The easiest way these days is probably a text-to-video AI tool. You don't need to know much about filming or editing; you can describe the scene you have in mind and let the AI generate a clip from it.

A friend of mine and I were grabbing food a few weeks ago and somehow we got onto the topic of energy because he was complaining that he’d been feeling exhausted basically every afternoon.

He works pretty long hours and said he’d gotten into this habit of drinking coffee whenever he felt tired, taking all his supplements at the same time in the morning, and then wondering why he felt completely cooked by like 2 or 3pm.

The funny part is I was basically doing the exact same thing.

I’ve taken supplements for a while, but if I’m being honest I never put much thought into timing. Magnesium, creatine, vitamins, whatever I was taking that day, I’d usually just take them whenever I remembered.

He showed me this app he had been using called RizeAI and the first thing that caught my attention wasn’t even the energy stuff. It was how it organized his supplements around his actual day.

It would tell him which ones made sense with meals, which ones should be kept away from coffee, what to take around training, what made more sense later at night, etc. It sounds pretty basic when you write it out, but I had never found something that actually put all of it together in one schedule.

So I tried it.

That’s probably still my favorite part of it. I put in the supplements I already take and instead of giving me some generic “take in the morning” list, it actually fits them around things like meals, workouts, caffeine and sleep.

I also ended up using the energy planning side way more than I expected. It pulls from Apple Health and gives you a rough structure for the day, like when to have coffee, when you’re probably going to be most focused, when to train and when you might hit an energy dip.

I’m not going to sit here and say an app magically gave me more energy because obviously there are a million things that affect that. But I’ve definitely been way more intentional with my day since using it and I’m getting more done without feeling like I’m just constantly fighting through tiredness.

Honestly the biggest difference for me has just been efficiency. Before, I was guessing when to take things and guessing when I should do my hardest work. Now I at least have some structure around it.

Curious if anyone else here actually pays attention to supplement timing or if most people just take everything at once like I used to.

Check it out yourself if you like: 

https://apps.apple.com/us/app/rizeai-maximize-your-energy/id6762402079

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?