Your coding agent bill doubled. Here’s how to fix it.
Learn why coding agent bills spiral out of control — and how to trace, compare, and govern spend across Claude Code, Cursor, Copilot, and more in one place. Context & related coverage →
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Embabel Agent Framework Reaches 1.0
Embabel has reached its 1.0 release, providing a framework for AI agents on Java It allows Java and Kotlin developers to define agents as typed domain objects. Built on Spring AI, Embabel supports multiple model provi... Context & related coverage →
Open letters about AI development
Open letters about AI development I wrote this summary of the past few weeks of open letters as a section of my sponsors-only newsletter but I've decided to share it here as well. Open Weights and American AI Leadersh... Context & related coverage →
Alibaba Releases AI Model With Performance Rivaling Anthropic - Bloomberg.com
Alibaba Releases AI Model With Performance Rivaling Anthropic Bloomberg.com Context & related coverage →
How LangChain Built an Agent-First Data Stack
Learn how LangChain used Hex, dbt, semantic models, and observability to build a trusted data agent and scale self-service analysis by 40x. Context & related coverage →
condense-json 1.0
Release: condense-json 1.0 I'm trying to get braver at releasing 1.0 versions. This little library is a year and a half old now - I've applied some sensible and non-disruptive fixes and shipped the big 1.0 for it. Her... Context & related coverage →
ExtractBench: A Benchmark for Schema-Guided Enterprise Document Extraction
Enterprise workflows increasingly rely on agents for \emph{schema-guided extraction}: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evide... Context & related coverage →
Studying quantization trade-offs for efficient inference deployment in machine translation
Deploying large language models in realistic server environments poses challenges, as the system needs to provide high-quality responses with low latency. Quantization is a common approach to reduce the memory footpri... Context & related coverage →
MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification
Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end-to-end network updates. Although PEF... Context & related coverage →
SERUM: State Extraction and Refinement for User Modeling
Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow. However, building these models from raw, unstructured screen activity remains an open challenge... Context & related coverage →
Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World Centric Foundation GUI Agents
GUI agents have the potential to become a general purpose executor over existing digital devices. To advance them toward real-world use, we envision agents that operate reliably on real devices, execute workflows acro... Context & related coverage →
Building abundant intelligence
A full-stack approach to making advanced AI more capable, more affordable, and more widely useful. Context & related coverage →