Show HN: Validatum – build fluent validation functions in .NET https://ift.tt/3s7cUOf February 19, 2021 at 03:43PM
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Showing posts with label February 19. Show all posts
Showing posts with label February 19. Show all posts
Saturday, February 20, 2021
Show HN: Split Keyboards Gallery https://ift.tt/3se815MTHE RIGHT PEOPLE https://ift.tt/3brlZdQTHE RIGHT PEOPLE https://ift.tt/3bg4fSA https://ift.tt/3bg4fSATHE RIGHT PEOPLE https://ift.tt/2Nn8ofMTHE RIGHT PEOPLE https://ift.tt/3qPjisY https://ift.tt/3qPjisY
Show HN: Split Keyboards Gallery https://ift.tt/3bjXGyn February 18, 2021 at 05:01AM
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(https://ift.tt/3pDP6zVShow HN: Validatum – build fluent validation functions in .NET https://ift.tt/3udD2stTHE RIGHT PEOPLE https://ift.tt/3k9a8W2THE RIGHT PEOPLE https://ift.tt/3aB6LE0 https://ift.tt/3aB6LE0
Show HN: Validatum – build fluent validation functions in .NET https://ift.tt/3s7cUOf February 19, 2021 at 03:43PM
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(https://ift.tt/3pKNVyNShow HN: ClubCircle – gradient borders and status badges for Clubhouse avatars https://ift.tt/3ayVZOsTHE RIGHT PEOPLE https://ift.tt/3udyQJjTHE RIGHT PEOPLE https://ift.tt/2ZxULge https://ift.tt/2ZxULgeTHE RIGHT PEOPLE https://ift.tt/2NG6ZReTHE RIGHT PEOPLE https://ift.tt/3s7zYwj https://ift.tt/3s7zYwj
Show HN: ClubCircle – gradient borders and status badges for Clubhouse avatars https://clubcircle.app February 19, 2021 at 01:23PM
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(https://ift.tt/3dvJHIAShow HN: Augmented Reality Route Setting for Climbing https://ift.tt/3bqGtU4THE RIGHT PEOPLE https://ift.tt/3ufdT0GTHE RIGHT PEOPLE https://ift.tt/3qGiRBl https://ift.tt/3qGiRBlTHE RIGHT PEOPLE https://ift.tt/2M6hT2bTHE RIGHT PEOPLE https://ift.tt/3uiBQEr https://ift.tt/3uiBQEr
Show HN: Augmented Reality Route Setting for Climbing https://www.youtube.com/watch?v=_z9797LFm4c February 19, 2021 at 10:35AM
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Show HN: Crypto Mining Pools Aggregator https://ift.tt/3azwIUD February 19, 2021 at 06:03AM
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Show HN: Split Keyboards Gallery https://ift.tt/3bjXGyn February 18, 2021 at 05:01AM
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(https://ift.tt/3blSsm2Show HN: ClubCircle – gradient borders and status badges for Clubhouse avatars https://ift.tt/3ayVZOsTHE RIGHT PEOPLE https://ift.tt/3udyQJjTHE RIGHT PEOPLE https://ift.tt/2ZxULge https://ift.tt/2ZxULge
Show HN: ClubCircle – gradient borders and status badges for Clubhouse avatars https://clubcircle.app February 19, 2021 at 01:23PM
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Show HN: Augmented Reality Route Setting for Climbing https://www.youtube.com/watch?v=_z9797LFm4c February 19, 2021 at 10:35AM
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Show HN: Crypto Mining Pools Aggregator https://ift.tt/3azwIUD February 19, 2021 at 06:03AM
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(https://ift.tt/3k7s6I9Friday, 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
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(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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(https://ift.tt/3udwjinShow HN: Archive as you browse, store locally and/or share with others via IPFS https://ift.tt/3qAQ01dTHE RIGHT PEOPLE https://ift.tt/3ucFtvuTHE RIGHT PEOPLE https://ift.tt/3qC0nBX https://ift.tt/3qC0nBXTHE RIGHT PEOPLE https://ift.tt/2ZrJaPITHE RIGHT PEOPLE https://ift.tt/3ueNYGs https://ift.tt/3ueNYGs
Show HN: Archive as you browse, store locally and/or share with others via IPFS https://archiveweb.page February 18, 2021 at 06:04PM
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