1,013 karma · joined February 8, 2019
Website => https://vididoo.vercel.app/ Github => https://github.com/btahir/vididoo
That said, I agree that human + AI can cause damage and it’s precisely why, from a game theory perspective, the right move is to go full steam ahead. Regulation only slows down the good actors.
Valhalla is within reach, but we have to leap across a massive chasm to get there. Perhaps the only way out of this is through: accelerating fast enough to mitigate the “mid-curve” disasters, such as population revolt due to mass inequality or cyberattacks caused by vulnerabilities in untested systems.
Bill Gurley, a General Partner at Benchmark (an early Uber backer), fired back. His main point: the professor’s critical mistake was assuming the TAM stayed fixed. When you remove friction, markets expand. Opening your phone to hail a ride in two minutes is a completely different product than hoping to flag a cab on a street corner.
Fast forward 10 years and Uber is worth ~$200B, about 10x higher so its safe to say Gurley was right. I love this story because it shows how making something easier can create massive first, second, and third-order effects on demand.
I think we’re about to see the same thing happen with agents. Most of the current conversation is about human-agent interaction (tech support, booking flights, etc.). But the real unlock will be the agent-agent economy. Agents can transact with each other 24/7, making millisecond-level decisions at global scale. This economy could be hundreds or thousands of times bigger than the human economy.
What makes this possible? - AI infrastructure (more chips, more energy) - Smarter, cheaper models - Payments (instant, low-fee transactions)
That last piece is crucial. Agents need seamless payments. This is where crypto, and especially stablecoins, come in. Blockchains like Base, Solana, or Ethereum L2s make micro-transactions feasible.
Coinbase understands this better than anyone. They recently launched x402, an open protocol for internet-native payments. It revives the unused HTTP 402 status code to let you pay for resources directly through APIs—no registration, no OAuth, no messy signatures. And because it’s literally HTTP, every client already supports it.
Vercel recently created a Nextjs starter showing how x402 works and I used it to build NickelJoke, a fun little app where you can buy a joke for a nickel using USDC on Base testnet. With Stripe this would have been impossible (the transaction fee alone is at least a nickel).
Stablecoins unlock true microtransactions. Imagine charging an agent a cent to read your blog, or giving an agent $10 to transact with others toward a goal. The agent-to-agent economy is just getting started and people who understand these protocols early will have a huge advantage.
You can find the starter code here: https://github.com/vercel-labs/x402-ai-starter
Most companies with these models will die simply because they won't have the courage and long term thinking to do this.
Ultimately the reason the ecosystem is so fragile is because a ton of packages are maintained by solo devs. So it only takes one hack to impact a ton of code bases.
The only thing I can think of to prevent this is automated LLM scanning of every npm package when any dependency or subdependency (and that's its own gnarly tree) is updated.
https://www.linkedin.com/posts/biltahir_sometimes-i-get-into...
I wonder if Gemini Diffusion (and that class of models) really popularize this concept as the tokens streamed in won't be from top to bottom.
Then we can have a skeleton response that checks these chunks, updates those value and sends them to the UI.
I've been experimenting with a custom workflow to turn long-form videos into short, engaging highlight clips. I know a bunch of startups are in this space, but I wanted to build something lightweight and flexible just for fun — and ended up integrating it into one of my products.
Here’s the general flow:
Transcribe the video Use tools like Whisper, AssemblyAI, or Deepgram to get an accurate transcript.
Extract interesting clips Feed the transcript into an LLM (I recommend Gemini because of its long context and quality) and prompt it to segment the video based on criteria like "viral", "engaging", "funny", etc. Make sure it returns timestamps.
Generate the clips Use ffmpeg to slice the original video using those timestamps.
(Optional) Auto-crop for vertical If the user selects a mobile/short-form format, use something like Sieve to auto-crop and center the subject for vertical (9:16) output.
(Bonus) Enhance captions Run the extracted clip transcripts back through the LLM to pick out keywords or phrases to emphasize in the captions.
Add captions with styling Use Remotion or similar to render the clips with styled, animated captions. The component handles logic for timing and highlighting.
Render and download Batch render your clips and you’re done.
I used this exact pipeline to build a feature in one of my tools: https://www.shortsgenerator.com/highlights-generator
Appreciate any feedback!
I'm trying to be sensible here not dream up straw man scenarios of which there are many.
Embracing AI and moving forward is the only thing they can do.