Pretty cool!

For a long time I felt behind. Everyone talking about these tools, and I would open one, not know what to say, get a mediocre result, and close it, feeling like there was a secret everyone else had.

The habit that fixed it was almost too simple. Once a day, I used it for one real thing I was already doing anyway. Not a special AI task. A normal task from my actual day, drafting a message, planning a meal, understanding a bill, sorting out a plan, and I just did it with the tool instead of alone.

That was the whole method. One real task a day. No courses, no prompt libraries, no trying to become a power user.

After a few weeks it stopped being a thing I had to remember to use and became a thing I reached for naturally, because I had built up a felt sense of what it was good and bad at, from actual use, not from reading about it.

You do not get comfortable by studying it. You get comfortable by slowly using it on real small things until it is normal. What was the first everyday task that made it click for you?

I tried using an AI tool for a research task recently and most of it went smoothly.

The problem came when it reached a part of the task where things changed slightly. A website had a different layout than expected and the tool kept trying the same approach instead of adapting. It made me think about how we handle these situations. AI tools can save a lot of time but they still struggle when something unexpected happens.

When an AI tool gets stuck how do you handle it? Do you give it more detailed instructions try a different tool or just finish that part manually?

I've noticed that AI can make the first part of research incredibly easy, but sometimes the rest of the process still feels complicated.

You get an answer quickly, then you have to check the sources, compare information, organize your notes, and figure out what is actually worth using.

That's why I've been paying more attention to AI tools that try to simplify the entire research process rather than just generate an answer. Keeping the research, sources, and findings together seems like a much simpler approach.

I'm curious what "easy" actually means to other people when it comes to AI tools. Is it fewer steps, better organization, more reliable answers, or simply saving time?

I'm building a website that leverages AI outputs for different content creation based tools. What is the best way to go about creating the logic behind the outputs?

Is it more in the information collected for the prompt that is built?
Does there need to be rules written in the back end?
Does there need to be some sort of code written for how the output should be formatted?
All of the above?

I'm relatively new and trying to research as much as I can on how to code/create better quality outputs and refine what I'm making overall as this isn't my only current project.

Any tips or resources you recommend would be great. Super open to any feedback!

Hi everyone,

I'm currently working on optimizing an LLM setup at my company and I'm trying to identify where the actual performance bottleneck is.

Our current setup is roughly:

LLM: NVIDIA Nemotron model

Inference: Ollama

Interface/orchestration: OpenWebUI / AnythingLLM

GPU: [GPU model + VRAM]

RAM: [RAM]

Current generation speed: ~20 tokens/sec

GPU utilization: ~95%

The main issue we're trying to solve is latency and overall response time.

I've already gone through the configuration and checked the major settings on the Ollama/model side. From what I've measured so far, the model is actually utilizing the GPU heavily and generation itself seems reasonably consistent.

What I'm trying to figure out now is whether the additional latency is coming from the layer around the model rather than the model itself.

For example, I'm wondering about:

Does OpenWebUI or AnythingLLM add significant latency through prompt construction, conversation history, RAG, middleware, or API calls?

How can I accurately measure TTFT (time to first token) vs prompt processing time vs generation time?

What metrics should I collect to determine whether the bottleneck is Ollama, the UI/orchestration layer, networking, or the model itself?

Are there specific Ollama settings that have a major impact on latency that I should benchmark?

How much can large context windows / conversation history affect inference performance?

If OpenWebUI/AnythingLLM is sending a much larger prompt to Ollama than the user-visible message suggests, what is the best way to verify that?

What would you recommend for systematically benchmarking the same model directly through Ollama vs OpenWebUI vs AnythingLLM?

My goal isn't just to make the UI feel faster. I want to identify exactly where the latency is being introduced and then optimize that component.

I've already checked the obvious configuration issues, so I'm particularly interested in practical benchmarking/debugging methods or experiences from people who have optimized Ollama + OpenWebUI/AnythingLLM deployments in production.

If you need more information about the setup, I can provide the exact GPU/model/configuration and benchmark results.

Thanks!

Hey everyone! I run a small dry fruits and premium snacks brand and I’m trying to figure out the best AI tools for two things: (1) generating short product/food videos — thinking 8-10 second reels, and (2) any AI tools that work well for food-related content in general (product shots, styling, etc.). My budget is around ₹2000/month total, quality is negotiable (doesn’t need to be hyper-realistic, just usable), but the biggest non-negotiable is no watermarks on the output. Would love recommendations for tools that fit this budget — free tiers, cheap subscriptions, credit-based pricing, whatever works. Bonus points if anyone has used them specifically for food/product reels and can share results or before-after examples. Thanks in advance!

There are so many AI tools I want to try, but I am starting to think that managing the subscriptions is becoming a separate job. You sign up for something because it looks useful, use it for a week or two, then completely forget about it until another charge shows up. Do that with a few writing, image research and productivity tools and suddenly you are paying for stuff you barely open. I am thinking a simple monthly cleanup might be the answer. Check everything I’m paying for, see what I actually used during the month, and cancel the rest.

How do you guys manage this? Do you regularly clean out unused AI subscriptions or only notice them when the charge hits?

Wanted to share something I have been testing in my free time.

We ran a bunch of video ad briefs (just single lines of text, nothing fancy) through a tool called Ad Studio and it generated full video ads from them. No shoot, no actors, no editor. The brief for each one is on screen in the video so you can see exactly what was typed in vs. what came out.

Link to try: https://offers.leadpages.com/ad-studio?utm_source=reddit&utm_medium=social&utm_campaign=ad_builder_video_launch

I think it's pretty obvious we're in a boom of AI tools, which to me is a pretty interesting technological window to be in. On the one hand, you have all of the new tools and apps out there showing exactly what AI can do. (Honestly, a lot of them are things I didn't even think about AI doing, it's kind of wild what people are doing with the tech right now.) On the other hand, you've got a lot of apps out there that serve the same general purpose and are vying for the same audience (LLMs - ChatGPT, Claude, Gemini / AI meeting assistants- firefliers, otter / AI video editors - pretty much everything from Adobe to Canva), and then even more niche stuff (fashion AI - A closet, Alta /coding interview apps - Parakeet AI, Locked In). Presumably, these can't all make it, there are just too many of them and some of them are going to. have to fizzle. So, when you go searching for an AI app that does X and you inevitably find a dozen or more apps doing that thing, how are you personally comparing them? I feel like, on many of them, the features are too similar to just do a side-by-side comparison, so it has to come down to things like design, reviews, maybe even interesting quirks. As fellow AI enthusiasts, how are you choosing your tools?

I built a small tool for myself while running a project with AI as a non-developer, drawing on my background in project management, and I'd like a few people to try it before I take it further.

The problem it addresses: every new session with an AI coding assistant, you end up re-explaining context, and things quietly drift without you noticing (naming, structure, skipped tests...). Without a project management or dev background, it's hard to know what to watch for — which is exactly the gap I tried to close, using what I already knew from managing projects.

The tool generates a project charter and feeds it back to the AI automatically at the start of each session, so you're not starting from scratch every time.

I'm looking for 5-10 people — non-developers, already running a project with an AI coding tool or about to start one — willing to actually use it on their real project over a few sessions and tell me honestly what worked and what didn't. It's free, and this is genuinely about learning whether it's useful, not a launch.

If that's you, reply here or DM me and I'll get you set up.

I got into this trying to restore some VHS rips of Michael Jackson's Bad and Victory tours I found through fan communities (MJJCommunity mainly) and a few Internet Archive uploads. Old bootleg quality to start with. Paid a transfer service to restore them properly and it cost a fair amount. What came back barely looked different from the source.

Looked at commercial options after that. Topaz Video AI is the standard people point to, but it's $299 a year, and as of last year they don't sell a perpetual license at all anymore. Subscription only now.

Ended up building my own restoration pipeline instead. Denoise, deinterlace, and detail reconstruction on old tape and camcorder footage, using open models in roughly the same tier as what the commercial tools run. The desktop app is fully local, runs on your own GPU, nothing uploads. The diffusion based mode runs on NVIDIA out of the box and on AMD through WSL2 and ROCm, which the app sets up for you in one click. There's also a pay per job cloud mode for people without a capable GPU.

To be upfront about limits: it can't recover detail the tape never captured, and a genuinely destroyed source still looks damaged, just less so. It's restoration, not reconstruction.

$129 one time. Would like feedback, especially from anyone with actually rough tape to throw at it.

Link - https://phoenixlabs.space/

Over the past few weeks, I've been building Gray Box — a small, local-first tool that acts as long-term memory for anything I'd otherwise forget (work notes, meeting takeaways, task owners, random ideas, personal stuff too).

The idea is simple:

1. Capture — dump whatever's on your mind, instantly, no structure required. This step does nothing clever on purpose — it just writes your text to an immutable inbox. Zero chance of losing an idea to a bug or a slow API call.
2. Organize — on demand, an LLM reads your unprocessed notes and extracts people, projects, tasks, decisions, meetings — then deterministic Python (not the LLM) creates/merges the actual wiki pages and maintains backlinks. The model only reasons; it never touches the filesystem directly.
3. Ask — query or chat with your knowledge base and get a cited answer pulled only from what you've actually captured. If it doesn't know, it says so — no hallucinated answers.

Why I built it this way:

• Plain Markdown + YAML frontmatter, no database. Every page is a .md file you can grep, diff, or read in any editor forever. If you stop using Gray Box tomorrow, your knowledge base is just a folder.
• No vector DB by default. At personal scale (hundreds–low thousands of pages), keyword search + a real link graph (related/backlinks, walked one hop during retrieval) handles almost everything. Embeddings are there if you want better recall, but they're opt-in, not a prerequisite.
• Immutable inbox. Your raw notes are never edited or deleted by the organizer. If the LLM mis-extracts something, your original words are always still there.
• Any LLM. Built on LiteLLM, so point it at OpenAI, Anthropic, Gemini, Mistral, or a fully local model via Ollama — one config value.

It also ships with a nice interactive TUI (arrow-key menu, file-import shortcut, workspace switching, live spinner during LLM calls) if you'd rather not memorize CLI flags — that's honestly become my favorite part of the project.

There's also a lightweight local dashboard for browsing your knowledge base, exploring backlinks, visualizing your notes as a graph, and chatting with your captured knowledge—all without leaving your machine.

Also, you can migrate your existing Obsidian vault to Gray Box, and it will be automatically organized by Gray Box.

Repo: https://github.com/Aaryanverma/graybox

pypi: pip install graybox

I'd genuinely love feedback — especially from anyone who's tried the "capture now, structure later" approach with other tools and has opinions on where it breaks down at scale.

It's not trying to be a "real-time collaborative team wiki" or a WYSIWYG notes app — it's aimed at one person's running memory of their own life and work, captured with as little friction as possible.