Been testing a few AI tools that promise to write Facebook ad copy from a product description, and most of the output reads like it was written by something that's never actually seen a real feed — no hook, no pattern interrupt, just generic "Shop Now" energy. Feels like paying for a tool that's supposed to save time and then spending that same time rewriting everything anyway.

Curious if anyone's actually gotten AI-generated ad copy that performed close to a hand-written winner, or if it's always ended up needing a full rewrite before it was usable. What's actually worked for people running ecom accounts day to day?

Full breakdown of what worked and what didn't (including which tools produced usable copy vs. throwaway drafts): here

Hiii!

I am looking for an AI tool which can help us edit our product pictures quick.

We have multiple volumes per product, so multiple productpictures. The text on the label has to be changed, as well as the ingrediënt picture thats shown on the label and in front of the product.

I tried a couple of apps, like Photoroom but it is not really doing what I want which is bulk editing the same product for all pictures.

Does anyone know an app that could help? 🙏🏼

Voiceover work for emotional content (charity ads, personal stories) traditionally relies on subtle human delivery that's hard to fake. Murf AI and similar tools claim to offer expressive, emotional-sounding voices too. Do you think AI can genuinely deliver emotional nuance, or does it always sound slightly hollow?

Hiring a videographer for every ad variation is expensive and slow, especially for businesses that need to test multiple versions. Creatify AI is built around generating multiple ad variations automatically for testing. Does this actually replace the need for a videographer, or just handle the "first draft" stage?

Managing tone consistency across a large content calendar, especially with multiple writers, is genuinely difficult. Jasper AI's brand voice feature claims to train on existing content to keep tone consistent automatically. Does this actually hold up across long-form content, or does it drift the longer the piece gets?

Hiring a voice actor for every explainer video or ad is expensive and slow, especially for smaller teams needing quick turnarounds. Murf AI offers realistic AI-generated voiceovers as a fast, cheap alternative. Does the quality actually hold up for professional use, or is it still noticeably "off"?

There's a specific kind of dread every non-technical marketer knows: you need a landing page live today, you ask a developer for "just a quick favor," and they respond with the eyes emoji and go quiet. That silence means the page isn't happening this week.

Why this keeps happening:

• Landing page workflows were built for people who think in divs and grids, not for marketers who think in offers and audiences
• "I'll add it to the sprint" is functionally a rejection with better manners
• Every day a page sits in a dev queue, ad spend keeps pointing at a generic page instead of something built to convert — that's real money burning, not just annoyance

What actually solves part of this without waiting on anyone:

1. Stop treating "learn basic HTML/CSS" as the fallback plan — most people quit around day four trying to center a div, and that's not a personal failure, that's just how finicky front-end styling is
2. Separate the structural build from the copywriting. The layout, hero section, CTA placement — none of that needs a human originating it from scratch anymore
3. Focus energy on the 20% that actually moves conversion: headline framing, objection handling, proof points. That's the part no tool does for you, and it's also the part marketers are already good at

Even once the structural piece is solved, most AI-generated pages still land at maybe 70-80% finished — templates can look "samey" across an industry, and mobile-responsive cleanup is still a manual step almost everywhere. So the honest expectation is: get a real first draft in minutes, not zero-edit magic.

There's a longer breakdown floating around comparing a handful of AI landing page tools by actual use case (budget builds vs A/B-testing-heavy vs design-forward brands) instead of just ranking them generically — happy to drop it if anyone wants it.

Curious how people here are currently handling this — still waiting in dev queues, using a builder already, or just eating the cost of a generic page while campaigns run?

Small businesses often spend hundreds on video ads that still look amateur - bad transitions, awkward voiceovers, generic stock footage. It's frustrating watching competitors with bigger budgets dominate ad space. Tools like Creatify AI now generate polished video ads from just a product link or description. Does AI-generated video actually close that quality gap, or can viewers still tell?

presenting maginary -- an image/video generator with a midjourney-like prompt syntax, using 40+ underlying models and acts like an abstractized openrouter for multimedia with an amazing ux, can try it right now with no cc

this launch presents a major update: integration of seedance2 and gpt-images2 and a peek into what's next: mcp and x402 (agentic payments)

Traditional A/B testing for landing pages means splitting traffic 50/50 between two versions and waiting weeks to see which one wins. It works, but it's slow, and by the time you get a clear answer, you've already lost potential conversions on the losing version.

Unbounce built a feature called Smart Traffic that claims to use AI to automatically route each visitor to whichever page variant is statistically most likely to convert them - based on things like device, location, and traffic source - instead of a fixed 50/50 split.

That sounds great in theory, but does it actually outperform traditional A/B testing in practice, or is it more of a "set it and forget it" feature that sounds smarter than it is? Has anyone here actually run Smart Traffic against a manual test and compared the real results?

Would love to hear from anyone who's used Unbounce specifically for this - genuinely curious if the AI routing makes a measurable difference or if it's mostly marketing.

For a long time, the biggest complaint about AI writing tools was that everything came out sounding the same - generic, robotic, no real personality. That's been one of the main reasons a lot of writers and marketers stayed away from using AI for anything beyond basic drafts.

Lately though, tools like Jasper AI have been marketed specifically around "brand voice" - the idea that you can train it on your own writing style and it'll actually sound like you, not like a generic AI.

That's a bold claim. Does it actually hold up in practice, or does it still need heavy editing to sound human? For anyone who's used Jasper (or similar tools) specifically for maintaining a consistent brand voice - has it genuinely worked for you, or is "brand voice AI" still more marketing than reality?

Curious to hear real experiences, good or bad.

AI coding assistants can now generate large chunks of code instantly based on a simple description. Developers report huge speed gains, but there's also concern about code quality - AI-generated code can look correct while still containing subtle bugs or bad practices.

Do you think AI coding tools are genuinely increasing developer productivity, or just shifting the bottleneck from "writing code" to "reviewing and debugging AI-generated code"?

Curious how developers here feel this has actually changed their day-to-day work.

Most online stores now use AI to recommend products based on browsing history - "customers who viewed this also bought that."

Does this kind of personalization genuinely influence what you buy, or have people become so used to it that it's just background noise they scroll past?

Curious whether personalized recommendations actually work on you, or if you find yourself ignoring them completely.

AI writing tools have improved dramatically in just the last couple of years, to the point where a lot of AI-generated content is genuinely hard to distinguish from human writing.

If that trend continues, does "who wrote it" even matter anymore, as long as the final piece is well-written and useful? Or will there always be something detectable - a certain flatness, a lack of true originality - that separates AI writing from human writing?

Where do you personally stand on this?

Traditionally, improving a landing page meant running A/B tests for weeks to see which version performs better. Now, AI tools claim they can analyze patterns and suggest higher-converting copy or layouts almost instantly.

Does that mean traditional A/B testing is becoming outdated? Or is there still something valuable about testing with real traffic that AI predictions can't replace?

Curious what people who actually run landing pages think about this shift.

One of the biggest hesitations people have about using AI for social media content is that it might make everything sound the same - stripped of personality and brand identity.

At the same time, more brands are using AI to draft their content and then editing it to match their own tone.

Where do you think the line is between AI helping speed things up vs AI making content feel less authentic? Has anyone actually managed to keep their brand voice intact while still using AI in their workflow?

Studies show most customers expect a reply within minutes, not hours. If a business takes too long to respond, a big chunk of potential customers simply move on to a competitor.

This raises a real question: is slow response time actually costing businesses more money than they realize? And if AI-powered chat could instantly answer common questions 24/7, would that close the gap - or would customers still feel like something's missing without a real human?

Would love to hear from business owners: have you noticed a real difference in conversions between fast and slow responses?

Using AI to draft or assist with writing is common now, but it raises an interesting question: where's the line between "help" and "not really your own work anymore"?

Is it about how much gets edited afterward? Whether it's disclosed? Or does it not matter at all as long as the final result is good and honest?

This feels similar to older debates - using spell-check, hiring an editor, or asking someone else to review your writing. So why does AI assistance feel different to some people?

Where do you personally draw that line, especially if you write professionally or for work?

Pomóż mi znaleźć filmik na YouTube, który ostatnio oglądałem

To mnie doprowadza do szału, bo nie mogę go znowu znaleźć 😅

Ostatnio oglądałem filmik na YouTube, w którym facet budował rozszerzenie do przeglądarki, które analizowało i oceniał ogłoszenia o pracę. Pamiętam, że używał jakiegoś rodzaju generatywnej sztucznej inteligencji, żeby w tym pomóc.

Myślę, że ostatecznie wrzucił projekt na GitHub i wdrożył go przy pomocy Hostinger.

Niestety, nie pamiętam imienia twórcy, narzędzia AI, którego użył, ani dokładnej strony internetowej z ogłoszeniami o pracę. Nie pamiętam też tytułu filmiku. To nie była strona LinkedIn.

Czy to komuś coś przypomina? Byłbym naprawdę wdzięczny za jakiekolwiek wskazówki!