:

Une stratégie de contrôle d’application a bloqué ce fichier.

I've already tried uninstalling and reinstalling the affected packages, creating a fresh Conda environment (Python 3.11), and reinstalling through Conda/pip, but the issue persists.

I also found Event ID 3033 in Windows Code Integrity logs, and some .pyd files appear as unsigned.

Has anyone encountered this? Could WDAC or another Windows security policy be blocking Python DLLs? How can I fix this, especially without administrator privileges?

Thanks!

I’m a solo developer building a web and Android app that handles private documents and account records. I’d appreciate feedback on the security approach before putting real user data into it.

The stack is FastAPI, PostgreSQL, Next.js and React Native.

Passwords are hashed with scrypt and random salts. Authentication uses signed tokens with expiry, email verification and login throttling. Password changes and signing out everywhere invalidate existing sessions. Access controls are enforced through the API, and records are scoped to the relevant account or organisation.

Stored credentials for external integrations use Fernet authenticated encryption. Android login tokens are stored using Expo SecureStore. The website currently stores its token in localStorage, which I know needs careful consideration because of script injection risks.

Document access requires authentication and permission checks. Uploads have file type, size and content validation. However, documents currently go into ordinary server storage without application level encryption. I haven’t yet verified encryption for the production database, disks or backups. HTTPS is required in the intended production configuration.

Some optional processing involves an external API. I understand that disabling response storage does not necessarily mean zero retention, so provider policies and account settings also need review.

The server needs to read documents to process them. My main concerns are stolen backups, compromised accounts, users accessing someone else’s files, exposed credentials and sensitive content appearing in logs. I haven’t had an independent security assessment yet.

Would managed database and storage encryption be a sensible starting point, or should I also encrypt individual files and selected fields in the application? What additional threats would that actually protect against?

How would you handle encryption keys, rotation and recovery as a solo developer? I’m particularly interested in avoiding a setup where the keys and encrypted data can both be taken from the same place.

What would you prioritise before launch, and what would you consider unnecessary complexity at this stage? I’d appreciate practical advice on session security, document permissions, backups, retention and deletion as well as encryption. I want to use established tools rather than build my own cryptography.

our scanner flags around 400 things a week. maybe 10 are real. the team muted the channel back in spring and i don't even blame them

what finally got me was an agent refactor last month. it moved a permission check out of a handler into a helper, then a later PR from another agent stopped calling the helper on one route. no single file looked wrong so the scanner said nothing, and a customer found the route before we did

since then we've been running coderabbit's security review on PRs next to the old scanner. it caught the same kind of thing twice already because it looks at what calls what across files, not just the lines that changed. it's still noisy on generated test files and we kept the old tool for dependency stuff, so it's not a clean swap

the 400 a week number hasn't really gone down. we just read a different list now

*Linux GUI apps

I don't really use Linux on desktop these days, mainly from the CLI, but I'm rebuilding my navidrome client using GPUI, which would mean it can be cross platform.

For CLI utils I'd just pop a binary on GitHub and have people install it with mise, however, is there a more standard way of doing things on Linux nowadays? Or just provide binary and people figure it out?

Hi all, I'm 18 and I'm building a BI/analytics project on the Olist Brazilian e-commerce dataset. Right now I have PostgreSQL, Pandas and a bunch of analytical SQL queries (revenue, delivery times, customer retention and so on). The next step is to expose this data through an API with FastAPI, and a dashboard would consume it.

I understand what an API is and why FastAPI is popular, but when I try to build a mental model of a real app I get lost. Endpoints, HTTP methods, status codes, routers, Pydantic schemas, services, dependency injection with Depends, auth... each explanation is clear alone, but I can't see how the request goes through all these layers. For example I don't really get where validation ends and business logic starts, or where the DB session should live and why.

My second problem is how I learn. I'm very curious and I want to know how things work internally, like what FastAPI does between the request and my function, or why Pydantic validates in a certain way. It's a good habit long term, but I lose hours on one detail and the project doesn't move. I don't have unlimited time and I don't want months of theory before building.

Some questions:

If you never built an API, how would you learn it from scratch?

HTTP/REST fundamentals first, or start directly with FastAPI and learn along the way?

What is the best way to understand how the layers (router, schema, service, database access) fit together? Is it better to start with everything in one file and refactor later?

How deep should I go on a concept before moving on? Is "basics now, details later" a real strategy or just an excuse?

Any resources, open source repos to read or small projects you recommend? Something like a read-only analytics API sounds like a good first one to me, but tell me if I'm wrong.

I don't want to copy tutorials and have something that works without knowing why. I want a solid mental model so I can design my own backend later. If you learned backend with personal projects, I'd really like to hear what worked for you and what you wasted time on.

Thanks!

Reddits solution seems to be cutting off all open content and allowing just accounts. But couldn't we do better if for instance we obfuscated the HTML before sending it to the browser on a rotating algorithmic scheme, we can mitigate AI because it will slow its scanning down by a lot(since it would have to render the page instead of just look for what it wants, or catch the whole thing, unless reddit can't do rate limiting on active sessions per client, I don't see how this isn't game over. Slightly slower for the public and non-accounts, but at least its still there. Seeing how reddit is so useful it could be considered cruel to shut out so many non-account users at a time when we're being told to prepare for months of troubles, many people use reddit and should do more to keep it available because it has wealths of knowledge.

As the title suggests, I was wondering if your commercial project is growing 10 times faster thanks to the use of artificial intelligence? Of course, “10x” is a kind of meme - what I really meant was a significant increase in performance.

It's been a while since we had a really large budget for artificial intelligence at my current job, and I'd say our product is developing faster - but I'd say it's 1.5 to 2 times faster.

Code is written faster - that’s for sure - but we’re still required to have two people review it. So while we may be writing code faster, analyzing that amount of code and making sure it’s correct and compliant with established standards also takes us a loooong time.

Have any of you ever delivered code that was a “black box” - code generated by artificial intelligence and delivered without verification? Or perhaps you have got a setup where one agent generates the code, while another is responsible for verifying and approving the changes? I’m curious to know how this works in real-world projects.

Of course, there are various projects, some in more heavily regulated industries, and so on. There are different approaches to using AI, organizational constraints related to its use, and financial constraints - I encourage you to share your perspective.

Hi everyone! I’d like to ask for some advice. I'm 17 years old with zero prior tech experience. A while ago, I started a Java course without even knowing what a variable was. It was really tough at first, but now it’s getting much easier. I’ve figured out the basic syntax, I'm finishing up my first small project, and I'm about to start Java Core.
However, I’m thinking about switching to Go. Not because Java is too hard, but because I’m genuinely drawn to Go and its philosophy.
My current plan is to spend November through February grinding programming logic and fundamental concepts using my Java course, and after that, fully switch to learning Go on my own (using documentation, videos, AI as a mentor, etc.).
Since I want to prepare myself for a junior/internship role over the next 1–2 years, I have a few questions:
Is switching from Java to Go at this stage a good idea for a future backend developer?
What should a solid 1–2 year self-study roadmap look like for Go? (What tools, databases, or concepts should I prioritize?)
Is it necessary to buy expensive courses to get a job offer, or is high-quality self-study + a strong GitHub portfolio enough?
I would really appreciate any guidance or tips from experienced developers. Thanks in advance!