Zuhayeer Musa

Excited to announce that Pipeshift (YC S24) has raised a $2.5M seed round. Over the past year, enterprises have increasingly adopted open-source models for better privacy, control, and performance. New releases like DeepSeek R1 are driving even greater adoption, but deploying these models efficiently and securely remains a challenge. While models continue to improve, the tooling needed for engineering teams to train, deploy, and scale them in production is severely lacking. The amount of time and effort required to piece together multiple tools is significant, not to mention the effort needed to build new workflows. At Pipeshift, we're building a modular orchestration platform that allows teams to build with open source AI components, including LLMs, audio models, image models, vision models, embeddings, and vector DBs. Teams can deploy these models on their infrastructure, whether cloud or on-prem, while optimizing for performance, cost, and quality. In recent months, we've helped several teams train and deploy use case specific models for their workflows, and we’ll continue to do so. If you're currently building with open source models or thinking about making the switch, let's chat. We’re incredibly grateful to our earliest supporters at Y Combinator (GPs: Dalton Caldwell, Tyler Bosmeny, Michael Seibel, David Lieb), SenseAI Ventures, Arka Venture Labs, Good News Ventures, Nivesha Ventures, Astir Ventures, MyAsiaVC, gradCapital, 100X.VC and amazing angels including Kulveer Taggar, Umur Cubukcu, Krishna M., Nate Matherson, Max Kolysh, Fred Zhang, Ruchir Patwa, Arjun Maheswaran, Kishore Ganji, Praneeth Kannah, Anshul Bhide, Hasan Abu Sheikh, Uday Sodhi, Aabhas Khanna and Maria Nigam. Full throttle ahead 🏎️ Link to the article: https://lnkd.in/gj-DXiSA

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SiMa.ai’s $85M Raise Marks a New Chapter for Physical AI SIMA AI, a startup focused on edge inference, just secured an $85 million Series C led by Maverick Ventures, with participation from Lockheed Martin and Amplify Partners. But the number isn’t what matters. The thesis is. SIMA is building a fully integrated chip + software stack that enables real-time AI processing on devices that don’t have the luxury of hyperscaler latency budgets. In other words, the future of AI isn’t just chatbots and copilots. It’s the physical world, where inference must be deployed without cloud contracts, without power-hungry GPUs, and without sacrificing latency. CEO Krishna Rangasayee put it bluntly: “Inference shouldn’t need 500 watts and a cloud contract.” As AI moves beyond screens and into machines, companies like SIMA are not just enabling performance—they’re defining the terms of where and how intelligence lives. The future may be foundation models. But increasingly, it also looks like edge-native, domain-specific, silicon-optimized intelligence. And that, perhaps, is the quieter revolution unfolding. #ai #inference #cloud #physicalAI

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Per Similarweb, Threads is close to eclipsing X in terms of DAU. While much of this growth can be credited to the aggressive cross-promotion Meta is undertaking from Instagram, I think two other facts are equally important to note: - Threads has maintained a consistent drumbeat of new feature launches, which likely contributes to engagement - Threads is now being monetized through Meta’s ads infrastructure, which almost certainly delivers a higher ARPU than X’s

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the last few weeks feels like a mini breakthrough for AI apps hiding in plain sight - partial autonomy, high agency models, and context eng vs prompt eng. are all incredibly important concepts a lot of these principles were apparent but were just recently articulated (mostly by Karpathy - legend!!). one is the concept of partial autonomy apps - autonomy sliders, app specific UI, and “keeping the AI on a leash” two is the idea of small, high agency models that know how to solve problems vs knowing answers via tool-use / reasoning / multimodality (“maximally sacrifices encyclopedic knowledge for capability”) three is context engineering vs prompt engineering - putting the right stuff into working memory, building around the “jagged intelligence” of models etc these should probably be the starting point for founders thinking through AI native apps Refs: Partial autonomy apps: [Karpathy @YC startup school] High agency mini models: https://lnkd.in/gthCXtMe Context Eng: https://lnkd.in/gha2i7BX

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