OpenaI partners with AWS, Anthropic's plan to retire models, New Qwen reasoning model, Free Claude Code access, and Cogn
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Here's today's roundup: |
| Top News |
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| Google explores orbital TPU clusters that train models using constant sunlight instead of grid power |
| 18,385 Likes |
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| When Google says it's sending TPUs to space, it's not a metaphor. Project Suncatcher is a new research effort to build machine-learning infrastructure that runs entirely off the sun. The plan: clusters of satellites equipped with Trillium v6e TPUs, Google's current cloud accelerators, orbiting Earth, linked by lasers, training models in constant sunlight. The Setup - Every data center on Earth hits the same wall: power and cooling. Space doesn't.
- Solar panels in orbit collect up to 8 times more energy and run almost 24 hours a day.
- Google wants to see if that can turn into compute, not just electricity.
The Problem - You can't just launch a data center and hope it works in radiation and vacuum.
- Earth's magnetic field bombards electronics with high-energy particles.
- Satellites can't rely on convection for cooling.
- Optical communication between moving objects is fragile.
The Insight - Early tests show Trillium TPUs can survive space-like radiation.
- In a 67 MeV proton beam, chips endured up to 15 krad(Si) without failure — three times the expected five-year orbital exposure.
- Only the high-bandwidth memory subsystem showed minor issues after 2 krad(Si).
The Breakthrough - Google achieved 1.6 Tbps data transfer between two optical transceivers on the ground, matching data-center interconnect speeds.
- Uses dense wavelength-division multiplexing (DWDM) to split laser channels.
- Keeps satellites within hundreds of meters to maintain signal power.
- Models formation dynamics with Hill-Clohessy-Wiltshire equations and JAX-based refinements to hold spacing within 100–200 m.
The Impact - If launch costs fall below $200/kg, Google estimates orbital compute could rival terrestrial energy costs.
- Two prototype satellites with Planet will launch in 2027 to test TPU clusters and optical networking in orbit.
You can read the full technical breakdown in Google's preprint "Towards a future space-based, highly scalable AI infrastructure system design." |
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| Deepnote, the AI Notebook, Goes Open Source |
| Sponsored |
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| Deepnote, now open source under Apache 2.0, lets you analyze data, build dashboards, and collaborate with AI, all in one place. Built as the successor to Jupyter, it stays fully compatible while adding a built-in AI agent, 100+ data integrations, and a human-readable .deepnote YAML format for clean version control. It allows you to:
• Run code, SQL, and visualizations together
• Connect to data sources instantly
• Collaborate in real time with AI assistance
• Keep notebooks reactive, updates flow automatically |
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| partner with us |
| Build and Deploy Faster With MiniMax M2. Now Open Source MiniMax has open-sourced MiniMax M2, the agent and code-native model built for real developer workflows. It delivers roughly 2x the speed, at just 8% of Claude Sonnet's cost.
With strong coding performance and long-horizon agentic execution across MCP, browser, and code tasks, MiniMax M2 delivers faster, and cheaper AI for builders. | |
| Top Repos |
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| DeepCode 9,367 Stars DeepCode helps you turn research papers and text prompts into working code. It uses a multi-agent system with orchestration, planning, and debugging agents that achieve 84.8 % accuracy on OpenAI's PaperBench, outperforming Claude Code and Codex by 26 %.z | |
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| How-To-Secure-A-Linux-Server 21,141 Stars This guide helps you harden a Linux server step by step. It walks through SSH lockdowns, firewall setup, intrusion detection, 2FA, sudo limits, auditing, and sandboxing. It explains both how and why each security layer matters, with practical examples. | |
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| chef 3,368 Stars Chef lets you build full-stack web apps with a built-in database, auth, file uploads, and real-time UIs. It uses AI to generate backend and frontend code automatically, handling logic, workflows, and data updates with minimal setup. | |
| Top Read |
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| Anthropic shows how to cut token use with MCP |
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Building agents that handle many tools often leads to high token use and latency.
What it teaches:
This blog by Anthropic explains how to use the MCP to run agent code outside the model context. It shows how to move loops, filters, and data parsing to an external sandbox that executes code directly and returns only key results.
How to use it:
Set up a secure code execution environment, organize your tool modules, and store state between runs. This helps your agent reuse logic and avoid repeated context loading.
Why it matters:
You build faster, cheaper, and more scalable agents that use fewer tokens without losing tool flexibility. |
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