New capabilities for building agents on the Anthropic API | Claude

Claude now offers code execution, MCP server connections, file storage, and extended prompt caching through the API—giving developers powerful tools to build agents that analyze data, connect to external systems, and maintain context for longer periods of time.

LLM Agents Are the Antidote to Walled Gardens

While the Internet's core infrastructure was designed to be open and universal, today's application layer is dominated by closed, proprietary platforms. Open and interoperable APIs require significant investment, and market leaders have little incentive to enable data exchange that could erode their user lock-in. We argue that LLM-based agents fundamentally disrupt this status quo. Agents can automatically translate between data formats and interact with interfaces designed for humans: this makes interoperability dramatically cheaper and effectively unavoidable. We name this shift universal interoperability: the ability for any two digital services to exchange data seamlessly using AI-mediated adapters. Universal interoperability undermines monopolistic behaviours and promotes data portability. However, it can also lead to new security risks and technical debt. Our position is that the ML community should embrace this development while building the appropriate frameworks to mitigate the downsides. By acting now, we can harness AI to restore user freedom and competitive markets without sacrificing security.

PDS as-a-service

Decouple data from apps.

Spinning Up Your Own Corner of the ATmosphere - Mostly Protocols

How to set up your own PDS from scratch - server, domain, email, migration, and all the little details that actually matter when you're trying to run your own piece of the ATmosphere.

Environments in Managed Agents  |  Gemini API  |  Google AI for Developers

Learn about persistent execution environments for managed agents in the Gemini API, including pre-installed software, snapshots, and configuration options.

Replacing Middle Management with APIs by Peter Reinhardt

There’s a trend in Silicon Valley startups to create a software layer in industries that were traditionally pure human services. Uber and Lyft have created software layers in the taxi industry, 99designs Tasks in the visual design industry, Homejoy in the cleaning industry, and so on. These new software services employ armies of human workers, optimizing their output, productivity and quality while driving price down.

3W for In-Browser AI: WebLLM + WASM + WebWorkers

What if AI agents could run entirely in your browser? Not just the UI part—the actual model inference, agent logic, and response generation, all happening locally without a single API call.

Building Managed Agents  |  Gemini API  |  Google AI for Developers

How to create and use custom agents with managed agents in the Gemini API.

Supermemory

The memory layer for AI agents. Context engineering platform powering enterprise APIs, developer plugins, and a personal app that remembers everything.

DREAM — Dynamic Retention Episodic Architecture for Memory

Modern AI systems lack persistent, user-specific episodic memory. Existing approaches rely on short-term context windows, shallow preference storage, or static conversation logs that do not scale and cannot preserve meaningful long-term continuity. This paper introduces DREAM (Dynamic Retention Episodic Architecture for Memory), a scalable, opt-in, episodic memory framework designed to work with current LLM and agent architectures. DREAM integrates episodic summarization, user-controlled opt-in memory, semantic retrieval via per-user vector indexes, an adaptive retention mechanism that expands TTL based on user engagement, and horizontal sharding of orchestrators and storage for large-scale deployments. The paper details the architecture, components, data flows, and implementation examples, and argues that DREAM provides a practical path toward AI systems capable of consistent, privacy-aligned long-term reasoning. This project has been extended with a conceptual analysis and simulation of a "DREAM-as-a-Support" (DaaS) hybrid layer. This extension demonstrates DREAM's architectural extensibility, reframing it from a standalone framework into a foundational platform component. The DaaS model provides core memory governance such as adaptive retention (ARM) and user-centric opt-in as an on-demand service to complementary cognitive systems, validating the original four-pillar design through a scalable, internal API. Reference Implementation A reference implementation of the DREAM architecture is available as an open-source Python framework:Official Reference Implementation This implementation is intended for experimentation and architectural validation and does not represent a production-ready system DREAM Architecture — Official GitHub Repository

Running a PDS in Kubernetes for fun & profit - at://hayden.moe

The trials and tribulations of running a PDS on self-hosted Kubernetes with the Gateway API and other such fun things

Why Executive Interest In APIs Is Booming

A decade ago, API awareness was limited, with evangelists educating a mostly unaware C-suite. Today, APIs are integral to business, widely embraced by executives and developers alike, reflecting a significant shift in digital transformation strategies.

Introducing Gemini Enterprise Agent Platform | Google Cloud Blog

Gemini Enterprise Agent Platform is our new platform to build, scale, govern, and optimize agents. It integrates the model selection, model building, and agent building capabilities of Vertex AI, with new features for agent integration, DevOps, and orchestration, and security.