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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Showing posts with label February 18. Show all posts
Showing posts with label February 18. Show all posts
Friday, February 19, 2021
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://ift.tt/3qAQ01dTHE RIGHT PEOPLE https://ift.tt/3ucFtvuTHE RIGHT PEOPLE https://ift.tt/3qC0nBX https://ift.tt/3qC0nBX
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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(https://ift.tt/37thZsiThursday, 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/3s6inVALaunch 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/2ZreFt5Launch 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
via Blogger https://ift.tt/2ZstHiA
(https://ift.tt/3s2iFg0
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