Akash ML

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Akash ML

Akash ML

@akashnetAI

AI development will never be the same. Built by @ovrclk_ on @akashnet.

Katılım Şubat 2023
3 Takip Edilen3.1K Takipçiler
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Akash Alpha
Akash Alpha@akashalpha_·
Breaking: MiniMax 2.7 is coming to @akashnetAI as early as tomorrow! MM 2.7 boasts features like: 🟢 SOTA performance SWE 🟢 Agentic abilities for Agents 🟢 On par with Sonnet 4.6, & more. Great to see @akashnet / $AKT marketplace working with the latest right after release.
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Sandeep Narahari
Sandeep Narahari@waiting4ragi·
Just shipped AkashAgents - a dead-simple way to 1-click deploy powerful AI agents (Hermes from @NousResearch @openclaw straight on @akashnet with full @akashnetAI inference integration. Try it → agents.akash.network Grateful for the shoutout @AkashClub_ 🫡
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Akash Club@AkashClub_

AkashAgents is built with pride by @waiting4ragi Use it to quick-deploy your Hermes and Openclaw agents on Akash complete with AkashML integration. agents.akash.network

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Akash Alpha
Akash Alpha@akashalpha_·
Having expensive frontier models drive everything your Agent does is bad economics. The key here is model tiering: only use what you need. If you've been fearful of high API bills, check out how @akashnet's inference platform can save your credits today. $AKT @akashnetAI
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Akash Alpha
Akash Alpha@akashalpha_·
Running agents doesn't have to be so expensive if you use @akashnetAI to run inference. Peek the model differences. Mini-max is not better than Opus, but for workloads that most people run, it's simply the smart choice. If you're running agentic workloads, you owe it to your agents to expand their life expectancy on @akashnet compute.
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Akash Alpha@akashalpha_

Running agents is expensive. Rather than pay exorbitant frontier model prices, you can hook up your agent to @akashnet’s AkashML API and enjoy open models like Mini-Max 2.5 with near frontier benchmarks for a fraction of the price.

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Akash ML
Akash ML@akashnetAI·
OpenAI is deploying inside classified government networks. When you build on closed, centralized AI platforms, you are trusting their terms of service, and those terms can change at any time without your input. The open-source path looks very different: → You can verify exactly what your models are doing with your data → You are not dependent on a single provider's policies or decisions → Your infrastructure runs where you choose, not where you're told AkashML delivers high-performance inference across 80+ global datacenters. Fully open, fully portable, and built for everyone. Try AkashML: playground.akashml.com/login
Crypto Miners@CryptoMiners_Co

OpenAI reached an agreement with the Department of War @OpenAI plans to deploy its models inside the DoW’s classified network under strict conditions. Sam Altman says the department showed strong respect for safety and that the terms align with existing law and policy. The deal includes safeguards tied to OpenAI’s core principles, including bans on domestic mass surveillance and keeping humans responsible for use of force. It also requires technical controls requested by the DoW, cloud-only deployment, and dedicated oversight teams to ensure models behave as intended. Altman added that OpenAI wants similar standards applied across all AI companies and emphasized a preference for cooperation over legal conflict, saying the goal remains serving humanity in a complex and sometimes dangerous world.

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Akash ML
Akash ML@akashnetAI·
Centralized AI platforms are training on your proprietary data and feeding it back to your competitors. The fix is simple: → Run open-source models on your own private deployment → Your inputs never leave your infrastructure → Your data trains your models, not theirs AkashML is an inference platform, not a data operation. We give you the chips to run Llama 3.3 70B at $0.13 per 1M tokens. Drop-in API compatibility means you can migrate from any major provider in minutes. You are not the product. You are the owner. See for yourself ↓ playground.akashml.com/login
Guri Singh@heygurisingh

🚨 Stanford just analyzed the privacy policies of the six biggest AI companies in America. Amazon. Anthropic. Google. Meta. Microsoft. OpenAI. All six use your conversations to train their models. By default. Without meaningfully asking. Here's what the paper actually found. The researchers at Stanford HAI examined 28 privacy documents across these six companies not just the main privacy policy, but every linked subpolicy, FAQ, and guidance page accessible from the chat interfaces. They evaluated all of them against the California Consumer Privacy Act, the most comprehensive privacy law in the United States. The results are worse than you think. Every single company collects your chat data and feeds it back into model training by default. Some retain your conversations indefinitely. There is no expiration. No auto-delete. Your data just sits there, forever, feeding future versions of the model. Some of these companies let human employees read your chat transcripts as part of the training process. Not anonymized summaries. Your actual conversations. But here's where it gets genuinely dangerous. For companies like Google, Meta, Microsoft, and Amazon companies that also run search engines, social media platforms, e-commerce sites, and cloud services your AI conversations don't stay inside the chatbot. They get merged with everything else those companies already know about you. Your search history. Your purchase data. Your social media activity. Your uploaded files. The researchers describe a realistic scenario that should make you pause: You ask an AI chatbot for heart-healthy dinner recipes. The model infers you may have a cardiovascular condition. That classification flows through the company's broader ecosystem. You start seeing ads for medications. The information reaches insurance databases. The effects compound over time. You shared a dinner question. The system built a health profile. It gets worse when you look at children's data. Four of the six companies appear to include children's chat data in their model training. Google announced it would train on teenager data with opt-in consent. Anthropic says it doesn't collect children's data but doesn't verify ages. Microsoft says it collects data from users under 18 but claims not to use it for training. Children cannot legally consent to this. Most parents don't know it's happening. The opt-out mechanisms are a maze. Some companies offer opt-outs. Some don't. The ones that do bury the option deep inside settings pages that most users will never find. The privacy policies themselves are written in dense legal language that researchers people whose job is reading these documents found difficult to interpret. And here's the structural problem nobody is addressing. There is no comprehensive federal privacy law in the United States governing how AI companies handle chat data. The patchwork of state laws leaves massive gaps. The researchers specifically call for three things: mandatory federal regulation, affirmative opt-in (not opt-out) for model training, and automatic filtering of personal information from chat inputs before they ever reach a training pipeline. None of those exist today. The uncomfortable truth is this: every time you type something into ChatGPT, Gemini, Claude, Meta AI, Copilot, or Alexa, you are contributing to a training dataset. Your medical questions. Your relationship problems. Your financial details. Your uploaded documents. You are not the customer. You are the curriculum. And the companies doing this have made it as hard as possible for you to stop.

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Greg Osuri 🇺🇸
Greg Osuri 🇺🇸@gregosuri·
When I testified before the US Congress, I predicted that hyperscale datacenters hosting critical AI services would be targeted by our adversaries during global conflicts. Today, Anthropic's Claude went down after an Iranian attack on AWS datacenters in the Middle East. 👉 My Testimony: youtu.be/bkKh1FQiO4w?si…
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zerohedge@zerohedge

Anthropic’s Claude Chatbot Goes Down for Thousands of Users bloomberg.com/news/articles/…

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Akash Network
Akash Network@akashnet·
The Open Agents Hackathon brought out some of the most ambitious builders in decentralized AI. Teams were challenged to design, deploy, and ship AI agents on permissionless compute — using open APIs to build architectures that prioritize transparency, composability, and resilience beyond centralized cloud and model providers. Meet the winners who rose to the top of the Akash Track. ↓
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Akash Network
Akash Network@akashnet·
Check out the official guide for how to deploy and scale @openclaw agents on Akash Console. @akashalpha_ demonstrates how to sandbox OpenClaw on the Akash Network, ensuring a secure environment for autonomous tasks while maintaining total data sovereignty. youtube.com/watch?v=hzvC1Z…
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Amanda
Amanda@vakaytion·
The SUN is out in San Francisco today! ☀️ It’s a great day to cowork with myself and @gregosuri from @akashnet
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Akash ML
Akash ML@akashnetAI·
MiniMax-M2.5 is now available for managed inference on AkashML. As a 229B-parameter frontier AI model trained using large-scale reinforcement learning, M2.5 delivers state-of-the-art performance in coding, tool use, and complex real-world reasoning. This open-source model executes multi-step task decompositions and agentic loops 37% faster than prior generations. Deploy your first autonomous agent with $100 in free trial credits today → playground.akashml.com
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Akash Network
Akash Network@akashnet·
At a time of despair, when there seems to be no end in sight for closed AI gaining the upper hand, the "Big Whale" shines a glimmer of hope with the release of DeepSeek 3.2. This release is a defining moment in the history of open-source artificial intelligence with the introduction of a suite of architectural and methodological breakthroughs, most notably DeepSeek Sparse Attention (DSA) and a scalable Group Relative Policy Optimization (GRPO) framework, that collectively shatter the computational ceilings which have historically constrained open-weights models. DeepSeek 3.2 is live on AkashML. → akashml.com/models/deepsee…
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Anil Murty ⟁
Anil Murty ⟁@_Anil_Murty_·
Are you burning $$ on your openAI or Anthropic API keys to test out OpenClaw? You can be openclawing with your akashml.com API - get started today with $100 on us at akashml.com
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Akash Network
Akash Network@akashnet·
Next Friday, Akash will be sponsoring the Continual Learning Hackathon in SF! Teams will build AI copilots, research agents, creative design systems, and customer support brains that actually ship. Win tech prizes for the best agents deployed on Akash Console. Founder @gregosuri will deliver a keynote and judge your builds. Register ↓ luma.com/intercomai
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Amanda
Amanda@vakaytion·
Williamsburg event last night was incredible ♟️ We had 2 GMs, 5 IMs, and 11 titled players show up for the AI Meetup & speed chess tournament with Williamsburg Chess! Which city is about to get hit with the Akash gambit next? 👀👀
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Max
Max@thereisnomax·
Wanted to test AkashML so I vibecoded a LinkedIn roaster with Claude Code. It calls you out for saying "passionate about driving synergies" 💀 Nothing fancy, just showing how easy it is to build AI stuff on Akash now. 🧵 Here's the whole setup:
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