Made an introductory 📕(draft) about using Python for Bayesian Inference and unifying narrative, math, and code. People seem to find it helpful. Check it out. Feedback encouraged. persuasivepython.com/ #DataScience #Python #bayes #Stats #probabilisticprogramming

Very excited to announce that the #BayesianWorkflow book by @StatModeling, @avehtari, @rlmcelreath et al publishes in June! routledge.com/9780367490140 #RStats #DataScience #Bayesian @mcmc_stan

Keep levelling up your AI toolkit by using and writing skills.

Prof. Donald Knuth opened his new paper with "Shock! Shock!" Claude Opus 4.6 had just solved an open problem he'd been working on for weeks — a graph decomposition conjecture from The Art of Computer Programming. He named the paper "Claude's Cycles." 31 explorations. ~1 hour. Knuth read the output, wrote the formal proof, and closed with: "It seems I'll have to revise my opinions about generative AI one of these days." The man who wrote the bible of computer science just said that. In a paper named after an AI. Paper: cs.stanford.edu/~knuth/paper…

This question is fun and the answer about mindset is fabulous. Reflect and tinker.

People love to say that "smart" people fail...but a cardiologist just placed 3rd out of 13,000 at Anthropic’s hackathon. Smart the right Initiative is insane leverage & where breakthroughs will come from...don't buy the hype. x.com/BoWang87/status/202490…

Claude Code isn’t just for writing code. Our new post shows how to use it as a lightweight agentic workflow library, going from messy ideas to working agents in a single session, without setting up a full framework first. landeranalytics.com/post/cla… #AIWorkflows

Feels like Simpson’s paradox

Vibe coding is the new product management. Training and tuning models is the new coding.

Interesting read on how the criteria by which we judge programming languages changes in the post-AI era.

Agree. AI makes breadth >> depth for most work: strong fundamentals good judgment lets you mimic deep expertise when you need it. I can switch languages and produce code as if I’ve studied them for years.

{causact} 📦 v0.5.8 is on its way to CRAN. Learn computational Bayesian inference using this intuitive and visual #rstats package. Release includes a little intro vignette: cran.r-project.org/web/packa…

The invention of modern writing instruments like the typewriter made writing easier, but they also led to the rise of writer’s block, where deciding what to write became the bottleneck. Similarly, the invention of agentic coding assistants has led to a new builder’s block, where the holdup is deciding what to build. I call this the Product Management Bottleneck. Product management is the art and science of deciding what to build. Because highly agentic coding accelerates the writing of software to a given product specification, deciding what to build is the new bottleneck, especially in early-stage projects. As the teams I work with take advantage of agentic coders, I increasingly value product managers (PMs) who have very high user empathy and can make product decisions quickly, so the speed of product decision-making matches the speed of coding. PMs with high user empathy can make decisions by gut and get them right a lot of the time. As new information comes in, they can keep refining their mental models of what users like or do not like — and thereby refine their gut — and keep making fast decisions of increasing quality. Many tactics are available to get user feedback and other forms of data that shape our beliefs about users. They include conversations with a handful of users, focus groups, surveys, and A/B tests on scaled products. But to drive progress at GenAI speed, I find that synthesizing all these sources of data in a PM's gut helps us move faster. Let me illustrate with an example. Recently, my team debated which of 4 features users would prefer. I had my instincts, but none of us were sure, so we surveyed about 1,000 users. The results contradicted my initial beliefs — I was wrong! So what was the right thing to do at this point? - Option 1: Go by the survey and build what users told us clearly they prefer. - Option 2: Examine the survey data in detail to see how it changes my beliefs about what users want. That is, refine my mental model of users. Then use my revised mental model to decide what to do. Even though some would consider Option 1 the “data-driven” way to make decisions, I consider this an inferior approach for most projects. Surveys may be flawed. Further, taking time to run a survey before making a decision results in slow decision-making. In contrast, using Option 2, the survey results give much more generalizable information that can help me shape not just this decision, but many others as well. And it lets me process this one piece of data alongside all the user conversations, surveys, market reports, and observations of user behavior when they’re engaging with our product to form a much fuller view on how to serve users. Ultimately, that mental model drives my product decisions. Of course, this technique does not always scale. For example, with programmatic online advertising in which AI might try to optimize the number of clicks on ads shown, an automated system conducts far more experiments in parallel and gathers data on what users do and do not click on, to filter through a PM's mental model of users. When a system needs to make a huge number of decisions, such as what ads to show (or products to recommend) on a huge number of pages, PM review and human intuition do not scale. But in products where a team is making a small number of critical decisions such as what key features to prioritize, I find that data — used to help build a good mental model of the user, which is then applied to make decisions very quickly — is still the best way to drive rapid progress and relieve the Product Management Bottleneck. [Original text: deeplearning.ai/the-batch/is… ]

this is very good 👀

Vibe coding is the way. While not perfect, it significantly boosts productivity and, when used with understanding, can also enhance quality.

🧠 LLMs aren't technically Bayesian, but they're conditional probability powerhouses! They train with maximum likelihood yet output evolving token probabilities that update with each new piece of info. This visual perfectly captures how a neutral stock trend shifts up or down as new words enter the prompt. Prediction in action!