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Sonar

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Sonar (sound navigation and ranging or sonic navigation and ranging) is a technique that uses sound propagation (usually underwater, as in submarine navigation) to navigate, measure distances (ranging), communicate with or detect objects on or under the surface of the water, such as other vessels. "Sonar" can refer to one of two types of technology: passive sonar means listening for the sound made by vessels; active sonar means emitting pulses of sounds and listening for echoes. Sonar may be use

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AI-native API lifecycle: Postman Git workflows and LLM-ready specs

Postman introduced AI-native, Git-based API workflows and a central API catalog while LLMs begin to consume and co-author API specs, pushing teams to make documentation machine-optimized and governed. Postman’s latest platform update brings Agent Mode into Git, where it understands collections, definitions, and underlying code to cut manual work in debugging, test writing, and keeping collections in sync, alongside native Git workflows for specs, tests, mocks, and environments and a new enterprise-wide API Catalog for visibility and ownership tracking ([InfoWorld](https://www.infoworld.com/article/4140102/postman-api-platform-adds-ai-native-git-based-workflows.html)). It can also coordinate multi-step changes using inputs from MCP servers tied to Atlassian, Amazon CloudWatch, GitHub, Linear, Sentry, and Webflow, and publish docs, sandboxes, and SDKs in one place. As agentic access to APIs grows, specs must be unambiguous for machines as well as humans, emphasizing well-structured descriptions, precise natural language, sample requests/responses, and consistent versioning to avoid drift and misuse ([Nordic APIs](https://nordicapis.com/how-llms-are-changing-the-way-we-build-api-specifications/)). This shifts API design from merely machine-readable to truly machine-optimized. For teams building research copilots or smarter portals, Perplexity’s APIs offer web-grounded answers (Sonar), agentic research workflows, and ranked search that can backstop doc Q&A, discovery, and RAG without maintaining your own crawl pipeline ([DataStudios overview](https://www.datastudios.org/post/perplexity-ai-api-access-and-developer-use-cases-overview-platform-structure-key-capabilities-and)).

calendar_today 2026-03-03
postman postman-agent-mode postman-api-catalog openapi atlassian

Inside Perplexity’s Model Routing and Citation Stack

Perplexity’s approach combines model routing, retrieval orchestration, and grounded generation with citations to deliver fast, verifiable answers. A recent architecture deep dive details how Perplexity blends its proprietary Sonar models with partner LLMs (e.g., GPT-4, Claude, Gemini) and routes queries via an automatic “Best” mode or explicit model selection for Pro users, optimizing for speed, reasoning depth, and output style while keeping the experience seamless for most users ([read the explainer](https://www.datastudios.org/post/perplexity-ai-models-explained-and-how-answers-are-generated-architecture-retrieval-model-selecti)). The retrieval pipeline ranks evidence and tightly links generation to citations, yielding traceable responses and real-time relevance—an effective blueprint for RAG at scale that balances latency, cost, and quality while improving user trust through sourced outputs ([details here](https://www.datastudios.org/post/perplexity-ai-models-explained-and-how-answers-are-generated-architecture-retrieval-model-selecti)).

calendar_today 2026-02-24
perplexity sonar gpt-4 claude gemini