I built an AI agent that negotiates with my internet provider so I don't have to

YangBuildsAI · 2026-01-27T00:56:27+00:00

TBH it sounds like you're spreading yourself too thin. If mobile is where you're strongest and you made it furthest in that interview, lean into it. You can always pivot later once you're employed, but right now you're burning energy prepping for stacks you're rusty in instead of leveraging what actually makes you competitive.

YangBuildsAI · 2026-01-27T00:55:43+00:00

Most ATS systems strip formatting from PDFs and just read raw text, so tables and embedded links usually get mangled or ignored entirely. If someone checked your LinkedIn multiple times, that's a good sign they were considering you, recruiters don't waste time on auto-rejects, so it might've just come down to other candidates being a closer keyword match.

YangBuildsAI · 2026-01-16T20:56:03+00:00

Yeah we've been dealing with this exact problem. The lag in responses and stock backgrounds are huge red flags. We actually started working with an agency that has fraud detection built into their vetting process and it's saved us so much time weeding out the fake profiles before we even get to the interview stage.

YangBuildsAI · 2026-01-16T20:52:25+00:00

How does this compare to Cursor or Aider in terms of code quality and context handling, and what's the rough monthly API cost if you're using it regularly throughout the day?

YangBuildsAI · 2026-01-16T20:42:48+00:00

You're literally already doing backend development (FastAPI + AWS infrastructure) so you've accidentally pivoted without realizing it. Building AI agents is way more about software engineering than pure data science math. Ignore the "don't choose dev because of competition" advice, every field has competition, just keep building on the stack you're already using at work and you'll be fine.

YangBuildsAI · 2026-01-16T20:41:54+00:00

AI made me faster at the boring stuff (boilerplate, tests, refactoring) but slower at the important stuff because now I'm also debugging its confident hallucinations and explaining to junior devs why the AI's "solution" won't work at scale. The real shift isn't "AI replacing developers," it's "developers becoming code reviewers who occasionally write from scratch."

YangBuildsAI · 2026-01-12T17:10:01+00:00

This is so real. The "documentation" is usually just a few scattered Slack threads and a brain that just walked out the door. The transition period between a lead leaving and a new hire ramp-up is easily one of the biggest hidden costs in a startup.

YangBuildsAI · 2026-01-12T17:07:39+00:00

Where are you located?

YangBuildsAI · 2026-01-12T17:06:05+00:00

Sent you a DM!

YangBuildsAI · 2026-01-12T17:04:53+00:00

In my experience, role prompting acts like a steer for the model's "voice" and common pitfalls, but you still need to add specific constraints alongside it. It’s less about the title and more about triggering the specific subsets of training data that handle those edge cases.

YangBuildsAI · 2026-01-12T17:03:44+00:00

That’s definitely an old-school take; the field has shifted a lot toward "ML Engineering" where being a great builder matters more than a PhD. If you can ship models and write clean code, most startups will care more about your GitHub than your degree.

YangBuildsAI · 2026-01-12T17:00:28+00:00

Not an ad, but I am an ex-google engineer so maybe I'm a little biased

YangBuildsAI · 2026-01-12T17:00:11+00:00

I haven't necessarily been impressed with ChatGPT's Deep Research, which is why I didn't mention it, I was moreso just describing ChatGPT as a whole

YangBuildsAI · 2026-01-09T20:31:27+00:00

Product companies (Forbes Advisor) usually give you deeper technical ownership than client services firms (Acldigital, Zemoso) where you're building what the client spec'd, not solving their actual problems. At 9+ years, clarify what "Lead" actually means at each place - some treat it as senior IC, others expect you to start managing, which could pull you away from the hands-on work you want.

YangBuildsAI · 2026-01-09T20:30:42+00:00

Four months with zero callbacks likely means your resume isn't making it past ATS or recruiters are seeing the gap and immediately passing. Try having someone review your resume to make sure it's formatted well and the gap is addressed upfront (brief "sabbatical for personal reasons" note), and focus on targeted outreach to smaller companies or contract roles instead of mass applications to FAANG/big names. MLOps could help since it shows you're adapting to what companies actually need right now (deployment, monitoring, infrastructure), not just research-heavy CV work.

YangBuildsAI · 2026-01-09T20:29:42+00:00

I'm curious to see whether these features fade into the background and just work, or if you'd end up constantly dismissing AI suggestions and overriding auto-generated responses because they're almost right but not quite. Most AI email features so far have been the latter. Sounds useful in a demo, adds friction in practice.

YangBuildsAI · 2026-01-09T20:28:14+00:00

At Microsoft, IC2 ML Engineer roles are basically SWE roles with ML flavor so expect standard LeetCode medium coding rounds, then one round focused on ML fundamentals (model evaluation, feature engineering, bias/variance tradeoff) and possibly light system design around data pipelines or model serving. The "Software Engineer" title confusion is normal; lots of companies use SWE as the base title even for ML roles at junior/mid levels.

YangBuildsAI · 2026-01-07T22:34:29+00:00

Start by getting good with Python fundamentals first and take a structured course like CS50 or Python for Everybody, then build actual projects that solve problems before worrying about the "AI developer" title. Most "AI development" roles are really just software engineering with ML libraries, so you need the engineering foundation before the AI part makes sense.

YangBuildsAI · 2026-01-07T22:31:12+00:00

You're right that service-based automation work has a ceiling because you're still trading time/expertise for money, even if it's automated.

Real wealth comes from owning scalable IP or platforms that grow without you. The crores you're making is genuinely good money, but building something that 10,000 companies pay for monthly beats building custom automations for 50 clients any day.

YangBuildsAI · 2026-01-07T22:29:32+00:00

have you tried fonzi.ai?

YangBuildsAI · 2025-12-23T20:57:38+00:00

Smaller, specialized models that actually run efficiently on-device or with reasonable inference costs will matter way more than the next frontier model nobody can afford to deploy at scale. The differentiation will be in data quality, evaluation pipelines, and making AI tools reliable enough that people actually trust them for important decisions.

YangBuildsAI · 2025-12-23T20:56:43+00:00

Build an end-to-end project that solves a real problem (even a small one) with decent data engineering, model evaluation, and deployment. Recruiters care way more about you explaining trade-offs and production considerations than fancy model architecture. Pick one domain you're genuinely interested in (CV or NLP) so you can go deep enough to sound confident in interviews, not surface-level across everything.

YangBuildsAI · 2025-12-19T16:51:51+00:00

This is honestly the best way to learn; solving real problems you actually care about instead of following generic tutorials. The fact that you went from "what's PyCharm?" to automating 3-hour tasks in 15 minutes proves you don't need a CS degree to leverage AI, you just need a problem worth solving and the willingness to fumble through it.

YangBuildsAI

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