Introduction If you follow the message streaming space at all, you know it is dominated by a few big names. Apache Kafka has been the 800-pound gorilla since LinkedIn open-sourced it back in 2011. I’ve covered streaming-adjacent projects a few times in this series, like WarpStream, Apache Paimon, and Proton. So when I started seeing chatter about a message streaming platform written from scratch in Rust that was processing millions of messages per second, I had to take […]

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Need advice!!! I had a very nice discussion with the founder of a well funded startup company. The problem mentioned to me got me excited and over the weekend I spend time just drafting the problem into the MDP as they would like to move to pure RL. The following week I had an interview with a guy who works as a consultant at the same company and the interview was okay. I gave good answers but got […]

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arXiv:2603.17074v1 Announce Type: new Abstract: We present PRISM, a comprehensive empirical study of mid-training design choices for large language models. Through controlled experiments across seven base models spanning four families (Granite, LLaMA, Mistral, Nemotron-H), two architecture types (dense Transformer and attention-Mamba hybrid), and scales from 3B to 24B parameters, we show that mid-training on approximately 27B high-quality tokens yields consistent gains of +15 to +40 points on math, +5 to +12 points on code, and +6 to +13 […]

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Google Antigravity Changes the Way Everyone Operates with Computers :::warning The world we knew—where you were the bottleneck, the typist, the clicker—is gone. ::: :::tip Imagine a reality where the laws of digital physics no longer apply. ::: A world where your computer isn’t just a reactive tool waiting for a keystroke, but a proactive partner that defies the gravity of mundane tasks. :::tip Welcome to Google Antigravity. ::: This isn’t just another chatbot wrapper or a fancy […]

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arXiv:2404.10899v2 Announce Type: replace-cross Abstract: Classic Bayesian methods with complex models are frequently infeasible due to an intractable likelihood. Simulation-based inference methods, such as Approximate Bayesian Computing (ABC), calculate posteriors without accessing a likelihood function by leveraging the fact that data can be quickly simulated from the model, but converge slowly and/or poorly in high-dimensional settings. In this paper, we propose a framework for Bayesian posterior estimation by mapping data to posteriors of parameters using a neural network […]

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Table of Links Abstract and 1. Introduction Related Works 2.1 Traditional Index Selection Approaches 2.2 RL-based Index Selection Approaches Index Selection Problem Methodology 4.1 Formulation of the DRL Problem 4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection System Framework of IA2 5.1 Preprocessing Phase 5.2 RL Training and Application Phase Experiments 6.1 Experimental Setting 6.2 Experimental Results 6.3 End-to-End Performance Comparison 6.4 Key Insights Conclusion and Future Work, and References 5.1 Preprocessing Phase The preprocessing phase is […]

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arXiv:2603.03388v2 Announce Type: new Abstract: Recent neural solvers have achieved strong performance on vehicle routing problems (VRPs), yet they mainly assume symmetric Euclidean distances, restricting applicability to real-world scenarios. A core challenge is encoding the relational features in asymmetric distance matrices of VRPs. Early attempts directly encoded these matrices but often failed to produce compact embeddings and generalized poorly at scale. In this paper, we propose RADAR, a scalable neural framework that augments existing neural VRP solvers with […]

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arXiv:2602.17721v1 Announce Type: new Abstract: This commentary explores how the platform economy shapes labour market responses during times of crisis, with a focus on gendered experiences. Drawing on cases of economic crisis, natural disasters, and refugee displacement, it examines how digital labour platforms offer flexible work opportunities while also reinforcing existing inequalities. Women face distinct constraints (such as caregiving responsibilities, limited mobility, and economic insecurity) that hinder their employment opportunities and earnings potential. These constraints are more pronounced […]

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arXiv:2601.17041v1 Announce Type: new Abstract: Arabic Sign Language (ArSL) is an essential communication method for individuals in the Deaf and Hard-of-Hearing community. However, existing recognition systems face significant challenges due to their reliance on single sensor approaches like Leap Motion or RGB cameras. These systems struggle with limitations such as inadequate tracking of complex hand orientations and imprecise recognition of 3D hand movements. This research paper aims to investigate the potential of a multimodal approach that combines Leap […]

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