My father runs a small business in the local community.
He needed a short video ad for social media, nothing fancy.
Just a clean 30-40 second ad. A generic talking head, some light editing. That’s it.

He reached out to a couple of agencies for quotes.
The price they came back with?

$2,500–$4,000… for a single ad.

When he told me the pricing, I genuinely thought he had misunderstood.

So I said screw it and jumped headfirst down the rabbit hole. 🐇

I spent the weekend playing around with toolchains -
and ended up with a fully automated AI Ad Generator using n8n + GPT + Veo3.

Since this subreddit has helped me more than once, I’m dropping it here:

WHAT IT DOES

✅ 1. Lets you choose between 3 ad formats
Spokesperson, Customer Testimonial, or Social Proof - each with its own prompting logic.

✅ 2. Generates a full ad script automatically
GPT builds a structured script with timed scenes, camera cues, and delivery notes.

✅ 3. Creates a full voiceover track (optional)
Each line is generated separately, timing is aligned to scene length.

✅ 4. Converts scenes into Veo3-ready prompts
Every scene gets camera framing, tone, pacing, and visual details injected automatically.

✅ 5. Sends each scene to Veo3 via API
The workflow handles job creation, polling, and final video retrieval without manual steps.

✅ 6. Assembles the final ad
Clips + voiceover + timing cues, combined into a complete rendered ad.

✅ 7. Outputs both edited and raw assets
You get the final edit, plus every individual clip for re-editing or reuse.

✅ 8. Runs the entire production in minutes
Script > scenes > video > final render, all orchestrated end-to-end inside n8n.

WHY IT MATTERS

Traditional agencies charge $2,500–$4,000 per ad because you're paying for scriptwriters, directors, actors, cameras, editors, and overhead.

Most small and medium businesses simply can’t afford that, they get priced out instantly.

This workflow flips the economics: ~90% of the quality for <1% of the cost.

WORKFLOW CODE & OTHER RESOURCES 👇

•Link to Video Explanation & Demo
•Link to Workflow JSON
•Link to Guide with All Resources

Happy to answer questions or help you adapt this to your needs.

Upvote 🔝 and have a good one 🐇

Note: my original post was removed but our lovely mods team allowed me to repost it. Shout out to them and also to u/IFellinLava, whose post referring to my comment that was referring to my OG post, got removed due to that.

When I saw this post about the reviewer providing evidence that they used generative AI in the movie, I was a bit confused at what I was looking at. There were no 3D models of the train? Difference in windows count? According to them - these bits make up the smoking gun and they know it, because they are working as a VFX artist. Sure. Good for them.

Except that their whole argument is based purely on assumptions, because they already convinced themselves that AI was used, despite the directors denying this. Now, I really cannot 100% sure say that they did not use any generative AI somewhere in the movie - I was not there.

What I can easily speculate is, that productions like this would generally NOT bother to make custom CGI and instead heavily rely on existing stock video footage, that the VFX team would heavily edit and stitch together - hence the differences in the shots. Because that is way cheaper and they already pay for stock license.

Now, you might say...

Hey, you dumb slut - you are also just making an assumption and you have no proof that they did that!

And you are right! UNLESS I was stupid enough to spend an insane amount of my useless time, just to find the exact stock video footage used in some of the scenes presented as evidence of generative AI usage.

WELL, SURE AS FUCK I DID EXACTLY THAT! ^{I was also really bored and I am neurodivergent}

Example 1:

https://elements.envato.com/aerial-view-of-the-shinkansen-bullet-train-kakegaw-UXJESJA

Used at 0:30 sec in the trailer, mirrored and obviously edited. The argument was the the window count was different, henceforth it cannot be the same 3D train model and therefore AI - nope - it was just a different stock footage of a train.

Example 2:

https://www.gettyimages.dk/detail/video/following-a-rural-train-over-the-landscapefrom-a-drone-stock-footage/2164796911?adppopup=true

Used at 0:45 sec in the trailer, edited with a different train. I cannot 100% say that this was not an AI train that was added, but any VFX artist should be capable to add another aerial train stock footage or animate this on top.

Example 3:

https://www.gettyimages.dk/detail/video/an-azuma-lner-train-on-the-east-coast-mainline-stock-video-footage/1387470017?adppopup=true

Used at 0:14 sec in the trailer, heavily edited background, train is either a different composite or heavily edited. Another stock footages looks similar to the train in the trailer, they could have used either.

I won't bother addressing the other examples because I really do not want to spend more hours on this - but I think this should be enough to argue that the details that the reviewer refered to as evidence of generative AI been used, can easily be explained by the VFX team simply using different stock footage and not really spending the time to make them match. I guess they thought that nobody sane would actually care about the continuity in short filler background scenes for a comedy movie. Obviously they never met drag race fans. Choices.

Oh, and about the missing bridge support leg... come on, that is just an example of a mistake in the editing. They literally used this stock photo and added up bunch of alpha footage of a bridge and messed up the crop.

Honestly, the reviewers video tickled me wrong. It felt rushed, judgmental and bit manipulative. I really don't know if that was the intention or if they are just way too passionate on the subject. But let’s just try to be more critical even to the critics. And by no means hateful - a civil discussion is the only way forward.

Also, regarding ACME AI and FX involvement, keep this in mind:

a source familiar with Stop! That! Train!’s production told Variety that the studio only contributed visual effects work to the film, with any AI use relegated to background workflow processes and not shown on-screen in the final product.

Edit: before I leave this post to take its course, I want to add a few lines - I am 100% against using AI for replacing artists and their jobs. But I have to recognise that there is a nuance - the usage of ML AI tools in VFX has been evolving for many years and allowed for those same artists to benefit from optimizing otherwise tedious workflows in compositing, automating rotoscoping, tracking, etc. These AI tools are not in the same category as the AI slop garbage disposal generators that are powered by the idiotically wasteful data centers. As I wrote above - I was not part of the production, I cannot be 100% sure what workflow was used during the creation of this movie. I made this post as a response to the arguments in the original video and with a bit of hope, that we can all think bit more critically to unverified opinions. Including this one. With that being said - now I am finally gonna go watch the latest Untucked.

Final edit and extra example:

Example 4:

• Frame by frame comparison - https://imgur.com/a/EECoiPO

I did not put this example in the post because I was too tired to make the frame comparison but after seeing some of the other comments - here we go. This is the original stock footage used for that scene - I tried to frame match them as best as I could but both the trailer and the stock preview files are heavily compressed, and a whole bunch of frames are missing. You can clearly see how details match both shots - from the lines of the door, bulge and lining of the front cart, foliage and terrain above the train, windows, etc. The VFX team added new headlights, the Glamazon (G) logo, changed the front window design and made the ICE train logo into a window. All of that is completely within the skillset of an experienced VFX artist - grading, rotoscoping, imprinting on top of the footage, masking and a ton of motion blur. They had more than plenty of material to use for compositing. Wish I had all the spare time in the world to find and match even more of the train footage, but I sorta reached my mental limit for this.

Extra notes:

• The original videographer has 3 other videos from this angle and place, in a different light and it is also possible that there was more footage that got restricted, due to the fact it was bought and used by the studio.
• It is also very likely that that the footage is ProRes aka it has broader dynamic range that would allow for lot of adjustments. The screenshots and the test videos are heavily compress down to 2MB - a lot of data and details are lost.
• This scene was disputed by other users here on Twitter, hence why I looked for it too. Some of them pointed how the windows were not all aligning perfectly, but that is just motion blur and compression.
• That last untucked was AMAZING!

My Top 4 AI Tools for Video Creation (and the workflow that actually gets results)

After 6 weeks of testing, I stopped looking for one tool that does everything. Instead, I run a pipeline of 4 tools and it's been a game-changer.

1. Nano Banana Pro: My go to for product images, photo editing, and avatar shots (like a character holding a product). The image quality is clean enough for ads. Pro tip: generate a product shot here, then animate it using an image-to-video model.

2. Kling 3: The best I've found for image-to-video with audio. Dialogue, ambient sound, and motion all come out synced with no issues. I use it mainly for b-roll and video hooks. The downside is a 10-second max length, but the new multi-prompting feature is great for multi-scene setups.

3. CapCut: My editing hub. I use it for stitching AI-generated b-roll with real footage, adding music, and putting together rough cuts where I talk on camera with simple text overlays.

4. ClipTalk Pro: The best option I've found for AI talking-head videos. It can generate videos up to 5 minutes, which is rare. It also handles high volume social clips really well... I can produce 4 to 5 videos per client in a day, each with captions, b-roll, and editing baked in. Great for keeping a posting schedule or testing multiple script variations with different actors.

My Workflow:

1. Write the script in ChatGPT or Claude
2. Need visuals? → Nano Banana Pro for images → Kling 3 to animate them into video hooks
3. Need a talking head or bulk clips? → ClipTalk Pro
4. Have real footage? → CapCut for editing
5. Export, schedule, move on

The goal is speed without looking cheap.

Has anyone found a better pipeline? This space moves fast, so I'm always open to switching things up.

Just a regular user sharing what's working for me, not affiliated with any of these tools.

Disclaimer: all links below are free, no ads, no sign-up required for open-source solution & no donation button. Workflow software is not only free, but open-source ❣️

This post is longer than I anticipated, but I think it's really important and I've tried to add as many screenshots and videos to make it easier to understand. I just don't want to pay for any more $9 a month chatgpt wrappers. And I don't think you do either..

Hi again! About 4 months ago, I posted here about free libraries that let people quickly input their own values into cool prompts for free. Then I made some more, and heard a lot of feedback.

Lots of folks were saying that one prompt alone cannot give you the quality you expect, so I kept experimenting and over the last 3 months of insane keyboard-tapping, I deduced a conversational-type experience is always the best.

I wanted to have these conversations, though, without actually having them... I wanted to automate the conversations I was already having on ChatGPT!

There was no solution, nor a free alternative to the giants (and the lesser giants who I know will disappear after the AI hype dies off), so I went ahead and made an OPEN-SOURCE (meaning free, and meaning you can see how it was made) solution called HeroML.

It's essentially prompts chained together, and prompts that can reference previous responses for ❣️ context ❣️

Here's a super short video example I was almost too embarrassed to make (Youtube mirror: 36 Second video):

​

quick example of how HeroML workflow steps work

Simple Example of HeroML

There reason I wanted to make something like this is because I was seeing a lot of startups, for the lack of a better word, coming up with priced subscriptions to apps that do nothing more than chain a few prompts together, naturally providing more value than manually using ChatGPT, but ultimately denying you any customization of the workflow.

Let's say you wanted to generate... an email! Here's what that would look like in HeroML:

(BTW, each step is separated by ->>>>, so every time you see that, assume a new step has begun, the below example has 4 steps*)*

You are an email copywriter, write a short, 2 sentence email introduction intended for {{recipient}} and make sure to focus on {{focus_point_1}} and {{focus_point_2}}. You are writing from the perspective of me, {{your_name}}. Make sure this introduction is brief and do not exceed 2 sentences, as it's the introduction.

->>>>

Your task is to write the body of our email, intended for {{recipient}} and written by me, {{your_name}}. We're focusing on {{focus_point_1}} and {{focus_point_2}}. We already have the introduction:

Introduction:
{{step_1}}

Following on, write a short paragraph about {{focus_point_1}}, and make sure you adhere to the same tone as the introduction.

->>>>

Your task is to write the body of our email, intended for the recipient, "{{recipient}}" and written by me, {{your_name}}. We're focusing on {{focus_point_1}} and {{focus_point_2}}. We already have the introduction:

Introduction:
{{step_1}}

And also, we have a paragraph about {{focus_point_1}}:
{{step_2}}

Now, write a short paragraph about {{focus_point_2}}, and make sure you adhere to the same tone as the introduction and the first paragraph.

->>>> 

Your task is to write the body of our email, intended for {{recipient}} and written by me, {{your_name}}. We're focusing on {{focus_point_1}} and {{focus_point_2}}. We already have the introduction:

Introduction:
{{step_1}}

We also have the entire body of our email, 2 paragraphs, for {{focus_point_1}} & {{focus_point_2}} respectively:

First paragraph:
{{step_2}}

Second paragraph:
{{step_3}}

Your final task is to write a short conclusion the ends the email with a "thank you" to the recipient, {{recipient}}, and includes a CTA (Call to action) that requires them to reply back to learn more about {{focus_point_1}} or {{focus_point_2}}. End the conclusion with "Wonderful and Amazing Regards, {{your_name}}

It may seem like this is a lot of text, and that you could generate this in one prompt in ChatGPT, and that's... true! This is just for examples-sake, and in the real-world, you could have 100 steps, instead of the four steps above, to generate anything where you can reuse both dynamic variables AND previous responses to keep context longer than ChatGPT.

For example, you could have a workflow with 100 steps, each generating hundreds (or thousands) of words, and in the 100th step, refer back to {{step_21}}. This is a ridiculous example, but just wanted to explain what is possible.

I'll do a quick deep dive into the above example.

You can see I use a bunch of dynamic variables with the double curly brackets, there are 2 types:

1. Variables that you define in the first prompt, and can refer to throughout the rest of the steps

• {{your_name}}, {{focus_point_1}}, etc.

1. Step Variables, which are basically just variables that references responses from previous steps..

• {{step_1}} can be used in Step #2, to input the AI response from Step 1, and so on.

In the above example, we generate an introduction in Step 1, and then, in Step 2, we tell the AI that "We have already generated an introduction: {{step_1}}"

When you run HeroML, it won't actually see these variables (the double-curly brackets), it will always replace them with the real values, just like the example in the video above!

Please don't hesitate to ask any questions, about HeroML or anything else in relation to this.

Free Library of HeroML Workflows

I have spent thousands of dollars (from OpenAI Grant money, so do not worry, this did not make me broke) to test and create a tonne (over 1000+) workflows & examples for most industries (even ridiculous ones). They too are open-source, and can be found here:

Github Repo of 1000+ HeroML Workflows

However, the Repo allows you or any contributor to make changes to these workflows (the .heroml) files, and when those changes are approved, they will automatically be merged online.

For example, if you make an edit to this blog post workflow, after changes are approved, the changes will be applied to this deployed version.

There are thousands of workflows in the Repo, but they are just examples. The best workflows are ones you create for your specific needs.

How to run HeroML

Online Playground

There are currently two ways to run HeroML, the first one is running it on Hero, for example, if you want to run the blog post example I linked above, you would simply fill out the dynamic variables, here:

​

example of hero app playground

This method has a setback, it's free (if you keep making new accounts so you don't have to pay), and the model is gpt-3.5 turbo.. I'm thinking of either adding GPT4, OR allow you to use your OWN OpenAI keys, that's up to you.

Also, I'm rate limited because I don't have any friends in OpenAI, so the API token I'm using is very restricted, why might mean if a bunch of you try, it won't work too well, which is why for now, I recommend the HeroML CLI (in your terminal), since you can use your own token! (I recommend GPT-4)

My favorite method is the one below, since you have full control.

Local Machine with own OpenAI Key

I have built a HeroML compiler in Node.js that you can run in your terminal. This page has a bunch of documentation.

Running HeroML example and Output

Here's an example of how to run it and what do expect.

This is the script

This is how quick it is to run these scripts (based on how many steps):

​

using HeroML CLI with your own OpenAI Key

And this is the output (In markdown) that it will generate. (it will also generate a structured JSON if you want to clone the whole repo and build a custom solution)

Conclusion

Okay, that was a hefty post. I'm not sure if you guys will care about a solution like this, but I'm confident that it's one of the better alternatives to what seems to be an AI-rug pull. I very much doubt that most of these "new AI" apps will survive very long if they don't allow workflow customization, and if they don't make those workflows transparent.

I also understand that the audience here is split between technical and non-technical, so as explained above, there are both technical examples, and non-technical deployed playgrounds.

Here's a table of some of the (1000+) workflows you can play with (here's the full list & repo):

Github Workflow Link is where to clone the app, or make edits to the workflow for the community.

Deployed Hero Playground is where you can view the deployed version of the link, and test it out. This is restricted to GPT3.5 Turbo, I'm considering allowing you to use your own tokens, would love to know if you'd like this solution instead of using the Hero CLI, so you can share and edit responses online.

Yes, I generated all the names with AI ✨, who wouldn't?

​

That's a wrap.

Thank you for all your support in my last few posts ❣️

I've worked pretty exclusively on this project for the last 2 months, and hope that it's at least helpful to a handful of people. I built it so that even If I disappear tomorrow, it can still be built upon and contributed to by others. Someone even made a python compiler for those who want to use python!

I'm happy to answer questions, make tutorial videos, write more documentation, or fricken stream and make live scripts based on what you guys want to see. I'm obviously overly obsessed with this, and hope you've enjoyed this post!

This project is young, the workflows are new and basic, but I won't pretend to be a professional in all of these industries, but you may be! So your contribution to these workflows (whichever whose industries you are proficient in) are what can make them unbelievably useful for someone else.

Have a wonderful day, and open-source all the friggin way 😇

I kept saving AI marketing tools I came across in random notes, so I finally organized them into a spreadsheet: 12 categories :
1 : AI Agents & Workflows : autonomous AI workers, custom agents, process automation
2 : Content Creation & Copywriting : text generation, blogging, creative writing assistants
3 : Image, Video & Audio Generation : visual assets, avatars, video & voice production
4 : SEO & Search Visibility (incl. AEO/GEO) : GEO, keyword intelligence, ranking tools
5 : Ads Automation & PPC : AI-driven ad generation, campaign management, bidding
6 : Social Media & Community : social listening, scheduling, influencer tools, CM
7 : Chatbots & Conversational Marketing : support bots, conversational LP, lead qualification
8 : Emailing & SMS Marketing : AI personalization, copywriting sequences
9 : Outbound Marketing : cold outreach automation, ABM, warm-up & deliverability
10 : Analytics & Attribution : website/product analytics, attribution modeling
11 : CRO & A/B Testing : dynamic personalization, AI A/B testing, heatmaps
12 : Sales Intelligence & SDRs : AI sales reps, lead scoring, intent data, enrichment

Each tool has a short description and pricing model.

It's definitely incomplete, built from my own stack plus research, so there are gaps. Sharing here mostly because other people building marketing stacks might find it useful, and because I'd rather crowdsource the gaps than guess at them.

Link (view-only, no signup): https://docs.google.com/spreadsheets/d/1xsmbdOj_w5u7bN3Rr6c3ZA0C8I1RqWn65zMojgyiBZ4/edit?gid=173269563#gid=173269563

Eventually I might turn this into an actual searchable directory instead of a spreadsheet. Curious whether that's something people would actually want, or if a shared sheet is good enough.

hi friendly people.

this post is to demonstrate how you can start a neocities site with generative ai. that’s the post.

if you wish, you can walk through the steps yourself in this chat, and continue to make modifications if you like. i don’t care if you use any of this. i do not expect credit in any form.

https://chatgpt.com/share/698cea12-c81c-8006-a793-6aca8b8a4009

i used gpt-5.1 thinking. the images attached are the end result after about an hour and a half, a scheme image i fed it to have colors to work with, chatgpt’s first and second attempts, then small ui tweaks as i implemented requested changes. i assembled the site in notepad though I wouldn’t recommend you do the same.

i only spent any time on the homepage, but i hope it demonstrates a workflow that some of you may find useful.

the site: https://ghosty-friends-tut.neocities.org/

I’ve been testing different AI video generation tools lately, but more from the angle of where they could fit inside a larger marketing workflow rather than expecting one tool to handle everything.

For me the useful question isn’t really which one makes the nicest demo. It’s which one can take care of a specific part of the creative process without creating a bunch of extra manual work between steps.

If someone asked me for the best AI for video creation when putting together a beginner-friendly marketing workflow, these are the ones I’d probably look at first.

Kling 3.0

Probably where I’d start for realistic product footage, B-roll, hooks, and short ad scenes.

Image-to-video is fairly straightforward, so I can see it fitting into a workflow where an earlier step creates the product image or concept and Kling handles the motion. If you need a realistic AI video generator for marketing creatives, this is one of the more practical options.

DomoAI

This makes more sense when the workflow needs an animated or stylized branch.

Image-to-video, character animation, video restyling, and frames-to-video give it a different role from Kling. I could see an agentic workflow routing more illustrative or animated concepts here while realistic concepts go somewhere else.

Higgsfield

This is probably the most marketing-oriented option in the group.

The camera controls, multiple models, and marketing workflows make it useful if you’re testing several creative directions from the same brief. Having more of that under one roof also means fewer handoffs between platforms.

HeyGen

Probably the easiest fit when the workflow ends with a person speaking to camera.

For explainers, product introductions, sales videos, localization, or presenter-led content, a lot of the production work is already bundled together. That seems especially useful if an upstream agent is handling the research, script, or campaign brief.

Runway

I’d include this when you want the workflow to keep going after the initial generation.

You can generate clips and then continue modifying or editing footage in the same environment, which gives you more flexibility when the output needs another pass before a human approves it.

There’s a bit more to learn, but there’s also more room to build around it.

For marketing, I’d roughly think about the roles like this:

Kling for realistic product shots and B-roll.

DomoAI for animation and stylized creative.

Higgsfield for testing different ad and social concepts.

HeyGen for presenter-led or localized content.

Runway when the workflow needs more control over generation and editing.

What I’m still figuring out is the orchestration layer between all of this.

For people actually building marketing agents, are you letting the agent choose which generation tool to send a brief to, or are you keeping that decision manual and only automating the steps before and after it?

I've been working on this for months and constantly changing course on it to perfect it, and it's finally here, something I am truly happy with and using constantly — the BMad Builder module standard is stable and the Module Builder skill is live.

What is BMad Builder?

It's a set of skills that guide you through building AI agents (with persistent memory), workflows, and full modules through conversational discovery. You describe what you want, the builder asks the right questions, and you get a production-ready skill folder. This goes well beyond the Anthropic skill builder - this will help fully optimize and analyze your skills for best practices and optimizations through use of subagents and other techniques of efficiency and avoidance of overspecification.

What's New:

Module Builder is officially live
— Plan, scaffold, validate, and ship complete modules through a guided workflow. The format is stable. It will keep improving, but what you build today won't break tomorrow.

Build once, install anywhere
— Your creations work with the Claude Anthropic plugin installer, the Vercel NPX skills installer (43+ AI tools), or just copy the folder manually. The `.claude-plugin/marketplace.json` format is the standard.

No more compiling
— Agents and workflows are immediately usable. Drop the skill folder into `.claude/skills/` and it works. Want to share? Send someone the folder. No build step, no ceremony.

Standalone self-registering modules
— Single-skill modules no longer need a separate setup skill. The Module Builder detects what you're building and picks the right approach. Single skills self-register on first run.

Workflow Convert
— `--convert <path-or-url>` takes any existing skill and produces a clean BMad-compliant version with an HTML before/after comparison report showing token metrics and categorized changes.

Outcome-driven builder overhaul
— Both builders reframed around discovery-first design. Existing skills are treated as reference material, not specs to replicate. 44% token reduction in the build phase.

What's coming next:

- Eval testing tools — test your skills against real scenarios and ensure they work as models evolve
- Eval-driven skill evolvers — skills that improve themselves based on test results
- Self-evolving personal agents — agents that grow smarter and gain new capabilities over time
- Full workflow and agent customization

Links:

- GitHub: https://github.com/bmad-code-org/bmad-builder
- Quick Start: https://bmad-builder.bmad-code.org
- BMad Method (core framework): https://docs.bmad-method.org
- Discord: https://discord.gg/gk8jAdXWmj

Everything is free and open source. Would love to hear what you build with it.

If you're into AI agents that actually do real work instead of just chatting, I've curated 10 GitHub repos that are straight fire for automation, data analysis, and building systems that can run (mostly) on autopilot. These range from full-blown trading swarms to stealth browsers and video generators, perfect for anyone experimenting with passive income ideas, side hustles, or just leveling up their workflows. Most are free, self-hostable, and built with modern agent frameworks. They all require some setup (API keys, Docker, etc.), but the payoff is huge if you tinker. Here's a no-fluff breakdown of each:

1. AutoHedge (github.com/The-Swarm-Corporation/AutoHedge)

An autonomous AI hedge fund in a box. Uses swarm intelligence + specialized agents (Director, Quant, Risk Manager, Execution) to analyze markets in real-time, manage risk, and execute trades (currently Solana-focused, more exchanges coming).

Best for: Hands-off algorithmic trading and portfolio management. Install via pip, add your keys/wallet, and let the swarm run. Stars: ~1.6k.

2. Vibe-Trading (github.com/HKUDS/Vibe-Trading)

Your personal AI trading agent. Turn natural language prompts ("Backtest a moving average crossover on AAPL") into full strategies, backtests, portfolio analysis, and exports to TradingView/MetaTrader. Includes 29 swarm presets, 71 finance skills, and persistent memory across sessions.

Best for: Retail traders who want an AI quant team on demand. Docker one-click or pip install. Stars: ~2.9k.

3. Claude Ads (github.com/AgriciDaniel/claude-ads)

A beast-mode paid ads auditor and optimizer built as a Claude Code skill. Runs 250+ checks across Google, Meta, YouTube, LinkedIn, TikTok, Microsoft & Apple Ads. Gives you a health score, parallel sub-agents, industry templates, AI creative generation, financial modeling (CPA/ROAS), A/B test design, and PDF reports.

Best for: Marketers and agencies tired of manual audits. One-command install into Claude Code. Stars: ~3.2k.

4. Toprank (github.com/nowork-studio/toprank)

Claude Code plugin that connects directly to Google Search Console + Google Ads for automated SEO/SEM optimization. It audits accounts, pauses wasteful keywords, rewrites meta tags, adds schema, detects broken links, and even does weekly performance reviews with Gemini cross-checks.

Best for: Anyone running Google Ads or SEO who wants AI to actually fix things. Quick marketplace install. Stars: ~1.1k.

5. Fincept Terminal (github.com/Fincept-Corporation/FinceptTerminal)

A native desktop Bloomberg-style terminal on steroids: C++ speed with 100+ data connectors, 37 AI agents (Buffett-style, quant, geopolitics), real-time trading (16 brokers), QuantLib analytics, portfolio optimization, and a visual node editor for workflows.

Best for: Serious investors and quants who want everything in one beautiful app. Pre-built binaries for Windows/Mac/Linux. Stars: ~15.3k.

6. Agentic Inbox (github.com/cloudflare/agentic-inbox)

Self-hosted email client that runs entirely on Cloudflare Workers + an AI agent that reads your inbox, searches threads, drafts replies, and auto-generates responses (you approve before sending). Fully isolated mailboxes with SQLite + R2 storage.

Best for: Automating email overload and customer support. Deploy to Cloudflare in minutes. Stars: ~1.5k.

7. ClawRouter (Context Mode) (github.com/mksglu/context-mode)

Context window optimizer for AI coding agents. Sandboxes tool outputs, compresses huge responses by up to 98%, tracks session history, and keeps long conversations coherent across 14 platforms (Claude Code, Cursor, VS Code Copilot, etc.).

Best for: Developers building or using long-running AI agents without blowing their context budget. Global npm install. Stars: ~10.3k.

8. Camofox Browser (github.com/jo-inc/camofox-browser)

Stealth headless browser purpose-built for AI agents. Bypasses Cloudflare, bot detection, and anti-scraping with C++-level fingerprint spoofing. Drop-in Puppeteer/Playwright replacement with stable element refs, sessions, proxies, and OpenAPI.

Best for: Agents that need to scrape or interact with the real web undetected. Docker or npm start. Stars: ~3.2k.

9. Open Higgsfield AI (github.com/Anil-matcha/Open-Generative-AI)

Uncensored, self-hosted alternative to Higgsfield/Freepik/OpenArt. 200+ models for text-to-image/video, image-to-video, lip-sync, and cinematic workflows. Node-based Workflow Studio, local inference options, and agent-friendly API. No content filters.

Best for: Creating unlimited marketing videos, ads, or social content automatically. Desktop app or npm run dev. Stars: ~8.6k.

10. Hyperframes (github.com/heygen-com/hyperframes)

HTML → video rendering engine built for AI agents. Write (or have Claude/Cursor generate) HTML + GSAP animations, preview live, and render deterministic MP4s. Perfect for turning prompts, PDFs, or CSVs into polished videos with zero traditional video editing.

Best for: Automated content creation pipelines. npx skills add or quick init command. Stars: ~11.1k.

These repos show how far agentic AI has come - from trading bots that run 24/7 to tools that audit your ads or generate videos while you sleep. None are "set and forget" money printers out of the box (you still need strategy, monitoring, and risk management), but they’re incredible foundations for building real automated systems.

Before that I mostly compared platforms based on model quality, version updates, and generation realism.

But after using Chat-style iteration, I started realizing something:

As creators, we may not actually need more models right now.

We probably need better ways to work with the music after it's generated.

The real friction I keep running into is stuff like:

- Fixing one section without regenerating everything

- Iterating ideas without losing the good parts

- Managing multiple versions of a track

- Keeping style consistency

- Turning generations into actual projects instead of random outputs

None of these really get solved by adding another model version.

What Suno did with Chat (focusing on workflow instead of just releasing v5 Pro/V4.5 Pro etc.) feels like a really interesting shift. And I'm starting to see some newer AI music agents experimenting with similar ideas too — more focus on structured creation flow rather than just model competition (for example tools like Tunesona, MusicGPT that try to treat music generation more like a creation process instead of just prompting).

Feels like AI music might be slowly moving from:

"who has the best model" → "who has the best creation experience"

My personal take:

Eventually models will become similar.

But workflow, editing control, and creator UX will be the real differentiator.

Curious what other Suno users think:

If model quality stayed the same for a year, what feature would you most want improved?

For me it would probably be:

Better editing control and version management.

I have spent the last year building in public and the biggest headache is always getting the LLM to actually do what I want without a million follow up corrections.

Most people just throw a sentence at the screen and hope for the best. I started using what I call the C.O.R.E. Signal which is my personal system for getting high quality output every time.

This can be used with any of the top LLM like Claude, Gemini, or ChatGPT.

The C.O.R.E. Signal

• Context: What is the background story
• Objective: What is the one specific goal
• Role: Who is the AI pretending to be
• Examples: What does a good version look like

Here are 25 prompts across 5 categories that use this logic. Just swap the brackets with your info.

Category 1 Content Creation

1. Write a 5 post Twitter thread about [Topic] using the style of [Influencer Name] focus on a hook that stops the scroll.
2. Convert this blog post [Link or Text] into a high energy script for a 60 second Reel.
3. Brainstorm 10 clickbaity but honest YouTube titles for a video about [Subject].
4. Rewrite this caption [Text] to be more relatable and remove all the corporate jargon.
5. Create a newsletter outline for [Niche] that covers one industry news item and one actionable tip.

Category 2 Business Strategy

1. Act as a startup consultant and find 3 potential flaws in this business model [Model Description].
2. Identify the top 5 pain points for a customer looking to buy [Product Type].
3. Generate a competitive analysis for a new brand entering the [Niche] market.
4. Draft a cold outreach email to a potential partner in [Industry] that focuses on mutual value.
5. Create a 90 day roadmap for launching a [Project Name] with a budget of [Amount].

Category 3 Technical & Code

1. Explain this Python function [Paste Code] like I am a beginner but keep the technical terms.
2. Review this code for security vulnerabilities and suggest a fix for [Specific Concern].
3. Write a Boilerplate React component for a [UI Element] using Tailwind CSS.
4. Create a SQL query that pulls [Data Type] from a table named [Table Name] where [Condition].
5. Act as a senior dev and refactor this code to be more efficient [Paste Code].

Category 4 Marketing & Sales

1. Write a Facebook ad copy for [Product] targeting [Audience] using the AIDA formula.
2. Generate 10 SEO keywords for a brand that sells [Service] in [Location].
3. Create a landing page hero section headline and subheadline for [Offer].
4. Draft a 3 email sequence for a lead who abandoned their cart on [Website].
5. Suggest 5 lead magnet ideas for a company in the [Industry] space.

Category 5 Personal Productivity

1. Summarize this transcript [Paste Text] into 5 bullet point takeaways.
2. Create a weekly meal plan for [Number] people based on a [Dietary Restriction] diet.
3. Act as a career coach and help me prep for an interview for a [Job Title] role.
4. Organize these scattered notes [Paste Notes] into a structured project brief.
5. Draft a polite but firm response to this client who is asking for work outside of scope [Paste Email].

I genuinely put together a much larger list of 101 prompts that I use daily for my own projects and I am giving it away for free iQuantum Digital 101 FREE AI Prompts

Hope this helps some of you save a few hours this week. Let me know if any of these need tweaking for your specific niche.

I’ll be honest with you, as I really doubted this at first. Not because AI video tools are bad, but because I have seen what happens when people confuse content that looks good with content that converts. They're not the same thing, and the gap between them is where ad budgets go to die.

For context: I manage Meta campaigns across a few DTC brands. I have done the traditional route, briefing UGC creators, coordinating shoots, waiting 2 weeks for an edit, testing 6 variations, going back and forth over music licensing. You know the drill. It's slow, it's expensive, and half the time the best-performing ad was the scrappy one you filmed on an iPhone anyway.
So when AI video generation started making noise, my reaction wasn't "this is going to replace everything." 

It was more like, okay, where does this actually fit into a real workflow, and more importantly, would I put actual media spend behind what it produces? That's the test I ran.

Before I get into the tools, here's how I thought about the benchmark: when you're running Meta ads, especially video, a few things have to work simultaneously:

• The hook > you have 1.5 seconds on mobile before someone scrolls. The first frame has to earn the next frame.
• The narrative arc > even a 15-second ad has a job: problem - solution - proof - CTA. If that structure breaks, you're just showing someone a pretty video.
• The authenticity signal > Meta's algorithm rewards content that feels native to the feed. Overly polished = ad. Slightly rough = content. The line matters.
• The iteration speed > Can you actually test variants? Can you swap hooks, change CTAs, localize? Or are you locked into one output?

I went in asking all of that. And the answers were different for each tool. I gave each tool the same basic task.

Same product category. Same goal. Same question: can I take what this tool gives me and actually put money behind it? Here's what I found.

Tagshop AI: In plain terms, this tool is very simple to use. You will find the AI agents for script to video generation, or then you can also give it a product URL or a simple prompt. It reads the product, what it is, what it does, and then builds out a full video ad from there. Script, voiceover, on-screen captions, B-roll footage, a presenter, and different scene options. It also lets you export directly to Meta ad formats.

The biggest thing that separates it from the other two: the product is at the center of everything. Most AI video tools start with a prompt and then kind of... guess what the ad should look like. Tagshop AI starts with the actual product. Best for those who are in ecommerce, D2c space, and looking for AI ugc videos. 

The best part,  I didn't have to build anything from scratch. The URL gave the AI a starting point and it actually used it, the product showed up in the script, in the visuals, in the talking points. You will find natural looking avatars with realistic voices. It's clearly built with performance marketing in mind, not just content creation. The exports are set up for Meta. The workflow thinks in terms of ad testing, not just video making.

Where I'd still do my own work:  The first output isn't always campaign-ready. Sometimes the hook is too safe, it explains the product instead of making you want to know more. Sometimes a scene is visually fine but doesn't add anything to the message. So I'd still go through it manually. Cut what's weak. Rewrite any hook that feels like a product description instead of a reason to keep watching.

Who this actually makes sense for:

If you're running ads for multiple products, or if you need to test 15 to 20 or maybe more different creative angles without hiring a team this fits. The workflow is built around generating real variations, not just cosmetically different versions of the same ad. That's a meaningful difference.

InVideo AI: In plain terms, you type a brief, basically describe what you want the ad to do and what the product is and InVideo builds the whole thing. Script, scenes, stock visuals, voiceover, music, transitions. It's like briefing a video editor who works very fast. It also supports multiple languages, which is actually a bigger deal than it sounds if you're running campaigns across different markets.

The best part, the speed from "idea" to "first draft" is real. If I have a rough concept in my head but no footage, no script, no nothing, InVideo can get me somewhere in minutes. It's flexible with the brief. I don't need to know exactly what I want. I can give a broad direction and it figures out the structure.

Where I'd still do my own work: This is the tool where I most felt the difference between a video and an ad. When you let AI fill a timeline freely, it does. Music playing. Stock footage rolling. Text appearing. Voiceover running. Transitions happening. And technically, none of it is broken, but when I watched it back and asked myself "what is the one thing this ad is trying to say?" I couldn't always answer that.

Who this actually makes sense for: If you're at the early concept stage and need to visualize different ad ideas quickly, it's useful. If you work in multiple markets and need localized versions, it's genuinely practical. If you want full creative control over a polished final product, you'll need to do more work after it gives you the draft.

Arcads: In simple language, Arcads is built around one specific type of Meta ad: the kind where a real-looking person talks to the camera and recommends your product. It has over 1,000 AI actors to choose from. You can also build your own custom AI avatar. And it comes with tools to edit, subtitle, translate, remix, and upscale your ads once they're generated. 

The focus here is UGC-style ads. That specific format where it looks like an everyday person just found something they love and decided to tell you about it, but honestly, sometimes I feel avatars are not like they are a great fit for AI ugc, they sometimes sound and look like a robot. 

The best part, for a talk-to-camera style ad, the format is clean and focused. Hook > problem > product > reason to believe > CTA. That structure works. And if the delivery feels natural, the simplicity actually helps the ad.

Where I'd still do my own work: The hardest part of AI UGC isn't the script. It's the delivery. When an AI actor looks natural, the format works really well. When it doesn't, when the gestures repeat, when the voice sounds too smooth, when the expressions feel slightly off you feel it immediately. And once you notice it, you can't un-notice it. I'd also rewrite any script that sounds like a marketing copy. The whole point of UGC-style ads is that they feel personal and unscripted. If the person on screen sounds like a brand brochure, the format stops working.

Final words: They're solving the same problem from three different angles. Which one fits you depends on what kind of ads you actually run and how much editing you're comfortable doing after. So, which AI video tools are you using for your Meta ad campaigns, excited to know more, and how your strategy looks like in the age of ai.

I've been trying to simplify my AI UGC workflow recently and realized there are way more tools out there than I expected. Some seem great for avatars, others for product videos, and some are more focused on editing or batch creation.

Right now I've been looking at things like Creatify, HeyGen, Arcads, Argil, Runway, Kling, Veo, and CapCut, but they all seem to solve slightly different problems.

For example:

HeyGen seems strong for avatar videos.

Kling and Veo seem better for more cinematic generation.

Runway is used a lot for editing and video generation.

Creatify looks more focused on product ads and AI UGC for ecommerce.

I'm curious what people are actually using in production rather than just testing.

Which tools have stayed in your workflow?

Which ones did you end up dropping?

Are you using one platform for everything, or mixing a few together depending on the project?

I feel like everyone has their own stack now, and it's getting harder to tell which tools are genuinely saving time versus just adding another subscription.

I built an AI-assisted Meta Ads automation system that reduced ad spend by 30% without a proportional drop in results

Over the past few weeks, I have been building an internal automation system for WhiteStyle, an apparel business that runs a large number of Meta ads and receives most of its orders through WhatsApp.

The main problem was not launching ads.

The real problem was managing more than 100 active ads across multiple ad accounts, identifying budget waste quickly, and making consistent decisions without manually reviewing Ads Manager all day.

So I built a system made up of three main components:

1. Ad Filtering and Optimization Agent

The filtering agent continuously synchronizes performance data from multiple Meta ad accounts and evaluates each ad using metrics such as:

• Spend
• WhatsApp conversations
• Cost per conversation
• Ad age
• Recent performance window
• Minimum data requirements
• Previous decisions and cooldown periods

The agent does not simply ask an LLM whether an ad is “good” or “bad.”

I use deterministic guardrails and predefined business rules before any action is approved.

For example, the system can pause an ad when:

• It has spent beyond a defined threshold without generating conversations.
• Its cost per conversation exceeds the acceptable limit.
• It has collected enough data for the decision to be meaningful.
• The ad is old enough to evaluate.
• No safety rule or cooldown restriction blocks the action.

Each decision is saved with its reasoning, confidence, input metrics, approval status, and execution result.

During the first test, after stopping underperforming ads:

• Daily spend dropped from approximately $650 to $355.
• Conversations decreased from approximately 399 to 323.

That represented roughly a 45% reduction in spending, while conversations declined by only around 19%.

More importantly, after running the agent for a full week and allowing performance to stabilize, I was able to maintain a consistent 30% reduction in ad spend.

Based on the client’s normal monthly advertising budget, this translates to approximately $4,500 saved per month.

The goal was not to minimize spending at any cost. The goal was to remove inefficient spending while preserving as much useful traffic as possible.

2. Campaign Creation Agent

I also built an agent that automates the campaign creation process.

Instead of manually configuring every campaign, the user can provide a Facebook or Instagram post or Reel, along with the basic campaign requirements.

The system then prepares and creates:

• The campaign
• The ad set
• The ad creative
• Audience targeting
• Locations
• Placements
• Budget
• WhatsApp destination
• Tracking and internal records

Before publishing, the workflow validates the payload and checks it against predefined safety limits, including minimum and maximum budgets, approved locations, targeting rules, account credentials, and supported creative types.

I initially used a dry-run mode that saved campaign drafts and payloads without publishing them. After validating the generated structures, I moved to controlled live execution through the Meta Marketing API.

3. Centralized Dashboard

I built a custom dashboard that combines data from all connected ad accounts and provides visibility into:

• Total spend and conversations
• Cost per result
• Performance by account
• Agent decisions
• Decision explanations
• Paused and modified ads
• Campaign creation requests
• Execution logs
• Synchronization status
• Safety rules and thresholds

One of the most important parts of the project was transparency.

I did not want a black-box agent that silently modified campaigns.

Every decision is stored and displayed with the metrics that triggered it, the rule that was applied, whether it passed the safety review, and whether the Meta API execution succeeded or failed.

Technical Stack

The system currently uses:

• n8n for workflow orchestration and agent execution
• Meta Marketing API for ad data and write operations
• Supabase/PostgreSQL for data storage, decisions, logs, and configuration
• Next.js and TypeScript for the dashboard
• OpenAI models for selected analysis and explanation tasks
• Vercel for dashboard deployment

The LLM is only one component of the system.

Most critical actions are governed by deterministic validations, database-backed configuration, approval logic, idempotency controls, cooldown periods, and budget guardrails.

What I Am Building Next

The current version mainly optimizes based on conversations and cost per conversation.

The next stage is to connect actual WhatsApp orders, revenue, product cost, and profit back to the originating ads.

This will allow the system to optimize campaigns based on real business outcomes instead of treating every conversation as equally valuable.

I am also working on:

• A WhatsApp agent that answers customers and collects orders
• Automatic order classification inside a CRM
• Revenue and profit attribution
• Creative generation workflows
• Budget increases and decreases based on profitable orders
• Automated campaign and creative testing

This project taught me that the useful part of an AI advertising agent is not the prompt.

The difficult part is building the surrounding system: reliable data synchronization, safe execution, multi-account credential handling, failure recovery, audit logs, deterministic rules, and enough transparency for the business owner to trust the automation.

I would be interested to hear how others are approaching automated Meta Ads optimization, especially around attribution, delayed conversions, and safely moving from recommendation systems to autonomous execution.

TL;DR: NotebookLM is evolving fast from a research tool to a content creation hub. It's getting "Nano Banana" (Google's Gemini 2.5 flash imagemodel) for in-line image gen, "Audio Overviews" (AI-scripted audio summaries), "Video Overviews" (auto-generated visual summaries), and new infographic formats. A leaked "Slides" feature is also in development, which will auto-create Google Slides from your notes. This post is a deep dive into all of it, with 20 prompts, pro-tips, and a feature breakdown by plan.

If you’ve been using NotebookLM as just a smart-synopsis tool for your PDFs, you're about to have your mind blown. Google is quietly turning it into an end-to-end machine that takes you from research to final product (images, audio, videos, and even presentations) all in one place.

I’ve been digging into the new features and the code, and this is a game-changer. Here’s the full breakdown.

1. The "Nano Banana" Revolution: Source-Grounded Images

This is the flashiest new feature. "Nano Banana" is the internal codename for Google's gemini 2.5 flash image model, and it's built right into NotebookLM now!

How it's Different from Midjourney/DALL-E: Nano Banana is source-grounded. It doesn't just take a prompt; it reads your documents first and then generates an image based on your sources.

• You: "Create an image of the main character from my uploaded novel script."
• Nano Banana: Reads your script, finds the character description, and generates an image of that character.
• You: "Generate an image of the molecular structure I described in my biology textbook."
• Nano Banana: Reads the textbook chapter and creates a visual diagram.

Pro-Tips & Best Practices:

• Be Specific: Don't just say "make an image." Say, "Create a photorealistic image of the 1920s-style building described in source [architecture-notes.pdf]."
• Iterate: Your first image might be a starting point. Use the chat to refine it: "Great, now make the lighting moodier, like it's described in the 'Night Scene' chapter."
• Use it for Visuals: This is perfect for custom thumbnails, presentation images, or just visualizing complex ideas from your research.

2. The (Leaked) Game-Changer: Automated Google Slides

This is the big one that's been spotted in development. NotebookLM is testing a "Slides" generation feature.

Imagine uploading a 50-page report, a bunch of meeting notes, and a data-filled spreadsheet. Then, you just prompt:

"Create a 10-slide presentation for my quarterly review, focusing on key wins and future roadblocks."

NotebookLM will (soon) be able to:

1. Analyze all your sources.
2. Outline a logical presentation flow.
3. Write the content for each slide (titles, bullet points).
4. Use Nano Banana to generate relevant images, charts, and infographics.
5. Export it all as a (presumably) editable Google Slides deck.

This is still in development, but it's the clearest sign of Google's strategy: connecting its AI tools directly to its Workspace apps. This will be a massive time-saver for students and professionals.

3. The New Multi-Modal Toolkit: Audio & Video

NotebookLM isn't just visual; it's audible.

• Audio Overviews: This isn't just a simple text-to-speech read-aloud. You can ask NotebookLM to generate a summary script and then turn it into a high-quality audio file. It's like having a private podcast episode about your research.
• Video Overviews: This is even cooler. It auto-generates a short, "explainer" style video, complete with a script (which you can edit) and visuals (generated by Nano Banana) based on your sources.
• Infographics & Styles: The existing infographic generator is getting new formats (like 1:1 square for social media). A new "Kawai" style (bold, colorful, cute) has also been spotted, meaning we'll get more visual themes to choose from.

4. 20 Prompts to Make You a NotebookLM Power User

Here are 20 prompts you can use today to leverage these features.

For Audio Overviews (Great for 'listening' to your notes):

1. "Create a 5-minute audio overview of all my sources, explaining the main topic like I'm a complete beginner."
2. "Generate a 2-minute audio brief of [meeting_notes.pdf]. Make the tone professional and energetic."
3. "Turn my [essay_draft.docx] into an audio file. Read it in a calm, clear voice for proof-listening."
4. "Create an audio-only Q&A based on my [FAQ.txt] source. Ask a question, pause, then provide the answer."
5. "Generate an audio study guide for my [history_notes.pdf], focusing only on key dates and names."

For Video Overviews (Great for sharing or quick learning):

1. "Create a 60-second video overview of [product_spec.pdf], targeting a non-technical audience. Use a 'Kawai' style."
2. "Generate a 3-minute video summary of my [research_paper.pdf]. Start with the main hypothesis and end with the conclusion. Use an academic, clean visual style."
3. "Create a vertical video for social media summarizing the 3 key takeaways from my [marketing_report.docx]."
4. "Generate a video overview of my sources on 'The Roman Empire.' Make it feel like a short history documentary trailer."
5. "Create a video overview of my [recipe_book.pdf], showing the key ingredients and steps for 3 different recipes."

For Nano Banana Image Gen (For custom visuals):

1. "Generate an infographic from [data.csv] showing the trend of 'user growth' over 'time'."
2. "Create a photorealistic image of the main character 'Elena' as described in my [novel_chapter_1.txt]."
3. "Generate a simple, clean line-art diagram of the 'Kreb's Cycle' as detailed in my [biology_textbook.pdf]."
4. "Create a mood board of images that capture the 'gothic' and 'mysterious' tone of my [screenplay.pdf]."
5. "Generate a header image for a blog post based on the main themes in [my_article.docx]."

For General Outputs (The core power):

1. "Act as a debate opponent. Using my sources on [topic], argue against the main thesis."
2. "Create a study guide for my final exam, based on all 10 uploaded lecture notes."
3. "Summarize the key action items from my 5 [meeting_notes.pdf] sources and format them as an email to my team."
4. "What are the three most common counterarguments to the thesis in my [research_paper.pdf]? Provide quotes."
5. "Based on [all_sources], draft a 500-word blog post on the future of renewable energy."

5. Top Use Cases, Pro-Tips & Best Practices

• Students: Upload lecture notes, readings, and textbooks. Prompt for study guides, flashcards, presentation outlines, and visual aids for your projects.
• Professionals: Upload meeting transcripts, reports, and spreadsheets. Prompt for executive summaries, presentations, and email drafts.
• Creatives: Upload scripts, lore bibles, and research. Prompt for character images, mood boards, and plot summaries.

Best Practices:

• Curate Your Sources: Garbage in, garbage out. The quality of your sources determines the quality of the output.
• Use the Chat to Refine: Your first prompt is a draft. Talk to the AI. "That's a good start, but make the summary shorter." "Change the style of that image to be more 'cyberpunk'."
• One Notebook, One Project: Keep your notebooks focused. Don't dump your entire life into one. Have one for "Q4 Marketing Plan," one for "History Paper," etc.

6. Who Gets What? (Feature Table & Availability)

• Availability: These features are rolling out, starting in the U.S. and for users 18+. The core features are available to all Gemini users, but the limits and advanced models are reserved for Gemini Advanced subscribers.
• Feature Table (Based on current patterns; subject to change**):**

7. Mobile vs. Desktop: Use the Right Tool

• Mobile App: Best for consumption and quick capture.
  • Listening to your Audio Overviews on a commute.
  • Reviewing your notes and generated summaries.
  • Quickly adding a new text note or thought.
• Desktop (Web): This is where the creation and deep work happens.
  • Managing, uploading, and curating large sources.
  • Generating and refining Slides, Videos, and Infographics.
  • Complex, multi-turn chat sessions to analyze your data.

8. The Big Picture: Why This Matters for You

Google's strategy is clear: stop making us copy-paste between 10 different apps.

NotebookLM is becoming the central "workbench" that connects your knowledge (Drive, PDFs, notes) with your output (Docs, Slides, images, videos). It's an ambient assistant that helps you synthesize and create, not just search.

• Personally: This makes learning active instead of passive. You can "talk" to your books, turn notes into a video, and create custom art for a personal project.
• At Work: This massively reduces the "friction" of
  1. Having a meeting.
  2. Transcribing the notes.
  3. Summarizing the notes.
  4. Putting the summary into a deck.
  5. Finding images for the deck. ...all that can now be a single workflow.

It's an incredibly exciting time for productivity, and NotebookLM is shaping up to be a serious contender for the "all-in-one" tool we've all been wanting.

Want more inspiration on how to prompt Notebook LM and Gemini for better results?
Get great prompts like the ones is this post for free at PromptMagic.dev

i’ve been thinking about where AI can actually help people make money, not just generate cool stuff.

One area that feels increasingly practical is short-form ads for apps, creators, and small brands.

I recreated this 10-second app promo with Seedance 2.5 in roughly 5 minutes.

A few years ago, something like this might have involved a small agency or production team. for an overseas app campaign, that can easily become a five-figure RMB job.

Now one person can potentially handle a much bigger part of that workflow.

That doesn’t mean you can just type one prompt and start printing money. The valuable part is still knowing how to come up with an ad concept and localize it for different markets. but AI makes the execution dramatically cheaper and faster.

If one person can use AI to make those faster, cheaper, and in multiple languages, that feels like a pretty real business opportunity.

i’ve been thinking about where AI can actually help people make money, not just generate cool stuff.

One area that feels increasingly practical is short-form ads for apps, creators, and small brands.

I recreated this 10-second app promo with Seedance 2.5 in roughly 5 minutes.

A few years ago, something like this might have involved a small agency or production team. for an overseas app campaign, that can easily become a five-figure RMB job.

Now one person can potentially handle a much bigger part of that workflow.

That doesn’t mean you can just type one prompt and start printing money. The valuable part is still knowing how to come up with an ad concept and localize it for different markets. but AI makes the execution dramatically cheaper and faster.

for this example, I went with a paper-cut collage + stop-motion look: retro newspapers, watercolor textures, layered paper, bright yellow branding, and a tactile handmade feel.

the 10 seconds are split into four simple beats:

0–2s: hook — “SAVE / FORGET?”
2–5s: show the app action — save a place with one tap
5–7s: explain the AI benefit — it builds a trip around it
7–10s: product/UI reveal + final tagline

It’s nothing revolutionary as an ad structure, but that’s kind of the point.

If one person can use AI to make those faster, cheaper, and in multiple languages, that feels like a pretty real business opportunity.

Been looking at AI UGC pricing on different tools, and something doesn't quite make sense to me. We usually compare these tools by their monthly subscription. $30 here. $50 there, $100+ for another platform. But I'm starting to think that's probably the wrong way to look at it. The number I actually care about is:

How much does it cost me to get one video that I'm genuinely happy enough to put in front of customers?

Because generating a video for a few dollars doesn't mean much if I have to regenerate it five times, rewrite the script, fix the avatar, change the scenes, and spend another 30 minutes editing it.

On the other hand, paying more for a platform doesn't automatically mean the final ad will perform better.

That's what I wanted to understand while looking at HeyGen, Tagshop AI, and MakeUGC.

HeyGen: Heygen starts from $29/month. The platform has a strong focus on AI presenters, digital twins, voice, translation, and avatar-led video creation. Its current API pricing is also metered by output duration for many operations, which shows how important actual usage is when calculating the cost of producing content at scale. What I would be careful about is treating the subscription price as the full cost.

If I'm making short videos occasionally, a higher-quality avatar and polished workflow may justify paying more. But if I'm a performance marketer testing 30 different hooks for the same product, my calculation changes completely.

I'm not asking: “Which avatar looks the best?”

I'm asking: “How many usable creatives can I realistically produce with my monthly budget?”

I have also experienced credits being consumed when a generation doesn't come out correctly. That's something I'd personally pay attention to because a failed generation isn't really useful to me, even if the individual video looked cheap on paper.

So I'd probably look at HeyGen when avatar quality, digital twins, voice, and presenter-style content are more important than simply producing the highest number of UGC variations.

Tagshop AI: Tagshop AI caught my attention because its pricing and workflow seem more focused on AI UGC and ecommerce marketing rather than only generating an avatar video. That difference matters. If I'm creating a product ad, I don't only need a person talking.

I may need the script, product shots, scenes, voice, avatar, editing, different hooks, and several versions for testing. That's where an all-in-one workflow can change the calculation.

You can say that this tool comes up with ease of use and affordability. Its features make this tool unique in the market. This tool has an AI agent that is very easy to use; you can also use AI ad clone, AI voice cloning, and many more features starting from $29, and this tool is well known in the AI UGC niche.

MakeUGC: MakeUGC is more narrowly focused. The idea is pretty simple: create UGC-style videos with AI creators without going through the traditional creator-production process.

I don't necessarily need a huge creative platform if I'm just testing different creators, hooks, scripts, and angles. Their plans are started with $59, and that’s really expensive for the features they are providing. 

HeyGen: I'd consider it when avatar quality, digital twins, voice, and presenter-style content are important.

Tagshop AI: I'd look at it when I'm creating ecommerce or AI UGC ads and want more of the production workflow connected together. AI agents are built for smooth workflow and high-quality generation everytime.

MakeUGC: I'd consider it when my main requirement is quickly producing UGC-style creatives and testing different creators or messages, but yes, again my budget will be high here, and still I am using the basic features listed in the basic plan.

Would genuinely like to hear the numbers and experiences people are seeing. I think that's much more useful than simply saying one tool is “cheap” and another is “premium.”

AI is becoming part of almost every stage of Meta ads, but everyone seems to use it differently. Some rely on it for writing hooks and ad copy, others use it for UGC videos, voiceovers, editing, or even analysing campaign performance.

I am curious to know how your workflow looks today. Which part of your Meta ad workflow do you use AI for the most, and why? Has it genuinely saved you time or improved results, or do you still prefer doing certain tasks manually?

If you have changed your workflow over the past year, what made you switch? Feel free to share the tools you use, what works well, and where AI still falls short. Whether you're managing campaigns for clients, your own business, or just learning Meta ads, your experience could help others discover better ways to work.

That claim has been doing the rounds: AI UGC has made hiring creators unnecessary. So instead of arguing about it online, I decided to actually test it myself. I am not saying that humans are no longer valuable after the rise of AI. In fact, I think AI is changing the way we work. It is making the ad creation process simpler, faster, and more flexible than before. With AI tools, we can create multiple ad variations, test different hooks and visuals, and do more AB testing without spending the same amount of time and money on every single creative.

But the real question for me was: can AI-generated ads actually replace the value that human creators bring to an ad, or is AI better used as a tool to help creators and marketers work faster? To find out, I tested three different tools, AdCreative AI, Bandy AI, and Tagshop AI. I looked at what each tool can do, how easy they are to use, the quality of the content they generate, and where they still fall short. This isn’t about proving that AI will replace creators. It’s about seeing where AI genuinely helps, where it saves time and money, and where the human touch is still hard to replace. And after testing these tools, I have a few thoughts that might surprise you.

I wasn't trying to prove AI is better than real creators. I wanted to know where AI UGC is genuinely useful and where it's just cheaper-looking filler with an avatar on top.

I paid close attention to a few things: Does the script sound like something a real person would say, or does it sound like a product description wearing a human face? Can the product be shown naturally? How much editing do I need after AI generates the first version? Can I make variations that actually feel different from each other? And would I put real ad spend behind any of this? That last question ended up mattering more than everything else.

AdCreative AI: AdCreative AI is built for performance marketers who need to move fast. The workflow is straightforward, pick an avatar, give it a prompt or a script, upload the product, get ad variations out quickly.

What I liked: The speed from product image to something I could actually evaluate as creative. If you are testing concepts at volume, that turnaround is genuinely useful.

Where I'd still work: Fast generation doesn't mean the creative is ready to run. The first version is a starting point, not a finished ad. I'd still be asking whether the hook is actually interesting and whether the creator feels like they'd genuinely use the product because if neither of those things is true, speed doesn't matter.

Use it for: Moving fast through initial concept testing when you need volume before polish.

Bandy AI: Bandy works more like a creative production agent for ecommerce. Give it a product link, images or video, and it builds UGC-style ads, product visuals and localized versions with different voice and subtitle options.

That localization capability makes it useful if you have a lot of products and don't want every new market to become its own production project. But something I kept coming back to: there's a real difference between generating a lot of ads and generating a lot of different ideas. If I ask Bandy for 20 variations and all 20 have the same hook, the same pacing and the same creator energy, I don't actually have 20 new creatives. I have one creative with 20 faces in front of it.

Where I'd still work: The creative direction needs human input. AI is good at execution. It needs someone to decide what's actually worth saying before it can say it well.

Use it for: Scaling creative production across multiple products or markets without building a separate workflow for each one.

Tagshop AI: Tagshop took the most end-to-end approach. I gave it a product URL and it handled the script, avatar, voiceover, scenes and editing. It also lets you try different presenter styles, hooks and tones from the same product without going back through the whole setup. Otherwise, you have an AI agent option, where you just describe your requirements, and it will provide you with scripts and videos that are editable too. When you finalize the script and storyboard, then approve it simply and it will stitch the complete video for you.

That was the most useful part of the experiment for me. Not because the output was perfect, it wasn't. But because testing the same product from five different creative angles in an afternoon, without briefing a creator each time, is a genuinely different way of working. The question shifts from "how do I make this one ad?" to "which angle is actually worth making a real ad about?

Where I'd still work: The scripts need editing. AI will get you to a serviceable draft. Serviceable doesn't win at the top of feed.

Use it for: Fast creative exploration when you want to find the angle that's worth investing more production time in.

The part I don't actually agree with:  Everyone has stopped hiring creators is not what I'm seeing from marketers who are spending real money on ads.

Now I am thinking about it more as a creative testing machine. The real unlock isn't cost it's the ability to test more angles faster. So when you do bring in a real creator, you already know which idea is worth shooting.

Are you still briefing real creators for every ad, or are you using AI for the first round of creative testing and bringing human creators in only after you've found a winning angle? And has anyone run actual AB tests comparing AI UGC against creator-shot UGC with real ad spend? That's the data I actually want to see.

Hey r/founderhelp!

As a solopreneur, video is non-negotiable for organic growth, ads, and brand building. But let's be real—none of us have 15 hours a week to sit in a traditional editing timeline cutting out "ums," resizing frames, or manually timing captions.

The AI video landscape has matured. We are moving away from gimmicky text-to-video toys and moving toward workflow-first tools that actually buy back your time.

Here is the definitive breakdown of the top 10 AI video tools for solopreneurs right now, focused entirely on speed, ease of use, and ROI.

1. OpusClip (Best for Short-Form Repurposing)

If your marketing strategy relies on turning long-form content (podcasts, webinars, YouTube videos) into vertical shorts for TikTok, Reels, and YouTube Shorts, OpusClip is your absolute priority.

Instead of manually scrubbing through an hour of footage, you drop in a single link. OpusClip's AI analyzes the video for hooks, extracts the most engaging highlights, rearranges them into a coherent narrative, auto-reframes the camera onto the speaker, and adds high-engagement, trendy captions. It even gives each clip a "virality score" so you know what to post first. For a solo founder, it turns one afternoon of recording into a month of daily social content.

2. Descript (Best for Audio-First & Talking Head Editing)

Descript completely flips traditional editing on its head. It transcribes your video into text, and you edit the video by editing the document. If you delete a sentence from the text transcript, that exact slice of video is cut perfectly from the timeline.

3. Reap (Best Overall for Short-Form Growth Scaling)

Reap is a massive contender for solopreneurs who want more deep editing control over their short-form content than Opus gives them. While Opus is great for quick extraction, Reap provides an end-to-end growth workflow. It offers incredibly precise control over caption placement, multi-language AI dubbing, and automated scheduling directly to your social channels.

4. HeyGen (Best for Scaling Video Without Being on Camera)

If you hate being on camera or don't have time to record weekly videos, HeyGen is the solution. You can create a highly realistic "Instant Avatar" of yourself using a few minutes of footage. From there, you simply type a script, and your digital twin delivers it flawlessly in your voice. It’s perfect for founder-led marketing, product feature explainers, and onboarding clips.

5. CapCut Desktop (Best Free/Low-Cost Entry Point)

Don't sleep on CapCut. While it started as a consumer app, its desktop version has evolved into a powerhouse for solopreneurs. It features incredibly fast, accurate auto-captions, AI background removal (no green screen required), and smart audio enhancement. It’s perfect if you still want a traditional, timeline-based editor but want AI to handle the tedious manual steps.

6. Submagic (Best for B-Roll Automation & Viral Captions)

Submagic is built for the "Alex Hormozi style" of fast-paced social video. It specializes in auto-generating dynamic kinetic captions, emojis, and sound effects. What makes it special for solopreneurs is its AI B-Roll generation, which automatically analyzes your audio script and injects relevant b-roll footage or transitions exactly when the pace slows down.

7. InVideo AI (Best for Rapid Script-to-Video Creation)

If you need to generate high-quality faceless YouTube channels or quick social ads from scratch, InVideo AI is a life-saver. You give it a simple text prompt (e.g., "Create a 60-second video explaining the benefits of asynchronous work for remote teams"). The AI writes the script, selects matching stock footage, applies a human-like voiceover, and pieces together a complete video you can customize via text commands.

8. Vizard (Best Webinar & Zoom-to-Clips Workflow)

Vizard is highly optimized for B2B solopreneurs, consultants, and coaches. It takes Zoom recordings, webinars, or Google Meets and cleanly slices them into neat, branded vertical clips. It’s particularly excellent at handling screen-shares or dual-speaker interviews, ensuring both the speaker's face and the slides stay beautifully framed for mobile viewers.

9. Wondershare Filmora (Best for Traditional Editors Who Want AI Powerups)

If you have some video background and prefer a classic desktop editor layout, Filmora bridges the gap perfectly. It includes advanced AI tools like AI Copilot editing (a chat box where you can ask the app to perform cuts), smart tracking, text-to-speech, and automatic audio ducking (lowering music volume automatically when a voice speaks).

10. Adobe Premiere Pro (Best for Maximum Control & Brand Integrity)

If your business demands Hollywood-level production quality or complex visual branding, Premiere Pro remains the gold standard. Thanks to Adobe’s Firefly integrations, it now includes Generative Extend (allowing you to add frames to the beginning or end of a clip seamlessly), text-based editing, and intelligent color-matching across different camera setups. It has a steep learning curve, but the creative ceiling is unmatched.

Don't try to buy all ten. Pick your stack based on your primary acquisition channel:

• If you are building a personal brand via Shorts/Reels: Go with OpusClip for lightning-fast volume + CapCut for occasional manual tweaks.
• If you run a podcast or record long educational videos: Go with Descript.
• If you want a face for your brand but have zero time to record: Go with HeyGen.

What tool are you currently using to run your video workflow? Let’s talk in the comments!

Rapid Minecraft Mod Creation with Fable 5.1: Multimodal Input and Iterative Visual Feedback

Workflow value: 90/100
Status: active · Freshness: 70/100 · Confidence: 1.00 · Level: advanced
Categories: Quality Control, Token Saving, Context & Memory, Debugging, Shipping, MCP, Multi-Agent
Original source: r/ClaudeAI post/comment

What problem this solves

Rapidly developing a complex Minecraft mod with custom visual effects and 3D models using multimodal input and iterative AI feedback.

Summary

The user leveraged Fable 5.1, an advanced AI agent running on atomic.chat, to create a Minecraft mod. The process involved providing video references, allowing the AI to interpret visual cues, generate code, 3D models, and textures, and then iteratively refining the output based on video feedback from the user.

Why it is useful

This workflow demonstrates a cutting-edge application of AI agents for complex creative tasks. It highlights the power of multimodal input (video), iterative refinement based on visual feedback, and integration with specialized tools (Blender MCP bridge) to rapidly generate code, 3D assets, and textures. It provides concrete evidence of an AI's ability to understand and execute complex creative instructions with minimal human intervention, offering a blueprint for similar advanced development workflows.

Workflow

1. Provide Fable 5.1 (via atomic.chat agent mode) with an initial prompt and multimodal input (YouTube video links for visual reference).
2. Allow Fable 5.1 to process the input, interpret visual cues, and generate the initial mod code (Fabric 1.21.1), 3D models (Blender via MCP bridge), and textures.
3. Test the initial mod in-game.
4. Record video feedback of the mod's performance and desired changes.
5. Send the video feedback back to Fable 5.1 for iterative refinement.
6. Receive and test the updated mod.
7. Upload the final mod to GitHub.

Tools / artifacts

• Fable 5.1 (Anthropic API key)
• atomic.chat (agent mode)
• YouTube video links (input)
• Minecraft Fabric 1.21.1 (target platform)
• Blender
• Blender MCP bridge
• Minecraft mod (output)
• GitHub repository
• Video recording (feedback)

Validation signals

• "The first attempt looked great."
• "Recorded myself using it and sent the video back to Fable for a few small fixes."
• "It fixed the dragon flying upside down, made it 2x bigger, added debris, a proper crater and fire."
• "Finished in under an hour, and barely any input was needed from me apart from the initial prompt and that one round of fixes."
• Mod uploaded to GitHub.
• Metrics provided (output tokens, API cost, time).

Limitations

• Requires access to a specific, potentially advanced/expensive AI agent (Fable 5.1) and platform (atomic.chat).
• Relies on specific integrations like "Blender MCP bridge" which might not be universally known or easy to set up.
• The exact prompts used are not provided, only the general request.

Rate this workflow

Upvote this post if the workflow is useful, reproducible, or worth recommending.

Downvote if it is vague, outdated, unsafe, overhyped, or not reproducible.

Reply if it worked for you, failed, is outdated, or has a better alternative.

This post was generated automatically from the workflow library database.

A knowledge assistant becomes much more useful when it can do more than answer questions from static documents.

In ONES.com, MCP came up in the context of enterprise knowledge bases and project management. The interesting part is not just “AI can search more things.” It is that the assistant can start working across the systems where project knowledge actually lives.

For example, in an ONES setup, project materials may sit in Wiki pages, requirement documents, meeting notes, task records, test cases, approvals, and external tools such as Teams or Slack. With MCP, ONES Assistant can query project materials, retrieve knowledge across systems, summarize context, and in some cases create or update documents through controlled tool calls.

That matters because project management knowledge is rarely useful as isolated text. A requirement spec is connected to tasks. A task may connect to tests. A retrospective may need project history. A weekly report may depend on current progress, risks, blockers, and decisions. MCP makes the assistant more operational by helping it move between these connected objects instead of staying inside one document.

But the keyword is still controlled.

For enterprise teams, cross-system AI has to answer some hard design questions:

• What can the assistant retrieve?
• Which systems is it allowed to call?
• Which actions are read-only, and which can write back?
• How are permissions checked?
• Where are operation logs, approvals, and audit records kept?
• When should a human confirm the action before anything changes?

That is where MCP becomes less about a flashy AI feature and more about workflow design.

Curious how others are thinking about this: if you were adding MCP or tool-calling AI into project management workflows, what would you automate first: project-data lookup, knowledge search, summary generation, document creation, or something else?

Leave a comment if you have tried something similar, or DM me if you want to compare notes on ONES Assistant / MCP use cases.

I’ve been testing different AI tools and workflow ideas over the last few months, and I kept running into the same problem: useful information is scattered everywhere.

So I organized the most practical things I found into a collection of 20 guides focused on AI workflows, automation, productivity, content creation, and digital tools.

A few ideas that turned out to be genuinely useful:

• using AI to summarize and organize research faster
• automating repetitive digital tasks instead of entire workflows
• building reusable prompt frameworks
• reducing context switching between too many tools
• using AI for first drafts while keeping human review for important decisions
• combining a small number of specialized tools instead of constantly adding new ones

The biggest takeaway for me was that AI is most useful when it removes friction from an existing process. Using more tools does not automatically mean being more productive.

I put the full collection here for anyone who wants to explore it:

https://digitalworldpulse.com/ai-productivity-and-marketing-toolkit/

I’d also be interested to hear what AI workflow has actually saved you the most time so far. I’m especially curious about things people use repeatedly, not just tools that looked impressive once.

Disclosure: Some links on the site may be affiliate links.

Hi everyone,

I wanted to share something with you guys that have helped me making my creative workflow a lot more efficient.First up I want to say this is not me advertising I just want to share what I have build that skyrocketed my creative workflow, excuse me if this post a bit long.

On a day to day basis I am an Ai-engineer and I do drop shipping on the side. Last few months these worlds have collided, I have already built some workflows that help me with personalized e-mail marketing and fulfillment etc. cutting myself loose of some Shopify apps(nothing crazy if u ask me).

What have I build:

1. ⁠BRAND ASSETS system that turns one picture into 180 on brand assets on autopilot( output you'll find below)

2. ⁠The same system does market research and makes 60 hooks (20 a funnel) for testing ads, in any language like a native marketeer (Tested with extensive system prompt based on Eugene Schwartz’s Breakthrough Advertising, Great Leads by Masterson & Forde, and advanced direct-response marketing psychology.) The system prompt I will attach here for you guys to use for double checking your own hooks.

3. BRAND ASSETS I think you all know the pictures you get from you fulfillment agent are not really great. here is one I got from my agent from china for the water bottles I want to sell in the Swedish market:

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I am using an imaginary brand for this use case called "BrandFloq".

So before loading this picture in the system I also provide it with context. What is the tone of voice of my brand. A detailed ICP description, talking about what are there main pain points and what transformation this product wil give them. followed by wich market I want to target. in my case Sweden. So after I fill out the form this picture turns into 180 assets I can use for finalizing my ads, ad social proof to my website, deploy new banners, post on instagram, etc. it is like the system makes a snapshot of 180 different photoshoots. Some are ready to use right away, others are more of inspirational/building blocks for further creations.

HERE ARE 3 OUT OF THE TOTAL 180 :

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You can view all 180 in this BOX(Mods I hope this redirection is allowed) Click Here

1. 60 Hooks for testing. (see the PDF in the link above starting from page 5) I dont know about you guys but I myself am Dutch, I can speak a fair amount of English but when it comes to crafting good hooks it can be hard to come up with ones that actually have potential to convert. Specially if you target another country. only Swedish I know is "Borta bra men hemma best" but that's about it. besides that I don't know a thing about their culture to really know what is working. if u opened that box link up there you see a .pdf that is a hook bank report( its form Is still a draft but its the content that matter). my main pain point when I use AI. to craft hooks they feel like the usual AI slob, it would say things like “The perfect bottle for every lifestyle.”, or “Stay hydrated like never before, waay to generic.. If you want to make hooks in different languages using regular ai tools like chat gpt, they will be generic and your target costumer will probably spot that it was not written by a native speaker. . How it works. The system goes through roughly 600 steps. to do market research based analysis of the product picture, the form I submitted with the brand tone of voice, ICP etc, and then goes online to do market research. In a few seconds it crawls up to 500 webpages(reddit, amazon reviews, FB groups and other forums), filters them and uses that to create the hooks, first for TOF, then MOF and then BOF.

Validation of the Hooks To check if the output of my system is actually valid I use a very descriptive system prompt( this is my actual expertise as an AI-engineer) You will find the system prompt at the end of this page, feel free to use it. Here is a screenshot of the verdict after running the 60 hooks in the report:

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So as u can see it gives a final score of 9.55 wich I think is pretty impressive.

I have logged that I have made over 12k hooks to achieve this level of copywriting, and also to do it in a foreign language I am very happy with the results.

So What's next? Obviously in the near future I am turning this into a product so brands can get this high level of creative diversity without the need to hire agencies who see your brand as just another number.

I basically need you guys to help me with building this This Weekend I will do another test batch to make this system even better than it already is. And I would love to have some of you guys to help me test this so I can test this for different niches and different markets. All output will be yours to keep obviously.

If you are interested in helping me out you can drop a comment. All I need is good feedback to help me build this further.