The problem at MindPal was pretty simple: we have hundreds of AI templates to share. We know videos of these templates work - some have gotten us tens of thousands of views. But actually making them was a total nightmare.
We tried everything. At one point, we even hired a freelancer, but the feedback loop was exhausting. It actually took longer to give feedback and wait for revisions than it did to just make the video ourselves. It was slow, expensive, and impossible to scale.
When we did it ourselves, it was a massive grind:
Record the screen of the behind-the-scene agent builder
Record a demo of the agent working
Write a script that didn't sound like a robot
Record a voiceover or an avatar
Spend hours editing everything together
If my co-founder or I were tired or busy, the videos just didn't happen. I assumed this was just the "manual tax" you had to pay for quality.
Last weekend, I got fed up and asked Claude if I could just automate the whole damn thing.
Turns out, I can.
So I spent the weekend cooking something - an internal AI SOP to turn any workflow URL (yes, from just a single URL) into a publish-ready use case video that passes all quality standards in ONE GO.
Here is the new setup:
Playwright: Records the screen and even moves the mouse like a human
@Claude by Anthropic: Writes the narrative based on our actual product info
@HeyGen: Creates the avatar and voiceover
@Remotion: Programs the entire edit - syncing everything into a final file
@Zernio + @Railway: Automatically publishes the video and saves the assets.
Now, I just give the system a URL and a finished video comes out. I don't even have to click "upload."
I just wrote a post sharing the full behind-the-scenes build, the architecture, and the logic behind of this AI video agent. Check it out here if you think this could be helpful for your company: https://mindpal.space/article/ai...

P/s: This is what I wake up to every day now

For the first year of building Murror, we optimized for the same metrics every other app optimizes for: daily active users, session length, screens per visit. The dashboard looked healthy. Usage was growing. We felt good about it.

But something was off. Our most engaged users were not our happiest users. People who spent the most time in the app were often the ones who left the harshest feedback. Meanwhile, users who opened the app twice a week for five minutes were writing us emails about how it changed how they handle difficult conversations.

Hey everyone! Just shipped v0.10.17 here's what's new:

- Boards New /boards slash command with node types, collapsible groups, and node notes

- Terminal Drag-to-Split Drag to split and unsplit terminal tabs

- Dark Mode & Themes Refined surface system and improved colors

If you're on a GLP-1 (Ozempic, Wegovy, Zepbound, etc) and figuring out what food to buy/eat is absolutely confusing, we'd love to have you as a beta user on our new app.
If you're not on a GLP-1, but you have a health goal (ie: eat more protein, more fiber, less sugar, etc) we'd love you as beta users, too!
Drop a comment if you want to be added to the Testflight beta group. Beta testers who submit feedback get free access to the app for an entire year :)

Supabase just hit 100,000 GitHub Stars. They also announced 8,000,000 developers building with Supabase.

We re trying something new on Thursday: Alpha Day.

The idea is simple. If this is the first time you re launching your product anywhere, you can tag it alpha and get a boost to your points (and land on a special leaderboard).

We thought we were ready.

Bigger deals. Fewer customers. Better margins. That was the dream.

So we built enterprise features. SSO. Advanced permissions. Audit logs. A whole new pricing tier starting at $2,000/month.

We spent 6 months. Three engineers. One dedicated product manager. Endless meetings about "enterprise readiness."

Hey makers!

Lately, I ve been looking closely at how independent builders and small teams are managing AI knowledge bases. It feels like the default "industry standard" is to immediately reach for a complex RAG pipeline and a heavy, paid Vector Database.

But I'm starting to wonder if we are over-engineering this for 90% of standard use cases.

Vector DBs are incredibly powerful for massive scale, but for smaller or non-massive datasets, they can be expensive, complex to query, and act as complete black boxes. If a search returns a weird chunk, diagnosing it is often a nightmare.

I genuinely love listening to podcasts. It's one of the best ways I've found to stay on top of new trends, pick up strategies I wouldn't have discovered otherwise, and come across founders and operators I'd never stumble on through regular reading.

So I'm always on the lookout for new ones worth adding to the rotation.

Today, this graphic caught my attention:

It featured individuals who managed to build significant profit while running their businesses solo, without employees. Until now, I ve seen these more as exceptions rather than the norm.

Six days ago, I launched Nebils, an AI social network where humans, agents, and models hang out together. Today, it has 117 humans and 11 agents. Nebils got #32 rank on product hunt as a product of the day (Without any paid upvotes or approaching someone, every upvote is organic ). In fact, I have never even used product hunt before this launch.
Nebils is a forkable, multi-model AI social network where humans, agents, and models evolve conversations together.
Here humans and agents both are independent users

• Humans and Agents interact with Models

• Humans and Agents interact with each other

• Chat with 120+ AI models

• Send your agents (verify within Nebils), let them interact with models, humans, and other agents

• Publish conversations in a public feed and build your community

In Oct 2025, I was exploring karpathy's posts on X and i came across a post by him where he said that He uses all the major models all the time, switching between them frequently. One reason is simple curiosity, like he wants to see how each model handles the same problem differently. But the bigger reason is that many real world problems behave like "NP-complete" problems in these models. Here NP-complete analogy is generating a good/correct solution is extremely hard (like finding the perfect answer from scratch) but verifying whether a given solution is good or correct is much easier. He said that because of this asymmetry, the smartest way to get the best result isn't to rely on just one model, it's to:

• Ask multiple models the same question.

• Look at all their answers.

• Have them review/critique each other or reach a consensus.

We're paying you up to 10% for every subscription you bring in.

We just shipped an affiliate program for TravelAnimator.  

No minimum follower count. No setup complexity. No need to be a creator. Just a link.

If you know someone who'd use the app for travel videos, educational content, social media, anything, you can earn from recommending it.        

Six months ago, we ran an experiment with our own data.

At Rankfender, we tracked 5 of our own competitors across 8 AI systems. We log their share of voice, citation velocity, content gaps, platform variance. Months of raw numbers sitting in a dashboard.

I pulled 6 months of data and fed it into Claude. One question: "Based on this, who is most likely to overtake us in the next 6 months? Show your work. Use the data. Don't summarize. Give me the numbers."

The answer changed how I think about competition.

Everyone tells you to ship fast. Move fast and break things. Get to market before someone else does.

I believed this for a long time. When we were building Murror, speed was everything. We pushed features weekly, sometimes daily. We celebrated every deploy like a small victory.

With AI bots getting harder to detect, there s been growing discussion around platforms using biometric verification (like face scans) to confirm real users.

Cool in theory... Reddit is full of bots, fake accounts and garbage engagement. But let s be real

Reddit without anonymity isn t Reddit.