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Huy Nguyen

Similarity search 101 - Part 2 (search with vp-trees). Similarity search 101 - Part 1 (overview). Faster numpy dot product for multi-dimensional arrays. The JSON Streaming Record (JSRec) data format. A guide to analyzing Python performance. Boost your productivity with workspaces using Tmux and iTerm2. Modifying Python's SimpleHTTPServer to accept directory aliases. Could testing in Python be simplified? Textmate hack: A keyboard shortcut for opening files in a new window. So that's how tracing JITs work .

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Similarity search 101 - Part 2 (search with vp-trees). Similarity search 101 - Part 1 (overview). Faster numpy dot product for multi-dimensional arrays. The JSON Streaming Record (JSRec) data format. A guide to analyzing Python performance. Boost your productivity with workspaces using Tmux and iTerm2. Modifying Python's SimpleHTTPServer to accept directory aliases. Could testing in Python be simplified? Textmate hack: A keyboard shortcut for opening files in a new window. So that's how tracing JITs work .

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Similarity search 101 - Part 2 (search with vp-trees). Similarity search 101 - Part 1 (overview). Faster numpy dot product for multi-dimensional arrays. The JSON Streaming Record (JSRec) data format. A guide to analyzing Python performance. Boost your productivity with workspaces using Tmux and iTerm2. Modifying Python's SimpleHTTPServer to accept directory aliases. Could testing in Python be simplified? Textmate hack: A keyboard shortcut for opening files in a new window. So that's how tracing JITs work .

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Faster numpy dot product for multi-dimensional arrays « Huy Nguyen

http://www.huyng.com/posts/faster-numpy-dot-product

Faster numpy dot product for multi-dimensional arrays. When installed and linked correctly against a blas implementation like ATLAS or OpenBlas, numpy’s. Product can run incredibly fast. What I hadn’t known until recently is that for some special cases, you can perform dot products an order of magnitude faster just by calling the underlying blas routines directly. In order to do so, I’ve written a module called fastdot. Whose source code is located in a github gist. As you can see. I build computer visio...

2

Exceptions are your Friends « Huy Nguyen

http://www.huyng.com/posts/exceptions-are-your-friends

Exceptions are your Friends. Robust code cries often and loudly as soon as something is not right. It does not cower away in corners of obscurity hoping that no one will notice, until one day, shit hits the fan. Any serious python code contains proper use of. In fact, I would argue that their presence defines the difference between one-off throwaway scripts and robust library code. Imagine a python interpreter without these facilities. What would the scenario look like? Statement around every piece of co...

3

How I Located a Camera in your Back Yard « Huy Nguyen

http://www.huyng.com/posts/how-i-located-a-camera-in-your-back-yard

How I Located a Camera in your Back Yard. I found a webcam in. Neighborhood. As I type, I see. Dog easing out a steady stream of its steady-stream onto your neighbor's freshly lacquered patio. Don't believe me? See the results for yourself:. Unsecure) Webcams Around the World. It first started with this link. Which means that you can use it to programatically harvest urls to thousands of these cameras. Below, I use this module to locate the first 40 webcam urls on a Yahoo Search Page:. After extracting t...

4

Boost your productivity with workspaces using Tmux and iTerm2 « Huy Nguyen

http://www.huyng.com/posts/productivity-boost-with-tmux-iterm2-workspaces

Boost your productivity with workspaces using Tmux and iTerm2. If you do a lot of context switching between projects, recreating your terminal environment and window layout can easily eat up hours of your day. Here’s a quick tip to help you create and manage persistent terminal workspaces so that with a few keystrokes, you can jump straight back into whatever you were working on as quickly as possible. All of this is achieved with the following shell script which we’ll configure iTerm2. Or if you don’t w...

5

A guide to analyzing Python performance « Huy Nguyen

http://www.huyng.com/posts/python-performance-analysis

A guide to analyzing Python performance. While it’s not always the case that every Python program you write will require a rigorous performance analysis, it is reassuring to know that there are a wide variety of tools in Python’s ecosystem that one can turn to when the time arises. Analyzing a program’s performance boils down to answering 4 basic questions:. How fast is it running? Where are the speed bottlenecks? How much memory is it using? Where is memory leaking? Coarse grain timing with time. Our ne...

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Huy Nguyen

Similarity search 101 - Part 2 (search with vp-trees). Similarity search 101 - Part 1 (overview). Faster numpy dot product for multi-dimensional arrays. The JSON Streaming Record (JSRec) data format. A guide to analyzing Python performance. Boost your productivity with workspaces using Tmux and iTerm2. Modifying Python's SimpleHTTPServer to accept directory aliases. Could testing in Python be simplified? Textmate hack: A keyboard shortcut for opening files in a new window. So that's how tracing JITs work .

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