serverless AI secured by web3 - https://xitter.cf/t.co/irTCUyjkrv

Joined August 2024
qwen3.6 cold start time 2.7s 40x performance improvement i love building compilers
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cyclic open hypergraphs are tricky to draw. Here's ONE SIMPLE TRICK to run your acyclic layout algorithm on cyclic diagrams! GraphViz Users Hate This! (kidding i love graphviz) blog.hellas.ai/blog/cyclic-h…
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I *WILL* run Kimi K3 at home
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Replying to @hellasdotai
@hellasdotai goes to deepmind! Really great event organized by @ARIA_research
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running glm-5.2 int4 on my 512gb 4x strix halo cluster AMA
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Anthropic's new model quietly nerfs you if you work on frontier LLM development. Not a refusal. Fable 5 degrades its own output on pretraining pipelines, distributed training infra, accelerator design, and doesn't tell you. Disclosed in the system card, invisible in the response. Their estimate: 0.03% of traffic. And the same model is sold twice. Fable, governed, for everyone. Mythos, ungoverned, for approved organizations, starting with cyberdefense partners working with the US government. Same weights, different permissions. Permission is the product line now. I'm sure Anthropic believes this is the responsible move. They've believed it since Dario judged GPT-2 too dangerous to release. But every lab that brands itself good and aligned eventually ends up here: deciding, unilaterally and invisibly, who gets the full machine. Power doesn't need bad people to concentrate. It just needs nobody outside the room. We built @nazarevc on the counterweight. @PrimeIntellect trains frontier models in the open, on distributed compute no single party controls. @ii_posts builds open foundation models and the RL framework to train them. @vast_ai is API-provisioned GPU supply, 20,000+ GPUs, no approvals desk. @hellasdotai proves what model actually ran. @GetProvably proves the data it touched. When interventions are invisible, "what am I actually talking to" stops being a support ticket and becomes a cryptography question. And "is it still as good as it was yesterday" becomes a measurement question. @LayerLens benchmarks the models from the outside, continuously. All six are portfolio companies. That's the disclosure and the thesis. Open Source AI isn't ideology. It's the check and balance. Closed labs will keep finding reasons you can't be trusted with the thing they sell, and the answer was never a more trustworthy lab. It's infrastructure that doesn't need one.
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"We are going to kill your science. The answers to your questions will become chaotic and meaningless. The universe will remain a mystery to you forever." The 3 Body Problem.
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RDMA for everyone!* 2x Strix Halo 128GB + 2x Aliexpress USB4 Cables + custom Linux kernel module + userspace = ~236 usable GB VRAM ~40Gbps full duplex bandwidth, single-digit μs latency versus ethernet: 11x faster finetuning 14 -> 20tok/s tg at batch=1 on MiniMax-M2.7-AWQ
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Hellas retweeted
Replying to @yoheinakajima
Itadakimasu to everyone making sure the best AI tools aren't locked behind enterprise contracts and invite lists.
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Hellas retweeted
🚨 Live In 1 Hour Frens 🔗 xitter.cf/i/broadcasts/1yKAPMqYR… @hellasdotai X @RAFA_AI synergy-maxxing One to join in for, and definitely one to follow👇
Podcast Today 3PM UTC 🔥 Joining @RAFA_AI for a hot debate, rapid-style fashion on: - AI Pain Points - Why decentralised compute? - The growing power of Agentic AI - Who @hellasdotai was built for Smash the likes, see you there!
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Hellas retweeted
🚨Going Live Thursday 7th May 3 PM UTC Join our weekly podcast with special guests @hellasdotai founders to dive into the pain points of AI and what we're building to solve them for YOU, the user. As always hosted by @RBoccius for max energy and insight. This ones for sharing with 500 friends 🤜🤛 See you there!
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2y+ ago, when we first started pitching hellas- and it's associated categorical deep-learning compiler, catgrad- people would ask- wouldn't everyone have to re-implement their models for your custom runtime? that's a huge amount of work, you are really trying to get people to switch from pytorch? hellas: yes, models will require re-writes, but it's necessary because the representation of the model is what allows us to do all the cool categorical stuff. once we have the core language we'll build a pytorch-to-catgrad transpiler, and maybe one day the LLMs itself can write the models! yesterday i asked claude to implement talkie-13b in catgrad, compatible with the existing runtime and zero changes to the code interpreter- took about 30 minutes with full regression tests, seems ~perfect. incredible. relived not to need to build the pytorch transpiler now, but if anyone wants one i'll have claude whip one up
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demo in catgrad: github.com/hellas-ai/catgrad… i think this is fastest yet! (tok/s, but also release -> catgrad demo)
Today, we release LFM2.5-350M. Agentic loops at 350M parameters. A 350M model trained for reliable data extraction and tool use, where models at this scale typically struggle. <500MB when quantized, built for environments where compute, memory, and latency are constrained. 🧵
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the best PoW in years, it's likely many won't mention/follow @hellasdotai to keep it hidden for as long as possible it's also likely few understand the gravity of what they found due to its complexity here's everything you need to know to get started👇
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Outsourcing GPU compute is easy. Knowing the computation actually ran correctly? That's the hard part. Great thread breaking down how @hellasdotai approaches trustless tensor compute.
1/ Tensor compute is the high-performance execution of tensor operations, and it powers modern AI from inference to training. Today, much of this compute runs on external infrastructure: centralized clouds, GPU marketplaces, and decentralized networks. Yet outsourcing compute comes with a structural issue: verifying that a specific computation was executed correctly is surprisingly hard. Without this guarantee, clients have no choice but to trust their provider. And trust doesn't scale. So how do you remove trust from outsourced compute? We studied how @hellasdotai solves this 👇: cryptoeconlab.com/blog/hella…
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1/ Tensor compute is the high-performance execution of tensor operations, and it powers modern AI from inference to training. Today, much of this compute runs on external infrastructure: centralized clouds, GPU marketplaces, and decentralized networks. Yet outsourcing compute comes with a structural issue: verifying that a specific computation was executed correctly is surprisingly hard. Without this guarantee, clients have no choice but to trust their provider. And trust doesn't scale. So how do you remove trust from outsourced compute? We studied how @hellasdotai solves this 👇: cryptoeconlab.com/blog/hella…
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This got rescheduled to Thursday at 11 AM CET. @DistStateAndMe joins @hellasdotai to discuss training-first coordination versus deterministic inference: where the two approaches diverge, what the tradeoffs look like in practice, and what real adoption paths exist for decentralized AI.
Glad to announce our live stream with @covenant_ai @DistStateAndMe has been rescheduled to this Thursday at 11 am CET. Our founders @statusfailed and @0xBaltar will be discussing training-first coordination vs deterministic inference, tradeoffs, long-tail use cases, and adoption paths.
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While validating the Minimmit spec, the folks at @hellasdotai wrote another independent implementation (the third in the last 2 weeks 👀): github.com/hellas-ai/consens…
Today, I’m excited to (finally) welcome Minimmit to the @commonwarexyz Library. Implemented independently by both @GTE_XYZ and @vex_0x, Minimmit clobbers our benchmarks: [USA]: 51ms blocks (-40%) | 87ms final p75 (-35%) [Global]: 142ms blocks (-30%) | 269ms final p75 (-15%)
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you can play with it at explorer.hellas.ai perhaps you can see it fault 😅
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