Show HN: Go-FrodoKEM a Practical quantum-secure key encapsulation in Go https://ift.tt/3duueZr February 21, 2021 at 03:09AM
via Blogger https://ift.tt/3pJZEgF
(https://ift.tt/3qKc3m8Sunday, February 21, 2021
Show HN: Write plain SQL, generate Typescript types of result row and parameters https://ift.tt/3dxNzZHTHE RIGHT PEOPLE https://ift.tt/2OY8A5ETHE RIGHT PEOPLE https://ift.tt/2ZCTzYU https://ift.tt/2ZCTzYU
Show HN: Write plain SQL, generate Typescript types of result row and parameters https://ift.tt/2ZCKAa4 February 20, 2021 at 10:16PM
via Blogger https://ift.tt/3sjMnxj
(https://ift.tt/2Mh63TaShow HN: Python Wheel Obfuscator https://ift.tt/3sigR2rTHE RIGHT PEOPLE https://ift.tt/2ZzcHXVTHE RIGHT PEOPLE https://ift.tt/3buzf1x https://ift.tt/3buzf1x
Show HN: Python Wheel Obfuscator https://ift.tt/2NogzZ6 February 20, 2021 at 11:26PM
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(https://ift.tt/3dzg4q0Show HN: CompreFace is a free and open-source face recognition software https://ift.tt/37zTwS0
Show HN: CompreFace is a free and open-source face recognition software https://ift.tt/37zw4nT February 21, 2021 at 07:18AM
Show HN: Force Directed Graph of Singapore MRT and LRT Networks https://ift.tt/3sbyoJKTHE RIGHT PEOPLE https://ift.tt/3qGyXeiTHE RIGHT PEOPLE https://ift.tt/3aESnun https://ift.tt/3aESnun
Show HN: Force Directed Graph of Singapore MRT and LRT Networks https://ift.tt/3kaKAHK February 21, 2021 at 12:42AM
via Blogger https://ift.tt/3sc2SLI
(https://ift.tt/3ueYCwZShow HN: Go-FrodoKEM a Practical quantum-secure key encapsulation in Go https://ift.tt/3kl6W9X
Show HN: Go-FrodoKEM a Practical quantum-secure key encapsulation in Go https://ift.tt/3duueZr February 21, 2021 at 03:09AM
Show HN: Write plain SQL, generate Typescript types of result row and parameters https://ift.tt/3dxNzZH
Show HN: Write plain SQL, generate Typescript types of result row and parameters https://ift.tt/2ZCKAa4 February 20, 2021 at 10:16PM
Show HN: Python Wheel Obfuscator https://ift.tt/3sigR2r
Show HN: Python Wheel Obfuscator https://ift.tt/2NogzZ6 February 20, 2021 at 11:26PM
Show HN: Force Directed Graph of Singapore MRT and LRT Networks https://ift.tt/3sbyoJK
Show HN: Force Directed Graph of Singapore MRT and LRT Networks https://ift.tt/3kaKAHK February 21, 2021 at 12:42AM
Show HN: Tape Machine https://ift.tt/3pCB5CATHE RIGHT PEOPLE https://ift.tt/3qLNPs0THE RIGHT PEOPLE https://ift.tt/3k68550 https://ift.tt/3k68550THE RIGHT PEOPLE https://ift.tt/2ZxxSJMTHE RIGHT PEOPLE https://ift.tt/2NuigEp https://ift.tt/2NuigEp
Show HN: Tape Machine https://www.youtube.com/watch?v=XlQkZrrQx3U&feature=youtu.be February 20, 2021 at 07:08PM
via Blogger https://ift.tt/3qGqVSK
via Blogger https://ift.tt/3dxAGi9
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Show HN: Jambook.io – A “don’t break the chain” dashboard for GitHub writing https://www.jambook.io/ February 20, 2021 at 03:40PM
via Blogger https://ift.tt/3ducOw5
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(https://ift.tt/3aDVW44Show HN: Peppa Peg – An Ultra Lightweight Peg Parser in ANSI C https://ift.tt/2ZBEsPqTHE RIGHT PEOPLE https://ift.tt/3ukrmV2THE RIGHT PEOPLE https://ift.tt/3sgqS0q https://ift.tt/3sgqS0qTHE RIGHT PEOPLE https://ift.tt/2ONftGHTHE RIGHT PEOPLE https://ift.tt/3pJbesy https://ift.tt/3pJbesy
Show HN: Peppa Peg – An Ultra Lightweight Peg Parser in ANSI C After reading the [PEG Parsers series] written by Guido van Rossum, I started thinking writing a PEG Parser in ANSI C. Here are the reasons: – It’s FUN. I’ve made several parser libraries, such as JSON, Mustache, Markdown, and I think I can take the challenge now. – I haven’t had any opportunity to work on an Open Source project written in ANSI C. – Having a PEG parser in ANSI C can benefit whoever is developing a parser, as adding C bindings for other programming languages are not too difficult. And after SIX months’ development, my project is now kinda feature complete. It’s named Peppa PEG and you can find it here: https://ift.tt/3aBmrqW I have learned quite a lot during the journey of creating it, such as gdb, valgrind, cmake, etc. And I wouldn’t make it to the end without learning from some awesome projects, such as pest.rs, cJSON, etc. Appreciate any feedbacks! Thank you! [PEG Parsers series]: https://ift.tt/2M0QQTs February 20, 2021 at 06:10PM
via Blogger https://ift.tt/2M9fXWK
via Blogger https://ift.tt/37uliiX
(https://ift.tt/2Npm7CNShow HN: Tape Machine https://ift.tt/3pCB5CATHE RIGHT PEOPLE https://ift.tt/3qLNPs0THE RIGHT PEOPLE https://ift.tt/3k68550 https://ift.tt/3k68550
Show HN: Tape Machine https://www.youtube.com/watch?v=XlQkZrrQx3U&feature=youtu.be February 20, 2021 at 07:08PM
via Blogger https://ift.tt/3qGqVSK
(https://ift.tt/2Zz5o2vShow HN: Jambook.io – A “don’t break the chain” dashboard for GitHub writing https://ift.tt/37wUPRITHE RIGHT PEOPLE https://ift.tt/3boRCEZTHE RIGHT PEOPLE https://ift.tt/3aEhD48 https://ift.tt/3aEhD48
Show HN: Jambook.io – A “don’t break the chain” dashboard for GitHub writing https://www.jambook.io/ February 20, 2021 at 03:40PM
via Blogger https://ift.tt/3ducOw5
(https://ift.tt/3dBQsZtShow HN: Peppa Peg – An Ultra Lightweight Peg Parser in ANSI C https://ift.tt/2ZBEsPqTHE RIGHT PEOPLE https://ift.tt/3ukrmV2THE RIGHT PEOPLE https://ift.tt/3sgqS0q https://ift.tt/3sgqS0q
Show HN: Peppa Peg – An Ultra Lightweight Peg Parser in ANSI C After reading the [PEG Parsers series] written by Guido van Rossum, I started thinking writing a PEG Parser in ANSI C. Here are the reasons: – It’s FUN. I’ve made several parser libraries, such as JSON, Mustache, Markdown, and I think I can take the challenge now. – I haven’t had any opportunity to work on an Open Source project written in ANSI C. – Having a PEG parser in ANSI C can benefit whoever is developing a parser, as adding C bindings for other programming languages are not too difficult. And after SIX months’ development, my project is now kinda feature complete. It’s named Peppa PEG and you can find it here: https://ift.tt/3aBmrqW I have learned quite a lot during the journey of creating it, such as gdb, valgrind, cmake, etc. And I wouldn’t make it to the end without learning from some awesome projects, such as pest.rs, cJSON, etc. Appreciate any feedbacks! Thank you! [PEG Parsers series]: https://ift.tt/2M0QQTs February 20, 2021 at 06:10PM
via Blogger https://ift.tt/2M9fXWK
(https://ift.tt/3pEphj9Show HN: Tape Machine https://ift.tt/3pCB5CA
Show HN: Tape Machine https://www.youtube.com/watch?v=XlQkZrrQx3U&feature=youtu.be February 20, 2021 at 07:08PM
Show HN: Jambook.io – A “don't break the chain” dashboard for GitHub writing https://ift.tt/37wUPRI
Show HN: Jambook.io – A “don't break the chain” dashboard for GitHub writing https://www.jambook.io/ February 20, 2021 at 03:40PM
Show HN: Peppa Peg – An Ultra Lightweight Peg Parser in ANSI C https://ift.tt/2ZBEsPq
Show HN: Peppa Peg – An Ultra Lightweight Peg Parser in ANSI C After reading the [PEG Parsers series] written by Guido van Rossum, I started thinking writing a PEG Parser in ANSI C. Here are the reasons: - It's FUN. I've made several parser libraries, such as JSON, Mustache, Markdown, and I think I can take the challenge now. - I haven't had any opportunity to work on an Open Source project written in ANSI C. - Having a PEG parser in ANSI C can benefit whoever is developing a parser, as adding C bindings for other programming languages are not too difficult. And after SIX months' development, my project is now kinda feature complete. It's named Peppa PEG and you can find it here: https://ift.tt/3aBmrqW I have learned quite a lot during the journey of creating it, such as gdb, valgrind, cmake, etc. And I wouldn't make it to the end without learning from some awesome projects, such as pest.rs, cJSON, etc. Appreciate any feedbacks! Thank you! [PEG Parsers series]: https://ift.tt/2M0QQTs February 20, 2021 at 06:10PM
Saturday, February 20, 2021
Show 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/3aB6LE0THE RIGHT PEOPLE https://ift.tt/37vgnhNTHE RIGHT PEOPLE https://ift.tt/3azAYDu https://ift.tt/3azAYDu
Show HN: Validatum – build fluent validation functions in .NET https://ift.tt/3s7cUOf February 19, 2021 at 03:43PM
via Blogger https://ift.tt/3bmdeln
via Blogger https://ift.tt/3qE6fe6
(https://ift.tt/37yVyBXShow HN: Share your workstation setup and earn with Amazon Affiliate links https://ift.tt/3scmoYA
Show HN: Share your workstation setup and earn with Amazon Affiliate links https://ift.tt/3qPcn2Y February 19, 2021 at 06:44PM
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
via Blogger https://ift.tt/37x5Ylv
via Blogger https://ift.tt/3sc77ag
(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
via Blogger https://ift.tt/3bmdeln
(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
via Blogger https://ift.tt/3bomYLP
via Blogger https://ift.tt/3aBEkWE
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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
via Blogger https://ift.tt/2OMr1Kk
via Blogger https://ift.tt/3k3Ts27
(https://ift.tt/37w35RTShow HN: Crypto Mining Pools Aggregator https://ift.tt/3uc77J0THE RIGHT PEOPLE https://ift.tt/3dws5fMTHE RIGHT PEOPLE https://ift.tt/3k6rnHd https://ift.tt/3k6rnHdTHE RIGHT PEOPLE https://ift.tt/2ZEsfJHTHE RIGHT PEOPLE https://ift.tt/3kdAOVC https://ift.tt/3kdAOVC
Show HN: Crypto Mining Pools Aggregator https://ift.tt/3azwIUD February 19, 2021 at 06:03AM
via Blogger https://ift.tt/3dCNJPk
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(https://ift.tt/3sdLTJ8Show HN: Split Keyboards Gallery https://ift.tt/3se815MTHE RIGHT PEOPLE https://ift.tt/3brlZdQTHE RIGHT PEOPLE https://ift.tt/3bg4fSA https://ift.tt/3bg4fSA
Show HN: Split Keyboards Gallery https://ift.tt/3bjXGyn February 18, 2021 at 05:01AM
via Blogger https://ift.tt/37x5Ylv
(https://ift.tt/3blSsm2Show HN: Validatum – build fluent validation functions in .NET https://ift.tt/3udD2st
Show HN: Validatum – build fluent validation functions in .NET https://ift.tt/3s7cUOf February 19, 2021 at 03:43PM
Show 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
via Blogger https://ift.tt/3bomYLP
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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
via Blogger https://ift.tt/2OMr1Kk
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Show HN: Crypto Mining Pools Aggregator https://ift.tt/3azwIUD February 19, 2021 at 06:03AM
via Blogger https://ift.tt/3dCNJPk
(https://ift.tt/3k7s6I9Friday, February 19, 2021
Show HN: Split Keyboards Gallery https://ift.tt/3se815M
Show HN: Split Keyboards Gallery https://ift.tt/3bjXGyn February 18, 2021 at 05:01AM
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
via Blogger https://ift.tt/3pCzjkE
via Blogger https://ift.tt/3dHdFK3
(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
via Blogger https://ift.tt/37uLnOF
via Blogger https://ift.tt/2OQgwpn
(https://ift.tt/3qFW3SfShow HN: ClubCircle – gradient borders and status badges for Clubhouse avatars https://ift.tt/3ayVZOs
Show HN: ClubCircle – gradient borders and status badges for Clubhouse avatars https://clubcircle.app February 19, 2021 at 01:23PM
Show HN: Augmented Reality Route Setting for Climbing https://ift.tt/3bqGtU4
Show HN: Augmented Reality Route Setting for Climbing https://www.youtube.com/watch?v=_z9797LFm4c February 19, 2021 at 10:35AM
Show HN: Crypto Mining Pools Aggregator https://ift.tt/3uc77J0
Show HN: Crypto Mining Pools Aggregator https://ift.tt/3azwIUD February 19, 2021 at 06:03AM
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/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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(https://ift.tt/3udwjinLaunch HN: MindsDB (YC W20) – Machine Learning Inside Your Database https://ift.tt/2ZuqrTN
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
Launch HN: HiGeorge (YC W21) – Real-time data visualizations for public datasets https://ift.tt/2NGzfTM
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
Show 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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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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Show HN: Archive as you browse, store locally and/or share with others via IPFS https://archiveweb.page February 18, 2021 at 06:04PM
Thursday, February 18, 2021
Show HN: Kalaksi: a social-network built on top of RSS https://ift.tt/3aue6VWTHE RIGHT PEOPLE https://ift.tt/3dpiGGJTHE RIGHT PEOPLE https://ift.tt/3dCZzJF https://ift.tt/3dCZzJFTHE RIGHT PEOPLE https://ift.tt/3bhZyYGTHE RIGHT PEOPLE https://ift.tt/2Na84kz https://ift.tt/2Na84kz
Show HN: Kalaksi: a social-network built on top of RSS https://www.kalaksi.com February 18, 2021 at 07:37AM
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(https://ift.tt/3s6inVAShow HN: ScreenToVideo – Record your videos flawlessly https://ift.tt/2NbjODq
Show HN: ScreenToVideo – Record your videos flawlessly https://ift.tt/3bNnemf February 18, 2021 at 12:15PM
Launch HN: Ontop (YC W21) – Easily hire and pay remote workers in LATAM https://ift.tt/3s2MTj0THE RIGHT PEOPLE https://ift.tt/3dmX5yDTHE RIGHT PEOPLE https://ift.tt/3jZfkeZ https://ift.tt/3jZfkeZTHE RIGHT PEOPLE https://ift.tt/2Nlk2rxTHE RIGHT PEOPLE https://ift.tt/3dqIJNG https://ift.tt/3dqIJNG
Launch HN: Ontop (YC W21) – Easily hire and pay remote workers in LATAM Hi YC! We are Santiago Aparicio, Julian Torres and Jaime Abella and we are from Colombia. We are building Ontop (www.ontop.ai) to help companies do remote hiring and payouts, all the way from contract creation, to compliance documentation and easy money transfers. COVID-19 has taught us all that remote works. Our bet is that companies in the US and Europe will start hiring more people in LATAM because talent is increasing in quality at a fraction of price compared to what they can get elsewhere. Paying people in LATAM requires local knowledge to get the level of speed and compliance that workers need to get their money on time. We are building a solution so companies hiring in LATAM have to do less paperwork, can easily be compliant and disperse payments to different countries in a single place. In our previous startup Fitpal (multi gym membership in LATAM) we experienced the pain behind signing contracts, collecting documents and sending money to different countries. We had to pay hundreds of gyms in LATAM and were frustrated by the amount of time we spent doing administrative work, when we should have been thinking on how to hack our way to growth. We handle all paperwork, compliance and payments so onboarding new people is really easy. And most importantly, everything done legally, by the book, so that companies are always due diligence proof. Our solution is tailored for LATAM guaranteeing the best speed and compliance in the market. We want to hear your thoughts on our solution. We value feedback and case uses that you might have. Email us at founders@ontop.ai and we will personally give you a demo. February 18, 2021 at 04:58AM
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(https://ift.tt/3pyjMCkShow HN: Merge multiple PDFs into one using WebAssembly https://ift.tt/3s6ywtUTHE RIGHT PEOPLE https://ift.tt/3drwLDETHE RIGHT PEOPLE https://ift.tt/2ZuVr5W https://ift.tt/2ZuVr5W
Show HN: Merge multiple PDFs into one using WebAssembly http://localpdf.tech/ February 18, 2021 at 08:52AM
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(https://ift.tt/3pzVgAZShow HN: Create APIs for static datasets without writing a single line of code https://ift.tt/3pxS8W4THE RIGHT PEOPLE https://ift.tt/3k4yqjYTHE RIGHT PEOPLE https://ift.tt/3bjE5OR https://ift.tt/3bjE5OR
Show HN: Create APIs for static datasets without writing a single line of code https://ift.tt/3k7WYsb February 18, 2021 at 08:15AM
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(https://ift.tt/3k1dJVWShow HN: Kalaksi: a social-network built on top of RSS https://ift.tt/3aue6VWTHE RIGHT PEOPLE https://ift.tt/3dpiGGJTHE RIGHT PEOPLE https://ift.tt/3dCZzJF https://ift.tt/3dCZzJF
Show HN: Kalaksi: a social-network built on top of RSS https://www.kalaksi.com February 18, 2021 at 07:37AM
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(https://ift.tt/3qsQVklLaunch HN: Ontop (YC W21) – Easily hire and pay remote workers in LATAM https://ift.tt/3s2MTj0THE RIGHT PEOPLE https://ift.tt/3dmX5yDTHE RIGHT PEOPLE https://ift.tt/3jZfkeZ https://ift.tt/3jZfkeZ
Launch HN: Ontop (YC W21) – Easily hire and pay remote workers in LATAM Hi YC! We are Santiago Aparicio, Julian Torres and Jaime Abella and we are from Colombia. We are building Ontop (www.ontop.ai) to help companies do remote hiring and payouts, all the way from contract creation, to compliance documentation and easy money transfers. COVID-19 has taught us all that remote works. Our bet is that companies in the US and Europe will start hiring more people in LATAM because talent is increasing in quality at a fraction of price compared to what they can get elsewhere. Paying people in LATAM requires local knowledge to get the level of speed and compliance that workers need to get their money on time. We are building a solution so companies hiring in LATAM have to do less paperwork, can easily be compliant and disperse payments to different countries in a single place. In our previous startup Fitpal (multi gym membership in LATAM) we experienced the pain behind signing contracts, collecting documents and sending money to different countries. We had to pay hundreds of gyms in LATAM and were frustrated by the amount of time we spent doing administrative work, when we should have been thinking on how to hack our way to growth. We handle all paperwork, compliance and payments so onboarding new people is really easy. And most importantly, everything done legally, by the book, so that companies are always due diligence proof. Our solution is tailored for LATAM guaranteeing the best speed and compliance in the market. We want to hear your thoughts on our solution. We value feedback and case uses that you might have. Email us at founders@ontop.ai and we will personally give you a demo. February 18, 2021 at 04:58AM
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(https://ift.tt/2ZreFt5Show HN: Merge multiple PDFs into one using WebAssembly https://ift.tt/3s6ywtU
Show HN: Merge multiple PDFs into one using WebAssembly http://localpdf.tech/ February 18, 2021 at 08:52AM
Show HN: Create APIs for static datasets without writing a single line of code https://ift.tt/3pxS8W4
Show HN: Create APIs for static datasets without writing a single line of code https://ift.tt/3k7WYsb February 18, 2021 at 08:15AM
Show HN: Kalaksi: a social-network built on top of RSS https://ift.tt/3aue6VW
Show HN: Kalaksi: a social-network built on top of RSS https://www.kalaksi.com February 18, 2021 at 07:37AM
Launch HN: Ontop (YC W21) – Easily hire and pay remote workers in LATAM https://ift.tt/3s2MTj0
Launch HN: Ontop (YC W21) – Easily hire and pay remote workers in LATAM Hi YC! We are Santiago Aparicio, Julian Torres and Jaime Abella and we are from Colombia. We are building Ontop (www.ontop.ai) to help companies do remote hiring and payouts, all the way from contract creation, to compliance documentation and easy money transfers. COVID-19 has taught us all that remote works. Our bet is that companies in the US and Europe will start hiring more people in LATAM because talent is increasing in quality at a fraction of price compared to what they can get elsewhere. Paying people in LATAM requires local knowledge to get the level of speed and compliance that workers need to get their money on time. We are building a solution so companies hiring in LATAM have to do less paperwork, can easily be compliant and disperse payments to different countries in a single place. In our previous startup Fitpal (multi gym membership in LATAM) we experienced the pain behind signing contracts, collecting documents and sending money to different countries. We had to pay hundreds of gyms in LATAM and were frustrated by the amount of time we spent doing administrative work, when we should have been thinking on how to hack our way to growth. We handle all paperwork, compliance and payments so onboarding new people is really easy. And most importantly, everything done legally, by the book, so that companies are always due diligence proof. Our solution is tailored for LATAM guaranteeing the best speed and compliance in the market. We want to hear your thoughts on our solution. We value feedback and case uses that you might have. Email us at founders@ontop.ai and we will personally give you a demo. February 18, 2021 at 04:58AM
Launch HN: Datrics (YC W21) – No-Code Analytics and ML for FinTech https://ift.tt/3beSuvYTHE RIGHT PEOPLE https://ift.tt/3au23b2THE RIGHT PEOPLE https://ift.tt/3dpJ439 https://ift.tt/3dpJ439THE RIGHT PEOPLE https://ift.tt/3ukECciTHE RIGHT PEOPLE https://ift.tt/3s67vqw https://ift.tt/3s67vqw
Launch HN: Datrics (YC W21) – No-Code Analytics and ML for FinTech Hey everyone, we’re Anton (avais), Kirill (Datkiri), and Volodymyr (vsofi), the founders of Datrics ( https://datrics.ai ). We help FinTech companies build and deploy machine learning models without writing code. We provide a visual tool to work with structured data by constructing a diagram of data manipulations from lego-like bricks, and then execute it all on a backend. This lets our users accomplish tasks that usually need a team of software engineers, data scientists, and DevOps. For instance, one of our customers is a consumer lending company that developed a new risk model using just our drag-and-drop interface. I used to lead a large data science consultancy team, being responsible for Financial Services (and Risks specifically). Our teams’ projects included end-to-end risk modeling, demand forecasting, and inventory management optimization, mostly requiring combined efforts from different technical teams and business units to be implemented. It usually took months of work to turn an idea into a complete solution, going through data snapshot gathering to cleansing to experimenting to working with engineering and DevOps teams to turn experiments in Jupyter notebooks into a complete application that worked in production. Moreover, even if the application and logic behind the scenes were really simple (could be just dozens or hundreds of lines of code for a core part), the process to bring this to end-users could take ages. We started thinking about possible solutions when a request from one of the Tier 1 banks appeared, which confirmed that we’re not alone in this vision: their problem was giving their “citizen data scientists” and “citizen developers” power to do data-driven work. In other words, work with the data and generate insights useful for business. That was the first time I’d heard the term “citizen data scientist”. Our users are now these citizen data scientists and developers, whom we’re giving the possibility to manipulate data, build apps, pipelines, and ML models with just nominal IT support. Datrics is designed not only to do ML without coding, but to give analysts and domain experts a drag and drop interface to perform queries, generate reports, and do forecasting in a visual way with nominal IT support. One of our core use cases is doing better credit risk modeling – create application scorecards based on ML or apply rule-based transactional fraud detection. For this use-case, we’ve developed intelligent bricks that allow you to do variables binning and scorecards in a visual way. Other use cases include reports and pivot tables on aggregating sales data from different countries in different formats or doing inventory optimization by forecasting the demand without knowing any programming language. We’re providing 50+ bricks to construct ETL pipelines and build models. There are some limitations – a finite number of pre-built building blocks that can be included in your app, but if there is no block that you need, you can easily build your own ( https://youtu.be/BQNFcZWwUC8 ). Datrics is initially cloud-native, but also can be installed on-prem for those customers who have corresponding security policy or setups. The underlying technology, the pipeline execution engine is rather complex and currently built on top of Dask, which gives Python scalability for big datasets. In the next release, we are going to support Pandas as well as to switch intelligently between small datasets for rapid prototyping and big datasets for pipeline deployments. We’re charging only for private deployments, so our web version is free: https://ift.tt/2Nfu1i1 . Try it to create your analytical applications with a machine learning component! We’ve put together a wiki ( https://wiki.datrics.ai ) to cover the major functionality, We are super-excited to hear your thoughts and feedback! We’re big believers in the power of Machine Learning and self-service analytics and are happy to discuss what you think of no-code approaches for doing ML and analytics generally as well as the availability of them for non-data scientists. Or anything you want to share in this space! February 18, 2021 at 12:12AM
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(https://ift.tt/3dondt3Launch HN: Datrics (YC W21) – No-Code Analytics and ML for FinTech https://ift.tt/3beSuvYTHE RIGHT PEOPLE https://ift.tt/3au23b2THE RIGHT PEOPLE https://ift.tt/3dpJ439 https://ift.tt/3dpJ439
Launch HN: Datrics (YC W21) – No-Code Analytics and ML for FinTech Hey everyone, we’re Anton (avais), Kirill (Datkiri), and Volodymyr (vsofi), the founders of Datrics ( https://datrics.ai ). We help FinTech companies build and deploy machine learning models without writing code. We provide a visual tool to work with structured data by constructing a diagram of data manipulations from lego-like bricks, and then execute it all on a backend. This lets our users accomplish tasks that usually need a team of software engineers, data scientists, and DevOps. For instance, one of our customers is a consumer lending company that developed a new risk model using just our drag-and-drop interface. I used to lead a large data science consultancy team, being responsible for Financial Services (and Risks specifically). Our teams’ projects included end-to-end risk modeling, demand forecasting, and inventory management optimization, mostly requiring combined efforts from different technical teams and business units to be implemented. It usually took months of work to turn an idea into a complete solution, going through data snapshot gathering to cleansing to experimenting to working with engineering and DevOps teams to turn experiments in Jupyter notebooks into a complete application that worked in production. Moreover, even if the application and logic behind the scenes were really simple (could be just dozens or hundreds of lines of code for a core part), the process to bring this to end-users could take ages. We started thinking about possible solutions when a request from one of the Tier 1 banks appeared, which confirmed that we’re not alone in this vision: their problem was giving their “citizen data scientists” and “citizen developers” power to do data-driven work. In other words, work with the data and generate insights useful for business. That was the first time I’d heard the term “citizen data scientist”. Our users are now these citizen data scientists and developers, whom we’re giving the possibility to manipulate data, build apps, pipelines, and ML models with just nominal IT support. Datrics is designed not only to do ML without coding, but to give analysts and domain experts a drag and drop interface to perform queries, generate reports, and do forecasting in a visual way with nominal IT support. One of our core use cases is doing better credit risk modeling – create application scorecards based on ML or apply rule-based transactional fraud detection. For this use-case, we’ve developed intelligent bricks that allow you to do variables binning and scorecards in a visual way. Other use cases include reports and pivot tables on aggregating sales data from different countries in different formats or doing inventory optimization by forecasting the demand without knowing any programming language. We’re providing 50+ bricks to construct ETL pipelines and build models. There are some limitations – a finite number of pre-built building blocks that can be included in your app, but if there is no block that you need, you can easily build your own ( https://youtu.be/BQNFcZWwUC8 ). Datrics is initially cloud-native, but also can be installed on-prem for those customers who have corresponding security policy or setups. The underlying technology, the pipeline execution engine is rather complex and currently built on top of Dask, which gives Python scalability for big datasets. In the next release, we are going to support Pandas as well as to switch intelligently between small datasets for rapid prototyping and big datasets for pipeline deployments. We’re charging only for private deployments, so our web version is free: https://ift.tt/2Nfu1i1 . Try it to create your analytical applications with a machine learning component! We’ve put together a wiki ( https://wiki.datrics.ai ) to cover the major functionality, We are super-excited to hear your thoughts and feedback! We’re big believers in the power of Machine Learning and self-service analytics and are happy to discuss what you think of no-code approaches for doing ML and analytics generally as well as the availability of them for non-data scientists. Or anything you want to share in this space! February 18, 2021 at 12:12AM
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(https://ift.tt/3s2iFg0Launch HN: Datrics (YC W21) – No-Code Analytics and ML for FinTech https://ift.tt/3beSuvY
Launch HN: Datrics (YC W21) – No-Code Analytics and ML for FinTech Hey everyone, we're Anton (avais), Kirill (Datkiri), and Volodymyr (vsofi), the founders of Datrics ( https://datrics.ai ). We help FinTech companies build and deploy machine learning models without writing code. We provide a visual tool to work with structured data by constructing a diagram of data manipulations from lego-like bricks, and then execute it all on a backend. This lets our users accomplish tasks that usually need a team of software engineers, data scientists, and DevOps. For instance, one of our customers is a consumer lending company that developed a new risk model using just our drag-and-drop interface. I used to lead a large data science consultancy team, being responsible for Financial Services (and Risks specifically). Our teams’ projects included end-to-end risk modeling, demand forecasting, and inventory management optimization, mostly requiring combined efforts from different technical teams and business units to be implemented. It usually took months of work to turn an idea into a complete solution, going through data snapshot gathering to cleansing to experimenting to working with engineering and DevOps teams to turn experiments in Jupyter notebooks into a complete application that worked in production. Moreover, even if the application and logic behind the scenes were really simple (could be just dozens or hundreds of lines of code for a core part), the process to bring this to end-users could take ages. We started thinking about possible solutions when a request from one of the Tier 1 banks appeared, which confirmed that we’re not alone in this vision: their problem was giving their “citizen data scientists” and “citizen developers” power to do data-driven work. In other words, work with the data and generate insights useful for business. That was the first time I’d heard the term “citizen data scientist”. Our users are now these citizen data scientists and developers, whom we’re giving the possibility to manipulate data, build apps, pipelines, and ML models with just nominal IT support. Datrics is designed not only to do ML without coding, but to give analysts and domain experts a drag and drop interface to perform queries, generate reports, and do forecasting in a visual way with nominal IT support. One of our core use cases is doing better credit risk modeling - create application scorecards based on ML or apply rule-based transactional fraud detection. For this use-case, we’ve developed intelligent bricks that allow you to do variables binning and scorecards in a visual way. Other use cases include reports and pivot tables on aggregating sales data from different countries in different formats or doing inventory optimization by forecasting the demand without knowing any programming language. We’re providing 50+ bricks to construct ETL pipelines and build models. There are some limitations - a finite number of pre-built building blocks that can be included in your app, but if there is no block that you need, you can easily build your own ( https://youtu.be/BQNFcZWwUC8 ). Datrics is initially cloud-native, but also can be installed on-prem for those customers who have corresponding security policy or setups. The underlying technology, the pipeline execution engine is rather complex and currently built on top of Dask, which gives Python scalability for big datasets. In the next release, we are going to support Pandas as well as to switch intelligently between small datasets for rapid prototyping and big datasets for pipeline deployments. We’re charging only for private deployments, so our web version is free: https://ift.tt/2Nfu1i1 . Try it to create your analytical applications with a machine learning component! We've put together a wiki ( https://wiki.datrics.ai ) to cover the major functionality, We are super-excited to hear your thoughts and feedback! We're big believers in the power of Machine Learning and self-service analytics and are happy to discuss what you think of no-code approaches for doing ML and analytics generally as well as the availability of them for non-data scientists. Or anything you want to share in this space! February 18, 2021 at 12:12AM
Show HN: Job Alerts for the Freelancing Economy https://ift.tt/3awjXKuTHE RIGHT PEOPLE https://ift.tt/3u87i82THE RIGHT PEOPLE https://ift.tt/3aujgkI https://ift.tt/3aujgkITHE RIGHT PEOPLE https://ift.tt/3qxB6JhTHE RIGHT PEOPLE https://ift.tt/37nYu4r https://ift.tt/37nYu4r
Show HN: Job Alerts for the Freelancing Economy https://www.ginevar.com February 17, 2021 at 02:58PM
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(https://ift.tt/3k1sGYgShow HN: Job Alerts for the Freelancing Economy https://ift.tt/3awjXKuTHE RIGHT PEOPLE https://ift.tt/3u87i82THE RIGHT PEOPLE https://ift.tt/3aujgkI https://ift.tt/3aujgkI
Show HN: Job Alerts for the Freelancing Economy https://www.ginevar.com February 17, 2021 at 02:58PM
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Show HN: Job Alerts for the Freelancing Economy https://www.ginevar.com February 17, 2021 at 02:58PM
Wednesday, February 17, 2021
Show HN: DeltaCI: Bare Metal CI/CD with much faster builds https://ift.tt/37nyWEw
Show HN: DeltaCI: Bare Metal CI/CD with much faster builds https://deltaci.com February 17, 2021 at 07:17AM
Show HN: Commitlog – Simple CLI Changelog Generator Using Commit History https://ift.tt/3qv0AqGTHE RIGHT PEOPLE https://ift.tt/3jUWgP0THE RIGHT PEOPLE https://ift.tt/3av7FSC https://ift.tt/3av7FSC
Show HN: Commitlog – Simple CLI Changelog Generator Using Commit History https://ift.tt/34XukUa February 17, 2021 at 04:49AM
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(https://ift.tt/2NDn75PShow HN: Mailoji – Emoji Email Addresses https://ift.tt/3dnjxYKTHE RIGHT PEOPLE https://ift.tt/3jY6MEXTHE RIGHT PEOPLE https://ift.tt/3ptXdim https://ift.tt/3ptXdim
Show HN: Mailoji – Emoji Email Addresses https://mailoji.com/ February 17, 2021 at 04:13AM
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(https://ift.tt/3poGSeMShow HN: Book Launch – Writing Maintainable Unit Tests https://ift.tt/3aoNTrMTHE RIGHT PEOPLE https://ift.tt/3dlcX4MTHE RIGHT PEOPLE https://ift.tt/3biwosa https://ift.tt/3biwosa
Show HN: Book Launch – Writing Maintainable Unit Tests https://ift.tt/3apBtjp February 17, 2021 at 02:19AM
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(https://ift.tt/2ZnUr3pShow HN: Experiments on Machine Translation in Pure Go https://ift.tt/3u5GMfCTHE RIGHT PEOPLE https://ift.tt/37og9J1THE RIGHT PEOPLE https://ift.tt/3jV6TBo https://ift.tt/3jV6TBo
Show HN: Experiments on Machine Translation in Pure Go https://ift.tt/3bfjqeQ February 17, 2021 at 12:47AM
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(https://ift.tt/3dlXBwRShow HN: Pure Golang PostgreSQL Parser powered by CockroachDB https://ift.tt/2M4D0SITHE RIGHT PEOPLE https://ift.tt/3jU1HO8THE RIGHT PEOPLE https://ift.tt/3pu1KBh https://ift.tt/3pu1KBh
Show HN: Pure Golang PostgreSQL Parser powered by CockroachDB https://ift.tt/3arMe4H February 17, 2021 at 12:43AM
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(https://ift.tt/3u1dC18Show HN: A social platform with music in focus. Download the app https://ift.tt/37nIDmdTHE RIGHT PEOPLE https://ift.tt/3qsv1h2THE RIGHT PEOPLE https://ift.tt/2OPFhlP https://ift.tt/2OPFhlPTHE RIGHT PEOPLE https://ift.tt/3rYwDzBTHE RIGHT PEOPLE https://ift.tt/3u9ktpi https://ift.tt/3u9ktpi
Show HN: A social platform with music in focus. Download the app https://syncc.app/ February 17, 2021 at 01:46AM
via Blogger https://ift.tt/3jYrwwh
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(https://ift.tt/3pnIlC8Show HN: Ray.so – Create beautiful images of your code https://ift.tt/3u6Ff8Z
Show HN: Ray.so – Create beautiful images of your code https://ray.so February 17, 2021 at 08:35AM
Show HN: Moneto – Cash planning tool for running a profitable business https://ift.tt/3at8mvD
Show HN: Moneto – Cash planning tool for running a profitable business https://ift.tt/3dik5Pq February 17, 2021 at 07:45AM
Show HN: Personal messaging with a 2-7 day delay https://ift.tt/3bge5Eg
Show HN: Personal messaging with a 2-7 day delay https://tardamail.com/ February 17, 2021 at 04:57AM
Show HN: Commitlog – Simple CLI Changelog Generator Using Commit History https://ift.tt/3qv0AqG
Show HN: Commitlog – Simple CLI Changelog Generator Using Commit History https://ift.tt/34XukUa February 17, 2021 at 04:49AM
Show HN: Mailoji – Emoji Email Addresses https://ift.tt/3dnjxYK
Show HN: Mailoji – Emoji Email Addresses https://mailoji.com/ February 17, 2021 at 04:13AM
Show HN: Book Launch – Writing Maintainable Unit Tests https://ift.tt/3aoNTrM
Show HN: Book Launch – Writing Maintainable Unit Tests https://ift.tt/3apBtjp February 17, 2021 at 02:19AM
Show HN: Experiments on Machine Translation in Pure Go https://ift.tt/3u5GMfC
Show HN: Experiments on Machine Translation in Pure Go https://ift.tt/3bfjqeQ February 17, 2021 at 12:47AM
Show HN: Pure Golang PostgreSQL Parser powered by CockroachDB https://ift.tt/2M4D0SI
Show HN: Pure Golang PostgreSQL Parser powered by CockroachDB https://ift.tt/3arMe4H February 17, 2021 at 12:43AM
Show HN: A social platform with music in focus. Download the app https://ift.tt/37nIDmdTHE RIGHT PEOPLE https://ift.tt/3qsv1h2THE RIGHT PEOPLE https://ift.tt/2OPFhlP https://ift.tt/2OPFhlP
Show HN: A social platform with music in focus. Download the app https://syncc.app/ February 17, 2021 at 01:46AM
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(https://ift.tt/3dyIO2fShow HN: Crane – A self-hosted research literature management web service https://ift.tt/3qrqyLyTHE RIGHT PEOPLE https://ift.tt/3jXv02iTHE RIGHT PEOPLE https://ift.tt/2NzQ2rK https://ift.tt/2NzQ2rKTHE RIGHT PEOPLE https://ift.tt/3dmXocHTHE RIGHT PEOPLE https://ift.tt/2OLYx3v https://ift.tt/2OLYx3v
Show HN: Crane – A self-hosted research literature management web service https://ift.tt/3dofH1g February 16, 2021 at 04:02PM
via Blogger https://ift.tt/2NuiB9U
via Blogger https://ift.tt/3asEdfN
(https://ift.tt/37oXzR5Show HN: A social platform with music in focus. Download the app https://ift.tt/37nIDmd
Show HN: A social platform with music in focus. Download the app https://syncc.app/ February 17, 2021 at 01:46AM
Show HN: Crane – A self-hosted research literature management web service https://ift.tt/3qrqyLyTHE RIGHT PEOPLE https://ift.tt/3jXv02iTHE RIGHT PEOPLE https://ift.tt/2NzQ2rK https://ift.tt/2NzQ2rK
Show HN: Crane – A self-hosted research literature management web service https://ift.tt/3dofH1g February 16, 2021 at 04:02PM
via Blogger https://ift.tt/2NuiB9U
(https://ift.tt/3bfMXFgShow HN: Crane – A self-hosted research literature management web service https://ift.tt/3qrqyLy
Show HN: Crane – A self-hosted research literature management web service https://ift.tt/3dofH1g February 16, 2021 at 04:02PM
Tuesday, February 16, 2021
Show HN: Pageturner – a community for building a reading habit https://ift.tt/3allnHw
Show HN: Pageturner – a community for building a reading habit https://ift.tt/3jUffZT February 16, 2021 at 02:54AM
Show HN: My ultimate make file for Golang services https://ift.tt/3qrn5N0THE RIGHT PEOPLE https://ift.tt/2ZhEImyTHE RIGHT PEOPLE https://ift.tt/3djp0jc https://ift.tt/3djp0jcTHE RIGHT PEOPLE https://ift.tt/3preMj9THE RIGHT PEOPLE https://ift.tt/3pnN5ru https://ift.tt/3pnN5ru
Show HN: My ultimate make file for Golang services https://ift.tt/3difFb6 February 15, 2021 at 11:11PM
via Blogger https://ift.tt/3atj5Gx
via Blogger https://ift.tt/3bblNPR
(https://ift.tt/3dg8qAvShow HN: Opul – find and optimise your financial freedom age https://ift.tt/3pkZz2RTHE RIGHT PEOPLE https://ift.tt/37kxIJZTHE RIGHT PEOPLE https://ift.tt/3b9ITX7 https://ift.tt/3b9ITX7
Show HN: Opul – find and optimise your financial freedom age https://ift.tt/3dehG8p February 16, 2021 at 01:25AM
via Blogger https://ift.tt/3apthQ1
(https://ift.tt/3b3KqxKShow HN: An async API and a CLI tool to search YouTube without using the DataAPI https://ift.tt/3bc0XQmTHE RIGHT PEOPLE https://ift.tt/3djjaOSTHE RIGHT PEOPLE https://ift.tt/2ZhOHbw https://ift.tt/2ZhOHbwTHE RIGHT PEOPLE https://ift.tt/2NA0XkUTHE RIGHT PEOPLE https://ift.tt/3b9VYjl https://ift.tt/3b9VYjl
Show HN: An async API and a CLI tool to search YouTube without using the DataAPI https://ift.tt/2ZhIcFN February 15, 2021 at 04:28PM
via Blogger https://ift.tt/3diAgwc
via Blogger https://ift.tt/2NwXtj8
(https://ift.tt/3dijfSTShow HN: My ultimate make file for Golang services https://ift.tt/3qrn5N0THE RIGHT PEOPLE https://ift.tt/2ZhEImyTHE RIGHT PEOPLE https://ift.tt/3djp0jc https://ift.tt/3djp0jc
Show HN: My ultimate make file for Golang services https://ift.tt/3difFb6 February 15, 2021 at 11:11PM
via Blogger https://ift.tt/3atj5Gx
(https://ift.tt/3diapVdShow HN: Opul – find and optimise your financial freedom age https://ift.tt/3pkZz2R
Show HN: Opul – find and optimise your financial freedom age https://ift.tt/3dehG8p February 16, 2021 at 01:25AM
Show HN: An async API and a CLI tool to search YouTube without using the DataAPI https://ift.tt/3bc0XQmTHE RIGHT PEOPLE https://ift.tt/3djjaOSTHE RIGHT PEOPLE https://ift.tt/2ZhOHbw https://ift.tt/2ZhOHbw
Show HN: An async API and a CLI tool to search YouTube without using the DataAPI https://ift.tt/2ZhIcFN February 15, 2021 at 04:28PM
via Blogger https://ift.tt/3diAgwc
(https://ift.tt/3rYTj2uShow HN: My ultimate make file for Golang services https://ift.tt/3qrn5N0
Show HN: My ultimate make file for Golang services https://ift.tt/3difFb6 February 15, 2021 at 11:11PM
Show HN: SendFiles.online – Make a file into a URL quickly https://ift.tt/3jONWQMTHE RIGHT PEOPLE https://ift.tt/3jTWtltTHE RIGHT PEOPLE https://ift.tt/3dggL7g https://ift.tt/3dggL7gTHE RIGHT PEOPLE https://ift.tt/3k4mZJ7THE RIGHT PEOPLE https://ift.tt/3b5imdw https://ift.tt/3b5imdw
Show HN: SendFiles.online – Make a file into a URL quickly https://ift.tt/37kdgZN February 15, 2021 at 05:06PM
via Blogger https://ift.tt/3u4HZnB
via Blogger https://ift.tt/3u3UVtB
(https://ift.tt/3b96mYnShow HN: Buy and Sell domain names before they expire https://ift.tt/2OL2p4VTHE RIGHT PEOPLE https://ift.tt/3amvEDeTHE RIGHT PEOPLE https://ift.tt/37ihwZH https://ift.tt/37ihwZHTHE RIGHT PEOPLE https://ift.tt/3jTjFA7THE RIGHT PEOPLE https://ift.tt/3poGUmQ https://ift.tt/3poGUmQ
Show HN: Buy and Sell domain names before they expire https://ift.tt/3rREFKk February 15, 2021 at 03:40PM
via Blogger https://ift.tt/2N1LJ8N
via Blogger https://ift.tt/3b7UxBO
(https://ift.tt/3rXdUUTShow HN: Jam, an Open Source Clubhouse (w/ WebRTC) https://ift.tt/3dixZkyTHE RIGHT PEOPLE https://ift.tt/3dkKBYmTHE RIGHT PEOPLE https://ift.tt/3baXVM9 https://ift.tt/3baXVM9THE RIGHT PEOPLE https://ift.tt/3podkhwTHE RIGHT PEOPLE https://ift.tt/37inWIe https://ift.tt/37inWIe
Show HN: Jam, an Open Source Clubhouse (w/ WebRTC) https://jam.systems February 14, 2021 at 02:32AM
via Blogger https://ift.tt/3jQgcT9
via Blogger https://ift.tt/3jZvRzt
(https://ift.tt/3jVXpWCShow HN: An async API and a CLI tool to search YouTube without using the DataAPI https://ift.tt/3bc0XQm
Show HN: An async API and a CLI tool to search YouTube without using the DataAPI https://ift.tt/2ZhIcFN February 15, 2021 at 04:28PM
Show HN: SendFiles.online – Make a file into a URL quickly https://ift.tt/3jONWQMTHE RIGHT PEOPLE https://ift.tt/3jTWtltTHE RIGHT PEOPLE https://ift.tt/3dggL7g https://ift.tt/3dggL7g
Show HN: SendFiles.online – Make a file into a URL quickly https://ift.tt/37kdgZN February 15, 2021 at 05:06PM
via Blogger https://ift.tt/3u4HZnB
(https://ift.tt/2NaB23BShow HN: Buy and Sell domain names before they expire https://ift.tt/2OL2p4VTHE RIGHT PEOPLE https://ift.tt/3amvEDeTHE RIGHT PEOPLE https://ift.tt/37ihwZH https://ift.tt/37ihwZH
Show HN: Buy and Sell domain names before they expire https://ift.tt/3rREFKk February 15, 2021 at 03:40PM
via Blogger https://ift.tt/2N1LJ8N
(https://ift.tt/3u1L8ECShow HN: Jam, an Open Source Clubhouse (w/ WebRTC) https://ift.tt/3dixZkyTHE RIGHT PEOPLE https://ift.tt/3dkKBYmTHE RIGHT PEOPLE https://ift.tt/3baXVM9 https://ift.tt/3baXVM9
Show HN: Jam, an Open Source Clubhouse (w/ WebRTC) https://jam.systems February 14, 2021 at 02:32AM
via Blogger https://ift.tt/3jQgcT9
(https://ift.tt/3rUQbVqShow HN: SendFiles.online – Make a file into a URL quickly https://ift.tt/3jONWQM
Show HN: SendFiles.online – Make a file into a URL quickly https://ift.tt/37kdgZN February 15, 2021 at 05:06PM
Show HN: Buy and Sell domain names before they expire https://ift.tt/2OL2p4V
Show HN: Buy and Sell domain names before they expire https://ift.tt/3rREFKk February 15, 2021 at 03:40PM
Show HN: Jam, an Open Source Clubhouse (w/ WebRTC) https://ift.tt/3dixZky
Show HN: Jam, an Open Source Clubhouse (w/ WebRTC) https://jam.systems February 14, 2021 at 02:32AM
Monday, February 15, 2021
Show HN: Puffer, SNI based tracker and adblocking for Android https://ift.tt/3pk82DGTHE RIGHT PEOPLE https://ift.tt/3qzH4ZSTHE RIGHT PEOPLE https://ift.tt/3detNCk https://ift.tt/3detNCk
Show HN: Puffer, SNI based tracker and adblocking for Android https://ift.tt/2ZhUBJM February 15, 2021 at 01:04AM
via Blogger https://ift.tt/3jSmMIU
(https://ift.tt/3rZ11toShow HN: ustaxes.org – open-source tax filing webapp https://ift.tt/3jPvvLGTHE RIGHT PEOPLE https://ift.tt/2ZhjbdNTHE RIGHT PEOPLE https://ift.tt/3ahjXhc https://ift.tt/3ahjXhcTHE RIGHT PEOPLE https://ift.tt/3qlwUMETHE RIGHT PEOPLE https://ift.tt/2ZhCDHj https://ift.tt/2ZhCDHj
Show HN: ustaxes.org – open-source tax filing webapp https://ift.tt/39ZMiHw February 14, 2021 at 06:37PM
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(https://ift.tt/3qja9c2
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