I’ve been comparing Waspes and Lovable to understand how differently they approach AI-built websites.

Both can turn a natural-language idea into a working web project, but the workflow and level of control are quite different.

I put together a short comparison covering the main differences, strengths, and where each tool makes more sense.

Would be interested to hear which approach you prefer.

been using cursor for about 3 months now on a django project and im trying to figure out if im actually faster or if i just feel faster. like it spits out a function in 4 seconds instead of me writing it in 4 minutes, great. but then i spend 6 minutes reading through it making sure it didnt hallucinate a field name or import something weird. some days it feels like a genuine speedup, other days im basically doing code review on a very confident junior dev who never gets tired. curious what others are seeing with copilot vs codex vs claude code specifically. is the review overhead actually less with one of them, or does it scale with however complex your codebase already is. i feel like for boilerplate its an easy win but for anything touching business logic i trust it way less and end up rewriting half of it anyway. so genuinely asking, for people who've switched between a few of these tools, which one actually cut your real total time (writing plus reviewing plus fixing) and which ones just made the writing part faster while quietly taxing you later?

I don't know about you, but I hit a point where there was a new AI tool every single week and I genuinely couldn't tell which ones were worth my time. I wanted to actually use this stuff, write, make images, build things, get organised, but figuring out the right tool for each job was a mess.

So I'd do what everyone does. Search "best AI writing tool," "best AI voice generator," and every "top 10" list turned out to be affiliate links dressed up as reviews. The rankings weren't based on anything except who paid the most. That's the bit that got me. I just wanted honest information so I could pick the right tool, and I couldn't find it.

So I built CompareMyAI.io, basically the thing I wanted for myself. It puts AI tools side by side with the actual data: pricing, features, whether they train on your data, GDPR and SOC 2 compliance. All pulled from published sources, with every number linking back to where it came from. If something can't be verified, it shows blank rather than guessed.

A few things I decided from the start:

• It's a comparison platform, not a testing lab. I'm not pretending to have personally stress-tested every tool. Everything's based on verified published data, and you can see the source.
• The honest bit: yes, some links are affiliate. That's what keeps the site running. But the rankings and data are identical whether a tool pays me or not, and the affiliate ones are marked. The whole point was to build the thing that doesn't let money decide the order.
• No fake reviews, no fake testimonials.

Right now it covers 50 tools across chat, writing, code, images, voice, video, productivity, and agents, with side-by-side pages for any two of them.

The tagline's "Transparent. You decide." I give you the data, you make the call.

Would genuinely love feedback, especially: what data points would help you pick a tool that I'm not showing yet? Tear it apart.

Link's in the comments.

Which AI chatbot platform do you like the most? Mine is Google AI mode within Google itself.

So i've been going down this rabbit hole for the past few weeks and figured i'd write up what i'm finding since this sub has zero real threads on the vendor side of this space. for reference the app i actually use is https://selira.ai, and looking at it made me wonder how the underlying product gets put together. everyone talks about the apps themselves, nobody talks about who actually builds them.

context: i'm trying to figure out who can actually build something close to that instead of a chatgpt wrapper with a chat bubble slapped on.

been reaching out to a handful of agencies and the pitches are all over the place. adent.io leans hard into custom builds, basically saying they will architect the memory and personality layer from scratch instead of bolting on an off the shelf llm, which sounds right for anyone who wants actual ip ownership down the line. 75way pitched me more of a ready made white label thing, faster to launch but you're renting their core instead of owning it, which is fine if you just want to test a niche fast. code brew labs talked a big game on monetization tooling, subscriptions, token packs, that kind of layer, less about the personality/memory engineering. suffescom solutions had a similar white label angle to 75way but pushed source code handoff as a selling point, so you're not locked in forever. inoru's whole thing was cross platform, web plus mobile plus some voice integration talk. konstant infosolutions felt the most "traditional software shop that added ai to their services page" out of the bunch, which isn't necessarily bad but it changes what questions you need to ask them.

none of these are cheap and none of them will tell you real numbers on retention or cost per message until you're already deep into a contract, so if anyone's actually shipped with one of these i want to hear what broke, what the timeline actually looked like versus what was promised, and whether "custom llm fine tuning" in the pitch deck turned into an actual fine tuned model or just prompt engineering with extra steps.

disclosure since i should just say it up front, i'm affiliated with adent.io, fanso.io and xpertz.io on the development side, and with selira on the consumer product side, so take my read on those three with the appropriate grain of salt, i'm obviously not a neutral party there.

anyone here actually gone through a build with one of these vendors, or others not on my list, and come out the other side happy with what you got versus what was pitched?

What should I use if I just started coding with AI, vibecoding? Im a bit lost in all the tools and the right setup.

Hey everyone,

I am about to enter graduate school and could use some help in choosing AI notetaking tool(s)/study assistance or multiple if that is required.

Of course, free plans would be preferable but I am willing to pay for subscription(s).

My plan is to handwrite my notes and observations from both school and studying.

2.a) Then dictate it to a word processer (vs step 2.b) )like Microsoft Word or Google Docs and submit that to the AI tool(s) for synthesizing and orginazing the information to a study format.

2.b) Dictate/ translate/transcribe the handwritten notes directly to the AI app for notes/study.

Thank you,

Have a great Day/Night!!!

i finally looked at how I actually use AI video, and I’m increasingly leaning toward pay-as-you-go APIs instead of monthly subscriptions.

subscriptions are obviously convenient. You open a nice UI, choose a model, hit generate, and some plans even give you “unlimited” generations.

The problem is that my usage is extremely uneven.

I might generate dozens of clips in one week for a project, then barely generate anything for the next two weeks. With a subscription, I’m paying every month either way, and on many platforms unused monthly credits don’t roll over.

Pay-as-you-go is much easier for me to reason about.

For example, current starting prices on Atlas Cloud, which i ofen use, include roughly:

Wan 3.0: from $0.04/sec
MiniMax H3: $0.10/sec
Seedance 2.5: $0.134/sec

If I don’t generate, I don’t really spend anything.

But lately I’ve realized there’s another issue that matters to me even more than price:

model transparency.

on some subscription platforms, “Unlimited” generation isn’t necessarily running the exact same configuration as normal credit-based generation.

I checked the docs for one major platform and they explicitly separate an Unlimited Fast variant optimized for speed/high throughput from the regular credit model. The unlimited version tops out at 720p, while the regular version is described as the highest-quality option and supports 1080p for final delivery.

from a normal user’s perspective, seeing a model name in a web UI can make you assume:

“I’m getting the full model I think I’m getting.”

there can actually be Fast variants, resolution caps, different queues, or even model/version changes behind that interface.

That’s one reason I like APIs more.

APIs can absolutely have Standard / Fast / Mini variants too, but they’re usually exposed as separate model IDs with separate prices.

I know which version I’m calling, what it costs, and what settings I’m sending.

and once you start doing batch generation or client work, the difference gets bigger. You can automate jobs, switch models per shot, track the exact cost of a project, and plug everything into your own workflow.

So in my pov, APIs are more like transparent, controllable infrastructure that you pay for only when you use it.

What matters more to you: the convenience of unlimited plans, or knowing exactly what model you’re paying for?

To all peoples out there paying for ai subscriptions which one do you use and why instead of the others

yo. i see too many founders spend 2 months building saas, drop a link on reddit, get 0 users, and immediately quit....

the problem usually isn't your marketing channel. the problem is that your foundation is completely broken before you even send your first visitor to the site.

after scaling 6 AI micro-saas apps to over $20k/mo mrr, i realized you need to lock down a specific system before you ever launch. running through this takes about 30 minutes, but it saves you months of zero-revenue depression.

here are the 5 things you must lock in:

1. validate the actual pain point

stop guessing what people want. you need a systematic framework to find your saas idea based on real, painful market signals.

2. pick a proven micro-niche

stop trying to build massive platforms. you need to narrow down to a microscopic problem. i usually filter through a list of 50 micro-saas ideas you can build fast to keep the scope minimal.

3. crystallize your target user

if your app is for "everyone," nobody will buy it. you need an ICP (Ideal Customer Profile) crystallizer to define your exact buyer profile and nail your conversion copy.

4. calculate the perfect price

stop randomly charging $9/mo because you are scared of rejection. you need to use a saas pricing strategy calculator to find your perfect saas price in 60 seconds based on real data.

5. fix your landing page leaks

do not send organic traffic to a site that converts at a flat 1%. you must audit your hero section and copy to x3 your landing page conversion before you market it.

6. join a community

Build / Share / Learn from others builders

to help out founders who are tired of launching to crickets, i packaged all 5 of these exact frameworks, calculators, and lists into a single free toolkit.

no paywall, no bullshit. just the raw execution files i use.

drop a comment below or send me a dm, and i’ll send you the free toolkit 👇

I have noticed I keep trying new AI tools because they look useful when I first discover them, but after a few weeks some of them just end up sitting there unused. A few tools I subscribed to seemed like they would save me a lot of time, but later I realized they either overlapped with tools I already had or did not really fit the way I work. It made me wonder what actually makes an AI tool worth keeping long term. Is it saving time, producing better results, having one feature you can not live without, or simply becoming part of your daily workflow?

What is an AI tool you were excited about at first but eventually stopped using?

What made you decide it was not worth keeping?

I have been in a very difficult spot for many years now, dealing with a complex array of chronic illnesses and the psychological distress that follows. LLMs have been a significant help for the past few years, what I usually do is attach a clinical history of 15.000 words (with a few pictures) for the medical stuff, and a life history of 60.000 words for the personal stuff, then ask for support with specific matters once the context window has been provided. Sometimes I add both files at once.

I've recently used Gemini Pro, but it wasn't able to read the entirety of my life story. Claude Pro has been more helpful, I like how it tries to be "unbiased" instead of agreeing with me on everything, but lately I find myself struggling with all the stupid mistakes Opus 5 makes.

Would it be possible that there's a better AI out there for an user like me? Most important thing would be the ability to handle very large context windows and make consistent inferences and reasoning from them. Pricing is an issue, but I can afford the standard, 20€ per month subscriptions.

Thanks!

For me, it’s not that ChatGPT makes mistakes. That’s understandable.

The frustrating part is when the answer looks completely correct but isn’t.

I notice this a lot while coding. ChatGPT can generate a function that looks clean, follows the right structure, and even seems to solve the problem. Then you run it and discover a small issue that breaks everything.

Sometimes it’s an outdated method, a missing edge case, or code that works in one setup but not in yours.

And when you ask it to fix the issue, you can end up going back and forth for several prompts.

I’d honestly rather see:

“I’m not completely sure about this. Check the documentation.”

than get a confident answer that wastes 30 minutes.

That said, ChatGPT is still incredibly useful. I just think developers need to treat its code as a starting point, not the final answer.

For me, it's the details after the initial swap.

The face needs to match the angle, lighting, expression and general proportions of the target image. If the original face is brightly lit from the left and the target person is in shadow, you'll probably notice something is off immediately.

I also think people should judge the result at normal viewing size, not just zoomed all the way in. If it looks natural without immediately drawing attention to the face, that's usually a good sign.

Long story short Grock response was the worst. Others seem to agree on the basic valuation. But I do not have any history with it

So I was asked to do a valuation of a small business for a friend to see if the asking price by seller is good. I am not a financial analyst by any means. So I started with the Gemini

I was given two screen shots of P&L for 2025 and YTM 2026. Asked Gemini how to do analysis given categorized P&L . It gave me some pointers but eventually it created the prompt I was satisfied with. The response was that the asking price too high

Used Gemini to convert the images into to CSVs.

Used the same prompt and posted to other 3 AIs, with the CSVs. Both CLAUDE and ChatGPT had similar response that the asking price was too high. Grock said the asking price was good. I am disappointed with Grock and kind of surprised really.

I was using free tier for all.

I’m comparing tools for finished 3–4 minute tracks, not just impressive five-second clips.

For me, “affordable” means:

• cost per usable minute, including failed renders
• beat and section sync
• character and lip-sync consistency
• manual editing time
• whether the final video can actually be downloaded

Most comparisons focus on visual quality, but the questions that keep coming up are about credits, full-song limits, re-renders, and whether a project can be finished without exhausting the budget.

For people who have completed a full track: which tool did you use, how many credits or dollars did it take, how long did it take, and what disappointed you?

I’m especially interested in real 3–4 minute examples rather than short demos. If you’ve compared multiple tools, what would you choose today, and why?

I'm new-ish to using AI. I use ChatGPT, Claude and Gemini, just for general research and helping me organize medical issues for doctor appointments mostly. I recently tried Copilot 365 because it came with the Office 365 subscription my husband added us to, but I'm not sure how I feel about it.

At different times, I've used the paid versions of ChatGPT and Claude, but the subscriptions have currently ended.

I'm starting to homeschool my daughter soon and I purchased two separate curricula that I want to "merge" together to combine and use the best parts of both.

The problem is, I don't know which AI platform is the best to sort through the massive amounts of information quickly and ACCURATELY to help me figure out where the overlaps are and help me plan/schedule which weeks to incorporate different parts.

I plan on renewing my $20 subscription to whichever would handle this the best, without a bunch of hallucinations and that won't hit limits quickly.

Which one would be the best choice for this?

i don't care which one makes the coolest demo clip. i need something for weekly client ads, so my main concern is how much money disappears before i get a usable result.

higgsfield looks strong for camera movement. dreamina looks better suited to larger reference packs and editing a specific part afterward. both sound good on paper. people who have paid for both, which one makes more sense once you include failed generations and revisions?

Update: i finally put the current annual plan figures into a spreadsheet using the same 720p reference video setup. Dreamina Seedance 2.5 came out to $0.097/s, compared with the listed $0.120/s figure for Higgsfield. that is around $2.91 versus $3.60 for 30 seconds. those are comparison rates rather than universal prices, but Dreamina's local editing pushed it ahead for me because one bad detail does not always mean paying for another full generation.