[Hands-on] Build a Real-Time Satellite Tracker with Claude Code
The recent shutdown of Mythos highlights the inherent risks of building a business on third-party AI APIs that the company cannot control or influence. While the industry has focused on the cost of tokens, the real issue is operational sovereignty; frontier APIs can revoke access at any time, whereas open model weights remain on a user's hardware once downloaded. Ultimately, the primary value for a company lies in post-training, proprietary data, and custom evaluations rather than the base model itself. Claude Code was used to build a full-stack real-time satellite tracker that visualizes over 10,000 active satellites on an interactive 3D globe. The application leverages Tiger Cloud for a managed TimescaleDB backend on Postgres to handle high-velocity time-series orbital data. Claude Code managed the entire development lifecycle in a single session, including database provisioning via the Tiger CLI MCP server and frontend development using Next.js and Three.js. The Google DeepMind paper "From AGI to ASI" explores the transition from human-level artificial intelligence to superintelligence. It posits that the ability to replicate and scale AI instances will lead to collective intelligence that far outpaces human organizations. The authors identify four primary pathways to superintelligence, including self-improvement loops and organizational scaling, while noting roadblocks like data scarcity and energy constraints. Rather than a single breakthrough, the paper suggests progress will arrive in accelerating waves that reshape science and the economy.
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Mythos taught everyone a lesson this week
The recent shutdown of Mythos highlights the inherent risks of building a business on third-party AI APIs that the company cannot control or influence. While the industry has focused on the cost of tokens, the real issue is operational sovereignty; frontier APIs can revoke access at any time, whereas open model weights remain on a user's hardware once downloaded. Ultimately, the primary value for a company lies in post-training, proprietary data, and custom evaluations rather than the base model itself.
- Mythos was shut down because it relied on external intelligence platforms it could not control.
- The debate over open models is shifting from token costs to the security of technological control.
- Frontier APIs present a risk of sudden service termination or rule changes that can destroy dependent businesses.
- Open models provide security because weights hosted on private hardware cannot be taken back by the provider.
- Business value is increasingly found in post-training and proprietary data rather than base model weights.
- Base weights for open models still originate from external labs, but local hosting ensures continuity.
Build a real-time satellite tracker with Claude Code
Claude Code was used to build a full-stack real-time satellite tracker that visualizes over 10,000 active satellites on an interactive 3D globe. The application leverages Tiger Cloud for a managed TimescaleDB backend on Postgres to handle high-velocity time-series orbital data. Claude Code managed the entire development lifecycle in a single session, including database provisioning via the Tiger CLI MCP server and frontend development using Next.js and Three.js.
- Claude Code can provision and manage database infrastructure directly using the Tiger CLI MCP server.
- The tracker visualizes orbital paths for over 10,000 satellites, including more than 6,000 Starlink units.
- TimescaleDB's hypertables and continuous aggregates are utilized to efficiently manage and query large volumes of time-series data.
- The frontend architecture uses Next.js and Three.js for 3D visualization and timeline-based data rendering.
- Tiger Cloud offers a managed Postgres environment optimized for TimescaleDB with a $1,000 credit for new users.
- The Tiger CLI is an open-source tool licensed under Apache 2.0 that integrates with multiple AI coding assistants.
From AGI → ASI
The Google DeepMind paper "From AGI to ASI" explores the transition from human-level artificial intelligence to superintelligence. It posits that the ability to replicate and scale AI instances will lead to collective intelligence that far outpaces human organizations. The authors identify four primary pathways to superintelligence, including self-improvement loops and organizational scaling, while noting roadblocks like data scarcity and energy constraints. Rather than a single breakthrough, the paper suggests progress will arrive in accelerating waves that reshape science and the economy.
- Reaching ASI may depend more on scaling and replicating human-level AI than on creating a single 'lone genius' model.
- Collective AI intelligence could potentially outperform any existing human research lab or company through instant knowledge sharing.
- The paper outlines four concurrent pathways to ASI: scaling current methods, architectural innovation, self-improvement loops, and organizational scaling.
- Significant barriers to progress include the depletion of high-quality training data and the rising costs of energy and research.
- A key benchmark for true intelligence is the ability to invent genuinely new ideas, such as discovering relativity from 1900s-era knowledge.
- Progress toward superintelligence is expected to occur in a series of accelerating waves rather than a single overnight transformation.