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Stripe built a company-wide AI agent in one week, used weekly by 83% of employees
Stripe has open-sourced the architecture of its company-wide AI agent Kai: it was built by one engineer in a week. Instead of preloading all 500+ internal tools and 1,000+ skills, it dynamically selects only what it needs on demand. The core lies in two key designs: skill selection and code sandbox isolation. However, it has a known limitation: output quality starts to decline when the number of skills exceeds 150.
Let AI Decide What to Remember: A Paper That Integrates Memory Management Into Agent Policy
Most AI agent architectures rely on static rules for memory management, but a new paper proposes directly training memory operations as an integrated part of a model's policy. AgeMem enables agents to autonomously invoke tools like ADD, UPDATE, and SUMMARIZE, simultaneously improving accuracy and reducing token overhead across 5 benchmarks.
Finally, a headless UI for the Vue ecosystem that doesn’t force React’s conventions on us
Akaza UI has released v0.0.6-alpha, a headless UI library built from the ground up for Vue 3. It doesn’t wrap React components under the hood, replaces the subcomponent tree pattern with slots and single-file component templates, and leverages v-model and named slots to their full potential.
Stop Building Agents as Linear Chains: Graph Engineering Is the Correct Approach to Running Thousands of Agents in Parallel
The single loop is doomed to fail: it only fixates on its own metrics and ends up fooling itself. Combining Andrew Ng's newly released free course with Codila's 5-step practical guide, you can upgrade your Agents from linear queues to a mutually supervising graph network. After reading this, you'll be able to get your own implementation up and running.
Supavec: Open-Source RAG Service That Deploys Vector Search in 5 Minutes, With a Built-In Scraping Tool
As an open-source alternative to Carbon.ai, Supavec offers a fully-featured RAG service that supports any data source and can be deployed in just 5 minutes. It comes with the matching DeepScrape web scraping tool to solve common data collection challenges.
Stop Dumping Garbage Into Vector Databases: The Right Way to Handle AI Memory
The A-MEM framework developed by the Rutgers team transforms AI memory from a fixed-slot database into a self-evolving network of notes. It delivers significant performance improvements and cuts token costs by a staggering 93%. More importantly, research from four leading labs reveals a counterintuitive truth: the core problem with AI memory isn't that it forgets—it's that it never actively forgets.
The Open-Source Project Popularized by Karpathy: Give Claude Permanent Memory, and It Becomes More Than Just a Chat Window
Your ideas are scattered all over the place. Every time you start a new chat, you have to explain everything from scratch. The LLM Wiki concept proposed by Andrej Karpathy is now being brought to life by an open-source project called claude-obsidian. It lets Claude Code manage your Obsidian vault, automatically organizing content, creating links, and flagging contradictions. All files are stored locally as plain text.
Coding Agents Stay Static After Deployment? This Paper Says: It’s Time to Learn to Evolve
Most coding agents stop learning after they are deployed. alphaXiv recommends a survey from researchers at Nanjing-based universities that reframes coding agents as systems that can continuously evolve from their own coding history—compiler errors, runtime traces, code reviews, and failed patches can all serve as learning signals.
When Agents Learn to Graph: A 14-Step Guide to Graph Engineering
An OpenAI engineer revealed that 85% of internal teams run hundreds of Agents using Codex, with graph engineering at its core. This article breaks down the 14-step process to transform linear Agents into parallel graphs—where nodes execute tasks and edges pass data, delivering lower costs and faster speeds.
Containerized, Sequenced Agents with Personal Gateways: Why You’ll Never Go Back Once You Try It
Hrishi shared a take on X: once you experience the combination of containerized, sequenced agents and personal gateways, it’s almost impossible to go back to how you worked before. The comment section was flooded with curiosity and confusion—what exactly is this all about?
