Martian Raised $9M from @NEA @Prosus_Ventures and @GC and is launching its beta today! Our LLM router dynamically routes each request to the best LLM. We beat GPT-4 on @OpenAI's own evals (colab to replicate in thread) while cutting costs up to 98%+ 🧵

Thanks to @ForbesTech and @juliaaneagu for highlighting us in their piece on open models. These models allow us to expand our routing options, enabling our customers to achieve significant cost savings and quality improvements compared to only using closed models. By studying these open models through our interpretability research, we're constantly improving our routing methods—better understanding leads to better routing.

We're proud to be spotlighted by the Google for Startups program! Looking forward to a fantastic partnership with GCP: Cool to see our friends @magicailabs @MistralAI @pinecone @SakanaAILabs here too 🥰

@AaronNam `promise.then(goSignUp)`

@cbandieth We're bringing the

@shriyashku Yup, almost as amazing as

These moves aim to accelerate enterprise AI adoption by simplifying model integration — we let companies use every AI model, instead of being stuck with just one. You can read the full announcement here:

@elonmusk 2y5y22,,

Although coal and LLMs couldn’t seem farther apart, the same relationship applies for any general-purpose technology. Our founder, Yash, talks more about Jevons Paradox as it relates to frontier models below – take a look!

Continued thoughts from @chrislmann as follow-ups to our recent article on automated prompt optimization (APO).

Claude 3.5 Sonnet in your application through Martian. Now in OpenAI SDK format.

@chrislmann @aparnadhinak Nice!

RT @onjas_buidl: Super excited to share our work on benchmarking LLM routers at Compound AI Systems workshop at @Data_AI_Summit ! Had the…

RT @onjas_buidl: The best kind of paper is one where the idea is initially tricky to conceive, but once you grasp it, it seems obvious.

At Martian, we are fortunate to work with many of the world's most advanced users of AI. We see the problems they face on the leading edge of AI and collaborate closely with them to overcome these challenges. In this first of a three-part series, we share a view into the future of prompt engineering we refer to as Automated Prompt Optimization (APO). In this article we summarize the challenges faced by leading AI companies including @mercor_ai , @G2dotcom, @copy_ai, @autobound_ai, @6senseInc, ZeltaLabs, @EDITED_HQ, @supernormalapp, and others. We identify key issues like model variability, drift, and “secret prompt handshakes”. We reveal innovative techniques used to address these challenges, including LLM observers, prompt co-pilots, and human-in-the-loop feedback systems to refine prompts. We invite the broader AI community to collaborate with us on research in this area. If you are interested in participating, please reach out to us!