Alternatives
Products that do what Kernopy does
A Digital Ecosystem for Physical Devices
- 1

A fast, rootless sandbox and virtual resource runtime for any workload, including untrusted and AI-generated code. Daemonless: a real, kernel-enforced container in ~3.5 ms from an OCI image, out of one static binary - getkern/kern
13d ago · github.com
- 2

- 3KI
Hey HN! Anibal, Sean, Vlad, Maxi and Karim here we're the co-founders of Kerno (https://www.kerno.io/). We're building a zero-hassle tool to help developers monitor and troubleshoot their services in a way that makes sense. No complex rollouts, no custom infra, no tons of instrumentation, no sidecars… none of that! Just a minimum effort, out-of-the box, highly contextualized and interactive experience so you and your team can focus on shipping! We basically scan your cluster resources, build usable abstractions on top of all the kubernetes craziness, identify monitor your…
2024 · kerno.io
- 4PA
2022 · github.com
- 5A1
Jan 2026 · github.com
- 6WM
We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.
2025 · github.com
- 7

- 8AE
2018 · github.com
- 9BA
2013 · thebinaryapp.com
- 10

- 11OA
Directed acyclic graphs are muched discussed in comp-sci, but octopus appears to be the first reusable, turnkey, ready-to-wear, off-the-shelf implementation of a DAG for application development, in any language, that I'm aware of. This is remarkable because DAGs hit a sweet spot in the middle of the three common programming paradigms (OO, event-driven, functional). Let's have a DAG as the top-level structure of our applications. Data-fetching and onChange handlers live in DAG nodes, next to the data they act on. The UI flows out from the DAG with fine-grained reactivity. Our app state is…
2023 · github.com
- 12

- 13KA
I'm building out Kerns, as an AI environment for research. You can seed a space with a topic and multiple source documents, and complete your research completely in one space. There's interactive mindmaps for exploration, podcast mode, powerful source readers with original plus chapter level summaries that let you zoom into source on demand, a powerful chat agent that lets you control context and cite refs, and AI assisted note taking. My goal is to have one place to do research on any topic which minimizes manual context engineering, and jumping around between chat/notes/readers.…
Nov 2025 · kerns.ai
- 14FA
2019 · github.com
- 15GI
2017 · github.com
- 16

- 17AK
Mar 2026 · github.com
- 18SF
I've made a small Python library, designed for quick-and-easy prototyping of machine learning models. It's built on top of scikit-learn, to serialize and deserialize data from the forms you're likely to have, to the format used in scikit-learn. https://github.com/madman-bob/Smart-Fruit It's pretty bare-bones at the moment, but I thought I'd see if there was any interest before spending too much time on it. Let me know what you think.
2018
- 19SF
2015 · github.com
- 20AC
2024 · github.com
- 21FI
2016 · forestry.io
- 22EB
2017 · elasticbyte.net
- 23KO
Mar 2026 · github.com
- 24SC
2016 · stdlib.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →