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“ The demonstration that no possible combination of known substances, known forms of machinery and known forms of force, can be united in a practical machine by which man shall fly long distances through the air, seems to the writer as complete as it is possible for the demonstration of any physical fact to be.” - Simon Newcomb (1906) J. Storrs Hall’s book ‘Where Is My Flying Car?’ asks why certain anticipated technologies haven’t become mainstream despite our technological capabilities. Hall uses the flying car as a metaphor to discuss innovation stagnation that has plagued since the 1970s, a period he refers to as the “Great Stagnation.” ...

AI is not purely digital infrastructure. It is an industrial system. Last year, went down the rabbit hole on earth materials and how they fit into the theme of supply chain demand, particularly with the growth of compute required AI. Defense contracts heavily depend on lithium, especially for electronic components and warfare applications. Currently, the US mainly rely on outsourcing lithium from countries such as China and Australia. The interesting realisation is that the bottlenecks increasingly look less like traditional software constraints and more like industrial coordination problems. ...

I want to talk about a rabbit hole I have fallen down since reading a paper on The Promises and Predicaments of Federated Learning in Healthcare. Last year, I had the privilege of working with an awesome team, we explored ideas applying machine learning to tackle data interoperability challenges within our healthcare system. The problem: disparate data formats, strict privacy regulations, and the sheer volume of sensitive patient information scattered across multiple institutions. Our thesis was that the near-term impact of data interoperability is obvious, time saved and admin (huge burden to the NHS) reduced. We validated this with our study across thousands of hospital reported EHR systems, and with firsthand NHS clinical experience in the team. But the bigger picture? The data unlock. ...

LLMs like GPT-o1 are demonstrating remarkable capabilities in knowledge and reasoning. Solving challenging mathematical problems, assisting scientists in writing proofs, retrieving related works, generating code, and discovering patterns. These feats hint at a future where AI doesn’t just follow human instructions but contribute creatively to human endeavours. The Promise and the Question A growing number of researchers propose autonomous agents that can generate and validate new ideas independently. CrewAI is a framework designed to build autonomous multi-agent systems that can generate and validate ideas without human intervention. ...

Cursor tutorials are going viral on Twitter. Applications built with little to no coding experience. Tools like Cursor, Claude, Lovable, Bolt allow you to stay inside your own mental model. You describe the problem in plain language, in terms of what the thing needs to do, and the AI handles the translation. The gap between “I know exactly what this needs to do” and “I know how to make a computer do it” closes in a way it never did with Bubble or Webflow. ...

A recent study involving 759 startups across four randomised control trials found that teaching entrepreneurs to adopt a scientific approach significantly boosts revenue. Founders trained in this way become quicker to abandon bad ideas, make strategic pivots more effectively, and enhance their overall performance. Key Insights: Entrepreneurs who apply scientific thinking are more likely to terminate non-viable projects earlier, conserving resources and time. These entrepreneurs pivot more effectively, making a few well-considered changes rather than none or many unfocused ones. ...

Conversations with engineers and insights from the TypeScript Congress 2023 highlight its growing prominence. JetBrains’ Developer Survey shows TypeScript’s user share has tripled from 12% in 2017 to 34% in 2022 and 2023. TypeScript is no longer just a frontend language. It is becoming the orchestration layer for AI native applications. Java dominated the infrastructure era of the internet. It became a standard that triggered a massive retooling across industries, leading to the rise of Databricks, Confluent, and Atlassian. Java’s influence was particularly profound in data infrastructure, underpinning projects like Spark, Kafka, Hadoop, and Cassandra. ...