Open Source Stash

Discover the best open-source alternatives for tools you use everyday – be it for design marketing or more.
Personal collective of ideas, thoughts and notes
Open Source Stash
Discover the best open-source alternatives for tools you use everyday – be it for design marketing or more.
Languages | Javascript/Typescript |
Frontend Frameworks | Angular, Bootstrap 4, WordPress |
Backend Frameworks | Express |
Build Systems | |
Development Tools | Nodemon |
Database | Postgres (AWS RDS) |
Deployment | Shell scripts |
Development | Github, VS Code, Posgres.app |
Linting | ESLint |
Testing | Puppeteer, Headless Chrome |
3rd Party | Google Services |
Git Client | Sourcetree |
HTTP Client | Postman |
Source Code | Github |
Infrastructure | Lightsail, AWS |
Certs | Lets Encrypt |
Domains | 1&1 |
CLI Tools | htop, pm2, shell |
SSH Client | Termius |
SFTP | Filezilla |
Monitoring | Sentry |
AWS SES | |
Email Templates | |
UI Components | Envato Elements |
Usage | Matomo |
Marketing | ProductHunt, |
Design | Dribbble, Adobe Behance, |
Mockups | Sketch, Photoshop |
Photo Editing | Photoshop |
Vector Design/Editing | Illustrator |
Icons | Envato Elements |
Photos | Unsplash |
Color Palettes | |
Issue Management | Github Issues |
Knowledge base | Github Wiki |
Payment Processing | Stripe |
Chat Support |
As an industry, we’ve gotten exceptionally good at building large, complex software systems. We’re now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. We’re seeing quick-moving impacts of this trend across the industry, including the emergence of new roles, shifts in customer spending, and the emergence of new startups providing infrastructure and tooling around data.
In fact, many of today’s fastest growing infrastructure startups build products to manage data. These systems enable data-driven decision making (analytic systems) and drive data-powered products, including with machine learning (operational systems). They range from the pipes that carry data, to storage solutions that house data, to SQL engines that analyze data, to dashboards that make data easy to understand – from data science and machine learning libraries, to automated data pipelines, to data catalogs, and beyond.
Sitting on land that had been handed down in the family for generations, the cabin with a 750-square-foot footprint is simple in form, but rich in detailing. https://archive.curbed.com/2020/1/27/21077762/wooden-cabin-retreat-vermont-olson-kundig
This is a great personal site from Ben Eater showing how some fundamental computer components are built: https://eater.net
Here is a nice summary of a user curated list of technical talks: https://news.ycombinator.com/item?id=18217762
Some I have watched and enjoyed:
Control engineering:
Gunter Stein’s inaugural Bode prize lecture from 1989 titled “Respect the Unstable” [0]. In this talk, he uses a minimum of mathematics to clearly demonstrate the fundamental (and inevitable!) trade-offs in control systems design. He effortlessly makes the link between his (in)ability to balance inverted rods of various lengths on his palm (with shorter rods being harder to balance) to why the X-29 aircraft was almost impossible to control and why Chernobyl blew up.
The fundamental message is extremely important and the derivation is so crystal clear that it is simply marvelous to watch him present it. I like it so much that I re-watch it about once a year.
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