| // | |
| // Regular Expression for URL validation | |
| // | |
| // Author: Diego Perini | |
| // Created: 2010/12/05 | |
| // Updated: 2018/09/12 | |
| // License: MIT | |
| // | |
| // Copyright (c) 2010-2018 Diego Perini (http://www.iport.it) | |
| // |
| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
People
 :bowtie: |
😄 :smile: |
😆 :laughing: |
|---|---|---|
😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
| #!/usr/bin/env python | |
| """ | |
| Sniff a specific port for Bit Torrent DHT traffic and print | |
| requests/responses in human readable form. | |
| Reference: http://www.bittorrent.org/beps/bep_0005.html | |
| """ | |
| from pcapy import open_live | |
| from bencode import bdecode |
Memory Optimization (Christer Ericson, GDC 2003)
http://realtimecollisiondetection.net/pubs/GDC03_Ericson_Memory_Optimization.ppt
Cache coherency primer (Fabian Giesen)
https://fgiesen.wordpress.com/2014/07/07/cache-coherency/
Code Clinic 2015: How to Write Code the Compiler Can Actually Optimize (Mike Acton)
http://gdcvault.com/play/1021866/Code-Clinic-2015-How-to
This is a step-by-step tutorial for hosting your website under your domain on IPFS, from zero, on a DigitalOcean Ubuntu 16.04.3 x64 Droplet (i am using the $10 variant with 2GB RAM).
Log in as root.
First, make sure the system is up to date, and install tar and wget:
| def club_similar_keywords(emb_mat, sim_score=0.9): | |
| """ | |
| :param emb_mat: matrix having vectors with words as index | |
| :param sim_score: 0.9 by default | |
| :return: returns list of unique words from index after combining words which has similarity score of more than | |
| 0.9 | |
| """ | |
| if len(emb_mat) == 0: | |
| return 'NA' | |
| xx = cosine_similarity(emb_mat) |
Let suppose I have two github accounts, https://github.com/rahul-office and https://github.com/rahul-personal. Now i want to setup my mac to easily talk to both the github accounts.
NOTE: This logic can be extended to more than two accounts also. :)
The setup can be done in 5 easy steps:
This episode of Recsperts was transcribed with Whisper from OpenAI, an open-source neural net trained on almost 700 hours of audio. The model includes an encoder-decoder architecture by tokenizing audio into 30-second chunks, normalizing audio samples to the log-Mel scale, and passing the data into an encoder. A decoder is trained to predict the captioned text matching the encoder, and the model includes transcription, as well as timestamp-aligned transcription, and multilingual translation.
The transcription process outputs a single string file, so it's up to the end-user to parse out individual speakers, or run the model [through a sec
The screenshots were taken on different sessions.
The entire sessions are included on the screenshots.
I lost the original prompts, so I had to reconstruct them, and still managed to reproduce.
The "compressed" version is actually longer! Emojis and abbreviations use more tokens than common words.
