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I built a live dataset of clinical trials worldwide using Parallel Web Systems If you know me you know I care a lot about the healthcare space and in particular frontier medical research. Parallel Web Systems released native connectors with Clinical Trials.gov, the NPI registry & pubmed a few weeks ago. 🩺 With those connectors, I built out this app that watches the competitive landscape within key indications (like MASH, Alzheimer's and Alopecia Areata). Every piece of data is generated directly from the Parallel Task API, Monitor API (to update it daily) or our amazing data connectors! Github + link in comments, would love to hear feedback :)

Introducing the Parallel Decisions API: the first web-grounded decisions engine. Think of it as a hybrid search engine and decision model: Questions in, typed answers with citations out. System One models like Jev (a.k.a. decision models) can answer in an instant, but they lack the recency of current events, just like LLMs. The Decisions API combines the speed, efficiency, and utility of this exciting new class of models with Parallel's best-in-class web search. It's live today at $0.001 per request ($1 CPM). Get 5,000 free monthly requests: https://lnkd.in/eWeabstU

With Parallel Search, agents stay grounded in fresh, high-quality, and token-efficient data from the open web.

Ultra fast agents need ultra fast web search. Parallel Search Turbo delivers 200ms responses at $1 per 1,000 requests.

Ultra fast agents need ultra fast web search. Parallel Search Turbo delivers 200ms responses at $1 per 1,000 requests.

For the last decade, infrastructure was one of the most reliable bets in software, for the people who built careers in it and for the investors who funded it. The 34 companies that defined that era, from Snowflake to Datadog to MongoDB, are worth roughly $800B today. Every one of them made a company's internal data better for humans to use. The new generation is turning the world's external knowledge into infrastructure that machines consume. Most of these companies are barely five years old, and training, reasoning and inference are already worth about $2.6T combine . This stack is increasingly used to build agents. These agent are only as good as what they can retrieve. The model brings what it learned in the past during training, and everything else has to come from the live web. That's where Parallel Web Systems comes in. Some thoughts below: https://lnkd.in/gUGu9HMP