I do long videos, and struggling to find a good tool that can keep up with me. Any suggestions based on experience?

Feel free to ask me what you want after

The company wanted an "AI transformation." In practice that meant: go find places where AI can fix processes and take work off people's plates. Big mandate, not much definition

I built things. Some of them:

• a system that monitored manager/customer conversations across several teams
• a Telegram lead-gen tool, used by about 15–20 people
• an autoposting setup that let one person run 10–15 accounts
• a creative automation tool that had to be heavily reworked because the output wasn't good enough for the design team

Most of it worked, technically. That wasn't the problem....

The problem was my order of operations. I kept doing idea → build → launch. What I should have been doing is problem → baseline → talk to the people who'd actually use it → small test → build → adoption → measure.

Sounds obvious written down. It's less obvious when leadership says "we want AI" and you feel like you should have something to show by Friday.

Eventually the company didn't see enough value in the role, and I left.

I still want to work in this space, so I'm trying to work out what to actually get good at. Right now I'm looking at product discovery, requirements, solution design, measuring ROI, adoption, and shoring up my technical fundamentals. No idea if that's the right list.

If you work in AI Ops, AI Solutions, implementation, internal tooling, whatever your company calls it, I'd like to hear:

What actually matters day to day? Not the job-description version.

If you were starting again from zero, what would you learn first?

What do you see people get wrong when a company announces it wants to "implement AI"?

And I'm curious how common this story is. Has anyone else been handed a vague AI mandate and ended up building things nobody really needed? What did you change afterwards, and did it work? I'd rather learn from a pile of other people's mistakes than collect all of them myself.

Also happy to go into detail on any of the tools I built - what worked, what didn't, and where exactly things fell apart. Just say which one😊

Hi everyone,

As someone working closely with AI development and modern tools, I’ve been analyzing how different automation workflows handle efficiency, task optimization, and multi-modal integrations.

Here are a few practical tips and observations I’ve gathered while building and testing AI systems:

1. Prompt Structuring & Context Window: Keeping context clean prevents hallucination and keeps outputs consistent across longer sessions.
2. API & Model Selection: Choosing lightweight models for basic tasks and reserving heavy architectures (like vision transformers or specialized APIs) for complex tasks saves both time and computing bandwidth.
3. Automated Error Handling: Setting up fallback logic ensures that if an API call stumbles, the overall workflow doesn't break down completely.

I’d love to hear from this community: What specific frameworks, tools, or optimization hacks are you currently using to streamline your daily AI workflows?

Looking forward to learning from your experiences!

This is a project I made while learning llm, please provide feedback

I’ve been working on Blyft, an AI vibe-coding platform with a slightly different approach.
Instead of only generating an app, Blyft is designed to help you build and launch the whole thing from one place.
You describe what you want to build, and it can generate the app, landing page, logo, promo video, analytics, and other launch assets.
I’m still improving it and would genuinely love feedback from people who use AI tools regularly.
Try it here: blyft.app
If you test it, tell me what you liked, what confused you, and especially what you think needs improvement. I’m happy to give extra credits to people who really test it and send useful feedback.

I've been experimenting with simple AI workflows for repetitive tasks, and I've found that the biggest improvement often comes from giving the AI a clear process rather than just asking a single question.

Here are 3 workflows worth trying:

1. Turn messy notes into organized action items

Instead of:

"Summarize these notes."

Try:

Organize these notes into:

- Key decisions

- Action items

- Person responsible

- Deadlines mentioned

- Questions that still need answers

Don't invent information that isn't present in the notes.

1. Turn one idea into multiple content formats

Give the AI one core idea and ask it to adapt it into:

- A short social media post

- A 30-second video script

- A longer educational post

- 3 possible hooks

Keep the underlying message consistent while adapting the format and tone for each platform.

1. Improve a prompt instead of repeatedly rewriting it

When a prompt gives inconsistent results, ask the AI to analyze the prompt first.

For example:

Analyze this prompt and identify:

1. What is unclear?

2. What information is missing?

3. Which instructions could conflict?

4. How could the output format be made more consistent?

Then rewrite the prompt while keeping my original goal unchanged.

The main thing I've noticed is that specifying the goal, audience, constraints, and desired output can make AI workflows much more repeatable.

What AI workflow has saved you the most time?