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- Responsible AI is dying. Long live responsible AI. How you, yes you, can help build a better future with AI.
- A Billion Dollars isn't cool. You know what is? Ten million dollars. AI can revive the middle class business model
- Superagency and ADHD Imagining a world look like where more people can just do things
- Solving LLM hallucinations is (mostly) a UX problem Why LLM hallucinations are primarily a UX challenge rather than an algorithmic one, and how design improvements can mitigate their impact on users.
- Building Successful Responsible AI Teams A practical guide to building responsible AI teams that succeed by aligning with business goals, hiring builders over framework-creators, and focusing on empirical risk management.
- Skeptical optimists are the key to an abundant AI future Beyond doomers and accelerationists: why skeptical optimists who believe AI problems are solvable with effort are essential for building an abundant AI future.
- Selecting on the dependent variable Why business books and productivity advice often commit the error of selecting on the dependent variable, and how to spot this common reasoning flaw.
- Quantification bias Why new systems that afford quantitative measurement face disproportionate scrutiny compared to incumbent systems, and why we should hold all systems to the same standards.
- The rise of the data product manager The emergence of a new kind of product manager who understands data infrastructure, ML, and how to build products where data is at the core of the value proposition.
- Software development skills for data scientists Notes from my own job search on what to expect in data science interviews, from technical screens to on-site interviews, and how to prepare for both type A and type B data scientist roles.
- The acute pain and chronic reward of public-facing work Reflections on the emotional toll of producing public-facing work like open source or writing, where criticism flows fast while rewards reveal themselves slowly.
- Do you have time for a quick chat? Guidelines for requesting informational coffee chats that respect the other person's time and increase your chances of getting a response.
- Software development skills for data scientists Essential software engineering skills that data scientists need to collaborate effectively: version control, code review, testing, documentation, and writing modular code.
- Why good data scientists make good product managers The natural affinities between data science and product management, plus the challenges data scientists face when making the transition to PM roles.
- Hiring data scientists A better approach to hiring data scientists: treat candidates as intelligent humans, use homework assignments over whiteboard coding, and build a process that leads to equitable outcomes.
- Getting started in data science A guide to getting started in data science, covering the essential foundations in math, statistics, experiments, machine learning, and tooling that aspiring data scientists need.
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