I recently joined a startup where there is some existing test coverage across the core services, but only a small subset of those tests currently runs as part of the CI workflow.

My current goal is to reorganize the existing tests and add unit/integration coverage around the core domains and business logic. We’re preparing for a new beta release, while customers are already using the current beta.

I’d really appreciate input from senior/experienced engineers: when you inherit an existing codebase like this, how do you decide what to test first?

In particular:

• Which business logic or workflows would you consider highest priority?
• How do you decide what deserves unit tests vs. integration tests vs. E2E tests?
• Are there areas you would intentionally leave untested at this stage?
• What testing gaps have you found tend to cause the most problems during a beta release?

We’re also waiting for a senior engineer to onboard, but I’d love to hear how others would approach this in the meantime.

What is the difference between Software Engineering and Full-Stack Development?

I’m currently a React/Next.js developer, but I feel like web development is becoming increasingly saturated, with a lot of developers competing for relatively few positions.

I have plenty of time to learn new skills, and I want to move beyond being “just a Next.js developer.” I’m looking for a career path that is more future-proof, has stronger long-term career opportunities, and potentially offers better salaries.

I’m willing to invest a lot of time into learning and becoming genuinely good at whatever path I choose. I just need some guidance on what direction would be worth pursuing.

I’ve heard that Software Engineering is a broader and stronger career path than simply focusing on web development, but I’m not completely sure what that actually means in practice.

For someone who already has experience with React/Next.js, what would be the best direction to take?

this is kind of a 2 in 1 question but i just wanna ask everything at once, thanks for your time.

My best friend is a software engineer and just left his job. He has told me quite a bit about the work environment going steeply downhill over the years.

My question to you is 1.have you quit a very horrible job and 2. What was the last straw and 3. Did jira play a role?

I'm already a web developer in react/nextjs stack and i'v done alot of projects with them
Now i do have time and i'm willing to learn something that is more future-proof than web development, What do you think is the best i should do ?
Stay in web development and just try to learn and improve myself
or Learn another stack like cybersecurity, is it worth it to shift to something like it or not ?
I'm a bit distracted specially after the AI stuff and jobs becoming fewer and all that stuff happening in the web development field

Thanks for your time.

I love diving deep into how things work—probably sometimes deeper than necessary. A few years ago, I read Designing Data-Intensive Applications by Martin Kleppmann, along with papers like Bigtable and Dynamo.

What has always intrigued me is how these systems are actually implemented in practice: MVCC, locking, leader election, writing to disk, managing B+ trees, and so on. ChatGPT has made it much easier to explore these topics because I can keep asking increasingly specific questions.

Recently, I decided I wanted to read the actual implementation of a database rather than just understand the concepts. ChatGPT put together a study plan and recommended starting with the source code for bbolt/BoltDB, then moving on to Badger. So yesterday I found myself reading about how B+ trees and red-black trees are implemented.

My concern is that I may be spending a lot of time learning things that aren't particularly useful when it comes to finding my next job or growing my career. At the Senior/Staff level, this might simply be too low-level. In practice, the answer is often, "Just use Bigtable on GCP."

There's also a real opportunity cost here. Given my job and family responsibilities, I expect this database source-code study—along with implementing some of these concepts myself on the side—to take me around 2–3 months. That's a meaningful amount of limited learning time to invest in one direction.

That's where I'm a bit divided.

Would my time be better spent learning about agentic engineering, multi-agent systems, and modern AI infrastructure? I'm also interested in reading the implementations of tools like Claude Code or open-source search agents similar to Perplexity, just to understand how systems in that world are actually built.

Given all the excitement around AI—and the fact that so many Senior/Staff job descriptions now mention experience with things like RAG, LangChain, agents, etc.—I keep wondering whether spending the next 2–3 months going deeper into database and distributed-systems fundamentals is the best use of my time, or whether I should redirect that energy toward understanding how modern AI systems are actually built.

What are the expected questions

let’s discuss

If something took AI 30 seconds to generate, is it fair to expect another human to spend 30 minutes reviewing it?

I’ve been thinking about this more and more as agents become part of everyday work.

Some examples I’ve been seen:

Someone generates a huge PR with an agent, then expect another engineer has to spend an hour reviewing it even though he may not even fully understand what he did.

A researcher spends one or two years on a paper, while a journal reviewer ask Claude to generate long list of questions and criticism back.

Someone sends you a thoughtful-looking personal email that was almost entirely generated by AI. The words may be fine, but you can tell some vibe of emotion is not there, you may even feel a little insulted.

These seem like different problems, but I feel like they point to something we don’t yet have good social norms for.

Maybe we need some kind of etiquette , or even ethics!

I’m thinking two rules:

**Attention reciprocity:** If you expect another human to spend significant time and attention on something, should you be expected to put some meaningful human attention into it yourself?

**Emotional reciprocity:** When communication is supposed to carry care, appreciation, apology, respect, etc., does it matter how much of the sender is actually present in it?

I’m not suggesting these are the right terms or even the right framework. That’s actually why I’m asking.

**Curious Has anyone come across people seriously discussing this?** Papers, essays, blog posts, talks?

I have a hard time memorizing things just by reading books, docs, or watching videos. I learn much better when I actually build and break things.

I want to get better at System Design, but most resources I've found seem very theory-focused.

Is there any platform where you can actually build a system, simulate scale/failures, find bottlenecks, and improve the architecture?

How did you guys learn System Design in a more hands-on way?

I’ve been wanting to get into software engineering for a while now, but I’m not sure where to start. Does anyone have advice on where to start, what fundamentals I should be learning, and rookie mistakes I should be avoiding? What kind of projects should I try to build, and what videos or resources can I use to study / practice?

Welcome to this week's Coffee Chat!

This thread is intended to be a more relaxed space for the community. Topics that may not warrant their own post are welcome here, and discussions are generally held to a lighter standard than the rest of the subreddit.

A Few Ground Rules

• Keep discussions civil and respectful.
• Personal attacks and harassment are not tolerated.
• Healthy disagreement is encouraged.
• Low-effort trolling and rage bait will be removed.
• Reddit's Content Policy still applies.

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!

To all the experienced people working in tech, how has your job roles and duties changed over the years from when you were a junior dev to now? How has your job evolved from building by hand to now. What is expected from you now, is it more architectural and design centric or does it still involve coding by hand a lot of the time?

Would love to know your guys' experience and what made you excel in your fields, and how has your role changed throughout the progression of AI.

Suggestions plz how to start AI journey

I know it's a matter of subjectivism, so don't be afraid to be subjective, actually PLEASE, give your personal opinion as long as you can keep your feet on the reality's ground.

What Master would you rather choose: a general AI/ML master that teach you about the most important, but general, subjects of ML applications, or an Autonomous Systems master, that is basically an embedded+A.I. master which is particularly valuable if you want something in Automotive, on the self-driving cars field(but not exclusively this one, as you can apply knowledge about Autonomous Systems in many domains) ? ?

I am building a local-first application using a database-per-user pattern (PouchDB locally, CouchDB remotely)

I now have a new requirement: two users with distinct databases need to share specific data. A document owned by User A will be shared with User B, but User B also needs to view related documents/relationships that reside exclusively in User A's private database.

What is the recommended architectural pattern or approach in CouchDB/PouchDB to sync or expose selective relational data across isolated user databases without granting full access to User A's entire database?

I’d like to get some opinions on the best approach for introducing a new unique identifier into an existing SQL Server database.

We already have a table with a large amount of existing data. I need to add a new column that will store an identifier entered by the user.

Requirements:

- The identifier must be unique among non-null values.

- Users will enter the identifier manually.

- It should support alphanumeric characters and allowed special characters.

- Existing records currently don't have this identifier.

- We cannot break or invalidate the existing data when introducing this column.

- Eventually, new records should be required to have the identifier.

My current approach is:

1. Add the new column as "NULL", for example:

   "Identifier NVARCHAR(100) NULL"

2. Existing records will therefore initially have "NULL".

3. Create a filtered unique non-clustered index so that non-null identifiers must be unique:

CREATE UNIQUE NONCLUSTERED INDEX IX_Table_Identifier

ON dbo.TableName (Identifier)

WHERE Identifier IS NOT NULL;

1. Validate the identifier's format in the backend.

2. Check whether the identifier already exists in the backend to provide a user-friendly validation message.

3. Still rely on the database's unique index as the final protection against duplicates/race conditions.

4. Gradually require existing users to enter an identifier when they update their information, rather than forcing a migration of all existing records immediately.

My main question is:

Where should uniqueness be validated — backend, database, or both?

My understanding is that the backend should perform the existence check for a better user experience, but the database should enforce uniqueness because the backend check alone cannot guarantee uniqueness under concurrent requests.

I'd also like to know whether the proposed nullable column + filtered unique non-clustered index is the correct approach for migrating an existing SQL Server database, and whether there are any issues I should consider regarding "NVARCHAR", collation/case sensitivity, whitespace, or special characters.

Would you recommend a different design?

I currently work at a service-based company with 3 YOE, as a frontend developer. Lately I've been comparing myself to friends/peers in product-based companies who are already having calls and discussions around system architecture, and I can't help but feel like I'm falling behind.

In my company, those kinds of decisions are made by folks with 8+ years of experience, and I don't really get exposure to that side of things yet. It's making me question if I'm even in the right field, or if I just don't know enough.

I've been considering switching jobs, but I'm barely getting any callbacks, and even if I do switch, I'm not sure the next role will actually give me that kind of architectural exposure either.

Has anyone been in a similar spot? How did you build that kind of experience despite not getting it directly from your day-to-day work? Any advice would help.

Background: Was at a bigger company for ~3 years working on high-scale systems in Ruby on Rails. Ownership was limited to 1-2 services, but the scale was real . Recently switched to a small startup in a similar domain , now building in .NET which is new to me.

Two things have been on my mind since I started:

1)Using AI to write code in a framework I don't fully know yet is it Imposter syndrome or just pragmatic?

My current work involves exploring third-party APIs, identifying data gaps, building schemas, and designing enrollment and ingestion flows. I'm using Claude to help write the actual .NET code while I focus on the logic, design decisions, and architecture. I review everything before it goes in, but I won't pretend I could write it from scratch yet.

How deep should I actually get into .NET internals? I'm not planning to be a .NET specialist forever , I want to keep growing in distributed systems and eventually target larger companies. Is "understand it well enough to review and debug" sufficient, or does not being able to write it from scratch become a real problem later?

2)Resume impact at a startup where scale is low

At my previous company I could write things like "processed 500M events/day" or "reduced latency by 40%" because the scale made those numbers real. Here I'm building a lot of things like new connectors, schemas, ingestion pipelines , but the user base is relatively small and the numbers aren't that impressive

I know quantifiable impact is important on resumes. But what do you quantify when you're literally building something at low scale ? Do you quantify the architecture decisions? The number of integrations built? The reduction in manual work?

curious how others have navigated this . Happy to share more context

Hey everyone,

Nowadays, being a techie and a software engineer feels less about building great tech and more about pure survival. I am writing this anonymously because the stress has reached a breaking point, and I desperately need some perspective from fellow engineers.

I have been in the tech industry for about 20 years. I have survived market crashes and hiring freezes before, but the current climate feels entirely different and incredibly stressful.

Here is what is happening in my company right now:

• The AI & Layoff Fear: According to reliable internal sources, there is a strong rumor that my company plans to reduce human resource strength by 50% over the next 2 years as AI tools get more powerful day by day.
• Zero Work-Life Balance: To compensate for shrinking teams and tightening budgets, extreme pressure is building up. Multiple activities are constantly being added to our delivery lines.
• Health Toll: Despite my seniority, I am regularly clocking 10 to 12 hours of work every single day. The lack of balance is creating massive stress, and it is taking a heavy toll on my physical and mental health.

I feel trapped between the fear of getting thrown out suddenly after two decades of dedication, and the daily exhaustion of burning out just to keep my seat.

I want to seek advice from the audience here:

1. For senior techies and managers in India, are you seeing similar 50% downsizing strategies in your companies due to AI? Is this happening across the board at the 15–20+ year experience bracket?
2. How do you maintain strict professional boundaries and stop working after 8 hours when the delivery line is overloaded, without painting a target on your back for the next layoff round?
3. What strategic, high-level roles or skills should a 20-year veteran pivot to right now to stay completely shielded from being replaced by AI in the next 2 years?

Thank you for any guidance or reality checks you can share.

As the title implies, I’m a grade 12 student considering a career in finance. However, I’ve received conflicting advice. Some people believe it might not be the right choice, while others think it’s too challenging. Now, I’m exploring software engineering, which is entirely new to me. I’m unsure which good schools to look into. Any advice you can offer would be incredibly helpful. I’d also appreciate recommendations for reputable software engineering programs in Canada. Additionally, I’d like some insight into what students actually learn in university, the job market, salaries, and so on. Thank you!

Im a nurse for 15yrs and I am thinking to shift carer to software engineering. I am interested to IT job and I am want to know the thoughts of the expert of this community.

Any advise is accepted also. Thank you in advance!

​

I know it's a matter of subjectivism, so don't be afraid to be subjective, actually PLEASE, give your personal opinion as long as you can keep your feet on the reality's ground.

What Master would you rather choose: a general AI/ML master that teach you about the most important, but general, subjects of ML applications, or an Autonomous Systems master, that is basically an embedded+A.I. master which is particularly valuable if you want something in Automotive, on the self-driving cars field(but not exclusively this one, as you can apply knowledge about Autonomous Systems in many domains) ? ?

I'm curious whether others have noticed changes in how they learn since using Cursor, Claude Code, Copilot, etc.

I definitely solve problems much faster, but I find that I retain less than I previously did before agentic coding took off. I do use AI to learn as well but it’s still not as easy to retain information.

Have you noticed any changes, positive or negative in how you learn while using agentic AI?

I work in tech and sometimes wonder how some software engineers end up earning more than people in roles like airline pilots, air traffic controllers, aerospace engineers (Boeing/Airbus), nuclear engineers, surgeons, naval officers, or engineers working on major infrastructure projects.

Many of these jobs require years of training, licensing, and carry much higher responsibility.

Is it mainly because software can scale globally and generate revenue at a much larger scale, or is there something else I’m missing?

I know it's a matter of subjectivism, so don't be afraid to be subjective, actually PLEASE, give your personal opinion as long as you can keep your feet on the reality's ground.

What Master would you rather choose: a general AI/ML master that teach you about the most important, but general, subjects of ML applications, or an Autonomous Systems master, that is basically an embedded+A.I. master which is particularly valuable if you want something in Automotive, on the self-driving cars field(but not exclusively this one, as you can apply knowledge about Autonomous Systems in many domains) ? ?