u/ustype

Some background first. I'm a software engineer from Lahore, Pakistan, with 5+ years of experience, all of it with Australian and US startups. For the last 3 years I was with an Australian startup and things were fine. Then about six months ago they shut down the Lahore office. Their reasoning was that they'd rather train their team in Australia, AI is doing a lot of the work now, and they couldn't train us too. So all of us got laid off. None of us were ready for what the market looks like right now.

In the last six months I've applied to 2,000+ jobs and got around 12-13 interviews. I couldn't convert any of them.

Money-wise I'm okay for now, with some savings and small freelance work.

I've been using this time to learn and build. I pay for two Claude Code subscriptions and one Cursor subscription, and I keep building side projects as if they were startups. I took some courses too. Two things I'm proud of: an open source repo with 1,000+ stars on GitHub and 100k+ downloads a month, and an AI app I put on the Play Store that got 8,000+ downloads in three months with zero marketing. I had to shut the app down because I couldn't keep paying the AI API costs. Recently I also started making Instagram reels. I'm terrible at video editing, so I vibe coded my own editing skills with Claude Code, and honestly they edit my videos more professionally than I ever could. That's the direction I'm trying to keep going in.

Now the reason I'm posting.

A friend referred me to a company here in Lahore, and that's how I got called to their recruitment drive. I spent about a week preparing day and night for it. The technical interview went 10/10, and I answered every single question. The coding test was basic and went well too. The last round was the managerial round with the CEO, the CTO and a manager. The manager has 15+ years in the industry.

He started with a few technical questions and things were going fine. I mentioned my open source repo, 1,000+ stars on GitHub and 100k+ downloads a month.

He asked what my biggest strength is. I said I'm a good learner, especially in tech. Even before AI, whenever something new came out or was trending on GitHub, I'd explore it and try it, and if it helped, I'd keep using it day to day. I told him I still do that, but with AI there's something new every single day, and it's really hard to keep up with the pace.

Then he asked how I use AI.

I told him I have my whole workflow set up: my own skills, my own MCP servers. When I work on a feature or a problem, I first prompt for a plan and ask the AI to ask me 10 questions about it, because that makes it really easy to catch hallucinations. I also gave my video editing tool as an example of how I use AI. I was bad at editing, I hate editing, so I built my own tool with AI and now I edit my own videos. My point was that AI can do a lot right now, and I use it to build things that solve my own problems.

Then he asked: if a person uses AI and has good analytical thinking, can he be a good engineer?

I said yes, he can be a good engineer. That's when I noticed he didn't agree with me. I could tell he wanted to hear something more, but I couldn't figure out what. He asked a few more AI questions after that.

Since I've been vibe coding a lot in the last six months, building fun projects and learning, I ended up talking a lot about that side of things. In my last role I was the one who unofficially got a Cursor subscription first and started doing things with AI before most people around me. Most of my friends weren't using it at that pace. A lot of my code was written by AI and I kept building my own solutions on top, but AI moves so fast that I can't match it either.

Looking back, maybe I should have answered differently. Something like: programming is one thing and AI is another, and a programmer should be able to debug an issue, plan, and review the system design before implementing anything. That's actually what we were doing in my last role six months ago. But AI has changed so much in those six months that I'm not sure what the industry expects anymore.

They rejected me. When I asked for feedback, all they said was "the panel rejected you." That's it.

The reason I'm taking this seriously is that this isn't the first time. I've been asked "how do you use AI?" in screening calls and technical interviews too. So I'm genuinely confused about what the right answer is right now. Is it me? Am I doing something wrong, or am I actually fine and this is just the market?

So my questions:

1. When someone asks you how you use AI in your day to day work, what do you answer?
2. For the hiring managers here: what are you actually trying to hear with that question, and what makes you reject someone?
3. Based on all this, is it me? What am I doing wrong?
4. Anything else that could help me survive this market. I'm happy to listen, even if it's harsh.

Thanks for reading this far.

Every agent I built that touched news had the same hole in it. It calls a news source, gets a set of articles, uses them, forgets. Next run it refetches, gets a slightly different set back, and has no idea it already saw two thirds of them. There was never a "what have I already read about this company" and that turned out to be the thing I actually wanted.

So I built the memory layer instead of writing that glue for a fifth time. The design decisions were less obvious than I expected, so here they are in case anyone else is stuck on the same thing.

Dedup is the entire problem. Google News hands back four or five URL variants for one article. Matching on URL fails on all of them. What worked was hashing the normalised title together with the normalised publisher. Note that Reuters and BBC covering the same event stay as two separate rows on purpose. Two outlets carrying a story is information, and collapsing them throws it away. Title normalisation also has to handle unicode apostrophe variants or you get "duplicates" that look identical to a human and different to a hash.

Store the embedding model on every row. Learned this the annoying way. Swap your embedding model six months in and the old vectors are silently in a different space, and retrieval just gets quietly worse with no error anywhere. Now every row carries the model name and dimension, so a mismatch fails loudly instead.

Recency belongs in ranking, not filtering. First version filtered to the last N days, which meant an agent asking about a company could not see the thing from five weeks ago that explained everything happening now. Now similarity gets blended with an exponential decay on age, three day half life. Old but very relevant still surfaces.

Same operations everywhere. ingest, search, timeline, brief, sentiment, stats, all exposed identically through a Python API, a CLI, and an MCP server. The MCP part changed my usage more than any prompt engineering did. Handing Claude a news memory it can query across sessions is a different experience from pasting articles into context every time.

The thing I have not solved: article revisions. Publishers rewrite headlines and bodies within the first hour, and my store keeps whatever version it caught first with no record that anything changed. Someone pointed this out to me last week and I have not found a clean answer for agent memory specifically. If you have handled it, I would like to hear how.

Storage is SQLite plus a vector store, all local. The LLM steps are optional and point at whatever provider you want including Ollama, so the memory itself runs with no keys at all.

It is open source. Putting the link in the comments per rule 3.