BlingFire
parabol
BlingFire | parabol | |
---|---|---|
2 | 33 | |
1,781 | 1,849 | |
0.3% | 1.0% | |
3.6 | 9.8 | |
6 months ago | 1 day ago | |
C++ | TypeScript | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
BlingFire
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[D] SentencePiece, WordPiece, BPE... Which tokenizer is the best one?
SentencePiece -> implementation of some algorithms (there are several others, https://github.com/microsoft/BlingFire https://github.com/glample/fastBPE https://github.com/huggingface/tokenizers )
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Ask HN: Who is hiring? (March 2021)
• Develop the best technology to bring deep learning solutions to unprecedented scale, for example we built the world's fastest tokenizer. [https://github.com/microsoft/BlingFire]
parabol
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How to Run a Sprint Retrospective
Parabol: Does much of the heavy lifting of facilitating for you. Applies a pre-defined structure to your retro agenda.
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Retrospective Tools
similar to teamretro: https://www.parabol.co/
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Any recommendations for improving remote only retrospective sessions?
Not sure it helps with the issues you mention, but I found https://www.parabol.co/ to stimulate discussion. Everyone writes their thoughts on their own first, then they get shown to the group, you group them, vote, and discuss in order of most votes.
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PSA don't use Datadog agent in a GraphQL project
We faced something similar. To improve GraphQL performance, we use graphql-jit. We turned off all other tracing that datadog turns on by default. Then, we then wrote a custom tracer to connect graphql-jit to dd-trace. Hopefully this same pattern works for you!
- When you use Parabol to run a meeting, you don't have to be a well-seasoned facilitator—but with features that nudge and guide you along the way, you'll feel like a pro in no time! Don’t let pricing stop you: Parabol is free for up to 2 teams. Yup, 100% free.
- You don’t have to be an agile team to benefit from regularly iterating and improving on projects. Anyone can run great retrospectives and create continuous improvement in their work. - even if you lose track, we won't. Don’t let pricing stop you: Parabol is free for up to 2 teams. Yup, 100% free.
- TIL 92% of users agreed that Parabol improves the efficiency of their meetings. By keeping meetings democratic and fair with anonymous voting, they learn what development teams want to talk about giving everyone a voice. Don’t let pricing stop you: Parabol is free for up to 2 teams. Yup, 100% free.
- Discover patterns, prioritize what matters as a team, and implement them with multiplayer grouping. Parabol’s AI automates naming groups so scrum masters don’t have to, leaving only the change up to you and your team. Don’t let pricing stop you: Parabol is free for up to 2 teams. Yup, 100% free.
What are some alternatives?
tokenizers - 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
Baserow - Open source no-code database and Airtable alternative. Create your own online database without technical experience. Performant with high volumes of data, can be self hosted and supports plugins
Mattermost - Mattermost is an open source platform for secure collaboration across the entire software development lifecycle..
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
OpenKP - Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural m
orchest - Build data pipelines, the easy way 🛠️
sgr - sgr (command line client for Splitgraph) and the splitgraph Python library
python-fake-data-producer-for-apache-kafka - The Python fake data producer for Apache Kafka® is a complete demo app allowing you to quickly produce JSON fake streaming datasets and push it to an Apache Kafka topic.
sucrase - Super-fast alternative to Babel for when you can target modern JS runtimes
fargate-game-servers - This repository contains an example solution on how to scale a fleet of game servers on AWS Fargate on Elastic Container Service and route players to game sessions using a Serverless backend. Game Server data is stored in ElastiCache Redis. All resources are deployed with Infrastructure as Code using CloudFormation, Serverless Application Model, Docker and bash/powershell scripts. By leveraging AWS Fargate for your game servers you don't need to manage the underlying virtual machines.
k6 - A modern load testing tool, using Go and JavaScript - https://k6.io