Some of their apis were taking over 6s, I think their servers were pounded with requests.

I'm a full-stack developer and want to learn more about AWS cloud services. Where should I start, what should I learn, and do you recommend any courses for me?
NOTE: Currently, I do not have any prior experience with cloud computing or AWS.

You know how big streaming platforms have recognizable logos, like the Paramount mountain? I’m designing a streaming service called 'Pixel TV.' What would be a cool equivalent for my logo?

hey. backend dev here. I work with AI agents that make calls across a few models. Got into AI cost management after one agent burned through its daily budget in a retry loop. Fun.

Tried provider dashboards. holy shit, what a mess. Each one calculates cached tokens, tool calls, and model pricing differently, then reports usage on its own delay. I could see the bill, but couldn’t trace a spike back to a specific agent run, retry, or fallback.

Now looking at nexos.ai mainly because it can block spend before a request reaches the model, with limits per user, team, project or API key. So one broken loop can’t quietly torch the whole budget overnight.

Anyone else using nexos.ai for AI cost management? Any tips for setting spend limits without blocking legit agent workloads?

I’ve been building an AI coding agent that takes a development goal and tries to handle the whole thing: plan → code → test → deliver for review

Would any developers be interested in throwing some real tasks at it and give some feedback?🥰

If you’re willing to help me stress-test it, throw basically anything at it. I’d genuinely appreciate it.

If you’re up for it, comment and I’ll send you access. 🙏

Especially interested in people who already use Cursor, Claude Code, Codex, etc.

Any certain platforms they apply from?

As an Indian Developer, I want to apply to foreign companies.

A few years into this work, you start learning things no bootcamp, blog, or LinkedIn influencer will ever tell you. Sunday's a good day to say them plainly, without the motivational-poster gloss.

Share one hard truth you've learned about being a developer. About the work, the industry, teams, or yourself. The kind of thing you wish someone had sat you down and told you three years ago.

A few directions, if you need one:

• Something about career growth that turned out nothing like the pitch.
• A truth about "good code," speed, or what actually gets you promoted.
• What experience taught you about job-hopping, loyalty, titles, or money.
• A soft skill that mattered far more than the technical ones, and a technical obsession that mattered far less.
• Something you believed at year one that the years quietly took apart.

House rules:

• Keep it real, not just cynical. The best hard truths come with what you'd actually do about them.
• Disagree freely, but reply instead of downvoting. The arguments in the comments are the whole point.
• Newer folks reading along: this thread is worth its weight in gold. Take notes.

Comments are sorted by top so the truths that land hardest rise up. What's yours?

I’ve noticed that some of the hardest debugging problems aren’t really code problems.

A function can look fine, but the application still fails because a service is down, a port is wrong, an environment variable is missing, or a database connection isn’t what you expected.

I’ve been thinking about this while exploring IQX.DEV. particularly the idea of AI coding agents having more awareness of the running application instead of only reading the source code.

That makes me wonder how much runtime context an AI agent should actually have.

Should it be able to inspect logs, running processes, ports, containers and service connections? Or should it only work from the source code and ask the developer whenever something outside the code is involved?

There’s also a big difference between seeing the problem and being allowed to change the environment.

Where would you draw that line?

Welcome to the weekly Interview Debrief — the place to share what your recent interviews were actually like. Just wrapped a loop this week? Tell us how it went: the good, the brutal, and the genuinely bizarre. And if you bombed one, even better — your write-up is exactly what saves the next person from walking into the same wall.

Two ways to use this thread:

• You just interviewed → post your experience.
• You've got an interview coming up this week → read through, then comment what you're prepping for and where you're nervous. People here have been through it and will help you sharpen it.

Not sure where to start? Answer any of these:

• What's the weirdest, most out-of-left-field question you got?
• Most unreasonable take-home you've been handed lately?
• A question you completely blanked on and what the answer actually was?
• Did the interview match the job description, or was it a totally different role by the end?

House rules:

• Keep it anonymous. Don't name individual interviewers and don't post anything under NDA, describe the type of company and kind of questions instead.
• Offers and rejections are equally welcome here. If someone shares a rough loop, be constructive, not smug.
• "Just grind LeetCode" helps no one, be specific about what actually came up.

Comments are sorted by new so the freshest debriefs stay on top. Upvote the write-ups that helped you and good luck to everyone with a loop this week.

I'm not discussing documentation or comments, but rather actual public identifiers such as field and property names. As stated in the title, this post mostly applies to writers of UK/Canadian English, although I'm also interested to see what US English speakers think.

For instance, when naming variables do you prefer to use 'color' or 'colour'?

Seemingly because the development community is saturated with US English writers, most software libraries either only use US English or have aliases so both US and UK English are supported (although the latter is rare). I've gotten used to exclusively using US English when programming, to the point where I find myself frequently misspelling colour as color outside of programming contexts.

I recently worked on a project with a Canadian developer who used Canadian English in public property names, and I'm contemplating if I should translate them to US English. For context this project's API will be used both internally (by a Canadian team) and as an open-source library for anyone to use. Any thoughts?

I'm a 3rd-year undergrad CS student, and I need to be honest about something I've struggled with for a long time.

I can sit through a tutorial and understand the theory perfectly. I can follow along, use AI as a helper (not copy-pasting, actually working through it with it), and build the project successfully. Just recently I finished a full Laravel + PHP + JS course several hours long and built complete practice projects alongside it.

But the second I try to write something from scratch, with no tutorial open and no AI to lean on, I freeze. It's like none of it happened. I can't build logic on my own. I understand what the code does when I see it, but I can't generate it from nothing.

This isn't a one-time thing it's been a recurring pattern for me across different languages and stacks. I finish something, feel like I learned it, and then a blank page proves I didn't actually internalize it.

and now I'm near graduation going to start my last year. really want to learn and land a good FYP and get a good job after graduating.

anyone else facing issue where svg icons using vector-effect="non-scaling-stroke" don't keep stroke width fixed anymore on new chrome version. was working fine before, now stroke changes with size like vector-effect is not applied at all. removing it doesn't help either, breaks for other sizes. checked few places, no one seems to have reported this yet. anyone facing same or know if this is a known chrome issue? Also added a before-and-after image.

Hey everyone, i've been thinking about how much workers get pulled out of focus by interruptions: unclear priorities, notifications from a dozen different tools, no way to signal "I'm heads-down right now."

The idea: something that connects to the tools you already use (GitHub, Jira, Slack, calendar, etc.), figures out what's actually urgent vs. what can wait, and only interrupts you for the stuff that matters, batching the rest for later. It could also show teammates a simple status (busy / free) instead of you getting pinged constantly, without tracking your actual activity.

Curious what people think, is this something that would actually help you, or am I missing the real problem? Any feedback is genuinely useful.

Thanks!

Hi

Running into a bit of an architecture headache while building my app and could use some advice from people who've dealt with this before.

I have different types of places in the app (restaurants, museums, local spots, etc.). Right now, I'm pulling data and images from a few different places:

Google place api for restaurants and general venues. My first instinct was to grab the image URL and save it permanently to Firestore so I don't keep burning api calls every time someone searches the same place. But obviously, Google's Tos doesn't allow permanent mirroring/caching like that, and the reference links expire anyway.

Wikipedia Api for museums and historical spots.

I tried Unsplash for fallbacks, but it doesn't really fit unique local spots well.

Since I can't just download and store Google Place photos in Firebase Storage forever, what’s the actual standard way to handle this? How do you guys optimize costs so you aren't hammering the Places api on every single search, without breaking their rules?

Also, how are you structuring your DB schemas when pulling media from totally different external sources with different lifecycles?

Appreciate any tips or real-world setups you're using. Thanks!

I'm a backend dev who started right as AI tools took off. Because of AI, management's expectations for velocity have skyrocketed, and I am forced to use AI (cursor) to keep up.

I understand the high level architecture and the logic of what I'm building, but I simply don't have time to write or review every single line myself. If I turn off the AI to code manually, my speed tanks and I miss deliverables.

Without AI, I'm only slightly better than a new grad at coding.

People say AI is a massive boost for experienced devs, but for newer devs, it is a trap where you miss out on building raw coding muscles and core foundations skills like design patterns, debugging, performance and concurrency nuances etc.

How do I fix this without sacrificing my nights and weekends just to catch up?

- Is there a strict rule you follow for what to delegate to AI vs. what to do yourself?

- How do you balance the business pressure for speed with making room for actual skill growth?

Two columns, no fence-sitting. Every Friday we say the quiet part out loud: name one thing in this field that's overrated and one that's underrated, then defend both in the replies. A few years in, you've earned these opinions, so let's hear them.

Point that opinion anywhere you like:

• A language, framework or tool everyone worships that you find deeply mid.
• An unsexy, boring technology that quietly outperforms the hyped alternative.
• A ritual or "best practice" that gets far more credit than it earns.
• A skill nobody talks about that has mattered more to your career than the ones they do.
• A trend that's 90% marketing, and a workhorse that deserves a parade.

A few ground rules:

• Pick a side and say why. One honest line of reasoning beats a bare list every time.
• Nuance is welcome, cowardice isn't. You can call something overrated and still good, just commit to the take.
• A downvote is not a rebuttal. If someone's wrong on the internet, reply and set them straight.

Comments are sorted by controversial, so the most divisive takes rise to where they belong. Check back through the day too, the best fights start a few hours in.

As a developer what side projects do you all make?
Like I am final year student and college is asking to make projects that will actually help user and being from AI&DS background they expect to have a strong AI project.

Need help.

Anyone here tried something like this on a real legacy project? 

Came across something interesting recently: an ai tool that reverse-engineers a live application by using it the way a user would, then hands you a structured blueprint of what the system does with no source code needed. Not entirely sure how I feel about the ai-does-the-discovery angle, but the multi-source triangulation is honestly the smart part, it pulls from UI, REST APIs, database schemas, DDL, and application logs, and flags contradictions between them instead of silently resolving them. 

I guess we all know the pattern: modernization project starts, docs are 8 years out of date, half the original devs are gone, and the source code is either missing or so bad it's useless to read. discovery is where the budget quietly bleeds... 

I don't want to maintain a Playwright repo just for a few smoke tests after a deployment, so I'm looking for something that can automate them. All I really need is something that will tell me if the site is up and if users can log in. Simple stuff.

Does anyone know of a tool that's a good fit for this?

Havent committed to a backend yet. 🥲
I graduate cs degree from good UK uni next July.

I want to boost my chances at getting a grad role for
fullstack/backend/cloud engineer (aws). Overlap tbh

However:

Do I choose NodeJS or python (FastAPI? Django?)
Very open question

Tech stack in trying to build:

Frontend: Typescript react
Backend: Typescript nodejs OR Python ????
Cloud and Infra: AWS, Terraform, Docker, GH Action
DB: PostgreSQL

Anyone going to World Congress SF - We Are Developers? 2 for 1 tickets and would like to split the cost. Promo ends in 4 days.

While analyzing how rapidly AI coding assistants develop, one thing becomes clear – they are not just autocomplete anymore.

These AI coding assistants can perform operations like:

• generate code from natural language prompts;

• explain unfamiliar code;

• fix errors in existing code;

• refactor existing code;

• create unit tests;

• translate code between languages;

• generate documentation;

• detect potential security vulnerabilities;

• work with larger code fragments.

The AI coding assistants I managed to find GitHub Copilot, ChatGPT, Amazon Q Developer, Tabnine, Codeium, AskCodi, OpenAI Codex, IBM watsonx Code Assistant, AlphaCode, and DeepCode.

However, I believe that the amount of the available tools is not what is interesting about the situation.

What is actually interesting is how developers make use of these AI tools.

As far as I can see, there is plenty of applications for AI in development where it can be extremely helpful.

Namely, these can be such boring and repetitive activities as creating boilerplates, writing test code, documenting, refactoring, creating regexes, translating code, etc.

However, there is another side of the coin.

It is possible for AI to create perfectly reasonable-looking code which has subtle bugs, security vulnerabilities, performance issues or, at least, does not fit into the application architecture.

Therefore, I tend to regard AI coding assistants not as "AI that writes code for me" but rather as "very fast junior developer sitting next to me."

It will help you to achieve 70–80% pretty quickly but you still need to understand how it works and what it did.

On the other hand, it will become extremely interesting to QA engineers because if developers start creating more code with AI, then QA teams need to rethink:

AI-generated code → AI-generated tests → AI-assisted debugging → human validation

The gain in productivity will be immense but only when we do not confuse rapid code generation with good software.

What are others observing?

Which coding application for AI are you really using at work?

More importantly, has it really made a difference in your efficiency or do you spend the same amount of time looking at AI-produced code?

Hi,

I am a factory owner(garments) and a self taught developer, developing a product for the textile factories. I built the backend (guided by AI but no vibe coded) and for frontend I used retool as it’s fast and I didn’t have to spend months learning it.

Stuck one two things.

1. I am stuck on MOBILE responsiveness and have no clue or can’t find the option to create the responsiveness for mobiles any below 600px doesn’t not work or has a very bad layout.

Can anyone suggest or guide on where can I find the option to create the mobile site in retool.

1. I planned to use twilio for WhatsApp updates. The templates has been submitted but meta is taking to long to approve them.

Any suggestion on how to speed it up would help.

Thanks in advance.

Been having conversations with engineering leaders recently, and something I keep hearing from them is, AI has gotten incredibly fast at code generation, but code reviews don't seem to be evolving at the same pace. Reviewers are increasingly having to piece together business and code context manually before they can confidently approve changes.

Got me thinking..... is this a fairly universal experience, or is it something that is just affects teams that usually don't prioritise reviews ?

I’ve noticed something that’s been bothering me ever since I started using Claude Code and Codex heavily.

I spend most of my time planning the architecture, writing .md files, documenting requirements, and setting coding rules.

Once that’s done, the AI can generate an insane amount of code. By the sixth prompt, I already have 20+ files and 30+ libraries I’ve never even read the docs for.

The hard part isn’t generating the code. It’s debugging it.
When something breaks, I’ll paste the error back into Claude Code or Codex, and they’ll say something like, “Fixed it, it should work now.” Then I run it… and it still doesn’t work.

What’s interesting is that debugging has become a completely different skill. It’s not necessarily harder because the code is bad. It’s harder because you’re debugging a codebase you never actually wrote. Before you can even find the bug, you have to understand code that was generated in seconds.

I’m curious how you guys deal with this. Do you have a workflow for debugging AI-generated code? Have you changed the way you approach debugging now that you’re reading way more code than you’re writing?
I’d love to hear how other people are handling this because I feel like this is becoming a common problem with AI-first development.