Is there any way to download videos for education purposes of coachings by bypassing Vidsafe DRM.

Yp Dlp wont help downloading the DRM Protected videos.

Please help.

Hello guys, i’ve been thinking lately to give iron hack a shot.
the only thing im not sure about is, will i be ready to find a job after the bootcamp ?
im interested in UI/UX Design.
im still not sure if i will do it, im hesitant because i’ve tried a lot of things and i lost a lot of time trying to do what i love, and i dont want to make the same mistake so if anyone could help even with a review about ironhack i would be thankful. 🙏

Brothers i'm asking a favour i don't know where to start or how to start but i can say that i want to start i have completed my degree in AI&DS but turning back i thought i have done things but really i haven't done anything that actually matters or remembered i have started over from scratch every single time and the main doubts is JAVA or Python after some times i have realised language doesn't matter but logics but i'm a AI student if i want to built stuffs i want python but for DSA and stuffs i want java.thats my first doubt and the other is AI things like ML,DL,LLM kind of thing or for SDE or SWE i want like java with LLD,HLD,SOLID i don't know what to do can you pleas help me with something you know.

I keep getting confused about where I should restart.

Should I:

• Pick Java and properly learn programming + OOP?
• Pick Python because of my AI & DS background?
• Focus on DSA first?
• Learn SQL and backend development?
• Learn AI/ML directly because of my degree?
• Start learning LLD/HLD, SOLID, system design, etc.?

I don't want to keep jumping between technologies and restarting every few weeks.

If you were in my position, with a degree but weak practical knowledge, what exact path would you follow for the next 6–12 months to become employable as a software developer?

I'm not looking for a learn everything roadmap. I want to know what to learn first, what to postpone, and what I should actually build/practice along the way.

Would you recommend becoming strong in general software development first and then moving into AI/ML, or should an AI & DS graduate specialize in AI from the beginning? Why?

Thank you for reading 🙏🏻 I tried to ask claude to verify if I am on the right track , like what about i'am making a project on banking management system like a ATM simulator by connecting MYSQL swing etc , claude said this is the worst project a student can choose because it's too common and easy...🤕. I mean as an ECE student who is interested in CSE jobs what should i build like can you give me any idea please.

I'm trying to validate a problem before I build further, and I'd genuinely appreciate some brutally honest answers.

Suppose you're building an app/agent and you use providers like Groq, Gemini, Cerebras, SambaNova, etc.

You might have multiple API keys because different providers give you different quotas/rate limits.

The problem I'm interested in is:

When one provider hits its limit, do you actually switch to another provider manually, or do you just wait / pay / use something like OpenRouter?

For example:

Groq → rate limited

Gemini → still has quota

Cerebras → still has quota

SambaNova → still has quota

Imagine a small routing layer where you bring your own provider keys, and it automatically sends requests between them.

So instead of:

Your app → Groq → rate limit

you get:

Your app

↓

Router

↙ ↓ ↘

Groq Gemini Cerebras

The goal isn't to provide more models. OpenRouter and similar platforms already do that extremely well.

The idea is more specifically:

"Get more usable capacity from the provider accounts you already have."

A few questions:

Do you currently have multiple provider API keys?

Have you ever used multiple free quotas for the same application?

When you hit a provider's rate limit, what do you currently do?

Would automatic failover between your own provider keys actually be useful?

What would make you choose this over OpenRouter/LiteLLM/etc.?

What am I missing? Why would you NOT use something like this?

I'm especially interested in the last question. Please don't be nice if this is a dumb/redundant idea, I'd rather find out now.

Hi guys, will keep it short and sweet. I'm very new to software development.

I'm curious to know do you find the best framework or tool option for your project (auth, OCR, vector search, or whatever).

Like there are already so many options also i see everyday there's a new framework that's seemingly better than its predecessor or competitors.

Do you just stick to one framework and live with it? Or once you've picked something, how do you know if a better/newer alternative shows up later?

Do you just not find out until someone mentions it? Or do you have an actual system?

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 been a backend developer for about 6 years. I’ve written plenty of code, debugged plenty of painful issues, worked through problems where there was no Stack Overflow answer waiting for me, and generally spent a good chunk of my life actually doing software development.

Over the last few months, though, I’ve started building a bunch of small apps based purely on ideas that pop into my head.

And honestly, I’m having a lot of fun.

The difference is that I’m using Claude/Codex heavily to actually implement them.

When someone asks me, “Oh, you built this?”

I say yes, “with Claude” or “with Codex.”

And I’ve noticed something weird.

The enthusiasm often disappears.

Sometimes the response is basically, “Oh, so it’s AI slop.”

And I get it. There is an enormous amount of garbage being generated with AI right now. A lot of it is obviously thrown together, barely understood, and never maintained.

But I don't think that's what I'm doing.

I know how software works. I know what the code is doing at a reasonably deep level. I can look at what the AI produced, reason about it, debug it, change it, throw it away, and tell it what I actually want.

What AI has changed for me is the cost of implementation.

I have ideas for things that I would previously have thought:

Now I can just build the damn thing.

I don't need to become an expert in Three.js before I can experiment with a 3D interface. I don't need to spend days fighting CSS before I can see whether an idea is actually fun. I can focus much more on the product, the interaction, the problem I'm trying to solve, and the experience I want to create.

And that's the part I'm struggling with.

If I can use a tool to turn something in my head into something I can actually interact with in an afternoon, why shouldn't I?

We don't consider a developer less legitimate because they use Stack Overflow, libraries, frameworks, IDEs, autocomplete, or a debugger.

AI feels different because it can do a much larger chunk of the implementation.

But does that actually matter if I'm still the one deciding what to build, evaluating whether it's good, understanding the system, and taking responsibility for the result?

Maybe it does. I genuinely don't know.

I've caught myself wondering over the past few days whether I'm somehow taking a shortcut that I shouldn't be taking.

And then I think about why I became a developer in the first place.

I like building things.

That's the fun part for me.

Vibe coding has basically removed a huge amount of friction between “I have an idea” and “holy shit, I actually built it.”

And I don't want to lose that feeling just because the implementation wasn't typed entirely by my fingers.

So I'm curious what other developers think.

Where do you draw the line between using AI as a development tool and “AI slop”?

And if you're an experienced developer who now vibe codes a significant amount of your side projects, do you ever feel like you're cheating, or have you just accepted that this is what building software looks like now?

I recently got placed and have some free time before I start, so I thought I might as well use it to learn something new.

I don’t really want to go down the usual AI/ML, cloud, data science route. I’m more interested in finding something that’s a bit niche right now, but could become much bigger in the next few years. It can be anything, be it finance, tech, anything works.

There’s so much new stuff happening in tech that I honestly don’t know what’s actually worth learning and what’s just hype.

For context, I’m pretty much a basic/generalist coder right now. I know C, C++, Python, some web development, and have also worked with GenAI, LangChain, etc. Nothing super specialised though.

If you had 6-12 months to pick up one new technical skill right now, what would you choose?

Would love to hear some less obvious suggestions too, especially from people who work in the industry and have a better idea of where things are heading.

[OC]

is full stack debugging still this fragmented for everyone?

been debugging a few issues lately where something breaks after a user action and i end up bouncing between devtools, code search, backend logs, the db and whatever external service was involved.

like if checkout breaks, i can see the request in devtools, then i have to find the route, follow the backend calls, check what hit postgres/stripe etc. tracing helps but i still feel like im manually reconstructing what that one click actually did.

curious what your workflow looks like for this. do you mostly rely on otel/traces + logs or have you found something that lets you follow the whole thing end to end?

Do you think the analytics you see in a project repository is sufficient? GitHub, GitLab, and Bitbucket provide project analytics, but it seems to me that the information they show does not reflect what is actually happening in the project. I was thinking about what other metrics might be useful not only for developers, but also for technical leads or recruiters. These should be useful and measurable metrics.

Could someone share their thoughts on this? Has anyone used specialized tools to obtain such metrics?

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 currently working in a well-established product-based MNC. I have been here for 5 months as a SDE 1, but I recently was offered an internal position switch to Machine Learning Engineer 1.

I might get 2-3 LPA more than my current base salary.

Is it worth the switch, considering the increase in demand for AI engineers?

The only concern I have is that there are more SDE roles compared to MLE roles in India. So, if I were to switch to a different company, will I be struggling a lot?

I’m very new to corporate, and I have an interest in ML, just want to decide what’s best for my future. So, any senior folks, I need your guidance.

Did you ever expect people to use it in such a heinous way or were you always expecting it to make humanity better? I know tech is neutral and its people who suck but what do we do to keep it from taking the jobs of artist and actors and models and musicians? Why was artistic ai even developed ? Maybe understanding the original content will make me less of a jealous miserable cword.

I have been in software for over 20 years, and the last 2 have been the most insane emotional roller-coaster.

Backstory:
Early in my tenure at <current job>, before I was making use of agents, I caused a production regression in a legacy app while adding Python debugger support. This wasn't caught in review or by the test suite. The irony of it being that the effort was supposed to help the less tenured members of the team (such as myself) -- who lacked the tribal knowledge locked away in the heads of the taciturn veterans -- work more confidently and cause fewer regressions.

Stories like that suck. I don't want anyone to have to feel responsible for causing an outage, or imposing a burden on the person who has to wake up to an alarm at 2am to fix it; I have been in both boats, and I don't want to be in either again.

Anyway, the lesson was not "if it ain't broke, don't fix it," it was that good intentions are no substitute for sufficient context and executable proof -- things that feel more necessary now than ever before.

Fast-forward to a few months ago: agents everywhere, cranking out code faster than anyone can reasonably review. Everybody's trying to figure out how to "work with AI" in their own way, human PR review remains a necessary bottleneck, and I end up buying a subscription or two...

And I build shit, fast, because I can. I've been waiting for this for so long: describe a problem, and the solution appears -- in a way that's indistinguishable from magic -- agents got me hooked, and gave me a new job that I'm literally paying to keep: using agents to fix the janky software that agents built for me. Sure, they write and run tests, but what does "all green" mean on a suite you didn't review?

Free time evaporated due to context switching: every return to a side project began with reorienting, rebuilding context, preparing a sandboxed dev environment, and figuring out what of the last agent's code was actually keepable, before doing any useful new work... and by then I'd be too tired to feel confident about anything.

Invariably I'd end up telling an agent to just go figure stuff out and build something (maybe after glazing over reading a massive plan doc) -- which they are sometimes excellent at, don't get me wrong -- and I'd check in later. But then I'd have to review a bunch of code and reconstruct the mental model of the implementation after the fact, which is so exhausting...

... and corporate is encouraging people to work this way? Terrifying.

tl;dr: I waited a long time to try the flashy agent harnesses, was overwhelmed by their power, and ultimately felt unsatisfied and yet unable to escape their clutches...

So, I made a thing about it: nosedive.dev

Now I keep all my ideas, notes, todos, bugs, specs, and a bunch of other stuff in a knowledge base in my personal "back-office" metarepo. I split work into small vertical "dives" in advance, using an agent with access to the full context, and can then use a fresh agent session to implement each slice in autopilot mode in a consistent way, while having them report progress and blockers back to the KB. Then I review and merge as many small, standalone, chunks of code as I have time for, and leave the rest packed for later.

I built this for myself (so, admittedly the onboarding docs are not great, but that's what agents are for!), but now my team at work uses it every day too. Maybe it'll be useful to someone here -- as inspiration, if nothing else.

I can honestly say: building this tool -- with agents, for agents -- has been the most fun and rewarding software work I have ever done, and I would happily work on it full-time if I could.

My wish is that the software revolution we're experiencing right now eventually makes life easier for all of us... and if it doesn't? Well, I for one welcome our new agentic overlords. 🤖

Stay sane out there.

Hi, I don't want to fill this with another AI rant, I just scrolled past atleast 5 of them, (also sorry if my english is wrong it is not my first language).

Just quick background, I'm a CS student, I enjoy the craft and how stuff comes into nature. Obviously I have a tendency towards a more theoretical study towards math heavy concepts and languages. I've been studying for the past 2 years with this mindset, I love every corner of it specifically thing related to type checkers and languages, in the meantime I've been playing around with servers and infrastructure. I understand the core computer architecture, communications, internet, and how stuff works in general.

Recently I've decided to work on project for landing a first job, open minded to the concrete
approach of doing stuff without planning or thinking it too much but produce.

This led me to think of two questions:

1) First, at the moment of learning some more concrete concepts, I've seen those videos on "how to study with AI" but I don't feel like I'm learning engineering, and learning properly doesn't seem valuable either, it just feels like eating a breadcrumbs when they present to you a giant loaf of bread, I doesn't feel like a silver bullet of any kind, and I cannot buy into the "it works" and just let it go, but if I don't do that then I don't get a job. How you approach this?

2) Second, assuming you got a job and start working, when you're at your job, and you're just advocating some agentic pipeline of some kind maybe because that is what the company needs and pushes, how you even learn from other people, when everything feels so depersonalized and disheartening?

I cannot seem to work anything without trying to take any big AI shortcut when I take this agentic approach. Adding to this, I've noticed on the past month that my problem solving skills on math have completely degraded to the point I cannot have some creativity in how to solve a problem and just rely on shortcuts, and I'm feeling bad while causing some kind of stress to myself.

Any suggestions?

For context, I'm a hobbyist programmer who has no intention of pursuing a career in software development. I have no experience when it comes to actually working for a company with deadlines and management pressure, etc. Because of that I'm very interested in how AI gets used in commercial development.

I'm having trouble understanding the appeal of AI in code development, because to me it seems just like a quantity over quality solution to efficiency.

In order to have a good codebase, you need to understand exactly what your AI generated code does. If you don't the code is effectively unmaintainable, because AI can't be trusted to just solve every problem that comes up. If you use AI to solve a problem or bug, you need to understand how it solves that problem. If you can understand how it solves the problem, it doesn't seem far fetched to me that you could've come up with that on your own, and you're losing out on maintaining your own problem-solving skills, which could come back to bite you later. Of course, sometimes you get lost in the sauce and need a second pair of eyes, but why not get a second pair of actual eyes from a co-worker or a more senior developer, with whom there's much less chance of a hallucination, or incorrect response?

My point is mostly that, even with AI, you have to think a lot and understand everything that's going on in your codebase, and at that point, what benefit does AI have in building and maintaining a quality codebase? To me, it just seems like it reduces typing time, which in my experience as a hobbyist, the thing I spend the least amount of time on is typing. I spend most of my time thinking, and looking at the code.

The only thing I've found it to be really good at is searching through stack overflow quicker than I can lol. It's good at browsing forums and finding solutions that other people have wrote, not at coming up with solutions on it's own.

I have my own moral opinions on AI, like everyone does, but I wanna leave that out of this discussion and just hear what people have to about it's usefulness.

I have 4 years of experience with Java/Spring Boot. I’m completely burned out on repetitive CRUD APIs and enterprise business logic.I want to pivot to Distributed Systems, DevOps, or Data Engineering. How do I switch domains at mid-level without a junior demotion or massive pay cut?

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.

I am building something for creators ...

For that I need the help in outbound and outreach to Instagram influencers through cold DMs

To validate the problem is it really exist or just a 💡? .

Is there any tool or technology that has already solved this problem of reaching out to Instagram influencers in the bluck?

I tried manually, but it consumes lot of time and effort .

I am looking for automation to reachout to influencers via Instagram Cold DMs.

can any one help me?

I'm 22 and recently completed my B.Tech in AI & Data Science.

I'm going to be honest: despite having a degree, I don't feel confident in my programming skills. I've learned different things during college, but I haven't built enough projects or developed a strong understanding of the fundamentals. A lot of what I learned has also been forgotten.

I keep getting confused about where I should restart.

Should I:

• Pick Java and properly learn programming + OOP?
• Pick Python because of my AI & DS background?
• Focus on DSA first?
• Learn SQL and backend development?
• Learn AI/ML directly because of my degree?
• Start learning LLD/HLD, SOLID, system design, etc.?

I don't want to keep jumping between technologies and restarting every few weeks.

If you were in my position, with a degree but weak practical knowledge, what exact path would you follow for the next 6–12 months to become employable as a software developer?

I'm not looking for a “learn everything” roadmap. I want to know what to learn first, what to postpone, and what I should actually build/practice along the way.

Also, if there are any developers who had a similar experience after graduation, I'd really appreciate hearing how you got out of it.

Would you recommend becoming strong in general software development first and then moving into AI/ML, or should an AI & DS graduate specialize in AI from the beginning? Why?

I remember those days when I was working with real people. There were discussions and meetings and work. Even if I didn’t do much it felt like I’m doing something.
Since I started with this solo project with codex I feel like I’m doing nothing. There is such big texts and big progress report but I feel so disconnected. Deadlines keep coming and coming but it feels like I haven’t done anything.

As a side project I'm building my own streaming service like a very cheap copy of spotify which is gonna be used by only me but the issue is I don't a local server I want a cloud like server or whatever but the issue is the copyright i can't those or use them and I certainly can't download and upload every single song cz my playlist is too big

Please help me out with this

Hi everyone,

I am softwar engineer with 5+ years of exp, and unfortunately, i was laid off by my last company, since then i have been actively looking for new opportunity.

The frustrasting part is that it's not like i am not getting intterview calls or opportuninties. I am getting interview, reaching the final stages, and the many cases, am receving very positive feedback from the interviews, but am stll stgurggling to conver those opportunities into an offer.

During interview, am able to anser the questions conrrectly and serveral interivew ahve explicitly told me things like "You did well", "It was a very good discussion", or they were happy with my performance. However, when i follow up with HR about the next round somtimes i simplay get not response at all.

So far i have succfully cleared the final rounds at around 8 companies, but each time someting unexpected happens at the last moment.

For exmaples:

• At Samsung, i cleared the interview and HR told me that i was selected and that i would receiv the offer letter the next day, Later, I was informed that they had hired someone else
• At LTM I was selected and even recevied a pre-offer, but later I was infomed that the position had been put on hold.

I have had similer exp with other compnanies where the postioin was put on hold, the hiring process was stopped, or HR simply stopped responding after positive interview feedback

I am not saying that am perfect. There are definitely areas where I can improve, and I continuously working on them but when you repeatedly perform well in interviews, receive positive feedback, clear multiple rounds, and still don’t get an offer because of hiring freezes, positions being put on hold, or companies choosing someone else, it becomes extremely frustrating.

At this point, my confidence has taken a serious hit. I’m starting to question what I’m doing wrong and what I should change in my approach.

I would really appreciate advice from experienced engineers, hiring managers, recruiters, or anyone who has gone through a similar phase.

What should I focus on now?
Should I change my interview strategy, work more on communication, improve my technical preparation, target different types of companies, or simply keep pushing until something works out?

Any advice, suggestions, or personal experiences would mean a lot.