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.
The post B...
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.
The post Introduction to Deep Evidential Regression for Uncertainty Quantification appeared first ...
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...
SciFi: A Safe, Lightweight, User-Friendly, and Fully Autonomous Agentic AI Workflow for Scientific Applications
arXiv:2604.13180v1 Announce Type: new
Abstract: Recent advances in agentic AI have enabled increasingly autonomous workflows, but existing systems still face substantial challenges in achieving reliable deployment in real-world scientific research. In this work, we present a safe, lightweight, and ...
Exploration and Exploitation Errors Are Measurable for Language Model Agents
arXiv:2604.13151v1 Announce Type: new
Abstract: Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. A core requirement in these settings is the ability to both explore the problem space and exploit acquired knowledge effective...
Design Conditions for Intra-Group Learning of Sequence-Level Rewards: Token Gradient Cancellation
arXiv:2604.13088v1 Announce Type: new
Abstract: In sparse termination rewards, intra-group comparisons have become the dominant paradigm for fine-tuning reasoning models via reinforcement learning. However, long-term training often leads to issues like ineffective update accumulation (learning tax)...
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...
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...
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...
Accelerating the cyber defense ecosystem that protects us all
Leading security firms and enterprises join OpenAI’s Trusted Access for Cyber, using GPT-5.4-Cyber and $10M in API grants to strengthen global cyber defense.
AI Weekly Issue #484: Your AI chats can be used against you in court
Quick Hits
Chery sells humanoid robot to consumers for $42,000: The Chinese automaker ships the first mass-market humanoid. A car company is now a robotics company. The price will halve by next year.
Claude Code Routines launches: Hit 686 points on Hacker News. Automate repetitive dev workflows wit...