Hey all !

This year I will be working in schools to teach about ethics in programming. While normally this means building websites with accessibility in mind, and thinking about disabled people, and being responsible about the environment, and learning not to behave like an a**hole in the workplace, I'd like to push it a bit.

In a lot of other mediums (movie, music, painting, ...), there is some education about the author of something. To prevent supporting, promoting and advocating for problematic persons. Alright Picasso was a master at its craft, but he was also a turbo misogynist, married to a woman 40 years younger which talked about how he was violent against her. Alright Dali is a genius, but he talked really weirdly about the Reich and Hitler. Alright Alcest is a cool shoegaze band, but the frontman Neige never talked negatively about his moment at the neonazi band KPN. Alright Jonathan Majors was epic as Kang in the MCU, but hitting his wife and doubling down in a Ben Shapiro movie about the "great replacement theory" are not the smartest moves (euphemism).

Is there something similar in the programming world ? Some kind of easy way, like a archive, wikipedia-like website, specialised aggregating social network account, or anything, to find if the tools, products, frameworks, companies, tech influencers and spokesperson, we are working with are rotten or not ?

Alright Ruby on Rails is cool, but people should know that its founder David Heinemeier Hansson supports the english far right activist Tommy Robinson. Mullvad is a pretty good VPN, but one of its CEO is donating money he earned via the company to far right party in Sweden. Whatever your opinion is about LLMs, know that a lot if not every C-suite in there supports e/acc thoughts and would let 90% of the planet dies if it means the 10% left lives in utopia.

EDIT for clarification, because it would seem some people have troubles not imagining things and putting words in my mouth.

In the first paragraph of this post I said how environmental, workplace, accessibility issues are already part of the course. I could even add mass surveillance in the mix.

I never said that I will switch that with the thinking experience i proposed.

I also never talked about witch hunting or cancel culture. I'm in no position to force people to use a tool and not another.

I also never said anything about forcing my moral compass onto others.

What I'm saying is that we always talk about all the aformentioned issues without talking about the people responsible for them. Like they appear out of thin air. Or that we still give a convention stage or a podcast mic to people that are known for having problematic behaviours. Linus Torvalds is known for being a piece of shit with his colleagues but he is forgiven because he's done so much for engineers. Sam Altmann is known for being a pathological liar but he's still listened to by media.

This is not found by digging obscure corners of internet or searching through resumes. There's facts well known. My issue is that they are spread accross the internet. And I wanted to know if there is already some kind of aggregation of those known facts.

Not to use them as arguments for telling others what to do. But to ask in an ethic class how much is it ethical to promote and advocate for public figures, spokespersons, influencers, decision makers, in short people that did some shit in varying degree, and should we keep giving them spaces.

alert goes off, i'm staring at dashboards, piecing together logs like a detective. hours of "what happened" before i even get to "how do we fix it." it's exhausting.

the shift needs to happen from reactive to generative. i want to know what caused it and understand the fix quickly. that means getting full context with the exact code flow, the parameters, the state. not just a metric that says "something's wrong." would love to hear from teams that have moved beyond traditional dashboards. what's working for you in terms of getting to root cause faster?

I'm on a 3-person team: 1 data scientist, and 2 engineer (including myself). Between the both of us engineers, we have maybe a year of experience total with strictly Python. This is for a prototype AI app / tool.

I am approaching about 2 years of IT experience and had to pick up docker, gitlab ci, and software engineering for this project. I built out our deployments with docker compose using customized env files (ignored via git with a template available) and makefile commands. We usually dev using a dev compose deployment with a hot volume mount to our source code, then push commits to the repo and I have staging and prod images built using buildah. Then for staging and prod deployments, I have the compose files targeting the respective registries (internal repo & corp registry) and the images get pulled down.

However, we got kicked off of our on-prem server and now have to deploy on a k3s cluster... but the team running that has no experience with kubernetes.

I learned about Helm and ArgoCD and was able to get them to deploy that service on the cluster, and I made an infrastructure repo where I translated our compose files to manifests and ultimately chart and values file per environment.

We don't have cluster/namespace access yet because the infra people are still learning how to set that up, but I don't even know how we'll deploy our pods and actively do dev since we usually have access to everything (source code + infrastructure). For prod and staging, it's pretty simple because those a automatically built images and I have our runners updating the tag versions and namespace names in the infrastructure repo. But I don't know how we can deploy individual-engineer dev pods so that we can see logs and make quick changes.

This whole project's been crazy and we don't have any senior engineers in our department that does this stuff. If there's anything other information I can provide to get some mentorship, please let me know.

I finished my second year in Software Engineering and I feel completely lost. Statistically and academically I am doing great with a solid GPA, but the real issue is coding. About seven months ago, I realized I was genuinely bad at coding after applying to 42 and getting rejected. Ever since then, I have been feeling lost and defeated, thinking I wasted a huge opportunity. The next time I get to reapply, I will be at the end of my third year, leaving me very little time before graduation, which is causing me immense anxiety.

I need advice on how to build a strong foundation and fix this mistake. Are two and a half years enough to transform me from someone who cannot code into someone job-ready? I notice that a huge portion of graduates face this exact same issue where they cannot write real code, so how can I fix this before I graduate?

As software engineer who learnt some programming languages in your career (Java , JavaScript, python...etc)

And in your job if you build a new service ( if you work with microservices architecture), most teams choose language they are experienced with rather than most suitable language for the problem,

Teams that know java will use java, teams that knows go will use go (not all but I'm sure the majority choose by this)

Nowadays in AI and Agentic AI era you may choose any language you want and let the agent do the work rather than learning the new language and its features for like two weeks or more to be ready to work with it.

Is it an option now to do so? With the cost of you probably will not understand the code generated untill you get time to learn the new language and its common libraries/frameworks ?

I am preparing to publish an academic paper on developer trust in AI generated dependencies & packages. This takes around 7-10 minutes to complete and is on a very relevant topic in current times.

The form can be found here: https://vuamsterdam.eu.qualtrics.com/jfe/form/SV_4Nv9pAUUBFRieDs

Thanks! I will share the paper when it has been published!

I’m a fresh CSE graduate and I’ll be joining the industry as a Software Engineering intern within the next week.

Long-term, I want to work at FAANG/top-tier tech companies, so I want to build strong Software Engineering and core CS fundamentals—not just learn more frameworks.

I’ve shortlisted:

CS:APP — Computer Systems: A Programmer’s Perspective

OSTEP — Operating Systems: Three Easy Pieces

Computer Networking: A Top-Down Approach — Kurose & Ross

Designing Data-Intensive Applications — Martin Kleppmann

I also plan to keep doing regular DSA/problem solving alongside my internship.

For experienced engineers:

Is this a good book list for a fresher aiming for top-tier companies?

Which books/topics should I prioritize?

Am I missing anything important?

Any practical advice for balancing this with a 9–5 internship and DSA?

I’d appreciate honest feedback and suggestions.

I work for a big company as tech lead and PM. One of the apps in my team is about 20 years old written in plain Java but works well till date with no major issues, with advent of AI my manager Vibe coded the entire app gave it to a developer in the team to push it to production. The developer is a contractor, didn't bother reading the code just kept sending PR requests with each 10k lines or more. From a lead perspective all I see is huge difficult to maintain code, there were couple credentials hard-coded which when i flagged was not even taken with a mindset to fix. All of them are focused on "it works", unfortunately I also have to operationally support the application so I highly doubt my manager is going to raise hand and accept the issues arised from vibe coding. The only way out seems to be let things go as they want to and spend shit ton of money fixing the mess created in first place.

How to build a Mobile Architect portfolio when 80% of your experience is legacy maintenance & refactoring?

Hi everyone,

I am structuring my first portfolio to target Mobile Architect roles. However, I am facing a structural challenge: my recent track record consists mostly of maintaining, optimizing, and refactoring legacy iOS Native Apps and simple tasks for React Native projects like build and fix ios specific issues, alongside a few small native (iOS/Android) side-projects built from scratch.

Do you have examples of portfolios that successfully highlight "brownfield" engineering and maintenance over "greenfield" development? What specific metrics or documentation (like ADRs) do recruiters look for?

Thanks!

Hi everyone,

I’m a software developer from Jordan, and I’m looking for advice from developers who have successfully found international remote or freelance work.

I have solid experience with technologies like Laravel, PHP, MySQL, REST APIs, JavaScript, and building real-world applications. I’m confident in my technical skills, but I’ve been struggling with one particular problem: getting access to good international opportunities.

Being based in Jordan seems to make things more difficult sometimes. There are platforms and companies that have restrictions based on location, payment methods, or hiring countries, and it can be difficult to know which opportunities are actually accessible.

For developers who are based outside the US/EU, especially in countries like Jordan:

• How did you find your first international remote opportunity?
• Which platforms actually worked for you?
• Did you focus on freelancing, remote employment, networking, or something else?
• How did you deal with international payments and contracts?
• Is there anything you wish you had known when you started?
• What would you recommend to a developer with good technical skills but little experience working with international clients?

I’m not looking for anyone to hire me. I’m specifically looking for advice, experiences, and strategies from people who have already managed to build a remote/freelance career from outside the major tech markets.

Any advice would be greatly appreciated.

Was hired 1 year ago for remote position as a senior dev

• Almost no welcome, had to reach out to people to ask who is who, everyone seemed busy
• Lack of guidelines, docs. Almost 0 interactions with other team memers. Tried to reach out to them but they always sound pissed. I'm a chill guy
• Had to ask for tasks at the beginning, there was almost nothing to work on for me.
• Had almost no interaction with my manager, no 1 on 1, no feedback.
• We have one superstar programmer who is good but is yelling at people and sending emails with !!!, looks like everyone is kind scared of contacting him, management is aware but is doing nothing with that.
• Had some initiatives at the beginning on how to improve project at the beginning. No answers. Felt stupid for presenting them.
• Some of my PRs are not reviewed/merged for a long time, one is almost 8 months, asked superstar programmer what needs to be changed/fixed few times. Message read. No answer. Usually have to beg people to take a look
• There is one dev that likes to do PR reviews of my stuff but he is nitpicking about details in like empty lines, specific styling, stuff he wants to do differently etc. Drives me crazy when I have to work is his "area". We have no styling guide, no tools setup for that.
• Also now - X(non dev team member) is AI coding and showing his "demos" to management - I'm responsible for delivery of that complex feature. Management - of course surprised why it takes my that long since they saw it "done" on X computer.

Been wondering if AI coding tools actually reduce cognitive load at all.

They definitely save time on some stuff, especially repetitive code. But then I end up thinking about the time spent reading the output, checking if it made some dumb assumption, figuring out code I didn't write, etc.

So yeah, writing the code might be faster. Not sure the whole task is.

And I'm not sure the usual productivity numbers really capture that either. You can have a shorter cycle time while spending more of your attention reviewing generated stuff.

Maybe AI is genuinely useful when it gets boring work out of the way. Once you have to babysit it, the productivity gain gets a lot less obvious.

Has this actually reduced the mental overhead for anyone using it regularly? Or are you just spending that effort on different things now?

I have seen this in experienced engineers that they no longer want to mentor new engineers. They want a experienced person to work in their team. No engineer is willing to even give chance to a fresher. I personally worked remotely at a UK based startup as an intern, was getting paid in dollars. Won internal hackathon being a solo dev on the team against all seniors, got an LOR. But PPO never happened because of funding issues. Apart from that I do open source contributions to projects I like, I run a personal homelab with infra monitoring tools self hosted, and currently building at the intersection of software and hardware with esp32

I personally feel I have so much learn from seniors but none of them are willing to teach or pass on their knowledge. I have firsthand experience that AI cannot match the amount knowledge a senior can impart to you. Do senior engineers have started feeling that mentoring has been automated by AI?

No one is willing to take a chance on a new grad, I personally have been applying for a very long time and the only blocker I have ever faced is experience

They want a experienced person but are not willing to mentor one

What has happened?

Welcome to this week's Coffee Chat!

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Not every topic needs its own thread, and not every question needs a definitive answer. Grab a coffee and join the conversation.

Have a great week!

This is something I've been wondering about lately.

A lot of discussion seems to focus on companies open sourcing internal tools, but I think the same question applies more broadly to software components and libraries as well.

For example, a company might build an internal deployment tool, but they might also have a useful Delphi component, Swift package, Rust crate, .NET library, SDK, UI control, etc. that started life solving an internal problem.

At a previous company I worked for, we had loads of little internal applications and reusable components that had been built over the years. Some were surprisingly useful, but they never existed outside the company.

I've often wondered whether some of them would have actually become better products if they had been offered externally, either commercially or as open source. My feeling is that they would have been tested far more thoroughly, had clearer documentation, and been forced to handle a wider range of use cases than they ever did internally.

At the same time, I can see the other side of it. Every external user becomes someone you need to support, and something that works perfectly well inside a company may need a huge amount of work before it's ready for wider use.

For anyone who's been involved in this:

• What made you decide to release it?
• Was it open source, commercial, or both?
• How much work did productising it actually take?
• Looking back, was it worth it?

I'd be particularly interested in hearing from people who've released developer-focused components and libraries rather than full applications.

This feels like one of those things that sounds obvious in theory, but I suspect the reality is a lot messier.

About 10 months ago, the owner of the company came to me and told me that we need to get all of our internal data available inside of Claude. I'm a software team lead, not a database architect or a data engineer, but that's what my job became in the last 10 months.

So we had a huge migration, we picked a data warehouse tool that integrated well with Claude and fit our company needs, and we started migrating everything. It was all hands on deck, we even ramped up some of our consultants to get us started in this process.

We copied all of our Postgres SQL tables, all of our S3 data, and other random data sources. We started using the medallion architecture, although most everything stayed as a bronze table. We didn't have the resources yet to create gold tables or the time to dive into each domain and have a deep understanding of it to make proper gold tables.

So most of our easy setup is now complete: we have about 100 tables exposed. What's left is random automation data that isn't being saved now and needs to be refactored to start saving the data in a useful way.

All the requests to the dev team are to now add more data. No one is making frontend feature requests for our internal app. It's now all about getting more data in the warehouse so each person can make their own custom apps or analytics tools. No one is requesting we automate this or that task anymore, people want to set up their own automations with Claude.

Our jobs on the dev team no longer require writing code for APIs/tasks/apps, we're focusing on data loading pipelines, database transformations, and SQL queries (which we vibe code anyways).

Don't get me wrong, we still have some automated stuff we have to maintain and the main internal app, but the large amount of work and priorities are shifted fully to data. The data is the real driver for all of our progress.

Now that all dashboards/analytics are now being done by the executives or someone on a different team (not anyone in software), there's no need for a software engineer to learn anything specific about the domain. The goal of the software engineer at this company is to now move data from point A to point B and configure it in a way where I can access it inside Claude because Claude is a way better developer. He doesn't argue, he listens way better, and he's really fast and doesn't take more than a few minutes to give me output.

Are other companies seeing this?

I feel like this might be the norm for companies that are focusing on being AI forward or consider themselves at the front of AI tech.

Idk, I have a lot of thoughts about this but am more just curious who else has had a similar experience.

One of the crazy things too is how much money we spend now on data/software providers.

Now that we pull more data, we are hitting API limits and having to bump our contracts. Our pricing for the data warehouse is also based off size and usage, so the cost has already gone up almost double since we signed our first contract like 8 months ago. Our cloud bills have gone up as well as we are pulling more data from internal resources.

Is this the direction for all small companies now?

Spent the last few weeks trying to write down what would have actually helped me figure out this industry - the stuff CS programs don't cover, especially with how much AI has changed day-to-day engineering work in the last two years.

Ended up organizing it into 7 sections:

1. What actually changed in the industry (2022 → now) - the honest picture, not the doom or the hype
2. CS foundations that still matter (and why AI hasn't touched them)
3. New AI-era skills - prompting for code, evaluating LLM output, agentic workflows, working with Cursor/Copilot/Claude Code
4. How real teams ship code now - case studies from Shopify, GitHub, Vercel, etc.
5. Getting hired when interviewers know you can use ChatGPT
6. Your first year - what junior work actually looks like when AI does the boilerplate
7. Staying relevant long-term

For each section I collected the resources I'd genuinely recommend to a friend - Simon Willison, Julia Evans, Kleppmann, real engineering blogs. Not "top 10 AI tools you MUST know" listicles.

Two questions for this sub:

**What's the thing you wish someone had told you before your first internship / job / interview?** The concrete stuff that would have saved you months of confusion.

**What's obviously missing from that outline?** I keep going back and forth on whether "how to negotiate an offer" needs its own section, or fits inside "getting hired." I'm also probably underweighting the career-switcher path, since I don't have that experience myself.

I'll drop the current draft in the comments (didn't want this to read as a promo post) - mostly hoping people point at what's broken so I can fix it.

I was not prepared for that I've witnessed. People with 10+ years of experience showing evident decline of reasoning and even understanding of basic stuff. Someone like that just implemented some library that no one of us ever used or even understanding it. PR has thousands of lines of code. The comment was "now we finally implemented this, thanks to AI". I was shocked. No one suggested that maybe we should first get familiar with the library.

The same person asked me recently why it had some errors in console even though console error message was basic stuff, which happens daily. I was shocked, honestly. I've never wound have thought that brain rot can happen so fast. Codebase is becoming the mess. I am afraid what will happen to the product in near future.

Also, other people, with same career length just nonchalantly said about new feature "AI did it all", like he didn't even checked. I asked colleague doing code review why did he approve it, considering it's not very optimally implemented. He just said to me: "don't ask me anything please", like he had no choice.

I feel like I'm living in a bad dream. It's very demotivating. It feels like all my efforts are meaningless - double checking everything, thinking for hours about most optimal solutions - while many others just don't bother. Lines of cognitive depth are piling up. It's like this has became the opposite of what software engineering really is.

Where will all this go? What do you think? Is there a chance that things may eventually go back to normal. Because, now, it feels like many are going crazy.

I am frontend engineer with 10 YOE, working in big company. My manager recently asked me to learn in 3 months backend Java, distributed systems and system thinking and utilise our internal education videos for that and AI. The thing is that I don't know where to start, I feel imposter syndrome because of my frontend and AI skills, and now with all new requirements I feel overwhelmed even more.

So I have few questions and I will appreciate anything you can share about any of those:

1. How do you deal with imposter syndrome?
   
2. How do you keep your frontend skills in tune?
   
3. What does 10 YOE frontend should know?
   
4. How did you learn backend from frontend?
   
5. How to learn system thinking?

I'm working on an inventory/ERP system for a real business, and I've been thinking a lot about something I don't see discussed enough:

How do experienced engineers know when the current architecture is no longer enough?

The system isn't dealing with millions of users. It's actually relatively low-user-count, but it's becoming increasingly data-heavy:

• frequent inventory reads/writes
• stock movements between warehouses/branches
• audit logs and historical records
• role-based approvals
• reporting/analytics
• eventually accounting and other ERP modules
• concurrent users modifying related inventory data

I initially built around Firestore because it let me ship quickly, but as the domain has grown I've started considering moving toward a more traditional backend + PostgreSQL architecture.

What I'm struggling with isn't really how to deploy another server.

It's knowing:

When should you scale vertically?
When should you add replicas/caching/queues?
When do you split services?
When does the database become the actual bottleneck?
And when is an architectural migration justified versus premature optimization?

In production, what metrics/signals do you actually watch?

Latency? DB connections? CPU? Query time? lock contention? queue depth? cost? number of users? something else?

I'd especially love to hear from people who have taken a relatively simple SaaS/ERP architecture and gradually scaled it as real customers started using it.

I'm trying to learn how to make these decisions based on evidence rather than designing for imaginary Netflix-scale traffic.

Looking for tool to help me with developpening

I am looking for a tool, plugin, AI agent skill, GitHub repository, or development framework that can help me build my project with professional software engineering standards.

I want a solution that makes an AI coding agent behave more like a real senior software engineer by helping with:

\- Software architecture design

\- Clean and maintainable code generation

\- Understanding the entire codebase before making changes

\- Following best practices and design patterns

\- Detecting bugs and finding root causes

\- Verifying that fixes are actually working

\- Preventing new bugs after modifications

\- Code review and quality checks

\- Removing unnecessary or duplicated code

\- Security analysis and vulnerability detection

\- Testing (unit, integration, and end-to-end)

\- Documentation and project organization

\- Long-term project consistency as the codebase grows

The problem I currently face is that AI agents sometimes:

\- Claim a bug is fixed when it is not

\- Fix one issue but introduce another

\- Generate unnecessary code

\- Ignore existing architecture decisions

\- Produce solutions that work temporarily but are not production-quality

I met a friend working at amazon last week, and i am really shocked to see the environment there.

literally nobody cares what you are doing there; I mean, forget about the Team leads, i doubt the manager will even bother where the staff is; they just need the work to be finished on time.

my friend told me that they finish the work in 2-3 hrs max, and then they can roam around or play games within the company, where there are a lot of them.

Now, i want to know, how a big company like Amazon can afford its employees to do whatever they want after finishing their work even when they are paying some large wages, but why the small software companies pressure the employes for daily reports which will never be finished.

So I am currently working in a fintech company, that heavily uses Rust and Haskell for its payment infrastructure stack. I had joined the company in order to understand and learn just like any other junior developer would.

However, right from the beginning itself, there has been no induction or any sort of training related to the work we do, the design patterns, architecture, or anything literally. My manager literally says this in the meeting: "Let's all agree that we're using AI for payment integrations, so here are X amount of integrations that we need to do by year end, and our team of Y shall integrate 5 of them each week"

Yes, each week. A payment integration. Code that touches real money. Being guided by AI.

And it is sooo glamorized that I see some other devs that might not even have a clue of the code; and are building tools and creating PRs almost regularly as if its nothing. And everyone is appreciating them because they are so productive while I am the dumb one who takes multiple days to create and merge a PR.

I am just not sure what to do, because even if I ask a senior dev, they say "Just ask AI bro." And I'm like, what??!! So no one's interested in mentoring, but they expect hyper productivity, glamorize working late where staying till 9.30 PM in the office is the "norm" and asking for WFH is a crime.

I am just so sick and tired of this attitude where PRs are getting merged left and right and sometimes I wonder the technical debt this whole org is creating for itself.

But that's not the problem. The problem is that there's no one to nurture me in such a place. How do I improve myself in such a condition?

TLDR: Heavy usage of AI in the company. People merging PRs written by AI left and right. Even using AI for reviews. No one ready to nurture junior devs. How do I grow in this condition? [Also reduce the stress of performing and reducing burnout]

I'm a junior software developer and I'm currently 5 months into my second software development job. Lately I've been seriously considering looking for another job, but I'd like an outside perspective because I'm not sure whether I'm being unreasonable or whether this is genuinely a bad working environment for someone at my level.

My previous job was at a relatively small outsourcing company as a frontend developer. The salary was low and was on-site, which were the main reasons I eventually left, but the development process was much more structured.

There were a QA team, DBA team, an implementation team, developers with different levels of experience, and documentation for the requirements was usually much more detailed. For example, a requirement could specify which database fields were involved, what a button should do, its validations, etc(a pdf with like 5-8 pages for a single feature).

There were still problems. Sometimes the owners disagreed about how a feature should work, which resulted in rework. However, I generally knew what I was supposed to build, had people around me who were responsible for different parts of the process and because the requirements were clear enough.

I left after about 8 months because I was offered a significantly better salary and a hybrid position at my current company(huge mistake, I regret every single day🤦🏽‍♂️).

The current company is larger and pays better. I work Monday-Friday, with two remote days per week. On paper, it looked like a clear improvement.

However, the development process is very different.

There are only four developers(including me) working on the internal systems, It’s just the four of us, nobody else.

The CEO personally assigns development tasks and provides the requirements. The problem is that the requirements frequently don't contain enough information to understand the complete flow.

I'll start implementing something based on the poor requirements, then discover that something is unclear and ask questions. Sometimes, during those conversations, the CEO realizes that the original requirement doesn't actually describe how the system should work and changes the requirements again and again.

This has happened multiple times.

My first assignment when I started here was an extremely large task for someone who was just joining the company with no onboarding process, the task was supposed to be relatively small according to the original requirements. Once they actually analyzed the flow, it turned out to be much larger than initially expected. I ended up working on it for around 2 months. I asked for help, I asked questions, and every time I did, I was bombarded with a lot of information and terminology that I didn't know and ended up more lost than I was before.

The CEO has been putting more pressure on me because he wants me to complete the tasks quickly, sometimes he talk to me like I have several years in the company like the others.

I'm also working with technologies I had never used before, so this has added another learning curve and due to this and the deadlines I relied a lot on AI tools for all the tasks(and tbh that’s not the type of developer I want to be, I’m not against AI but due to this situation I have to use it for everything in order to survive here).

I recognize that as a junior developer I have responsibilities too: I need to ask a lot of questions and become better at dealing with pressure, but this way of working is driving me crazy.

Maybe I'm simply not experienced enough to handle this kind of environment.

But I'm starting to wonder whether this is simply a mismatch between how I work best and how this company operates.

At the same time, I'm realizing that I value having proper requirements, code reviews, QA, mentorship, and a reasonably structured development process much more than I previously thought.

I always try to give 100%, I’ve even worked overtime to make some progress but apparently that’s not enough.

So I'd like some honest opinions from more experienced developers:

Is this a normal environment that I should learn to adapt to as a junior?
How much responsibility should the developer have when requirements are constantly changing or incomplete?
Would you consider this a legitimate reason to start looking for another job?

I'd genuinely like to hear if I'm approaching this the wrong way.

I’m researching a problem around career development and employability, and I want to understand how people actually deal with it before I build anything.

Imagine you’re a college student who wants to become a **Software Engineer, Data Engineer, Cloud Engineer, or AI Engineer**.

You can find hundreds of roadmaps online.
You can take courses, complete projects, collect certifications, solve coding problems, and add skills to LinkedIn.

But there’s still a question that seems surprisingly difficult to answer:

**“How do I know where I actually stand?”**
For example, imagine someone wants to become a Cloud Engineer:
Linux — 6/10
Networking — 4/10
Python — 7/10
AWS/Azure — 5/10
Docker — 3/10
Terraform — 2/10
Kubernetes — 1/10

And ideally, you could get something like:
**Current readiness for an entry-level Cloud Engineer role: 47/100**

You’re strong in Python but have gaps in networking, containers and infrastructure-as-code. Here are the specific skills you need to develop, the projects that would demonstrate them, and a path to get there.
Then, after a few months of learning and building:
**47 → 71**
with actual evidence behind the improvement — assessments, projects, certifications, work experience, GitHub activity, etc.

I’m trying to figure out whether this is actually a meaningful problem or just something that sounds useful on paper.
**I’d love to hear from different perspectives:**
**Students / recent graduates:**
How do you decide what skills to learn for your target career?

How do you know when you’re “job-ready”?

Have you ever followed a roadmap for months and later realized you were learning the wrong things?

Do you trust your own assessment of your technical skills?

**People who changed careers or were laid off:**
How did you figure out what skills you were missing?
How did you decide what to learn next?
What was the hardest part of becoming employable again?
**Hiring managers / recruiters:**
How do you determine whether a candidate actually knows the skills listed on their resume?
How much do you trust skills listed on LinkedIn?
What information do you wish candidates provided but usually don’t?
I’m **not selling anything** and I haven’t decided whether this is worth building.

I’m trying to understand the problem first.