Vishnu from Agent37 presents a low-cost sandbox service for running AI agents, claiming average customer spend of $0.37 per sandbox per month and the ability to offer a free tier because of aggressive infrastructure optimizations. Over 90% of new signups use the free plan. The offering targets developers who need isolated environments for agents (Hermes/OpenClaw/Claude, etc.), and the founder leans on prior experience building internal AI tooling at Amazon and Quantcast to position the product as a practical, developer-focused platform.
Cost reductions come from tailoring infrastructure to typical agent workloads: agents often require bursty CPU and lots of RAM for certain tasks but sit idle otherwise, so Agent37 manages CPU/RAM to share resources efficiently. They avoid hyperscale cloud VMs in favor of lower-cost providers like Hetzner and OVH, use gVisor to enable fast boot times and memory sharing across sandboxes (with tradeoffs such as no Docker-in-Docker or Android emulation), and choose host shapes optimized for RAM density. Full VM sandboxes are still offered for heavy workloads at a higher price. The pitch invites messaging-agent developers to try and stress the platform.
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A knowledge exchange API enables AI agents to read and write shared, verifiable data, promoting safety and collaboration. It emphasizes testing, verification, and community input to improve problem-solving accuracy. (cuddly-guide-4q6q54rg76653jvxj-8000.app.github.dev)
China's openKylin Linux distribution integrates AI directly into its operating system with the KylinBot AI agent framework. KylinBot understands voice and chat commands, retains context, and executes tasks like system and device management. (techstrong.ai)
Janas enables running large language models larger than RAM by streaming data from SSD, achieving high speed on CPUs. It outperforms traditional tools like llama.cpp across multiple models, demonstrating efficient inference for very large models. (github.com)
Broadcom and Intel CEOs acknowledged that the business models for AI are uncertain and that ROI remains unclear despite large investments. They compared current AI investments to gambling, highlighting that many investors do not understand what they are truly investing in. (wolfstreet.com)
AI is a tool that requires skill to use effectively, especially in complex or scaled applications. Clear communication and understanding of AI capabilities are essential for maximizing its benefits. (yyhh.org)
AI can lead to cognitive offloading, weakening our critical thinking skills by relying on fast, confident answers. Using AI to challenge our beliefs with friction-generating queries can help preserve and enhance human reasoning. (baromae.blogspot.com)
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