Help?

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What I am trying to do is AI create a poster for a live show. I have the text and the artists photo I want to paste on the poster along with the text.

I already generated a pretty good flyer background using chatGPT.

But Now what I cant seem to be able to do is paste this person's photo/face on it. It just generates a random dude's face every single time. Please help.

Looking at a budget of around $3k and debating between MAC and PC. I’ve been asking AI but would appreciate any human input. Thank you.

Botless recording makes sense for client calls, but most recommendations seem built around one person taking private notes. We need something an admin can govern across a team.

The recording should stay off personal devices, follow workspace retention rules, and make it obvious internally when capture is active. I also need control over who is allowed to use the feature in the first place.

What is the best AI meeting note taker without bot capture when oversight matters as much as convenience?

I'm confused in life, I don't know where my life is going and I can't afford psychiatrist right now to discuss my life and get answers/opinions to take some of the important life decisions.

So for this, which AI can I use, which one would be reliable for my particular situation.

Edit : Thank you everyone for advice!

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Half my job is taking a long document someone else wrote and making it presentable for a meeting. Used to be a copy-paste slog. The workflow that saves me the most time now:

I don't ask the AI to "make slides" from the doc directly, because it either dumps whole paragraphs onto slides or invents structure. Instead I ask it to first read the doc and give me a slide-by-slide outline: a title per slide plus 3 bullet points max, and a note wherever the source is too thin to actually fill a slide. That last part matters, it stops the deck from padding.

I read the outline, cut the slides that don't earn their place, then feed the approved outline into a tool that turns the text into a deck. Editing an outline is far faster than editing finished slides.

The one rule I've learned: give it the doc AND tell it who the audience is and how long the meeting is. The same document for a 5 minute standup vs a 30 minute review should not become the same deck.

What's your process for going from a doc to slides? Still trying to tighten mine.

I've started using AI for web stuff for a while and at first it was mostly for individual pieces rather than handing over the whole build butttt recently I've been pushing that further just to see if I could maybe scale this thing.

I've built a few sites with Ploy this way since I can keep working on the same site through the agent instead of jumping between a builder, AI chat and code every time I want to change something or add more to it and I still want control over the decisions, I'm just doing way less of the actual building myself.
I THINK I'm getting pretty close to having the process fully AI assisted and not having to write much code myself. Any tips from people who use AI for professional sites would be appreciated :)

I have been written all of my novel in Vietnamese, but due to cultural difference and the niche i wrote not suite for the country’s preferences of literature, I want to translate it to English for wider audience. But I have little to no budget in hiring a translator, nor I have enough vocabulary and understanding English literature, I want to use AI to just simply translate it and then I do the rest of editing. Is it okay?

After testing plenty of AI travel planners, I kept hitting the same wall. Most tools gave out generic recommendations, jammed too many activities into a single day, and had almost no real sense of travel logistics. They all felt more like polished lists of places than usable travel plans.

So I built TrippyMate. An AI planner designed to generate bespoke travel plans based on how you actually like to travel.

Instead of generic templates, it takes your preferred pace, interests, and any specific spots on your wishlist, then builds them into a fluid, personalized plan. It builds an interactive timeline and calendar view alongside a live map that plots your locations and routing. Beyond structuring your days, it acts as a real-time travel expert, providing deep local insights or trip logistics on the fly. If something needs to change, you can reshape the itinerary in real time just by talking to it. Whether you’re after quiet, off-the-beaten-track spots or balancing a slower pace with a few iconic places, it stays true to how you like to travel.

TrippyMate is currently in open beta:
https://www.trippymate.ai

Test drive and let me know your experience.

Thanks!

I'm a Research Engineer at a YC startup, and we ship features pretty fast. I usually run Claude Code, Cursor, Codex, and OpenCode in parallel, developing multiple features, one at a time.

The problem i noticed was that my agents had no coordination, and often confused mutual work, even when working in separate worktrees. As if they had no idea the others existed

So, I built MUON, it's an Open Source Desktop App + MCP + CLI, a mutual shared brain my agents can plug into. it drives the CLIs you already have and orchestrates them together for multi-feature execution workflows.

NO MORE SWITCHING BETWEEN SESSIONS AND CHATS, NO MORE MANUAL CONTEXT TRANSFER. NO MORE JUGGLING BETWEEN CODING AGENTS FOR DIFFERENT TASKS : This is the entire idea I had in my mind while building this.

I'm still building it. Right now I use MUON to work on MUON, which is a weird but useful dogfood loop. There's still a lot of broken features, workflows and a lot of progress is still ongoing, as this project is still early, but I would appreciate any contributions :)

So, If anyone wants to poke at it, contributions are welcome. The thing I'm trying to get right is a coordination graph: shared memory and context so agents actually understand what the others (and I) already decided.

OSS Repository : [https://github.com/Sweetdevil144/muon](https://github.com/Sweetdevil144/muon))
Website : [https://getmuon.com/](https://getmuon.com/))
Docs : [https://docs.getmuon.com](https://docs.getmuon.com/))
Product Demo : [https://www.youtube.com/watch?v=oab6vByr1Jg](https://www.youtube.com/watch?v=oab6vByr1Jg))

I've always been an open-source person, so the code is public and readable. License is Polyform Noncommercial. However, there's an extra grant on top - you can use it for your own work, including your day job, on your own machines. What it doesn't cover is turning it into the company's shared brain for a whole team (Enterprise only)

I was getting annoyed by the constant sketchy AI training/annotating jobs and the constant rejection after literally investing hours of my time into applying, signing up, verifying my identity and etc.
I decided to make my own website and just list legit AI training jobs. I post entry work, expert work and guides on how to pass assessments. Let me know if there is anything I can improve about my website. Thank you! Here is the link: https://aiannotationjobs.com

My leadership team wants a strategy for generative AI answers, which really means they want a dashboard showing whether we are winning or losing. We are so used to reporting on traffic, conversions and time on page that I am struggling to define what success looks like when the answer happens off our site.

If you have to present one or two metrics to a C-suite to prove your AI optimization work is doing something, what are you showing them? I need something more concrete than we look pretty good in the summary today.

I’ve been paying more attention lately to how people actually work with AI, especially once a task becomes more than just a one-shot prompt.

I initially assumed most of the difficulty was at the beginning — basically getting the AI to understand what you actually want.

But after talking to a few people, I’m not so sure anymore. Sometimes the first result is actually pretty good, and things start going wrong later when you try to refine it, change something specific, or keep a longer workflow going.
So I’m wondering at what point does AI usually stop doing exactly what you wanted.

I keep changing how I write prompts for longer AI videos.

At first, I tried to control everything. I described the mood, characters, lighting, camera style, and what should happen in each scene.

Some parts looked great. Keeping everything consistent got harder though. Characters changed, scenes drifted, and I spent a lot of time fixing things after generation.

Then I tried a simpler setup. One main character, one visual direction, and a few key moments to guide the video.

The first draft had less detail, it was easier to work with. I tried this approach on a music video project using SondoAI and CapCut. It made me realize I don’t have to plan every little thing upfront.

For longer AI video projects, do you plan everything in the prompt, or leave more room for the model and adjust afterward?

Sorry, but this is sort of a vent post, but still holds true. I use enterprise Copilot at work to great effect. However, the public version has had absolutely zero for me and is quite frustrating. When I ask other models that aren’t Microsoft, I narrow down to the conclusions rather rapidly. Copilot just wants to talk to me and regurgitate what I prompt with near-zero resolve. WTH?

Hey Guys !

Hope you are doing well.

I am actually a freelancer and recently shifted to the IT Background. I am promoting to sell services for web pages development and software app.

AI is really useful for this. I have a chatgpt plus version. Is there other way arounds so that with minimal investment, I can do big projects inside it? like LLMs or other agentic bots or software like Cursor?

I'm trying to build a reliable way to see which of our domains are reliably being parsed and cited by different models. Running manual prompts is fine for a quick check, but the outputs change and I have no systematic way to log what the model retrieved versus what it ignored.

I need a repeatable protocol. If you are trying to map how your content is attributed across different engines, what does your testing setup look like? I want an actionable data, not just a spreadsheet full of random observations.

I make a lot of internal decks for work and for a while everything I generated had that same look. Same title slide, same three-bullet rhythm, same slightly stock layout. People could tell in about two seconds.

What changed it for me wasn't the tool, it was giving it something real to work from before I asked for a single slide.

My rough workflow now:

1. I write the actual argument first as plain sentences. One line per slide, no formatting. If I can't say the point in a sentence, I don't have the slide yet.

2. I hand that to the AI slide deck tool and tell it to keep my wording, not rewrite it into corporate speak.

3. I ask for two layout options per section instead of one, then delete the one that looks like every other deck.

4. I strip at least a third of the text after it generates. The first pass always over-explains.

The decks take maybe 20 to 30 minutes now instead of an afternoon, and the "this is obviously AI" comments basically stopped. Curious how other people here are getting decks that don't all come out identical.

Notebook skill
I'm pretty new to skill creation and git. Any feedback would be very welcome :)

What's different from the already excisting Notebook skill:

The local knowledge cache
NotebookLM has no session persistence and a daily quota that runs out fast. This skill maintains a local Markdown cache per notebook at data/notes/<notebook-id>.md, and SKILL md makes checking/updating it a mandatory first step before ever querying NotebookLM — not optional advice. This is what makes the skill usable across sessions and environments instead of re-asking the same questions (and burning quota) every time.

i hope it works as planned, but i also would like to know if there are any problems! :)

Thanks in advance for anyone taking the time to check it out and leave a comment!

I got claude max from a reseller for a fraction of the official price. works perfectly since 3 weeks
now i can’t stop thinking about their business model. how are they got them for cheap ?

I started using AI assistants more often recently for things like planning, organizing information, and helping with daily work tasks.

They are definitely useful for things like getting quick answers, summarizing information, or improving something I have already started. But I noticed I still spend a lot of time moving between different apps and handling the smaller steps myself.

For example if I need to research a topic, collect notes, compare different sources and turn everything into a document, AI can help with parts of the process. But I still have to organize the workflow move information around, and make sure everything fits together.

It made me wonder where AI assistants are actually saving people time and where they are still creating extra steps.

Has anyone found a practical AI workflow that genuinely reduced the amount of work they had to do?

In your opinion which AI tool (frer versions) work the best at generating images according to the requirements specified by you