THE RIGHT PEOPLE

Launch HN: Narrator (YC S19) – a data modeling platform built on a single table Hi HN, We’re Ahmed, Cedric, Matt, and Mike from Narrator ( https://www.narrator.ai ). We’ve built a data platform that transforms all data in a data warehouse into a single 11-column data model and provides tools for analysts to quickly build any table for BI, reporting, and analysis on top of that model. Narrator initially grew out of our experience building a data platform for a team of 40 analysts and data scientists. The data warehouse, modeled as a star schema, grew to over 700 data models from 3000+ raw production tables. Every time we wanted to make a change or build a new analysis, it took forever as we had to deal with managing the complexity of these 700 different models. With all these layers of dependencies and stakeholders constantly demanding more data, we ended up making lots of mistakes (i.e. dashboard metrics not matching). These mistakes led to loss of trust and soon our stakeholders were off buying tools (Heap, Mixpanel, Amplitude, Wave Analytics, etc…) to do their own analysis. With a star schema (also core to recently IPO-ed Snowflake), you build the tables you need for reporting and BI on top of fact tables (what you want to measure, i.e. leads, sales…) and dimension tables (how you want to slice your data, i.e. gender, company, contract size…). Using this approach, the amount of fact and dimension tables grow in size and complexity in relation to the number of questions / datasets / metrics that need to be answered by the business. Over time the rate of new questions increases rapidly and data teams spend more time updating models and debugging mismatched numbers than answering data questions. What if instead of using the hundreds of fact and dimension tables in a star schema, we could use one table with all your customer data modeled as a collection of core customer actions (each a single source of truth), and combine them together to assemble any table at the moment the data analyst needs that table? Numbers would always match (single source of truth), any new question could be answered immediately without waiting on data engineering to build new fact and dimension tables (assembled when the data analyst needs it), and investigating issues would be easy (no nested dependencies of fact and dimension tables that depend on other tables). After several iterations, Narrator was born. Narrator uses a single 11-column table called the Activity Stream to represent all the data in your data warehouse. It’s built from sql transformations that transform a set of raw production tables (for example, Zendesk data) into activities (ticket opened, ticket closed, etc). Each row of the Activity Stream has a customer, a timestamp, an activity name, a unique identifier, and a bit of metadata describing it. Creating any table from this single model made up of activities that don’t obviously relate to each other is hard to imagine. Unlike star schema, we don’t use foreign keys (the direct relationships in relational databases that connect objects, like employee.company_id → company.id) because they don’t always exist when you’re dealing with data in multiple systems. Instead each activity has a customer identifier which we use, along with time, to automatically join within the single table to generate datasets. As an example, imagine you were investigating a single customer who called support. Did they visit the web site before that call? You’d look at that customer’s first web visit, and see if that person called before their next web visit. Now imagine finding all customers who behaved this way per month — you’d have to take a drastically different approach with your current data tools. Narrator, by contrast, always joins data in terms of behavior. The same approach you take to investigate a single customer applies to all of them. For the above example you’d ask Narrator’s Dataset tool to show all users who visited the website and called before the next visit, grouped by month. We started as a consultancy to build out the approach and prove that this was possible. We supported eight companies per Narrator data analyst, and now we’re excited for more data folks to get their hands on it so y’all can experience the same benefits. We’d love to hear any feedback or answer any questions about our approach. We’ve been using it ourselves in production for three years, but only launched it to the public last week. We’ll answer any comments on this thread and can also set up a video chat for anyone who wants to go more in-depth. September 30, 2020 at 09:30AM

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Show HN: A simple SSH CA service with only 150 lines of Python code https://ift.tt/3cIloEWTHE RIGHT PEOPLE https://ift.tt/3ibYOFSTHE RIGHT PEOPLE https://ift.tt/3n7RnTZ https://ift.tt/3n7RnTZ

Show HN: A simple SSH CA service with only 150 lines of Python code https://ift.tt/2Sc8UvV September 30, 2020 at 07:54AM

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(https://ift.tt/34fT7BV

Show HN: Parenthing – a parenting companion for parents with children aged 3 – 9 https://ift.tt/2S9yr8WTHE RIGHT PEOPLE https://ift.tt/34eKKGCTHE RIGHT PEOPLE https://ift.tt/2Sbt5tI https://ift.tt/2Sbt5tI

Show HN: Parenthing – a parenting companion for parents with children aged 3 – 9 https://ift.tt/34ecRWj September 30, 2020 at 06:26AM

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(https://ift.tt/2SdiLBz

Show HN: Buku – browser-independent private portable bookmark manager https://ift.tt/3n0BVchTHE RIGHT PEOPLE https://ift.tt/2SckTJRTHE RIGHT PEOPLE https://ift.tt/349XbDz https://ift.tt/349XbDzTHE RIGHT PEOPLE https://ift.tt/30mF5NxTHE RIGHT PEOPLE https://ift.tt/2Ge5Z3h https://ift.tt/2Ge5Z3h

Show HN: Buku – browser-independent private portable bookmark manager https://ift.tt/2RKNVkm September 30, 2020 at 06:18AM

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(https://ift.tt/3l1DYe4

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Show HN: Ruby One-Liners Cookbook https://ift.tt/30ihptWTHE RIGHT PEOPLE https://ift.tt/2ERoWZaTHE RIGHT PEOPLE https://ift.tt/2SgAOGU https://ift.tt/2SgAOGUTHE RIGHT PEOPLE https://ift.tt/3ifyzyjTHE RIGHT PEOPLE https://ift.tt/30jCBzq https://ift.tt/30jCBzq

Show HN: Ruby One-Liners Cookbook https://ift.tt/2Sa7Usd September 30, 2020 at 04:57AM

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Show HN: A simple log-based, transactional, embedded relational database in Java https://ift.tt/2EKJ1A7THE RIGHT PEOPLE https://ift.tt/3n8dtWbTHE RIGHT PEOPLE https://ift.tt/3cRgPrZ https://ift.tt/3cRgPrZ

Show HN: A simple log-based, transactional, embedded relational database in Java https://ift.tt/2Gk685c September 30, 2020 at 07:45AM

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(https://ift.tt/3jhCKLf

Show HN: Telegram bot to get new HN stories by keywords https://ift.tt/3cOoIyj

Show HN: Telegram bot to get new HN stories by keywords https://ift.tt/3n7yNLJ September 30, 2020 at 10:00AM

Launch HN: Narrator (YC S19) – a data modeling platform built on a single table https://ift.tt/34bcMD0

Launch HN: Narrator (YC S19) – a data modeling platform built on a single table Hi HN, We’re Ahmed, Cedric, Matt, and Mike from Narrator ( https://www.narrator.ai ). We’ve built a data platform that transforms all data in a data warehouse into a single 11-column data model and provides tools for analysts to quickly build any table for BI, reporting, and analysis on top of that model. Narrator initially grew out of our experience building a data platform for a team of 40 analysts and data scientists. The data warehouse, modeled as a star schema, grew to over 700 data models from 3000+ raw production tables. Every time we wanted to make a change or build a new analysis, it took forever as we had to deal with managing the complexity of these 700 different models. With all these layers of dependencies and stakeholders constantly demanding more data, we ended up making lots of mistakes (i.e. dashboard metrics not matching). These mistakes led to loss of trust and soon our stakeholders were off buying tools (Heap, Mixpanel, Amplitude, Wave Analytics, etc…) to do their own analysis. With a star schema (also core to recently IPO-ed Snowflake), you build the tables you need for reporting and BI on top of fact tables (what you want to measure, i.e. leads, sales…) and dimension tables (how you want to slice your data, i.e. gender, company, contract size…). Using this approach, the amount of fact and dimension tables grow in size and complexity in relation to the number of questions / datasets / metrics that need to be answered by the business. Over time the rate of new questions increases rapidly and data teams spend more time updating models and debugging mismatched numbers than answering data questions. What if instead of using the hundreds of fact and dimension tables in a star schema, we could use one table with all your customer data modeled as a collection of core customer actions (each a single source of truth), and combine them together to assemble any table at the moment the data analyst needs that table? Numbers would always match (single source of truth), any new question could be answered immediately without waiting on data engineering to build new fact and dimension tables (assembled when the data analyst needs it), and investigating issues would be easy (no nested dependencies of fact and dimension tables that depend on other tables). After several iterations, Narrator was born. Narrator uses a single 11-column table called the Activity Stream to represent all the data in your data warehouse. It’s built from sql transformations that transform a set of raw production tables (for example, Zendesk data) into activities (ticket opened, ticket closed, etc). Each row of the Activity Stream has a customer, a timestamp, an activity name, a unique identifier, and a bit of metadata describing it. Creating any table from this single model made up of activities that don’t obviously relate to each other is hard to imagine. Unlike star schema, we don’t use foreign keys (the direct relationships in relational databases that connect objects, like employee.company_id → company.id) because they don’t always exist when you’re dealing with data in multiple systems. Instead each activity has a customer identifier which we use, along with time, to automatically join within the single table to generate datasets. As an example, imagine you were investigating a single customer who called support. Did they visit the web site before that call? You’d look at that customer’s first web visit, and see if that person called before their next web visit. Now imagine finding all customers who behaved this way per month -- you’d have to take a drastically different approach with your current data tools. Narrator, by contrast, always joins data in terms of behavior. The same approach you take to investigate a single customer applies to all of them. For the above example you’d ask Narrator’s Dataset tool to show all users who visited the website and called before the next visit, grouped by month. We started as a consultancy to build out the approach and prove that this was possible. We supported eight companies per Narrator data analyst, and now we’re excited for more data folks to get their hands on it so y’all can experience the same benefits. We’d love to hear any feedback or answer any questions about our approach. We’ve been using it ourselves in production for three years, but only launched it to the public last week. We’ll answer any comments on this thread and can also set up a video chat for anyone who wants to go more in-depth. September 30, 2020 at 09:30AM

Show HN: A simple SSH CA service with only 150 lines of Python code https://ift.tt/3cIloEW

Show HN: A simple SSH CA service with only 150 lines of Python code https://ift.tt/2Sc8UvV September 30, 2020 at 07:54AM

Show HN: Parenthing – a parenting companion for parents with children aged 3 – 9 https://ift.tt/2S9yr8W

Show HN: Parenthing – a parenting companion for parents with children aged 3 – 9 https://ift.tt/34ecRWj September 30, 2020 at 06:26AM

Show HN: Buku – browser-independent private portable bookmark manager https://ift.tt/3n0BVchTHE RIGHT PEOPLE https://ift.tt/2SckTJRTHE RIGHT PEOPLE https://ift.tt/349XbDz https://ift.tt/349XbDz

Show HN: Buku – browser-independent private portable bookmark manager https://ift.tt/2RKNVkm September 30, 2020 at 06:18AM

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Show HN: Ruby One-Liners Cookbook https://ift.tt/30ihptWTHE RIGHT PEOPLE https://ift.tt/2ERoWZaTHE RIGHT PEOPLE https://ift.tt/2SgAOGU https://ift.tt/2SgAOGU

Show HN: Ruby One-Liners Cookbook https://ift.tt/2Sa7Usd September 30, 2020 at 04:57AM

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Show HN: A simple log-based, transactional, embedded relational database in Java https://ift.tt/2EKJ1A7

Show HN: A simple log-based, transactional, embedded relational database in Java https://ift.tt/2Gk685c September 30, 2020 at 07:45AM

Show HN: Buku – browser-independent private portable bookmark manager https://ift.tt/3n0BVch

Show HN: Buku – browser-independent private portable bookmark manager https://ift.tt/2RKNVkm September 30, 2020 at 06:18AM

Show HN: Ruby One-Liners Cookbook https://ift.tt/30ihptW

Show HN: Ruby One-Liners Cookbook https://ift.tt/2Sa7Usd September 30, 2020 at 04:57AM

Show HN: Live dashboard of every email Trump and Biden are sending https://ift.tt/2Ga1G9iTHE RIGHT PEOPLE https://ift.tt/36ffIBbTHE RIGHT PEOPLE https://ift.tt/36mdaBc https://ift.tt/36mdaBcTHE RIGHT PEOPLE https://ift.tt/2G2HiXLTHE RIGHT PEOPLE https://ift.tt/3n3lAmS https://ift.tt/3n3lAmS

Show HN: Live dashboard of every email Trump and Biden are sending https://ift.tt/33epHot September 29, 2020 at 07:25PM

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Show HN: Live dashboard of every email Trump and Biden are sending https://ift.tt/2Ga1G9iTHE RIGHT PEOPLE https://ift.tt/36ffIBbTHE RIGHT PEOPLE https://ift.tt/36mdaBc https://ift.tt/36mdaBc

Show HN: Live dashboard of every email Trump and Biden are sending https://ift.tt/33epHot September 29, 2020 at 07:25PM

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Show HN: Live dashboard of every email Trump and Biden are sending https://ift.tt/2Ga1G9i

Show HN: Live dashboard of every email Trump and Biden are sending https://ift.tt/33epHot September 29, 2020 at 07:25PM

Tuesday, September 29, 2020

Show HN: MindPad – complete life organizer – notes, habits, to-do's, reminders https://ift.tt/2EHQnnZ

Show HN: MindPad – complete life organizer – notes, habits, to-do's, reminders https://www.mindpad.io September 29, 2020 at 03:54AM

Show HN: ML News – Like HN, but for Machine Learning https://ift.tt/3je2yIlTHE RIGHT PEOPLE https://ift.tt/2GlgOAgTHE RIGHT PEOPLE https://ift.tt/30jryGx https://ift.tt/30jryGxTHE RIGHT PEOPLE https://ift.tt/33eQL6MTHE RIGHT PEOPLE https://ift.tt/3kXuBMr https://ift.tt/3kXuBMr

Show HN: ML News – Like HN, but for Machine Learning http://mln.dev September 29, 2020 at 02:15AM

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Show HN: Elsa is a minimal, fast and secure runtime for JavaScript written in Go https://ift.tt/2SbcXssTHE RIGHT PEOPLE https://ift.tt/3jhwEdOTHE RIGHT PEOPLE https://ift.tt/2S6jy7v https://ift.tt/2S6jy7v

Show HN: Elsa is a minimal, fast and secure runtime for JavaScript written in Go https://ift.tt/3hQxZa4 September 29, 2020 at 05:05AM

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Show HN: Go library for loading Python’s data serialized with pickle https://ift.tt/339RINYTHE RIGHT PEOPLE https://ift.tt/33cGsjZTHE RIGHT PEOPLE https://ift.tt/33b2YcM https://ift.tt/33b2YcM

Show HN: Go library for loading Python’s data serialized with pickle https://ift.tt/37o4eK1 September 29, 2020 at 03:35AM

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Show HN: A progress bar that plays lofi music while waiting https://ift.tt/3j7WC3vTHE RIGHT PEOPLE https://ift.tt/33bUfXSTHE RIGHT PEOPLE https://ift.tt/2S8JFKR https://ift.tt/2S8JFKRTHE RIGHT PEOPLE https://ift.tt/30jW9nkTHE RIGHT PEOPLE https://ift.tt/3j961Yy https://ift.tt/3j961Yy

Show HN: A progress bar that plays lofi music while waiting https://ift.tt/36392WQ September 28, 2020 at 10:35PM

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(https://ift.tt/3n47mCc

Show HN: ML News – Like HN, but for Machine Learning https://ift.tt/3je2yIlTHE RIGHT PEOPLE https://ift.tt/2GlgOAgTHE RIGHT PEOPLE https://ift.tt/30jryGx https://ift.tt/30jryGx

Show HN: ML News – Like HN, but for Machine Learning http://mln.dev September 29, 2020 at 02:15AM

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(https://ift.tt/349lUb9

Show HN: Elsa is a minimal, fast and secure runtime for JavaScript written in Go https://ift.tt/2SbcXss

Show HN: Elsa is a minimal, fast and secure runtime for JavaScript written in Go https://ift.tt/3hQxZa4 September 29, 2020 at 05:05AM

Show HN: Go library for loading Python's data serialized with pickle https://ift.tt/339RINY

Show HN: Go library for loading Python's data serialized with pickle https://ift.tt/37o4eK1 September 29, 2020 at 03:35AM

Show HN: A progress bar that plays lofi music while waiting https://ift.tt/3j7WC3vTHE RIGHT PEOPLE https://ift.tt/33bUfXSTHE RIGHT PEOPLE https://ift.tt/2S8JFKR https://ift.tt/2S8JFKR

Show HN: A progress bar that plays lofi music while waiting https://ift.tt/36392WQ September 28, 2020 at 10:35PM

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(https://ift.tt/34bmgOm

Show HN: ML News – Like HN, but for Machine Learning https://ift.tt/3je2yIl

Show HN: ML News – Like HN, but for Machine Learning http://mln.dev September 29, 2020 at 02:15AM

Show HN: Keywords2lyrics – Automatic Songwriting from Keywords https://ift.tt/36easOfTHE RIGHT PEOPLE https://ift.tt/3kTNJuSTHE RIGHT PEOPLE https://ift.tt/3385osK https://ift.tt/3385osKTHE RIGHT PEOPLE https://ift.tt/339ydVKTHE RIGHT PEOPLE https://ift.tt/3cN61v6 https://ift.tt/3cN61v6

Show HN: Keywords2lyrics – Automatic Songwriting from Keywords https://ift.tt/3kZV3oV September 28, 2020 at 03:20PM

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(https://ift.tt/3cHXObe

Show HN: lit-request – 852 bytes fetch API based Javascript microlibrary https://ift.tt/30fWE1WTHE RIGHT PEOPLE https://ift.tt/3idBkQOTHE RIGHT PEOPLE https://ift.tt/2GbqopR https://ift.tt/2GbqopRTHE RIGHT PEOPLE https://ift.tt/2Hx5sd4THE RIGHT PEOPLE https://ift.tt/36m1peh https://ift.tt/36m1peh

Show HN: lit-request – 852 bytes fetch API based Javascript microlibrary https://ift.tt/2HE3Q1n September 28, 2020 at 08:22AM

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Show HN: A progress bar that plays lofi music while waiting https://ift.tt/3j7WC3v

Show HN: A progress bar that plays lofi music while waiting https://ift.tt/36392WQ September 28, 2020 at 10:35PM

Show HN: Keywords2lyrics – Automatic Songwriting from Keywords https://ift.tt/36easOfTHE RIGHT PEOPLE https://ift.tt/3kTNJuSTHE RIGHT PEOPLE https://ift.tt/3385osK https://ift.tt/3385osK

Show HN: Keywords2lyrics – Automatic Songwriting from Keywords https://ift.tt/3kZV3oV September 28, 2020 at 03:20PM

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Show HN: lit-request – 852 bytes fetch API based Javascript microlibrary https://ift.tt/30fWE1WTHE RIGHT PEOPLE https://ift.tt/3idBkQOTHE RIGHT PEOPLE https://ift.tt/2GbqopR https://ift.tt/2GbqopR

Show HN: lit-request – 852 bytes fetch API based Javascript microlibrary https://ift.tt/2HE3Q1n September 28, 2020 at 08:22AM

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Show HN: Keywords2lyrics – Automatic Songwriting from Keywords https://ift.tt/36easOf

Show HN: Keywords2lyrics – Automatic Songwriting from Keywords https://ift.tt/3kZV3oV September 28, 2020 at 03:20PM

Show HN: lit-request - 852 bytes fetch API based Javascript microlibrary https://ift.tt/30fWE1W

Show HN: lit-request - 852 bytes fetch API based Javascript microlibrary https://ift.tt/2HE3Q1n September 28, 2020 at 08:22AM

Monday, September 28, 2020

Show HN: A tool that finds formulas from values without supervision https://ift.tt/3n02z4U

Show HN: A tool that finds formulas from values without supervision https://ift.tt/30f9r4t September 28, 2020 at 10:08AM

Show HN: All duckduckgo bang operators on one page (metasearch tool) https://ift.tt/2HEODx2

Show HN: All duckduckgo bang operators on one page (metasearch tool) https://ift.tt/30ax1iN September 28, 2020 at 09:34AM

Show HN: Make web longform content print optimised(beta),physical book(release) https://ift.tt/3jc0RLl

Show HN: Make web longform content print optimised(beta),physical book(release) https://ift.tt/3ibbRr8 September 28, 2020 at 08:37AM

Show HN: GRT – track code review turnaround for your GitHub repositories https://ift.tt/348pu5k

Show HN: GRT – track code review turnaround for your GitHub repositories https://ift.tt/36kXhLc September 28, 2020 at 04:55AM

Show HN: Go Budget, a full-featured manual budget tracker built with Flutter https://ift.tt/3cE7L9K

Show HN: Go Budget, a full-featured manual budget tracker built with Flutter Over the weekend I launched Go Budget, a side project of mine from the last few years that originally was just an Android app used by me and my wife, but was recreated in Flutter during the pandemic. https://gobudget.io [0] App Store [1] Google Play [2] Privacy Policy Go Budget lets you set up your income and bills (repeating expenses), automates tracking them wherever possible, then makes it as painless as possible to log every day purchases. For individual users the app is free/adfree. I offer a premium subscription that allows you to back up budgets online and invite others to join your budgets to stay in sync. My experience with Flutter was nothing short of amazing, and I think the end result turned out pretty nice. I'm happy to answer any questions about Flutter or anything else related to the app. -- The backend stack is pretty basic: - Flask + Redis + Gunicorn + Traefik running in docker on a Digital Ocean VM - Managed Digital Ocean postgres cluster -- [0] https://ift.tt/30ckH1F [1] https://ift.tt/2GaY3j3 [2] https://ift.tt/3jbzxwV September 28, 2020 at 07:28AM

Show HN: Vocabulary 90 – vocabulary learning automation https://ift.tt/3kV6w90

Show HN: Vocabulary 90 – vocabulary learning automation https://ift.tt/3ibRCtc September 28, 2020 at 03:58AM

Show HN: Grayscale/reading mode for i3 and other WMs https://ift.tt/2GggDGE

Show HN: Grayscale/reading mode for i3 and other WMs https://ift.tt/36gJfdE September 28, 2020 at 03:18AM

Show HN: Remote Oil Tank Monitoring Solution https://ift.tt/3j8G3EJTHE RIGHT PEOPLE https://ift.tt/339eh5qTHE RIGHT PEOPLE https://ift.tt/2HDjgD3 https://ift.tt/2HDjgD3THE RIGHT PEOPLE https://ift.tt/3jar9ObTHE RIGHT PEOPLE https://ift.tt/2S8lsEB https://ift.tt/2S8lsEB

Show HN: Remote Oil Tank Monitoring Solution http://myoilguage.com/ September 27, 2020 at 03:34PM

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(https://ift.tt/3kXKX83

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Show HN: Remote Oil Tank Monitoring Solution https://ift.tt/3j8G3EJTHE RIGHT PEOPLE https://ift.tt/339eh5qTHE RIGHT PEOPLE https://ift.tt/2HDjgD3 https://ift.tt/2HDjgD3

Show HN: Remote Oil Tank Monitoring Solution http://myoilguage.com/ September 27, 2020 at 03:34PM

via Blogger https://ift.tt/3mXMcpv

(https://ift.tt/3kXKX83

Show HN: Fast Variant data type for Go https://ift.tt/2S12GiiTHE RIGHT PEOPLE https://ift.tt/36gD4pZTHE RIGHT PEOPLE https://ift.tt/36erFHs https://ift.tt/36erFHsTHE RIGHT PEOPLE https://ift.tt/3mZ8TJJTHE RIGHT PEOPLE https://ift.tt/339UiU2 https://ift.tt/339UiU2

Show HN: Fast Variant data type for Go https://ift.tt/30gzg4q September 27, 2020 at 04:56PM

via Blogger https://ift.tt/3mRxhgB

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via Blogger https://ift.tt/2S5wDOs

(https://ift.tt/3cDZ8vZ

Show HN: File2DB, Upload CSV/JSON/Excel to a (PostgreSQL/MySQL/) Database https://ift.tt/3jfe5Y0THE RIGHT PEOPLE https://ift.tt/2S5zO8ITHE RIGHT PEOPLE https://ift.tt/36buvwL https://ift.tt/36buvwLTHE RIGHT PEOPLE https://ift.tt/2Gk98ybTHE RIGHT PEOPLE https://ift.tt/3cEC7c4 https://ift.tt/3cEC7c4

Show HN: File2DB, Upload CSV/JSON/Excel to a (PostgreSQL/MySQL/) Database https://ift.tt/3kRWANz September 27, 2020 at 11:36AM

via Blogger https://ift.tt/3cFvuXk

(https://ift.tt/368ZCch

via Blogger https://ift.tt/3kU23n2

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Show HN: Remote Oil Tank Monitoring Solution https://ift.tt/3j8G3EJ

Show HN: Remote Oil Tank Monitoring Solution http://myoilguage.com/ September 27, 2020 at 03:34PM

Show HN: Fast Variant data type for Go https://ift.tt/2S12GiiTHE RIGHT PEOPLE https://ift.tt/36gD4pZTHE RIGHT PEOPLE https://ift.tt/36erFHs https://ift.tt/36erFHs

Show HN: Fast Variant data type for Go https://ift.tt/30gzg4q September 27, 2020 at 04:56PM

via Blogger https://ift.tt/3mRxhgB

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Show HN: File2DB, Upload CSV/JSON/Excel to a (PostgreSQL/MySQL/) Database https://ift.tt/3jfe5Y0THE RIGHT PEOPLE https://ift.tt/2S5zO8ITHE RIGHT PEOPLE https://ift.tt/36buvwL https://ift.tt/36buvwL

Show HN: File2DB, Upload CSV/JSON/Excel to a (PostgreSQL/MySQL/) Database https://ift.tt/3kRWANz September 27, 2020 at 11:36AM

via Blogger https://ift.tt/3cFvuXk

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Show HN: Fast Variant data type for Go https://ift.tt/2S12Gii

Show HN: Fast Variant data type for Go https://ift.tt/30gzg4q September 27, 2020 at 04:56PM

Show HN: File2DB, Upload CSV/JSON/Excel to a (PostgreSQL/MySQL/) Database https://ift.tt/3jfe5Y0

Show HN: File2DB, Upload CSV/JSON/Excel to a (PostgreSQL/MySQL/) Database https://ift.tt/3kRWANz September 27, 2020 at 11:36AM

Sunday, September 27, 2020

Show HN: Submit your startup to 38 communities https://ift.tt/2FVODZbTHE RIGHT PEOPLE https://ift.tt/36a7MRJTHE RIGHT PEOPLE https://ift.tt/2GdGgI8 https://ift.tt/2GdGgI8THE RIGHT PEOPLE https://ift.tt/334R1W1THE RIGHT PEOPLE https://ift.tt/3czlXRz https://ift.tt/3czlXRz

Show HN: Submit your startup to 38 communities https://ift.tt/303EoZg September 26, 2020 at 11:55PM

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Saturday, September 26, 2020

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