“Almost all the cases where I’ve heard of a system that was built as a microservice system from scratch, it has ended in serious trouble… you shouldn’t start a new project with microservices, even if you’re sure your application will be big enough to make it worthwhile.” -Fowler
SHAPECRAFT WINNERS
congratulations to the projects below! you shaped new worlds & will each win a share of the $250k prize pool
full prize details will be announced live this thursday! set a reminder ⬇️
Now that $LGCT is live, it’s taking things to the next level!
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🚀 Own quanty.eth: The ideal ENS domain for $QUANT enthusiasts! Perfect for branding, influencers, or crypto projects in the $QUANT ecosystem.
🌟 Listed now on OpenSea! Grab it before it’s gone: https://t.co/qjlcNo4q0E
Trump just won and Bitcoin hit $75,000 🔥
As promised, I'm giving away 1 $BTC to 20 followers!
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Chainsage @asjv408
ChainSage is an AI-powered DeFi strategy optimizer across multiple chains. Leverages Zircuit for high-performance smart contracts, LayerZero for cross-chain messaging, and Phala Network for confidential AI computations.
Technologies:
🍵 Phala TEE Docker Simulator SDK
🔗 Zircuit
Demo Video: https://t.co/UigjGU6l6b
Live Context Retrieval will change AI forever.
Last week, Grass open sourced UpVoteWeb - a dataset consisting of 600 million posts & comments from Reddit - on @huggingface. I'd like to take a moment to explain why Grass would open source something so valuable.
There are two reasons:
1. Our values. Data is the foundational base layer of any AI model, and open sourcing datasets like these levels the playing field for developers across the world. We're making the public web public again.
2. As @BradUSV eloquently said: data has a very short half-life. Data is the most valuable it'll ever be the moment it's created, and this is why we don't expect most of the value accruing to Grass to come from static data snapshots like these.
What Grass excels at is retrieving information from the internet, very quickly.
If we take a moment to think about the future outlook of AI, it'd be silly to think that there'll be a single Fortune 500 company not using LLMs in some capacity. It's also impossible to imagine any use case that won't need to leverage real-time data.
The value that Grass provides to the AI industry goes well beyond large corpuses of data that can be used to train models. The real impact will come in the form of its Live Context Retrieval (LCR) engine, which will enable any model to leverage any data from the public web, in real-time.
As a result, every LLM in the world will be able to plug into Grass and leverage the entire internet as a resource, enhancing any prompt with the most up to date information in the world.