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Si tienes garaje y haces menos de 300 kilómetros al día, el eléctrico ya gana. Sin discusión.

Si tienes garaje y haces menos de 300 kilómetros al día, el eléctrico ya gana. Sin discusión.

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Hubo un tiempo en que comprar un coche eléctrico podía considerarse una apuesta, una declaración de intenciones o incluso una extravagancia tecnológica. Ese tiempo ha pasado. En 2025 se vendieron más de 20 millones de coches eléctricos en el mundo, más del 25% de todas las ventas de automóviles nuevos, una cifra que ya no describe una rareza, sino una transición industrial en toda regla. Quien siga hablando del vehículo eléctrico como si fuese una curiosidad está discutiendo […]

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Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning

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Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entropy-based rewards. However, their performance remains far from satisfactory due to the substantial annotation cost and issues such as model collapse or reward hacking. To address these issues, we introduce a new perspective inspired by cognitive learning theory and propose a novel approach called EasyRL. The core of EasyRL is to simulate the human cognitive acquisition curve by integrating […]

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A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions

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Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, reinforcement learning for LLMs faces substantial data scarcity challenges, including the limited availability of high-quality external supervision and the constrained volume of model-generated experience. These limitations make data-efficient reinforcement learning a critical research direction. In this survey, we present the first systematic review of reinforcement learning for LLMs under data scarcity. We propose a bottom-up hierarchical framework […]

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Building a Production-Ready RAG System with Incremental Indexing

Building a Production-Ready RAG System with Incremental Indexing

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A comprehensive guide to building a Retrieval-Augmented Generation (RAG) system that efficiently manages document updates, deletions, and additions without re-indexing everything. Source: Gemini Introduction Retrieval-Augmented Generation (RAG) has become the go-to architecture for building AI applications that need to answer questions based on custom knowledge bases. However, most RAG tutorials skip over a critical production concern: how do you efficiently update your knowledge base without re-indexing everything? In this article, I’ll walk you through building a RAG system […]

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How to Design Offline Eval Gates That Actually Catch Regressions Before Release

How to Design Offline Eval Gates That Actually Catch Regressions Before Release

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A practical guide to implementing offline release gates, with a reference implementation. Article 2 in a series on eval loops for production LLM systems. A release gate is not a benchmark report. It is a decision system. Most teams I’ve seen treat it like a scoreboard instead. They run a dataset, watch one number move, and call that release discipline. The problem shows up later, in production, when a candidate that looked flat on the headline metric turns out to […]

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The TechBeat: The Trap of “Vibe Coding” and the Rise of Engineering as a Service (4/19/2026)

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## 12 OpenCode Skills Every Dev Team Should Steal By @bezgin [ 4 Min read ] A practical guide to coding agent skills and commands from OpenCode and beyond, with reusable patterns for debugging, planning, review, and repo memory. Read More. Pretext Does What CSS […]

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PAC-Bayes Bounds for Gibbs Posteriors via Singular Learning Theory

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We derive explicit non-asymptotic PAC-Bayes generalization bounds for Gibbs posteriors, that is, data-dependent distributions over model parameters obtained by exponentially tilting a prior with the empirical risk. Unlike classical worst-case complexity bounds based on uniform laws of large numbers, which require explicit control of the model space in terms of metric entropy (integrals), our analysis yields posterior-averaged risk bounds that can be applied to overparameterized models and adapt to the data structure and the intrinsic model complexity. The […]

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Changes in the system prompt between Claude Opus 4.6 and 4.7

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Anthropic are the only major AI lab to publish the system prompts for their user-facing chat systems. Their system prompt archive now dates all the way back to Claude 3 in July 2024 and it’s always interesting to see how the system prompt evolves as they publish new models. Opus 4.7 shipped the other day (April 16, 2026) with a Claude.ai system prompt update since Opus 4.6 (February 5, 2026). I had Claude Code take the Markdown version […]

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Trials and tribulations fine-tuning & deploying Gemma-4 [P]

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Hey all, Our ML team spent some time this week getting training and deployments working for Gemma-4, and wanted to document all the things we ran into along the way. PEFT doesn’t recognize Gemma 4’s custom layers. Google wrapped vision/audio projections in a new ClippableLinear class that doesn’t inherit from nn.Linear, so PEFT refuses to attach LoRA, even for text-only fine-tuning. Fix: unwrap the wrappers after loading weights but before calling PEFT. SFTTrainer killed training silently. TRL hardcodes […]

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A Coding Guide for Property-Based Testing Using Hypothesis with Stateful, Differential, and Metamorphic Test Design

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In this tutorial, we explore property-based testing using Hypothesis and build a rigorous testing pipeline that goes far beyond traditional unit testing. We implement invariants, differential testing, metamorphic testing, targeted exploration, and stateful testing to validate both functional correctness and behavioral guarantees of our systems. Instead of manually crafting edge cases, we let Hypothesis generate structured inputs, shrink failures to minimal counterexamples, and systematically uncover hidden bugs. Also, we demonstrate how modern testing practices can be integrated directly […]

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