Rakesh Kothari’s Post

Shoutout to the team that built https://lnkd.in/g3Y-Zj3W . Really neat site that benchmarks the speed of different LLM API providers to help developers pick which models to use. This nicely complements the LMSYS Chatbot Arena, Hugging Face open LLM leaderboards and Stanford's HELM that focus more on the quality of the outputs. I hope benchmarks like this encourage more providers to work on fast token generation, which is critical for agentic workflows!

Excited to share that we're on the lookout for a talented front end / product engineer to join our team at Deductive AI! Previous experience with React is a plus, but not required. If you’re interested in working on a product at the intersection of Observability, AI, and data visualization, drop me a note to learn more.

We're excited to welcome Aditya B. to Deductive AI as our Founding UX Designer! Aditya joins us from Splunk, where he led the product design for several key observability products at the company. Before Splunk, Aditya spearheaded the design of Spektra and Ultima at Diamanti, and played a pivotal role in designing Deserve's consumer credit card management platforms. He worked at VMware and T-Labs during his foundational years as a UX designer. At Deductive, we are building the next generation of foundational data processing systems that can reason on (and not just query) extremely large amounts of data. If you are exceptional at what you do and are passionate about problems at the intersection of data and AI, please drop us a note!

A good article from Unusual Ventures on future trends in observability: https://lnkd.in/dCXEn-wZ. We're very grateful for their nod to Deductive AI as one of the innovators in the AI-powered reasoning space! Thinking along these lines, as we look at the evolution of databases and large-scale observability systems over the past decade, our industry has achieved remarkable progress in querying large datasets characterized by high dimensionality and cardinality. However, despite these advancements, the process of root cause analysis remains significantly hindered by the cost of storing this data and the domain knowledge required of engineers and on-calls to know what needs to be queried. It's fascinating to see how organizations are paying millions of dollars to collect, store, and process their metrics, logs, events, traces, and it still takes hours, if not days, for people to debug any non-trivial issues in production. While we're still in stealth, it has been a really fascinating and humbling journey over the last few months to build (and experience) firsthand the power of AI and approximations in fundamentally disrupting some of these age-old inefficiencies by enabling systems that can reason on (and not just query) large amounts of data. Looking forward to sharing more about what we're building in the coming months!

We're delighted to welcome Madeline Cripps to Deductive AI as our newest Founding Engineer! Maddie joins us from Meta, where she spent the last 6 years building the next generation of Meta's observability stack, with numerous key contributions spanning across Scuba, ODS, and Tracery. At Deductive, we are solving some very hard/interesting problems at the intersection of AI, approximations, and data analytics, with a focus on enabling real-time reasoning on massive data streams. If you are exceptional at what you do and are passionate about problems at the intersection of data and AI, please drop us a note!

We're extremely excited to welcome Pratyush Verma to Deductive AI as our newest Founding Engineer, based in London! Pratyush joins us from Meta, where he spent the last several years building large-scale data platforms from the ground up as part of the Ads Experimentation and Data Infrastructure Teams. Before Meta, Pratyush was an early engineer at Flipkart and Grab, where he helped build their infrastructure for scale. At Deductive, we are solving some very hard/interesting problems at the intersection of AI, approximations, and data analytics, with a focus on enabling real-time reasoning on massive data streams. If you are exceptional at what you do and are passionate about problems at the intersection of data and AI, please drop us a note!