Api
Research papers, repositories, and articles about api
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SynthID Detector: Identify content made with Google's AI tools
Google announces SynthID Detector, a web portal that lets you upload images, audio, video, or text generated with Google AI tools and automatically checks for imperceptible SynthID watermarks, highlighting which parts of the content are likely watermarked. For developers and media teams, it’s a turnkey authenticity check for content produced with models like Gemini, Imagen, Lyria, and Veo, designed to plug into editorial and trust-&-safety workflows. ([blog.google](https://blog.google/technology/ai/google-synthid-ai-content-detector/))
daytona
Daytona is a secure, elastic runtime for executing AI-generated code and agent workflows in isolated sandboxes, with Python and TypeScript SDKs to spin up environments in sub‑100ms and run arbitrary code, processes, or dev tools. It’s quickly becoming a go-to “agent runtime” layer for teams that need safe, persistent, and massively parallel sandboxes (including LangChain’s open-source coding agent), instead of gluing together ad‑hoc Docker or VM setups. ([github.com](https://github.com/daytonaio/daytona?utm_source=openai))
pandas-ai
pandas-ai turns DataFrames and SQL/CSV/Parquet sources into a conversational interface, translating natural-language questions into code or SQL, running them in a (configurable) sandbox, and optionally using RAG and semantic schemas to answer more complex queries. It’s attractive for quickly giving analysts or business users an LLM front-end on top of existing data, though you do need to pay attention to security configurations given its history of prompt-injection/RCE issues that were later mitigated with new settings. ([github.com](https://github.com/sinaptik-ai/pandas-ai?utm_source=openai))
TensorStore for High-Performance, Scalable Array Storage
TensorStore is an open-source C++ and Python library for working with massive n‑dimensional arrays, providing a uniform API over formats like Zarr and N5 and backends like GCS, local filesystems, HTTP, and in‑memory storage, with ACID transactions and async I/O. For ML and scientific developers, it’s a practical way to manage petascale datasets and large model checkpoints (e.g., PaLM) without custom sharding logic, while keeping read/write concurrency and performance under control. ([ai.googleblog.com](https://ai.googleblog.com/2022/09/tensorstore-for-high-performance.html))