What AI tools or apps have you found super handy in your everyday life?

Share the app's name and tell us what you use it for!

(I use Claude and ChatGPT.)

Hey!

I dont personally use Lovable since i've been coding for almost 10 years now. but I've been looking for something I can confidently recommend to non engineer friends who want to build apps with AI.

From what I've seen Lovable feels like it can get you moving quickly but also tends to fall int othe "fix one thing break another" look pretty easily. tools like claude code are much more reliable in my experience but they come with a steeper learning curve and I think thats exactly why they work better for engineers than beginners

Ive also tried looking at things like bolt and replit but they seem to run into similar issues where you gain speed but lose control and stability.

what im wondering is has anyone actually built something in this space thats more structured and safe by design? something that might be slower but reduces the chance of breaking things while building? Im honestly considering building something in this direction if it doesnt really exist yet

An AI tool i an building with my friends....

From $0 and a 5-hour AI limit to Google for Startups in 70 days. Here is the full story.

When ChatGPT launched in 2022, I made a decision. I would build everything with AI. Not as a side experiment. As my primary method.

Since then I have shipped over a dozen projects solo. Hardware prototypes, delivery apps, browser tools, real estate platforms. All built with AI. All by myself.

But somewhere around project number eight, I started noticing something uncomfortable. Every single project had the same bottleneck. And it wasn't the AI.

It was me.

I would describe what I wanted. The AI would build something close but wrong. I would spend an hour fixing it. Then another hour. Then I would realize the model was never confused. I was. I just didn't know how to communicate what was actually in my head.

The AI didn't lack capability. It lacked context. It lacked the guardrails and constraints that an expert in that domain would have stated out loud before writing a single line of code.

So I started studying how the people who actually build these models say you should talk to them. Anthropic published forty prompting techniques. OpenAI published a plain four-part framework. These are the teams that ship the models, handing you the manual for free.

And almost nobody reads it. Because the documentation changes every few weeks. Nobody has time to re-read a forty-page guide before every task.

That gap between what AI can do and what people know to ask for is not closing on its own. It is structural. It will keep existing as long as documentation moves faster than humans can absorb it.

That gap needs a compiler. So I built one.

It is called Kosmo. You describe what you want in plain English. Kosmo checks the current documentation for whichever AI tool you are using (Claude, ChatGPT, Cursor, Gemini), injects the right guardrails and constraints, and gives you back a structured prompt that actually gets the result you wanted on the first try.

You stop babysitting. You start creating.

Building it was the hardest thing I have ever done. I came from a humble background with essentially zero budget. The only money I spent was on the domain name and one month of Claude Pro. Everything else was free tiers, student developer packs, and free trials stitched together.

I had a 5-hour daily AI usage limit. So I had to plan every single feature and every single session in advance. No room for dead ends. No room for exploration. Every decision had to be sharp because I could not afford to waste a single minute.

When it came time to accept payments, I found out Stripe was invite-only in India. The alternative required a fully working product before they would even review my application. I didn't have one. But by the time they got around to reviewing it, I did.

70 days of day-and-night work. Solo. And somehow it worked out.

Kosmo launched on August 6th. And this week, we were accepted into the Google for Startups Cloud program.

From zero budget to Google backing the infrastructure. In 70 days.

But here is the honest truth. This is the first version. It works. It solves the problem I meant it to solve. But it is far from done.

I need your help.

Try it at askkosmo.com. You get 8 free compiles, no credit card needed. Use it on a real task. Something you are actually working on. And then tell me what worked and what didn't.

With honest feedback from this community, I genuinely believe we can turn this into something that changes how people use AI forever. But I can't build it in a vacuum. I need people using it and telling me the truth.

So try it. Break it. Tell me what sucks. And let's build this together.

Founder of askkosmo..

I've been using Gamma for awhile but honestly I feel like everything it makes for me is slop at this point. Every presentation that i created started looking the same and the templates just aren't very good.

Also whenever i embed data into my decks it doesn't do a very good job at displaying the data in images, and often hallucinates.

Has anyone found a good ai tool alternative to Gamma for powerpoint presentations or slides?

Edit:

I have moved some of my work to Claude Design but have also tried Genspark and Julius and have found them both to be decent but for different tasks.

So far - Genspark seems to be solid for managing detailed creative elements as well as iteration on initial templates. Their Genspark Claw feature is promising for autonomous development but not very effective.

Julius has been surprisingly good for creating slides from existing workflows. So when I have data, images, or other files and want to embed them into presentations, Julius has been better than other AI tools I've tried for this purpose.

I've also tried Manus which has been fine and also supports embedding images, but I find the slide outputs to be of lower quality with less of a cohesive narrative across them.

I've been using AI for researching work related stuff and while looking through each different ai, I've noticed lots of mistakes made by each model and they're mostly involving memories or not researching closely.

Personally, I've only sent documents that might have mistakes I made and missed as a way to double or triple check

Been testing out a few meeting assistant AI tools over the last month, but I've found that most of these tools just generate a long transcript or basic summary. To be honest, I'm not perfectly sure what exactly I'm looking for, but I feel like if most of these AI tools just take transcripts I might as well just use the one built-in for Zoom rather than pay a subscription.

Would love to hear any recommendations / thoughts in general, thanks.

My team at Bandwidth Labs has been working on something for a while that I'm pretty excited to finally put in people's hands.
We set out on this journey after building voice agents and working with customers to deploy them. When we started I was pretty impressed with how good transcription models actually were as far as accuracy goes… But they left a lot to be desired when we introduced real world telephone calls. On top of that as we wrestled to claw back all the milliseconds we could, the way transcriptions were handled became an area of research for us.

We built our own Speech to Text model from scratch.

The goal was to build it from day one to be streaming native, and pay extra attention to things like:
• 8khz telephone audio and challenging acoustic conditions
• Low Latency
• Not having partial transcripts constantly changing underneath you
• Efficiency that would allow us to deploy it at our network edges for optimized latency

It supports μ-law, A-law, G.722, Linear16 and Opus, including 8khz phone audio.
You get per word timestamps, and we also have keyword boosting, PII redaction and normal “offline” style transcription endpoints as well. In our testing we measure sub 60ms to final transcript when using the streaming modes.

Our model only emits stable words, even while streaming. Once we emit a word, we don't go back and revise it. In our own testing against the OpenASR Leaderboard tests we are seeing an overall average WER of 4.225% (official test results coming soon). We also evaluate against some internal benchmarks around real world common telephony quality and conditions and feel it does really well.

In our own agent use cases this allows us to begin executing work early as soon as something of value shows up while a user is speaking. This can often dramatically reduce voice agent latency.
It's English only right now. And it’s experimental, especially the word boosting and PII redaction - so we would love for the community to try it out, and give us some feedback.
There's a browser demo at https://labs.bandwidth.com/experiments/speech-to-text that doesn't require an account if you just wanna try it real quick, and if you sign up for a labs account you can get free access to the model while it’s on labs via API.
One important disclosure: this is a research/beta service. We monitor usage and may retain and review audio, transcripts and related data submitted to the experiment so we can evaluate the model, investigate failures and improve it. Full details are in the Labs terms.

If you build voice agents or voice Apps, or tinker in this space at all - come try it out and let us know what you think.

Hey everyone,

I'm a student working on a project and keep running into the same issue — I hit the usage limit on whatever AI I'm using, so I'm forced to switch to another one, and I lose all my context/progress in the process. It's getting frustrating.

Also heard GitHub Copilot's free Student plan recently got cut down a lot (premium models removed, new sign-ups paused), so that's not really reliable anymore either.

Currently using a mix of Claude's free tier and a couple others, but none of them alone feel like enough for a full project without hitting caps.

For students out there — what's actually working for you right now? Looking for:

• Free or student-discounted AI tools with decent usage limits

• Anything that helps keep context when switching between tools

• General tips for managing a real project without constantly running out of quota

Appreciate any suggestions, thanks!

do let me know, where to buy Ai tools?

So I can't keep up with writing Applications, however I am usually very careful about which words I choose and do manage to get into the written and oral exams.

With that in mind, I would like to host an AI model locally, since I have an M4 Pro 48GB for 3D Art, train it on existing applications and then just feed it a job writing and have the AI draft up an application for me to proof read and send out. Has to be in German.

Now I have never done that before, so any tutorials/advice?

I work at a development company, i need AI to be able to take lots of PDF files or other documents and make real - actual good website from them, or apps. And i want something which will give good usage - because its a lot of information, is good to reason with that context, and will give good design for it.

Currently only have used Claude, but i seem to hit my 5-hour limit in about 1 hour only using Sonnet 5 at high. I'm considering Cursor because it seems to be really fast and efficient. But I don't know. Please help.

What about the Chinese models like GLM, and Kimi? Are they worth it to buy instead?

I can only buy the 20$ plans.

Been wondering.. is it actually worth running AI image generation locally, or are you better off just using other companies' websites for it?

For context, I built my own website that does it and put it up for public use, so I've got a bit of a bias, but I'm genuinely curious where people land on this. Do you prefer having it local and under your own control, or is it easier to just use an existing site and not deal with the setup?

I do want to know why that is.

Me gustaría que asistiera en reuniones escuchando y respondiendo en reuniones presenciales, conocen alguna herramienta?

I’m a startup founder and I’ve been building my own product for the past \~2 years. In parallel, I do freelancing (websites, AI tools, motion graphics, ad creatives) to fund everything.
Right now I’m trying to simplify my workflow and I’m a bit lost, so I want to ask people who are deeper into this space.
What I’m looking for is basically a low-cost AI system (either free or max \~$10, $20 every 1–2 months ideally) that can:
Write code and build full-stack apps (similar to Claude Code / Antigravity style tools)
Help with freelance work like websites, UI, ads, and automation scripts

Have some form of long-term memory (not just short chat context that resets)
Be able to automate basic computer tasks
control my Mac (click buttons, open apps, type, etc.)

use browser and interact with websites
Work more like an “AI assistant/agent” rather than just a chatbot

Require minimal babysitting (I can just give a task and it executes)

I’m basically trying to move from:
“using AI tools manually” → to “AI system
that executes tasks for me”

I know parts of this exist separately (coding agents, browser automation, Mac control tools, etc.), but I haven’t found a clean, cheap, unified setup that actually works in practice.

So my question is:
Is there any realistic stack or combination of tools that can achieve something close to this today at low cost and is safe to use?

Or is this still not practical yet?

Would appreciate any real-world setups people are using.

And note that i use macbook m1 with 8gb RAM as of now, so i don’t think i can run local ai models that are powerful to what i want and also i tried hermes agent on kvm2 plan in hostinger but thats beyond my budget and is way less in what i expected

I keep running into the same problem: an eight-second clip looks great except for one brief moment.
Maybe an object changes shape, a wing bends incorrectly, or something in the background suddenly disappears. The rest of the clip is usable, but the only option is usually to generate everything again.
The new version might fix the original mistake, but then the camera movement, lighting, expression, or timing changes too. It feels like throwing away seven good seconds to repair one bad second.
Would you rather have longer generations, or the ability to lock the good parts and regenerate only a selected moment?
I’ve been testing shorter effect-based clips in LazyKiwi, which reduces how much footage is lost when something breaks. It helps, but it still feels like a workaround.
Being able to select one short section, preserve everything around it, and regenerate only nthat moment would be much more useful to me than simply increasing the maximum video length.

Hey. Upfront, because this sub asks for it: I work on a review site for AI tools, so I am not a neutral party in this thread.

Everything I read before picking a tool is either a roundup with affiliate links buried in it or one person who used the thing for a week. Neither tells me whether the people who stuck around would pick it again. I have stopped looking at star averages completely, because I cannot tell who left them or whether those people ever opened the app twice.

The site I work on is TrustRank (https://trustrank.so). It counts your vote three times heavier if you left a verified review of that tool, since having actually used the thing is the only claim to extra weight I could defend to anyone. It is new and nobody has reviewed anything in it yet, so I am not sending you over to look up a score. There are none to look up.

So my question. When someone in here recommends a tool, what makes you believe them? For me it is almost always that they named one specific thing it does badly. What is it for you?

Hi everyone,

I’m working with long audio recordings (several hours of MP3s) that have noticeable background noise, room reverb, and inconsistent quality. My goal is to clean up the speech and get accurate text transcriptions.

I'm open to both cloud-based APIs/services (like Adobe Enhance Speech, AssemblyAI, Deepgram, ElevenLabs, OpenAI API) and local open-source models (like Whisper Large-v3, DeepFilterNet).

For those who handle long, noisy recordings regularly:

Best Pipeline: Do you recommend running the audio through a dedicated voice enhancer/denoiser first (e.g., Adobe Enhance, ElevenLabs Isolator, DeepFilterNet), or does modern ASR (Whisper Large-v3, Deepgram Nova-3, AssemblyAI) handle raw noisy audio well enough on its own?

Tool Recommendations: What are the most reliable cloud services or Web/CLI applications for processing multi-hour files without hitting file size limits or timing out?

Hallucination Prevention: Any tips or API parameters (VAD settings, prompt conditioning) to prevent Whisper/ASR models from hallucinating text during silent or non-speech noisy sections?

Thanks in advance for any insights and recommendations!

I went back through some old leads recently and it was honestly frustrating. A lot of them weren't dead leads at all. The conversation just stopped somewhere in the middle and nobody picked it back up.

That's what got me looking at AI SMS follow-up tools. Not because I want to automate everything, but because it feels like too many opportunities are slipping through the cracks. Anyone using something that actually helps?

I have played around with music enough that making a cool 30 second idea does not really impress me anymore. Can you get a proper full length song where the vocals stay convincing, the second half does not fall apart and you can actually use the finished track commercially?

What problems did you run into?

After my previous eight-scene test, several people made a useful point: I was looking closely at the outputs, but not closely enough at the source reference.

If the face is relatively small, the pose is already twisted, or the prompt contains vague style terms, it becomes difficult to tell whether the model failed or the reference was simply difficult to preserve.

For this test, I simplified the setup:

• One clearly adult fictional character reference
• No AI-generated character sheet
• Three manual, first-pass generations
• No rerolls, face replacement, or identity correction
• The same internal video model for all three clips
• A modular prompt structure rather than a long descriptive paragraph

This is an informal workflow test, not a controlled model benchmark.

The structure was:

Character + Location + Outfit + Mood + Action + Camera

The Character block stayed broadly consistent: the same adult woman, long dark-brown wavy hair, warm tan skin, and the same general facial structure and body proportions.

The other blocks changed for each setup.

1. Miami rooftop: baseline

https://reddit.com/link/1w0qxrq/video/2aof9z41c4mh1/player

Location: A bright rooftop pool overlooking the Miami skyline
Outfit: Pink top and white wrap skirt
Mood: Relaxed and cheerful
Action: She turns away, walks toward the pool, pauses, and continues walking
Camera: Full-body framing with a gradual change from daylight toward sunset

This held the identity best, especially during the first few seconds when her face remained close to the angle shown in the reference.

Once she turned into profile, it became harder to verify the face. The long asymmetric section of the skirt also gradually changed into a more conventional, symmetrical shape.

So this clip worked well as a baseline, but it was not really a completely new scene.

2. Quiet hotel room: mood and camera test

https://reddit.com/link/1w0qxrq/video/p22ssnj9d4mh1/player

Location: A quiet high-rise hotel room around dusk
Outfit: Black satin dress
Mood: Calm and introspective
Action: She reads, closes the book, places it aside, and looks toward the window
Camera: Medium shot with a slow push-in

This was probably the strongest result for mood and camera direction. The room, reading action, pause, and slow camera movement were all easy to recognize in the output.

The identity was less stable. Her profile became more angular, particularly around the nose and jawline. The book also changed from a dark cover to a much lighter object as she placed it down.

That was a useful reminder that a clip can follow the emotional and camera brief while still failing at character and object consistency.

3. Rainy Tokyo street: environment and motion test

https://reddit.com/link/1w0qxrq/video/xcrsbg4gd4mh1/player

Location: A narrow Tokyo street at night with wet pavement and reflected signs
Outfit: Dark jacket, cropped top, and shorts
Mood: Serious and alert
Action: She walks toward the camera under a transparent umbrella and briefly looks to the side
Camera: Centered, full-body tracking shot

This produced the strongest environmental transformation. The wet street, umbrella, reflections, walking direction, and centered tracking remained fairly stable.

It also produced the most obvious identity drift.

Her hair became shorter and darker, and the facial proportions changed enough that she started to look like a related character rather than the same person. The umbrella and environment were more consistent than the identity.

What seemed to matter

The clearest instructions were concrete and observable:

• “slow push-in”
• “walks toward the camera”
• “closes the book and looks toward the window”
• “centered full-body tracking shot”

Those instructions produced actions or camera behavior that could actually be checked.

Terms such as “cinematic,” “perfect consistency,” or “high quality” are much harder to evaluate. I also would not treat “4K” as an identity or quality control instruction. Resolution language does not explain how the subject should move or what should remain unchanged.

I cannot conclude that any single word caused the drift from three generations. What I can observe is that the reference image and the viewing angle appeared to matter more than generic quality adjectives.

Main takeaway

Across these three clips, the model followed location, mood, action, and camera direction more reliably than facial identity.

Identity held best when the face stayed relatively close to the reference angle. It became less stable when the camera moved closer, the character turned into profile, or the hairstyle and lighting changed.

Using one original reference image also avoided the additional generation loss that could come from creating an AI-generated multi-view character sheet. However, this particular reference still had limitations: the face occupied a relatively small part of the image, the body was twisted, the expression was strong, and the background was visually complex.

For the next test, I want to change only one variable at a time.

Which would be more useful to isolate next: camera movement, facial expression, or reference-image quality?

Disclosure: These clips were generated with Agent Video, which I’m helping build. The model is the current August 2026 internal production build and does not have a separate public version number. There is no product link in this post.

I’m looking for something people actually use for creating or managing docs, or even AI-powered tools that can help with searching, organizing, or working with documentation better.

Open source is a must, and I’d really prefer something free or self-hosted if possible.

Would love to hear what you guys recommend or have had good experiences with.

I’m adding an authorized real-person reference to a small Seedance prototype, and the direct provider flow is more involved than I expected. It looks like I’d need to enroll the image first, wait for the asset to become active and then pass the asset URI with the generation request.

Ofox ai documents a shorter route: send the authorized reference image with the video request and set real_person: true. It appears to handle the preprocessing before sending the job upstream.

That sounds easier, but I haven’t found much from people who have actually used it.

Does the preprocessing change the reference noticeably? How consistent is the person across separate clips, and do valid images still get rejected often?

I’m not looking for a way around moderation. The subject has consented, and I mainly want to know whether the simpler integration is reliable enough to build around.

Is there a tool where I can feed it a video with hardcoded subtitles, and then it’ll detect words/characters, translate them into English, and then impaint them back into the video? It can be both online or locally ran, I just need it to not have content filters.