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Friday, 19 February 2021

Show HN: Share your workstation setup and earn with Amazon Affiliate links https://bit.ly/3bnMFfF

Show HN: Share your workstation setup and earn with Amazon Affiliate links https://bit.ly/3kd6j1V February 20, 2021 at 03:44AM

Show HN: Validatum – build fluent validation functions in .NET https://bit.ly/2OUslLo

Show HN: Validatum – build fluent validation functions in .NET https://bit.ly/2ZAM6cO February 20, 2021 at 12:43AM

Show HN: Split Keyboards Gallery https://bit.ly/3qHaCVG

Show HN: Split Keyboards Gallery https://bit.ly/3k5jpyc February 18, 2021 at 02:01PM

Show HN: ClubCircle – gradient borders and status badges for Clubhouse avatars https://bit.ly/2ZurYt3

Show HN: ClubCircle – gradient borders and status badges for Clubhouse avatars https://bit.ly/3sa9RVA February 19, 2021 at 10:23PM

Show HN: Crypto Mining Pools Aggregator https://bit.ly/3qG7nOq

Show HN: Crypto Mining Pools Aggregator https://bit.ly/2NnAkjB February 19, 2021 at 03:03PM

Launch HN: MindsDB (YC W20) – Machine Learning Inside Your Database https://bit.ly/2OPzo7X

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://bit.ly/3s8kUyr ). 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://bit.ly/3pEX23M ) 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://bit.ly/3aBrPKB or https://bit.ly/2ZtKq53 Thanks in advance, Adam & Jorge February 19, 2021 at 05:55PM

Launch HN: HiGeorge (YC W21) – Real-time data visualizations for public datasets https://bit.ly/3dv3xnA

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://bit.ly/3dx85to ). 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://bit.ly/37x44Bz... [2] https://bit.ly/2ZzAU02... [3] https://bit.ly/3bkbmJP... [4] https://bit.ly/3dtf9XT February 19, 2021 at 04:57PM

Thursday, 18 February 2021

Show HN: Archive as you browse, store locally and/or share with others via IPFS https://bit.ly/3k3Wza1

Show HN: Archive as you browse, store locally and/or share with others via IPFS https://bit.ly/2Nj2LiE February 19, 2021 at 03:04AM

Show HN: Kanban board that helps Engineers learn Marketing https://bit.ly/3qzMTGZ

Show HN: Kanban board that helps Engineers learn Marketing https://bit.ly/3pEYFyE February 18, 2021 at 11:44PM

Show HN: Stateless note sharing via URL Hash https://bit.ly/3bghT8x

Show HN: Stateless note sharing via URL Hash https://bit.ly/3dsgxKu February 18, 2021 at 06:45PM

Show HN: ScreenToVideo – Record your videos flawlessly https://bit.ly/3dutrHR

Show HN: ScreenToVideo – Record your videos flawlessly https://bit.ly/2MF3S8u February 18, 2021 at 09:15PM

Show HN: Merge multiple PDFs into one using WebAssembly https://bit.ly/3bfIOBj

Show HN: Merge multiple PDFs into one using WebAssembly https://bit.ly/3bhqd7Y February 18, 2021 at 05:52PM

Show HN: Create APIs for static datasets without writing a single line of code https://bit.ly/3qCE8Mg

Show HN: Create APIs for static datasets without writing a single line of code https://bit.ly/3bh8Jsb February 18, 2021 at 05:15PM

Show HN: Kalaksi: a social-network built on top of RSS https://bit.ly/3pv6cjq

Show HN: Kalaksi: a social-network built on top of RSS https://bit.ly/2ORQJgD February 18, 2021 at 04:37PM

Launch HN: Ontop (YC W21) – Easily hire and pay remote workers in LATAM https://bit.ly/3awhv6H

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 (https://bit.ly/3k4J8Xt) 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 01:58PM

Launch HN: Datrics (YC W21) – No-Code Analytics and ML for FinTech https://bit.ly/3avf7wT

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://bit.ly/3pwzr56 ). 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://bit.ly/3uaB3Fd . Try it to create your analytical applications with a machine learning component! We've put together a wiki ( https://bit.ly/37lP7SE ) 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 09:12AM

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