The best Hacker News stories from Show from the past day
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Show HN: Vellum – An interactive list of nonfiction books reviewed by academics
Show HN: Archive as you browse, store locally and/or share with others via IPFS
Show HN: Archive as you browse, store locally and/or share with others via IPFS
Show HN: Archive as you browse, store locally and/or share with others via IPFS
Launch HN: HiGeorge (YC W21) – Real-time data visualizations for public datasets
Hi HN!<p>Anuj here. My co-founder Amir (Aazo11) and I are building HiGeorge (<a href="https://hi-george.com/" rel="nofollow">https://hi-george.com/</a>). 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.<p>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.<p>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.<p>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.<p>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.<p>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].<p>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.<p>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.<p>[1]<a href="https://www.northcarolinahealthnews.org/2021/02/09/coronavirus-today-feb-9-deaths-top-10000-vaccine-roll-out-focuses-on-equity-and-efficiency/" rel="nofollow">https://www.northcarolinahealthnews.org/2021/02/09/coronavir...</a>
[2] <a href="https://www.sfgate.com/bayarea/article/vaccine-sites-San-Francisco-Bay-Area-appointments-15935161.php" rel="nofollow">https://www.sfgate.com/bayarea/article/vaccine-sites-San-Fra...</a>
[3] <a href="https://lakehighlands.advocatemag.com/2021/02/data-crime-trends-in-dallas-lake-highlands-in-early-february/" rel="nofollow">https://lakehighlands.advocatemag.com/2021/02/data-crime-tre...</a>
[4] <a href="https://missionlocal.org/crime-data/" rel="nofollow">https://missionlocal.org/crime-data/</a>
Launch HN: HiGeorge (YC W21) – Real-time data visualizations for public datasets
Hi HN!<p>Anuj here. My co-founder Amir (Aazo11) and I are building HiGeorge (<a href="https://hi-george.com/" rel="nofollow">https://hi-george.com/</a>). 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.<p>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.<p>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.<p>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.<p>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.<p>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].<p>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.<p>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.<p>[1]<a href="https://www.northcarolinahealthnews.org/2021/02/09/coronavirus-today-feb-9-deaths-top-10000-vaccine-roll-out-focuses-on-equity-and-efficiency/" rel="nofollow">https://www.northcarolinahealthnews.org/2021/02/09/coronavir...</a>
[2] <a href="https://www.sfgate.com/bayarea/article/vaccine-sites-San-Francisco-Bay-Area-appointments-15935161.php" rel="nofollow">https://www.sfgate.com/bayarea/article/vaccine-sites-San-Fra...</a>
[3] <a href="https://lakehighlands.advocatemag.com/2021/02/data-crime-trends-in-dallas-lake-highlands-in-early-february/" rel="nofollow">https://lakehighlands.advocatemag.com/2021/02/data-crime-tre...</a>
[4] <a href="https://missionlocal.org/crime-data/" rel="nofollow">https://missionlocal.org/crime-data/</a>
Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database
Hi HN,<p>Adam and Jorge here, and today we’re very excited to share MindsDB with you (<a href="http://github.com/mindsdb/mindsdb" rel="nofollow">http://github.com/mindsdb/mindsdb</a>). 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.<p>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.<p>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!<p>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.<p>When we talk to our 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.<p>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: (<a href="https://mindsdb.com/bring-your-own-ml-models" rel="nofollow">https://mindsdb.com/bring-your-own-ml-models</a>)<p>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.<p>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.<p>We’d also love to hear about your experience, so please share your feedback, thoughts, comments, and ideas below. <a href="https://docs.mindsdb.com/" rel="nofollow">https://docs.mindsdb.com/</a> or <a href="https://mindsdb.com/" rel="nofollow">https://mindsdb.com/</a><p>Thanks in advance,
Adam & Jorge
Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database
Hi HN,<p>Adam and Jorge here, and today we’re very excited to share MindsDB with you (<a href="http://github.com/mindsdb/mindsdb" rel="nofollow">http://github.com/mindsdb/mindsdb</a>). 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.<p>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.<p>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!<p>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.<p>When we talk to our 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.<p>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: (<a href="https://mindsdb.com/bring-your-own-ml-models" rel="nofollow">https://mindsdb.com/bring-your-own-ml-models</a>)<p>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.<p>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.<p>We’d also love to hear about your experience, so please share your feedback, thoughts, comments, and ideas below. <a href="https://docs.mindsdb.com/" rel="nofollow">https://docs.mindsdb.com/</a> or <a href="https://mindsdb.com/" rel="nofollow">https://mindsdb.com/</a><p>Thanks in advance,
Adam & Jorge
Show HN: Split Keyboards Gallery
Show HN: Split Keyboards Gallery
Show HN: Split Keyboards Gallery
Show HN: Rails N+1 queries auto-detection with zero false negatives
Show HN: ExampleOfiOSLiDAR – sample codes using the Lidar sensor on iOS device
Show HN: ExampleOfiOSLiDAR – sample codes using the Lidar sensor on iOS device
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 (<a href="https://datrics.ai" rel="nofollow">https://datrics.ai</a>). We help FinTech companies build and deploy machine learning models without writing code.<p>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.<p>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.<p>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.<p>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.<p>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.<p>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 (<a href="https://youtu.be/BQNFcZWwUC8" rel="nofollow">https://youtu.be/BQNFcZWwUC8</a>).<p>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.<p>We’re charging only for private deployments, so our web version is free: <a href="https://platform.app.datrics.ai/signup" rel="nofollow">https://platform.app.datrics.ai/signup</a>. Try it to create your analytical applications with a machine learning component! We've put together a wiki (<a href="https://wiki.datrics.ai" rel="nofollow">https://wiki.datrics.ai</a>) to cover the major functionality,<p>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!
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 (<a href="https://datrics.ai" rel="nofollow">https://datrics.ai</a>). We help FinTech companies build and deploy machine learning models without writing code.<p>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.<p>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.<p>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.<p>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.<p>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.<p>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 (<a href="https://youtu.be/BQNFcZWwUC8" rel="nofollow">https://youtu.be/BQNFcZWwUC8</a>).<p>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.<p>We’re charging only for private deployments, so our web version is free: <a href="https://platform.app.datrics.ai/signup" rel="nofollow">https://platform.app.datrics.ai/signup</a>. Try it to create your analytical applications with a machine learning component! We've put together a wiki (<a href="https://wiki.datrics.ai" rel="nofollow">https://wiki.datrics.ai</a>) to cover the major functionality,<p>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!
Show HN: A social platform with music in focus. Download the app
Show HN: An Event Sourced Minesweeper
Show HN: An Event Sourced Minesweeper
Launch HN: Worksphere (YC W21) – Manage flexible in-office or remote workspaces
Hey HN,<p>Aakhil, Theresa & Mark here at Worksphere (<a href="https://worksphere.com" rel="nofollow">https://worksphere.com</a>). Our software helps companies manage a safe workplace where employees can work flexibly in-office or remote. Worksphere automates desk reservations, safe entry, and gives companies office usage data to right-size their workplaces.<p>We previously worked together at Lish, a corporate catering marketplace startup catering (pun intended) to tech companies. Last March our revenue went off a cliff. No people in office = no lunch orders. We love our clients and our team, so we got to work on solving a new problem for our primary users - HR, Office, and Facilities Managers.<p>At the start of the pandemic, our users were struggling with how to return to their offices safely. New problems like enforcing social distancing, screening for COVID-19 symptoms, and contact tracing could not be solved at scale with existing tools. New coronavirus workplace regulations have cropped up, which are a pain and come with big financial risk if companies don’t comply (we’re looking at you CA businesses dealing with SB 1159 & AB 685 - learn more at <a href="https://www.worksphere.com/blog/covid-19/guide-to-ab-685-and-sb-1159" rel="nofollow">https://www.worksphere.com/blog/covid-19/guide-to-ab-685-and...</a>).<p>As the pandemic has continued (and continued, and continued) businesses are facing a new challenge. 75% of office employees report that they want a hybrid in-office and remote schedule. No one misses their 5-day-a-week commute. At the same time, collaboration and culture are hard to foster on Zoom and Slack alone. Employees still want access to an office when needed, but don’t want to come to work in an empty space. Businesses want the upsides of flexibility, but don’t want to pay for empty or only occasionally used desks — office space costs $10-12k per employee annually in major US cities. We believe that a hybrid workweek is a better workweek, and that an active approach is needed to make it a win-win for employees and businesses.<p>Our features help businesses reopen safely and realize the full potential of a flexible workplace. We automate the wellness surveys, contact tracing, and capacity limits needed to enforce internal safety guidelines and regional regulations. This alleviates a ton of manual work for Office and HR managers. Employees can create in-office schedules and see who else is in the office to increase collaboration. We track office utilization data so companies can make smart decisions about their office space and lease.<p>Worksphere is $6 per active employee/month, and our clients only pay for employees that come to the office.<p>If you’re one of the 3 of 4 people who wants to work flexibly post-pandemic, or if you manage a workplace, we’d love to hear from you. How frequently do you think you’ll need office space? What problems are you facing in this changing work environment? We’ll reply in the comments or you can email us at info@worksphere.com. Thanks so much for your feedback!