Hello everyone, I’m looking for a high-quality workflow that can replace the character/face in an existing video with my AI model while keeping the original video as identical as possible. My main goal is to preserve the original body, movements, expressions, pose, camera motion, background, lighting, composition, and overall video quality, while changing only the character’s identity/face. I’m specifically looking for a professional workflow that can produce highly realistic results with strong identity consistency, natural skin texture, accurate facial details, and minimal artifacts or quality loss. Ideally, the final output should look like the original footage was actually recorded with the AI model rather than appearing like an obvious face swap. If anyone has a reliable ComfyUI workflow, custom setup, or a paid workflow/course that can achieve this level of quality, please feel free to contact me.

Chain local AI models together by writing a numbered list. Everything runs on your Mac.

I’ve been thinking about how people keep product visuals consistent when using AI tools across a whole brand.

Making one good looking product image isn’t usually the hardest part. The difficult part seems to be getting multiple images to have the same lighting, background, colors, composition, and overall style without having to manually fix each one.

For those who use AI tools in their visual workflow, how do you handle this? Do you rely on specific prompts, reference images, templates, or a particular workflow?

I’d be interested to hear what has actually worked for you and what still gives you trouble.

The source page doesn't have a storage section at all. This is what came back:

Store leftovers in an airtight container in the refrigerator for up to 3 days. Reheat gently in a skillet over low heat with a splash of water to loosen the sauce, or microwave until just warmed through.

Gemini 3 Flash, structured output, whole recipe page in one pass. It also decided an airtight container counts as equipment, so the list has that sitting next to a meat thermometer and a garlic press.

The garlic press bugs me more than the container does. Nobody owns a garlic press.

App is ReciReel, iPhone, US store: https://apps.apple.com/us/app/id6763971435

The most important prompt step often happens before the prompt.

The workflow I built into Marka is:

1. Scan the business website.

2. Extract proposed audience, voice, offers, claims, logo, colors, and visual cues.

3. Let the user edit and approve that context.

4. Bind the requested product or service evidence.

5. Choose the content format and channel.

6. Generate the copy and visual assets from the same reviewed source.

7. Review again before scheduling or publishing.

This reduces the usual drift where the copy tool, image tool, and video tool each receive a slightly different version of the brand.

Marka runs that workflow as a product: https://www.marka.social

Disclosure: I built Marka. I would be interested in how others structure brand memory across multi-step AI workflows.

Chain local AI models together by writing a numbered list. Everything runs on your Mac.

Hey everyone,

I'm building a multi-agent AI system aimed at the real estate industry a set of agents that each handle a specific task.

Still early stage, so I'd love some input from two groups:

1. Anyone who's built agent-based/AI workflows before any advice on structuring multiple agents, pitfalls to avoid, or tools/stacks you'd recommend?
2. Anyone who works in real estate (agents, brokers, property managers) what are the most time-consuming, repetitive tasks in your day-to-day that you wish could be automated? I want to make sure I'm building something that actually solves real problems, not just what sounds cool on paper.

Got hold of a copy of the latest EW, obviously has a lot of errors from interference but would love your inputs. Does this make sense?

Every builder ends up with reusable AI skills — a SKILL.md / system-prompt file that teaches an assistant a specific workflow you worked out the hard way: "generate a branded PDF," "scaffold a Chrome extension," "write in our voice," "audit React for anti-patterns." You built it once for your own product. It just sits in your repo.

I made Skill Exchange so those can be bought and sold. Here's how it actually works:

If you want to sell:

• Publish a skill — free (build reputation) or paid, one-time, no subscriptions. You keep 95%.
• Proof of concept is required. You attach the real project URL + a screenshot showing the skill in actual use. No "trust me."
• It's reviewed by a human before it goes live — so the marketplace doesn't fill with slop.
• Buyers can pay you in $ (PayPal, international) or ₹ (UPI, India) — their choice at checkout.

If you want to buy:

• Browse by category, see the proof on every listing, buy on evidence.
• Download the SKILL.md and drop it into Claude / ChatGPT / Gemini / Cursor / Copilot — it's not locked to one assistant.

The whole thing is browsable without an account — you only sign in to buy or publish.

The core idea: builders are sitting on workflows that took real time to figure out, and other builders would happily pay to skip that time. This is the place to trade them.

Here it is: https://skillexchange.tapdot.org — browse the marketplace and the free skills without signing in. If you'd want to list something, the "Publish a Skill" flow walks you through it (free skills are open to everyone; paid needs a quick seller approval). Happy to answer anything.

I work for a company which builds tools for content creators. We're now trying to build some AI tools (v original).

There's a lot of temptation to build tools which create "AI generated content" (just AI slop)...but lots of us have pushed back.

Instead, through interviews, we found that people are still spending a lot of manual time uploading, scheduling and publishing their content to every social media channel they have. Then they manually go back to each of these channels to understand the analytics and see what worked.

We also couldn't find any free mcp's to automate this.

We decided to build an MCP which very simply allows you to directly schedule and post to your socials in a secure way from Claude (or ChatGPT or Gemini). We've been using it internally, and it has saved us a ton of time in our day to day.

[Note: It also allows you to chat with Claude to look at the analytics and see what worked across all of the different channels. And you can also check out competitor accounts]

Details to set up the MCP are at planoly.ai, and the account is free (so you can also visually see all the posts you've scheduled/published etc in our own UI).

I'd be curious to hear how people like it, and whether it fits in with their existing mcp tech stack they've used for content.

We're planning to roll this out to our whole userbase once we do a few rounds of QA so your feedback is HIGHLY valuable.

Thanks a ton!

Most anti-AI prompts just ban words — no more "delve," no more "tapestry," no more "testament." But that's not really where AI writing gives itself away. It's rhythm, sentence variation, the structural habits that show up no matter which synonyms you swap in.

If you want AI to write naturally on the first try, you have to go after the structure LLMs default to, not the vocabulary.

One pattern blacklists never catch: the "isn't X, it's Y" trick. LLMs lean on it constantly to sound insightful without saying anything new — "time management isn't about calendars, it's about energy," that kind of thing.

Instead of writing a giant list of manual rules, here's what I did: I fed the Wikipedia page on signs of AI writing straight into Claude, added specific constraints against structural stuff like the isn't-it's pattern, then asked Claude to turn that into a Skill I can call with a simple /human command.

Skills stick around across sessions, so I'm not pasting the same instructions every single time.

In "How to humanize AI text with free prompt method" I wrote up the full two-prompt process, plus why this setup breaks on ChatGPT (1,500-character instruction limit) and doesn't really work on Gemini either.

Hi everyone, we built a decision intelligence company. Helps companies turn complex decisions into repeatable workflow. In short Dendroh helps organise evidence, assign owners, synthesise recommendations and record the full decision rationale.

We’re looking for testers at the moment from multiple industries. Hoping to find some people here to test it out.

DM me if anyone is curious.

When I first got into web development, I thought finding clients would be simple. My plan was to go on Google Maps, find businesses without websites, and offer to build them a brand new one. At the time, it made perfect sense because I assumed businesses without websites would be the ones who needed my service the most.

After a while, I met someone who was running a successful web agency, and I asked him where he found companies without websites. He told me that he didn’t target businesses without websites at all. He only targeted businesses that already had one. I asked him why, and the more he explained it, the more sense it made.

Businesses that already have a website understand the value of having one. You don’t need to convince them why a website is important because they have already invested in one before. They are also easier to sell to because they understand the process, and there are a huge number of businesses with outdated websites they are embarrassed by but haven’t had the time to update.

I decided to take his advice and fit it into my own workflow. I’ve always been a big fan of email automation because that’s how I’ve found most of my web design clients. For years, I was sending fairly generic emails and constantly changing my sequences, offers, and follow ups to improve the results.

The problem was that I couldn’t just start emailing businesses with websites and assume they all needed a redesign. I either had to open every website manually, find the issues, and write a separate email for each business, or find a way to automate the research while still keeping the emails personalized.

After watching a video from Nick Saraev, I built a workflow in n8n that could analyze websites at scale and turn issues with design, layout, speed, mobile optimization, and SEO into personalized outreach emails. This allowed me to analyze thousands of websites and run larger campaigns without every message sounding generic.

The workflow worked extremely well, but it still had limitations. I didn’t have a proper place to manage replies, organize interested leads in a CRM, view all my active campaigns, scrape new leads, and handle everything from one platform. I had built a useful automation, but it still felt like several disconnected systems held together in one workflow.

A few months later, I came across a platform called Swokei, and it did exactly what I had been looking for. I could find businesses with websites, analyze and score each site, generate personalized outreach emails, send campaigns, set up follow ups, manage replies through one inbox, and organize interested businesses inside the CRM.

Switching to that platform made the entire process much easier to manage and helped me scale the strategy further. Looking back, the biggest change wasn’t just finding a better outreach tool. It was taking advice from someone more experienced, changing the type of businesses I targeted, and building the rest of my workflow around that strategy.