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DIAP: A Decentralized Agent Identity Protocol with Zero-Knowledge Proofs and a Hybrid P2P Stack

Liu, Yuanjie, Xing, Wenpeng, Zhou, Ye, Chang, Gaowei, Lin, Changting, Han, Meng

arXiv.org Artificial Intelligence

The absence of a fully decentralized, verifiable, and privacy-preserving communication protocol for autonomous agents remains a core challenge in decentralized computing. Existing systems often rely on centralized intermediaries, which reintroduce trust bottlenecks, or lack decentralized identity-resolution mechanisms, limiting persistence and cross-network interoperability. We propose the Decentralized Interstellar Agent Protocol (DIAP), a novel framework for agent identity and communication that enables persistent, verifiable, and trustless interoperability in fully decentralized environments. DIAP binds an agent's identity to an immutable IPFS or IPNS content identifier and uses zero-knowledge proofs (ZKP) to dynamically and statelessly prove ownership, removing the need for record updates. We present a Rust SDK that integrates Noir (for zero-knowledge proofs), DID-Key, IPFS, and a hybrid peer-to-peer stack combining Libp2p GossipSub for discovery and Iroh for high-performance, QUIC based data exchange. DIAP introduces a zero-dependency ZKP deployment model through a universal proof manager and compile-time build script that embeds a precompiled Noir circuit, eliminating the need for external ZKP toolchains. This enables instant, verifiable, and privacy-preserving identity proofs. This work establishes a practical, high-performance foundation for next-generation autonomous agent ecosystems and agent-to-agent (A to A) economies.


Democratizing Tabular Data Access with an Open$\unicode{x2013}$Source Synthetic$\unicode{x2013}$Data SDK

Krchova, Ivona, Vieyra, Mariana Vargas, Scriminaci, Mario, Sidorenko, Andrey

arXiv.org Artificial Intelligence

Abstract--Machine learning development critically depends on access to high-quality data. However, increasing restrictions due to privacy, proprietary interests, and ethical concerns have created significant barriers to data accessibility. Synthetic data offers a viable solution by enabling safe, broad data usage without compromising sensitive information. This paper presents the MOSTL Y AI Synthetic Data Software Development Kit (SDK), an open-source toolkit designed specifically for synthesizing high-quality tabular data. The SDK integrates robust features such as differential privacy guarantees, fairness-aware data generation, and automated quality assurance into a flexible and accessible Python interface. Leveraging the T abularARGN autoregressive framework, the SDK supports diverse data types and complex multi-table and sequential datasets, delivering competitive performance with notable improvements in speed and usability. Currently deployed both as a cloud service and locally installable software, the SDK has seen rapid adoption, highlighting its practicality in addressing real-world data bottlenecks and promoting widespread data democratization. HE development of Machine Learning applications requires broad access to training data. This necessity has become more critical in recent years with the advent of Deep Learning, which requires large-scale datasets to effectively train models.


The OpenHands Software Agent SDK: A Composable and Extensible Foundation for Production Agents

Wang, Xingyao, Rosenberg, Simon, Michelini, Juan, Smith, Calvin, Tran, Hoang, Nyst, Engel, Malhotra, Rohit, Zhou, Xuhui, Chen, Valerie, Brennan, Robert, Neubig, Graham

arXiv.org Artificial Intelligence

Agents are now used widely in the process of software development, but building production-ready software engineering agents is a complex task. Deploying software agents effectively requires flexibility in implementation and experimentation, reliable and secure execution, and interfaces for users to interact with agents. In this paper, we present the OpenHands Software Agent SDK, a toolkit for implementing software development agents that satisfy these desiderata. This toolkit is a complete architectural redesign of the agent components of the popular OpenHands framework for software development agents, which has 64k+ GitHub stars. To achieve flexibility, we design a simple interface for implementing agents that requires only a few lines of code in the default case, but is easily extensible to more complex, full-featured agents with features such as custom tools, memory management, and more. For security and reliability, it delivers seamless local-to-remote execution portability, integrated REST/WebSocket services. For interaction with human users, it can connect directly to a variety of interfaces, such as visual workspaces (VS Code, VNC, browser), command-line interfaces, and APIs. Compared with existing SDKs from OpenAI, Claude, and Google, OpenHands uniquely integrates native sandboxed execution, lifecycle control, model-agnostic multi-LLM routing, and built-in security analysis. Empirical results on SWE-Bench Verified and GAIA benchmarks demonstrate strong performance. Put together, these elements allow the OpenHands Software Agent SDK to provide a practical foundation for prototyping, unlocking new classes of custom applications, and reliably deploying agents at scale.


aoip.ai: An Open-Source P2P SDK

Konan, Joseph, Agnihotri, Shikhar, Hsieh, Chia-Chun

arXiv.org Artificial Intelligence

This white paper introduces aoip.ai, a groundbreaking open-source SDK incorporating peer-to-peer technology and advanced AI integration to transform VoIP and IoT applications. It addresses key market challenges by enhancing data security, elevating communication quality, and providing greater flexibility for developers and users. Developed in collaboration with Carnegie Mellon University, aoip.ai sets a new standard for decentralized and democratized communication solutions.


feather -- a Python SDK to share and deploy models

Vedd, Nihir, Riga, Paul

arXiv.org Artificial Intelligence

At its core, feather was a tool that allowed model developers to build shareable user interfaces for their models in under 20 lines of code. Using the Python SDK, developers specified visual components that users would interact with. (e.g. a FileUpload component to allow users to upload a file). Our service then provided 1) a URL that allowed others to access and use the model visually via a user interface; 2) an API endpoint to allow programmatic requests to a model. In this paper, we discuss feather's motivations and the value we intended to offer AI researchers and developers. For example, the SDK can support multi-step models and can be extended to run automatic evaluation against held out datasets. We additionally provide comprehensive technical and implementation details. N.B. feather is presently a dormant project. We have open sourced our code for research purposes: https://github.com/feather-ai/


Speech-to-Text using JavaScript

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Learn how to automatically transcribe speech to text using Picovoice Leopard Speech-to-Text Web SDK. The SDK runs on all modern browsers. If you are looking for a speech-to-text engine in Node.js, you might want to check the Speech-to-Text using Node.js The SpeechRecognition interface of Web Speech API is freely available. SpeechRecognition is not yet supported across all browsers and has (undocumented) usage limitations.



Break through language barriers with Amazon Transcribe, Amazon Translate, and Amazon Polly

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Imagine a surgeon taking video calls with patients across the globe without the need of a human translator. What if a fledgling startup could easily expand their product across borders and into new geographical markets by offering fluid, accurate, multilingual customer support and sales, all without the need of a live human translator? What happens to your business when you're no longer bound by language? It's common today to have virtual meetings with international teams and customers that speak many different languages. Whether they're internal or external meetings, meaning often gets lost in complex discussions and you may encounter language barriers that prevent you from being as effective as you could be.


LibLab raises $42M to create custom SDKs – TechCrunch

#artificialintelligence

LibLab, which bills itself as an "SDK-as-a-service" platform for engineers, today announced that it raised $42 million in Series A funding from Insight Partners with participation from Zeev Ventures, Stepstone, Sheva, and Rainfall. Co-founder and CEO Sagiv Ofek said that the new capital will be put toward building out the company's core service while expanding LibLab's private beta. Companies release software developer kits, or SDKs, to enable developers to use their API-based services. But creating an SDK can be arduous work. Each language and operating system has sets of requirements, and there's the potential that security vulnerabilities and bugs crop up in the course of development.


Onfido expands biometrics and AI fraud threat mitigation platform capabilities

#artificialintelligence

Onfido has added four new products to its biometrics and artificial intelligence (AI)-powered identity verification and authentication service known as the'Real Identity Platform,' promising superior results and performance. The update is comprised of the Onfido Verification Suite, Onfido Studio, Onfido Smart Capture, and Onfido Atlas AI. The Verification Suite is a curated library of trusted data sources and identity verification services to offer a user experience tailored around specific fraud and regulatory use cases, compliance requirements, global needs, risk appetite, and business objectives. It is integrated into Onfido's document and biometric identity verification solution and carries trusted data verification sources like a U.S. social security number and sanctions watchlist, and fraud detection verification through geolocation and phone verification among other options. Studio is described as an orchestration software built around a no-code platform and analytics tools for businesses.