2022 Blockly Developers Summit: Contributing to Blockly

Subscribers:
348,000
Published on ● Video Link: https://www.youtube.com/watch?v=vuW-L6ettx0



Duration: 12:20
361 views
5


A Google TechTalk, presented by Maribeth Bottorff, 2022/05/03.
ABSTRACT: Have you ever considered contributing to Blockly? There are many ways to contribute, from fixing bugs to answering questions in the forum. In this talk, Maribeth discusses why you should consider contributing, what you need to know, and how to get started.

About the speaker: Maribeth is a software engineer on the Blockly team. She works on the tooling for blockly-samples, documentation, and various Blockly features such as tooltips and toolboxes.

Maribeth spends her free time crocheting and playing board games (current favorite: Terraforming Mars).




Other Videos By Google TechTalks


2022-09-12Graph Attention Retrospective
2022-09-09Bayesian Optimization in the Wild: Risk-Averse Decisions and Budget Constraints
2022-07-16Fast Linear Algebra for Distance Matrices
2022-07-12Deep Learning 2.0: How Bayesian Optimization May Power the Next Generation of DL by Frank Hutter
2022-06-13Expressing High Performance Irregular Computations on the GPU
2022-05-24Building Developer Assistants that Think Fast and Slow
2022-05-052022 Blockly Developers Summit: Bad Blocks
2022-05-052022 Blockly Developers Summit: Debugging in Blockly
2022-05-052022 Blockly Developers Summit: Year in Review and Roadmap
2022-05-052022 Blockly Developers Summit: Customizing Blockly
2022-05-052022 Blockly Developers Summit: Contributing to Blockly
2022-02-14Probabilistic Numerics — moving BayesOpt expertise to the inner loop by Philipp Hennig
2022-02-08Information-Constrained Optimization: Can Adaptive Processing of Gradients Help?
2022-02-08Differential privacy dynamics of noisy gradient descent
2022-02-08Consistent Spectral Clustering of Network Block Models under Local Differential Privacy
2022-02-08The Skellam Mechanism for Differentially Private Federated Learning
2022-02-08Statistical Heterogeneity in Federated Learning
2022-02-08Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning
2022-02-08Tight Accounting in the Shuffle Model of Differential Privacy
2022-02-08Distributed Point Functions: Efficient Secure Aggregation and Beyond with Non-Colluding Servers
2022-02-08How to Turn Privacy ON and OFF and ON Again