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  1. Childhood Flashback

    I have very fuzzy memories of playing games like Tank Wars when I was a kid.

  2. Galactic-level Management

    I’ve always been interested in simulations.

  3. The Feynman Technique

    I’m reposting this.

  4. Org-mode cheatsheet

    Tags Search tags: SPC n m or SPC o a m Add tag: SPC m q Links Insert link: SPC m l l File links have the structure [[file:relative_link_to_file][some_title]] To link to a specific heading in a file [[file:relative_link::header title][some_title]] header t…

  5. Curl a GraphQL API

    1 2 3 4 5 6 7 curl -0 -v -X POST https://some.api.com/graphql \ -H 'Content-Type: application/json' \ -d @- << EOF { "query": "query { someQuery (someArg: false) { name } }" } EOF curl will probably complain about the -X POST being inferred, but it sho…

  6. Function Overloading

    Take a function like below… 1 2 3 4 5 6 7 8 type Combinable = string | number function add(a: Combinable, b: Combinable): Combinable { if (typeof a === 'string' || typeof b === 'string') { return a.toString() + b.toString() } return a + b } If we w…

  7. Nullish Coalescing

    1 2 // say you have some var hangin' around called `name` let x: string = name ??

  8. Recursive Type Aliases

    In TS@4 a type can reference itself, e.g. 1 2 3 4 5 6 7 8 9 type JSONValue = | string | number | boolean | null | JSONValue[] | { [k: string]: JSONValue } Previously, this would not be possible without some messy hacks.

  9. Labeled Tuple Types

    1 type Address = [number, string, string, number] Say you now have a function printAddress which takes an Address type as its arg. 1 2 3 function printAddress(...address: Address) { // ...stuff } Your editor (before TS@4) would hint that the arguments

  10. Variadic Tuple Types

    1 type Foo<T extends any[]> = [boolean, ...T, boolean] Before TS@4.0 ...T would need to be the last element, but now we can spread the T type nested between known types; e.g.

  11. Composite Builds

    TypeScript has a way of describing a build process as multiple subpieces of a project.

  12. Jest Setup for a Monorepo

    Out of the box, Jest mostly works in a Monorepo environment, with the exception of a few Babel plugins so that (as an example) TypeScript works.

  13. Monorepos

  14. Colocation

    “colocation” is a pattern wherein you keep the query/mutation as close to the consuming component as possible.

  15. Yarn-NPM

    package.json resolutions field yarn specific Allows you to force the use of a particular version for a nested dependency. e.g.: 1 2 3 4 "devDependencies": { "@angular/cli": "1.0.3", "typescript": "2.3.2" } yarn.lock will contain: 1 2 3 4 5 6 7 "types…

  16. Cipher

    A cipher is defined over the spaces of: All Keys, \(\mathscr{K}\) All Messages, \(\mathscr{M}\) All Cipher texts, \(\mathscr{C}\) Cipher (defined as a triple, \((\mathscr{K}, \mathscr{M}, \mathscr{C})\)) as a pair of algorithms \((\mathbf{E}, \mathbf{D})\)

  17. Distribution Vector

    A vector with non-negative components (representing specific probabilities) which add up to one e.g.

  18. Uniform Distribution

    \begin{equation} x \in U:P(x) = \frac{1}{|U|} \end{equation} Where \(|U|\) is the size of the universe (set).

  19. Point Distribution

    x0: P(x) = 1,∀ x ≠ x0: P(x) = 0 In Probability Theory, a point distribution is a distribution which assigns all the probability to a given point (set element).

  20. Cryptography

    Digital Signature

  21. Digital Signature

    A function of the content being “signed”

  22. Cost Function

    The measurement of accuracy of a hypothesis function.

  23. Machine Learning

    Gradient Descent Cost Function Hypothesis Function Artificial Neural Network

  24. Gradient Descent

    An optimization algorithm for finding the local minimum of a differentiable function.

  25. Hypothesis Function

    A function which maps values \(x\) to an output value \(y\).

  26. Artificial Neural Network

    Artificial Neural Network (ANN) Layers All learning occurs in the layers.

  27. Covectors

    A linear mapping from a vector space to a field of scalars.

  28. Differential Geometry

    Tensors Tensor Product

  29. Linear Algebra

    Bases Bases Transformation Coordinate Transformation Covectors Dual Space Identity Matrix Invertible Matrix Invertible Matrix Orthonormal Basis Tensor Product Tensors Vector Space Axioms (Vector Space)

  30. Linear Mapping

    A mapping from \(\mathbf{V} \rightarrow \mathbf{W}\) that preserves the operations of addition and scalar multiplication.

  31. Multilinear Map

    A function of several variables that is linear, separately, in each variable.

  32. Tensor Product

  33. Dual Space

    The space of all linear functionals \(f:V\rightarrow \mathbb{R}\), noted as \(V^{*}\) The dual space has the same dimension as the corresponding vector space or, given a space \(V\), with bases \((v_{1},…,v_{n})\), there exists a dual space \(V^{*}\) with…

  34. Dual Vector Space

    The space of all linear functionals \(f:V\rightarrow \mathbb{R}\), noted as \(V^{*}\) The dual space has the same dimension as the corresponding vector space or, given a space \(V\), with bases \((v_{1},…,v_{n})\), there exists a dual space \(V^{*}\) with…

  35. Heliosphere

    The bubble, created by the Sun’s plasma (see Solar Wind), which encompasses the Sun itself.

  36. Solar Wind

    TODO

  37. Tensors

    As a linear representation A tensor can be represented as a vector of x-number of dimensions.

  38. Kronecker Delta

    \begin{equation} \delta_{ij}= \begin{cases} 0 & \text{if i \(\neq\) j}\\\ 1 & \text{if i \(=\) j} \end{cases} \end{equation}

  39. Bases

    a basis for an n-dimensional vector space \(V\) is any ordered set of linearly independent vectors \((\mathbf{e}_{1}, \mathbf{e}_{2},…,\mathbf{e}_{n})\) An arbitrary vector \(\mathbf{x}\) in \(V\) can be expressed as a linear combination of the basis vect…

  40. Orthonormal Basis

    An orthonormal basis is a basis where all the vectors are one unit long and all perpendicular to each other (e.g. the Cartesian plane)

  41. Bases Transformation

    Consider two bases \((\mathbf{e}_{1},\mathbf{e}_{2})\) and \((\mathbf{\tilde{e}}_{1},\mathbf{\tilde{e}}_{2})\), where we consider the former the old basis and the latter the new basis.

  42. Identity Matrix

    \begin{equation} \mathbf{I}(\mathbf{X})=\mathbf{X} \end{equation} Where any \(nxn\) matrix is established via the Kronecker Delta, e.g.

  43. Invertible Matrix

    A matrix, which when multiplied by another matrix, results in the identity matrix.

  44. Vector Space

    also known as a linear space A collection of objects known as “vectors”.

  45. Coordinate Transformation

  46. Axioms (Vector Space)

    To qualify as a vector space, a set \(V\) and its associated operations of addition (\(+\)) and multiplication/scaling (\(\cdot\)) must adhere to the below: Associativity \begin{equation} \mathbf{u}+(\mathbf{v}+\mathbf{w}) = (\mathbf{u} + \mathbf{v}) + \m…

  47. Invariance (Mathematics)

    An property (of a mathematical object) is invariant if, after some operation(s) are applied, that property remains unchanged.

  48. Cosmology

    The Inflationary Universe Standard Model Cosmic Inflation Cosmological Constant Cosmic Microwave Background

  49. The Inflationary Universe

    source The Inflationary Universe: A Possible Solution To The Horizon And Flatness Problems (Guth, 1980) Questions I still have DONE WHY#1: why is that approximation unstable?

  50. This refers to the Cosmological “Standard Model”, i.e. the \(\Lambda\)CDM.