u/Obvious_Gap_5768

Let me give some background first.

My wife and I always wanted to build a company together. We started building and marketing some side projects on weekends and nights, nothing too serious just shipping things and seeing what worked, One of our side project was working great as well, organically reached 25k users but we weren’t satisfied enough to take it full time.

I've also worked with LLMs since 2023 and built a bunch of internal AI systems at my company over those years, including a multi-agent platform that ended up used across the company so I got promoted to senior staff along the way and ended up owning the AI architecture company wide.

Work was good but the itch to build our own thing never went away. So we started Repowise on the side. It's an open source codebase intelligence layer for AI coding agents. Your agent stops greping the same file four times and actually understands your repo: dependency graph, git history, docs, architectural decisions, and a code health score etc etc

We didnt do any outbounds just shipped it, wrote about it on our socials, and let people find it, been 3 months now.

It grew on its own. 2.7k+ stars. Around 50k downloads on PyPI. Then enterprises started reaching out, all inbound. That was the signal I needed, so I left my job to do this full time.

Still early and still terrifying but so far the user response has been exciting, We had focused mainly on perfecting the product and little bit organic marketing, Time to give it our everything now

I am working on an open source tool that scores how risky each file in a codebase is. 25 biomarkers per file, all deterministic, from the AST and git history, without LLM. I wanted to know which of those metrics actually predict bugs, so I scored every file at a point in time and counted how many bug-fixes each one collected over the next 6 months.

Did this across 21 repos and 9 languages, around 2800 files.

The metrics that predicted bugs best were behavioral, not structural. Co-change coupling, files that keep getting changed together, came out strongest. How spread out a file's ownership is and how erratic its change history is held up well too. The structural complexity metrics most teams watch, cyclomatic complexity, nesting depth, long methods, all are mid tier.

I posted an early version of this few days ago, and developer_congestion was my top signal then. That one turned out to be leakage. The metric reads recent git activity, and I was scoring files at their current state, so it was quietly counting the fix commits I was trying to predict. Moving the scoring point to before the bug window dropped it to almost nothing.

Mean AUC is around 0.74, basically 74% of times it can tell a bug inducing file from clean ones

What gets me is how much effort teams put into the complexity metrics while mostly ignoring coupling and ownership, which is what actually tracked bugs here.

Does any of this match your experience?

I'm building an open source codebase intelligence tool. One layer of it scores every file 1-10 using 15 deterministic biomarkers without LLM. Uses AST parsing via tree-sitter plus git history.

The biomarkers fall into five buckets:

Structural: brain_method, nested_complexity, bumpy_road, complex_method, large_method, complex_conditional, primitive_obsession

Duplication: dry_violation (Rabin-Karp rolling hash over tree-sitter tokens, survives variable renames)

Test coverage: untested_hotspot, coverage_gap

Organizational: developer_congestion, knowledge_loss, hidden_coupling, function_hotspot, code_age_volatility

I ran a time-travel experiment on FastAPI (104 files), Pydantic (216 files), and Django (542 files). Then score every file at time T, count bug-fix commits over the next 6 months, check correlation.

On Django: Spearman ρ = -0.34, p < 0.0001. Precision@20 = 70%, meaning 14 of the 20 worst-scoring files had real bugs in the following 6 months.

The two strongest single predictors were untested_hotspot (Cliff's delta +0.67) and developer_congestion (+0.78 in Django). Both are process signals. McCabe complexity and nesting depth ranked lower.

knowledge_loss went negative. Files where original authors left the project had fewer bugs.

My read is that stable legacy code that nobody touches doesn't break.

One thing I'm being upfront about is thatcontrolling for file size drops the correlation from ~0.3 to ~0.1. Bigger files carry more complexity and more bugs. CodeScene published a similar study claiming 15x more defects in unhealthy code but never reported this confound.

What would you add to this list? And has anyone else seen ownership metrics beat complexity in practice?

I am a Sr. Staff Data Scientist at a large public company. Good comp, good manager, good WLB. I'd been building things on the side for about a year with my wife. Open source dev tool an edtech product, and a hackathon project that got traction.

None of them replacing my salary but all of them taking more of my brain than my day job was. I submitted my resignation last week, currently serving notice.

The fear has got worse after resigning, not before. When it was a side project there was nothing at stake. Now there is. The salary comparison is brutal. I keep opening a spreadsheet that tells me I'm fine on runway and then feeling not fine anyway.

Enterprise interest exists. I've had calls with companies that have 500+ engineers, signed an NDA with one. But enterprise sales as a two-person team is a pipeline management problem I have zero muscle for. A single POC can take 8 weeks before any code runs.

The part I got right was starting while employed. Eight months of overlap where I built the core product nights and weekends. Would not have had the conviction to quit cold.

Would love any suggestions you might have, I am just feeling very anxious

I'm a Sr Staff Data Scientist at a large MNC in Bengaluru. Great CTC, a manager I genuinely like, the kind of WLB that lets you have a life outside of work. By most metrics it was the kind of job people would be happy to coast in for a few years.

At the same time I had three things running on the side.

One is an open source dev tool for AI coding agents I launched a month back. It started getting enterprise inbound faster than I expected. NDA already signed with one major mnc, POC scheduled with another, couple more in conversations. No revenue yet but things feel good.

Second is an AI interview prep platform I co-founded with my partner about 8 months back. Around 25K users now, real monthly revenue, covers DSA, system design, AI/ML, the whole stack. Not enough to retire on but enough that I stopped calling it a side project a while back.

Third is a voice AI tutor we built at a hackathon last month. 500 signups in the first couple of days. Way too early to call but the early response felt different from other things we've launched. Don't have enough time to market this so parked it aside.

The other reason this felt necessary was that my head was already somewhere else. Work was fine, I was hitting deliverables, but my real focus was going into the side stuff. Felt fairer to both sides to just pick one.

I also kept telling myself I'd quit once the time was right. After 6 months of saying that I realised the time is never actually going to feel right.

Last week I just did it, my partner really stepped in and helped me decide here.

Now I'm in notice period and I can't sleep properly. The same traction that made the decision feel obvious is now making it scarier somehow. Now there's something real to lose. Telling my parents was harder than I thought. They didn't say anything bad, but I could see their doubts.

Friends who were telling me to just do it three months ago are now asking are you sur. Atleast my partner is supportive and thats all that matters.

I keep coming back to feeling like this was the right call, but the anxiety hasn't gone down even after putting in the papers. If anything it's worse now.

Posting this here mostly to put it somewhere outside my own head. Two months till I find out if past me was an idiot or a genius.

I'm a Sr Staff Data Scientist at a large MNC in Bengaluru. Great CTC, a manager I genuinely like, the kind of WLB that lets you have a life outside of work. By most metrics on this sub it was the kind of job people would be happy to coast in for a few years.

At the same time I had three things running on the side.

One is an open source dev tool for AI coding agents I launched a month back. It started getting enterprise inbound faster than I expected. NDA already signed with one major mnc, POC scheduled with another, couple more in conversations. No revenue yet but things feel good.

Second is an AI interview prep platform I co-founded with my partner about 8 months back. Around 25K users now, real monthly revenue, covers DSA, system design, AI/ML, the whole stack. Not enough to retire on but enough that I stopped calling it a side project a while back.

Third is a voice AI tutor we built at a hackathon last month. 500 signups in the first couple of days. Way too early to call but the early response felt different from other things we've launched. Don't have enough time to market this so parked it aside.

The other reason this felt necessary was that my head was already somewhere else. Work was fine, I was hitting deliverables, but my real focus was going into the side stuff. Felt fairer to both sides to just pick one.

I also kept telling myself I'd quit once the time was right. After 6 months of saying that I realised the time is never actually going to feel right.

Last week I just did it, my partner really stepped in and helped me decide here.

Now I'm in notice period and I can't sleep properly. The same traction that made the decision feel obvious is now making it scarier somehow. Now there's something real to lose. Telling my parents was harder than I thought. They didn't say anything bad, but I could see their doubts.

Friends who were telling me to just do it three months ago are now asking are you sur. Atleast my partner is supportive and thats all that matters.

I keep coming back to feeling like this was the right call, but the anxiety hasn't gone down even after putting in the papers. If anything it's worse now.

Posting this here mostly to put it somewhere outside my own head. Two months till I find out if past me was an idiot or a genius.

Building two products full-time. Most of my day is babysitting Claude and Cursor. Prompt, wait, review diff, accept, prompt again. 10 hours of this.

Shipping more code than ever but still feel like I built nothing.

Used to love opening the editor at night, headphones on, disappearing into a problem for 4 hours straight. That flow state where you forget dinner is gone now

Opened code I wrote by hand 6 months back. Remembered every weird decision, every tradeoff. Pulled up something Ckaude shipped last month. Reads like a stranger wrote it.

Yes I can still write everything by hand. Then I ship 1 feature while the next founder ships 5. Cannot afford that as a solo guy competing for users.

I am not saying AI is bad. It is brilliant for boilerplate, refactors, migrations. But the part I actually loved is gone. Debugging a weird race condition for 3 hours until one print statement cracks it open. Writing a clean solution from scratch. Actually building something with my hands.

Anyone else feeling this?

I published repowise on PyPI a few weeks ago. It generates and maintains a wiki for your codebase, plus some git intelligence stuff like hotspots and ownership among other things

Soon after launch, three packages appeared on PyPI within hours of each other, all with the same description:

"Codebase intelligence that thinks ahead, outperforms repowise on every dimension."

Repowise is mine. They literally name it.

Looked inside the packages. They forked my AGPL-3.0 code, ran an LLM over it to fix a few small things, and republished under new names. No attribution, no license file, no source link.

Filed PyPI abuse reports. Filed a DMCA for the license violation. Sent email. Weeks in, all three packages are still live, still pulling downloads off my project's name.

PyPI's abuse flow seems to be a single form and silence. There's no copyleft enforcement path baked into the registry itself, so AGPL violations basically depend on DMCA, which is slow and easy to ignore.

Any suggestions would be very helpful

I work in AI daily. Staff Data Scientist, building multi-agent systems at enterprise scale, published at research venues. I also build products on the side. So I sit at an interesting intersection of "AI is my job" and "AI is eating my job."

Here's what I actually think plays out.

The junior dev pipeline dies first. Not junior devs. The pipeline. The "write CRUD, fix bugs, level up over 3 years" path is gone. Companies will hire fewer L1s and expect them to move faster. The entry bar got harder, not easier.

The middle gets squeezed. Engineers who are "good at implementing specs" are in trouble. That's a Claude/Cursor job now. Engineers who understand systems, make architectural calls, and can review AI output critically? More valuable than ever.

The top 10% gets a 10x multiplier. One good engineer with strong AI tooling is doing what used to take a small team. Companies will figure this out slowly, then all at once.

The Indian market specifically runs heavily on service contracts and staff augmentation. That model was already getting squeezed on margins. AI just compresses the timeline further.

The devs who thrive will be the ones who stopped thinking of themselves as coders and started thinking of themselves as builders.

Curious to hear about other people's take

TL;DR: The divide isn't AI replaces you vs. doesn't. It's people who use AI to think vs. people who use AI to avoid thinking. College is the best time to be in the first group. Build something real, go deep on one tool, learn to navigate codebases. The field is moving fast enough that a sharp fresher can outpace a complacent senior right now. That window won't stay open forever.

Let me start by saying: your anxiety is not irrational. The ground is actually shifting. I'm not going to give you the "AI is just a tool lol relax" cope.

Quick context so you know where this is coming from: 7 years in tech, senior staff level, currently leading AI initiatives at a large org. On the side I've been building and shipping things seriously: open source projects with real community traction, products with 20k+ users. I've been on the other side of GenAI hiring loops recently, so I see both ends of this, what gets built and what gets through.

Here's what I actually see.

The divide forming is not what you think

Everyone frames it as: will AI replace developers?

Wrong question.

The divide I see, in hiring, in who's getting traction, is between people who use AI to think and people who use AI to avoid thinking.

The second group is genuinely in trouble. I interview them. They can use Cursor, they can get something running, but ask them why they made an architectural choice or what happens when it breaks at scale: nothing. The AI wrote it and they don't know what it wrote.

The first group? They're shipping things I couldn't have shipped in the same time a few years ago. And a lot of them are not from pedigreed backgrounds.

What nobody tells you about enterprise AI

Most enterprise AI is a mess. Half the projects are LLM wrappers with no evals, no fallbacks, and someone's job title changed to "AI lead." People who can design agentic systems that are reliable, not just demo-able, are genuinely rare.

You don't need 5 years to understand this space better than most people in it. It's so new that a sharp 3rd-year who's been building seriously for a year can have better mental models than a 10-year veteran who started "exploring AI" last quarter.

That window won't stay open forever. But it's open right now.

The DSA question (honest answer)

Don't quit. But recalibrate why you're doing it.

The real value of DSA was never array rotations. It's learning to decompose problems and reason about tradeoffs. That's more useful than ever when you're debugging an agentic pipeline hallucinating in production.

The value of DSA as a placement filter is under pressure. Some companies are changing loops. But many haven't, and you need to clear placements to buy yourself options. Do the grind, clear the filter, just don't confuse clearing the filter with actually being good.

What I'd do if I were in college right now

1. Build something real before you graduate. Not a tutorial project. Something with users who aren't your friends. Even 10 strangers using your thing will teach you more than any course. With AI, a weekend is enough to have a rough but real product.

2. Go deep on one AI tool, not shallow on all of them. Listing 8 AI tools on a resume impresses nobody. Pick one and understand it well enough to explain its failure modes.

3. Learn to navigate a codebase, not just write one. AI writes code fast. The scarce skill is understanding why a codebase is shaped the way it is and knowing where to change things without breaking stuff. This is what separates a vibe-coder from someone a team actually wants.

4. Full-stack + LLM API calls is criminally underrated. If you can build a clean backend/frontend AND wire in a real LLM workflow, not just chat_completion but actual tool use, memory, fallbacks, you're genuinely useful at most AI startups right now. Most people don't have this. It's not a high bar.

5. Contribute to open source AI tooling. Agent frameworks, eval libraries, dev tools. People hiring in this space notice GitHub. More importantly, you'll be building alongside people who actually know what they're doing.

The uncomfortable thing

A lot of senior engineers, people at my level and above, are also scared and figuring this out in real time. "What's the right architecture for agentic systems in production?" Honest answer: we're still working it out

You're not as far behind as you think. The gap between a curious, building fresher and a complacent senior is closing faster than at any point I've seen. That's both a threat and an insane opportunity, depending on which one you decide to be.

Build something. Ship it. Let it fail. Repeat.

Good luck.