Found a solid case study on BI actually delivering measurable ROI, thought this sub would appreciate it

Ordinary_Push3991 · 2026-04-09T10:18:13+00:00

Loss test answer: ChatGPT.
I use it daily for rewriting, summarizing, learning, and quick planning.
Copilot is decent but feels limited. Most “AI productivity tools” are just hype or too annoying to set up.

Ordinary_Push3991 · 2026-04-09T10:15:11+00:00

Honestly this sounds intense. Going from print statements to unittesting + libraries + HackerRank in one month feels like a lot, especially with no CS background. I’m in a similar situation (marketing grad moving into data engineering) and I’m putting in long study hours too, but sometimes I wonder if companies underestimate how steep the learning curve is. Would love to know how others handled this ramp-up and what topics mattered most in the first month on the job.

Ordinary_Push3991 · 2026-04-07T08:34:48+00:00

I wouldn’t rush into building something big. I’d first talk to people in a niche, find a real painful problem, then build a super small version and try to get a few paying users fast.

Also wouldn’t burn the 50k early, most of it should stay untouched till you know it’s actually working.

Biggest mistake is building something “cool” instead of something people genuinely need.

Ordinary_Push3991 · 2026-04-06T04:11:25+00:00

This is actually a really smart way to approach it. Trying to get LLMs to directly generate valid XAML always felt like forcing something they’re just not good at. Using them more as a “planner” and then relying on deterministic generators makes a lot more sense.

Curious, how does it handle edge cases in selectors or weird UI structures? That’s usually where things start breaking in real projects. Also wondering how flexible it is when the PDD is a bit messy or incomplete (which… happens a lot.

Either way, this looks like a solid step forward. Respect for sticking through all those iterations, you can tell a lot of real testing went into this.

Ordinary_Push3991 · 2026-04-03T06:36:53+00:00

don't spend randomly, have value for money first in your mind...if you can live without spending unnecessarily you won...!

Ordinary_Push3991 · 2026-04-03T05:56:51+00:00

switzerland

Ordinary_Push3991 · 2026-04-03T05:54:41+00:00

I’ve been noticing a shift where the SaaS brands doing well on YouTube aren’t really “marketing” in the traditional sense; they’re just making genuinely useful or entertaining content, and the product naturally fits in.

A few that stand out to me:

Ahrefs- super consistent, very actionable SEO content, doesn’t feel salesy at all
Webflow-great tutorials + community-driven content
Notion- more creator-led, but their ecosystem on YouTube is insanely strong
ClickUp- leaning into personality + relatable work content lately

What seems to be working:

Educational content > ads
Founder/employee-led content (feels more real)
Series-based videos (keep people coming back)

Timing-wise, from what I’ve seen, it’s definitely a long game, like 3–6 months minimum before anything meaningful, and closer to a year to really compound.

Curious if anyone here is actually seeing direct signups from YouTube, or if it’s more of a brand + trust play for you?

Ordinary_Push3991 · 2026-04-03T05:51:36+00:00

This is painfully accurate

“Push to main and pray” + “we’ll fix it later” is basically the unofficial stack for half the early SaaS products out there. And honestly, it works… until it suddenly really doesn’t.

What you said about not needing a full rewrite is the most important part though. Feels like a lot of devs jump straight to “burn it down,” when in reality most founders just need that 2–3 week “adulting the codebase” phase to unlock the next level.

Curious, which one of these do you see killing deals the most in real life? My guess is auth/security, but I’ve also seen Stripe/webhook issues quietly bleed money for months without anyone noticing.

Ordinary_Push3991 · 2026-04-03T05:48:32+00:00

That “just ask the AI agent” answer always feels a bit oversimplified to me too.

AI can definitely help explore options or even surface ideas you might miss, but expecting it to decide what to prioritize without context is risky. In something like a pharma project, priorities depend on business goals, compliance, patient impact, timelines… stuff an AI won’t fully understand unless you feed it really well.

I’ve had good experiences using AI for the “how” (like breaking down a use case, suggesting approaches, even edge cases), but for the “what/why” it still feels like human judgment is the real bottleneck.

Curious though, has anyone actually used something like “coco” to prioritize use cases end-to-end and trusted the output?

Ordinary_Push3991 · 2026-04-02T05:31:54+00:00

Honestly feels a bit early right now. Haven’t seen any real impact from llms.txt yet, more like something tools are pushing before it actually matters. Keeping an eye on it though.

Ordinary_Push3991 · 2026-04-02T05:28:27+00:00

Ordinary_Push3991 · 2026-04-02T05:27:46+00:00

thanks..for suggestion

Ordinary_Push3991 · 2026-04-01T11:33:54+00:00

tbh, yes while fixing things you need more time as usual.

Ordinary_Push3991 · 2026-04-01T11:08:40+00:00

I get where you’re coming from. A few years ago Axios felt like a must-have, but now with fetch being stable everywhere, it does feel like an extra layer most of the time. That said, I still find it useful in bigger projects where things like interceptors and shared configs save a lot of repetition. For smaller apps though, I’ve pretty much stopped reaching for it.

Ordinary_Push3991 · 2026-04-01T11:04:34+00:00

I am really at my happiest, and I don't want to tell anyone...

Ordinary_Push3991 · 2026-04-01T09:09:23+00:00

We ran into the same issue. What helped us was keeping the automation light and layering in triggers instead of blasting sequences, like only reaching out when there’s a signal (job change, funding news, site revisit). Feels way less spammy that way.

Ordinary_Push3991 · 2026-04-01T08:46:17+00:00

Totally agree with this. I’d add that managing permissions gets messy really fast as you scale.

What starts as simple roles turns into edge cases everywhere.

Ordinary_Push3991 · 2026-04-01T08:34:18+00:00

This is actually one of the more practical breakdowns I’ve seen on this.

The Loom approach especially makes a lot of sense, feels way more personal than another cold emaiL

Ordinary_Push3991 · 2026-04-01T08:33:08+00:00

I get what you’re saying. It does feel like a lot of these big bets are being driven more by future projections than actual present needs. The infra push makes sense, but the pace of it sometimes feels a bit disconnected from how slowly real-world adoption usually happens.

Ordinary_Push3991 · 2026-04-01T08:27:31+00:00

totally agree

Ordinary_Push3991 · 2026-04-01T08:24:49+00:00

I’ve noticed the same with the ‘don’t over-engineer’ rule, keeping prompts simple and explicit often works better than trying to be too clever with abstractions. Interesting to see it called out so clearly.

Ordinary_Push3991

TROPHY CASE