$HYPE🥱

House of All Finance
Joined October 2024
arkhet.hl retweeted
Excited to work with Payward to forge a path for onchain derivatives built on Hyperliquid's infrastructure. Kraken is one of the most trusted and respected exchanges in crypto. They are also true believers in the technology and crypto ethos. We're honored that they are building on Hyperliquid to bring the next evolution of perpetual markets to the United States.
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arkhet.hl retweeted
Excited to work with Payward to forge a path for onchain derivatives built on Hyperliquid's infrastructure. Kraken is one of the most trusted and respected exchanges in crypto. They are also true believers in the technology and crypto ethos. We're honored that they are building on Hyperliquid to bring the next evolution of perpetual markets to the United States.
Payward, the parent company of Kraken, announced its intention to be the first CFTC-regulated deployer on HIP-3* markets. Payward will use Hyperliquid as the infrastructure layer to deploy perpetual futures markets for US clients. Subject to regulatory approval, Bitnomial, Payward’s CFTC-regulated DCM and DCO, would act as the HIP-3* deployer: creating, owning, and administering the markets and clearing and settling the contracts. If approved, NinjaTrader Clearing, Payward’s CFTC-registered FCM, would carry client accounts. HIP-3 will support optional deployer configuration for permissioned markets in a future network upgrade. This set of additional deployer functionality is collectively called HIP-3*. At its core, HIP-3* activation involves an onchain allowlist managed by the deployer or its sub-deployers. The functionality is strictly additive to HIP-3; its use is optional and up to the deployer. Existing markets remain unchanged. The initial release is available on testnet, where specifications are preliminary and subject to change based on feedback. Hyperliquid is a neutral infrastructure layer with the goal of supporting large parts of the financial system. HIP-3* gives deployers additional functionality to operate deployments consistent with the requirements applicable to them. As with HIP-3, HIP-3* deployers are independent operators who use Hyperliquid as the onchain infrastructure layer for their own markets, while retaining control and responsibility over their deployment.
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i’m giving one of you a fucking ROLEX like + repost + follow me one random winner picked 48 hours from this tweet. no purchase necessary
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arkhet.hl retweeted
The Pre-IPO Perpetual (IPOP) Market for Anthropic is now live.
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arkhet.hl retweeted
Payward, the parent company of Kraken, announced its intention to be the first CFTC-regulated deployer on HIP-3* markets. Payward will use Hyperliquid as the infrastructure layer to deploy perpetual futures markets for US clients. Subject to regulatory approval, Bitnomial, Payward’s CFTC-regulated DCM and DCO, would act as the HIP-3* deployer: creating, owning, and administering the markets and clearing and settling the contracts. If approved, NinjaTrader Clearing, Payward’s CFTC-registered FCM, would carry client accounts. HIP-3 will support optional deployer configuration for permissioned markets in a future network upgrade. This set of additional deployer functionality is collectively called HIP-3*. At its core, HIP-3* activation involves an onchain allowlist managed by the deployer or its sub-deployers. The functionality is strictly additive to HIP-3; its use is optional and up to the deployer. Existing markets remain unchanged. The initial release is available on testnet, where specifications are preliminary and subject to change based on feedback. Hyperliquid is a neutral infrastructure layer with the goal of supporting large parts of the financial system. HIP-3* gives deployers additional functionality to operate deployments consistent with the requirements applicable to them. As with HIP-3, HIP-3* deployers are independent operators who use Hyperliquid as the onchain infrastructure layer for their own markets, while retaining control and responsibility over their deployment.
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arkhet.hl retweeted
Most AI has an off switch. It looks like a building: one datacenter, one provider, one cloud account that can be closed, one government that can say no. If the thing you depend on lives in one place, someone else decides whether it stays on. Parallax is our attempt to train models that don’t have that switch.
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arkhet.hl retweeted
14 open models, one endpoint, every one of them running inside a hardware-attested TEE. That's the Chutes catalog today: Kimi K3 and GLM-5.2 with 1M context, DeepSeek V4 Flash, Qwen3.8-27B, Gemma 4 31B, and Nemotron-3-Nano from $0.0245 per million input tokens. The list changes often. Any OpenAI-compatible client works. Set the base URL to llm.chutes.ai/v1 and keep the rest of your code.
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arkhet.hl retweeted
Jon's comparison from the same slide, his estimate from a published report: Llama 3.2 1B at about 370,000 H100 hours and retail H100 rental of $2 to $4 per GPU-hour works out to $92 to $184 per billion tokens. The live training register: parallax.chutes.ai
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arkhet.hl retweeted
Pretraining a model used to mean a datacenter and a nine-figure budget. Jon did it on rented RTX 5090s over the open internet for $11.27 per billion tokens. The whole 8B run came to about $6,500. Retail hourly GPU prices, 574B tokens, 52 hours. The syncers were four CPU VMs with no GPU in them, and there was no object store anywhere in the system. If a model costs about what a used car costs, a lot more people get to own one. That's the point of Parallax.
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arkhet.hl retweeted
Introducing DecisionModels @decisionmodels_ I've spent the last four weeks benchmarking decision models (I run JevBench). Today I'm launching the place to actually use the best ones: decisionmodels.io ➡️Top open models ➡️Up to 4x faster on our own REAX engine ➡️From $0.019 per 1M input tokens. Expect more reveals soon, and read 👇
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arkhet.hl retweeted
There are ~two economies: attention and revenue. Focus on whichever you like but if you only pick one, it probably eventually breaks. Free high intelligence = maximum attention (but probably financial/resource crisis), and conversely maximally expensive commodities restricting access rugs the attention eventually and likewise probably leads to collapse. Parallax and the new "living model" type system created out of it is perhaps a way to foster both by decreasing prices (run on phone, macbook, spark, whatever) rather than increasing them further (with larger and larger models), and ideally maintaining the intelligence (and boosting creativity/customization via this epigenesis project to prevent too much concentration into any single company/model). Ideally, everyone just brings their own resources to fund the training (sparks + apple + amd) then they own the output forever and can shape it any which way with baking in their own memory/knowledge/rules into the otherwise sparse weights after training.
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arkhet.hl retweeted
Thank you to everyone who traveled near and far to make it to Monday's community event. Multiple members of the ecosystem hosted their own events to celebrate Hyperliquid and the community throughout the week. Thank you to every organizer for the hard work and dedication behind each one. The builder booths were a highlight for many. To everyone building on Hyperliquid: thank you for bringing more of finance onchain and spreading the word. Since last year, even more builders, users, and institutions have contributed to the ecosystem's growth. Hyperliquid's vision is to be infrastructure to house all finance, and every year, that vision becomes more of a reality.
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arkhet.hl retweeted
github.com/chutesai/epigenes… Still experimenting, but seems to check out. Smaller, faster, better? Models that grow with you (or your repo, or proprietary company docs, etc.) Short term exact recall is the context window. Context window feeds training continuously, binary optimizer. This becomes stored knowledge and higher order perceptual understanding over time, not just RAG. Sparse MSA global attention already selects most important tokens to attend to in longer context, so you can reinforce those parts more.
🤖 Made with AI
There's another magic trick (aka cool math) I believe this parallax enforced sparsity enables by the way... arxiv.org/abs/1711.05769 <- prune weights, adapt the pruned weights, sharply cuts catastrophic forgetting risk. If you have a model with the enforced sparsity, 50% of the weights are locked in to particular patterns. What if you mask the other expert weights leaving only those sparse contiguous weights to learn new data? That's the intuition, perhaps it works, perhaps not. But... if it does, it would make the base model significantly more malleable for new domains/tasks/whatever. SFT/continued pretraining with less risk of catastrophic forgetting and without lots of magic tricks and RL and re-sampling in your own words etc.
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arkhet.hl retweeted
parallax.chutes.ai/m/paralla… Still chugging along great, EDA seems much better! Also 7 host failures so far with automatic expert ownership failover. I did experiment also with triadic linear attention adapted to EDA and it didn't seem to have much benefit (probably skill issue on my part): arxiv.org/abs/2609.36529
EDA is superior to GDN-2 as it turns out, it avoids the failure mechanism entirely (so far), and the new run (lambda) is blowing past this one (kappa). Might add in triadic EDA next for better long context capability.
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arkhet.hl retweeted
My two cents on this "math is solved" and "maybe we lose cryptography" question (not that anyone asked of course). The questions are the important part, I think. What was the inspiration that led to the question which then ultimately created the answer? Having answers to old questions is very nice, but more importantly I think we just need to now use that knowledge to ask better questions. Don't be a doomer, ask bigger questions now that we have more powerful tools to help answer them. Don't let answers to old questions extinguish the creative spark, if you will.
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