Introducing - the Agentic OS you've been waiting for. 🧩 Compose teams of specialised AI agents from plugins. No model lock-in. No vendor platform. Your infrastructure, your data. ✅ Privacy-first: sensitive data never leaves your infra in plaintext. Tokenised before every LLM call, with a verifiable receipt per round. ✅ Plugin-driven: channels, integrations, tools, sub-agents as signed ZIPs. ✅ Self-hostable: Docker Compose up and running in minutes. ✅ Built for EU operations: GDPR-aware by design, EN/DE bilingual. Public preview is live. 🌐 🔗 #OpenSource# #AI# #AIAgents# #Privacy# #SelfHosted#

The Linux Foundation Announces $12.5 Million in Grant Funding (via @AlphaOmegaOSS and @OpenSSF) @AnthropicAI , @AmazonWebServices, @GitHub, @Google, @GoogleDeepMind, @Microsoft, @OpenAI to Invest in Sustainable Security Solutions for #OpenSource#

We’re excited to welcome Mooncake to the PyTorch Ecosystem! Mooncake is designed to solve the “memory wall” in LLM serving. By integrating Mooncake’s high performance KVCache transfer and storage capabilities with PyTorch native inference engines like SGLang, vLLM, and TensorRT-LLM, it unlocks new levels of throughput and scalability for large language model deployments. Mooncake enables prefill decode disaggregation, global KVCache reuse, elastic expert parallelism, and serves as a fault tolerant PyTorch distributed backend. 🔗 #PyTorch# #OpenSourceAI# #LLM# #AIInfrastructure#

Open-source AI is ruthlessly out-innovating the trillion-dollar monopolies. 🚀 Big labs are burning billions brute-forcing AGI on massive GPU clusters. Meanwhile, the open ecosystem is structurally forced to innovate on inference—and it's working. Look at what just happened: - DeepSeek v4 using SSDs for KV cache. - Breakthroughs like TurboQuant and Kimi K2 are aggressively compressing memory and driving the cost of intelligence to near zero. When you don't have infinite compute, you actually have to engineer better solutions. Constraints breed miracles. By solving the KV cache bottleneck, scrappy open-source builders are creating vastly cheaper and more profitable AI than the bloated closed-source giants. Hacker culture > GPU monopolies. Period.

Open-source and U.S. intelligence reporting indicate Iran retains sufficient asymmetric assets (mines, drones, fast attack craft) to disrupt commercial transit through the Strait of Hormuz despite recent degradation of larger naval and missile platforms. CIA assessments assess Tehran can sustain a naval blockade for 90-120 days using stockpiles and smuggling networks. Current incidents show continued IRGC capacity of harassment of U.S. warships.

Open source isn't just good for developers, it's one of America's strongest tools for AI security. More models means more defenders and more front doors protected. Earlier this week at the @MilkenInstitute Global Conference, Jensen sat down with @beckyquick to explain why 👇

Open source AI needs more than code. It needs trust. Excited to see our own @aurobindo_arman moderating this panel on privacy, security, and what it really takes to deploy open AI systems in the wild. If you care about where this space is going, this is one to checkout↴

Open source AI only works if people trust it. This panel brings together builders and researchers focused on privacy, security, and what it actually takes to deploy open AI systems in the real world. “Privacy, Security, and Open Source AI” is coming to our EthDenver (@EthereumDenver) Open AGI Summit. Moderated by Aurobindo Arman (@aurobindo_arman) from Horizen Labs (@horizenglobal). Speakers: Gabriel Andrade (@gab_p_andrade) from Gensyn (@gensynai) Prof. Salman Avestimehr (@avestime) from ChainOpera AI (@ChainOpera_AI) Jake Salerno (@JakeSalerno) from 0G Labs (@0G_labs) Grab your ticket below 👇

🚨 OPEN SOURCE AI IS LITERALLY UNSTOPPABLE 🚨 The legendary founder of Redis (Antirez) just dropped ds4 - a custom native inference engine built specifically for DeepSeek v4 Flash This is earth shattering! Here is why: DeepSeek v4 Flash is a quasi-frontier model with a massive 1M context window You can now run it LOCALLY on a 128GB Mac using specialized 2-bit quantization The architecture is reimagined—he moved the KV cache from RAM directly to the SSD disk! 🤯 We already know DeepSeek v4 Flash is insanely good for agentic loops - Now you don't even need the cloud to run it Closed-source labs are burning tens of billions on massive GPU clusters while single brilliant developers are running frontier-level AI on laptops! They told us open-source would be worthless against trillion-dollar monopolies Instead, pure hacker culture + incredible open-weight models are completely rewriting the rules Open Source will ALWAYS win 💕