Show HN: Preql – a database query language that compiles to SQL https://ift.tt/38loDBy March 13, 2021 at 07:14AM
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Showing posts with label 2021 at 10:20AM. Show all posts
Showing posts with label 2021 at 10:20AM. Show all posts
Saturday, March 13, 2021
Show HN: Authenticator by 2Stable – The missing Authenticator app https://ift.tt/38Bw6MATHE RIGHT PEOPLE https://ift.tt/3eNG6GLTHE RIGHT PEOPLE https://ift.tt/3lflbgP https://ift.tt/3lflbgPTHE RIGHT PEOPLE https://ift.tt/3t8RocqTHE RIGHT PEOPLE https://ift.tt/3vkyBwA https://ift.tt/3vkyBwA
Show HN: Authenticator by 2Stable – The missing Authenticator app https://ift.tt/2Pr3QFJ March 13, 2021 at 06:12AM
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(https://ift.tt/3thUtacShow HN: Preql – a database query language that compiles to SQL https://ift.tt/3co1246THE RIGHT PEOPLE https://ift.tt/2OUdujRTHE RIGHT PEOPLE https://ift.tt/3rPnXfa https://ift.tt/3rPnXfa
Show HN: Preql – a database query language that compiles to SQL https://ift.tt/38loDBy March 13, 2021 at 07:14AM
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(https://ift.tt/3ezZTsRShow HN: Authenticator by 2Stable – The missing Authenticator app https://ift.tt/38Bw6MATHE RIGHT PEOPLE https://ift.tt/3eNG6GLTHE RIGHT PEOPLE https://ift.tt/3lflbgP https://ift.tt/3lflbgP
Show HN: Authenticator by 2Stable – The missing Authenticator app https://ift.tt/2Pr3QFJ March 13, 2021 at 06:12AM
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Making Money From Music In 2021 | 3 Methods No One Is Talking AboutTHE RIGHT PEOPLE https://ift.tt/3vjNmQ7THE RIGHT PEOPLE https://ift.tt/3vcZZN7 https://ift.tt/3vcZZN7THE RIGHT PEOPLE https://ift.tt/3exiVQBTHE RIGHT PEOPLE https://ift.tt/3vm4gO2 https://ift.tt/3vm4gO2
Are you searching for the best way to make money from your music? In this episode of No Nonsense Music Marketing, we dive into 3 Methods No One Is Talking About that will make you money in 2021. Website Design & Management: https://ift.tt/2OorLWh Powerhouse Playlists: How Top Labels Legitimately Grow Their Spotify Lists & You Can Too 
Subscribe : http://bit.ly/2id0MGY 
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(https://ift.tt/3tdTQhHWednesday, March 10, 2021
Clubhouse: The #1 Thing Music Artists MUST Know About The New Viral AppTHE RIGHT PEOPLE https://ift.tt/3cn9Qr7 https://ift.tt/3cn9Qr7THE RIGHT PEOPLE https://ift.tt/3v67QvTTHE RIGHT PEOPLE https://ift.tt/3vkt6hF https://ift.tt/3vkt6hFTHE RIGHT PEOPLE https://ift.tt/3l12eOSTHE RIGHT PEOPLE https://ift.tt/2PIs2Ua https://ift.tt/2PIs2Ua
Ever wondered if you should start using Clubhouse to grow your fanbase? In this episode of No Nonesense Music Marketing, we dive into if artists should be using Clubhouse to promote their music.
Powerhouse Playlists: How Top Labels Legitimately Grow Their Spotify Lists & You Can Too
Subscribe : http://bit.ly/2id0MGY
Website : http://omarimc.com
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(https://ift.tt/3l2WuUJClubhouse: The #1 Thing Music Artists MUST Know About The New Viral AppTHE RIGHT PEOPLE https://ift.tt/38tyc11THE RIGHT PEOPLE https://ift.tt/38pxqSA https://ift.tt/38pxqSATHE RIGHT PEOPLE https://ift.tt/38ou42kTHE RIGHT PEOPLE https://ift.tt/3rA88cb https://ift.tt/3rA88cb
Ever wondered if you should start using Clubhouse to grow your fanbase? In this episode of No Nonesense Music Marketing, we dive into if artists should be using Clubhouse to promote their music. Powerhouse Playlists: How Top Labels Legitimately Grow Their Spotify Lists & You Can Too Subscribe : http://bit.ly/2id0MGY Website : http://omarimc.com ——————————————— ● Email: https://ift.tt/2knagGw ● Instagram : https://ift.tt/2BI5zLu ● Twitter : https://ift.tt/1uASU2k ● Facebook : https://ift.tt/2kjY5dA ● SoundCloud : https://ift.tt/1gjPNLb
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(https://ift.tt/3l4jP8nShow HN: Nextcloud as a rootless container & podman play kube with CentOS Stream https://ift.tt/3eqIWB9THE RIGHT PEOPLE https://ift.tt/3rAlFAuTHE RIGHT PEOPLE https://ift.tt/3qA6Rkb https://ift.tt/3qA6RkbTHE RIGHT PEOPLE https://ift.tt/2N3AigATHE RIGHT PEOPLE https://ift.tt/3bwMwrE https://ift.tt/3bwMwrE
Show HN: Nextcloud as a rootless container & podman play kube with CentOS Stream https://ift.tt/3eqEjH6 March 10, 2021 at 03:03AM
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(https://ift.tt/3epFXcfSaturday, February 27, 2021
Show HN: A tiny static site generator for publishing directory websites https://ift.tt/37TmcWmTHE RIGHT PEOPLE https://ift.tt/3krNW9ITHE RIGHT PEOPLE https://ift.tt/3pWcb0V https://ift.tt/3pWcb0VTHE RIGHT PEOPLE https://ift.tt/2ObQCwcTHE RIGHT PEOPLE https://ift.tt/3b2fgbA https://ift.tt/3b2fgbA
Show HN: A tiny static site generator for publishing directory websites https://ift.tt/3qZcpWo February 27, 2021 at 03:07AM
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(https://ift.tt/3szIi88Show HN: My custom computer case that acts as an air purifier https://ift.tt/3aY2HOgTHE RIGHT PEOPLE https://ift.tt/3q1kLLyTHE RIGHT PEOPLE https://ift.tt/3sCWzRJ https://ift.tt/3sCWzRJ
Show HN: My custom computer case that acts as an air purifier https://ift.tt/1FWS6eJ February 27, 2021 at 06:38AM
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(https://ift.tt/3bOJ2PUShow HN: Is it time to kill Scrum? https://ift.tt/3r03BPPTHE RIGHT PEOPLE https://ift.tt/3q0NbFITHE RIGHT PEOPLE https://ift.tt/3aYwmXC https://ift.tt/3aYwmXC
Show HN: Is it time to kill Scrum? https://ift.tt/3dQoaL1 February 27, 2021 at 06:34AM
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(https://ift.tt/3q00HJQShow HN: Ranking Data Sets by Quality https://ift.tt/3q1CwdZTHE RIGHT PEOPLE https://ift.tt/37PQfOvTHE RIGHT PEOPLE https://ift.tt/3syLAIV https://ift.tt/3syLAIV
Show HN: Ranking Data Sets by Quality https://ift.tt/384iNDz February 27, 2021 at 03:50AM
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(https://ift.tt/2NNEotJShow HN: GraphQL Zeus 3.0 – GraphQL Client now with subscription and JSON schema https://ift.tt/2Pj2qgATHE RIGHT PEOPLE https://ift.tt/37WaNVUTHE RIGHT PEOPLE https://ift.tt/3ksUk0p https://ift.tt/3ksUk0p
Show HN: GraphQL Zeus 3.0 – GraphQL Client now with subscription and JSON schema https://ift.tt/2lc2RtS February 27, 2021 at 03:50AM
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(https://ift.tt/2NEnE8iShow HN: A tiny static site generator for publishing directory websites https://ift.tt/37TmcWmTHE RIGHT PEOPLE https://ift.tt/3krNW9ITHE RIGHT PEOPLE https://ift.tt/3pWcb0V https://ift.tt/3pWcb0V
Show HN: A tiny static site generator for publishing directory websites https://ift.tt/3qZcpWo February 27, 2021 at 03:07AM
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(https://ift.tt/3bNflPdTuesday, February 23, 2021
Show HN: MobX-Style Observables in Svelte https://ift.tt/2ZELdjETHE RIGHT PEOPLE https://ift.tt/3usXLcaTHE RIGHT PEOPLE https://ift.tt/3qIYfZc https://ift.tt/3qIYfZcTHE RIGHT PEOPLE https://ift.tt/2P0fBTjTHE RIGHT PEOPLE https://ift.tt/3aNt9dC https://ift.tt/3aNt9dC
Show HN: MobX-Style Observables in Svelte https://ift.tt/2NOUClW February 23, 2021 at 12:29AM
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(https://ift.tt/3aMo1q1Show HN: Test your Gitlab CI Pipelines changes locally using Docker https://ift.tt/2ZG3VqTTHE RIGHT PEOPLE https://ift.tt/2NOFSUaTHE RIGHT PEOPLE https://ift.tt/37H1uZM https://ift.tt/37H1uZM
Show HN: Test your Gitlab CI Pipelines changes locally using Docker https://ift.tt/3kfmpYU February 23, 2021 at 04:47AM
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(https://ift.tt/3sfVsHaFriday, February 19, 2021
Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database https://ift.tt/2ZuqrTNTHE RIGHT PEOPLE https://ift.tt/37uGuVMTHE RIGHT PEOPLE https://ift.tt/3dsTaQS https://ift.tt/3dsTaQSTHE RIGHT PEOPLE https://ift.tt/3azvR68THE RIGHT PEOPLE https://ift.tt/3pzCb1W https://ift.tt/3pzCb1W
Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database Hi HN, Adam and Jorge here, and today we’re very excited to share MindsDB with you ( https://ift.tt/2UJWtY6 ). MindsDB AutoML Server is an open-source platform designed to accelerate machine learning workflows for people with data inside databases by introducing virtual AI tables. We allow you to create and consume machine learning models as regular database tables. Jorge and I have been friends for many years, having first met at college. We have previously founded and failed at another startup, but we stuck together as a team to start MindsDB. Initially a passion project, MindsDB began as an idea to help those who could not afford to hire a team of data scientists, which at the time was (and still is) very expensive. It has since grown into a thriving open-source community with contributors and users all over the globe. With the plethora of data available in databases today, predictive modeling can often be a pain, especially if you need to write complex applications for ingesting data, training encoders and embedders, writing sampling algorithms, training models, optimizing, scheduling, versioning, moving models into production environments, maintaining them and then having to explain the predictions and the degree of confidence… we knew there had to be a better way! We aim to steer you away from constantly reinventing the wheel by abstracting most of the unnecessary complexities around building, training, and deploying machine learning models. MindsDB provides you with two techniques for this: build and train models as simply as you would write an SQL query, and seamlessly “publish” and manage machine learning models as virtual tables inside your databases (we support Clickhouse, MariaDB, MySQL, PostgreSQL, and MSSQL. MongoDB is coming soon.) We also support getting data from other sources, such as Snowflake, s3, SQLite, and any excel, JSON, or CSV file. When we talk to our growing community, we find that they are using MindsDB for anything ranging from reducing financial risk in the payments sector to predicting in-app usage statistics – one user is even trying to predict the price of Bitcoin using sentiment analysis (we wish them luck). No matter what the use-case, what we hear most often is that the two most painful parts of the whole process are model generation (R&D) and/or moving the model into production. For those who already have models (i.e. who have already done the R&D part), we are launching the ability to bring your own models from frameworks like Pytorch, Tensorflow, scikit-learn, Keras, XGBoost, CatBoost, LightGBM, etc. directly into your database. If you’d like to try this experimental feature, you can sign-up here: ( https://ift.tt/3uhw05U ) We currently have a handful of customers who pay us for support. However, we will soon be launching a cloud version of MindsDB for those who do not want to worry about DevOps, scalability, and managing GPU clusters. Nevertheless, MindsDB will always remain free and open-source, because democratizing machine learning is at the core of every decision we make. We’re making good progress thanks to our open-source community and are also grateful to have the backing of the founders of MySQL & MariaDB. We would love your feedback and invite you to try it out. We’d also love to hear about your experience, so please share your feedback, thoughts, comments, and ideas below. https://ift.tt/3k6zsfm or https://mindsdb.com/ Thanks in advance, Adam & Jorge February 19, 2021 at 08:55AM
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(https://ift.tt/3pB6Mw6Launch HN: HiGeorge (YC W21) – Real-time data visualizations for public datasets https://ift.tt/2NGzfTMTHE RIGHT PEOPLE https://ift.tt/2ZKvRu5THE RIGHT PEOPLE https://ift.tt/3qGzeOk https://ift.tt/3qGzeOkTHE RIGHT PEOPLE https://ift.tt/3doGZ7QTHE RIGHT PEOPLE https://ift.tt/2ZudTM2 https://ift.tt/2ZudTM2
Launch HN: HiGeorge (YC W21) – Real-time data visualizations for public datasets Hi HN! Anuj here. My co-founder Amir (Aazo11) and I are building HiGeorge ( https://hi-george.com/ ). We make localized drag-and-drop data visualizations so that all publishers, even the small ones, can better leverage data in their storytelling. Think Tableau with all the necessary data attached. At the onset of the pandemic Amir and I were looking for local data on the spread of the virus. We visited the sites of large national newsrooms like the NYTimes and were impressed by the quality of data visualizations and maps, but they lacked the geographic granularity for our own neighborhood. We then turned to our local newsrooms but found they presented data in tables and lists that made it difficult to comprehend the virus’ spread and trends. We wondered why. After talking to local journalists and publishers, we found that newsrooms simply do not have the resources to make sense of large datasets. Public datasets are hard to clean, poorly structured, and constantly updated. One publisher explained to us that she would refresh her state health department’s website 5 times a day waiting for updated COVID data, then manually download a CSV and clean it in Excel. This process could take hours, and it needed to happen every day. This is where HiGeorge comes in. We clean and aggregate public datasets and turn them into auto-updating data visualizations that anyone can instantly use with a simple copy/paste. Our data visualizations can be drag-and-dropped into articles, allowing news publishers to offer compelling data content to their communities. Check out a few versions of what we’re doing with customers — COVID-19 data reporting at North Carolina Health News [1], COVID-19 vaccine site mapping at SFGATE [2], real-time crime reporting in Dallas, TX [3], and police use of force at Mission Local [4]. Today, HiGeorge works with dozens of newsrooms across the country. Our visualizations have driven a 2x increase in pageviews and a 75% increase in session duration for our partner publishers. We charge a monthly subscription for access to our data visualization library – a fraction of the cost of an in-house data engineer. In the long run, we are building HiGeorge so that it becomes the single place to collaborate on and publish data content. We’d love to hear from the HN community and we’ll be hanging out in the comments if you have any questions or feedback. [1] https://ift.tt/3k10ZPa… [2] https://ift.tt/3k8D6oL… [3] https://ift.tt/3aBfXbs… [4] https://ift.tt/2M5IVGZ February 19, 2021 at 07:57AM
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(https://ift.tt/3qFW3SfLaunch HN: MindsDB (YC W20) – Machine Learning Inside Your Database https://ift.tt/2ZuqrTNTHE RIGHT PEOPLE https://ift.tt/37uGuVMTHE RIGHT PEOPLE https://ift.tt/3dsTaQS https://ift.tt/3dsTaQS
Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database Hi HN, Adam and Jorge here, and today we’re very excited to share MindsDB with you ( https://ift.tt/2UJWtY6 ). MindsDB AutoML Server is an open-source platform designed to accelerate machine learning workflows for people with data inside databases by introducing virtual AI tables. We allow you to create and consume machine learning models as regular database tables. Jorge and I have been friends for many years, having first met at college. We have previously founded and failed at another startup, but we stuck together as a team to start MindsDB. Initially a passion project, MindsDB began as an idea to help those who could not afford to hire a team of data scientists, which at the time was (and still is) very expensive. It has since grown into a thriving open-source community with contributors and users all over the globe. With the plethora of data available in databases today, predictive modeling can often be a pain, especially if you need to write complex applications for ingesting data, training encoders and embedders, writing sampling algorithms, training models, optimizing, scheduling, versioning, moving models into production environments, maintaining them and then having to explain the predictions and the degree of confidence… we knew there had to be a better way! We aim to steer you away from constantly reinventing the wheel by abstracting most of the unnecessary complexities around building, training, and deploying machine learning models. MindsDB provides you with two techniques for this: build and train models as simply as you would write an SQL query, and seamlessly “publish” and manage machine learning models as virtual tables inside your databases (we support Clickhouse, MariaDB, MySQL, PostgreSQL, and MSSQL. MongoDB is coming soon.) We also support getting data from other sources, such as Snowflake, s3, SQLite, and any excel, JSON, or CSV file. When we talk to our growing community, we find that they are using MindsDB for anything ranging from reducing financial risk in the payments sector to predicting in-app usage statistics – one user is even trying to predict the price of Bitcoin using sentiment analysis (we wish them luck). No matter what the use-case, what we hear most often is that the two most painful parts of the whole process are model generation (R&D) and/or moving the model into production. For those who already have models (i.e. who have already done the R&D part), we are launching the ability to bring your own models from frameworks like Pytorch, Tensorflow, scikit-learn, Keras, XGBoost, CatBoost, LightGBM, etc. directly into your database. If you’d like to try this experimental feature, you can sign-up here: ( https://ift.tt/3uhw05U ) We currently have a handful of customers who pay us for support. However, we will soon be launching a cloud version of MindsDB for those who do not want to worry about DevOps, scalability, and managing GPU clusters. Nevertheless, MindsDB will always remain free and open-source, because democratizing machine learning is at the core of every decision we make. We’re making good progress thanks to our open-source community and are also grateful to have the backing of the founders of MySQL & MariaDB. We would love your feedback and invite you to try it out. We’d also love to hear about your experience, so please share your feedback, thoughts, comments, and ideas below. https://ift.tt/3k6zsfm or https://mindsdb.com/ Thanks in advance, Adam & Jorge February 19, 2021 at 08:55AM
via Blogger https://ift.tt/3pCzjkE
(https://ift.tt/3dt30CoLaunch HN: HiGeorge (YC W21) – Real-time data visualizations for public datasets https://ift.tt/2NGzfTMTHE RIGHT PEOPLE https://ift.tt/2ZKvRu5THE RIGHT PEOPLE https://ift.tt/3qGzeOk https://ift.tt/3qGzeOk
Launch HN: HiGeorge (YC W21) – Real-time data visualizations for public datasets Hi HN! Anuj here. My co-founder Amir (Aazo11) and I are building HiGeorge ( https://hi-george.com/ ). We make localized drag-and-drop data visualizations so that all publishers, even the small ones, can better leverage data in their storytelling. Think Tableau with all the necessary data attached. At the onset of the pandemic Amir and I were looking for local data on the spread of the virus. We visited the sites of large national newsrooms like the NYTimes and were impressed by the quality of data visualizations and maps, but they lacked the geographic granularity for our own neighborhood. We then turned to our local newsrooms but found they presented data in tables and lists that made it difficult to comprehend the virus’ spread and trends. We wondered why. After talking to local journalists and publishers, we found that newsrooms simply do not have the resources to make sense of large datasets. Public datasets are hard to clean, poorly structured, and constantly updated. One publisher explained to us that she would refresh her state health department’s website 5 times a day waiting for updated COVID data, then manually download a CSV and clean it in Excel. This process could take hours, and it needed to happen every day. This is where HiGeorge comes in. We clean and aggregate public datasets and turn them into auto-updating data visualizations that anyone can instantly use with a simple copy/paste. Our data visualizations can be drag-and-dropped into articles, allowing news publishers to offer compelling data content to their communities. Check out a few versions of what we’re doing with customers — COVID-19 data reporting at North Carolina Health News [1], COVID-19 vaccine site mapping at SFGATE [2], real-time crime reporting in Dallas, TX [3], and police use of force at Mission Local [4]. Today, HiGeorge works with dozens of newsrooms across the country. Our visualizations have driven a 2x increase in pageviews and a 75% increase in session duration for our partner publishers. We charge a monthly subscription for access to our data visualization library – a fraction of the cost of an in-house data engineer. In the long run, we are building HiGeorge so that it becomes the single place to collaborate on and publish data content. We’d love to hear from the HN community and we’ll be hanging out in the comments if you have any questions or feedback. [1] https://ift.tt/3k10ZPa… [2] https://ift.tt/3k8D6oL… [3] https://ift.tt/3aBfXbs… [4] https://ift.tt/2M5IVGZ February 19, 2021 at 07:57AM
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