Alternatives
Products that do what Kerns – A Continuous Research Workspace does
Most research tools help you collect links. Kerns is built for ongoing research. You define topics and sources once. Kerns continuously tracks them over time, surfaces what changes, and structures the material so understanding compounds instead of resetting each session. The key difference is the interface layer. Beyond feeds and summaries, Kerns organizes research into reasoning-ready views—maps, structured summaries, and synthesized perspectives—so you can actually think through complex areas rather than just store information. We built this for people doing deep, long-running research…
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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
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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
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Doing research with agents is fun until they blow way past budget, jumble the sources, and don't even give you the best possible answer, just sound confident. And if you want to run some research task on local data - you have no idea where your data ends up after the prompt consumes it. So I built this tool: a deep-research agent with an enforced budget, verified quotes, and a privacy boundary for local data. 1. Never spend more than you budgeted (measured overshoot is 0%). 2. Every claim carries a source 3. Data stays local (give a CSV, it'll analyze it without the data ever leaving your…
23d ago · github.com
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Understanding scientific articles can be tough, even in your own field. Trying to comprehend articles from others? Good luck. Enter, Now I Get It! I made this app for curious people. Simply upload an article and after a few minutes you'll have an interactive web page showcasing the highlights. Generated pages are stored in the cloud and can be viewed from a gallery. Now I Get It! uses the best LLMs out there, which means the app will improve as AI improves. Free for now - it's capped at 20 articles per day so I don't burn cash. A few things I (and maybe you will) find interesting: * This is…
Feb 2026 · nowigetit.us
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Hi HN! My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text. Some technical aspects you may be interested in: - Used structured generation to reliably parse unstructured text into a consistent schema - Tested multiple models (Mistral-7B-v0.3, Llama3.2-3B, gpt4o-mini) for information extraction - Created an interactive visualization using gephi-lite and Retina (WebGL) - Built (with Claude) a simple Flask web app to clean and…
2025 · theophilecantelob.re
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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
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2014 · coredemia.com
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