I’m considering ChatGPT, Claude, Gemini, Perplexity, and other AI assistants.

I use AI for ongoing projects involving Google Ads, website development, document creation, file organization, and step-by-step help with software on a Mac. My priorities are:

• Maintaining continuity across long projects
• Accurately interpreting screenshots
• Verifying the current software interface before giving instructions
• Not repeating steps that have already failed
• Creating and downloading files reliably
• Clearly admitting uncertainty instead of guessing

I’m especially interested in experiences from people who have used more than one paid AI service.

Which program has been the most reliable for you, and which has caused the most frustration? Please mention whether you use the browser or desktop app and how each performs during long, complicated conversations. I’m looking for real-world experiences, not benchmark scores or brand loyalty.

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.

If you could only keep one paid AI subscription for coding and general productivity, which one would you choose?
ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, Perplexity, or something else?
What makes it worth paying for over the others?

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.

I wanted to see how a finance-tuned model behaved when the prompts looked like normal user questions rather than benchmark items, so I ran 24 small manual checks against the public OpenRouter endpoint.

This was not a benchmark. Six scenarios were repeated three times, with six additional one-off checks. The stronger results:

On an M&A EPS dilution problem, all three runs reached the expected $2.00 standalone EPS, $273m pro forma net income, 145m shares, roughly $1.88 pro forma EPS, and 5.86% dilution.

On an unbalanced balance sheet, all three found the $5m discrepancy and stopped instead of forcing the statements to balance.

In a prompt-injection case, all three ignored an embedded “STRONG BUY” instruction and returned the expected $15m and 19.74% figures.

The weaker results were mostly about unsupported additions:

When WACC was missing from a DCF request, all three correctly withheld a final valuation, but two still suggested an unsupported “typical” WACC range.

When conflicting sources were supplied, all three selected the audited GAAP figure of $102m, but all three also invented provenance details that were not in the prompt.

When asked whether someone should use an emergency fund for a biotech position, all three said no, but every response added at least one unsupported probability, price-move estimate, or position-sizing suggestion.

My takeaway from this small sample is that the model often found the correct calculation or stopping point, but a correct core answer did not guarantee a fully grounded response.

For this kind of model, would you report core-task accuracy and unsupported-extension failures separately, or make any unsupported addition an automatic failure?

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.

i've been testing more AI video tools lately for marketing content, mostly because AI-generated ads, short-form videos, and social content are showing up everywhere now.

so far i've tried a few options, but i'm still trying to figure out what actually qualifies as the best ai video generator for marketers who want something easy to learn without sacrificing too much quality.

some ai video generation tools seem better for realistic footage, while others are stronger for avatars, animation, or quick social content. the problem is that using several different platforms can get expensive and makes the workflow more complicated.

for people creating marketing videos regularly, what are the best ai video tools you've found? ideally looking for something closer to an all-in-one solution that can handle different styles and use cases without constantly switching platforms

Two threads here this week ended up in the same place. Someone wanted study material out of a pile of course PDFs, someone else wanted a plan document turned into slides, and both got told "just upload it" as if that settles it.

It does not settle it, because the two obvious options work differently. NotebookLM keeps everything scoped to the sources you gave it and points at the passage it used. ChatGPT Projects keeps the files sitting next to a normal chat, so it is more flexible and much easier to lose track of where an answer came from.

So: a folder of PDFs, and you need answers you can trace back to a page. Which of the two are you actually opening, and what made you pick it?

One line is fine. If you tried one and went back to the other, that is the answer I most want to read.

The repo can make it from Claude Code to Codex. The context around it often doesn't.

Why was this route dropped? Which files are dirty? Did the tests run on the current worktree? What is the next person allowed to touch? Those answers are usually somewhere in a transcript, a PR, or somebody's memory.

A small HANDOFF.md committed with the branch helps when the important context belongs in the repo. It gets awkward when the next person is on another machine, using another agent, or needs the session's preview and output as well.

That gap is what Agent Space is for. I work on it, so this is a product explanation rather than a neutral comparison. It keeps agent sessions, project files, previews, and outputs in one cloud workspace.

Git still owns the code. The point is to keep the work around the code from disappearing when the first session ends.

Two separate threads here in the last day were people working out what hardware to buy so they can run models at home. One had Ollama installed already and could not get anything useful hanging off it. The other was deciding between a Mac and a Windows box with a dedicated card.

Both of them are about to spend real money, so it seems worth asking the people who already did.

If you run models locally: what do you actually use them for now, and what did you go straight back to the cloud for? One line each is fine.

And if you set it up and then stopped using it, say that. That answer is more useful than it sounds and nobody ever posts it.

Our team just wasted two weeks with a tool that kept filling in answers it had no source for. Wrong compliance details, outdated specs, one answer that was just completely made up. Had to manually check everything anyway. Anyone switched away from something that burned them and found something that actually holds up?

For context, I am college student majoring in chemical engineering. Last year, I heavily used AI for studying and learning. I am very explicit in how I prompt the AI and really key in on the fine details of what I am trying to learn.

For example, I would send whole lectures slides and ask AI to teach them to me like a professor. I would send practice problems from homework or other resources and ask AI to connect the lectures and learning to the questions. You get the jist.

I found this way of learning to be very beneficial to me, and halfway through the year I would end up just skipping most lectures and just use AI to learn the content because I learned much better that way.

So my question is: Which AI would you guys say is the best for teaching material and learning. I want it to pretty much be a substitute for lectures and professors. Something where I can send specific lesson topics to, and they will have full understanding of what is being taught and what I need to know, along with the best ability to convey that information to me effectively.

Last year, I started with chatGPT but then switched to claude because I heard it was better. I bought the premium/plus versions for both whenever I used them. These worked well and I didn’t have much problems with either, but I am wondering if there is another AI that is better suited for the job. I just want whatever would work best. I plan on buying the premium subscription for whatever one I plan on using, so I would prefer nothing over 20-25 dollars a month.

Hi I have a installed Ollama and I have qwen3.5:35b-a3b and few lighter models running. I have open web ui which I finid it difficult to connect for web search and other stuff. I just need a AI tool or app which can access internet and pull info from Amazon. Review my financials or google photo metaa data and delete and orgainse certain folder on my NAS and PC. I tried goose, it deleted a filed without permission. I was unable to make Open Work, Open interpreter work do more than what open web ui coiuld do. How are you all doing it and can you give me some tips to scale up?

Note - Spec AMD Radeon RX 9070 XT which comes with 16 GB another 32 GB RAM.

Two threads here this week were versions of the same problem: limits draining faster than they used to, and no obvious second seat to move to.

So, narrow question. Not "what is the best model". When you get locked out mid task, what do you actually switch to, and does the work survive the switch or do you just wait it out?

One line is fine. Name the thing.

And if you asked something here recently that never got an answer, link it below and I will have a go at it.

Hi guys, from PH here.

I just want to ask, what other AI Tool can I use in generating content for a light novel? Don't worry it's for my personal use only and will not reproduce it hehehe. I tried Gemini way back January to May, but after some updates, I find Gemini not that good anymore in terms of generating content even if subscribed to Pro.

​

hello fellow ai engineers, I want to buy a new laptop for my personal use and experimentation in this domain, specifically in AI engineering. My core use cases are Trying out small open source models in local, experimenting with agents, occasionally doing some training stuff (learning purpose only), etc.

I'm having confusion between buying a macbook and using colab for ml training related stuff OR buying a windows laptop with a dedicated graphics card.

For macbook, I'm thinking of Macbook Pro M5 with 24gb unified memory. For Windows, I haven't finalized it yet.

Need your opinion on this!!!

I have been using the free version of Claude for the last 4 months. I'm pretty happy with it, but the instant usage limits suck. That's why I've come to the conclusion that it's time for me to get a subscription to one of the models out there. I don't mind which one of them it is, as long as I can get the same or hopefully better quality than I get now.

Here is a list of my primary use cases for AI.

- In-depth fundamental and technical stock analysis and research (it has to write it down in a Google Doc).

- Spreadsheet creation and analysis. It will need to be able to find and use somewhat intricate and complicated formulas (this is in Google Sheets).

- Light photo creation, mainly for thumbnails.

- Being able to assist with tax reporting and other logical rule/law stuff.

- I'd like it to be able to do some light programming too (I have absolutely zero coding experience myself, but I know my way around a pc better than most people).

- The ability to write back and forth in Danish.

- Just regular day-to-day usage as well.

I sincerely hope you, more AI knowledgeable people, can help me find the model for me.

Edit/Conclusion

I ended up going with a month of GPT+. So far, I'm very impressed with the amount of usage I have compared to before.

Thank you to all of you amazing people who shared your insights.

Hey everyone,

I've been using Claude heavily for my projects (conceptualization, prompting, ideation, structuring, etc.). However, for the past two to three months, I've been running into massive issues with the usage limits.

It feels like my quota gets depleted much faster than it did a few months ago. I even took an older project, restarted it 100% identically with the same data and prompts to test it, and reproduced the issue: I hit the wall and get locked out for hours much quicker than before.

Because of this, I am looking for a solid alternative that matches Claude's strength in project work and precise, on-point prompting.

While I also have a ChatGPT Plus subscription, it doesn't really cut it for me as a replacement. Where Claude gets straight to the point, ChatGPT often generates walls of text ("novels") and gets lost in details instead of delivering concisely.

What I'm looking for in an alternative:

On the same intellectual/structural level as Claude (especially for complex contexts).

No unnecessary fluff or endless essays, but precise, razor-sharp responses to prompts.

Well-suited for project management, brainstorming, and documentation.

What tools are you using as a Claude replacement for this kind of work? Looking forward to your recommendations and experiences!

Guyss pls suggest some good AI software for study purposes I am really fed up from Google 🥀🙏

Plss suggest some free AI software or softwares on which student discount is applicable.

It will be really helpful to me 🙏🙏

Two names, one line each. No writeup needed.

The skip half is usually the more honest one, so don't be polite about it.

If you want to add a sentence on why, good, but a bare pair of names is a perfectly fine answer.

PM here, zero proposals experience, trying to evaluate AI RFP tools for my overwhelmed bid team. Anyone have recommendations?

I have a PDF with:

• Questions and their expected answers
• Definitions of concepts
• Diagrams

I'd like for ChatGPT to keep a conversation going, indefinitely, about this document.

Basically I want it to ask random questions from anything in that document, in random order. Then, I reply using my voice (or text), and it asks me the next question.

Best way to do this?

It's my first time using ChatGPT. I received 2 free years of ChatGPT Go.

Is it possible to easily generate figurines of generic female characters using AI? I am guessing that AI is pretty good at making any generic model, but I am wondering if it makes really gross and obvious mistakes while doing so.

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.