r/softwareengineer

I’m really into low-level programming (C, C++, x86 ASM, Rust). Since it's hard to find comprehensive video tutorials on YouTube for advanced topics, I usually resort to searching the web. However, I often find the documentation and resources quite overwhelming and hard to grasp.

​To bridge the gap, I’ve been using AI tools (like Gemini and Claude Code) to guide me through building things like a bootloader and a kernel from scratch using C and Assembly.

​While it works, I can't shake the feeling that I'm becoming a "vibe coder"—just pasting code and hoping it works without truly understanding the core concepts under the hood.

​Has anyone else been in this loop? How can I transition from relying on AI to actually understanding low-level documentation and OSDev independently? Would love some advice or resource recommendations!

I’m interested in becoming a software engineer and wanted to ask for advice from people who are already in the field or studying it.
I’m currently in my last year of university studying CIS (Computer Information Systems), and before I graduate I’d like to take a few courses that will actually help me in my career. I’m still not completely sure which specialization I want to pursue.
If you were starting from scratch, what courses would you recommend taking? Which topics or technologies do you think are essential (programming, data structures, algorithms, databases, web development, cloud, etc.)?
I’d also love to know which courses or certifications helped you the most, and which ones you’d skip if you could start over.

Started 10 months ago thinking the job was basically writing code and fixing bugs. Turns out that's like 40% of it.

Some context — I've shipped 200+ REST APIs across different projects, built a School ERP as a freelance side project (now actually used by 500+ students/staff at a school), and done a bunch of production deployments (VPS, DNS, SSL, the whole mess).

Things that actually shifted for me:

Writing code that works locally means nothing. Writing code that survives production, weird client environments, and users doing unexpected things — that's the actual job.

I gravitated toward backend/DevOps stuff way more than frontend. Something about deployments, CI/CD, infra just clicked more for me than UI work.

Client calls taught me more about "requirements" than any tutorial. What people ask for and what they actually need are often different things.

Debugged a production issue once around midnight — turned out to be one misconfigured env variable that nobody had touched in months. That one hurt but taught me a lot about how fragile "it works" can be.

Genuinely the part I enjoy most: picking up a task and watching it go from "in progress" to actually live. That feeling hasn't worn off yet.

Curious what others who're \~1 year in felt shifted the most for them — was it a specific incident, a mentor, or just time?

I have done everything people told me to do. I went to the Job Centre. They said I needed more keywords to get through the sift. I went to the university careers office. They said my CV was strong. I paid £500 for a professional CV review. They said it was basically perfect. I ran it through ATS scanners. It scores 9.2, 9.4 and sometimes 9.7 depending on the website.

I have tailored it for every role. I have added action verbs. I have added measurable impact. I have added stakeholder management. I have added cloud. I have added AI. I have added data. I have added delivery. I have added strategy. I have added resilience.

I have added every keyword I can think of because everyone keeps saying this is how you get through the first sift.

My background is:

2018 2020 Retail Assistant / Warehouse Operative

Worked in a fast-paced customer-facing environment. Built transferable skills across stock control, logistics, customer service, communication, teamwork, time management, problem solving, process improvement and working to KPIs.

2020 Python Bootcamp

Completed an 8-week Python bootcamp covering Python, data analysis, machine learning, APIs, Flask, SQL, Git, Docker, pandas, NumPy, scikit-learn, basic cloud, automation and software engineering fundamentals.

2021 Junior Data Analyst

Worked on dashboards, reporting, Excel, SQL, Python, Power BI, data cleaning, KPI tracking, business intelligence, stakeholder reporting and automation.

2022 AI Engineer AI Gym Trainer Enabled App

Worked at a startup building an AI-enabled gym trainer app. The product used computer vision and machine learning to help users improve form during workouts. I worked across Python, APIs, data processing, model testing, basic cloud deployment, user feedback, analytics and product iteration.

The startup later went under and I was made redundant.

2023 2024 AI/ML Engineer

Worked on machine learning and generative AI projects using Python, OpenAI APIs, LangChain, embeddings, vector search, RAG, FastAPI, Docker, AWS, SQL, GitHub, CI/CD, model evaluation and internal tooling.

I did fail my review at the second company. To be fair, I did not fully understand how Git worked at the time, especially branching, rebasing, pull requests and resolving merge conflicts. I was learning, but I know now that I should have understood the basics better before going into that role.

2024 2025 Senior AI/ML Engineer

Worked across generative AI, RAG pipelines, prompt engineering, LLM evaluation, data pipelines, API integrations, cloud deployment, stakeholder demos, internal automation, documentation, model monitoring and AI strategy.

My skills section now includes Python, SQL, JavaScript, Bash, AWS, Azure, Docker, Kubernetes, Terraform, Linux, Git, GitHub Actions, CI/CD, DevOps, MLOps, LLMOps, Agile, Scrum, Jira, Confluence, Power BI, Tableau, Excel, Snowflake, Databricks, Spark, Airflow, TensorFlow, PyTorch, scikit-learn, Hugging Face, OpenAI, Claude, Gemini, LangChain, LlamaIndex, Pinecone, Weaviate, Chroma, FAISS, FastAPI, Flask, Django, REST APIs, microservices, Lambda, RAG, embeddings, fine-tuning, prompt engineering, model governance, responsible AI, data engineering, data science, machine learning, deep learning, NLP, computer vision, generative AI, stakeholder management and commercial awareness.

I have also added Python packages because I was told recruiters search for specific tools: pandas, NumPy, SciPy, scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, LightGBM, Matplotlib, Seaborn, Plotly, Streamlit, FastAPI, Flask, Django, SQLAlchemy, Pydantic, Requests, BeautifulSoup, Selenium, Pytest, Jupyter, Dask, PySpark, boto3, LangChain, LlamaIndex, Transformers, SentenceTransformers, spaCy, NLTK, OpenCV, Pillow, MLflow, Airflow, Prefect, ChromaDB, Pinecone, Weaviate and FAISS.

I added networking as well because some AI roles mention infrastructure: TCP, UDP, IP, DNS, DHCP, HTTP, HTTPS, TLS, SSH, SFTP, SMTP, IMAP, WebSockets, gRPC, MQTT, VPNs, VLANs, NAT, load balancing, reverse proxies, API gateways, firewalls, OAuth2, OIDC, SAML, JWT and Zero Trust.

I added cloud because every job seems to want cloud now: EC2, S3, IAM, Lambda, ECS, EKS, CloudWatch, VPC, Route 53, RDS, DynamoDB, SQS, SNS, API Gateway, SageMaker, Bedrock, Azure Functions, Azure ML, Azure DevOps, Blob Storage, Cosmos DB, GCP Cloud Run, BigQuery, Vertex AI, Pub/Sub and Cloud Storage.

I added soft skills because I was told technical skills are not enough: communication, collaboration, adaptability, resilience, curiosity, problem solving, critical thinking, ownership, accountability, mentoring, stakeholder management, leadership potential, working at pace and being a self-starter.

I keep being told my CV is excellent. I keep being told it is ATS optimized. I keep being told I have all the right keywords. I keep being told I need to network more. I keep being told I need more projects. I keep being told I need to post more on LinkedIn. I keep being told I need to build a personal brand. I have done all of this and I am still not getting interviews.

I am now thinking about doing a Project Manager course as well, but I am not sure if that is worth it because I think a lot of those jobs will probably be automated within a year. I am trying to stay positive, but every entry-level AI role seems to want five years of production machine learning experience, Kubernetes, Terraform, AWS, stakeholder management, LLMOps, MLOps, commercial delivery, system design and the ability to explain business value to senior leadership.

I do not know what more employers want. The economy has made everything worse. Thanks Liz Truss.

Hey everyone,

​I’m looking for some career advice and resource recommendations because I’m feeling pretty stuck.

​I’ve been working as a Data Engineer for almost a year now, but honestly, I’m not finding it amusing or fulfilling at all. The biggest issue is the workload—or lack thereof. For the entire past year, I haven't been assigned any real work and haven't written a single line of code. I can feel my skills getting incredibly rusty, and it’s starting to freak me out.

​To make things more challenging, I don't come from a Computer Science background. In college, I only focused on Data Structures and Algorithms (DSA) to get through placements, so I’m missing a lot of the fundamental CS basics that a lot of peers take for granted (databases, operating systems, how systems actually talk to each other under the hood, etc.).

​Since I have a good amount of downtime at work right now, I want to use this time to fix my foundation, upskill, and prepare myself to look for better opportunities where I can actually build things.

​Could you guys recommend the best courses, roadmaps, or resources to get started with the core basics of Computer Science?

​Thanks in advance for the help!

I'm about 9 months into my first graduate software engineering role and I'm struggling to work out what my role should actually be now.

My company is extremely dependant on AI. Most engineers use Claude heavily, some to the point where entire features are implemented through agentic workflows. It's common for some seniors to have multiple Claude instances running at once, generating implementation plans, writing code, generating tests, reviewing PRs, etc.

The problem is that I'm starting to feel intellectually under-challenged and dissatisfied.

I consistently receive good feedback, complete my work to a good standard, and am trusted with important tasks. The issue is that many of those tasks can now be completed far faster than before using AI.

For example, a typical workflow might now be:

1. Understand the requirements.

2. Think through the architecture and implementation approach.

3. Have Claude generate a detailed plan.

4. Have Claude/sub-agent driven workflow to implement most of it.

5. Review, test and iterate.

The result is that I can get my work done while spending very little time in deep technical problem-solving. I'm worried that if I continue like this for several years, I'll end up with "5 years of experience" on paper but basically zero engineering growth.

I've recently had a conversation with a mentor at the company. His advice was essentially:

1. Use AI aggressively for implementation. Don't waste time manually writing code

2. Build strong foundations in architecture, DDD, system design, trade-offs, etc. Which, as a recent grad, I currently lack

3. Understand concepts deeply rather than blindly accepting AI output, and be curious.

That advice makes sense, but it leaves me with the question:

What should a junior engineer actually be doing to grow when implementation is increasingly automated?

Should I be focusing on:

Architecture and system design?

Reading books like Clean Architecture and applying the concepts?

Building personal projects?

LeetCode/interview prep?

I'm also curious whether others have experienced a similar feeling of being productive on paper (for example shipped several features) while simultaneously feeling under-stimulated intellectually.

For those of you who are senior engineers or tech leads, if you were a recent graduate entering the industry today, what would you be doing now to ensure that I grow over the next 2 to 5 years?

I’m curious about the average monthly AI token spend for developers who use AI in their development workflow.

If you’re comfortable sharing, how much do you spend per month, and roughly how many API requests do you make?

(22m) I’ve been trying to figure out what I should major in soon and thought of CS and wanting to be a SWE. Before choosing it and regretting it, I started python for beginners in my spare time. I can barely manage over an hour into it and am forcing myself to learn it as much as I can. Should I push through or is it not for me? (Never been a school guy but trying to get my life sorted)

Hey everyone,

I’m working in Generative AI and currently improving my CV for job applications. I already have a solid RAG project, so I’m looking for suggestions on other types of projects that would make my CV stronger and stand out to recruiters.

What kinds of GenAI projects do you think are most valuable for landing strong AI roles today?

Would appreciate your insights. Thanks!

I'm a developer with about 2 years of experience, and I'm curious about your perspective. Where do you see the future of software development heading? Do you think the current AI boom is a genuine long-term transformation, or is it just another tech bubble? Also, which technologies and skills do you believe will be most valuable over the next 5–10 years? I'd really appreciate hearing your thoughts and insights based on your experience.

Hi everyone,

I'm a software engineer at an early-stage product startup, and the company provides Cursor AI Ultra, which I use extensively for development, debugging, and planning. Most of the implementation is AI-generated, and my job is primarily to review, understand, and refine the code.

Over the past month, I've also learned a lot about AI-assisted development—writing better prompts, optimizing token usage, creating effective rules, using plugins, and building reusable AI workflows. While these skills have improved significantly, I feel like I'm growing more as an AI-assisted developer than as a software engineer.

Outside of work, I'm studying system design, databases, and software architecture.

My biggest concern is switching jobs in the future. Most companies still assess candidates on DSA and system design. I can continue learning system design, but I'm not strong at DSA, and AI has reduced the need for me to practice coding from scratch.

Should I spend a significant amount of time on DSA, even though I rarely use it at work? In the AI era, what should an early-career software engineer focus on to become a strong engineer and stay competitive?

Hello,

It is observed, especially among juniors, relying on AI to generate quick answers or solutions, skipping the learning process required to discover the solution. Nowadays, A beginner is able to come up with fine solutions without investing time in foundations, or spending time on difficult problems.

The modern engineering culture is centered on quick prototyping, where AI fits to generate a quick fine solution. The incentive to learn, think, and build well is degrading. Any engineer at some point adapts on the business, and probably enjoys building a hobbyist project.

Any experienced engineer knows the value of books like Database Design for Mere Mortals by Hernandez; the value of spending a long-time to understand a design pattern, or to solve an architectural trade-off.

Here is my workflow, where I try to retain good habits, while delivering on deadlines.

1. Query the LLM on the problem or question.

2. Query "Recommend foundational background" to generate fundamental information or methods, through which the LLM answered.

3. Upload personal markdown notes or a well-studied book, then query "cite relevant sections and explain their relevance".

In this way, the LLM hints familiar ideas as the key solution, and recommends new ideas one step beyond my mastered knowledge.

4. Then I attempt to answer the original question or problem in no. (1) without seeing the generated answer. Because I mastered the foundations of no (3), I can play with the generated hints very fluently to derive a new solution.

The goal is to deliver quickly, while maintaining a trace of foundations; To generate a short answer, while tracing the long-time reading and thinking.

Discussion. What about you? Did you suffer from AI brain rot? Did you face delivery expectations from the business at the expense of good engineering? How do you retain good habits alongside quick delivery? Did you use AI to become a more perfectionist engineer?

There I was, exhausted of seeing slop everyday in PRs. Being the only person in the team that raised any comments, probably the only one that cared enough.

PS: This is not a self-glazing post. I need guidance, as I am tired. And this is me sorta venting.

For the last 2 years, I've been grinding hard at a mid sized startup. Have been leading a team for a domain, contributing individually to another (all within software, mostly centred around web and cloud).

I have this habit of turning auto-assist / auto-completions off when I set my IDE up. I do use a fair amount of web based LLM conversations to understand things, learn, and solve animation code or to research the existence of modules for serverless code - but I always try to learn, and then write things. Since I have loved programming for quite a long while, I tend to prefer, what I would call, clean code.

No I didn't read a book, I'm not a prodigy per-se, but understandable variable names, caching modules, custom hooks, improving the flow and structure of code, adapting linting systems, thinking about the implications of certain blocks of code in a practical manner rather than just achieving the end result. Now, I used to think all of that is bare minimum, but then I started being the go-to guy (led the team for a while) for everything, every-single-thing. If ChatGPT couldn't answer, I would, help everyone in my team. If I didn't know, I'd just ask GPT better to teach me the required patterns, and I'd figure out the answer. Or I'd still find the solution somewhere on stackoverflow. Call me crazy but I still love that website.

Years passed, and a while ago, I decided to make the leap. I resigned. I tried figuring out some other way with the HR, they really wanted to keep me too, but things weren't gonna change. A hyper lenient reporting manager for our engineering team shields inefficiency to a level that breaks the morales of every single person tirelessly working there to improve systems, not just code. And I'm not the only one of that kind, but my kind suffers, in this team at the very least.

Lick his boots, talk of his life, praise thy lord, and then you could push the shittiest pieces of code that had active comments to prod without resolving them, and you'd still be safe.

I've about a month left, under notice, and I'm tired of applying for jobs as well. It's exhausting, as almost most of the listings are fake. My brain kinda stopped working the moment I saw the JD for the same job on one website asking for Angular and Azure, and the same Job, with the same ID, on another website asking for React and AWS.

I'm looking out for challenges, I want to work on systems. I can do things on my own. From scratch. If I don't know, I study, I learn, I experiment. I achieve results in a manner that doesn't hinder future development. And I have always helped everyone around me.

I have been holding everything in for quite a while. So here it is. Should I post my resume too? I need help finding opportunities.

Do you still find unit tests provide the same value they did a few years ago?

My experience has been that they started as useful guardrails, but over time they've become more of a maintenance burden. When implementation changes, AI usually updates or rewrites the tests along with the code, so they don't seem to catch as much as they used to. They also add a lot of context for AI to process.

Has anyone else noticed this, or am I missing something? Have your testing strategies changed because of AI?

I hope this post won't be removed.

I'm a Tech Lead.

A year ago, my developers were writing their own code.
Today, more and more PRs feel like they're 90% AI-generated.

The ticket says A, the code does B.
Business rules get ignored.
There are AI comments everywhere.
Tests exist, but don't actually test anything useful.
Random abstractions appear for problems nobody was trying to solve.

The company I work for is very pro-AI. Every time I complain about this stuff, I feel like I'm seen as the old guy not being able to live his time (I'm 28 btw).

The answer is always the same: "Well, you're the Tech Lead. Manage your team."

The problem is that AI made my developers faster, but it made me slower:

They generate code, open a PR, and move on.
I review it.
I leave 10 comments.
They ask the AI to fix the comments.
I review it again.
Half the fixes are still wrong.
Repeat.

My review time has easily tripled over the last year. And since I'm the one responsible for what goes to production, I can't just approve it and hope for the best.

What frustrates me the most is that nobody seems to count this cost. The developers save time. The Tech Leads and senior engineers pay for it.

Honestly, I'm starting to lose motivation.
I liked reviewing code written by developers.
I don't like reviewing code written by an LLM through a developer.

Has anyone else been dealing with this?
And if so, how did you get your team to understand the problem?

I am a software engineer by profession, but honestly, the main reason I got into this field was to earn money. Sometimes I feel anxious about not doing my work properly and receiving negative feedback that could affect my salary hike or career growth.

Lately, I have been wondering how I can become more involved in my work and develop a genuine interest in what I do. I really like the feeling of doing something well and knowing that I have done a good job. However, many times I have to do things that I don't particularly enjoy, even simple tasks like sweeping the floor. In those situations, I often feel irritated and resistant, and that has been bothering me.

Recently, I had a different thought. Whenever I choose to spend my time on a particular activity, person, or task, I am giving a part of my life to it. My time is one of the most valuable things I have. So if I have already decided to spend my time on something, why not fully involve myself in it and give it my best attention?

This attitude seems to encourage me. It helps me become more engaged in whatever I am doing, whether I enjoy it or not. I also feel that it is a great way to train the mind to stay focused when I want it to. Over time, it seems to strengthen my ability to use my mind consciously instead of being controlled by my likes and dislikes.

What do you think?

If you were in college right now in the final year what would you do to or learn to get the best of your placement drives?

I’ve recently seen a lot of resumes on Reddit where job seekers describe themselves as AI engineers, and I’m curious what that actually means.

Some of my friends in AI work on things like model training, hyperparameter tuning, loss function design, model deployment, and GPU inference optimization.

My own area is AI + rendering, mainly neural rendering. I use AI to approximate radiance, visibility, light transport, indirect lighting, material response, and similar rendering-related components.

That’s why I’m confused by the current use of the title “AI engineer.” A lot of resumes I see look more like backend engineering resumes. If someone is just building a RAG app, calling an API to make an AI agent, or putting together a chatbot, isn’t that something almost anyone can do at this point? Even people without a CS background can build that. So what is the actual engineering value there?

hey folks

I am primarily a mobile app developer

I have structured out a plan using my own experience and with the help of AI, the roadmap is to build an app

Dating app: It is very similar to tinder, except that users can women can earn money using the app

I will be working 2 hours per day on scrum basis, so this entire project will take me 4.7 months to complete

But i think that 4.7 months is too much time, and this entire process can be finished in under 2 months.

Please give your suggestions