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 |
Notes:
Quantum Systems are exponentially powerful
Based on particles: 2^500 – More particles in the universe
Challenges:
Qubit – simplest quantum system
Entanglement-
Lecture 1: Double Slit Experiment
If we add a measuring device just after the slits to track which slit the electron goes through, it “disrupts” the measurement and we get the 2nd pattern. If we use a very slight/dim light enough light, we get the 3rd “expected” pattern, but we also miss a lot of the electrons and may not capture the pattern.
= Hesienburgs uncertainty principle = Impossible to design apparatus which can detect which slit it went through without disturbing the interference pattern.
Traveling sales person problem
Solve problems which are NP hard – and they can’t be solved in polynomial time.
P versus NP problem: full polynomial versus nondeterministic polynomial problem
A P problem is one that can be solved in “polynomial time,” which means that an algorithm exists for its solution such that the number of steps in the algorithm is bounded by a polynomial function of n, where n corresponds to the length of the input for the problem. Thus, P problems are said to be easy, or tractable. A problem is called NP if its solution can be guessed and verified in polynomial time, and nondeterministic means that no particular rule is followed to make the guess.
http://www.paulgraham.com/ds.html#f1n
One of the most common types of advice we give at Y Combinator is to do things that don’t scale. A lot of would-be founders believe that startups either take off or don’t. You build something, make it available, and if you’ve made a better mousetrap, people beat a path to your door as promised. Or they don’t, in which case the market must not exist.
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.
RSA
A good simple overview: https://simple.wikipedia.org/wiki/RSA_algorithm
https://www.npmjs.com/package/big-integer
https://www.youtube.com/watch?v=vgTtHV04xRI
I recently purchased a Concept2 rower and started doing some indoor rowing to change up my workout routines. I was considering developing a live rowing platform to compete with friends and make the workouts more interactive. It turned out there were already a few options on the market, so I shelved the idea.
But, since the Concept2 allows 3rd party connectivity, I still was curious how integration, discovery, and notifications would work from an iOS device. I was able to find a nice SDK, but it was considerably outdated (5 years) so I decided to use that as a foundation and port the application to Swift 5 and get it working for anyone else interested in developing a rowing app for iOS.
I have been exposed to design thinking in a variety of ways over the past 13 odd years. From conferences, startups and projects – I have used the framework to develop and build services, software and hardware which incorporates one of the most important elements in the design: empathy.
Empathy ensures that the designers, developers and creators of these products “walk a mile in their shoes” and put the users at the center of the development lifecycle rather than technology, limitations or costs.
This TedX talk is a nice example of empathy in something critical that all of us can in some shape or form relate to, which is being born. The video centers around the design thinking process which went into the design and development of Neonatal Intensive Care Units and the equipment which nurses and parents have to deal with when a child is born prematurely. It is a great example of how empathy was an integral part of the process from start to finish.
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