Alternatives
Products that do what SnapThink does
Powerful Simulations & AI Notebooks - Without the Cloud
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Give your team one AI memory. Keep your own private.
Jul 2026 · thesecondbrain.dev
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2023 · tinyllms.vercel.app
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Hello Hacker News! We're Yangqing, Xiang and JJ from lepton.ai. We are building a platform to run any AI models as easy as writing local code, and to get your favorite models in minutes. It's like container for AI, but without the hassle of actually building a docker image. We built and contributed to some of the world's most popular AI software - PyTorch 1.0, ONNX, Caffe, etcd, Kubernetes, etc. We also managed hundreds of thousands of computers in our previous jobs. And we found that the AI software stack is usually unnecessarily complex - and we want to change that. Imagine if you are a…
2023 · lepton.ai
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Hi HN! I'm the author of mere.run a local first inference runtime built around an installable CLI. I believe that whenever possible we should use the stuff we already own (like our Mac laptops, decent machines gathering dust, our gaming PC) and the limited electrical power we have easy access to, like the socket in the wall next to most of us. We shouldn't have to send our data to the cloud hoping some T&C will prevent it from being used in a way that we'd regret. Most of the local AI solutions are technical, involved, and land a curious body in some package hell. People are optimizing for…
Jul 2026 · github.com
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Local▲107Super excited to launch our new app Local today. What we’ve learned at Base Compute over the last months is that running AI directly on your laptop or workstation gives you maximum privacy and it’s free, but it’s also a massive headache to configure. So we’ve decided what matters is making the experience completely frictionless for users. Local analyses the hardware of your laptop, optimises the AI for it, and recommends the best models for your specific device. It let’s you do what you’re doing with cloud AI already, just for free and on your own machine: Chatting with PDF’s, Recording and…
17d ago · basecompute.co
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Your Mac can run AI that holds its own against cloud models for the everyday stuff: chatting, making images, reading documents, transcribing voice. The hardware got there a while ago. The software to actually use it locally mostly didn't, so I built Off Grid. Download a model and it all runs on your machine. Ask it something on a flight with no wifi. Summarize a confidential document that never leaves your laptop. Run a hundred image generations in a loop and pay nothing, because it's your own GPU doing the work. Swap your paid dictation app for local Whisper. Talk through a coding problem…
Jun 2026 · github.com
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Hi HN, I'm excited to share Bodhi App, a tool designed to simplify running open-source Large Language Models (LLMs) locally on your laptops. While we currently support M2 Macs, we plan to support other platforms as our community grows. # Problem To use LLMs, you typically need to purchase a subscription from providers like OpenAI or Anthropic, or use OpenAI API credits with compatible Chat UIs. These options can not only burden you financially, but also raise data security and privacy concerns. Many laptops are capable of running powerful open-source LLMs, but for non-tech users, setting…
2024 · github.com
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2024 · github.com
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Hi HN, I built OpenGraviton, an open-source AI inference engine that pushes the limits of running extremely large LLMs on consumer hardware. By combining 1.58-bit ternary quantization, dynamic sparsity with Top-K pruning and MoE routing, and mmap-based layer streaming, OpenGraviton can run models far larger than your system RAM—even on a Mac Mini. Early benchmarks: TinyLlama-1.1B drops from ~2GB (FP16) to ~0.24GB with ternary quantization. At 140B scale, models that normally require ~280GB fit within ~35GB packed. Optimized for Apple Silicon with Metal + C++ tensor unpacking, plus…
Mar 2026 · github.com
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