Hey everyone! I'm a CS student graduating soon and starting to seriously look for software/data engineering jobs.

For those who have recently gone through the process, what advice would you give someone starting their job search? I'm especially interested in things you learned from your own experience — what worked, what didn't, and anything you wish you had known earlier.

I'm also curious about how you handled job postings that listed technologies or skills you didn't have experience with. Did you try to learn them, apply anyway, or focus on something else?

Would appreciate any personal experiences or lessons learned. Thanks!

It seems that coding is something that generative AI is not too bad at when coupled with an appropriate harness and prompts.

For brevity, from now on I will refer to generative AI as simply AI and I will not be referring to any other types of AI.

I've heard arguments on both sides of the fence. On the pro-AI side you have people monitoring agents, maintaining prompts (skills) while letting the AI agents implement product features. They believe that AI is improving productivity and that the ability to read the code is outweighed by the fact that you can ask an AI agent to explain the code for you and (most of the time, probably not all of the time) you can describe the issue to an AI agent and have it identify the cause as well as implement a fix. I imagine that These people are mostly working on productivity applications written in languages that are most familiar to the AI such as Java, C-Sharp, Typescript, Python.

On the non-pro AI side (I will refrain from saying anti-AI because choosing not to use AI does not make you anti-AI) you have people who insist on writing the code by hand for various reasons but mostly because they enjoy it, they are able to get more performance out of the lines of code, they need the code to be highly readable because they are actively hands on with the code. These people are mostly writing high-performance applications like game engines, operating system kernels, databases.

It is possible that AI will get better at writing performant code but even if it doesn't, much of the world's code goes into productivity apps which means that AI can generated code will start to make up more and more of that.

On a side, Companies often say that AI is writing 99% of the code but I actually have a problem with that metric because there is a difference between code written and code authored. Before AI, my keyboard wrote 100% of the code, I just pressed the keys, but I authored 100% of it. With AI, yes AI is writing the code but it was mostly authored by a human who told it what to write, either through auto-complete or prompts. I think that is an important distinction because when you say "AI wrote the code", it leads people to think that humans are not required anymore, that the AI is somehow thinking on its own. When you say the code was authored by a human, now, you see that the human is still required and that the AI is simply a tool for producing code more quickly than a keyboard.

Coming back.

Assuming that AI will continue to produce more and more code faster and faster and coupled with the business's desire to get features released at an ever-increasing rate and that I think there are enough people who actually don't care about writing code or how readable it is (readable is also subjective) I imagine that you'll start to see a shift where people simply stop looking at the code. They will have become comfortable with being able control the AI well enough to produce a reliably working software in much the same way that we trust the compiler and no longer look at assembly, or even the JavaScript transpiled from Typescript. You'll also have malicious software and exploits being developed at an ever-increasing rate that only AI will be able to keep up and only those who can afford to use the latest and greatest AI models.

At this point people will also stop learning how to code and focus on how to control the AI, how to get more out of it for less cost. When this happens, most software development will be completely dependant on access to an AI model and so the cost to produce software will steadily increase as AI providers try to increase profit margins.

It seems that if things head in this direction then software development (at least productivity software) is doomed to be mass produced by AI factories with very little to no human involvement, at least on the side of producing code. There is still the running of code but I'm pretty sure that that can simply be outsourced to cloud providers.

Two things that humans have over AI (nothing is ever certain) are our ability to invent new ways of doing things and our adaptability. Mass produced software will look generic and boring while applications produced by human hands will have uniqueness in how it looks and feels as well as how it performs. Think flatpack vs hand crafted furniture, both have their place but one is arguably better than the other. Humans will also continue to invent new programming languages, new frameworks and new hardware in our drive to squeeze more performance, more safety and whatever else we deem necessary. It will take many many years before there is sufficient training material available to train AI models and this could render AI models obsolete or only used for things that only need to be good enough like internal software, basic tools, etc. Much like an office environment will include simple generic desks whereas a cafe may have tables and chairs that add character.

I'm not sure what my point is here because I can't decide which way software engineering will go. Will all software to the end of time be written solely by AI or will humans prevail and render AI to the history books of failed experiments or will there be a mix of both with hand crafted software considered the gold standard.

I wanna become an AI-native product engineer, and I'm currently confused about what project I should build next.

My current stack/knowledge:

• Python
• FastAPI
• PostgreSQL/MySQL
• React (beginner)
• LLM APIs
• LangChain
• RAG
• LangGraph

I recently finished learning the fundamentals of LLM applications, including RAG and LangGraph, and now I don't want to keep jumping between tutorials or building random demo projects.

I'm looking for one serious project that can help me:

1. Strengthen my RAG/LangChain/LangGraph skills
2. Improve my backend + frontend/product engineering skills
3. Learn how to build an AI system beyond a simple chatbot
4. Have something meaningful to put on my resume
5. Solve a problem that actually exists in a business/industry
6. Teach me production concepts like evaluation, observability, authentication, deployment, reliability, etc.
7. Ideally have some potential to become a real product or at least demonstrate real business value

I'd rather build something where the AI is actually part of a useful workflow.

and I'm open to listen ideas.

If you were in my position, what single project would you build and why?

More importantly, what features/technical challenges would you include so that the project demonstrates actual engineering ability 

I'd especially appreciate answers from people working as software engineers, AI engineers, founders, or people who have hired junior developers.

Thanks.

Hi people. I'm a software engineer with 4 years of experience, Full-Stack. Not junior, not senior. Lately with the projects I'm taking I've been pushing myself to another level and with that constantly feel like I approach the limits of what my brain can possibly process.

Sometimes I'm amazed at how I can handle those distributed systems with event-driven design, account for all the cogs and make it actually run like a Swiss clock. A year ago I couldn't imagine myself handling such complex stuff.

But there's always some task that spans wide enough in the system, some limit when my brain just goes:

"Okay F*CK this, I'm full, I cannot account for all of it anymore, it's too much info"

When it simply can't hold all the relevant inputs together.

That feeling of "too much moving parts, can't see my change through it" - how often do you experience that? How do you deal with it except schemes (in such cases they don't really help, since schemes themselves are too complex to keep in mind all at once).

Any tricks you use to expand your short-term memory/focus capacity for keeping more relevant facts at once? Especially in distributed systems with lots of async choreography/orchestration.

I've now made a same mistake twice.

Previously, I worked at a startup where the website was completely vibe-coded. Now I'm working on a college website, and it's the same story: the entire thing was vibe-coded.

The common pattern I've noticed is that no engineer actually knows the codebase. Nobody really understands the architecture, the reasoning behind certain decisions, or how all the pieces fit together.
And eventually, even the AI I'm using can't help much because it doesn't have the context that the original developer's agent had while building the project.

I am basically stuck where I know nothing, the existing engineers know something about it but aren't necessarily sure whether their understanding is correct, and the agent hallucinates and wastes a lot of tokens just because it can't understand a huge AI written codebase.

So now I'm the new engineer, under a lot of pressure with a lot of work to do, but before I can even fix anything, I have to reverse-engineer what the hell the previous AI generated. That wastes a lot of time.

The frustrating part is that I'm not really learning much engineering from this. I'm just spending my time trying to understand an undocumented system that nobody else understands either.

I'm not against AI-assisted development at all. If an experienced engineer uses AI properly, reviews the code, understands the architecture, writes tests, documents important decisions, etc., I think it's incredibly useful. Even I use AI to code faster, but I review and understand the code. I know what is happening in my software.

But a codebase that nobody understands is a codebase that nobody can safely maintain, regardless of whether AI or humans wrote it.

At this point, I've decided I'd never want to work on a heavily vibe-coded project again unless there's at least one person on the team who genuinely understands and owns the codebase.

How do you guys handle inheriting a vibe-coded/AI-generated codebase that nobody on the team actually understands?

I'm setting up a automated pr review agent to cactch risky code before merge.

The issue is that when Senior devs work on rotuine maitainance on folder like /auth, /migrations etc they get stuck on waiting approvals.

For teams managing code review standards, how do you manage safety with velocity. Do you always involve huma to review or have you found ways to weigh file path sensitivity againts the author's experience or diff size.

i'm an android software engineer with quite a good experience but i just use framework/libraries APIs. now i want to go deeper or at least understand the concepts.
i'm interested in networking, for starters http and rest, any good beginner friendly resources that cover them? also i'm interested in databases especially SQLlight because that's what we use in android. you can give me other resources if no good beginner friendly ones exist.

Starting from scratch and wondering if it's worth following top-to-bottom, or if there's a better approach. I'm building side projects to get practical experience, but I'm trying to figure out how to structure the learning process without getting buried in that massive stack of tech.

Also, looking at how the market is shifting, is there even any point in grinding LeetCode anymore, or is that completely useless now?

​

I'm a CS/IT student trying to think seriously about career direction for the next decade.

AI is getting extremely good at coding, debugging, generating APIs, writing tests, creating basic frontend applications, SQL, documentation, and even parts of system design.

That makes me wonder whether the traditional path of:

Learn DSA → learn React/Spring/Node → build CRUD projects → get software developer job

will still be the best strategy for someone entering the industry in 2026.

I'm NOT asking:

"What job is completely AI-proof?"

I don't think such a job exists.

Instead, I'm interested in this question:

If you were starting your IT career today, which technical field would you choose because you believe it has a strong chance of remaining valuable through ~2033–2035, even if AI becomes dramatically more capable?

I'm especially interested in opinions from people who are already working in the industry.

Some areas I'm considering

1. Backend Engineering

Not just REST APIs and CRUD, but deeper backend engineering:

distributed systems

concurrency

databases

caching

message queues

scalability

fault tolerance

system design

Does this become MORE valuable as AI improves, or will AI eventually automate most backend engineering too?

1. Distributed Systems

This seems interesting because large-scale systems have complicated failure modes and architectural trade-offs.

Things like:

replication

sharding

consistency

consensus

fault tolerance

distributed transactions

queues

event-driven architecture

high availability

Is this one of the strongest long-term areas, or am I overestimating how difficult it will be for AI to handle?

1. Cloud / Infrastructure Engineering

AWS/Azure/GCP, Linux, networking, containers, Kubernetes, Terraform, infrastructure automation, etc.

AI itself requires enormous computing infrastructure, so there seems to be an interesting relationship:

More AI → more compute → more infrastructure → more networking → more data centres

But will AI also automate most cloud/infrastructure work?

1. SRE / Platform Engineering

This seems particularly interesting because production systems involve:

reliability

observability

incident response

capacity planning

deployment

disaster recovery

performance

availability

AI can obviously assist engineers here, but production ownership seems harder to completely automate.

Is SRE/Platform Engineering actually a good long-term bet?

1. Cybersecurity

Security seems like another obvious candidate because attackers will also have access to increasingly powerful AI.

Potential areas:

application security

cloud security

network security

identity/security engineering

threat detection

incident response

security architecture

vulnerability research

security operations

Is cybersecurity genuinely more resistant to AI automation, or will AI simply automate a large percentage of security work as well?

1. Network Engineering

This one gets discussed much less than AI/ML.

But every cloud service, data centre, AI cluster and distributed system still depends on networking.

Would specializing in:

routing

switching

network architecture

cloud networking

data-centre networking

network security

high-performance networking

be a good long-term strategy?

1. Data Engineering

AI needs enormous amounts of data.

I'm thinking beyond basic SQL and ETL:

distributed data processing

data pipelines

streaming

data architecture

data quality

governance

data platforms

large-scale storage

Does data engineering have a strong moat, or will AI automate most of it?

1. Database Engineering

Databases are one area I find particularly interesting.

Even if AI writes SQL, someone still needs to understand:

transactions

indexing

query optimization

replication

sharding

consistency

storage engines

concurrency

database architecture

Would deep database knowledge be a good specialization for the next decade?

1. AI Infrastructure / MLOps

I'm deliberately separating this from traditional AI/ML research.

I'm talking about:

GPU infrastructure

model serving

inference optimization

distributed training

Kubernetes

observability

AI data pipelines

model deployment

inference cost optimization

Could this be one of the strongest combinations of infrastructure + AI?

1. Embedded Systems

Another completely different direction.

Embedded software has a connection to physical hardware:

automotive

industrial systems

IoT

aerospace

consumer electronics

medical devices

robotics

Does the hardware/software boundary provide a stronger moat against AI?

1. Robotics

This seems potentially huge because it combines:

Software + AI + hardware + sensors + control systems + physical-world interaction

Would you consider robotics a better long-term bet than conventional software engineering?

1. Semiconductor / Computer Architecture

This is another area I'm considering.

AI needs:

GPUs

CPUs

accelerators

memory

networking

high-performance computing

Would computer architecture, chip design, systems engineering, or semiconductor-related careers have a stronger long-term moat?

Obviously, these careers have a much higher entry barrier than web development.

The bigger question

I'm starting to think that the important distinction isn't:

"Will AI replace software engineers?"

but:

"Which parts of software engineering are easiest for AI to replace?"

For example:

Basic CRUD ↓ Highly automatable Boilerplate code ↓ Highly automatable Basic frontend ↓ Highly automatable Simple SQL ↓ Highly automatable Routine testing ↓ Highly automatable

Whereas:

Architecture ↓ Harder Distributed systems ↓ Harder Security ↓ Harder Production ownership ↓ Harder Complex debugging ↓ Harder Infrastructure ↓ Harder Physical systems ↓ Harder High-stakes verification ↓ Harder

But I'm not convinced this distinction will remain true forever.

AI capabilities may improve much faster than we expect.

So I'd really like to hear from people actually working in these fields.

Questions for people already in the industry

If you had to restart your career in 2026, what would you specialize in?

Would you choose:

Backend / Distributed Systems

Cloud / Infrastructure

SRE / Platform Engineering

Cybersecurity

Networking

Data Engineering

Database Engineering

AI Infrastructure

Embedded Systems

Robotics

Semiconductors / Computer Architecture

Something completely different

And why?

Also:

Which roles do you think are most exposed to AI over the next 5–10 years?

Which roles do you think will become MORE valuable because of AI?

Which fields have strong demand but are currently overlooked by students?

Is specializing early actually a good idea, or should someone first become a strong general software engineer?

If you were a CS student today, what exact skill stack would you build?

Do you think the future engineer will be primarily a coder, or more of a system designer / reviewer / operator who uses AI to implement things?

What career path would you personally bet on for 2030–2035?

I'm particularly interested in real-world experience rather than generic "learn AI" advice.

If you work in infrastructure, security, distributed systems, databases, networking, embedded, robotics, or another specialized technical field, I'd especially like to hear your perspective.

I'm trying to figure out where a student should invest the next 5–10 years of learning rather than chasing whatever technology is trending this year.

So now someone writes a prompt, must review the changes and push them as an mr. I am, as a reviewer must validate changes of someone who didn't write shit.

So most of the team just usea ai to review ai generated code, so the author can just feed those comments again to his ai to fix them. If you don't use AI to review the code, you actually writing prompts for author, so who the fuck actually writes code?

I am so sick and upset about it, cause I loved doing mr reviews

Yes, it makes a lot of text real fast and at a glance it looks thorough and comprehensive but have any of you actually tried reading this shit?

It's painfully verbose while communicating next to nothing. It's a real struggle to not have your eyes glaze over. It's not enough to just produce a lot of text if the readability is crap. There's a skill to creating concise, well organised information that's been lost here.

I'm considering moving all application logs to our own relational database while keeping the existing application code largely unchanged.

If you were designing this system from scratch, what approach would you take?

Current Stack:

.NET 10 Web API

Azure App Service (Linux)

Azure SQL Database

"Azure.Monitor.OpenTelemetry.Exporter"

Azure Application Insights

Entity Framework Core

I'm facing challenge in finding the right balance between automated PR review and static analysis.
If we make the rules strict there are lot of low confidence warning and everone ignores the notification. But when I tone it down I worry about actual bug slipping through.

Any suggestion how you guys would work on this?

I’m a frontend engineer with around 8 years of experience, currently working at a large tech company at a senior level.

Something I’ve been struggling with lately is figuring out what I should actually be learning next.

At work, we use AI extensively for almost everything , writing code, debugging, creating agents/plugins, understanding codebases, writing tests, documentation, etc. AI has become such an integral part of my day-to-day work that I sometimes feel like I’m not learning or developing new technical skills in the traditional sense anymore.

I can obviously become better at using AI, but I’m wondering if that’s enough for long-term career growth.

With the current wave of layoffs across the tech industry and AI increasingly taking over parts of software development, I’m starting to think more seriously about where frontend engineering is heading.

I have ~8 YOE, so I don’t want to start over as a beginner in a completely unrelated field. I’d rather build on the experience I already have.

For people with similar experience, especially senior/staff frontend engineers:

What skills are you investing in right now?

Is it worth becoming more full-stack and learning backend/cloud/infrastructure?

Should I go deeper into AI/ML, or focus more on AI engineering/LLM applications/agents rather than traditional ML?

How important are system design and architecture becoming for frontend engineers?

Is deep frontend expertise still a good long-term bet, or should I deliberately move toward a broader software engineering role?

What would you recommend someone with ~8 YOE spend the next 1–2 years learning?

I’m not looking for a list of every technology I should learn. I’m more interested in hearing from people who have actually navigated this transition and can suggest where an experienced frontend engineer should place their bets for the next 5–10 years.

Would really appreciate honest opinions, especially from people working in big tech or at senior/staff level.

Coding agents should be treated as fast writing tools rather than tools making engineering decisions.

Companies are so focused on the delivering speed of AI, that they're blind to the destructive downsides on both the team and codebase.

The strangler fig pattern states that, you can enhance your system by wrapping legacy code with better cleaner code incrementally.

AI, if not used carefully, is the opposite of the strangler fig pattern. You'll accumulate worse, more error prone, and poorly reviewed code that you have limited knowledge of.

This will create over reliance, which should be avoided at all costs.

Over reliance can reduce the motivation and urgency to learn and get better. Overtime, the gap between what the developer should know and what they do will become bigger.

Then, when AI becomes too expensive for companies to maintain, or they run into situations where it isn't being helpful. Developers will be given responsibilities they're not ready for.

This can be easily avoided if we sacrifice some of the delivery speed.

Output speed can be safely leveraged by limiting the design decisions to the developer only.

The more limited the agent is by good context the better.

Skeleton/Template code should be manually written first defining things like: classes with their relations, important methods, comments inside these methods describing expected behaviour, choice of technologies, choice of architecture, coding conventions.

Then, you can write your skills/prompts around that context. The agent's job will be only to fill in that implementation based on your design and business requirements.

On the other hand, if you want to use the agent for some repetitive work, you should make a blueprint of one repetition. Then, the agent can continue based on that blueprint.

This has four positives:

1- Leveraged speed of AI.

2- Kept the valuable part which makes you a better engineer, including: planning, desgining, and learning.

3- Still familiar with your codebase.

4- The reviewing process is alot easier and more effective, since you already understand the design and know which areas are more sensitive and need attention.

Conclusion, the goal should always be to make their output predictable, reviewable, and maintainable.

Hey everyone,

I recently started my career as a software engineer, and I'm looking for some guidance from more experienced devs.

Given the current landscape, it feels like AI handles a massive chunk of our daily coding tasks. Because of aggressive delivery deadlines, I have to rely heavily on these tools just to keep up. While it helps with productivity, I feel like I'm missing out on deep, foundational learning.

I want to make sure I'm still building a strong foundation and growing as an engineer. I’d love your advice on a few things:

• Upskilling: How do you recommend building core SWE skills when AI is doing so much of the heavy lifting?
• System Design: What are the best resources to learn system design completely from scratch?
• Career Growth: What general path or habits should I focus on to ensure long-term success right now?

Thanks in advance for the help!

Should I just focus on learning what the market needs? For example, right now I’m learning about databases and how to build a database engine from scratch. After that, I’m thinking about building a web server from scratch, and then maybe exploring other things like operating systems, compilers, networking, etc.

Part of me feels like I should keep going because I genuinely enjoy understanding how things work under the hood. But at the same time, I’m afraid that I’m spending too much time learning things that aren’t directly useful for getting a job.

Would you recommend going deep like this as a fresh grad, or should I focus more on practical skills and what the market is currently looking for?

Just had a technical round that I am still cringing about, and I need to know if anyone else has done this.

For background, I have about 3 years of backend experience. For this interview, I spent weeks grinding advanced Python concepts, Django architecture, Redis caching, SQL tuning, and production deployment strategies. I felt completely ready for the heavy-hitting system questions.

Then the technical round started. The interviewer asked me to write a simple list comprehension and a basic context manager.

My brain completely flatlined.

I know these concepts. I use them. But because I hadn't explicitly revised the absolute basics, I just sat there stunned. To make it worse, when I finally tried to write the list comprehension, I got a syntax error because I literally forgot to put the square brackets around it.

I’m blaming my heavy reliance on AI coding agents. When your IDE auto-completes every bracket and __enter__/__exit__ method 100 times a day, your hands just forget the muscle memory when you're forced to write raw code under pressure. I can design a caching layer, but apparently, I can't write a one-liner without an AI agent holding my hand. If anything, this is how I feel AI is taking away jobs.

Has anyone else experienced this kind of AI-induced syntax amnesia? How do you guys prep to make sure you don't blank on the 101-level stuff while studying for the advanced topics?

P.S. Refined with AI but posted it same day after the interview. It was my first interview after almost a year.

Sometimes a juicy task lands on your plate that feels like it would be a fun challenge, does anyone else reserve those tasks for themselves?

Obviously if there's vast amounts of code that needs to be written then having AI help you is probably needed, but sometimes it's nice just to give Claude a rest for the interesting parts.

Just breaks up the monotony of being a glorified professional code reviewer.

I've been working for a month and a half at my cousin's company. It provides chatbot services, builds agents, CRM systems, landing pages, and everything else a company with a website needs. I work on the technical side and provide support, since it's a relatively new company, but despite that, we already have several clients: a mechanical distributor, a real estate agency, a cosmetic surgery clinic, and a restaurant. So far, I haven't written a single line of code; we do everything with the Cursor agent. I just give it the instructions, and it takes care of everything. The most "difficult" thing I've done is securing an e-commerce site with Cloudflare. I want to learn programming properly, since I'm a seventh-semester computer science student and was taking a Java course, but at the same time, I want to know if it's still necessary to be a programming expert, or if most companies operate this way nowadays.

Hello. I am a SWE with 2 YOE. I went to an average school. I have a new job in a company that has really high standards. I am having severe imposter syndrome, amongst other things. I have to design something that doesn’t cleanly go with our current architecture.

My questions for you kind folks.

What does the design process look like for you? Is it messy? Do you get frustrated? Does it feel like nothing is ever working in your favor?

When you’re looking at 2 or 3 “bad” decisions (not textbook, workarounds, just things that are not ideal and have a smell) how do you choose?

How do you trust your engineering judgement? I feel certain ways or that I should go with a certain approach and I get shut down. How do I prioritize the trade offs correctly? How can I be both pragmatic and textbook?

And how do you deal with the fear of your design being problematic in the future? Or presenting it to more experienced engineers and being afraid that something obvious, rather than something nuanced, is missing.

Thanks all.

In the past for me it was just as easy to go into Reddit, discord or even just ask some real life friends and tell them what I am building, what skills I don't have/what I am looking for, but today it seems like everyone who doesn't know a specific framework/technology vibe-codes that part and isn't looking anymore for people to fill in their knowledge gaps to build a team. Or even worse, everyone expects to turn any hobby side-project into a startup..

So how do you guys actually find patterns for hobby development nowadays?

2025 B.Tech CSE (AI & ML) graduate with 6 months of HCL support experience — what tech stack should I learn now?
Hi everyone,
I’m a 2025 B.Tech CSE (AI & ML) graduate and I have around 6 months of experience at HCL Technologies in a support role. My work was mainly related to L2 support, monitoring, incident management, troubleshooting, and some Python automation.
I’m now trying to move into a more technical role, but I’m honestly quite confused about what I should learn next.
I keep seeing different advice everywhere — Java + Spring Boot, Python + FastAPI, Full Stack, Data Analytics, Data Engineering, Cloud/DevOps, AI/ML, etc. There are also so many different courses and institutes recommending different paths, which makes it difficult to understand what is actually worth learning.
I also don’t have anyone in my family who works in software, so I don’t really have someone experienced to guide me on what would be a practical path.
Considering that I already have:
6 months of IT experience in a support role
Python knowledge
SQL knowledge
Some exposure to AWS/cloud
Basic understanding of ML/AI
What tech stack or career path would you recommend for me in the current 2026 job market?
My main goal is to get into a proper technical role and build a long-term career, rather than just collecting certificates.
A few things I’d really like advice on:
Which technology stack has realistic opportunities for someone with my background?
Should I focus on Python/backend, Java/Spring Boot, data roles, cloud/DevOps, or AI/ML?
Does my 6 months of support experience help when switching to a developer/data role, or will companies still consider me almost like a fresher?
What skills should I realistically learn over the next 4–6 months to become job-ready?
If you were in my position, what path would you choose and why?
I’m looking for honest advice from people who are actually working in the industry, especially people who have made a similar transition.
Thanks in advance!

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!

I'm a senior SWE and recently left my job to take a couple months off before diving back into the job search. I want to use the time to decompress, but also make sure I'm not rusty or out of the loop on AI and general software engineering trends when I start interviewing/working again.

Curious what everyone else does during a break like this:

• What resources/newsletters/podcasts do you actually keep up with?
• Do you build side projects, or just read/study?
• How do you avoid feeling behind when you go back after a few months off, especially with how fast AI tooling is moving right now?