Tech News
Agent memory as a file format
The article proposes treating agent memory as a file format, where agents generate and manage their own documents instead of relying on traditional memory systems.
Introducing wrapture
New from Graham Dumpleton (of wrapt, mod_wsgi, and New Relic's Python agent fame), who describes Wrapture as taking the monkeypatching ideas from wrapt and extending them to apply to testing and tracing at the same time.
Connect an AgentCore Runtime hosted MCP server to Amazon Quick
In this post, you will learn how to deploy and host your MCP server in AgentCore Runtime and integrate it with Amazon Quick, along with the prerequisites.
Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science
Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next.
vllm-project/vllm v0.28.0
v0.28.0 Highlights This release features 584 commits from 270 contributors (76 new)!
GitHub Repos
cobusgreyling/loop-engineering
Practical patterns, starters & CLI tools for loop engineering with AI coding agents.
# TypeScript# agentic ai# ai agentsOpenHands/OpenHands
🙌 OpenHands: AI-Driven Development
# TypeScript# agent# artificial intelligenceopen-multi-agent/open-multi-agent
TypeScript AI agent orchestration framework with dynamic workflows.
# TypeScript# agent framework# agent orchestrationResearch Papers
Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusions
This paper tests whether making AI benchmark evaluations cheaper (via batching, quantization, or using smaller subsets) changes the conclusions about model bias and accuracy. They find that some efficiency tricks preserve conclusions, but others, like INT4 quantization, can...
Scaling Large Reasoning Models beyond Human Supervision: A Path toward Superintelligence
This paper surveys how large reasoning models can keep improving when human supervision is reduced, proposing a five-level ladder (L0-L4) to categorize the shift from human-curated rewards and tasks to fully autonomous learning systems, while also discussing risks like reward...
Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions
This paper tackles a tricky problem in causal representation learning: figuring out cause-and-effect relationships when different clients only see parts of the puzzle. It introduces a method called Jigsaw-CRL that pieces together these fragments to recover the global causal...