Andrew King @AndysThinking

@mcahogarth It is also a bunch of tech needed for hyperloop pods

Three step transformation: Fleet-based self-driving electric vehicles will push personal vehicles per family way down (due to cost differential). With more people choosing transportation mode based on trip destination, use of all alternative modes will increase (as they realize it is nice to not always drag a car everywhere they go). The build environment heals as development is no longer expected to be car dependent.

Three forms of adaptive learning for Precision Med Edu: • Volume adaptive — extra content until the concept clicks, or to go deeper on what interests you • Sequence adaptive — order of material shifts to match case exposure in clinic • Medium adaptive — lecture, reading, animation, podcast… whatever fits the learner

@LimitingThe Speaking of wait times, it would greatly benefit the country if SpaceX built a factory for natural gas turbines

@DJSnM Grist House Command near Pittsburgh is a brewery in an old Nike command center

I found this insightful "learners engange in activities for 2 different reasons: strive to develop competence by learning as much as they can (mastery goals) or strive to display competence by trying to outperform others (performance goals)"

Proficiency is reps, pattern recognition, and situational awareness. -CP

Learning a new domain gives you both new frameworks for thinking and new language for communicating. Its the biggest level-up you can do.

@paulg It might be a slaughter. Once the self driving network is active, it can be coordinated to have chargeded vehicle for you to swap to midroute. Then cars w/ layflat seats. Finally, seemless swaps to tesla small electric planes at regional airports.

Jevons paradox is happening in real time. Companies, especially outside of tech, are realizing that they can now afford to take on software projects that they wouldn’t have been able to tackle before because now AI lets them do so. We’re going to start to use software for all new things in the economy because it’s incrementally cheaper to produce. Marketing teams at big companies will have engineers helping to automate workflows. Engineers in life sciences and healthcare will automate research. Small businesses will hire engineers for the first to build better digital experiences. And as long as AI agents still require a human who understands what to prompt, how to review when an agent goes off the rails, how it guide back, how to maintain the system that was built, how to fix the ongoing bugs, and more, we will still have humans managing these agents. This is why all the advice you get of not going into engineering is wrong. The world is going to increasingly be made up of software, and the people that understand it best will be in a strong economic position. This will happen in other roles as well where output goes up and demand increases.

Lenny Rachitsky @lennysan

7 months ago

Engineering job openings are at the highest levels we’ve seen in over 3 years There are over 67,000 (!!!) eng openings at tech companies globally right now, with 26,000 just in the U.S. We don’t know if there would have been more open roles if not for AI or if AI is actually

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Crowds are dumb, markets are smart—esp. vs "wisdom of crowds." Key takes: @naval: "Crowds aren’t wise - markets are wise, and mobs are dumb." @mert: Markets=wisdom w/o consensus. @CliffordAsness: coordination kills crowds & social media increases coordination #MarketsOverMobs

I don’t think we’ve have yet to grasp how much self driving cars will change how we live. It will help alleviate housing affordability because people will be able to commute further. Parking lots become obsolete, allowing city centers to reclaim millions of square blocks across the US. Car ownership will be optional, the car insurance industry dies, and car travel becomes affordable for all. Same day delivery becomes the norm, and mobile kitchens deliver food in mere minutes. The 2nd leading cause of death for young people drops to basically zero.

signüll @signulll

10 months ago

the self driving car is simply a couch that moves. cars won’t compete with other cars. they’ll compete with living rooms, offices, & beds.

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@SawyerMerritt Instantly turn each car into part of the starlink data center mesh network.

@JonasNi46301750 @nikillinit This at off-peak times

Anthropic just posted another banger guide. This one is on building more efficient agents to handle more tools and efficient token usage. This is a must-read for AI devs! (bookmark it) It helps with three major issues in AI agent tool calling: token costs, latency, and tool composition. How? It combines code executions with MCP, where it turns MCP servers into code APIs rather than direct tool calls. Here is all you need to know: 1. Token Efficiency Problem: Loading all MCP tool definitions upfront and passing intermediate results through the context window creates massive token overhead, sometimes 150,000+ tokens for complex multi-tool workflows. 2. Code-as-API Approach: Instead of direct tool calls, present MCP servers as code APIs (e.g., TypeScript modules) that agents can import and call programmatically, reducing the example workflow from 150k to 2k tokens (98.7% savings). 3. Progressive Tool Discovery: Use filesystem exploration or search_tools functions to load only the tool definitions needed for the current task, rather than loading everything upfront into context. This solves so many context rot and token overload problems. 4. In-Environment Data Processing: Filter, transform, and aggregate data within the code execution environment before passing results to the model. E.g., filter 10,000 spreadsheet rows down to 5 relevant ones. 5. Better Control Flow: Implement loops, conditionals, and error handling with native code constructs rather than chaining individual tool calls through the agent, reducing latency and token consumption. 6. Privacy: Sensitive data can flow through workflows without entering the model's context; only explicitly logged/returned values are visible, with optional automatic PII tokenization. 7. State Persistence: Agents can save intermediate results to files and resume work later, enabling long-running tasks and incremental progress tracking. 8. Reusable Skills: Agents can save working code as reusable functions (with SKILL .MD documentation), building a library of higher-level capabilities over time. This approach is complex and it's not perfect, but it should enhance the efficiency and accuracy of your AI agents across the board. anthropic. com/engineering/code-execution-with-mcp

Grid scale batteries are changing our electricity system. Excellent new visual story on batteries in FT today shows just how far this technology has evolved. Fasten your seatbelts, this is just the beginning. ig.ft.com/mega-batteries/

Prediction: The penetration of democratized education (@DeepLearningAI, chatbots as teachers, etc.) is going to tip the AI investment bubble balance from about to burst to being justified.

This roller coaster is surprisingly aggressive. It made me think that I was getting too old for thrill rides cbsnews.com/news/universal…

Who is going to create a drone swarm based surveillance product for all outdoor events?