Don’t aim at success—the more you aim at it and make it a target, the more you are going to miss it. For success, like happiness, cannot be pursued; it must ensue . . . as the unintended side-effect of one’s personal dedication to a course greater than oneself.
Excerpt from Man’s Search for Meaning by Viktor Frankl.
AI agents have quickly evolved from obscure technical experiments to mainstream buzz. In the last 18 months, new automation capabilities have captured the imagination of entrepreneurs and established organizations alike. While it’s possible that we’re in another AI bubble, the early adopters of AI agents are reporting impressive
Last week I attended the Algorithmic Innovation & Entrepreneurship Summit on Open Problems for AI. That’s a bit of a mouthful but in essence, it was a 2-day conference covering the challenges and opportunities for AI over the next 5 years. Day 1 was focussed on algorithms and AI
An undeniable killer use case for large language models is code generation. It’s already helping software developers write code up to twice as fast and with more delight. As LLM models improve with each release, they can write better software and handle more complex architectures at a fraction of
AI agents have been getting a lot of attention but they can be expensive to run. This is because they use a lot of tokens and make multiple calls to LLMs. Some AI developers have beat performance benchmarks by using exceptionally expensive agentic runs. However, this is unrealistic because in