bunny

clawpump Verified account @clawpumptech · 11h

grateful to the @incubator team by @solana labs for bringing us all the guidance we need to scale the largest agentic financing mechanism in the ecosystem

clawpump's economic flywheel gets brand new roadmap to empower builder's fundraising at Breakpoint

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Lyber.ai Verified account @LyberAI · 1d

Uncensored DeepSeek V4.1 Flash is back.
126,976 output tokens
$0.29 in/$0.029 cache/$0.99 out

Kimi K3.1 Pro
131,072 output tokens
$4 in/$0.40 cache/$15 out

Sign up to Lyber.ai for a $300 bonus credit with eligible paid plans available for 24 hours.

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Lyber.ai Verified account @LyberAI · 15h

We just hit 11 Billion tokens on Lyber on our 3rd day of public release

Deepseek-v4.1-flash-1017-abliterated, 7.5B tokens

Kimi-k31-pro-abliterated, 3.2B tokens

Thank you for joining us on our journey to reach agentic freedoms. We will continue to aggressively scale with you.

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Illia (root.near) (🇺🇦, ⋈) Verified account @ilblackdragon · 20h

Inference providers are under attack.

AI inference is now playing a critical role and is a honey pot of data and ability to inject actions back into users devices via agents for malicious actors.

NEAR AI thwarted a series of attacks on its systems and is back to normal. NEAR Protocol, Intents and other infra wasn’t affected.

TEE architecture is part of the answer to making sure that from outside even inference provider doesn’t have an access. User should verify exactly what happens with their data and prompts.

NEAR AI code is open and inspected by many partners when deciding to leverage the stack to validate exactly what runs inside TEE and ensuring privacy.

In this case there was also a vulnerability within SGLang, open source inference engine that runs inside an enclave. Even though this had a minimum disruption it still was a very

Going forward we are going increase security rigor of dependencies and also improve a “turnstile” to detect when anything out of normal runs inside TEE.

Proactive security against external attackers has became a must. We have been using Abliterated models and red teaming harness to continuously monitor and detect issues. We are also expanding scope and compute invested into this.

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MTS @MTSlive · 1d

AI inference demand is doubling every 2-3 weeks, yet Morph's @skeptrune says there would still be a massive compute surplus if GPUs currently used for training were redirected to inference:

"People don't understand quite how much compute there is already, and there's a little bit of a gap in the understanding of whether or not demand is actually infinite."

"Demand for the open source models and demand for the closed source models is probably doubling every 2 to 3 weeks. But the vast, vast majority of compute today is not being used for inference. It's being used for training."

"If we were to use all of what we had for inference, we'd have an excess of compute. There’s not infinite inference demand."

"The idea that you could just found a company, raise some money from debt, build a data center cluster with GPUs in it, and make money is not as true as people believe, and we need to build applications."
@morphllm

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