they do not form the basis for discovery,

Different ways to prepend a line: (echo 'line to prepend';cat file)|sponge file sed -i '1iline to prepend' file # GNU sed -i '' $'1i\\\nline to prepend\n' file # BSD printf %s\\n 0a 'line to prepend' . w|ed -s file perl -pi -e 'print"line to prepend\n"if$.==1' file

$ ed - var.c << end > 0a > xxx > . > wq > end

What I am attempting to do is to highlight a div with a certain id, when It has been referred to by an anchor on another page IE: User clicks link href="qw.html#test", when the page is loaded, then the div with the id="test" is highlighted so that the user can see it clearly.

You need to use the :target pseudo-class: :target { background-color: #ffa; }

More importantly, using a plain email would save lots of time and effort. As a goal-driven-lazy person, that’s a good enough reason to start experimenting.

They don't look like advertisements. The second the recipient interprets your email as an ad, promotion, or sales pitch—and it does take just a second—its chances of being read or acted upon plummet towards zero. A plain email leads people to start reading it before jumping to conclusions.

They feel more personal. It's no handwritten note, but it's much more personal than an over-designed email with the recipient's first name crammed somewhere inside.

The plain, unstyled emails resulted in more opens, clicks, replies, and conversions, every time.

They're less likely to go into the "Promotions" tab in Gmail (used by ~16% of all email users), for the same reasons above. From my testing, the plain emails typically end up in the Updates tab and some times even in the primary tab. Of course, the text in the email also affects this.

The plain email—which took no time to design or code—was opened by more recipients and had 3.3x more clicks than the designed email.

Vulgata

Ideally, GitHub would understand rich formats

Feedback from the faculty teaching team after teaching for almost 8 weeks is how to template and simplify space for students to use, here is a direct quote: “could we create dedicated blog page for students that would be a pre-made, fool-proof template? When a student’s WordPress blog does not work and we can’t fix the problem, it is very frustrating to be helpless beside an exasperated student.”

$ ./my_script Will end up in STDOUT(terminal) and /var/log/messages $ tail -n1 /var/log/messages Sep 23 15:54:03 wks056 my_script_tag[11644]: Will end up in STDOUT(terminal) and /var/log/messages

exec &> >(tee -a "$log_file")

exec &> >(tee -a "$log_file") echo "This will be logged to the file and to the screen" $log_file will contain the output of the script and any subprocesses, and the output will also be printed to the screen.

Write stderr and stdout to a file, display stderr on screen (on stdout) exec 2> >(tee -a -i "$HOME/somefile.log") exec >> "$HOME/somefile.log" Useful for crons, so you can receive errors (and only errors) by mail

What you want is not to detect if stdin is a pipe, but if stdin/stdout is a terminal.

This question does not show any research effort; it is unclear or not useful Bookmark this question. Show activity on this post. I'm trying to filter the output of the mpv media player, removing a particular line, but when I do so I am unable to control mpv with the keyboard. Here is the command: mpv FILE | grep -v 'Error while decoding frame' When I run the command, everything displays correctly, but I am unable to use the LEFT and RIGHT keys to scan through the file, or do anything else with the keyboard. How do I filter the output of the program while retaining control of it?

Quite a lot of programs actually detect if their output goes to a file (e.g. try man | grep -F a and you will not be able to scroll back and forth).

perl -ne 'chomp(); if (-e $_) {print "$_\n"}'

xargs -i sh -c 'test -f {} && echo {}'

i found that for the osx host "gonzo" , the vanished files (not the warning message itself) appear in stdout - for linux hosts they _both_ appear in stderr , but nothing in stdout (rsync.err.#num is stderr, rsync.log is stdout)

Enclosed text fields with a rectangular (box) shape performed better than those with a line affordance

However, some users didn’t know that they could interact with and click on the text field. It looked like an empty box. The line affordance under the old text fields was not clear to some users. The line was confused with a divider.

Note: The standard variant of the TextField is no longer documented in the Material Design guidelines (here's why), but Material-UI will continue to support it.

engage in technology enhancedmultigenre research

[...document.querySelectorAll("*")].filter(e => e.childNodes && [...e.childNodes].find(n => n.nodeValue?.match("❤")))

function contains(selector, text) { var elements = document.querySelectorAll(selector); return [].filter.call(elements, function(element){ return RegExp(text).test(element.textContent); }); }

Array.from(document.querySelectorAll('div')) .find(el => el.textContent === 'SomeText, text continues.');

Linux has plenty to offer

Group Rules from the Admins1NO POSTING LINKS INSIDE OF POST - FOR ANY REASONWe've seen way too many groups become a glorified classified ad & members don't like that. We don't want the quality of our group negatively impacted because of endless links everywhere. NO LINKS2NO POST FROM FAN PAGES / ARTICLES / VIDEO LINKSOur mission is to cultivate the highest quality content inside the group. If we allowed videos, fan page shares, & outside websites, our group would turn into spam fest. Original written content only3NO SELF PROMOTION, RECRUITING, OR DM SPAMMINGMembers love our group because it's SAFE. We are very strict on banning members who blatantly self promote their product or services in the group OR secretly private message members to recruit them.4NO POSTING OR UPLOADING VIDEOS OF ANY KINDTo protect the quality of our group & prevent members from being solicited products & services - we don't allow any videos because we can't monitor what's being said word for word. Written post only.

While there are always going to be cases where one is more appropriate than the other, border-bottom offers much more precise control over text-decoration and is therefore probably the preferred method. Here's a quick (likely not exhaustive) list of properties that border-bottom can control/enable that text-decoration cannot:

logInfoToStdOut (boolean) (default=false) This is important if you read from stdout or stderr and for proper error handling. The default value ensures that you can read from stdout e.g. via pipes or you use webpack -j to generate json output.

The heart of the JCT Charity is to restore dignity to those who have lost all. To show respect to all, (especially those who have little self-respect), and to hod out that helping hand. Never have people felt so isolated as now. Being heard is an important part of building ‘community’. Many we seek to help can’t speak English for example. JCT aims to give a voice to those who have not had one and to help them to interact and contribute again.

Many have said that COVID-19 has brought communities together. Neighborhoods have risen to the challenge of helping each other out in really practical ways, food banks have emerged, street Whatsapp groups popped up everywhere and friends doing each other’s shopping. And yet isolation and personal crisis are to be found everywhere. JCT sees the need to make an impact in these times, allowing connection, enabling a dialogue, joining communities together. For us Covid has forced us to close many of our previous services especially drop-ins for people who were homeless or in need. This crisis forced us to work in a more focused way, one to one, with many of our clients concentrationg of getting housing and funds for those in the greatest need. What we found was that we were able to make a greater difference to those we were able to work with over this time, and from this JCT were able to fashion a new, highly effective, method of casework. The lockdown has helped us identify a specific way to help in this time of great need.

I don’t think the right answer is to use something like the Mnemonic medium to memorize a cookbook’s contents. I think a likelier model is: each time you see a recipe, there’s some chance it’ll trigger an actionable “ooh, I want to make this!”, dependent on seasonality, weather, what else you’ve been cooking recently, etc. A more effective cookbook might simply resurface recipes intermittently over time, creating more opportunities for a good match: e.g. a weekly email with 5-10 cooking ideas, perhaps with some accompanying narrative. Ideally, the cookbook would surface seasonally-appropriate recipes. Seasonality would make the experience of “reading” a cookbook extend over the course of a year—a Timeful text.

MyScript MathPad

Make sure students can reach out to you privately to let you know if anything about the experience was uncomfortable for them.

19 Now the Lord God had formed out of the ground all the wild animals(AA) and all the birds in the sky.(AB) He brought them to the man to see what he would name them; and whatever the man called(AC) each living creature,(AD) that was its name. 20 So the man gave names to all the livestock, the birds in the sky and all the wild animals.

When the woman saw that the fruit of the tree was good for food and pleasing to the eye, and also desirable(AT) for gaining wisdom, she took some and ate it.

Original Instructions.

First class TypeScript support means that both of these two systems do a good job of handling TypeScript code.

To add documentation on a Svelte component that will show up as a docstring in LSP-compatible editors, you can use an HTML comment with the @component tag:

It’s 2020, doesn’t everyone use VSCode already?

Because the Web IDE is based on the Monaco Editor, you can find a more complete list of supported languages in the Monaco languages repository. Under the hood, Monaco uses the Monarch library for syntax highlighting.

Once running, kaniko will then get the data from STDIN and create the build context as a compressed tar. It will then unpack the compressed tar of the build context before starting the image build.

Pipes are great for taking output of one command and transforming it using other commands like jq. They’re a key part of the Unix philosophy of “small sharp tools”: since commands can be chained together with pipes, each command only needs to do one thing and then hand it off to another command.

Write programs to handle text streams, because that is a universal interface.

Here are the purposes included in each category:

Generally, these laws apply to any service targeting residents of the region, which effectively means that they may apply to your business whether it’s located in the region or not.

Subject–object–verb order. Learn more at Differences with Vi(m).

Python contributed examples¶ Mic VAD Streaming¶ This example demonstrates getting audio from microphone, running Voice-Activity-Detection and then outputting text. Full source code available on https://github.com/mozilla/DeepSpeech-examples. VAD Transcriber¶ This example demonstrates VAD-based transcription with both console and graphical interface. Full source code available on https://github.com/mozilla/DeepSpeech-examples.

Python API Usage example Edit on GitHub Python API Usage example¶ Examples are from native_client/python/client.cc. Creating a model instance and loading model¶ 115 ds = Model(args.model) Performing inference¶ 149 150 151 152 153 154 if args.extended: print(metadata_to_string(ds.sttWithMetadata(audio, 1).transcripts[0])) elif args.json: print(metadata_json_output(ds.sttWithMetadata(audio, 3))) else: print(ds.stt(audio)) Full source code

DeepSpeech is an open source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. NOTE: This documentation applies to the 0.7.0 version of DeepSpeech only. Documentation for all versions is published on deepspeech.readthedocs.io. To install and use DeepSpeech all you have to do is: # Create and activate a virtualenv virtualenv -p python3 $HOME/tmp/deepspeech-venv/ source $HOME/tmp/deepspeech-venv/bin/activate # Install DeepSpeech pip3 install deepspeech # Download pre-trained English model files curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/deepspeech-0.7.0-models.pbmm curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/deepspeech-0.7.0-models.scorer # Download example audio files curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/audio-0.7.0.tar.gz tar xvf audio-0.7.0.tar.gz # Transcribe an audio file deepspeech --model deepspeech-0.7.0-models.pbmm --scorer deepspeech-0.7.0-models.scorer --audio audio/2830-3980-0043.wav A pre-trained English model is available for use and can be downloaded using the instructions below. A package with some example audio files is available for download in our release notes.

Speech & Machine Learning

Library for performing speech recognition, with support for several engines and APIs, online and offline. Speech recognition engine/API support: CMU Sphinx (works offline) Google Speech Recognition Google Cloud Speech API Wit.ai Microsoft Bing Voice Recognition Houndify API IBM Speech to Text Snowboy Hotword Detection (works offline) Quickstart: pip install SpeechRecognition. See the “Installing” section for more details. To quickly try it out, run python -m speech_recognition after installing. Project links: PyPI Source code Issue tracker Library Reference The library reference documents every publicly accessible object in the library. This document is also included under reference/library-reference.rst. See Notes on using PocketSphinx for information about installing languages, compiling PocketSphinx, and building language packs from online resources. This document is also included under reference/pocketsphinx.rst.

Running the example code with python Run like this: cd vosk-api/python/example wget https://github.com/alphacep/kaldi-android-demo/releases/download/2020-01/alphacep-model-android-en-us-0.3.tar.gz tar xf alphacep-model-android-en-us-0.3.tar.gz mv alphacep-model-android-en-us-0.3 model-en python3 ./test_simple.py test.wav To run with your audio file make sure it has proper format - PCM 16khz 16bit mono, otherwise decoding will not work. You can find other examples of using a microphone, decoding with a fixed small vocabulary or speaker identification setup in python/example subfolder

Vosk is a speech recognition toolkit. The best things in Vosk are: Supports 8 languages - English, German, French, Spanish, Portuguese, Chinese, Russian, Vietnamese. More to come. Works offline, even on lightweight devices - Raspberry Pi, Android, iOS Installs with simple pip3 install vosk Portable per-language models are only 50Mb each, but there are much bigger server models available. Provides streaming API for the best user experience (unlike popular speech-recognition python packages) There are bindings for different programming languages, too - java/csharp/javascript etc. Allows quick reconfiguration of vocabulary for best accuracy. Supports speaker identification beside simple speech recognition.

Kaldi API for offline speech recognition on Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node

import all the necessary libraries into our notebook. LibROSA and SciPy are the Python libraries used for processing audio signals. import os import librosa #for audio processing import IPython.display as ipd import matplotlib.pyplot as plt import numpy as np from scipy.io import wavfile #for audio processing import warnings warnings.filterwarnings("ignore") view raw modules.py hosted with ❤ by GitHub View the code on <a href="https://gist.github.com/aravindpai/eb40aeca0266e95c128e49823dacaab9">Gist</a>. Data Exploration and Visualization Data Exploration and Visualization helps us to understand the data as well as pre-processing steps in a better way. 

TensorFlow recently released the Speech Commands Datasets. It includes 65,000 one-second long utterances of 30 short words, by thousands of different people. We’ll build a speech recognition system that understands simple spoken commands. You can download the dataset from here.