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Articles related to "run"


I'm Open Sourcing the Have I Been Pwned Code Base

  • Open sourcing the HIBP code base gives me the opportunity to address that original problem I set out to solve with the M&A process: finding other people that can help sustain the project.
  • People who live and breathe open source, people who understand .NET and Azure inside and out, people who know HIBP well and above all, people I trust to expose my own shortcomings so that they can help me make this thing more sustainable.
  • Let me be clear: I don't have a timeline for each step along the way yet as HIBP remains something I do in my spare time and I've always got a bunch of other stuff on my plate, but the process has already begun and I'll be sharing more on that as soon as I can.

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Chip and Joanna Gaines bringing 'Fixer Upper' back

  • The hit HGTV series starring husband and wife duo Chip and Joanna Gaines is being revived on the couple's new Magnolia Network.
  • It's been more than two years since the couple last did a house reveal on their massively successful show which featured them finding and renovating homes for their clients in Waco, Texas.
  • In the interim they announced the formation of Magnolia Network, Discovery Inc.'s joint-venture with the Gaines' home and lifestyle brand Magnolia which had been set to debut on October 4.
  • Discovery is the parent company of HGTV.
  • The new show will be one of a slate of series offered on Magnolia Network.
  • During its initial run, "Fixer Upper" was the No. 1 unscripted series on cable television with 75 million viewers tuning in for the show's fifth season, including 19.6 million on a weekly basis, according to Discovery.

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Take Gitlab to the command line with GLab, an open-source Gitlab CLI tool

  • GLab is an open source Gitlab Cli tool written in Go (golang) to help work seamlessly with Gitlab from the command line.
  • Read the documentation for more information on this tool.
  • Download a binary suitable for your OS at the releases page.
  • Available for download on scoop or manually as an installable executable file or a Portable archived file in tar and zip formats at the releases page.
  • Download and install now at the releases page.
  • The installable executable file sets the PATH automatically.
  • If go is not installed, follow instructions on the Go website.
  • Run glab help to check if it worked.
  • Thanks for considering contributing to this project!
  • Please read the contributions guide and Code of conduct.
  • Feel free to open an issue or submit a pull request!

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Texas scientist and mother of two killed during her regular morning run

  • Sarmistha Sen had gotten up early for a run and at some point in time came across her attacker, according to Plano Police Department public information officer David Tilley.
  • Less than two hours later, a witness saw what she believed was a body about 20 to 25 feet below near the creek.
  • Police were called to the scene and discovered Sen's body.
  • A person of interest is currently being held at the Collin County Detention Center for a burglary that happened nearby, according to a press release from Plano Police Department.
  • The killing is an extreme example of an issue women commonly face while out running.
  • Local residents paid tribute to Sen by dropping off pairs of running shoes near the site where she was killed.
  • Sen was born in Sindri, India, later moved to Bangalore and then got married and moved to Plano in 2004, the CaringBridge site said.

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Build a BERT-based Semantic Search System…For Star Trek

  • In this tutorial we’re going to build our own search engine to search all the lines from Star Trek: The Next Generation.
  • In pods/ we see chunk.yml, craft.yml, doc.yml, and encode.yml - these Pods are called from the Flows to process data for indexing or querying.
  • Now that we’ve got the code to load our data, we’re going to dive into writing our app and running our Flows!
  • Flows are the different tasks our app performs, like indexing or searching the data.
  • We’ll search through this index when we use the query Flow later.
  • We define Flows in app.py to index and query the content in our Star Trek dataset.
  • Now that you have a broad understanding of how things work, you can try out some of more example tutorials to build image or video search, or stay tuned for our next set of tutorials that build upon your Star Trek app.

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Build a BERT-based Semantic Search System…For Star Trek

  • In this tutorial we’re going to build our own search engine to search all the lines from Star Trek: The Next Generation.
  • In pods/ we see chunk.yml, craft.yml, doc.yml, and encode.yml - these Pods are called from the Flows to process data for indexing or querying.
  • Now that we’ve got the code to load our data, we’re going to dive into writing our app and running our Flows!
  • Flows are the different tasks our app performs, like indexing or searching the data.
  • We’ll search through this index when we use the query Flow later.
  • We define Flows in app.py to index and query the content in our Star Trek dataset.
  • Now that you have a broad understanding of how things work, you can try out some of more example tutorials to build image or video search, or stay tuned for our next set of tutorials that build upon your Star Trek app.

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Teaching New Tricks To An Old Camera

  • I could solve the delivery box notification problem with minimal cost, put the camera to use AND use some deep learning to get a modern detector… that was too good a project to pass up!
  • Running the detection off the files did introduce a bit of a delay, though: the recording is done in one-minute segments, and it isn’t processed until it is finished; add to that a few seconds for the script to notice that there is a new file, a few more for running inference on it, and a few more to send out any notifications.
  • Since the camera is PTZ, at the start I ambitiously thought I’d train a neural network to recognize where it was pointed and adjust the relevant detection area.

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Sub-10 ms Latency in Java: Concurrent GC with Green Threads · Hazelcast Jet

  • We determined how many threads the given GC uses, set the size of the Jet thread pool to 16 (c5.4xlarge vCPUs) minus that value and then did some trial-and-error runs to find the optimum.
  • Comparing ZGC's results below with those in the previous round, we can see the latency stayed about the same where it was already good, but the range of throughputs got extended from 8 to 10 M items/second, a solid 25% improvement.
  • The best news: the maximum throughput at which a single Hazelcast Jet node maintains 99.99% latency within 10 ms now lies at 20 million items per second, a 250% boost!
  • Can a single-node Hazelcast Jet handle this load and produce, say, the time integral of the measured quantity from each sensor over a 1-second window, at a 10 ms latency?

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Teaching New Tricks To An Old Camera

  • I could solve the delivery box notification problem with minimal cost, put the camera to use AND use some deep learning to get a modern detector… that was too good a project to pass up!
  • Running the detection off the files did introduce a bit of a delay, though: the recording is done in one-minute segments, and it isn’t processed until it is finished; add to that a few seconds for the script to notice that there is a new file, a few more for running inference on it, and a few more to send out any notifications.
  • Since the camera is PTZ, at the start I ambitiously thought I’d train a neural network to recognize where it was pointed and adjust the relevant detection area.

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Deploy a Scikit-Learn NLP Model with Docker, GCP Cloud Run and Flask

  • Be sure to check out the README and code in our GitHub repository instructions on setting up this app locally with Docker!
  • This Docker image now accessible at the GCP container registry or GCR and can be accessed via URL with Cloud Run. Note: Replace PROJECT-ID with your GCP project ID and container-name with your containers’ name.
  • You have just deployed an application packaged in a container to Cloud Run. You only pay for the CPU, memory, and networking consumed during request handling.
  • We’ve covered setting up an app to serve a model and building docker containers locally.
  • Next, we stored our docker image in the cloud and used it to build an app on Google Cloud Run. Getting any decently good model out quickly can have significant business and tech value.

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