🎉 Big news: RxInfer.jl is now a @NumFOCUS Affiliated Project! Born from PhD research on reactive message passing, it’s now an open-source tool for fast, scalable Bayesian inference — and part of a world-class ecosystem #JuliaLang #BayesianInference #NumFOCUS #OpenSource

This is a cool Julia version of my Jax library for Bayesian Structural Time Series modeling ( https://t.co/T5gQOQPN9o) from the folks at @ReactiveBayes. It can easily handle non-linear and non-conjugate likelihoods (eg Poisson distribution for integer count observations). For

New Bayesian Structured Time Series example: predicting taco demand during #NeurIPS 2025 ⚡️ Learnable Dynamics 🔢 Non-Conjugacies 🏎️ Blazing Speed https://t.co/t23bDp7Lab #JuliaLang #BayesianInference #DataScience

🏆 Best Technical Paper at @iwai_ws! Recasting EFE planning as Variational Inference → sample-efficient Active Inference agents that actually run. Built in RxInfer. Won by @wouterwln. Paper: https://t.co/ybnLLZsdL6 Code: https://t.co/gCGWi0SKAQ #IWAI2025 #RxInfer #Bayesian

🚀 We’re open-sourcing Gears.jl — a Julia package for precise task scheduling in simulation environments! ⚙️ Run timed, event-based, or ASAP jobs with flexible schedulers, multiple clocks, and multi-threading. 🔗 https://t.co/7ByKubG8dk #JuliaLang #OpenSource #Agents

We've been seeing some cool examples of fast Bayesian inference + LLMs - from trust/reliability to LLMs participating in inference (even learning to derive variational rules!). Check out the examples:

We’re on Discord! 🎧🚀 Moving beyond GitHub discussions—join the new RxInfer server (by @LazyDynamics ) to chat, ask Qs, get updates, and plan hackathons. Jump in: https://t.co/z6TV09yGO9 #RxInfer #BayesianInference #ProbabilisticProgramming

We’re on Discord! You asked for better community engagement around RxInfer & RxInfer Pro—so here it is. Join us for Q&A, updates, hackathons & more. It’s quiet now, but let’s build it together. 📷 https://t.co/z6TV09yGO9 #RxInfer #BayesianInference #ProbabilisticProgramming

In Active Inference, a lot of time is spent on computing Expected Free Energy. What if we could tweak the generative model such that EFE can be minimised with traditional variational inference methods?

🧠 We hooked LLMs into Bayesian inference in 30 minutes ChatGPT as a native node in message-passing. Working sentiment clustering with uncertainty quantification. 📖 Notebook: https://t.co/KjbKjkm5JV #BayesianInference #LLM

https://t.co/NYt4EizYAO can be powerful for robotics - but requires deep probabilistic programming knowledge. What if there was a simpler way? LLM → RxInfer → Robotics = 𝗖𝘂𝗿𝘀𝗼𝗿 𝗳𝗼𝗿 𝗿𝗼𝗯𝗼𝘁𝗶𝗰𝘀? #RxInfer #Robotics #LLM #CursorForRobotics #AIRobotics

Check out our DeepWiki page - it looks pretty accurate! https://t.co/g5JVOSLRYJ Thanks to @cognition_labs!

🎯 New Contextual Multi-Armed Bandits example in RxInferExamples.jl! Fresh take using message passing inference. No MCMC, no hyperparameters - just principled decision making. https://t.co/DhH1MrL6Wx #BayesianML #JuliaLang #RxInfer

100%.

🚀Behind the scenes at Lazy Dynamics: We are working on Cortex, our future reactive message-passing backend for RxInfer! Designed for fast, reliable, and asynchronous probabilistic inference on edge devices. Ideal for robotics, audio processing, and more. https://t.co/HrnvxT87Dl

That is 🤯

Probabilistic modeling in Julia just got smoother. With RxInfer + GraphPPL, you write models like regular code — loops, functions, submodels — and it handles the inference under the hood. From simple regressions to massive hierarchies. Check it out 👇 https://t.co/HHjUJSqDPZ

LLMs were a leap — but the next gen of AI won’t just generate. It will adapt in real-time. At Lazy Dynamics, we’re building reactive AI with RxInfer: real-time Bayesian inference for uncertain, dynamic environments. The future of AI is reactive. #EdgeAI #Bayesian #RxInfer #AI

Thanks to @LazyDynamics for open-sourcing latent vector autoregressive model for RxInfer! Handles 20 5th-order AR processes, 2000 noisy obs, and 50-step predictions in 3s. 🔗 https://t.co/cf7ONGx0UG #ProbabilisticProgramming #OpenSource #RxInfer

New paper alert! Active Inference (AIF) agents score policy candidates using a cost function called the Expected Free Energy (EFE), which has many desirable features, such as a parameter-free balance between information-seeking and goal-driven behavior. (1/4)