Show HN: A worker owned (CO-OP) version of Amazon but for sustainable products https://ift.tt/3pjnuAA

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Show HN: Don't Let Him Poo – Embedded in Blog Post https://ift.tt/36gdTDb

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Show HN: SharedOTP – Shared two-factor authentication mechanism https://sharedotp.com/ January 25, 2021 at 03:27PM

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Show HN: SharedOTP – Shared two-factor authentication mechanism https://sharedotp.com/ January 25, 2021 at 03:27PM

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Show HN: Retrospective on having my “Show HN” on the front-page https://ift.tt/3chGrjk

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Show HN: A weekly newsletter where I interview an open-source developer https://ift.tt/2YcSBBO

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Show HN: Filmulator – a streamlined, open-source raw photo editor https://ift.tt/3ocTJzW January 23, 2021 at 12:30PM

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Show HN: Fast, turnkey static web site recipe (Terraform and Jekyll and AWS) https://ift.tt/3qK6N1r January 23, 2021 at 11:45AM

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Show HN: I bought wutangforever.net a few days ago for $10 https://ift.tt/2Y61Ofd January 23, 2021 at 02:15PM

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Show HN: Full text search Project Gutenberg (60m paragraphs) https://ift.tt/2Y8gevw January 23, 2021 at 08:58PM

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Show HN: Filmulator – a streamlined, open-source raw photo editor https://ift.tt/3ocTJzW January 23, 2021 at 12:30PM

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Show HN: My Home Kubernetes Cluster Managed by Flux2 and Renovatebot https://ift.tt/2YbbXr4 January 23, 2021 at 04:13PM

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Show HN: Full text search Project Gutenberg (60m paragraphs) https://ift.tt/3oawHK3

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Show HN: Filmulator – a streamlined, open-source raw photo editor https://ift.tt/364VkSi

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Show HN: My Home Kubernetes Cluster Managed by Flux2 and Renovatebot https://ift.tt/3iGUFeP

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Show HN: Traindown – A language to help athletes express their training https://ift.tt/3c9Bp8D

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Show HN: Supercharge.dev – Accelerated Development in React/Next.js https://ift.tt/3o8Vf6b

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Show HN: Garnet – Distributed Python on Kubernetes Garnet (https://garnet.ai) is a framework to run distributed and parallel Python on Kubernetes. We make it easy to scale from laptop to cluster, without requiring any devops work. Garnet plugs into any existing Python environment (notebook, IDE, CI/CD pipelines, custom apps) using a lightweight client library, and enables users to scale popular data/ml libraries (including pandas, NumPy, scikit-learn, XGBoost, etc.) and custom code to a remote cluster for execution. Under the hood, we natively wire up Dask with Kubernetes for scheduling, which brings advantages such as dynamic resource allocation, ephemeral clusters and autoscaling, while abstracting the devops complexity for developers and data scientists. Our vision is to build the orchestration layer for distributed computing in Python. We’re starting off with Dask, but have goals of supporting other frameworks such as Ray and Modin in the future. Some of the cool use cases we’ve seen from our users are: Bursting to the cloud from local dev environments for data and memory intensive computations Standing up managed Dask and Kubernetes clusters programmatically in 3-4 lines of Python, without any devops knowledge (Docker, k8s etc.) Parallelize existing codebases in pandas, NumPy, scikit-learn, XGBoost etc. with minimal code changes, without opting for a more complex system such as Spark Currently, users can download our client library and run Dask workloads on our fully managed cluster (see docs). We’re working on adding the capability to run Garnet on your own cloud (Kubernetes) in an upcoming release. We’re excited to hear your feedback, and what you’d like to see. Please drop your email on our website so we can get in touch directly. January 22, 2021 at 04:49PM

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Show HN: Rysolv – Fix open source issues, get paid https://ift.tt/2WCT0wR January 22, 2021 at 07:14PM

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Show HN: Garnet – Distributed Python on Kubernetes https://ift.tt/3a0ibzwTHE RIGHT PEOPLE https://ift.tt/3c5qH3aTHE RIGHT PEOPLE https://ift.tt/3c5wLbC https://ift.tt/3c5wLbC

Show HN: Garnet – Distributed Python on Kubernetes Garnet (https://garnet.ai) is a framework to run distributed and parallel Python on Kubernetes. We make it easy to scale from laptop to cluster, without requiring any devops work. Garnet plugs into any existing Python environment (notebook, IDE, CI/CD pipelines, custom apps) using a lightweight client library, and enables users to scale popular data/ml libraries (including pandas, NumPy, scikit-learn, XGBoost, etc.) and custom code to a remote cluster for execution. Under the hood, we natively wire up Dask with Kubernetes for scheduling, which brings advantages such as dynamic resource allocation, ephemeral clusters and autoscaling, while abstracting the devops complexity for developers and data scientists. Our vision is to build the orchestration layer for distributed computing in Python. We’re starting off with Dask, but have goals of supporting other frameworks such as Ray and Modin in the future. Some of the cool use cases we’ve seen from our users are: Bursting to the cloud from local dev environments for data and memory intensive computations Standing up managed Dask and Kubernetes clusters programmatically in 3-4 lines of Python, without any devops knowledge (Docker, k8s etc.) Parallelize existing codebases in pandas, NumPy, scikit-learn, XGBoost etc. with minimal code changes, without opting for a more complex system such as Spark Currently, users can download our client library and run Dask workloads on our fully managed cluster (see docs). We’re working on adding the capability to run Garnet on your own cloud (Kubernetes) in an upcoming release. We’re excited to hear your feedback, and what you’d like to see. Please drop your email on our website so we can get in touch directly. January 22, 2021 at 04:49PM

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Show HN: Garnet – Distributed Python on Kubernetes Garnet (https://garnet.ai) is a framework to run distributed and parallel Python on Kubernetes. We make it easy to scale from laptop to cluster, without requiring any devops work. Garnet plugs into any existing Python environment (notebook, IDE, CI/CD pipelines, custom apps) using a lightweight client library, and enables users to scale popular data/ml libraries (including pandas, NumPy, scikit-learn, XGBoost, etc.) and custom code to a remote cluster for execution. Under the hood, we natively wire up Dask with Kubernetes for scheduling, which brings advantages such as dynamic resource allocation, ephemeral clusters and autoscaling, while abstracting the devops complexity for developers and data scientists. Our vision is to build the orchestration layer for distributed computing in Python. We’re starting off with Dask, but have goals of supporting other frameworks such as Ray and Modin in the future. Some of the cool use cases we’ve seen from our users are: Bursting to the cloud from local dev environments for data and memory intensive computations Standing up managed Dask and Kubernetes clusters programmatically in 3-4 lines of Python, without any devops knowledge (Docker, k8s etc.) Parallelize existing codebases in pandas, NumPy, scikit-learn, XGBoost etc. with minimal code changes, without opting for a more complex system such as Spark Currently, users can download our client library and run Dask workloads on our fully managed cluster (see docs). We’re working on adding the capability to run Garnet on your own cloud (Kubernetes) in an upcoming release. We’re excited to hear your feedback, and what you’d like to see. Please drop your email on our website so we can get in touch directly. January 22, 2021 at 04:49PM

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Show HN: Git News – Social News website for developers and GitHub fans https://ift.tt/3iAottG

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Show HN: 71 Lines – Build Chrome Extensions with No-Code Tools https://ift.tt/2XYFlAH

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Show HN: Analytics for LinkedIn http://lunaa.co.uk/ January 21, 2021 at 01:18AM

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Show HN: An automated security/performance checker for Laravel apps https://ift.tt/2LQbRmiTHE RIGHT PEOPLE https://ift.tt/3bYxBXNTHE RIGHT PEOPLE https://ift.tt/2LOE7pe https://ift.tt/2LOE7peTHE RIGHT PEOPLE https://ift.tt/3iycj4yTHE RIGHT PEOPLE https://ift.tt/3sIhSC4 https://ift.tt/3sIhSC4

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