Teepin is building on @shelbyserves Private Beta.
We are one of the first projects running real workloads on it.
Learn what we are building on Shelby and how Shelby enabled our business model 👇
28.5s, every time, on 1 to 2 GB files. private beta. @shelbyserves built on @Aptos. we're just here enjoying the flat line
Enterprise AI starts with the data no one else has.
@teepin, formerly Flashback Labs, is building around proprietary enterprise data, open-source models and changing compute.
Teepin CTO & Co-Founder Srikanth Alla, previously at Amazon, ran internal tests comparing Shelby with AWS S3 on 1-2 GB files:
“In our internal testing on 1-2 GB files, Shelby’s hot storage was on par with AWS. Typical uploads finished in about 28.5 seconds, and the slowest stayed around 28-30 seconds. On S3, typical uploads were about 25.7 seconds, but the slowest 5% stretched to 32.5 seconds. Shelby’s speeds were surprisingly consistent, despite still being in private beta.”
Early proof that the data layer can keep pace as the rest of the stack changes.
Teepin™ retweeted
Love the hustle, ambition and execution from the @teepin team.
That's where @shelbyserves comes in. Our data layer runs on Shelby, and its egress economics mean moving data to compute doesn't come with a giant bill. We can switch GPU providers without reworking the data layer every time.
shelby serves.
we're showing up for the private beta.
Private Beta starts with the workload.
Testnet began with a broad question: can decentralized storage be performant and cost-effective enough for hot data?
What we learned gave us a clearer order of operations.
Performance. Placement. Control. Economics.
Rather than expand the network around assumptions, we're starting with real customer requirements and letting the network model follow.
From Testnet to Private Beta.
Building on @shelbyserves testnet that already feels like production. Based on what they've shipped so far, mainnet must be enterprise grade.
3.4× a year changes the map. Optionality on where you run is the real unlock.
AI compute capacity has been growing at ~3.4× a year.
That’s creating more options than ever for where workloads could run.
Data: @EpochAIResearch
Flashback Labs is now @teepin
We started after winning @Aptos Code Collision. Built private personal AI as @FlashbackAI. Bootstrapped the whole way. No external funding.
Along that path we got to build with some incredible teams. Grateful to @io_net @akashnet_ @0G_labs @OasisProtocol @BNBCHAIN @Shenanigrahams. And a special thank you to @solanamobile for the developer SKR airdrop. That stretch mattered. It kept our bootstrapped journey going until we could make healthy revenue. We are committed to to launch the best on-chain AI product natively on @solanamobile soon.
We’ve turned a new leaf with this rebrand. Teepin is enterprise AI infrastructure, with paying enterprises already on board.
We’ll be now building something that actually world class with @shelbyserves. More details unveiled soon.
Thank you for reading.
Privately training AI on lived experiences allows models better to understand relationships, loss, joy, and identity.
With Trusted Execution Environments via @OasisProtocol, sensitive data held on @BNBCHAIN Greenfield remains private, protected & verifiable during compute ✨
Whether you are advancing decentralized AI in Web3 or protecting legacy in Web2, the foundation matters ✨
@FlashbackLabs is building the human layer of AGI on @BNBCHAIN Greenfield.
Start your first Flashback today at Flashbackai.xyz











