alex
2.9K posts
Excited to see @octra Circles become more useful as B2B infrastructure. In addition to individual Web2/3 developers, even some confidentiality-focused projects building around ZK and TEEs may eventually benefit from moving their back end from an AWS server to an encrypted Circle!
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@octra Rust is one of the most popular languages for back end development, and while we don't know what goes inside some of the Circles, activity around them is ramping up. This is probably the most underrated feature right now, and way more important that simple private transfer imo.
λ@lambda0xE
a mini-update to webcli that enables rust and aml in circles is already available, you can update and rebuild it now - github.com/octra-labs/web… we’ve also added a few examples: - public hmtl circle: oct://oct9rofAj2yrS4uaisrZ9yqPwPpYHHCiHzFEGWFNFVGbZhK/index.html pwd: not required - sealed html circle: oct://octAP8rLfUSwGc7asoZAdMEJLXcCyUz9qPmT3ztCaa2eKHo/index.html pwd: octra-circle-demo - AML circle counter: oct://oct5DvtkJEefjmTChcxGQ68hCvkGPQCnA64zSjUzw8QSNEb/index.html pwd: octra-circle-demo - rust circle counter: oct://oct3PBSWGkXPC2LtUvARmmUsivUnmFwqJgG71tjRPyDZvf9/index.html pwd: octra-circle-demo fyi: the runtime allows imports from the "octra" module. the list of host funcs: - response buffer - circle invoke - HFHE invoke - circle KV/state storage - epoch - caller/self - state path key - emit event rust and aml circle examples are available directly in git as well: github.com/octra-labs/cir… PS: the examples are prepared as a third party asset and are not directly related to the octra ecosys, the functionality can be anything circle is a universal, decentralized, and private droplet with its own runtime and memory for any programs, websites, services, etc., with a configurable access level via HFHE or native cryptographic functions on top of the octra network circles have access to the network state and can interact with it natively, and can also access the clear web via unikernels (to hide the exit node) through a zk or pvac-hfhe relayers * this is an alpha build, there may be oddities and errors that don't look critical
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alex retweetledi
alex retweetledi
alex retweetledi
alex retweetledi
alex retweetledi
incomplete may stats:
+100% ethereum:0x4647e1fe715c9e23959022c2416c71867f5a6e80 holders on ethereum (~3k)
58k active mainnet accounts
10k new active mainnet accounts (2x from april)
+1.37m oct bridged in
5000 @0xio_xyz users
76k txs w/ real funds
dasha@0xdasha
the only relevant metric worth tracking day to day for newer coins is not price but holders imo
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alex retweetledi
operating on a global encrypted state is much more flexible than shielding or mixing coins
example:
> have 100k oct
> encrypt 50k
> stealth send 30k
> decrypt 10k
proofs are only required on spend and decrypt, all other operations within the hypergraph space are near-instant
octra labs@octralabs
octra stealth transfers do not expose a plaintext sender→recipient→amount graph. they spend from encrypted balance, update encrypted state with proofs, and send an encrypted recipient payload. metadata at the edges still matters: key discovery, claims, timing and app routing must be handled carefully.
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@0xridey @lambda0xE Its in progress
alex@octralex
As @octra won the @novadotmarkets vote to list on Hyperliquid, we are in touch with the team for next steps. In the meantime, thought I'd provide some context on launch and listings. OCT started with an auto-deployed @Uniswap pool and a bridge because we wanted to provide a simple way to obtain and encrypt assets. Despite being one of the most liquid and traded v4 pools outside of majors, it was not really intended for trading. Rather, it was there to build up organic demand for OCT as the privacy solution, which has been on a steady rise since deployment. We deliberately chose not to force pre-market perps to create a higher price illusion or short-term exit liquidity during the @UniswapAuctions CCA. We also turned down several Tier 1 exchange offers to launch day 1 perps. Broadly, we are tech and product focused guys, and not trading experts, but some of the terms we received from every party and on every step of the way were straight up extractive and borderline dangerous for the project's survival. We have therefore decided that we will not be providing any OCT free of charge for any party. Octra's launch may look less flashy, but we believe that launching it and continuing to build in public is the cleaner and more honest way to build long term demand for the projects' native asset. We remain open to future listings as liquidity, trading history and demand grow organically on decentralized venues, such as Uniswap, Hyperliquid, or Solana. Thank you for your support.
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Hey Alex,
Have you considered listing Octra as a spot token on Hyperliquid? I'm confident it would gain strong traction, and you'd stay aligned with fair principles without having to pay any CEX for a listing. You could create a bridge similar to what you did with wOCT, perhaps calling it hOCT (Hyperliquid Octra), so it bridges over properly. Hyperliquid is very well aligned with DeFi and fairness, much like the approach you've taken so far (kudos for that).
A ticker currently costs 500 HYPE, which is around $31k at the moment. Since Circle and Coinbase signed the deal making USDC the primary stablecoin, spot stablecoins have been gaining significant momentum, with roughly $2B in stablecoins already sitting on HyperCore alone. The token could also be deployed on HyperEVM, since HyperCore supports linking the same token across both by simply connecting the smart contract, which would make it tradeable on HyperEVM DEXs as well.
HyperOctra
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AI with public and verifiable weights, but encrypted inputs and outputs, has been achieved externally, publicly, onchain
it makes use of exactly zero TEE (trusted execution environments) technology that powers venice, phala, near et al, who misleadingly claim llms to be private
λ@lambda0xE
another mini update with examples of octra state inference speed - it has grown even more with the latest updates the circle version is performing well and is already acquiring some kind of interface, the future plans are clear - expanding capabilities, adding ml-tools and deploying more complex mechanisms (this is an early version, but significant progress has already been made) the devnet version is written entirely in @AppliedLang circles will allow the creation of multiple, fully private and verifiable models, they can be assigned tasks, combined into rooms and given different access rights, the so called agents will come to life in the state and will be able to work directly with the network (this is useful for debugging and deep tuning) this direction definitely has potential and we will continue to work on it
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octra:
>net positive for crypto
>logo is beautiful
>the infrastructure for the encrypted internet
Elon Musk@elonmusk
Please post a bit more positive, beautiful or informative content on this platform
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alex retweetledi
octra stealth transfers do not expose a plaintext sender→recipient→amount graph.
they spend from encrypted balance, update encrypted state with proofs, and send an encrypted recipient payload.
metadata at the edges still matters: key discovery, claims, timing and app routing must be handled carefully.
octra@octra
octra stealth transfers are not mixers, nor monero-style ring/UTXO privacy they rely on encrypted balance state > hold encrypted value > spend from it privately > claim into encrypted balance > decrypt arbitrary amounts when needed > compute and compose without revealing state
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alex retweetledi
Btw, a fun fact about ML and contex mixing - all the time at the end of school and the beginning of university, I was fascinated by data compression and algorithmic codecs. This is essentially how I started my career as a C developer. There's one fun fact about compression that just occurred to me: the legendary Matt Mahoney, who created the most powerful algorithmic coder and founded the PAQ family, variations of which constantly won the Hutter Prize for data compression, essentially laid the foundation for the concept of ML through data compression, since algorithmic codecs are built on predictive models and sigmoid functions for constructing frequency context models based on the data type. So, what I'm getting at is that in our "narrow" circle of those involved in this topic, there was a clear justification for what AGI is expressed through data compression. As you know - you can't bypass the Shannon limit, that's a hard math law. However, the Shannon limit is bound to a specific predictive model. If an algorithmic codec can build such a profound, context aware model of the world that its 'guessing' pushes the practical compression down to the data's absolute, intrinsic complexity, that might just be the key to unlocking AGI.
If you want to start approaching AGI, you need to pay attention to data compression, bc if a context codec model can "decompress" and predict and compress the full context of Wikipedia (which in a compressed format has entropy practically within the limits of Shannon), then this may mean that one of the most important steps towards AGI has been made.
PVAC-HFHE uses our own version of algorithmic mixing to compress public keys for the HFHE engine. You can see it here: github.com/octra-labs/web…
Now that we're head over heels in working on circles in @octra with the goal of bringing interesting use cases, this has made sense again and we've developed a passion for trying out new hypotheses. So yes, one of the new directions for octra is ML and contextual transformation, because the treechain structure and data model are perfectly compatible with the goals of ML inference. We'll continue working on this (I think every day now) and will report if we find anything interesting.
London, England 🇬🇧 English