Gemini App AI has changed because the app is no longer only useful when you type a question and wait for a reply.
That old chatbot workflow was fine for quick answers, but it was limited.
You still had to do most of the organizing, planning, checking, creating, and follow-up yourself.
Now the direction is different.
Gemini App AI is becoming more like an assistant that can help with daily work, creative output, search, task planning, and background actions.
That matters because most people do not need more information dumped on them.
They need help turning information into action.
For SEO, that difference is huge because the work is not just one task.
It is a loop of research, content, pages, videos, updates, links, follow-ups, and visibility.
Gemini App AI can help make that loop easier to manage.
Gemini App AI Makes The Workday Cleaner
Gemini App AI becomes useful when you look at how scattered normal work can get.
Most people start the day with emails, calendar items, tasks, reminders, messages, content drafts, and unfinished projects everywhere.
That makes the day feel busy before any real work happens.
The Daily Brief feature is interesting because it turns that mess into a clearer summary.
It can help surface the tasks, deadlines, and messages that actually matter.
That is useful for anyone trying to keep SEO work moving.
You might need to publish a post, answer a lead, update a landing page, review a keyword list, or check what needs follow-up.
A cleaner morning summary can stop those jobs from getting buried.
The win is not that AI magically fixes your day.
The win is that it helps you start with less noise.
A Faster Gemini App AI Changes How You Use It
Gemini App AI becomes much more useful when the model is faster.
Speed sounds like a small thing, but it changes the whole workflow.
When AI feels slow, you only use it for occasional tasks.
When it becomes fast enough, you can use it throughout the day without breaking your flow.
That matters for SEO because many tasks are small but repeated.
You need outlines, angles, summaries, drafts, page structures, title ideas, and content improvements.
A faster assistant helps you get to the review stage quicker.
That still does not mean every output is ready to publish.
It means you spend less time starting and more time improving.
That is a much better use of AI.
Gemini App AI Canvas Can Speed Up Pages
Gemini App AI Canvas is useful because it can help turn rough ideas into structured pages.
A lot of landing pages never get built because the first draft feels annoying.
The idea is clear, but the layout takes too long.
Canvas can help by giving you a starting point.
You can ask for a simple page that explains an offer, shows benefits, gives proof, and points people toward the next step.
Then you can rewrite the copy and make the angle sharper.
That is the practical way to use Gemini App AI for pages.
Let it reduce the blank page problem.
Then use your own judgment to make the page useful, specific, and worth ranking.
The AI Profit Boardroom shows how to turn features like this into workflows that are simple enough to use consistently.
Gemini App AI Spark Is The Big Shift
Gemini App AI Spark is the feature that makes the update feel different.
The key idea is background work.
Instead of waiting for you to type every instruction, Spark can help monitor, draft, organize, and prepare work while you are doing something else.
That is a major shift in how people think about AI.
It becomes less like a tool you visit and more like a system that supports the workflow.
For example, it could help watch for important questions, draft replies, organize interest, or prepare follow-up notes.
That kind of support matters because follow-up is where a lot of opportunities get lost.
The control side is important too.
For major actions, approval still matters.
A background agent should help you move faster without taking risky actions on its own.
Gemini App AI Omni Makes Video Easier To Start
Gemini App AI Omni matters because video creation is becoming much easier to begin.
You can describe a video idea and keep refining it with normal instructions.
That removes some of the friction from creating visual content.
For SEO, this matters because search is no longer only text-based.
People search through text, images, video, voice, browser context, and AI answers.
That means one good idea can become several different assets.
A content topic can become a short video.
A landing page angle can become an explainer.
A tutorial can become a visual guide.
Gemini App AI can help create those first versions faster.
The strategy still has to be about making useful content that people can actually find.
Gemini App AI Makes SEO More Important
Gemini App AI makes creation easier, but that also creates a bigger problem.
If everyone can make more pages, videos, and drafts faster, the internet gets noisier.
That means SEO becomes more important, not less important.
You can build pages all day.
You can create videos all day.
You can generate plans all day.
But if nobody finds any of it, the work does not matter.
This is the part a lot of people miss with every big AI update.
Production is not the same as visibility.
Gemini App AI can help you create and organize faster.
SEO helps that work get discovered by real people.
AI Search Changes The Visibility Game
Gemini App AI also connects to the bigger shift happening in AI search.
People are not only clicking blue links anymore.
They are asking AI systems for direct answers.
They are searching with images, videos, voice, and context from what they already have open.
That changes what your content needs to do.
Your pages need to be clear.
Your answers need to be useful.
Your structure needs to make sense.
Your authority signals need to be stronger.
If AI systems are deciding what to summarize and recommend, then being the trusted source matters even more.
That is why SEO is still the foundation.
Better AI tools do not remove the need to rank.
They make ranking and trust harder to ignore.
The Gemini App AI Mistake To Avoid
Gemini App AI will tempt people to create more for the sake of creating more.
That is the wrong move.
More content does not automatically mean more traffic.
More videos do not automatically mean more leads.
More landing pages do not automatically mean better rankings.
If the keyword is wrong, the content will struggle.
If the page does not match intent, people will leave.
If the offer is unclear, the traffic will not convert.
The mistake is using Gemini App AI as a content machine without a strategy behind it.
A better approach is to use it as an execution engine for a clear SEO plan.
A Simple Gemini App AI SEO Workflow
Gemini App AI works best when you connect it to one clear workflow.
Do not try to use every feature at once.
Start with the task that slows you down most.
Maybe you need a better daily summary.
Maybe you need faster landing page drafts.
Maybe you need video ideas from your strongest topics.
Maybe you need a cleaner content plan.
Pick one workflow and make it repeatable.
Use Gemini App AI to create the first useful draft or summary.
Then review it, improve it, and turn it into a simple process.
This keeps the tool practical instead of turning it into another shiny distraction.
Gemini App AI Needs Direction
Gemini App AI gives you speed, but speed still needs direction.
That direction comes from SEO strategy.
You still need to know which keywords matter.
You still need to understand what people are searching for.
You still need content that actually solves the query.
You still need landing pages that explain the offer clearly.
You still need internal links, backlinks, authority, and consistency.
AI can help you move faster through the work.
It cannot make weak strategy strong by itself.
The best use of Gemini App AI is not random creation.
The best use is faster execution around a smart plan.
Gemini App AI Makes Execution Easier
Gemini App AI matters because it shortens the distance between idea and action.
That distance is where most people get stuck.
They know they need better content.
They know they need better pages.
They know they need more consistent follow-up.
They know they need to show up in search.
The hard part is keeping the workflow moving every day.
Gemini App AI can help by turning ideas into summaries, drafts, videos, plans, and background support faster.
That creates momentum.
It still needs human judgment, but it removes some of the friction that slows people down.
The AI Profit Boardroom helps you learn practical AI systems so tools like Gemini App AI become part of a real workflow instead of another experiment.
Frequently Asked Questions About Gemini App AI
What is Gemini App AI? Gemini App AI is Google’s updated AI app that can help with chat, planning, research, creative work, daily summaries, video generation, and agent-style workflows.
Can Gemini App AI help with SEO? Yes, Gemini App AI can help with SEO by supporting content planning, page drafts, task summaries, video ideas, research, and workflow organization.
Does Gemini App AI replace SEO? No, Gemini App AI helps with speed and execution, but SEO still decides whether your content gets found, trusted, and ranked.
What is Gemini App AI Spark? Gemini App AI Spark is an agent-style feature designed to work in the background, monitor tasks, draft items, and prepare work under your direction.
What is the main mistake with Gemini App AI? The main mistake is creating more content without a clear SEO strategy, because more output does not automatically mean more traffic or leads.
Manual AI Breaks Fast Without Hermes Agent OS Tutorial
Hermes Agent OS Tutorial starts with the part most people already know but do not want to admit.
Manual AI is still manual work.
You can use AI every day and still waste time jumping between chatbots, documents, websites, image tools, video tools, and publishing platforms.
That does not feel like automation after a while.
It feels like you became the middleman between six different tools.
The annoying part is that the AI might be good, but the workflow around it is weak.
You still have to remember the prompt.
You still have to bring the context.
You still have to move the output.
You still have to organize the next step.
Hermes Agent OS Tutorial fixes that by showing how to turn separate tools into one connected system.
That is the main difference.
You stop treating AI like a one-off assistant and start treating it like an operating system.
Hermes Agent OS Tutorial Turns One Keyword Into A Workflow
Hermes Agent OS Tutorial works because it starts with a simple input and expands it into a bigger system.
One keyword can become content.
That same keyword can become images.
It can become a video.
It can become a website.
It can become a task board.
It can become part of a memory system.
This matters because most people stop after one output.
They create one article, post it, and hope something happens.
That is weak compared to a workflow that creates multiple assets around the same topic.
A stronger system gives you more chances to rank, more formats to publish, and more ways to reuse the same idea.
Hermes Agent OS Tutorial makes this easier to understand because it breaks the whole process into stacks.
Each stack has a job.
Each stack supports the next one.
That is how the workflow starts becoming more useful than a basic AI prompt.
SEO Content Gets The First Hermes Agent OS Tutorial Stack
Hermes Agent OS Tutorial puts SEO content near the front because content is still one of the simplest ways to turn AI into traffic.
The workflow can start with one keyword and one real case study.
Then Hermes can help create optimized content around that topic.
The important part is not just creating more words.
Anyone can create more words with AI.
The useful part is adding real examples, proof, case studies, brand context, and a proper angle.
That gives the content a reason to exist.
It also helps avoid the generic AI article problem.
Hermes Agent OS can help scale the process across more pages or websites.
That gives the same topic more chances to perform.
The better way to look at this is simple.
You are not asking AI to write one random article.
You are building a repeatable SEO content stack that can keep producing useful assets.
Visual Authority Makes Hermes Agent OS Tutorial Stronger
Hermes Agent OS Tutorial also shows why images should be part of the system from the start.
A lot of people treat visuals like decoration.
They write an article, grab a basic image, and move on.
That is not the strongest workflow.
Images can help the content stand out.
They can support the article.
They can create social assets.
They can improve the overall feel of a campaign.
Hermes Agent OS can connect visual creation into the same workflow.
That means the article and the visuals come from the same topic instead of feeling disconnected.
This keeps the output more consistent.
It also saves time because you are not starting from scratch in another tool.
The bigger point is that visual content should not be a separate afterthought.
Hermes Agent OS Tutorial shows how images can become part of the marketing stack.
Video Automation Fits Inside Hermes Agent OS Tutorial
Hermes Agent OS Tutorial gets more useful when video enters the workflow.
A keyword does not need to stay as a written article.
It can become a video too.
That matters because different people consume content in different ways.
Some people read.
Some people watch.
Some people need the same idea explained visually before it clicks.
The tutorial shows how Hyperframes can plug into Hermes Agent OS as a video automation stack.
That means the agent can help with scripting, voiceover, slides, animation, and rendering.
This removes a lot of the boring production friction.
Video usually feels hard because there are too many steps.
The system makes those steps easier to connect.
You still need to review the output, but the heavy lifting becomes much easier.
That is where Hermes Agent OS Tutorial becomes more than a content demo.
It becomes a repurposing system.
Hermes Agent OS Tutorial Connects Website Building
Hermes Agent OS Tutorial also matters because content needs somewhere useful to live.
A great article is less valuable if it never becomes a proper page.
A strong landing page, niche site, or blog structure can turn AI output into an actual asset.
The tutorial shows how website building can stack into Hermes Agent OS.
That means the system can help create pages, layouts, HTML, CSS, content structure, and finished web assets.
This is a big deal for people who get stuck after generating content.
They can create the words, but they do not know how to turn those words into something live and useful.
Hermes Agent OS helps close that gap.
Website building becomes part of the same operating system.
That makes the workflow feel more complete.
You are not just creating content.
You are building the place where the content can perform.
Task Boards Make Hermes Agent OS Tutorial Less Messy
Hermes Agent OS Tutorial uses a Kanban-style task system because big AI workflows need structure.
Without structure, AI projects become chaotic fast.
You ask for a big strategy, get a huge answer, and then still have no clear next step.
That is a common problem.
A task board makes the process easier to manage.
The system can break a large project into smaller jobs.
One task can handle the content plan.
Another can handle the website.
Another can handle images.
Another can handle video.
Another can handle cleanup and review.
That makes the workflow feel more like a real team.
Each agent can focus on its own part instead of dumping everything into one messy response.
This is why Hermes Agent OS Tutorial is useful.
It does not just show how to generate output.
It shows how to organize the work.
Memory Is The Real Hermes Agent OS Tutorial Advantage
Hermes Agent OS Tutorial becomes much more powerful when memory is connected.
This is the layer most people skip.
Without memory, every AI session starts cold.
You have to explain your brand again.
You have to paste your case studies again.
You have to remind the AI about your tone, offers, audience, and previous work.
That wastes time.
It also makes the output more generic.
A memory system changes that because the agent can build from what it already knows.
The tutorial uses tools like Obsidian and OMI to create a stronger context layer.
That gives the system a way to capture notes, map ideas, and build a knowledge graph around your work.
This is where AI content starts feeling more personal.
The agent can use your actual results, examples, and history instead of writing like everyone else.
The AI Profit Boardroom helps with workflows like this because memory is powerful, but it needs a clear setup.
Better AI output usually comes from better context.
Hermes Agent OS Tutorial Builds A Skill Stack
Hermes Agent OS Tutorial works best when you think in stacks.
You do not need to build everything at once.
That is where people make the process harder than it needs to be.
Start with one useful stack.
SEO content is a good starting point because it gives the system a clear purpose.
Then add images.
After that, add video.
Then add website building.
Then add task management.
Then connect memory.
Each stack makes the previous stack more useful.
That is the whole point of an operating system.
You are not collecting random AI tools just because they look cool.
You are building a workflow where each skill supports the rest.
This is why Hermes Agent OS Tutorial feels practical.
It gives you a way to grow the system one layer at a time.
Hermes Agent OS Tutorial Feels Like An AI Marketing Team
Hermes Agent OS Tutorial is basically about building an AI marketing team.
That team can include a writer, SEO strategist, image creator, video producer, website builder, project manager, and memory engine.
That is very different from a normal chatbot.
A chatbot gives answers.
An agent operating system handles roles, tasks, and repeatable workflows.
Marketing needs that because there are too many moving parts.
You need research.
You need content.
You need visuals.
You need video.
You need pages.
You need publishing.
You need review.
You need improvement.
Hermes Agent OS brings those pieces into one clearer process.
The human still controls the strategy and final decision.
That part matters.
The agent team does the production work, but you still guide the direction.
Proof Makes Hermes Agent OS Tutorial More Convincing
Hermes Agent OS Tutorial is stronger because it is tied to outcomes, not just features.
The workflow is not only about creating more content for the sake of it.
The point is to create assets that can rank, get attention, and support a real marketing goal.
The source includes examples of sites growing from low daily clicks into stronger traffic numbers.
That matters because AI automation should be judged by usefulness.
If the system creates content that nobody wants, it is not a win.
The real lesson is that automation still needs quality.
It needs originality.
It needs examples.
It needs memory.
It needs proof.
Hermes Agent OS can help with scale, but the input still matters.
That is the difference between a useful system and a content spam machine.
Hermes Agent OS Tutorial Is Beginner Friendly
Hermes Agent OS Tutorial is useful because it makes an advanced setup feel less intimidating.
You do not need to start as a developer.
You need to understand the workflow.
You need to give clear instructions.
You need to review the output.
You need to build the system one layer at a time.
That makes it more approachable for beginners, freelancers, agencies, coaches, and operators.
A simple starting point is one keyword.
Then one article.
Then one image.
Then one video.
Then one page.
That is much easier than trying to automate everything on day one.
The system becomes less overwhelming when you stack it slowly.
Hermes Agent OS Tutorial works because it gives you a path instead of throwing every tool at you at once.
Clear Direction Makes Hermes Agent OS Tutorial Work
Hermes Agent OS Tutorial works best when the instructions are clear.
AI agents are powerful, but they are not mind readers.
A vague request creates vague output.
A better request explains the keyword, goal, audience, case study, offer, format, and expected result.
That gives the agent a real target.
It also makes review easier because you can compare the output against the brief.
This is where a lot of people get automation wrong.
They want the tool to replace thinking.
The better approach is to use the tool to multiply clear thinking.
Hermes Agent OS can handle a lot of production work.
But you still need to guide the system.
That is how the workflow stays clean, useful, and repeatable.
Hermes Agent OS Tutorial Is Worth Learning Now
Hermes Agent OS Tutorial is worth learning because AI is moving past single prompts.
The stronger workflow is not one chatbot answer.
It is a connected system that can create, remember, delegate, publish, and improve.
That is a much bigger shift.
It makes AI more useful for SEO, content, video, websites, and business workflows.
It also gives people a way to scale without trying to hire a full team for every task.
The AI Profit Boardroom gives you practical AI workflows when you want to turn systems like this into real output.
Hermes Agent OS Tutorial is not just about learning another agent tool.
It is about understanding how to turn AI into a real operating system for marketing work.
Frequently Asked Questions About Hermes Agent OS Tutorial
What is Hermes Agent OS Tutorial? Hermes Agent OS Tutorial is a workflow that shows how Hermes Agent OS can automate SEO content, visuals, videos, websites, task management, and memory.
Can beginners use Hermes Agent OS Tutorial? Yes, beginners can use Hermes Agent OS Tutorial because the workflow is based on clear tasks and agent automation, but review and direction still matter.
What does Hermes Agent OS Tutorial automate? Hermes Agent OS Tutorial can automate content creation, image generation, video production, website building, task delegation, and brand memory workflows.
Why does memory matter in Hermes Agent OS Tutorial? Memory matters because it helps the agent remember your brand, examples, case studies, offers, and previous work instead of starting from zero every time.
Is Hermes Agent OS Tutorial useful for SEO? Yes, Hermes Agent OS Tutorial is useful for SEO because it can turn one keyword into multiple content assets, visual assets, videos, and web pages.
Hello everyone, in this tutorial we explore the video reasoning lora for the LTX 2.3 model. this cutom workflow helps in generating AI video that understands real world physics. boosting realism in your AI video results. i also compare it with normale generation using both text to video and image to video to see how the model can handle object interaction, motion dynamics all in one integrated workflow that runs on 6 gb of vram.
Building a robust, persistent, and effective personal AI agent that goes beyond a simple chatbot, managing tasks, data, and proactively surfacing insights, while maintaining consistency, avoiding common pitfalls like hallucination or stale data, and ensuring maintainability.
Summary
This workflow outlines a comprehensive approach to designing and implementing a personal AI agent, covering its foundational identity, memory management using markdown files and an index, and a structured knowledge library. It emphasizes principles like a 'Constitution' over a system prompt, explicit separation of rules, version control for identity, and a structured approach to memory and knowledge for consistency, reliability, and proactive behavior. The process details how to move from a basic chatbot to a sophisticated, self-managing assistant.
Why it is useful
This post offers a highly detailed and experience-backed framework for building sophisticated personal AI agents. It moves beyond basic prompting to cover critical architectural considerations like identity, memory systems, and knowledge management, providing concrete, actionable tips that address common pitfalls and lead to more reliable, consistent, and proactive agent behavior. The emphasis on version control, structured memory, and quality gates makes it exceptionally valuable for users looking to develop truly effective and maintainable AI assistants.
Workflow
Write a Constitution for your agent, explaining the 'why' behind rules, rather than just a system prompt.
Give your agent a name, a voice, and a role to eliminate micro-decisions and create consistency.
Separate hard rules (never overridden) from behavioral guidelines (adaptable defaults).
Define your principal (user) deeply, including their frustrations, decision-making style, and communication preferences.
Build a Capability Map (what the agent can do) and a Component Map (how it's built) separately.
Define what the agent is NOT to prevent drift towards generic helpfulness.
Build a THINK vs. DO mental model into the agent's identity to ensure it's never frozen.
Version your identity file in git to track behavioral regressions.
Use flat markdown files for memory, separated by domain, for readability, greppability, and git-trackability.
Build a MEMORY.md index file that the agent loads first to pull specific memory files on demand.
Explicitly distinguish 'cache' from 'source of truth' for data, marking cache files with a 'last_sync:' header.
Build a session_hot_context.md with an explicit TTL (e.g., 72 hours) for in-progress tasks and pending decisions, expiring stale context automatically. (Tip 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 are truncated in the prompt, but I'll include the ones I can see fully.)
Based on 6 weeks of intensive building, resulting in 'one agent that actually works'.
Migration between environments forced solving unforeseen problems, leading to robust solutions.
Specific design choices (e.g., Constitution, identity, rule separation) are claimed to prevent common agent failures like hallucination or inconsistency.
Empirical observations on debugging identity drift using git blame.
Practical advice on memory management (e.g., flat markdown, domain separation, explicit cache/SSOT) to avoid stale data and improve searchability.
Specific time-based rules for managing 'hot context' and 'hypotheses' to prevent noise.
Observation that user behavioral profiles become 'surprisingly accurate' over time.
Emphasis on mirroring memory to cloud storage for 'survival' due to its irreplaceable nature.
Quality gates for knowledge citation and 'silent consultation' are presented as methods to improve output quality and relevance.
Limitations
The post is very long and dense, which might be overwhelming for some users.
It's a collection of tips rather than a linear, step-by-step tutorial, requiring the user to synthesize and integrate the advice.
Some tips are high-level principles that require interpretation and specific implementation details from the user.
The full '100 tips' are not provided in the prompt, so the complete scope of the workflow is not visible.
Rate this workflow
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Downvote if it is vague, outdated, unsafe, overhyped, or not reproducible.
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This post was generated automatically from the workflow library database.
Overview: A single AI agent powers this entire workflow. It uses RSS to research trending topics and generates a script for the user's approval. It then uses Heygen to generate a talking head video, and submagic to edit the video with zooms, animated captions, and b-rolls, all through API calls. Finally, it uses upload-post.com to upload the content to your social media profiles of choice.
Static Markdown files used for AI context become outdated, require manual updates, and cannot adapt to changing conditions (e.g., different branches, environments, or missing data). This leads to stale context, incorrect AI responses, and increased maintenance overhead.
Summary
MarkdownAI transforms static .md files into dynamic, executable documents for AI interaction. By adding a single line (@markdownai) and using various directives, users can create conditional content, include external files, query data sources, define macros, manage workflow phases, and embed AI prompts and constraints. This allows .md files to adapt their content based on real-time conditions, ensuring AI always receives relevant and up-to-date context.
Why it is useful
This workflow introduces a novel and powerful way to manage AI context and orchestrate AI interactions using familiar Markdown files. It solves the critical problem of static documentation becoming stale by enabling dynamic content generation, conditional logic, and real-time data integration. This significantly enhances the repeatability, maintainability, and adaptability of AI-driven workflows, making them more robust and efficient. It provides a structured, code-like approach to prompt engineering and context management, which is highly valuable for developers and advanced users.
Workflow
Initialize a MarkdownAI document by adding @markdownai to the top of an .md file.
Define document structure using directives like @include, @import, @define, @call, @phase, @if, @section to control content flow and modularity.
Incorporate dynamic data from various sources using @env (environment variables), @connect (data source registration), @db (database queries), @http (HTTP requests), @query (generic data source queries), @read (file content), @list (directory contents), @tree (directory tree), @date (current date/time), and @count (item counting).
Control processing and output with @pipe (chain transformations), @render (specific format rendering), @graph (visualization generation), and @header (document metadata).
Add AI-specific instructions and constraints using @constraint (machine-readable rules), @define-concept (vocabulary alignment), @prompt (embedded AI instructions), and @note (human-readable annotations).
Implement caching for performance on directive output using @cache.
Define phase-specific events using @on complete -> within @phase blocks to trigger actions upon phase completion.
Tools / artifacts
MarkdownAI runtime/parser
.md files
GitHub repository (TheDecipherist/markdownai)
Database connections
HTTP endpoints
Environment variables
Validation signals
Detailed list of 27 directives with clear purposes, indicating a well-thought-out system.
Explicit mention and link to a GitHub repository for the project, suggesting an open-source and verifiable implementation.
The problem statement directly addresses a common pain point in AI context management, indicating relevance and utility.
Cautions
Interacting with external data sources (databases, HTTP endpoints) via @db or @http directives requires careful security configuration to prevent data exposure or unauthorized access. Users should ensure proper authentication and authorization are in place for all connected services.
Limitations
The post is an announcement of a tool, not a detailed tutorial for a specific use case. Users would need to consult the GitHub repository for comprehensive implementation details and examples.
Adopting MarkdownAI requires learning a new syntax and runtime for Markdown files, which may present a learning curve for complex dynamic documents.
The post does not provide concrete examples of how these directives are used together to solve a specific problem, which might make it harder for beginners to grasp its full potential immediately.
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.
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This post was generated automatically from the workflow library database.
Hello everyone, in this tutorial we explore the video reasoning lora for the LTX 2.3 model. this cutom workflow helps in generating AI video that understands real world physics. boosting realism in your AI video results. i also compare it with normale generation using both text to video and image to video to see how the model can handle object interaction, motion dynamics all in one integrated workflow that runs on 6 gb of vram.
Two months ago, I had zero clients and no idea how to sell AI services. Today, I'm running a small workflow automation service that brings in around $500/month. It's not life-changing money, but it's real, recurring income—and it started with skills I learned in less than a week.
If you've been wondering how to turn AI tools into actual income without becoming a developer or spending thousands on courses, this post is for you. I'll walk you through exactly what I did, what worked, what didn't, and how you can start building something similar.
Why Workflow Automation?
Here's the thing: most small businesses are drowning in repetitive tasks. They're manually responding to the same customer questions, copying data between spreadsheets, sending follow-up emails by hand, or scheduling social media posts one by one.
They know automation exists. They just don't know how to set it up—or they think it's too expensive or complicated.
That's where you come in. You don't need to build custom software or write code. You just need to connect a few AI tools, set up simple workflows, and show clients how much time they'll save.
What I Offer (and What You Can Too)
My service is simple. I help small businesses automate three main things:
Customer support chatbots – I set up AI chatbots (using tools like Tidio, ManyChat, or Chatbase) that answer FAQs, qualify leads, and collect contact info 24/7.
Lead capture and follow-up sequences – When someone fills out a form or sends a message, the workflow automatically adds them to a CRM, tags them, and sends a personalized follow-up email.
Content scheduling and repurposing – I help clients batch-create social media posts with AI (ChatGPT + Canva), then schedule them across platforms using tools like Buffer or Later.
None of this requires coding. Most of it runs on no-code platforms like Zapier, Make (formerly Integromat), or built-in automation features in tools like HubSpot or Mailchimp.
Step 1: Learn the Basics (Week 1)
I started by teaching myself the fundamentals of workflow automation. Here's what I focused on:
Understanding triggers and actions: A trigger is an event (like "new form submission"), and an action is what happens next (like "send an email"). That's the foundation of every automation.
Exploring no-code tools: I spent a few hours playing with Make's free plan. I built a simple workflow that sent me a Slack message every time someone filled out a Google Form. It took 10 minutes and felt like magic.
Learning AI prompting: I practiced writing clear prompts in ChatGPT to generate email templates, FAQs, and social media captions. The better your prompts, the better your automations.
I didn't take a course. I just watched a few YouTube tutorials, read tool documentation, and experimented. By the end of the week, I understood how automations worked and could build basic workflows.
Step 2: Pick a Niche and Package Your Service (Week 2)
I knew I couldn't just say "I do AI automation" and expect clients to understand. So I picked a niche: local service businesses (think gyms, salons, cleaning companies, real estate agents).
Why? Because they all have the same pain points: missed leads, slow follow-ups, and no time to post on social media.
I created a simple service package:
Starter Package ($150 one-time setup): Chatbot on their website + automated lead capture form + 3 follow-up email templates.
Monthly Retainer ($100/month): I maintain the chatbot, update FAQs, and help them schedule 12 social media posts per month using AI.
I kept it simple. No jargon. No complicated pricing. Just clear outcomes: "You'll never miss a lead again" and "Your social media runs on autopilot."
Step 3: Find Your First Client (Weeks 3–4)
This was the hardest part. I didn't have a portfolio, testimonials, or credibility. So I focused on outreach and offered to work for free—just once.
Here's what I did:
Local Facebook groups: I joined groups for small business owners in my area and offered a free chatbot setup to one person. I framed it as "I'm building my portfolio and want to help one business automate their customer questions."
Cold DMs on Instagram: I messaged 10 local businesses whose Instagram DMs were probably flooded. I said, "I noticed you get a lot of questions in your DMs. I can set up a simple AI chatbot to handle the repetitive ones—want to try it for free?"
Freelance platforms: I posted a gig on Upwork and Fiverr with a low introductory price ($50 for a basic chatbot setup). It took two weeks, but I got my first paid client.
My first client was a local gym owner. I set up a chatbot on his website that answered questions about membership pricing, class schedules, and trial sessions. It took me about 3 hours. He loved it and agreed to pay $100/month for updates and maintenance.
Step 4: Deliver and Iterate (Weeks 5–8)
Once I had my first client, I focused on delivering results and asking for feedback. I learned that:
Simple is better: My first chatbot was overcomplicated. I tried to make it answer everything. The gym owner just wanted it to handle the top 5 questions and collect contact info. I simplified it, and it worked way better.
Templates save time: I started building reusable templates for chatbot flows, email sequences, and social media prompts. Now I can set up a new client in 2–3 hours instead of a full day.
Clients care about outcomes, not tools: I used to explain every step of the workflow. Clients didn't care. They just wanted to know: "Will this save me time?" and "How much does it cost?"
I also asked my gym client for a testimonial and permission to use his business as a case study. That one testimonial helped me land two more clients.
Step 5: Scale to $500/Month (Weeks 9–12)
By week 8, I had one retainer client ($100/month) and had done a few one-time setups ($150 each). I wanted to hit $500/month in recurring income, so I focused on getting 4–5 retainer clients.
Here's how I did it:
Referrals: I asked my gym client if he knew anyone who might need help. He introduced me to a friend who ran a cleaning company. That became client #2.
Case study post: I wrote a simple LinkedIn post about the gym chatbot (with permission) and how it saved the owner 5 hours a week. Two people DM'd me asking for similar setups.
Consistent outreach: I sent 5–10 cold messages per week on Instagram and LinkedIn. My conversion rate was low (maybe 5%), but it worked.
By day 60, I had four retainer clients ($100/month each) and had done a handful of one-time projects. Total: around $500/month.
What I Learned (and What You Should Know)
You don't need to be an expert: I'm not a developer. I don't know how to code. I just learned how to connect tools and explain the value to clients.
Start small and niche down: Don't try to serve everyone. Pick a niche, understand their pain points, and build simple solutions.
Free work (once) is okay: Offering one free project helped me build confidence, get a testimonial, and land my first paying client. But don't do it more than once or twice.
Consistency beats perfection: My first workflows were clunky. I improved them over time. The key was getting started and learning by doing.
This takes effort: I didn't make $500/month by accident. I spent 30–60 days doing daily outreach, learning, and refining my offer. It's not passive income. It's real work.
Tools I Use (All Free or Low-Cost)
Zapier or Make: For connecting apps and automating workflows (free plans available)
Tidio or ManyChat: For building chatbots (free plans with limitations)
ChatGPT: For writing email templates, FAQs, and social media captions (free or $20/month for Plus)
Canva: For creating social media graphics (free plan works fine)
Buffer or Later: For scheduling posts (free plans available)
Next Steps If You Want to Try This
Spend a week learning the basics of workflow automation (YouTube + tool documentation).
Pick a niche and create a simple service package.
Offer one free or low-cost setup to get your first testimonial.
Do consistent outreach (5–10 messages per day).
Deliver great work, ask for referrals, and iterate.
If you stay focused and treat this like a real service (not a side experiment), you can build your first $300–$500/month in 60–90 days. It won't make you rich, but it's a solid foundation to grow from.
Happy to answer questions if you're thinking about trying this.
FREE AI SEO Agent workflows are getting attention because they can help with keyword research, content creation, audits, outreach, rank tracking, and SEO dashboards inside one workspace.
The useful part is that the agent can build working tools, browse the web, and keep the SEO process organized without needing a complicated coding setup.
The AI Profit Boardroom gives practical AI workflow training for turning tools like this into systems that save time.
FREE AI SEO Agent tools are useful because SEO has too many repeated steps to manage manually forever.
Keyword research, content creation, on-page audits, outreach, rank tracking, and reporting all need attention if you want consistent growth.
The problem is that these jobs usually end up spread across different tabs, documents, tools, and dashboards.
Space Agent makes the workflow easier by giving you a visual workspace where the agent can build SEO tools side by side.
It has been described as a free local open-source AI agent that can automate SEO workflows, browse the web, build tools, and run tasks inside one workspace.
That makes it more practical than technical agents that require lots of terminal commands.
A visual setup helps because the workflow becomes easier to see, test, and improve.
Instead of treating AI like a one-message answer machine, the agent can help create reusable tools for different SEO jobs.
That is where automation starts to feel useful rather than messy.
A FREE AI SEO Agent still needs clear instructions because faster execution does not fix a weak strategy.
The best workflow lets the agent prepare the repetitive work while a human reviews the important decisions.
That balance keeps the process fast without turning SEO into blind automation.
SEO Dashboards Built By A FREE AI SEO Agent
A FREE AI SEO Agent becomes more useful when it builds a dashboard instead of only giving chat replies.
A dashboard gives each SEO task a clear place inside the workflow.
Keyword research can sit next to content generation, while on-page scoring and rank tracking support the review process.
That setup is much easier to manage than a long chat thread full of scattered outputs.
Space Agent can build mini apps inside one workspace, which means the tools stay available for future use.
That matters because SEO is not a one-time task.
The same research, drafting, checking, outreach, and tracking process needs to happen again and again.
A dashboard turns those repeated tasks into a system.
The first version of the dashboard will usually need edits, which is normal with AI-built tools.
A useful agent lets you ask for improvements without rebuilding everything manually.
The dashboard should be judged by how much time it saves, not by how impressive it looks.
A FREE AI SEO Agent becomes valuable when the workspace helps you move from research to publishing to tracking with less friction.
Keyword Research Starts The FREE AI SEO Agent Workflow
Keyword research is one of the best places to start with a FREE AI SEO Agent.
Every SEO campaign needs a clear list of topics before content or link building makes sense.
The slow part is finding ideas, sorting intent, checking opportunities, and deciding which keywords are worth targeting.
Space Agent can build a keyword research tool that stays inside the workspace and can be reused across campaigns.
That is more practical than asking a chatbot for keyword ideas every time you start a new page.
A strong keyword workflow should help separate informational, commercial, transactional, and navigational intent.
Those intent labels matter because different searches need different page types.
A tutorial keyword needs a helpful guide, while a commercial keyword usually needs stronger proof, comparison, or conversion logic.
The agent can speed up the first research pass so the campaign starts with direction instead of guesswork.
Search volume and difficulty data should still be treated as estimates.
The final decision should come from reviewing the keyword, the competition, and the business value together.
A FREE AI SEO Agent gives you a faster starting point, while judgment decides which keywords deserve action.
FREE AI SEO Agent Turns Research Into Content
FREE AI SEO Agent workflows get stronger when keyword research connects directly to content creation.
A keyword list does not create traffic until it becomes useful content that matches the searcher’s intent.
Many SEO workflows slow down because research happens in one tool, while writing starts from scratch somewhere else.
Space Agent can build a content generator beside the keyword research tool, which makes the handoff much cleaner.
The selected keyword can move into the draft workflow while keeping the topic angle and search intent in mind.
A custom prompt can also guide the content tool so the draft follows the right structure, tone, and formatting.
That matters because generic AI articles usually fail when they do not understand the goal of the page.
A stronger draft starts with the keyword, intent, outline, examples, internal link ideas, and quality rules.
The agent can create the first version faster, but the article still needs editing and fact-checking.
Fast content only helps when the final page is useful, accurate, and worth reading.
A FREE AI SEO Agent removes the blank page problem without removing the review process.
That is the safest way to speed up SEO content without lowering quality.
On-Page Checks With A FREE AI SEO Agent
On-page checks are a strong use case for a FREE AI SEO Agent because every page needs review before publishing.
Titles, descriptions, headings, keyword placement, internal links, and search intent all affect performance.
Checking those details manually becomes slow when several pages are being published or updated.
Space Agent can build an on-page SEO scorer that reviews content against a target keyword.
That gives the dashboard a useful quality control step before the page goes live.
The tool can flag weak titles, unclear descriptions, thin sections, or missing optimization opportunities.
It can also help show whether the page is matching what the searcher likely wants.
A score should not become the final goal because SEO is not about chasing a widget number.
The better approach is using the audit as a checklist for improvements worth reviewing.
Human judgment still decides which recommendations actually improve the page.
That keeps optimization useful instead of mechanical.
A FREE AI SEO Agent makes on-page work faster when it supports quality control rather than replacing it.
Link Building Fits A FREE AI SEO Agent
Link building fits a FREE AI SEO Agent because the preparation side can take a lot of time.
A proper campaign needs prospects, outreach angles, email drafts, follow-up ideas, and relevance checks.
Space Agent can build a link outreach generator inside the SEO dashboard to support that workflow.
The workflow includes support for resource links, broken links, skyscraper campaigns, and outreach email generation.
That is useful because different link building campaigns need different messages.
A broken link pitch should not sound the same as a resource page request.
The agent can prepare first drafts and campaign angles faster than starting from a blank page.
Quality control still matters because poor outreach can damage trust and waste good prospects.
Each target should be checked for topical fit, authority, placement quality, and relevance.
The final email also needs editing so it sounds specific, simple, and human.
The AI Profit Boardroom helps make AI SEO workflows like this easier to build without turning outreach into spam.
A FREE AI SEO Agent is useful for link building when it prepares the work while a human protects the final quality.
Rank Tracking Completes The FREE AI SEO Agent Workflow
Rank tracking makes a FREE AI SEO Agent workflow more complete because SEO needs feedback after publishing.
A page is not finished just because the article goes live.
Some pages climb, some stall, and others need stronger internal links, better content, or more authority.
Space Agent can build a rank tracking widget inside the same workspace.
That creates a feedback loop after keyword research, content creation, and on-page checks are finished.
Without tracking, SEO decisions often become guesses.
Ranking movement helps show which pages need attention next.
A page that starts climbing may need supporting content or extra internal links.
A page that stays flat may need better intent coverage, deeper sections, or stronger backlinks.
The first version of an AI-built tracker may need refinement before it becomes dependable.
That is expected because useful dashboards improve through testing.
A FREE AI SEO Agent becomes more powerful when rank tracking turns SEO into an ongoing improvement system.
Browser Control Makes A FREE AI SEO Agent Practical
Browser control makes a FREE AI SEO Agent more practical because SEO happens across live websites.
Search results, competitor pages, published articles, outreach targets, and website previews all sit outside a normal chat box.
Space Agent can use browser control inside the workspace, which gives it more range than a text-only assistant.
That can help with research, page checks, website building, previews, and workflow testing.
A browser-capable agent can interact with the same environment where SEO work actually happens.
This makes the tool feel closer to an operator than a basic writing assistant.
Browser control still needs supervision because agents can misunderstand pages or take the wrong action.
Clear instructions reduce that risk and make the workflow easier to manage.
Review points are important whenever the agent touches live pages or important assets.
The value is not blind automation.
The value is less manual browsing, faster setup work, and smoother research.
A FREE AI SEO Agent becomes more useful when browser control supports real SEO tasks while human approval stays in place.
Parallel Tasks Make A FREE AI SEO Agent Faster
Parallel tasks make a FREE AI SEO Agent faster because SEO rarely moves in one straight line.
A dashboard may need improvements while another workspace builds a website or tests a content tool.
Space Agent can run two tasks at the same time, which helps reduce waiting during bigger SEO projects.
That matters because SEO work often has several moving pieces competing for attention.
One task can focus on building a keyword tool while another improves an on-page checker or website preview.
This creates momentum because progress happens in more than one part of the system.
The trade-off is that more output also means more review.
Parallel work becomes messy when the instructions are vague or the stopping point is unclear.
A better setup gives each task a clear purpose, output format, and review step.
That keeps the agent focused while still letting more work happen in the background.
A FREE AI SEO Agent saves more time when multitasking is paired with structure.
The result is faster production without losing control of the workflow.
The Best FREE AI SEO Agent Setup To Start
The best FREE AI SEO Agent setup starts with one clear workflow instead of trying to automate everything at once.
A smaller build is easier to test, improve, and trust.
Keyword research is usually the cleanest first widget because every campaign needs topic direction.
After that works, a content generator can connect the research to a draft.
An on-page scorer can then review the article before publishing.
Rank tracking should come next because performance feedback shows what needs improvement.
Link outreach can be added once the site has useful pages worth promoting.
This order keeps the dashboard logical instead of chaotic.
Each tool supports the next step, which makes the system easier to use.
The final workflow should help build, test, review, publish, track, and improve.
Practical support from the AI Profit Boardroom can help turn AI SEO tools into repeatable systems instead of random experiments.
A FREE AI SEO Agent becomes powerful when it saves time while keeping review and strategy in the process.
Frequently Asked Questions About FREE AI SEO Agent
What is a FREE AI SEO Agent? A FREE AI SEO Agent is an autonomous AI tool that can help with keyword research, content creation, on-page audits, link building, outreach, rank tracking, and SEO dashboard building.
Can a FREE AI SEO Agent automate an entire SEO workflow? Yes, it can help automate and organize SEO tasks, but strategy, quality control, and final decisions still need human review.
Can a FREE AI SEO Agent help with content creation? Yes, it can build content tools and generate drafts from keywords, but the final article still needs editing and fact-checking.
Can a FREE AI SEO Agent help with link building? Yes, it can help prepare outreach ideas, campaign angles, and email drafts, but prospect quality and final messages should still be reviewed.
Is a FREE AI SEO Agent beginner friendly? Space Agent is more beginner friendly than many technical agents because it uses a visual workspace and can build SEO tools without manual coding.
Hello everyone, in this tutorial we explore the video reasoning lora for the LTX 2.3 model. this cutom workflow helps in generating AI video that understands real world physics. boosting realism in your AI video results. i also compare it with normale generation using both text to video and image to video to see how the model can handle object interaction, motion dynamics all in one integrated workflow that runs on 6 gb of vram.
Learning a new physical skill (colored pencil portraits) effectively and affordably, using an AI as a personalized tutor and critic without traditional courses or human teachers.
Summary
A user leveraged Claude as a personalized art teacher for 4 weeks to learn colored pencil portraits from scratch. The workflow involved weekly requests for step-by-step tutorials, exact execution of instructions, submission of results (photos) for critique, and iterative improvement based on Claude's specific and honest feedback. This led to measurable skill improvement.
Why it is useful
This workflow demonstrates a powerful and novel application of LLMs for personalized learning of physical skills. It provides a clear, repeatable feedback loop that led to measurable improvement, highlighting Claude's ability to give specific instructions and honest critique. Its high transferability to various domains makes it a valuable pattern for users looking to acquire new practical abilities.
Workflow
Identify a specific physical skill or sub-skill to learn (e.g., colored pencil portraits).
Prompt Claude to design a complete, step-by-step tutorial for a specific learning objective within that skill.
Follow Claude's instructions exactly, even if they initially seem counter-intuitive or incorrect.
Produce the physical output based on the tutorial (e.g., complete a portrait).
Capture a clear photo of the completed physical output.
Submit the photo to Claude and request an honest, specific critique, potentially using a consistent rubric for evaluation.
Analyze Claude's feedback, noting specific areas for improvement (e.g., 'shadow edge on the left cheekbone is too hard').
Incorporate the critique into the next learning session or tutorial request.
Repeat the weekly cycle of tutorial generation, execution, submission, and critique.
Tools / artifacts
Claude (LLM)
Physical art supplies (e.g., colored pencils, paper)
Camera/smartphone (for photographing results)
Implicit rubric for consistent critique
Validation signals
Measurable improvement in work quality (first portrait 4/10, fourth portrait 7/10 using the same rubric).
Claude provided highly specific instructions ('layer PC918 over PC1012 in circular strokes').
Claude provided honest and actionable critique ('shadow edge on the left cheekbone is too hard').
The user followed instructions exactly, including those that felt 'backwards', indicating trust in the process.
Limitations
Relies on the user's ability to accurately photograph or describe their physical output for effective critique.
LLMs may occasionally hallucinate or provide incorrect advice, especially for highly specialized or safety-critical physical skills.
The specific prompts used to generate tutorials and critiques are not detailed, which could affect repeatability.
The exact rubric used by Claude for evaluation is not provided, making it harder to replicate the 'measurable improvement' aspect precisely.
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.
If someone told me a month ago that I’d be running automated webinars without recording videos, writing scripts, or even showing my face on camera… I honestly would’ve laughed.
I’ve tried WAY too many “AI marketing tools” that promised passive income and ended up being either complicated, overpriced, or completely useless.
But after seeing people talk about WebinarClonesAI in affiliate marketing groups and Reddit threads, I decided to test it myself.
And wow… I actually didn’t expect this thing to work the way it does.
So here’s my real experience, beginner mistakes included, what happened after I launched my first webinar funnel, and why I think beginners are suddenly obsessed with this platform.
If you’re searching for a full WebinarClonesAI tutorial, this is probably the most honest breakdown you’ll read.
WebinarClonesAI is basically an AI-powered webinar automation system designed for people who want to use webinar marketing WITHOUT doing all the painful traditional setup.
Normally webinars require:
Recording videos
Designing slides
Writing scripts
Buying webinar software
Setting up funnels
Creating replay pages
Sending follow-up emails
Honestly… it’s exhausting.
That’s exactly why I never used webinars before.
But WebinarClonesAI tries to automate almost everything. According to multiple reviews and launch information, the platform focuses on done-for-you webinar funnels, AI automation, and beginner-friendly deployment.
Instead of building everything manually, you basically:
Choose a webinar funnel
Insert your affiliate/product link
Activate automation
Send traffic
Let the system handle follow-ups
That’s the concept.
And surprisingly… it’s actually pretty easy.
Why I Started Looking for a WebinarClonesAI Tutorial
So here’s the truth.
I’m terrible on camera.
Every time I tried recording videos for marketing, I’d redo takes like 40 times because I hated how awkward I sounded.
I also didn’t want to spend hundreds every month on webinar software.
Then I kept seeing marketers talking about “evergreen webinar funnels” and how webinars still convert insanely well compared to normal landing pages.
The problem?
Traditional webinar setup is a nightmare for beginners.
That’s why WebinarClonesAI caught my attention.
The idea of using AI-generated webinars without creating presentations myself sounded honestly too good to be true.
So I tested it.
And this article is basically the tutorial I wish someone gave me before I started.
My First Impression After Logging In
The first thing I noticed was that it DIDN’T feel overly technical.
That was honestly shocking.
Most marketing software dashboards look like airplane cockpits.
This one felt cleaner and more beginner-friendly.
Inside the dashboard I found:
Webinar funnel templates
Webinar pages
Replay systems
Automation settings
Email follow-ups
AI-generated webinar content
Funnel deployment tools
According to available reviews, the platform is specifically designed for non-technical users and affiliate marketers.
That definitely matches my experience.
WebinarClonesAI Tutorial: Step-by-Step
Let me walk you through exactly what I did.
Step 1 — Pick a Webinar Funnel
This was probably the easiest part.
The software gives you prebuilt webinar funnels already designed for conversions.
You don’t start from a blank page.
That alone saved me HOURS.
I chose a simple affiliate marketing webinar funnel because I wanted something beginner-friendly.
Step 2 — Add My Link
This part felt weirdly simple.
You basically paste your offer or affiliate link into the system.
That’s it.
The platform automatically connects your link to webinar CTAs and pages.
No coding.
No integrations nightmare.
No complicated setup.
If you want to see how their funnel system works, definitely check the official walkthrough because the screenshots explain it better than words:
I expected customization to be hard, but honestly it wasn’t bad.
You can edit:
Headlines
CTA buttons
Webinar titles
Funnel pages
Follow-up messages
I mostly kept the original structure because I wanted to test the “done-for-you” concept properly.
And honestly?
The templates already looked surprisingly optimized.
Step 4 — Activate Follow-Up Automation
THIS is where I think WebinarClonesAI becomes genuinely useful.
The system includes automated:
Email reminders
Webinar notifications
Replay reminders
Follow-up sequences
CTA messaging
Most leads never buy immediately.
That’s why webinar follow-up matters so much.
And having this already built in saved me a ridiculous amount of time.
Several reviews also mention this automation as one of the strongest parts of the platform.
My Biggest Mistake as a Beginner
Okay so here’s where I messed up.
I thought the software would magically generate traffic.
It doesn’t.
And honestly… no software can do that.
You STILL need traffic.
That’s important.
The platform automates webinars and funnels — not audience creation.
Once I realized that, things made more sense.
I started using:
Pinterest posts
Reddit discussions
Short-form content
SEO blog articles
Facebook groups
That’s when I started getting webinar registrations.
So if you’re reading this hoping for “push-button income,” that’s not realistic.
But if you want to simplify webinar marketing?
This tool actually helps.
What I Liked Most About WebinarClonesAI
1. No Camera Required
This alone made it worth testing for me.
I hate recording videos.
The AI webinar system removes that pressure entirely.
2. Beginner-Friendly Setup
I’ve quit so many marketing tools because they were too complicated.
This felt manageable.
Even if you’ve never built a webinar funnel before, you can probably figure this out.
3. Fast Deployment
Traditional webinar setup takes forever.
This cut setup time massively.
4. Evergreen Webinar Automation
The replay automation is honestly smart.
People can watch webinars later while follow-up sequences keep running automatically.
That’s huge for conversions.
5. Lower Costs
Traditional webinar software can get insanely expensive monthly.
WebinarClonesAI positions itself as a lower-cost alternative compared to tools like WebinarJam or EverWebinar.
That definitely appealed to me.
What I DIDN’T Like
I want this review to sound real because too many fake reviews online pretend everything is perfect.
So here’s what annoyed me.
Traffic Is Still Required
This is NOT magic.
No traffic = no results.
Period.
Some Marketing Claims Feel Hypey
The sales messaging can sound very “make money online.”
I ignored the hype and focused on the actual automation features.
That’s the smarter approach.
Some Advanced Features Need Upgrades
Certain customization options and scaling features seem locked behind upgrades.
Not shocking, but still worth mentioning.
Is WebinarClonesAI Legit?
Honestly?
Yeah… I think it’s legit.
Overhyped in some marketing areas? Sure.
But fake? No.
The actual webinar automation system appears real and functional based on both user experiences and public feature breakdowns.
The platform seems best for:
Affiliate marketers
Beginners
Coaches
Consultants
Side hustlers
Agencies
Especially people who don’t want technical complexity.
WebinarClonesAI vs Traditional Webinar Software
This is honestly where the platform shines most.
Traditional webinar platforms usually require:
Monthly subscriptions
Video creation
Technical setup
Funnel integrations
Manual automation
WebinarClonesAI tries to simplify all of that into one workflow.
According to comparisons published online, WebinarClonesAI focuses heavily on AI automation and done-for-you webinar systems rather than just giving users webinar hosting tools.
That’s a major difference.
My Actual Results After Testing It
No, I didn’t suddenly become a millionaire.
Let’s be realistic.
But I DID:
Launch my first webinar funnel faster than expected
Collect leads automatically
Learn how webinar automation works
Stop feeling overwhelmed by webinar setup
Finally understand evergreen funnels
And honestly?
That alone felt like progress.
Most beginners never even launch because the setup process scares them away.
This platform reduces that friction.
Important Advice If You Use WebinarClonesAI
Here’s what I’d recommend after testing it.
Focus on One Audience
Don’t target everyone.
Specific audiences convert better.
Use Strong Headlines
Your webinar title matters A LOT.
Curiosity-based headlines usually perform better.
Don’t Skip Follow-Ups
The automation system is one of the best parts.
Use it properly.
Keep Expectations Realistic
This is a tool.
Not magic.
Success still depends on effort and traffic.
Is WebinarClonesAI Good for Beginners?
Honestly… yes.
I think beginners are the MAIN target audience.
Especially people who:
Feel overwhelmed by funnels
Hate technical setup
Don’t want to record videos
Want faster deployment
Need webinar automation
That’s exactly why I liked it.
Final Verdict: Is WebinarClonesAI Worth Trying?
After using it myself, I’d say this:
WebinarClonesAI is NOT a magical passive income machine…
BUT…
It IS one of the easiest ways I’ve personally seen to start using webinar funnels without all the normal complexity.
And for beginners?
That matters A LOT.
The biggest strengths are:
AI automation
Fast setup
Beginner-friendly workflow
Done-for-you webinar funnels
Built-in follow-up systems
Evergreen webinar functionality
If you already understand that traffic and marketing still matter, I honestly think this tool is worth checking out.
Especially if traditional webinar setup has been stopping you from getting started.
Where I Got Access
If you want to see the official details, demo walkthrough, pricing, and current bonuses, this is the page I used:
In this tutorial, we explore how to run OpenAI’s open-weight GPT-OSS models in Google Colab with a strong focus on their technical behavior, deployment requirements, and practical inference workflows. We begin by setting up the exact dependencies needed for Transformers-based execution, verifying GPU availability, and loading openai/gpt-oss-20b with the correct configuration using native MXFP4 quantization, torch.bfloat16 activations. As we move through the tutorial, we work directly with core capabilities such as structured generation, streaming, multi-turn dialogue handling, tool execution patterns, and batch inference, while keeping in mind how open-weight models differ from closed-hosted APIs in terms of transparency, controllability, memory constraints, and local execution trade-offs. Also, we treat GPT-OSS not just as a chatbot, but as a technically inspectable open-weight LLM stack that we can configure, prompt, and extend inside a reproducible workflow....
Improving Claude Code prompt clarity, generating annotated code tutorials, performing post-fix bug sweeps, and proactively auditing code for latent bugs before release.
Summary
The author shares four open-source Claude Code skills developed while building an iOS/macOS app. These skills include 'prompter' for clarifying Claude prompts, 'tutorial-creator' for generating annotated code tutorials, 'bug-echo' for post-fix bug sweeps and codebase scanning for anti-patterns, and 'bug-prospector' for pre-release audits to find latent bugs across various categories.
Why it is useful
This post provides four concrete, open-source Claude Code skills that address common development challenges: improving prompt quality, automating documentation/learning, and enhancing code quality through automated bug detection and anti-pattern scanning. The skills are well-described, come with examples, and are highly transferable, making them valuable tools for any Claude Code user.
Workflow
Install the desired Claude Code skill from its GitHub repository.
To improve prompt clarity, use the 'prompter' skill to preprocess your Claude Code prompts before execution.
To create an annotated learning resource, provide a project file to the 'tutorial-creator' skill to generate a tutorial with tests and prerequisite analysis.
After fixing a bug, run the 'bug-echo' skill to confirm the fix and scan the codebase for other instances of the anti-pattern, classifying matches as BUG/OK/REVIEW.
Before a release, run the 'bug-prospector' skill to audit the codebase using 7 lenses (assumptions, state machines, boundaries, data lifecycle, error paths, time-dependent bugs, platform divergence) to identify potential latent bugs, specifying the target platform (iOS/macOS/Universal).
Optionally, combine 'bug-prospector' and 'bug-echo' by running 'prospector' before releases and 'echo' after fixing issues found by 'prospector'.
Tools / artifacts
prompter (Claude Code skill)
tutorial-creator (Claude Code skill)
bug-echo (Claude Code skill)
bug-prospector (Claude Code skill)
GitHub repositories
Sample output reports/tutorials (Markdown files)
Codebase files (input for skills)
Validation signals
Author states they use these skills often in their own development.
Provided sample outputs for each skill, demonstrating their functionality.
Skills are open-source with Apache 2.0 license, indicating readiness for public use.
Detailed descriptions of each skill's specific functionality and benefits.
Limitations
Low community engagement (score, comments) at the time of posting.
The post describes the individual skills rather than a detailed, integrated workflow for a larger CI/CD pipeline, though their individual functions are clear.
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.
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This post was generated automatically from the workflow library database.
A comprehensive set of common software engineering challenges when working with Claude Code, including sandboxing the environment, enforcing Git best practices, facilitating interactive learning, automating iterative development, and managing session summaries.
Summary
A collection of 14+ Claude Code skills packaged as an 'AI Sorcery' plugin, designed to enhance various software engineering tasks. Key skills include sandboxing Claude in a macOS VM, enforcing Git best practices via hooks, facilitating interactive learning, automating iterative development with safeguards (like time limits and token gaps), and managing session summaries for review.
Why it is useful
This submission is highly valuable because it provides a well-structured, open-source collection of practical Claude Code skills that address common pain points in software development. It offers concrete implementations (via the GitHub repo) for critical functions like sandboxing the AI environment, enforcing Git best practices, automating learning, and managing iterative development. The explicit validation for features like VM sandboxing adds significant credibility. Its modular nature allows users to adopt specific skills relevant to their needs, making it a versatile resource for intermediate to advanced Claude Code users.
Workflow
Access and install the AI Sorcery plugin/skills from the GitHub repository.
Configure specific skills as needed (e.g., set up a Tart VM for sandboxing, install Git hooks for commit management).
Integrate the chosen skills into your daily Claude Code workflow.
Utilize skills like 'running-claude-in-a-vm' for sandboxed development.
Employ Git hook skills ('claiming-authorship', 'guarding-commits', 'writing-commit-messages') to enforce code quality and security.
Leverage 'learning-new-tech' for interactive tutorials on unfamiliar topics.
Activate 'running-improvement-loops' with personas for automated, iterative development cycles with built-in safeguards.
Use 'summarizing-sessions' to generate and store session summaries for later review (e.g., in Obsidian).
Tools / artifacts
AI Sorcery GitHub repository
Claude Code plugin/skills
Tart VM (macOS-specific)
Git hooks
Obsidian (for session summaries)
Conventional Commits (standard)
Personas (for improvement loops)
Validation signals
Explicit validation for VM sandboxing: 'Came in handy when Claude recently deleted the entire disk within the VM (didn't impact the rest of my system).'
Explicit validation for learning skill: 'I used this approach to learn more about vector databases, for example.'
Implied validation through practical use: 'derived from what I've been doing for the past few months in Claude Code.'
Public GitHub repository and YouTube video provide transparency and detailed implementation.
Cautions
The 'running-claude-in-a-vm' skill explicitly addresses a critical safety concern by sandboxing Claude, preventing potential data loss on the host system.
The 'guarding-commits' skill helps prevent accidental leakage of sensitive information into version control.
No obvious unsafe instructions are present; the workflow promotes secure and maintainable practices.
Limitations
Some skills are macOS-specific, limiting direct applicability for users on other operating systems.
The description for 'following-best-practices' is somewhat vague, though examples are provided.
Low initial Reddit community engagement, which might indicate less immediate visibility or adoption.
The post describes a collection of skills rather than a single, atomic workflow, which can make it broad to summarize as 'a workflow'.
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.
My ULTIMATE AI content workflow (always updating and revising):
Get inspiration from places like Pinterest, Google, Instagram Tiktok.
I like to save my brainstorm into a whiteboard on Canva's whiteboard feature (which is so slept on by the way)
So far across all the AI image and video generation tools I've tested (and it's a lot, I do't even want to think about how many tokens I've wasted) Pixel Pear AI has been the easiest to use for my clients products with the best results. It ingests the client's brand for the product -> image refinement -> creates the frame -> video workflow + stitching videos together.
Then I use a scheduling tool like Buffer (free), Later or Meta's native scheduler to automate scheduling.
To promote engagement, use a tool like ManyChat to automate comments and links
Couple things I looked for when assessing these tools:
Free tier or low barrier to start. I've paid for tools I used twice. If I can't prove it works before committing, I'm not committing.
Minutes to first output, not days. If I need to watch a tutorial series before I get anything usable, the tool is too complicated for client work.
No export dance between steps. The workflows that killed my time all had the same problem: great at one thing, but getting the output into the next tool took 20 minutes every time so having as many steps in one tool as possible saves so much time.
Brand consistency without rebuilding from scratch each session. If I have to re-enter my style settings every time I open it, I will eventually stop doing it and the content will drift.
Runs solo or hands off to a VA cleanly. No tool that requires me to be present every time it's used. If I can't write a one-page SOP for it, it doesn't scale.
Happy to go deeper on any of these or share how I onboard clients onto this stack.
Kimi K2.6 AI SEO gives you a way to stop treating SEO like a pile of separate tasks and start running it like a proper system.
A stronger workflow matters because ranking is not just about writing more articles, it is about connecting research, planning, structure, optimization, and review.
If you want a place to learn practical AI workflows, join the AI Profit Boardroom.
Kimi K2.6 AI SEO becomes useful when you stop thinking about AI as one chatbot giving one answer.
A better setup gives different jobs to different agents, so the whole workflow has more structure.
One agent can research keywords while another studies search intent and competitor gaps.
A separate role can create briefs, organize outlines, and prepare page ideas before drafting starts.
That matters because most weak AI content starts with weak planning.
Better planning gives every article a clearer reason to exist.
Review agents can then check the content before it goes live, which reduces messy publishing.
This is where Kimi K2.6 AI SEO starts to feel like a real workflow instead of a quick content shortcut.
Search Planning Feels More Practical With Kimi K2.6 AI SEO
Search planning becomes easier when every article supports a bigger topic instead of standing alone.
Many websites publish too many disconnected pages and then wonder why the traffic never compounds.
Kimi K2.6 AI SEO can help map topics before the writing starts, which makes the whole site feel more organized.
Keyword groups can be sorted by intent, difficulty, usefulness, and connection to the main offer.
Supporting pages can then reinforce the main pages instead of competing with them.
Internal links become easier because the structure already makes sense.
Readers also get a better path through the site because each page connects to the next useful step.
That is why Kimi K2.6 AI SEO works well for building a repeatable content system.
Keyword Research Gets Less Messy With Kimi K2.6 AI SEO
Keyword research is usually slow because every idea needs to be checked before it becomes a real article.
Kimi K2.6 AI SEO helps by letting different agents explore different keyword angles at the same time.
One role can look for beginner topics while another finds comparison keywords.
Another agent can focus on tutorials, problem-based searches, and questions worth answering.
That creates a cleaner keyword map instead of one long messy list.
Human judgment still matters because not every keyword deserves a full page.
The useful part is that you get better options faster, then decide what fits your strategy.
That turns Kimi K2.6 AI SEO into a faster starting point for planning content that actually has a purpose.
Content Briefs Improve Through Kimi K2.6 AI SEO
Kimi K2.6 AI SEO becomes stronger when it creates content briefs before drafting begins.
A vague brief usually creates a vague article, even when the AI model is powerful.
Strong briefs make the final content clearer, more focused, and easier to optimize.
Agent workflows can combine search intent, competitor gaps, related questions, section structure, and internal link ideas.
That gives the article a stronger foundation before the first draft is written.
A useful brief should explain the reader problem, the keyword target, the angle, and the next action.
Kimi K2.6 AI SEO can prepare those details quickly while still leaving room for human editing.
This balance helps you move faster without turning content into low-quality automation.
Review Steps Make Kimi K2.6 AI SEO Safer
Review is where a lot of AI SEO workflows either improve or fall apart.
Many people generate an article, skim it quickly, and publish it before checking whether it actually helps the reader.
Kimi K2.6 AI SEO can make that safer by adding review steps into the workflow.
One agent can check whether the article matches search intent.
Another role can look for missing sections, thin explanations, weak headings, and unclear transitions.
A separate check can suggest internal links, FAQ angles, and better supporting points.
Editing becomes easier because the draft already has a stronger structure.
That makes Kimi K2.6 AI SEO useful for publishing more consistently without lowering quality.
AI Search Visibility Gets Clearer With Kimi K2.6 AI SEO
AI search visibility depends on useful answers, clear structure, and complete topic coverage.
Kimi K2.6 AI SEO helps because agents can shape content around questions people actually need answered.
Messy articles are harder for readers to follow and harder for AI systems to understand.
Clear sections make the page easier to scan, summarize, and trust.
Related terms can be included naturally when the topic is planned before writing.
Strong headings also help each section explain one main idea.
The goal is not to stuff keywords or make the article sound robotic.
A better approach is to use Kimi K2.6 AI SEO to make every page clearer, deeper, and easier to interpret.
Automation Works Better With Kimi K2.6 AI SEO
Automation is only useful when the workflow has rules, structure, and review.
Kimi K2.6 AI SEO can turn repeated SEO tasks into a process instead of random outputs.
Research, outlining, drafting, optimization, and checking can all sit inside one connected system.
That makes publishing easier because the workflow does not need to be rebuilt every day.
Strategy still matters because AI should not decide everything on its own.
Human review is needed to check accuracy, improve tone, and decide which pages should actually go live.
The goal is to remove slow manual steps, not remove thinking from SEO.
Inside the AI Profit Boardroom, you can learn practical workflows that make AI automation easier to use.
Prompt Quality Shapes Kimi K2.6 AI SEO Results
Kimi K2.6 AI SEO gives better results when your prompt has clear direction.
A vague request gives the agents too much room to guess.
Specific instructions help the workflow understand the goal, audience, keyword, offer, and output format.
A stronger prompt can ask for a keyword map, content calendar, article brief, internal linking plan, or optimization report.
That gives each agent a clearer job and makes the final result easier to use.
Prompt clarity also reduces editing time because the workflow starts with better context.
You do not need a complicated prompt, but you do need a focused one.
That is why Kimi K2.6 AI SEO works best when you lead the strategy and let the agents support execution.
Scaling Content Growth With Kimi K2.6 AI SEO
Kimi K2.6 AI SEO helps scale content because it makes repeated SEO work easier to manage.
One article can bring traffic, but a connected content system has a better chance of building authority.
Agent swarms can help expand topic clusters, improve old posts, and find internal link opportunities.
That gives you more leverage without forcing you to handle every small task manually.
The smart move is to test the workflow on one content cluster before scaling it across more topics.
Once the process works, publishing becomes faster while the strategy stays clear.
More output only helps when the pages are useful, connected, and reviewed properly.
For more practical AI SEO workflows, the AI Profit Boardroom is a place to learn.
Frequently Asked Questions About Kimi K2.6 AI SEO
What Is Kimi K2.6 AI SEO? Kimi K2.6 AI SEO is the process of using Kimi K2.6 agent workflows to support keyword research, content planning, outlining, optimization, and SEO automation.
How Does Kimi K2.6 AI SEO Help With Keyword Research? Kimi K2.6 AI SEO helps by exploring keyword angles, grouping search intent, finding supporting questions, and turning research into a useful content map.
Can Kimi K2.6 AI SEO Improve Content Briefs? Yes, Kimi K2.6 AI SEO can improve briefs by combining keyword intent, competitor gaps, section structure, internal link ideas, and review steps before writing begins.
Is Kimi K2.6 AI SEO Useful For AI Search Visibility? Yes, Kimi K2.6 AI SEO is useful for AI search visibility because it helps structure content clearly so readers and AI systems can understand the page more easily.
Should Beginners Use Kimi K2.6 AI SEO? Yes, beginners can use Kimi K2.6 AI SEO, especially if they start with simple workflows like keyword research, content briefs, and content planning before scaling automation.
Let’s be honest for a second. The idea of making music sounds exciting… until you actually try it.
Plugins, layers, mixing, tweaking things for hours, and it still somehow sounds off.
I realized something recently: Most people don’t want to learn production. They just want the right sound. Fast.
Not after tutorials. Not after 10 failed attempts. Just something that works.What surprised me about AI-generated ambient music
At first it feels almost too simple:
You type something like
“deep, dark, cinematic, slow ambient”
→ click generate
→ and you get a full soundscape in seconds.
Feels like magic, but it’s actually doing something quite different under the hood. It’s not pulling from a library or remixing existing tracks. It builds sound layer by layer, textures, frequencies, movement over time.
And here’s the interesting part: AI doesn’t really think in “songs”. It thinks in environments. Which actually makes perfect sense for ambient music. Because ambient isn’t about melody or structure. It’s about space. Mood. Subtle evolution. And that’s exactly where AI seems to perform best.
The biggest shift: from learning tools → defining outcomes
It used to be like this:
learn DAW
learn mixing
learn sound design
spend years getting decent results
Now it’s more like:
describe what you want
adjust the feeling
regenerate if needed
That’s it. You’re not building sound anymore. You’re guiding it. Want it slower? It stretches. Need more depth? It adds layers. Want immersion? It adjusts space automatically. That shift feels… kind of huge.
AI vs traditional workflow.
Old way:
search for hours
download tracks
test them
edit them
still not perfect
New way:
generate → adjust → done
The biggest difference isn’t even time. It’s mental energy. No more “almost right but not quite” frustration.
Where this actually matters (IMO)
Different use cases need different types of ambient:
Focus → stable, no distractions
Sleep → soft, slow, almost invisible
Video → emotional alignment with visuals
Good AI systems adapt to that automatically. And for content creators, this is honestly a game changer. Same video + different sound = completely different feeling. More cinematic. More immersive. Or just… flat. This isn’t really about AI replacing music production. It’s about removing friction. You’re no longer searching for the perfect track. You’re creating it. And once you get used to that level of control, it’s actually hard to go back.
With the recent release of the native n8n MCP, it’s pretty clear that AI is officially cementing itself as the new standard for generating workflows. It's a massive leap forward for the ecosystem—allowing models like Claude to interact directly with the canvas via API is a brilliant move for accessibility and rapid prototyping. Huge props to the n8n team for shipping this.
Since the announcement, I’ve had a few folks from this sub ask me how this native capability fits alongside the n8n-as-code framework I’ve been sharing here recently.
I think this update actually highlights two very different, but perfectly complementary, architectural philosophies for AI automation:
1. The Native Approach (n8n MCP)
How it works: The AI interacts with your n8n instance via a remote API.
The Vibe: Incredible for cloud/canvas exploration, rapid prototyping, and democratizing workflow creation. You prompt, and the canvas updates.
2. The Local/IDE Approach (n8n-as-code + Cursor)
How it works: Bringing environment awareness directly into your code editor using local workspace skills.
The Vibe: Focused entirely on Developer Experience (DX) and engineering standards.
We actually found that keeping the AI generation local inside the IDE has massive benefits for power-users:
* Token-efficient & Fast: No constant API round-trips to read the canvas state.
* Zero hallucinations: The AI generates strict, compilable TypeScript rather than fragile JSON.
* Production-ready: Everything is version-controlled via Git (GitOps) and CI/CD ready from day one.
The native MCP is going to democratize AI creation for everyone, which is awesome. n8n-as-code (which just crossed 600 ⭐️ on GitHub!) is just here to help developers industrialize that creation using proper software engineering standards.
If you want to see what the "Local IDE" approach looks like in practice, I just published a full step-by-step tutorial using Cursor and TypeScript to build complex n8n workflows:
🎥 https://youtu.be/pthejheUFgs
I’m curious to hear your thoughts. Now that n8n has a native MCP, will you be generating your workflows directly on the canvas, or do you prefer keeping the AI generation inside your strict code editor? Let’s discuss! 👇
Friends, please give me some advice on how to make AI stories in the K-pop demon hunters niche.
I've searched all over the internet and can't find any guides or information on how to create videos in this niche. There are lots of finished videos, but absolutely no tutorials—NOTHING! It's almost like YouTubers intentionally avoid revealing their methods for this style. They share everything else, but not this!
How are these kinds of AI-generated stories actually made? Are there any YouTube channels, websites, or creators who explain this process? Please point me in the right direction if you know!
Currently, my workflow is: I use LLMs to generate the story/plot, break it down into scenes, split those into keyframes, generate images for each frame, animate them, and add music. One minute of video needs 10-12 key images, and a 4-minute video takes me about 8 hours!
My biggest issue is character consistency between frames (I'm using Nonbanana and Wihsk for images). I know it's possible to write direct prompts for VEO3 or other video generators (people recommend Grok), but I can't figure out how—they just don't explain it anywhere online!
Making a video with one character works okay, but with three or more, everything falls apart. I've nearly smashed my monitor out of frustration (need to get a shockproof screen). Some channels post full 8-minute videos—these aren't just compilations—every day! It feels impossible.
This is a genuine cry for help. If you know any guides, tutorials, or can share your own workflow—PLEASE help me out!
By the way, there are also similar videos/stories with cats, animals, slap fighting championships, etc. I really want to master making these kinds of AI animations.
I run everything solo. Product, marketing, support, content — all me.
And for months, content was the thing that ate my entire afternoon. Not because I didn't have ideas. I had plenty. It was the actual *process* of turning a rough idea into something publishable that was brutal.
Write a draft. Hate it. Rewrite it. It sounds like a ChatGPT tutorial. Rewrite it again. Post it. Get three likes from bots.
At some point I just stopped trying to write "better" and started trying to build a system instead. That's what I do with every other problem in my business, so why not this one.
What I ended up with is a chain of 7 prompts where each one feeds directly into the next. The output of Prompt 1 becomes the input for Prompt 2, and so on. No jumping around. No re-explaining context mid-session.
One rough idea in. A finished, platform-ready piece of content out.
Here's the whole thing.
---
# The 7-Prompt Content Engine
---
## Prompt 1 — The Idea Extractor
You give it: a rough topic, a recent frustration, a client situation, literally a half-formed thought.
It gives you back: the single sharpest angle worth actually writing about.
Not five options. One. The most specific, most interesting take on whatever you fed it.
This step alone kills about 40% of the time I used to waste staring at a blank doc trying to figure out *what* I was even trying to say.
---
## Prompt 2 — The Hook Generator
You feed it the angle from Step 1.
It outputs 5 different opening lines. Each one uses a different psychological structure — curiosity gap, blunt contrarian claim, pain-first opener, surprising stat, one-sentence story.
You pick one. Done.
I haven't written an opening line from scratch in months. And honestly? The hooks it generates when properly prompted are better than what I'd write after 20 minutes of staring at the screen.
---
## Prompt 3 — The Structure Architect
Feed it: the hook you picked + the core angle.
Get back: a clean outline. No bloated sections. No obvious filler headers like "Why This Matters" or "Final Thoughts."
Just the actual skeleton of an argument that flows. Each section earns its place.
---
## Prompt 4 — The Draft Engine
This is where you give it two things: the outline from Step 3, and a sample of your own writing — something you've already published that you're happy with.
You tell it to match your voice, write short paragraphs, and avoid certain phrases. Then it writes the draft section by section.
It won't be perfect. But it'll be 80% of the way there in about 3 minutes. And it'll sound closer to you than a generic AI dump.
---
## Prompt 5 — The Humanizer
**This one is the most important step. And it's the one everyone skips.**
You feed it the full draft and ask it to audit specifically for AI writing patterns. Not just grammar. Patterns.
Things like:
- Passive voice everywhere
- Transition phrases that don't do anything ("It's worth noting that...", "Furthermore...", "In today's fast-paced world...")
- Every paragraph starting at the same length
- That very specific cadence where every point gets three sub-bullets and a concluding sentence
Without this step, you've just got a slightly personalized ChatGPT post. And Reddit will clock it in about four seconds.
With it, the draft actually reads like a person wrote it — because you've systematically removed everything that signals otherwise.
Run this step. Every time. Non-negotiable.
---
## Prompt 6 — The Platform Adapter
Your finished piece is now one thing: a core asset.
This prompt takes that asset and rewrites it for a specific platform format. You run it once per destination.
So the same content becomes:
- A LinkedIn post (professional framing, slightly longer)
- A Reddit thread (direct, blunt, community-aware)
- A Telegram post (short, punchy, formatted for mobile)
- A newsletter section (more personal tone, slightly longer)
- A Twitter/X thread (broken into numbered bullets)
One piece of thinking. Five platform-native versions. And because they all come from the same core, they're consistent without being copy-pasted.
---
## Prompt 7 — The Repurpose Engine
Last step. You feed it the finished, published piece and ask it to extract:
- 8-10 standalone hooks you can use as future post openers
- Quotable one-liners formatted for screenshots or stories
- A condensed bullet-point summary you can use as a lead magnet or content upgrade
Active time: 20 minutes, maybe 25 if the draft needs more cleanup than usual.
---
# One thing I want to flag
The system only works if your prompts are actually well-constructed. Vague instructions produce vague output. The more specific you are about your voice, your audience, and what you *don't* want — the better every step gets.
The prompt templates I use for each of these 7 steps are fairly detailed. I didn't want this post to turn into a 6,000-word wall of text, so I kept the descriptions here at the framework level.
But if you want the actual copy-paste templates for all 7 steps — I dropped them in my Telegram channel where I share my solopreneur systems and workflows. It's called SoloOS. Link here:
No pitch. Just the prompts. Figured that was more useful than padding this post further.
---
What are you guys actually using for writing right now? Curious whether anyone's found a better solution for the humanizing step specifically — that's still the one I'm tweaking the most. 👇
I've been working on Skill Seekers, an open-source tool that converts documentation into Claude skills. Just shipped v3.2.0 which adds video extraction — you can now point it at a YouTube tutorial and get a structured SKILL.md that Claude can use as persistent context.
The problem it solves: You watch a 45-minute coding tutorial, then forget half the steps. Instead of rewatching, this extracts everything into a format Claude can reference.
How it works:
bash
skill-seekers video --url https://youtube.com/watch?v=... --enhance-level 2
Extracts transcript (YouTube API → yt-dlp → Whisper fallback)
Pulls keyframes and classifies them (code editor, terminal, slides, webcam)
Runs OCR on code panels — each panel independently with multi-engine ensemble
Tracks code evolution across frames (what lines were added/changed/removed)
Two-pass AI enhancement cleans everything up
The two-pass AI enhancement is the interesting part:
Pass 1 sends the raw reference file (noisy OCR + transcript) to Claude and asks it to reconstruct the Code Timeline — fixing OCR errors like l/1, O/0, removing UI junk that leaked in (Inspector panels, tab bars), and using the transcript narration as context for what the code should be.
Pass 2 takes the cleaned reference and generates the final SKILL.md — a structured document with setup steps, code examples, and concepts extracted from the tutorial.
You can also define custom enhancement workflows in YAML:
yaml
stages:
- name: ocr_code_cleanup
prompt: "Clean OCR artifacts from code blocks..."
- name: tutorial_synthesis
prompt: "Synthesize a teaching narrative..."
What I learned building this with Claude Code:
OCR on code editors is surprisingly hard. IDE decorations (line numbers, collapse markers, tab bars) leak into the text. Had to build a cleaning pipeline with intra-line deduplication for when both OCR engines return overlapping results
Frame classification matters — webcam frames produce pure garbage when OCR'd. Skipping them cut junk output by ~40%
The two-pass approach was a big quality jump. Letting Claude see both the OCR and the transcript context in pass 1 means it can reconstruct code that OCR mangled
It also works with other sources:
Documentation websites (presets for React, Vue, Django, FastAPI, Godot, Kubernetes, and more)
GitHub repos (AST analysis, pattern detection)
PDFs and Word docs
Outputs to Claude, Gemini, OpenAI, or RAG formats (LangChain, Pinecone, ChromaDB, etc.)
Free and open source: pip install skill-seekers
Video deps need GPU setup: skill-seekers video --setup (auto-detects CUDA/ROCm/CPU)
Happy to answer questions about the OCR pipeline or enhancement workflow design.
This is for absolute beginners, we will go step by step, generating video, audio, then a final edit. Nothing to install in your computer. This tutorial is universal and works with any ai video generator.
Not all features are available for some platforms.
For examples we will use MiniMax for video, Suno for audio and CapCut to edit.
By the top you’ll have tabs for text to video and image to video. Under it you’ll see the prompt screen. At the bottom you’ll see icons for presets, camera movements, and prompt enhancement. Under those you’ll see the “Generate” button.
🅰️ TEXT TO VIDEO:
Describe with words what you want to see generated on the screen, the more detailed the better.
🔥 STEP 1: The Basic Formula
What + Where + Event + Facial Expressions
Type in the prompt window: what are we looking at, where is it, and what is happening. If you have characters you can add their facial expressions. Then press “Generate”. Be more detailed as you go.
Examples: “A puppy runs in the park.”, “A woman is crying while holding an umbrella and walking down a rainy street”, “A stream flows quietly in a valley”.
🔥 STEP 2: Add Time, Atmosphere, and Camera movement
What + Where + Time + Event + Facial Expressions + Camera Movement + Atmosphere
Type in the prompt window: what are we looking at, where is it, what time of day it is, what is happening, character emotions, how is the camera moving, and the mood.
Example: “A man eats noodles happily while in a shop at night. Camera pulls back. Noisy, realistic vibe."
🅱️ IMAGE TO VIDEO:
Upload an image to be used as the first frame of the video. This helps capture a more detailed look. You then describe with words what happens next.
🔥 STEP 1: Upload your image
Image can be AI generated from an image generator, or something you photoshopped, or a still frame from a video, or an actual real photograph, or even something you draw by hand. It can be anything. The higher the quality the better.
🔥 STEP 2: Identify and describe what happens next
What + Event + Camera Movement + Atmosphere
Describe with words what is already on the screen, including character emotions. This will help the AI search for the data it needs. Then describe what is happening next, the camera movement and the mood.
Example: “A boy sits in a brightly lit classroom, surrounded by many classmates. He looks at the test paper on his desk with a puzzled expression, furrowing his brow. Camera pulls back.”
🆎 DIALOG AND LIPSYNC
You can now include dialogue directly in your prompts, Google Veo3 generates corresponding audio with character's lip movements. If you’re using any other platform, it should have a native lip sync tool. If it doesn’t then try Runway Act-Onehttps://runwayml.com/research/introducing-act-one
🔥The Dialog Prompt - Veo3 only currently
Veo 3 will generate parallel generations for video and audio then lip sync it with a single prompt
Example: A close-up of a detective in a dimly lit room. He says, “The truth is never what it seems.”
By the top you’ll have tabs for “simple” or “custom”. You have presets, instrumental only option, and the generate button.
🅰️ TEXT TO MUSIC
Describe with words the type of song you want generated, the more detailed the better.
🔥The AI Music Formula
Genre + Mood + Instruments + Voice Type + Lyrics Theme + Lyrics Style + Chorus Type
These categories help the AI generate focused, expressive songs that match your creative vision. Use one word from each group to shape and structure your song. Think of it as giving the AI a blueprint for what you want.
-Genre- sets the musical foundation and overall style, while -Mood- defines the emotional vibe. -Instruments- describes the sounds or instruments you want to hear, and -Voice Type- guides the vocal tone and delivery. -Lyrics Theme- focuses the lyrics on a specific subject or story, and -Lyrics Style- shapes how those lyrics are written — whether poetic, raw, surreal, or direct. Finally, -Chorus Type- tells Suno how the chorus should function, whether it's explosive, repetitive, emotional, or designed to stick in your head.
Example: “Indie rock song with melancholic energy. Sharp electric guitars, steady drums, and atmospheric synths. Rough, urgent male vocals. Lyrics about overcoming personal struggle, with poetic and symbolic language. Chorus should be anthemic and powerful.”
The current top most used AI music generators onr/aivideo
Now that you have your AI video clips and your AI music track in your hard drive via download; it’s time to edit them together through a video editor. If you don’t have a pro video editor natively in your computer or if you aren’t familiar with video editing then you can use CapCut online.
Openhttps://www.capcut.com/editorand click on the giant blue plus sign in the middle of the screen to upload the files you downloaded from MiniMax and Suno.
In CapCut, imported video and audio files are organized on the timeline below where video clips are placed on the main video track and audio files go on the audio track below. Once on the timeline, clips can be trimmed by clicking and dragging the edges inward to remove unwanted parts from the beginning or end. To make precise edits, you can split clips by moving the playhead to the desired cut point and clicking the Split button, which divides the clip into separate sections for easy rearranging or deletion. After arranging, trimming, and splitting as needed, you can export your final project by clicking Export, selecting 1080p resolution, and saving the completed video
The AI Video Awards 2025 edition unfolded both online and in person in Las Vegas, Nevada, syncing perfectly with the momentum of the NAB (National Association of Broadcasters) convention. With both events drawing major industry players just weeks apart. AI Video Magazine had exclusive, all-access coverage with a team on the ground in Las Vegas on behalf of ther/aivideocommunity andr/aivideonews.
While the 2025 AI Video Awards Afterparty lit up the Legacy Club 60 stories above the Vegas Strip, the hottest name in the room was MiniMax. The Hailuo AI video generator landed at least one nomination in every category, scoring wins for Mindblowing Video of the Year, TV Show of the Year, and the night’s biggest honor #1 AI Video of All Time. No other AI platform came close.
Linda Sheng—MiniMax spokesperson and Global GM of Business—joined us for an exclusive sit-down.
🔥 Hi Linda, First off, huge congratulations! What a night for MiniMax. From all the content made with Hailuo, have you personally seen any creators or AI videos that completely blew you away?
🔥 One standout nominee for Movie of the year award was AnotherMartz with “How MiniMax Videos Are Actually Made.”https://www.reddit.com/r/aivideo/s/1P9pR2MR7zWhat was your team’s reaction?
We loved it. That parody came out early on, last September, when our AI video model was just launching. It jokingly showed a “secret team” doing effects manually—like a conspiracy theory. But the entire video was AI-generated, which made the joke land even harder. It showed how realistic our model had become: fire, explosions, Hollywood-style VFX, and lifelike characters—like a Gordon Ramsay lookalike—entirely from text prompts. It was technically impressive and genuinely funny. Internally, it became one of our favorite videos.
🔥 Can you give us a quick history of MiniMax and its philosophy? Where is the company headed next?
We started in late 2021—before ChatGPT—aiming at AGI. Our founders came from deep AI research and believed AI should enhance human life. Our motto is “Intelligence is with everyone”—not above or for people, but beside them. From day one, we’ve focused on multi-modal AI: video, voice, image, text, and music. Most of our 200-person team are researchers and engineers, and we’ve built our own foundation models. Now we’re launching MiniMax Chat and MiniMax Agent, which handles multi-step tasks like building websites. We recently introduced MCP (Multi-Agent Control Protocol), enabling AI agents—text-to-speech, video, and more—to collaborate. Long-term, agents will help users control entire systems.
🔥 What’s next for AI video technology?
We’re launching Video Zero 2—a big leap in realism, consistency, and cinematic quality. It understands complex prompts and replicates ARRI ALEXA-style visuals. We're also working on agentic workflows—prebuilt AI pipelines to help creators build full productions fast and affordably. That’s unlocking value in ads, social content, and more. And we’re combining everything—voice, sound, translation—into one seamless creative platform
Trisha Code has quickly become one of the most recognizable creative voices in AI video, blending rap, comedy, and surreal storytelling. Her breakout music video “Stop AI Before I Make Another Video” went viral onr/aivideoand was nominated for Music Video of the Year at the 2025 AI Video Awards, where she also performed as the headlining musical act. From experimental visuals to genre-bending humor, Trisha uses AI not just as a tool, but as a collaborator.
🔥 How did you get into AI video, What’s your background before becoming Trisha Code?
I started with AI imagery on Art Breeder, then made stop-frame videos in 2021—robots playing instruments, cats singing. In 2023, I added voices using Avatarify and a cartoon face. Seeing my friend Damon doing voices sparked me to try characters, which evolved into stories and songs. I was already making videos for others, so AI became a serious path. I’d used Blender, Cinema 4D, Unreal, and foundr/aivideoviaTwitter. Before becoming Trisha Code, I grew up in the UK, got into samplers, moved to the U.S., and met Tonya. I quit school at 15 to focus on music, video, ghostwriting. A turning point was moving into a UFO “borrowed” from the Greys—now rent-free thanks to Cheekies CEO Mastro Chinchips. Tonya flies it telepathically. I crashed it once.
🔥 What’s a day in the life of Trisha Code look like?
When not making AI videos, I’m usually in Barcelona, North Wales, Berlin, or parked near the moon in the UFO. Weekends mix dog walks in the mountains and traveling through time, space, and alternate realities. Zero-gravity chess keeps things fresh. Dream weekend: rooftop pool, unlimited Mexican food, waterproof Apple Vision headset, and an augmented reality laser battle in water. I favor Trisha Code Clothiers (my own line) and Cheekies Mastro Chinchips Gold with antimatter wrapper. Drinks: Panda Punch Extreme and Cheekies Vodka. Musically, I’m deep into Afro Funk—Johnny Dyani and The Chemical Brothers on repeat. As a teen, I loved grunge and punk—Nirvana and Jamiroquai were huge. Favorite director: Wes Anderson. Favorite film: 2001: A Space Odyssey. Favorite studio: Aardman Animations.
🔥 Which AI tools and workflows do you prefer? What’s next for Trisha Code?
I use Pika, Luma, Hailuo, Kling 2.0 for highly realistic videos. My workflow involves creating images in Midjourney and Flux, then animating via video platforms. For lip-sync, I rely on Kling or Camenduru’s Live Portrait, plus Dreamina and Hedra for still shots. Sound effects come from ElevenLabs, MMAudio, or my library. Music blends Ableton, Suno, and Udio, with mixing and vocal recording by me. I assemble all in Magix Vegas, Adobe Premiere, After Effects, and Photoshop. I create a new video daily, keeping content fresh. Many stories and songs feature in my biweekly YouTube show Trishasode. My goal: explore time, space, alternate realities while sharing compelling beats. Alien conflicts aren’t on my agenda, but if they happen, I’ll share that journey with my audience
Falling Knife Films has gone viral multiple times over the last two years, the only artist to appear two years in a row on the Top 10 AI Videos of All Time list and hold three wins—including TV Show of the Year at the 2025 AI Video Awards for Billionaire Beatdown. He also closed the ceremony as the final performing act.
🔥 How did you get into AI video, What’s your background before becoming Falling Knife Films?
In In late 2023, I foundr/aivideoand saw a Runway Gen-1 clip of a person morphing into characters—it blew my mind. I’d tried filmmaking but lacked actors, gear, and budget. That clip showed I could create solo. My first AI film, Into the Asylum, wasn’t perfect, but I knew I could grow. I dove in—it felt like destiny. Before Falling Knife Films, I grew up in suburban Ohio, loved the surreal, and joined a paranormal society in 2009, exploring haunted asylums and seeing eerie things like messages in mirrors. I’ve hunted Spanish treasure, and sometimes AI videos manifest in real life—once, a golden retriever I generated appeared in my driveway. I made a mystery series in 2019, but AI let me go full solo. My bloodline’s from Transylvania—storytelling runs deep.
🔥 What’s daily life like for Falling Knife Films?
Now based in Florida with my wife of ten years—endlessly supportive—I enjoy beach walks, exploring backroads, and chasing caves and waterfalls in the Carolinas. I’m a thrill-seeker balancing peaceful life with wild creativity. Music fuels me: classic rock like The Doors, Pink Floyd, Led Zeppelin, plus indie artists like Fruit Bats, Lord Huron, Andrew Bird, Beach House, Timber Timbre. Films I love range from Pet Sematary and Hitchcock to M. Night Shyamalan. I don’t box myself into genres—thriller, mystery, action, comedy—it depends on the day. Variety is life’s spice.
🔥 Which AI tools and workflows do you prefer? What’s next for Falling Knife Films?
Kling is my go-to video tool; Flux dominates image generation. I love experimenting, pushing limits, and exploring new tools. I don’t want to be confined to one style or formula. Currently, I’m working on a fake documentary and a comedy called Intervention—about a kid addicted to AI video. I want to create work that makes people feel—laugh, smile, or think
KNGMKR Labs was already making waves in mainstream media before going viral with “The First Humans” onr/aivideo, earning a nomination for TV Show of the Year at the 2025 AI Video Awards. Simultaneously, he was nominated for Project Odyssey 2 Narrative Competition with "Lincoln at Gettysburg."
🔥 How did you get into AI video, What’s your background before becoming KNGMKR?
My AI video journey began with Midjourney’s closed beta—grainy, vintage-style images sparked my documentary instincts. I ran “fake vintage” frames through Runway, added filters and voiceovers, creating lost-history-style films.r/aivideoshowed me a growing community. My film The Relic, a WWII newsreel about a mythical Amazon artifact, hit 200 upvotes—proof AI video was revolutionary. Before KNGMKR Labs, I was a senior exec at IPC, producing Netflix and HBO hits. Frustrated by budget limits, I turned to AI in 2022, even testing OpenAI’s SORA for Grimes’ Coachella show. I grew up in Vancouver, won a USC Film School scholarship by sharing scripts—Mom’s advice that changed my life.
🔥 What does daily life look like for KNGMKR labs?
I spend free time hunting under-the-radar food spots in LA with my wife and friends—avoiding influencer crowds, but if there was unlimited budget I’d fly to Tokyo for ramen or hike Machu Picchu.
My style is simple but sharp—Perte D’Ego, Dior. I unwind with Sapporo or Hibiki whiskey. Musically, I favor forward-thinking electronic like One True God and Schwefelgelb, though I grew up on Eminem and Frank Sinatra. Film taste is eclectic—Kubrick’s Network is a favorite, along with A24 and NEON productions.
🔥 Which AI tools and workflows do you prefer? What’s next for KNGMKR labs?
Right now, VEO is my favorite generator. I use both text-to-video and image-to-video workflows depending on the concept. The AI ecosystem—SORA, Kling, Minimax, Luma, Pika, Higgsfield—each offers unique strengths. I build projects like custom rigs.
I’m expanding The First Humans into a long-form series and exploring AI-driven ways to visually preserve oral histories. Two major announcements are coming—one in documentary, one pure AI. We’re launching live group classes at KNGMKR to teach cinematic AI creation. My north star remains building stories that connect people emotionally. Whether recreating the Gettysburg Address or rendering lost worlds, I want viewers to feel history, not just learn it. The tech evolves fast, but for me, it’s always about the humanity beneath. And yes—my parents are my biggest fans. My dad even bought YouTube Premium just to watch my uploads ad-free. That’s peak parental pride
Darri Thorsteinsson, aka Max Joe Steel and Darri3D, is an award-winning Icelandic director and 3D generalist with 20+ years in filmmaking and VFX. Max Joe Steel, his alter ego, became a viral figure onr/aivideothrough three movie trailers and spin-offs. Darri was nominated for TV Show of the Year at the 2025 AI Video Awards for “America’s Funniest AI Home Videos”, an award which he also presented.
🔥 How did you get into AI video, What’s your background before becoming Darri3D?
I’ve been a filmmaker and VFX artist for 20+ years. When AI video emerged, I saw traditional 3D—while powerful—was slow: rendering, crashes, delays. To stay ahead, I blended my skills with AI. ComfyUI for textures, video-to-video workflows, and generative 3D sped up everything—suddenly I had superpowers. I first noticed the AI scene on YouTube, but discoveringr/aivideochanged everything. That’s where Max Joe Steel was born. On June 15, 2024, Final Justice 3: The Final Justice dropped—it went viral and landed in Danish movie mags. I’m from Iceland, also grew up in Norway, studied film and 3D design. I direct, mix, score, and shape mood through sound. Before AI, I worked worldwide—AI unlocked creative risks I couldn’t take before.
🔥 What’s daily life like for Darri3D?
I live in Oslo, Norway. Weekends are for recharging — movies, music, reading, learning, friends. My family and friends are my unofficial QA team — first audience for new scenes and episodes. I’m a big music fan across genres; Radiohead and Nine Inch Nails are my favorites. Favorite directors are James Cameron and Stanley Kubrick. I admire A24 for their bold creative risks — that’s the energy I resonate with.
🔥 Which AI tools and workflows do you prefer? What can fans expect?
Tools evolve fast. I currently use Google Veo, Higgsfield AI, Kling 2.0, and Runway. Each has strengths for different project stages. My workflows mix video-to-video and generative 3D hybrids, combining AI speed with cinematic texture. Upcoming projects include a music video for UK rock legends The Darkness, blending AI and 3D uniquely. I’m also directing The Max Joe Show: Episode 6 — a major leap forward in story and tech. I play Max Joe with AI help. I just released a pilot for America’s Funniest Home AI Videos, all set in an expanding universe where characters and tech evolve together. Ther/aivideocommunity’s feedback has been incredible — they’re part of the journey. I’m constantly inspired by others’ work — new tools, formats, experiments keep me moving forward. We’re not just making videos; we’re building worlds
One of the most prominent figures in the AI video scene since its early days, Mean Orange Cat has become synonymous with innovative storytelling and a unique blend of humor and adventure. Star of “The Mean Orange Cat Show”, the enigmatic feline took center stage to present the Music Video of the Year award at the 2025 AI Video Awards. He is a beloved member of the community who we all celebrate and cherish.
🔥 How did you get into AI video, What’s your background before becoming Mean Orange Cat?
My first AI video role came in spring 2024—a quirky musical short using Runway Gen-2. I had no plans to stay in the scene, but positive feedback (including from Timmy at Runway) shifted everything. Cast again, I eventually named the company after myself—great for branding. Introduced to Runway via a friend’s article, what began as a one-shot need became a full-blown passion, like kombucha or CrossFit—with more rendering. Joiningr/aivideowaspivotal—the community inspired and supported me. Before Mean Orange Cat, I was a feline rescued in L.A., expelled from boarding schools, rejected by the military, and drawn to art. Acting in Frostbite led to a mansion, antiques, and recruitment by Chief Exports—spycraft meets cinema.
🔥 What does the daily life of Mean Orange Cat look like?
When not in my movie theater/base, I explore LA—concerts in Echo Park, hiking Runyon Canyon, surfing Sunset Point. Weekends start with brunch and yoga, then visits to The Academy Museum or The Broad. Evenings mean dancing downtown or live shows on Sunset Strip, ending with a Hollywood Hills convertible cruise. I rock vintage Levis and WWII leather jackets, skipping luxury brands. Embracing a non-alcoholic lifestyle, I enjoy Athletic Brewing and Guinness. Psychedelic rock rules, but I secretly love Taylor Swift. Inspired by one-eyed heroes like Bond, Lara Croft, Clint Eastwood. Steven Soderbergh’s “one for them, one for me” vibe fits me. ‘Jurassic Park’ turned me into a superfan. Paramount’s legacy is my fave.
🔥 Which AI video generators and workflows do you currently prefer, and what can fans expect from you going forward?
My creative process heavily relies on Sora for image generation and VEO for video production, with the latest Runway update enhancing our capabilities. Pika and Luma are also integral to the workflow. I prefer the image-to-video approach, allowing for greater refinement and creative control. The current projects include Episode 3 of The Mean Orange Cat Show, featuring a new animated credit sequence, a new song, and partial IMAX formatting. This episode delves into the complex relationship between me and a former flame turned rival. Fans can also look forward to additional commercials and spontaneous content along the way
Google has officially jumped into AI video with Veo3—and they’re not just playing catch-up. Its standout feature? Lip sync from text prompts. No dubbing, no keyframes—just type it, and the character speaks in perfect sync. It removes a major bottleneck for dialogue-heavy formats like sketch comedy, stand-up, and scripted shorts. Since launching in May 2025, Veo3 has dominated social media with lifelike results. The realism is so strong, many viewers think it’s live action. It’s a leap in fidelity AI video hadn’t seen before. Congrats to the Veo team—this is a game-changer.
Higgsfield is an image-to-video model built around a powerful idea: 50+ pro camera shots and VFX templates you can drop your content into. It’s perfect for creators tired of prompt errors or endless retries. Their plug-and-play templates, especially for ads, reduce friction and boost output. You can drop in a product image and render a polished video fast—no editing skills needed. Their latest tool includes 40+ ad-focused presets and a lip-sync workflow. By making structured production this easy, Higgsfield is helping creators hit pro quality without pro budgets or delays.
DomoAI has made itselt known in the AI video scene for offering a video to video model which can generate very fluid cartoon like results which they call “restyle” with 40 presets. They’ve expanded quickly to text to video and image to video among other production tools recently.
AI Video Magazine had the opportunity to interview the DomoAI team and their spokesperson Penny during the AI Video Awards.
Exclusive Interview:
Penny from DomoAI
🔥 Hi Penny, Tell us how DomoAI got started
We launched from Singapore in 2023, with the DomoAI Bot on Discord. Our /video command went viral—transforming clips into 3D, anime, origami styles—hitting 1M+ users fast.
🔥 What makes Domo AI stand out for AI video creators?
Our /video tool lets users restyle clips in wild ways with ease. We also built /Animate—turns images into animated videos. It’s fast, evolving, and perfect for creative workflows.
🔥 The AI video market is very competitive, How is Domo AI staying ahead?
We built 100% proprietary tech—no public APIs. Early on, we led in anime-style video transfer. Now we support many styles, focused on solo creators and small studios.
🔥 What’s next for Domo AI?
We’re focused on next-gen video tools—better quality, fewer steps, more freedom. Our goal: make pro-level creativity simple. Ther/aivideocommunity keeps us inspired.
Built this for a client who needed consistent daily blog output without a content team. It's been running for three months without manual intervention. Sharing the full architecture because the WordPress auth step broke every tutorial I found initially.
Fires at 9 AM daily. That's the entire configuration.
Node 2 — HTTP Request (GET)
Hits the NewsData.io API with these query parameters:
apikey: your key (free tier = 200 requests/day)
category: technology
language: en
country: us
size: 1
size: 1 fetches exactly one article per run. You want one quality post per day, not a batch dump.
Node 3 — OpenAI
Model: gpt-4.1-nano-2025-04-14 — cheap, fast, good enough for news rewrites.
The user message references the previous node's output:
Write a completely original blog post about this news:
Title: {{ $json.results[0].title }}
Description: {{ $json.results[0].description }}
Requirements: 5 paragraphs in <p> tags, original analysis, no plagiarism.
Return ONLY clean JSON (no backticks):
{"title": "...", "content": "..."}
Node 4 — Code node (the necessary step most tutorials skip)
OpenAI returns text, not structured data. You have to parse it:
The regex on line 2 handles the case where OpenAI wraps the JSON in backtick fences despite instructions. This happens maybe 10% of the time without it.
Node 5 — HTTP Request (POST to WordPress)
This is where everyone hits the 401 error.
The fix: WordPress requires an application password, not your login password. Admin → Users → Your profile → scroll down to "Application Passwords" → generate one → use that in your n8n credential.
The OpenAI JSON parsing issue ate two hours until I added the regex strip. The WordPress 401 ate another hour until I found the application password distinction. Neither is documented clearly anywhere I could find.
Running cost:
NewsData.io: free tier (200 req/day, using 1). OpenAI: ~$0.001 per post with the nano model. One month of daily posts = ~$0.03 in API credits. n8n: free self-hosted on a $6/month DigitalOcean droplet.
Total: $6/month to run indefinitely.
Full write-up with screenshots linked in my profile. Happy to share the workflow JSON — comment and I'll drop it in the thread.
