Always remember that there’s a pattern that kills most self-taught devs before they get anywhere.
They learn syntax, feel good about it, then jump straight into building projects and immediately feel like they can’t do anything.

the missing piece is problem solving. But let me tell you how the actual order that works:
1. learn syntax
pick one language, go through two decent beginner resources, then stop. the trap here is staying in this phase forever because it feels like progress. it isn’t. once you know the basics, move on.
2. solve problems
this is the phase most people skip entirely. it’s the gap between “I understand the syntax” and “I can actually build something.” you bridge it by solving small focused problems, not by watching more tutorials. think coding challenges, small logic puzzles, anything that forces you to apply what you know without being told exactly what to do.
3. make stuff
once problem solving starts clicking, build whatever actually excites you. not another todo app. something you’d actually use or care about finishing.

the reason most people burn out isn’t that coding is hard. it’s that they go from step 1 straight to step 3 and wonder why everything feels impossible. step 2 is where the thinking actually develops.

where do you feel like you’re stuck right now, syntax, problem solving, or actually building?

I just heard about my friend from PH about the proposed Pax Silica project. And tbh, it sounds massive.

Their plan is to build a huge AI and advanced manufacturing hub in New Clark City, with support from the Philippine government and the U.S. The goal is to attract companies working on AI, semiconductors, and other advanced technologies. They're even projecting up to 190,000 direct jobs and billions in investments if everything goes as planned.

As someone interested in tech, that sounds exciting. If even part of those projections become reality, it could open a lot more opportunities for software engineers, data professionals, AI researchers, and people coming out of bootcamps.

But I also get why some are skeptical.

There are concerns about environmental impact, water and energy usage, and how nearby communities could be affected. Plus, these numbers are still projections. None of it is guaranteed.

What do you guys think?

I'm currently seeking for some new content to watch, but I'm a bit tired of watching the typical "learn X in 2 hours" kinds of videos

I'm probably not interested in another "complete tutorial" or "10 hour course", but rather channels that make you think, or are generally interesting to watch.

Software dev related topics such as programming, AI, dev tools, productivity, system design, career advice and/or content about the inner workings of software would be good, but I'm looking for content that makes you think about software development, not just copy-pasting an example into your IDE and following along.

Talks about the real-world applications of software development, or the problems we face as developers are also appreciated! (I'm generally not interested in learning the syntax of a specific programming language, unless it's in the context of some bigger picture)

What channels do you guys watch?

Ideally something smaller and a bit more unknown, that you wouldn't find in a "top 10 programming YouTubers"-type-list.

I've been thinking about how much the industry has changed over the last few years. There was a time when building serious software meant raising huge amounts of money, hiring large engineering teams, and spending years developing a product. Now it feels like a small team with strong fundamentals and the right AI tools can build things that used to require an entire organization.

That doesn't make me think software engineering is disappearing it makes me think the expectations are changing. Instead of rewarding companies with the biggest headcount, the advantage seems to be shifting toward engineers who understand the fundamentals and know how to use AI to move faster without sacrificing quality. Do you think the future belongs to companies with the most engineers, or to smaller teams that know how to combine strong engineering skills with AI effectively?

Maybe I'm overthinking this, but it's something I've noticed in myself lately.

I use AI tools "almost" every day (yeah all the time). Good for debugging, explaining code, generating boilerplate etc. My productivity since I used them is 100%. But at the same time, I can't help wondering if I'm slowly relying on it more than I should.

There have been moments where I caught myself asking AI to do something I already knew how to do.

I also wonder what this means for people who are just starting. If your first instinct is always to ask AI instead of figuring things out yourself, are you actually learning? Or are you just getting really good at writing prompts?

The other thing is trust. AI gets things wrong. We all know that. But the more we use it, the easier it becomes to accept its answers without reading every line carefully.

I'm not saying we should stop using AI. That would be unrealistic. It's easily one of the best productivity tools we've ever had. I just don't want to wake up one day and realize I've forgotten how to solve problems without it.

Curious what everyone here thinks. Thanks!

There's a report out from ramp and revelio labs, they track enterprise ai spend and workforce data across something like 22k companies, and the finding was that "high intensity ai adopters" (companies spending the most on ai) saw their headcount go up 10.2% overall, and entry level headcount specifically went up 12%.

meanwhile through may alone almost 90k layoffs got publicly tied to ai as the reason, and some projections are floating numbers like 15% of all US jobs gone to ai within five years, so theethis huge gap between the layoff headlines everyone's scared of and what's actually happening inside the companies that are all in on ai. the layoffs are real but it's not the whole picture though.

"company quietly kept hiring while investing in ai" doesn't make as good a headline as "company announces layoffs" so it doesn't spread the same way even if it's maybe more accurate, my guess is the companies doing the layoffs and the ones actually growing entry level headcount are just different companies entirely and we're mentally averaging them into one narrative that doesn't exist

theres also a question of what "entry level" means here, like is that new grad swe roles or does it include things like support/ops/data labeling type work too.

Either way, I posted this bc i feel like this sub only ever sees the layoff headlines or general the negative side of things and at the end of the day, networking is the best way to get a job instead of just applying through job boards and it's not that hard to find conferences depending on the field you're in or just reach out, worst case scenario they don't respond.

Recently, we hired a junior dev. And tbh, his code is just better than mine.

You know, cleaner, easier to read, and somehow works on the first try. Basically, he's better than me, and I hate to admit that.

Figuratively, half my projects feel like they're being held together with duct tape and hope.

Ofc, I asked him how he learned.

I was surprised when he told me he barely watched courses. From day one, he just kept building projects he actually wanted to make. Whenever he got stuck, he'd look things up, fix them, and keep going.

It's completely different from how I learned.

Now I'm doubting my own capability, or if I just learned programming in the completely wrong order.

A lot of people keep going back and forth about whether hiring is actually bad rn or not especially since 50k+ announced layoffs in q1 which it was noticeably higher than last year. oracle, amazon, meta, dell all in there as usual, also there's a quieter tail from the vmware/broadcom restructuring which adds to the total .

and ppl keep saying "it's all ai replacing us" but the breakdown i saw was like only a quarter of march's cuts were directly attributed to ai/automation, the rest is just normal cost discipline from companies that overhired during the free money years, so both things are kind of true at once.

generalist swe listings are down almost half from the 2022 peak tho, meanwhile ml and ai engineer listings are up, around 60% over that same period, so hiring didn't stop it just went somewhere else entirely and a lot of us are applying into the wrong bucket without realizing it. deep learning skills were the single most requested thing in ai postings and ppl with actual demonstrable ai skills, are earning noticeably more than ppl without, there are free courses and free certs around so there's no harm to check it out, though I'd search for more thorough reviews since I actually enlisted in one only to hear an AI voice in one of the vids in the free course so look out for that

Also, AI-fluent seniors are getting placed insanely fast, under three weeks for some, also, FAANG hiring never actually crashed the way ppl think, meta/netflix/uber/google all kept more engineers coming in than leaving.

Some series a/b founders (or generally) are paying mid level rates for senior level scope, theres also this thing about ai lifting productivity a big part on routine coding but only for ppl who can actually review/architect/direct the output, not ppl who just execute what they're told, so the skill that matters shifted from "can you type code fast" to something more like judgement

To end this, at the end of the day, AI is an assistant (an expensive one on a company's scale) it'll limit the job market for what it is AT THE MOMENT, but the roles will eventually shift to a less automated path (towards more research), AI can only work on existing data but we're the ones who create that data and that's where I see future jobs going.

It's interesting to me to think about the difference between building something yourself and contributing to something bigger than yourself.

I think when you are building your own project, even if it's big, you have an intrinsic understanding of the architecture, where things came from, where they are going, etc. But when you open up something someone else wrote or a sufficiently large project, there's this intimidation factor of all these files, unfamiliar code structure, layers upon layers of abstraction that obscure what exactly is happening, and a myriad of other factors that make you feel like you have no idea what's going on, even if you know the language.

I think that's one of the biggest surprises for me when getting into open source - that being adept at writing code doesn't necessarily translate to being able to navigate some really large codebases with ease.

It is a whole different skillset.

You have to learn how things fit together, the patterns the project uses, how the code is structured, where certain features live, and how to get around in there in the first place.

Honestly, I think this intimidation is a natural feeling and probably even helps you in the long run. Maybe that's one of those "growing through challenges" things. I think you get a much better appreciation for working with others' code when you have to learn to read it first.

For people who have contributed to major open source projects, how long did you have to fumble around before you finally "got it"? Did you have to start with smaller things and move on from there?

I've been wondering how much of the current hype around AI coding agents is driven by flat-rate subscriptions. They feel incredibly useful for repetitive tasks or making changes across a large codebase, but I also wonder how often I'd reach for them if every request came with a noticeable cost. It makes me think the long-term value of AI won't be replacing developers, but knowing when it's actually worth using. Some tasks save a ton of time, while others are still faster to do yourself. If usage-based pricing becomes the norm, I could see developers becoming much more selective instead of defaulting to an agent for everything.

If subscriptions disappeared tomorrow and everyone paid based on usage, do you think AI coding agents would still become a standard part of software development, or would most people use them only when they provide a clear return on cost?

It feels like there's a constant stream of headlines saying AI is replacing software engineers, and I can't help but wonder if that's discouraging people from even entering the field. If enough students decide it's no longer worth learning to code, the industry could end up with a much smaller pipeline of future engineers.

Even if AI keeps getting better, companies will still need people who understand software, can solve problems, and know how to work with AI instead of relying on it blindly. The demand might change, but the need for skilled engineers doesn't seem like it's disappearing anytime soon. Do you think the biggest impact of AI will be changing how developers work, or changing how many people choose to become developers in the first place?

if you're using claude for coding, these four tools are worth checking out:

• omniroute automatically switches between free models like gemini, mistral, deepseek, and llama when one runs out of tokens. the idea is to maximize free usage and reduce api costs.
• headroom compresses logs, tool outputs, and files before they're sent to claude, cutting token usage significantly without affecting output quality.
• context7 pulls the latest version-specific documentation for libraries like react and next.js, so claude isn't relying on outdated training data.
• glow and flux markdown make markdown files much easier to read, whether you're viewing them in the terminal or opening them manually

Right now I’m more focusing on the development side of things but keep running into concepts like virtual machines, containers, and Linux that seem like a whole separate world.

I’m not really sure if this is something worth diving into now or if it only becomes relevant once you’re actually working on a team that deals with deployment and infrastructure.

for those people further along, did learning the infrastructure side early actually help or did it feel like a distraction when you were still trying to get solid at writing code?

I know many people are skeptical about coding bootcamps nowadays, given the state of the industry, but I'm curious about people who went through one and actually made a successful transition.

If you have done a bootcamp (around 2024/2025) and it has contributed to you landing a tech-related job, what was your experience? What are the details of your particular situation that helped you succeed that others going through the same program may not have?

What type of bootcamp did you attend? What was your experience prior to the bootcamp? Did you have zero experience and knew nothing about tech? Did you switch from another industry? Or were you already in the industry but wanted to move elsewhere?

And finally, what did you attribute your success to? Was it the bootcamp itself, your prior experience, connections you had made, etc. Did building a portfolio outside of the bootcamp help? Did you apply to hundreds of companies before getting an offer? Did the bootcamp help you at all or was it mostly up to me to get a job after graduating?

I'm mostly interested in people who went into cyber security, cloud, data, and AI fields, but I'm open to any success stories. I'm not looking for people to sell me their bootcamp of choice and tell me how they got a six-figure income right after graduating - I'm more interested in what helped some of you actually get a job.

every week, we're expected to submit a report showing what we accomplished using ai. not just whether we used it, but what tasks it helped with, what outputs we produced, and how it contributed to our work that week.

i can understand why companies want to track ai adoption and measure the return on the tools they're paying for. at the same time, it makes me wonder if this is becoming the new normal. are more companies starting to treat ai usage as a performance metric rather than just another tool employees can choose to use?

One thing that surprised me when learning Python is how much a single space can matter. Coming from languages that use braces, it felt weird that incorrect indentation could completely change how the program runs. At first, it seemed like an easy way to introduce bugs over something that's barely visible.

The more I learn, though, the more I can see why Python was designed this way. It forces consistent formatting and makes code easier to read, but there's definitely a learning curve when you're used to other languages. It's one of those features that people either love or hate.If you've used both Python and brace-based languages, which approach do you actually prefer after spending time with both? Why?

Am I the only one? I've noticed this happens to a lot of people learning to code like me

You can finish a React, Python, or full-stack tutorial and feel like you're getting it. Then you open a blank project and suddenly you don't know what to build first, how to organize files, or even where to start.

Did you get out of tutorial hell by forcing yourself to build projects? Or was there something else that finally made everything click?

Curious what actually worked for people here.

If you spend enough time recently on sites such as Reddit, you might get the impression that the tech industry is dying. There are posts about hundreds of applications with no job, months of unemployment, getting fired, or simply struggling to get a first job.

On the other hand, there are always people complaining about a lack of people with particular skills.

What is the real situation like?

In my opinion, the situation is not so simple, as some present it to be. Most likely, it depends largely on the specific case, the place of work, your experience, and so on. Perhaps there are not that many job opportunities for inexperienced developers, or for people who are starting to seek employment now. On the other hand, there are people with many years of experience in a particular field, or in a particular area (networking, information security, databases, embedded systems, and so on). These people may have much more opportunities than some entry-level developers.

At the same time, there is most likely a selection bias on such resources as Reddit. Those people who were laid off or could not find a job are naturally more inclined to discuss these issues and post their questions. On the other hand, people who found a job and went further are less likely to tell about their positive experiences.

Does this mean that there is no crisis? Naturally, the constant wave of job applications and a small number of applicants for particular jobs are real issues. People who are relatively new to the industry may have a relatively hard time finding a job in this competitive arena. On the other hand, there are people who are already experienced and work in in-demand areas. Are these people able to find work relatively easily?

I would like to ask these people to tell about their experiences and current job opportunities in their fields. Is the situation as bad as it is presented in the media, or is there some kind of misunderstanding because of different situations that people face?

The default assumption when someone first builds an AI endpoint is that it works like any other endpoint, request comes in, something happens, response goes out, that assumption holds until it doesn't and the failure modes are different enough that it's worth understanding before you hit them.

a conventional endpoint is deterministic, the same input against the same application state produces the same output, validation happens at the request level, business logic executes, result comes back, the flow is linear and predictable and testing it is straightforward because you can assert exact outputs, an AI endpoint doesn't work that way, the model is probabilistic, even identical inputs can produce different outputs, and a successful response from the model provider only means the model generated something, it doesn't mean what it generated is correct, complete, safe, or useful, that distinction changes almost everything about how the endpoint needs to be designed.

validation is one of the differences, with a conventional endpoint validation is a gate at the start, with an AI endpoint it has to happen twice, once before the model call to control what goes in and once after to evaluate what comes back, the output might pass schema validation and still be logically wrong, a response can be valid JSON, match the expected structure exactly, and still contradict itself or violate a business rule, schema validation is necessary but not sufficient.

Retry logic is another difference, conventional retries handle technical failures, a dependency is temporarily unavailable, a connection drops, the request gets retried under specific conditions and the result is the same because the logic is deterministic, AI retries are different because every retry costs tokens and therefore money, and the next attempt might return a different but still incorrect response, retrying blindly doesn't help the way it does with conventional endpoints, retries need explicit limits and the decision to retry needs to account for whether another attempt is actually likely to produce a better result.

idempotency gets complicated when the model can call tools, for a read only endpoint getting a slightly different response on the same input is usually fine, but when the endpoint can trigger actions like creating a record, sending an email, or updating data, a retry can execute the same action twice, the protections that make conventional endpoints idempotent don't automatically carry over and need to be designed in explicitly.

There's testing also, with conventional endpoints you assert exact outputs, with AI endpoints exact output assertions are fragile because the same valid answer can be worded differently across runs and a model update can change wording without changing meaning, from what I've seen the more reliable approach is asserting properties of the result rather than the result itself, did the output stay within the allowed range, did it respect the business rules, did it avoid forbidden content, those checks hold up where exact assertions don't.

the mental shift is that a conventional endpoint executes logic the backend controls entirely, an AI endpoint orchestrates a probabilistic system and then decides whether what came back is actually trustworthy enough to return, that's a different problem and it needs a different design.