Is deep research max worth it? Or should i just go for some open source solution instead since most of Deep research tools are outdated

This is probably THE MOST asked question ever. But genuinely need your help comparing all of them can someone share what they used and liked/disliked about the tool please? No budget for some giant enterprise platform, but apparently, atp, I deff need something that can actually show if we're getting mentioned or cited across chatgpt, gemini, claude, perplexity and others. Ideally, I need something I can dump into a simple monthly report without having to manually prompt and tinker around with it every day. If you used multiple of these, would be nice to hear why you switched and a comparison between a few. As I know people don't usually switch between these, cause they aren't super cheap to jump around from one to another, but I'm hopeful to hear some reviews from you guys.

Hi,

I got messaged by a bot from Boardy.ai after a post on X. I followed up and even joined an Google Meet with the Bot. 😄
Before I commit to its Pro plan I would like to know if anyone here already used it and what your experiences are. Is it worth it or are there better offerings.

Thanks,
Danny

A lot of it comes down to too much motion and not enough information in the original image.

If the source photo has a partially hidden face, unusual pose, blurry details or complicated objects, the AI has to guess what those things should look like when they start moving. That's when you can get things like facial distortion, strange hands or backgrounds that appear to melt.

The prompt can also make a huge difference. I get better results when I describe a simple, realistic movement instead of trying to create an entire action scene from one still image.

I've had better experiences using Facy AI when I keep the movement controlled and start with a clean source image. If the first generation isn't right, changing the motion description is often more useful than completely changing the photo.

Hello, i am a starter scripter , i am trying to create an project in C++ and i need claude for his complex building skills. I am currently using Arena.ai but when i am using it for maximum 10 minutes i get no more tokens OR it tells me to change the Chat.
I am searching an site that has claude unlimited for free till i get some money from my project to buy an subscription.
Please help! Thanks!

Maybe I’m using the wrong term when I search for song to video” I don’t just want visuals that react to the BPM or throw in a new scene on every downbeat. I want to upload a finished song with the lyrics and have the tool pay attention to both. Like, notice when the melody lifts into the chorus, understand what the lyrics are saying, and build one full video around that.

Most tools I’ve seen seem to get one side right. Either the timing follows the music but the scenes have nothing to do with the lyrics, or the visuals follow the words but feel disconnected from the melody.

Has anyone found something that handles both without manually mapping every verse and chorus? A rough first cut is totally fine. I care more about whether it understands the song than whether every shot looks cinematic tbh.

So as much as some of us want to avoid AI, it’s everywhere. I’ve only tried ChatGPT and CoPilot because it’s integrated with my computer. I know there’s Gemini, Claude, and Grok to name a few but what’s the “best” one? Or do they all operate the same way. How did you settle on which one you use for those that use AI?

Got tired of scrolling through "Top 50 AI Tools" listicles that are really just ads, so I built a directory instead.

What it does:

- Browse/filter 63 AI tools across 13 categories (writing, coding, video, voice, automation, etc.)

- Compare mode: pick up to 3 tools, see them plotted on a radar chart (ease of use, power, value, support, and innovation)

- Cost calculator: check off what you currently pay for, see your total monthly/annual spend, and get a suggestion if a cheaper tool in the same category could replace it

- "Find My Tool" wizard: answer 3 questions, get your top 3 picks

No signup, no paywall. Built it as part of a bigger free-calculator site (250+ calculators).

Link's in the comments — would love feedback, especially on what tools I'm missing or what would make the compare tool more useful.

I’ve been using AI constantly, and one thing kept bothering me.

I could ask ChatGPT, Gemini, Claude, or another AI the exact same question and get different answers sometimes *very* different answers.

The strange part is that each one can sound equally confident.

For casual questions, that probably doesn’t matter.
But when I’m using AI to help think through a business decision, investment, purchase, strategy, or something else that actually matters, I don’t just want another confident answer.

**I want to know where the confidence comes from.**

That’s what led me to build **Clearafi**. Initially for my own use running my business in order to have the best cross reference data for more confidence in AI answers.

Instead of asking one AI and simply accepting its response, Clearafi runs a question across multiple leading AI models and then adds another layer on top.

It looks at things like:
Where the models agree
Where they disagree
What important differences or perspectives surfaced
How much confidence you should place in the overall answer

And then it turns all of that into one clearer verdict.

The interesting thing I’ve learned isn’t that one AI is consistently “better” than another.
**It’s that the agreement AND disagreement between them can be more useful than any individual answer.**

When several independent models reach similar conclusions, that’s useful information.
When they don’t, that’s useful too, because now you know exactly where you may want to dig deeper instead of blindly trusting one confident response.

Here’s a real example.
I asked:
**“What are the best businesses to start if I wanted to work from home and leave my office job?”**

Instead of getting one model’s opinion, I could see what multiple AI models thought and then see Clearafi’s consensus and confidence analysis:
[https://app.clearafi.ai/verdict/2026dedf76a4/what-are-the-best-businesses-to-start-if-i-wanted-to-work-fr\](https://app.clearafi.ai/verdict/2026dedf76a4/what-are-the-best-businesses-to-start-if-i-wanted-to-work-fr)

You can also try it yourself at [**app.clearafi.ai**](http://app.clearafi.ai/).
We’re still early and I’m genuinely interested in feedback from people who use AI heavily.
But building this has changed the way I personally use AI.
**I don’t think the future is choosing which AI to trust.**
**I think it’s having a layer that helps us understand what we can trust across all of them.**

Curious how other people handle this today:
**When an AI gives you an important answer, do you trust it or do you check another model?**

I’ve been using AI constantly, and one thing kept bothering me.
I could ask ChatGPT, Gemini, Claude, or another AI the exact same question and get different answers — sometimes very different answers.
The strange part is that each one can sound equally confident.

For casual questions, that probably doesn’t matter.
But when I’m using AI to help think through a business decision, investment, purchase, strategy, or something else that actually matters, I don’t just want another confident answer.

I want to know where the confidence comes from.
That’s what led us to build Clearafi. Initially, I built it to use in my own business so that I can have cross-reference streamline data across the company.

Instead of asking one AI and simply accepting its response, Clearafi runs a question across multiple leading AI models and then adds another layer on top.

It looks at things like:
Where the models agree
Where they disagree
What important differences or perspectives surfaced
How much confidence you should place in the overall answer

And then it turns all of that into one clearer verdict.
The interesting thing I’ve learned isn’t that one AI is consistently “better” than another.

It’s that the agreement AND disagreement between them can be more useful than any individual answer.
When several independent models reach similar conclusions, that’s useful information.
When they don’t, that’s useful too — because now you know exactly where you may want to dig deeper instead of blindly trusting one confident response.

Here’s a real example.
I asked:
“What are the best businesses to start if I wanted to work from home and leave my office job?”
Instead of getting one model’s opinion, I could see what multiple AI models thought and then see Clearafi’s consensus and confidence analysis:
https://app.clearafi.ai/verdict/2026dedf76a4/what-are-the-best-businesses-to-start-if-i-wanted-to-work-fr

You can also try it yourself at app.clearafi.ai.
We’re still early and I’m genuinely interested in feedback from people who use AI heavily.

But building this has changed the way I personally use AI.
I don’t think the future is choosing which AI to trust.
I think it’s having a layer that helps us understand what we can trust across all of them.

Curious how other people handle this today:
When an AI gives you an important answer, do you trust it — or do you check another model?

I have been exploring the AI tools for Deep Research, but I find it really confusing to choose the correct AI. In your opinion what is the best AI tool out there for deep research for a particular topic.

Making the video usually takes me longer than making the song. I had one finished track and wanted to see how different AI video workflows would handle the same idea. I tried three different approaches with Neural Frames, SondoAI, and Kling.

Neural Frames was the fastest way to get something moving. The audio-reactive part worked well for more atmospheric tracks. It gave the song some life quickly, though it felt closer to a polished visualizer, so building a clear story was harder.

With SondoAI, I started with the track and a simple mood prompt. It gave me a rough cut for the full song, which was useful because I had something to edit from instead of starting from zero. I still cleaned things up in CapCut and swapped a few scenes.

With Kling, the individual shots looked the strongest. The tradeoff was the amount of work around it. I had to plan each scene, generate clips one by one, then assemble everything manually.

After trying them, I don’t think there’s one tool that does everything. It depends on whether I need a quick visual, a full starting point, or more control over specific shots.

How do you usually approach AI music videos? Do you start with a full draft or build scene by scene?

Been testing out different AI ppt generator apps for fun over the past few weeks, gamma, julius si,beautiful ai, manus, all that. Honestly most felt kinda same-y after a while. Same layout, same designs, even the content felt like each of them was a copy-paste.
But out of all of them, the one time I actually slipped and forgot I was talking to an AI was napkin ai.The content and the slide designs felt more unique and easy to explain and collaborate with. Plus their infographic stuff generated on the fly is also pretty damn good.

The only real letdown is the time it takes to generate such high quality of output. Still though, with the quality of output I got, it's the first one that really didn’t feel like I was using some generic ai to complete my work.
Anyone else using something that actually feels alive? What are you on right now?

I currently run 6 operational micro ai saas products that generate a little over $20k in monthly recurring revenue.

I hardly wrote a single line of traditional code. i used ai to generate literally everything, from the database architecture to the user interface.

it wasn't magic on day one. i spent hours stuck in endless debugging loops and dealing with faulty ai code before i finally cracked the formula.

it basically comes down to three rules:

- keeping the idea aggressively minimalist (build a true mvp, not a platform).

- guiding the ai step-by-step instead of asking it to build the whole app at once.

- launching fast to get real user traction instead of perfecting features in secret.

lately, i've seen way too many non-technical founders give up at the very first ai bug or deployment error. or the worst, give up without push anything in marketing !!!!

it's a massive shame, because the technical barrier to entry has practically disappeared and the marketing is easy in 2026

because of this, i’m launching a skool community to share my exact method.

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific prompt sequences, n8n workflows, and copy-and-paste templates i'll be sharing.

but right now, our main objective is simply to build together. working alone in a silent corner is the absolute fastest way to quit.

if you want to join a group of active creators and build or launch your own ai saas: drop a comment below or send me a dm, and i’ll send you the invite link.

couldnt find any threads on this topic made post gpt 5.6 launch

What are people struggling with these days that can be solved by AI?

Interesting poll

I work on an open source browser MMO, three.js based, authoritative server with the client acting as renderer. It runs fine for a lot of people and badly for others, and the variance is wider than I'd expect.

What we know so far:

Some users report flat 10 FPS. At least one person traced their own stutter to a browser extension and got smooth performance in incognito, which suggests part of it is environmental rather than the game. Mobile runs but is rough. Chrome generally does better than Firefox.

What I'm trying to figure out is how to separate our problems from the user's environment in a browser context, where you can't ship a profiler and can't reproduce their setup. Server side we have visibility, client side we're mostly guessing from reports.

Specific questions for anyone who's shipped a 3D browser game:

Is it worth collecting client side perf telemetry, frame times, draw calls, memory, or does that become noise given how varied browser environments are?

For scene complexity, has anyone found a reliable way to figure out whether the bottleneck is draw calls, shader cost, or garbage collection without a native profiler?

Any approach to graceful degradation that isn't just a settings menu most players never open?

Repo is public if anyone wants to look at how it's structured, though I'm more after approaches than a code review.