Samuel Marks

Scaling engineering is difficult. It shouldn't be. My mission is to create open-source technology, to scale from one engineer and one user, to 100s of engineering teams and millions of users.

I write open-source developer tools to speedup engineering of scalable software. Foci on: cross-platform, multi-ML, multicloud, and compilers to translate across codebases.

SamuelMarks

Deploy at any scale

From one [e.g., embedded] device to 10,000 servers:

PurposeRepo
Provision nodes specified in JSON, across 50+ cloudsoffstrategy
SSH into node provisioned by offstrategy|offsetoffshell
Deprovision node provisioned by offstrategy|offset from cloud providers offswitch
Bring Your Own Node (BYON) [so can use ↕]offset
Deploy any of 50 “offregister-” prefixed softwares—including clustered databases—to nodes provisioned by offstrategy|offset offregister

Competitive advantage

  • Support for more cloud vendors;
  • Uses normal Python packages deployable to PyPi, as opposed to Puppet/Chef/Ansible with their custom systems;
  • [WiP] Deploy to any operating system (cross-platform: SunOS, Windows, Linux, macOS, OpenBSD);
  • [WiP] Experiment with different versions of each package, including clustered variants.

Multicloud

From one cloud vendor to many:

  • [old] See aforementioned Apache Libcloud and Fabric utilising Python repos;
  • [new] C89 google-cloud-c library (soon: auto-generate entire library, and other vendors);
  • [planned] autogenerate vendors other than Google Cloud.

Competitive advantage

  • [C89] Can be called from most any programming language and runs in all environments;
  • [planned] Build specific abstractions for multicloud, like: container-as-a-Service; ML-as-a-Service; Storage-as-a-Service; &etc.

Multi-ML

GoogleOther vendors
tensorflowpytorch
kerasskorch
flaxsklearn
traxxgboost
jaxcntk

Competitive advantage

  • Keep up-to-date with latest innovations without porting to favourite framework;
  • Experiment with every model on all major Python ML frameworks.

Native development, cross-platform, without tradeoffs

Compilers to automatically translate—within and—between:

LanguageCompiler
Pythoncdd-python
Ccdd-c
Java (Android)cdd-java
Kotlin (Android)cdd-kotlin
Swift (iOS)cdd-swift
TypeScript (Angular)cdd-ts-ng
Rustcdd-rust

Competitive advantage

  • [intra-language] Automatically synchronise tests (& mocks), docs, types & interfaces;
  • [exolanguage] Translate changes across language boundaries;
  • Develop multi-language applications—e.g., Android, iOS, web, backend—as fast as single-language applications (compare with: Django or Ruby on Rails) and at a higher quality thanks to increased consistency, test coverage and doc coverage.