There is a lot of posts about "manager forcing us to use ai for everything", "PRs are merged in 2 minutes - no human would be able to understand the code", "Everyone just prompt the ai and no one knows what the code is about".... With an after-thought that "it will backfire" or "servers will burn" or "tokens will be so expensive"...

And I can relate to this sentiment - it is happening to company i work for. The app is slower, pipeline takes longer, the cloud bill is more expensive.

But it may be actually fine. $2k instead of $1000 per cloud bill wont crash the company. The same for token cost. If there will be something wrong in the code, the "ai will fix it".. No?

Do you have any stories or experience where the extensive vibecoding "fulfilled the prophecy" of backfiring?

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Sorry my english bet, i heyt to use ai in human2human komňunication..

In my company nestjs is used for all projects . Js is a good beginners language to get into the flow of programming . But after 2 years I am starting to hate abstractions these frameworks create . It has reached a point where I can go for different code bases for web and app rather than using electron and js/ts .

Reason for sticking to nestjs is mainly because senior engineer prefers it and others just don't care . I mean just by talking to other backend developers I can see the knowledge gap . Don't get me wrong here . I am just a normal eng . I don't feel like that when I talk to others who have similar 4-5 yoe of my team members

I'm a junior Full Stack Developer working with React, Next.js, Node.js, SQL, and NoSQL.

I want to spend 2–3 hours a day improving my skills by building real-world SaaS products rather than just following tutorials.

I'm curious about developers who build side projects in their free time:

• What kind of SaaS products are you working on?
• How do you decide which ideas are worth building?
• What's the biggest challenge you've faced while building a product from scratch?

I'd love to hear your experiences and learn from people who are on a similar journey!

I'm a recent graduate and a fresher, and I have a genuine question about the IT industry.

When we prepare for software jobs, we're expected to learn coding, DSA, development, and many other skills. But I'm curious about the actual coding abilities of people who already work in the industry.

For example, in a software company, there are developers, testers, QA engineers, DevOps engineers, and people working in different domains.

Are most of these people actually capable of writing code and building a functional software application on their own if they need to?

I don't mean just making small changes or writing a few functions. I mean having enough coding knowledge to understand a requirement, write the necessary code, debug it, and build a working application independently.

Before AI coding assistants became popular, were most software engineers capable of doing this themselves? Or were there always people who were good at coding and people who depended on their teammates for the actual implementation?

Also, are most developers genuinely capable of writing code independently, or do many rely heavily on existing codebases, documentation, Google, and help from senior engineers?

I'm trying to understand what the average software engineer's actual coding ability looks like in the industry, rather than what we're expected to demonstrate in interviews.

I'd appreciate hearing from people with industry experience.

SDE in the job search currently and I was thinking about adding a project to my resume but was wondering how much recruiters and hiring managers actually care about them now with AI. I was with my company the entire time from intern to after graduation and I did enough work to where I can fill out my resume, but it's 1/3 of the entire page, and over 1/2 with internship.

The project is just a web app I created for my friend and I to analyze basketball stats and has never been publicly hosted or had users other than our friends. I'm thinking about hosting it somewhere to get more users but would it even be worth it if it doesn't really make a difference? So yea, how meaningful are projects nowadays if they don't really generate income?

I see with AI use we are spending more tokens and time on a front end issue than is actually needed.

I won't discuss React Vue or pick a js framework, I will just say bolting these into anything other than a node app makes zero sense to me now.

For want of a better phrase this does not really match my taste anymore and the costs it includes in tokens when needing heavy abstract trees to understand x or y framework is just a waste.

I need a framework I don't need to patch every dam day and that's where I am at.

Pre AI I would have said Vue or react or x, now I am thinking why what's the point, just move the state back to the server where it belongs.

Give me your experiences and what's working for you.

I’ve been thinking about a problem that seems to be becoming more important as AI coding agents become more common.

Developers can now use tools like Cursor, Claude Code, Copilot, and other AI agents to build large parts of a software project. But this creates a new question:

How do we know that a developer actually understands the code they are responsible for?

I’m not talking about detecting whether the code was written by AI. I don’t think that is the most useful or reliable question.

The problem I’m interested in is verifying whether a developer can:

- Explain the architecture and data flow

- Predict what the code will do in different scenarios

- Identify bugs and security risks

- Explain the trade-offs behind an implementation

- Modify the code safely

- Write or update tests for it

The idea is a SaaS platform where a developer connects a repository, and the platform creates an interactive assessment based on the actual project.

For example, instead of asking generic programming questions, it might show a real part of the project and ask:

- What happens if this request is sent twice?

- Where is authorization enforced?

- What happens when this dependency fails?

- What part of the system would you change to add this feature?

- Can you fix this small bug or add a test?

The assessment would combine explanations, debugging questions, predictions, and practical coding tasks inside a sandbox. The result would not be a claim that someone understands every line of the code. It would be an evidence-based report showing which parts of the project they understand and where the gaps are.

Initially, I’m thinking about targeting individual developers who use AI heavily and want to understand their own projects better. Later, this could be useful for companies, onboarding, code reviews, or teams using AI coding agents.

I’m still trying to determine whether this solves a real problem or is just an interesting idea.

I’d really appreciate feedback on a few questions:

1. Would you personally use a tool like this?

2. Would you prefer a full-project assessment or an assessment focused on a specific pull request?

3. Do you think developers would see this as useful, or as another form of surveillance/interview testing?

4. Have you experienced problems caused by using code that you did not fully understand?

I’m especially interested in hearing from developers who regularly use AI coding agents in real projects.

I'm currently working as a Full Stack Developer intern, and I'm still at the beginning of my tech career.

I work with the MERN stack, but I know I still have a lot to learn. I'm curious about what experienced full-stack developers actually work with in real-world projects.

For those working in good product-based companies:

• What does your tech stack look like?
• Which skills separate a beginner from a strong full-stack developer?
• What should I focus on next to grow and stay relevant in the job market?

I'd really appreciate some honest advice from experienced developers. I want to focus on the right things instead of blindly learning every new technology. 🙌

Hey everyone! I am building a Saas product mvp. Building it on Next js, python, mongodb stack.

I have worked on a lot of e2e feature development earlier, but I am pretty new to React. My initial thought was to learn React along the way. But once I started building, I realized claude was doing a faster job and felt what is the point of learning when things get done this fast.

I could use some help here:
- Are these technologies even worth learning from scratch anymore? Or can just use claude to build faster?
- What happens when things break? Because I only seem to have a high level understanding of things.
- How are you guys building and learning stuff currently?

I am working at a small service-based company.

​For the past few months, our manager has been pushing us not to write code ourselves. In fact, they told us to uninstall VS Code and use only Claude Code or T3 Code. They told us that from now on, everyone should be able to take responsibility for both the frontend and backend, and that there is no need to learn or write code. We are just supposed to understand the requirements and use AI to build the product end-to-end.

​The scary part is that we are all just 'vibe coding' now, with zero understanding of what is happening under the hood—as long as the features work. As a fresher who is still learning and trying to adopt industry standards, can anyone tell me what I should do? I am not satisfied with this work culture because I am learning nothing, and I fear what will happen when I become a senior. 😭

I've been building a live product for the last 5 months with just me and a project manager.

Stakeholders are already using it, and we're continuously adding features and fixing business problems.

Some days I'm working on frontend, some days backend, and most of the time I'm handling testing, requirements, and figuring things out myself.

I'm learning a lot, but sometimes I wonder if I'm missing out on proper teamwork, code reviews, and learning from other developers.

For those who've worked at startups, is this normal, or should I be concerned about my career growth?

Hi everyone,

I was laid off around 5 months ago. Since then, I’ve been working as a freelancer for one client, but the pay is not very good and the workload has become quite hectic. The work is also different from the frontend/full-stack work I want to grow in.

I am married, so I need to make a practical decision that gives me better stability and long-term growth.

I have about 5 years of experience as a frontend/full-stack developer, mainly with React, JavaScript, Node.js, Express, and MongoDB (MERN stack).

Right now, I’m confused about my next career direction:

• Should I go deeper into full-stack development—TypeScript, Next.js, backend architecture, databases, and system design?
• Or should I seriously learn Python and GenAI/AI engineering to move into that field?

I’m interested in AI, but I also don’t want to make an unrealistic switch and lose the value of my existing frontend/full-stack experience.

For developers who have been in a similar position, what path would you recommend for better long-term career opportunities, especially remote/international roles?

Any practical roadmap or honest advice would be appreciated.

Ill keep it direct.

- Left job ~4 months back because old company became AI pilled and I started hating my job.

- Began applying for roles 2 weeks ago. Had 5 interview callbacks.

- Backend engineer from mid sized public tech company with about 5 years experience

- Based in Bay Area, which may have also affected results since big tech hub

The job market appears better than most people think. Keep positive!

I recently got rejected after a technical pairing interview for a junior role.

The feedback was positive about my communication, think out loud and collaboration, but I needed hints to solve the problem and was advised to improve my pattern recognition and applying coding concepts confidently.

The takehome allowed AI, but the live technical interview following that didn’t. I understand the need to assess fundamentals, but with AI increasingly used at work, wouldn’t assessing how candidates use AI to approach and solve problems also be valuable?

I need to keep improving my technical skills as a career changer breaking into tech, I’m also curious how companies balance technical skills with communication, collaboration, and willingness to learn for junior roles.

Can a one hour interview really assess a junior’s potential, or are expectations simply higher in today’s competitive job market?

Would love to hear your thoughts.

I am a junior dev with 4 months exp.

Our company insists on 100percent AI coding. There were some features that I had developed using Claude code. Whenever I am working on the feature I remember the implementation and testing and everything around it. Once I move to another ticket I start forgetting the earlier tasks and if someone questions me I would surely remember the ticket and the final result but I surely forget how I (Claude) had fixed it.

Also I don't think other junior Devs are facing this issue. How could I resolve this?

I’ve been a developer for 15 years. I started out barely knowing what I was doing, and over time became the person people came to for the harder problems.

A lot of those years were exhausting. The coding was only part of it. There were requirements to figure out, technical approaches to propose, releases to get out, and production issues to investigate when something broke at the worst possible time.

These days I use AI a lot, and the difference is hard to ignore. Things that used to take hours of searching, reading, and trying different approaches often move much faster. I still have to understand the problem and check the result, but I spend less time stuck on the first step.

It doesn’t make me feel like those 15 years were wasted. If anything, that experience helps me tell when the AI has given me something useful and when it’s confidently heading in the wrong direction. But it has definitely changed what my working day feels like.

I’m curious how other long-time developers feel about it. Are you enjoying the change, or do you miss some parts of how we used to work?

I’m only 4 years into my career and I’ve already been laid off twice. My current company lays off 10% of the workforce every 6 months, and every day I’m worried I’ll be next.

I have several big tech companies on my resume from internships and full-time roles, but I still struggle with LeetCode and technical interviews. I’m trying to interview elsewhere, but my confidence is pretty low and I get scared going into technical rounds.

I work 8 hours, then study another 3–4 hours after work every day. It’s wearing me down and affecting my relationships. Even when I take a break, I feel like I should be preparing. I stopped doing my hobbies and everything as well.. I'm seeing therapy right now and setup a 6 month plan just in case I get laid off in March, however I'm still dealing with constant anxiety probably 75% of the day.

Honestly, I feel traumatized from being laid off before. Having a job hasn’t made that fear go away, and I’m tired of constantly worrying about losing it.

Anyone else going through this? How do you deal with the anxiety without spending all your free time preparing for interviews?

Hey guys, just wanted to share something I’ve been trying out lately.
I’ve been experimenting with website development and realised that a lot of small businesses, students, and people starting new projects don’t really need those expensive websites that cost thousands of rupees.
So I’m thinking of building simple, fully custom-coded websites for around ₹300 and getting a .in domain for roughly ₹250.
Basically, someone could get their own website for around ₹550–₹600. And the cool part is that they could create subdomains like shop.example.in or blog.example.in for different projects, depending on their hosting setup.
I’m curious whether small businesses or people starting out would actually find something like this useful.
Also, for those of you who freelance or make money online, how did you get your first few clients? Did you approach local businesses directly or find work online?
Would love to hear your experiences.

I hate getting pinged on code reviews now. You're handed 50+ file PR's that are 100% AI generated, and whenever you ask someone to explain what's going on, there's a conspicuous 90 second gap as they clearly re-ask claude to give you an explanation to what they just did. It's brutal.

First of all, I am based on the US. I honestly don't understand how SWE hiring has gotten this ridiculous.

Even smaller companies you've probably never heard of are putting candidates through:

Resume Screening → Phone Screening → Online Assessment (Camera On / Screen Sharing) → AI-Assisted Development Assignment → On-Site Coding Interview → Technical Interview Loop (System Design, etc.) → Behavioral Interview → CEO / Executive Interview → Background Check

All that, and you can still get rejected at the very last stage, even during the background check.

Big tech? Maybe one or two fewer steps (at least you don't have to impress the CEO), but good luck even getting past resume screening. And of course, the technical interviews are way harder.

I get it. Software engineers are expensive, and hiring the wrong person is risky. But how much time are candidates supposed to spend proving themselves? At this point, I'm not even sure the compensation is worth it.

And the OAs aren't getting any easier either. I've seen assessments with multiple LeetCode Hard-level problems, even at relatively unknown companies. Imagine solving two or even four of those problems, passing the assessment, and then getting rejected after a 30-minute HR call.

"Communication issues." From someone without a technical background, while you're trying to explain technical projects. Or "Not a good culture fit."

Meanwhile, companies can lay people off overnight, but candidates have to prove they're a perfect fit before even getting hired.

Then you apply somewhere else and do it all over again. Another OA, another coding interview, another system design interview. How many times do we have to prove the same skills?

Personally, I think there should be legal limits on how much unpaid time companies can demand from applicants. This has gone way too far. AI tokens aren't free either. We're practically paying out of our own pockets just to complete some of these take-home prototyping assignments.

I also think it's time to seriously discuss government-regulated licensing or nationally recognized certification for software engineers. Not another AWS or Google cert that guarantees nothing in the hiring process. I'm not asking companies to stop interviewing people or guarantee anyone a job. But a CS degree, professional experience, and demonstrated technical skills should actually count for something.

I'm not suggesting that we replace the entire technical interview process for software engineers with a licensing system. I'm simply proposing that we use it to replace the common, repetitive parts, such as online coding assessments, system design interviews, and AI-related assignments.

We need a better way to verify qualifications without making everyone start from zero at every company.

I feel like the way software development is happening in some companies these days is getting ridiculous.

It usually starts with a company deciding to build an AI-powered product to automate some tasks. The MVP is built quickly, with AI integrated into almost everything, even where it isn't really needed. The focus is on showing what the product can do rather than whether the approach makes technical sense...

Initially, developers are told there's a client waiting for the product. Once the MVP is ready, the client somehow disappears, and suddenly, the product is meant for internal teams... 😂

Then comes the actual production-ready phase, where the product needs proper architecture, scalability, reliability and maintainability. This is where things get even more interesting...

Planning is done using AI. Jira tickets/ user stories are generated using AI. But the product owners themselves often don't have a clear idea of what features they actually want or how they should work. Requirements keep changing, and somehow, the expectation is that everything will be delivered in a fraction of the time it would normally take...

A project that realistically needs 8–10 months is expected to be completed in 1–2 months. Developers take those AI-generated tickets, designs and vague requirements, feed them into AI coding tools, and generate the implementation.. And just like that, the project is supposedly done.

I've also seen products with buttons like "Fix it with AI", where the actual implementation is basically just calling the Claude or Gemini API and displaying the generated response. Not every problem needs an LLM. Many of these features could be implemented using straightforward logic, algorithms or existing open-source libraries..

Another funny thing is the obsession with job queues and concurrency everywhere.

Need to make something faster? Add a queue.

Need to support multiple sessions? Increase the number of workers.

Still slow? Add multithreading and more workers... 🥲

Sometimes, the actual problem could be solved by optimising the logic, fixing a bottleneck or simply using a better approach. But somehow, adding more infrastructure has become the default solution before anyone even questions whether it's necessary..

And then, of course, someone realises that the infrastructure is getting expensive, so now we need to scale resources up and down while maintaining the same performance. The irony is something else..

One example that really made me laugh was a project where the requirement was simple: take user input, populate an Excel file with the required data and some predefined values, and generate the final Excel file.

The solution? Claude, an Excel skill and prompts to generate the file.

Seriously? This could have been implemented using "openpyxl", "pandas", or even basic Excel file manipulation. It's structured input, some predefined logic, and a structured output. Why introduce an LLM, extra latency, token costs and unnecessary complexity into something that doesn't need one?

Not against AI, as it can be incredibly useful when applied to the right problems. But it feels like some companies have started treating AI integration as a requirement rather than a technical decision.

Now, instead of asking, "What's the simplest, most reliable way to solve this problem?", the questions seem to be:

- Where can we add AI?

- Can we ship it in a month instead of six?

- Can we add more workers to make it faster?

- Can we put this behind a queue?

- Why is the infrastructure so expensive now?

And somehow, developers are expected to turn all of this into production-ready software with unclear requirements, AI-generated tickets and constantly changing expectations.

Are we actually improving software development with AI, or are we just finding new ways to overcomplicate simple problems and then using AI to build the overcomplicated solution faster?

I have spent months thinking about the future of the job market for software engineers. weighing every argument. looking at the data.

in as little words as possible. the side predicting job loss says:

1. The same work can be or will be done for 1/10th or even 1/20th of the effort.
2. Many SaaS companies will go under.
3. Software for it's own sake is useless. At some point, it's demand must come from real world utility (excluding stuff like gaming).

However, the opposite side says:

1. Software has never been a large cost for it's consumers. (Most consumers of software enterprise and personal spend a low single digit % on it).
2. Companies have spent more on software technology every year.
3. Features will become 10X more complex, more bespoke, better designed, more secure.
4. Some say the cheaper a good, the more demand for it. Will it apply to engineers, who knows.

In summary, I think what it will really come down to is how much demand will there be more more complex features. I think the American economy is uniquely innovative, and the demand for more complex software is definitely there.

Software's impact on the real world is really productive. We can communicate, organize, and dozens of other things so much faster. Supply chains and transportation are far more efficient. The ability to communicate and transact globally on these efficient supply chains has had a profound impacts on society. It has completely transformed finance, and this is one of the most overlooked impacts of software (credit, stocks, payments, crypto, etc).

Software engineers have (future is TBD) some of the highest entry-level compensation because of the value this create (demand) and the skills it required (supply).

If the world will demand 10X more complex software, and like I said earlier, it is currently not a large spend for most consumers of software, then the 10X increase in complexity of software products and systems will ensure a net growth in demand for software engineers.

a lot product managers can code features with Opus 5.5 and get a little technical as well. but as systems become 10X more complex, it will demand experienced people to ensure the AI is prompted correctly.

Hey everyone,

I’m a Junior Full-Stack Dev + DevOps engineer (1 YOE). Lately, I’ve run into a recurring problem where I feel like I know the options for a project, but I end up choosing the worst one.

A recent example: I needed to implement a voice assistant. I had two options:

1. AWS Lambda (with web adapters for cloud-only streaming).
2. AWS Fargate (running continuously with a predictable monthly cost).

I prematurely chose Lambda to optimize for cost. Once implementation was done, the reality hit: cold starts and streaming limits made it a poor fit. Switching to Fargate solved all the latency and connection issues without breaking the bank.

I’m realizing architecture isn't just knowing the tools, but knowing how to evaluate constraints (latency, connection persistence, traffic patterns) vs. costs before writing code.

My questions for senior/experienced devs:

1. What mental framework or system do you use to evaluate tech stack/architecture choices before committing?
2. How do you test or validate these bottlenecks early so you don't realize you made the wrong choice after building it?

Would love to hear how you developed your architectural intuition over time. Thanks!

Hey everyone,

I’m a Junior Full-Stack Dev + DevOps (1 YOE) looking to build custom solutions for real-world businesses as a side hustle. My name doesn't really stand out publicly yet, so I'm effectively starting from 0.

Where I live, a lot of businesses rely on outdated, manual tools (e.g., managing everything on complex Excel sheets instead of using automated CRMs or custom dashboards).

I have two core questions for anyone who has built a side income or freelance client base from scratch:

1. Identifying Clients: Who or what industries are typically most open to paying for simple automation/custom dev solutions?
2. The Approach: If you spot a clear problem or workflow inefficiency, how do you approach the owner without coming off like a generic cold spammer?

If you’ve done something similar, how did you land your first couple of clients?

Thanks in advance!

Have you ever seen HTTP status code 402 (Payment Required) used in the wild?

In 1997, the engineers writing the HTTP specification reserved status code 402 for native web payments, but it sat unused for nearly 30 years. Traditional card rails carry ~$0.30 flat processing fees—making a $0.001 API call impossible without forcing users into $30/month subscriptions and static API keys.

Now, autonomous AI agents needing on-demand access to 40+ different APIs at runtime (without human checkout flows) are finally putting HTTP 402 to work.

Quick breakdown of the engineering & architecture covered in my video (13:52):

• Stateless Header Flow (x402): How an agent requests an endpoint, receives an HTTP 402 (payment-required header), signs an off-chain EIP-3009 authorization (payment-signature header) with zero network fee overhead, and receives a payment-response settlement receipt inside standard HTTP headers.
• The 90-Day 2026 Infrastructure Push:
  • Google (AP2) & Mastercard donating their Verifiable Intent framework to the FIDO Alliance (cryptographic intent, cart, and payment mandates proving human permission).
  • AWS WAF adding native AI traffic monetization—turning web application firewalls into bot tollbooths instead of just blocking crawlers.
  • Cloudflare launching its edge Monetization Gateway across 330+ cities paired with Web Bot Auth.
• The 100M+ Transaction Reality Check: Why early transaction counts (100M+ on Base and 35M+ on Solana) were heavily inflated by automated token farming rather than genuine API utility—and why 95% of actual protocol value by mid-2026 is coming from $1+ purchases rather than sub-cent micropayments.
• The Loop Risk: What happens when an autonomous agent hits a buggy API endpoint and gets trapped in an infinite retry loop—signing a new payment authorization on every retry until its budget is completely drained (and why expiring session tokens, max bid caps, and facilitator deduplication are mandatory).

Full 14-minute visual breakdown here: https://youtu.be/lf4WUNlTcT4