OpenAI Announces GPT-5.4-Cyber But You Can’t Get it Just Yet
The question around AI, and I mean the pinnacle of AI, not your regular “write me an email”, is shifting. What used to be “what can it do for me?” has now become “who gets to use it?” We saw this recently with Anthropic’s Claude Mythos Preview – a supposed epitome of AI models that […]
The post Open...
The upstream decision no model, or LLM can fix once you get it wrong
The post Your Chunks Failed Your RAG in Production appeared first on Towards Data Science.
InsightFinder raises $15M to help companies figure out where AI agents go wrong
According to CEO Helen Gu, the biggest problem facing the industry today is not just monitoring and diagnosing where AI models go wrong, it's diagnosing how the entire tech stack operates now that AI is a part of it.
Building My Own Personal AI Assistant: A Chronicle, Part 2
Building a personal AI assistant is rarely a single, monolithic effort. In this piece, I walk through my latest addition: a task breaker module that decomposes complex goals into structured, actionable steps — and why that single component changed how I think about AI-driven productivity.
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Everyone is talking about Claude Code. With millions of weekly downloads and a rapidly expanding feature set, it has quietly become one of the most powerful tools in a developer's arsenal. But most people are barely scratching the surface.
memweave: Zero-Infra AI Agent Memory with Markdown and SQLite — No Vector Database Required
The problem with agent memory today
The post memweave: Zero-Infra AI Agent Memory with Markdown and SQLite — No Vector Database Required appeared first on Towards Data Science.
No Need for Space Gear — Capcom’s ‘PRAGMATA’ Joins GeForce NOW on Launch Day
Head straight for orbit with GeForce NOW — no space helmet required. PRAGMATA, Capcom’s long-awaited sci-fi action adventure, touches down on GeForce NOW the same day it launches worldwide. The futuristic journey through a cold lunar station in the near future can be streamed instantly from the clo...
There’s a fault line running through enterprise AI, and it’s not the one getting the most attention. The public conversation still tracks foundation models and benchmarks — GPT versus Gemini, reasoning scores, and marginal capability gains. But in practice, the more durable advantage is structural: ...
Introduction to Deep Evidential Regression for Uncertainty Quantification
Machine learning models can be confident even when they shouldn't be. This article introduces Deep Evidential Regression (DER), a method that lets neural networks rapidly express what they don't know.
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The updated Codex app for macOS and Windows adds computer use, in-app browsing, image generation, memory, and plugins to accelerate developer workflows.
UCSD and Together AI Research Introduces Parcae: A Stable Architecture for Looped Language Models That Achieves the Quality of a Transformer Twice the Size
The dominant recipe for building better language models has not changed much since the Chinchilla era: spend more FLOPs, add more parameters, train on more tokens. But as inference deployments consume an ever-growing share of compute and model deployments push toward the edge, researchers are increa...
A Coding Implementation to Build Multi-Agent AI Systems with SmolAgents Using Code Execution, Tool Calling, and Dynamic Orchestration
In this tutorial, we build an advanced, production-ready agentic system using SmolAgents and demonstrate how modern, lightweight AI agents can reason, execute code, dynamically manage tools, and collaborate across multiple agents. We start by installing dependencies and configuring a powerful yet ef...
Numerical Instability and Chaos: Quantifying the Unpredictability of Large Language Models
arXiv:2604.13206v1 Announce Type: new
Abstract: As Large Language Models (LLMs) are increasingly integrated into agentic workflows, their unpredictability stemming from numerical instability has emerged as a critical reliability issue. While recent studies have demonstrated the significant downstre...
Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach
arXiv:2604.13283v1 Announce Type: new
Abstract: Earth Observation (EO) satellite scheduling (deciding which imaging tasks to perform and when) is a well-studied combinatorial optimization problem. Existing methods typically assume that the operational constraint model is fully specified in advance....
WebXSkill: Skill Learning for Autonomous Web Agents
arXiv:2604.13318v1 Announce Type: new
Abstract: Autonomous web agents powered by large language models (LLMs) have shown promise in completing complex browser tasks, yet they still struggle with long-horizon workflows. A key bottleneck is the grounding gap in existing skill formulations: textual wo...
Sparse Goodness: How Selective Measurement Transforms Forward-Forward Learning
arXiv:2604.13081v1 Announce Type: new
Abstract: The Forward-Forward (FF) algorithm is a biologically plausible alternative to backpropagation that trains neural networks layer by layer using a local goodness function to distinguish positive from negative data. Since its introduction, sum-of-squares...
The Long Delay to Arithmetic Generalization: When Learned Representations Outrun Behavior
arXiv:2604.13082v1 Announce Type: new
Abstract: Grokking in transformers trained on algorithmic tasks is characterized by a long delay between training-set fit and abrupt generalization, but the source of that delay remains poorly understood. In encoder-decoder arithmetic models, we argue that this...
Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments
arXiv:2604.13085v1 Announce Type: new
Abstract: Autonomous AI agents operating in dynamic environments face a persistent challenge: acquiring new capabilities without erasing prior knowledge. We present Adaptive Memory Crystallization (AMC), a memory architecture for progressive experience consolid...