You’re not losing money by giving value early.
You’re buying attention 📣 and earning trust 🧠
When you launch the full thing 🚀
they won’t hesitate to pay
...because they already know you're the real deal 💯
Why?
Because trust > transactions 🤝
When people experience real value upfront,
they're way more likely to pay later 💳
They’ll know your style, your quality, your speed.
No guesswork. 🔍
Struggling to get your first customers?
Don’t pitch! PROVE!
Offer something valuable for free first. 🎁
Give away ~60% of the value.
✅ Enough to help
✅ Enough to impress
❌ Not enough to replace your product
#startup #trust #growth
🤷 Real talk: I haven't gotten stellar results from it yet, but more people experimenting with this could help refine the technique.
Worth checking out if you're working with AI video generation. 🎬
🎬 This opens up new possibilities for interactive content creation, virtual meetings, and automated video production.
🚫 The binding problem was a real blocker for multi-person scenarios.
What sets this apart from single-person talking head generation:
👥 - Handles multi-person dialogue scenes
⏱️ - Maintains temporal consistency across speakers 🎛️ - Prompt-driven interaction control
🎯 - State-of-the-art lip sync accuracy
The L-RoPE method assigns identical labels to audio embeddings and video latents, activating specific regions in the audio cross-attention map.
This prevents the common "speaker confusion" 😵💫problem plaguing existing methods.
📄 Paper: arxiv.org/abs/2505.22647
🔧 Code: github.com/MeiGen-AI/Mu...
What makes this technically interesting:
🎵 - Multi-stream audio injection with proper person-audio binding
🎭 - Works with conversations, singing, cartoon characters
⏱️ - Supports up to 15-second generation
👎 Most talking AI models break when you add multiple people to a conversation.
✅ MultiTalk shows a lot of potential to solve this, using a new approach that correctly binds multi-stream audio to specific characters.
Github repo 👇
🎯 What it does:
✅ Same person in different locations
✅ Two reference images at once
✅ Products stay accurate across scenes
✅ Facial features don't drift
#MAGREF #MultiSubjectVideo #VideoAI
Paper: arxiv.org/abs/2505.23742
Code: github.com/MAGREF-Video...
🛠️ Already has ComfyUI nodes ready.
Pro-tips:
- White background reference images work best
- Use Light X2V LoRa to speed things up
- Detail retention is really good
This image shows a demonstration of AI video generation technology. The left side displays reference inputs organized into three categories: "Single ID" (featuring a Van Gogh self-portrait), "Multi-ID" (showing headshots of a smiling man and woman), and "ID + Object + Background" (containing images of a cat, outdoor scene, and another person). The right side shows "Generated Video Frames" with four sequential frames for each scenario. The top sequence shows a man in a blue button-up shirt sitting at a wooden cafe table, holding coffee while looking at his phone. The middle sequence depicts two people in an art studio - a woman in a blue jacket standing behind a seated man in white, both near an easel. The bottom sequence features a man in a blue shirt and straw hat sitting outdoors, interacting with a cat.
I just discovered MAGREF.
This fine-tuned Wan 2.1 model keeps faces and objects consistent across video scenes.
Single subject or multi-reference mode - both work surprisingly well.
Sounds like Adobe and their cancellation penalties 🤪
Big fan of Wan2GP by DeepBeepMeep for running video generation on low-end GPUs. The name might be misleading, but it actually supports multiple open-source models including Wan (plus its derivatives), Hunyuan Video, and LTV Video.
Link to the repo, if you are interested 👇
That's it! Your AI assistant now:
- Searches automatically
- Saves everything to Notion
- Organizes research
- Builds your knowledge base
All hands-free 🧠 Questions? Drop them below! 🙌
5. The magic config
- Add to `~/Library/Application\ Support/Claude/claude_desktop_config.json` (or where you have Claude Desktop installed)
pastebin.com/rY62GMZy
4. Get the Notion server:
1) Clone: github.com/suekou/mcp-n...
2) cd mcp-notion-server/notion
3) `npm i && npm run build`
Remember where your build folder is! 🏗️
3. Notion setup:
1) Visit notion.so/my-integrati...
2) Create "New Integration"
3) Copy your secret token
4) Add integration to your workspace
2. Brave Search setup:
1) Sign up: brave.com/search/api/
2) Choose free tier (2K queries/month)
3) Generate API key
4) Save it for later
1. What you'll need:
- Claude Desktop: claude.ai/download
- Notion account (free)
- Brave Search API (free tier = 2K queries/month)
- 5 minutes
Let's automate your research workflow 🚀
🔥 5-min guide: Build your personal AI research assistant that automatically searches & saves everything to Notion. From research to organized notes, automatically. Here's how → 🧵 #ResearchTools
Wait until GTA VI gets released 😂
yeppp... Threads without hashtags is like a library without a card catalog... we are all just wandering around hoping to bump into something interesting. I'm surprised that they haven't made it a priority
8. Key lesson: Building powerful AI tools isn't just about the technology... it's about having a systematic way to process and structure information.
What's your information processing system looking like? 🤔
7. Want to see this in action? I'm building using this exact framework to create AI-powered tools for solopreneurs.
Follow along to see how I:
- Process information
- Finetune models
- Build automation tools
6. The beauty of CODE is its flexibility. While @fortelabs designed it for personal knowledge management, it's perfect for building AI systems:
Capture → Training Data
Organize → Data Structure
Distill → Feature Engineering
Express → Model Deployment
how to express what you learned diagram
5. E - EXPRESS 🚀
Turn knowledge into action:
- Train custom AI models
- Create automation workflows
- Share insights with your audience
I've built a few personal automation tools using this pipeline!
distill data using LLMs diagram
4. D - DISTILL 🌟
The magic happens here:
- Progressive summarization
- Extract core ideas
- Find key patterns
This creates clean, structured data that LLMs can learn from effectively.
organize data using notion databases diagram
3. O - ORGANIZE 🗂️
PARA system is key here:
- Projects (active)
- Areas (ongoing)
- Resources (reference)
- Archives (inactive)
This structure helps both humans AND machines understand context better!
