Series C and Scale
We've raised $900m to push the frontier of AI coding research.
Perspectives and announcements from PWV, our founders and community.
We've raised $900m to push the frontier of AI coding research.
By Pedro Piñera
Our lessons building a dev tools business: community focus, human-centered marketing, technological pragmatism, and creating sustainable innovation.
Discover new insights on the future of construction tech on Million Dollar Days. Discover what’s planned for Builders and the construction industry.
StackOne – the next-gen, AI-powered platform fuelling the future of enterprise AI agents and SaaS integrations – has raised $20 million in a Series A round led by GV (Google Ventures). Workday Ventures, XTX Ventures, existing investors Episode 1 and Playfair, and angels from OpenAI, Deepmind, Microsoft and Mulesoft also participated. The funding, which takes the total raised by StackOne to $24 million, will be used to continue building StackOne’s state-of-the-art tool-calling LLM, invest in R&D, and further expand the number of integrations and depth of actions available in the StackOne platform.
By Steve Ruiz
Announcing new funding from Lux Capital, Definition, and more
By Scott Chacon
Twenty years ago, Git was born. How did this unlikely "information manager" take over the world?
Today we announced that we are being acquired by CoreWeave, the AI Hyperscaler.
By Andreas Stuhlmueller, Jungwon Byun
Elicit has raised $22M in Series A funding at a $100M valuation led by Spark Capital and Footwork. Existing investors Fifty Years, Basis Set, and Mythos also participated, reinforcing their conviction in our mission to deploy AI to radically increase good reasoning in the world.
We've raised $105M to further our mission of automating code.
By Flock Safety
Flock Safety acquires Aerodome to enhance DFR technology, accelerating crime response with NDAA-compliant, American-made drones.
By Devansh Jain, Tze-Yang Tung, and Tomás Hernando Kofman
RoRF (Routing on Random Forests) is an open-source LLM router that efficiently directs prompts between pairs of large language models using a Random Forest Classifier trained on evaluation data and prompt embeddings. This lightweight yet powerful tool includes six pre-trained routers for various model pairs including GPT-4o, Claude 3.5 Sonnet, and Llama 3.1 variants, offering a more cost-effective solution compared to using individual models while outperforming other open-source router architectures.