Future of AI Development - #SFTechWeek
Photos from Future of AI Development at SFTechWeek — hosted by DevSwarm, showcasing how parallel AI coding unlocks high-velocity engineering and unstoppable innovation.
Photos from Future of AI Development at SFTechWeek — hosted by DevSwarm, showcasing how parallel AI coding unlocks high-velocity engineering and unstoppable innovation.
The Future of AI Development event at SFTechWeek, hosted by DevSwarm, brought together engineers and founders exploring how AI is transforming the way we build software.
Founders Mike Biglan and Trevor Dilley shared how DevSwarm is evolving engineering stacks with parallel AI coding — empowering teams to build more, better, and faster.
From tools like Claude Code, Codex, and Cursor to real-world discussions on MCP servers, reviews, and automation, the evening was packed with insights and innovation.
Browse the gallery below and relive the energy of a community redefining what it means to code in parallel — and build unstoppably.

















































































PhysicsX, a London-based AI engineering startup, has raised $300M at a $2.4B valuation to scale its physics simulation platform across industries like aerospace, semiconductors, and automotive.
Suno raised $400 million at a $5.4 billion valuation—more than doubling its worth in seven months—despite facing copyright lawsuits from Universal Music Group and Sony alleging unauthorized use of over 61,000 copyrighted works in its AI training data.
OpenAI expanded Codex with six role-specific plugins for jobs like sales and investment banking, a Sites feature for sharing work as hosted interactive webpages, and inline Annotations for targeted edits, as non-developer users grow three times faster than developers on the platform.
Inherent emerged from stealth with a $50M seed round co-led by Index Ventures and Radical Ventures to develop Faraday, an AI system designed to reimagine scientific discovery by enabling open-ended human-AI collaboration on unsolved research problems.
XCENA raised $135 million at a $570 million valuation to commercialize its MX1 chip, which places compute capabilities directly inside memory modules to eliminate the costly data relay between CPUs, GPUs, and DRAM that bottlenecks every AI inference request.
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