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Ankush Goyal ankushgoyal

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@stevenringo
stevenringo / reinvent-2016-youtube.md
Last active March 6, 2025 19:18
Links to YouTube recordings of AWS re:Invent sessions
@rondy
rondy / Effective_Engineer.md
Last active October 31, 2025 10:44

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

Effective Engineer - Notes

What's an Effective Engineer?

@pandafulmanda
pandafulmanda / Python3 Virtualenv Setup.md
Last active August 6, 2025 13:42 — forked from akszydelko/Python3 Virtualenv Setup.md
Setting up and using Python3 Virtualenv on Mac

Python3 Virtualenv Setup

Requirements
  • Python 3
  • Pip 3
$ brew install python3
@kevin-smets
kevin-smets / iterm2-solarized.md
Last active October 31, 2025 08:25
iTerm2 + Oh My Zsh + Solarized color scheme + Source Code Pro Powerline + Font Awesome + [Powerlevel10k] - (macOS)

Default

Default

Powerlevel10k

Powerlevel10k

@debasishg
debasishg / gist:8172796
Last active October 19, 2025 00:47
A collection of links for streaming algorithms and data structures

General Background and Overview

  1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
  2. Models and Issues in Data Stream Systems
  3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
  4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
  5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
@adamwiggins
adamwiggins / adams-heroku-values.md
Last active September 6, 2025 20:29
My Heroku values

Make it real

Ideas are cheap. Make a prototype, sketch a CLI session, draw a wireframe. Discuss around concrete examples, not hand-waving abstractions. Don't say you did something, provide a URL that proves it.

Ship it

Nothing is real until it's being used by a real user. This doesn't mean you make a prototype in the morning and blog about it in the evening. It means you find one person you believe your product will help and try to get them to use it.

Do it with style

@hellerbarde
hellerbarde / latency.markdown
Created May 31, 2012 13:16 — forked from jboner/latency.txt
Latency numbers every programmer should know

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs