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source{d} @sourcedtech

Data Platform for the Software Development Life Cycle. #EngineeringEffectiveness #MLonCode⠀| GitHub: github.com/src-d⠀| website: sourced.tech

Joined November 2015
  • Tweets 1,834
  • Following 1,272
  • Followers 3,086
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source{d}@sourcedtech
2 Jul 2019
In case you missed today exciting news from @sourcedtech 🎉📢🗞️ - Private beta of @sourcedtech Community Edition is now open - more info in this blog blog.sourced.tech/post/intro… - @sourcedtech Enterprise Edition is now GA blog.sourced.tech/post/annou… #CodeAsData #EngineeringOps #SDLC
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source{d} analysis of the @cloudfoundry codebase: The results show a mature and complex architecture yet extraordinarily active and agile. buff.ly/2oSXjGW #MLonCode
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Data Retrieval pipeline at source{d} "Data collection and processing might be less sexy than #MachineLearning but nevertheless is crucial for any progress" buff.ly/2JZ0WTa #MLonCode
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Managing with Context, Not Control by @aspyker, #Engineering Manager, @netflix Container Platform buff.ly/2PShiAO
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Software Delivery Performance, Multi-GPU #DeepLearning and Overton buff.ly/34DRmwJ
How do you know if your software is good enough? @rich_archbold, VP of #Engineering @intercom shares his engineering standards that will help protect you from easily avoidable mistakes buff.ly/2riyFQU #DevOps
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We analyzed all the @TensorFlow git repositories with source{d} EE to extract interesting insights for the Tensorflow community. Check out our summary here: buff.ly/2PSnY1K View the dashboard of the entire analysis here: buff.ly/2WQATCT #MLonCode
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source{d} retweeted
P.-E. Fraisse@p_e_fraisse
7 Nov 2019
👏 Not only for architects but for a whole organization This is what I am used to call #CodeAsData from a few years. Hope to be able to spend time on it very soon! #MLOnCode @eisokant @natfriedman @sourcedtech @Semmle
This tweet is unavailable
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"Many of the threats facing #DevOps and #Agile transformation fall into four main camps." 1. No sense of urgency and an unclear vision 2. Lack of senior stakeholder sponsorship 3. Cultural resistance to change 4. Not understanding the change maturity curve buff.ly/32oHPIx
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💥 "...Vendors such as source{d} are enabling developers to build increasingly complex software faster, with high quality, and better user experience.” 💥@marksdriver , @Gartner_inc buff.ly/2POU7ay #MLonCode #GartnerCoolVendor
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Yesterday we shared Part 1. Here is Part 2 of "Clean code... Why bother?" buff.ly/34DzTVn by @pauxdsantamaria, co-founder of @uppernauts 1. Clean code leads to better practices 2. Reviewing PRs is hard work 3. Reusability 4. Faster bug fixing
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Check out source{d} Lead #MachineLearning Engineer, @vadimlearning's article on @kdnuggets "What is #MLonCode?" buff.ly/36Cjh1Z 🤖🎓 💻 📄
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@sourcedtech CEO, @Eisokant had his article, "How #MLonCode Could Disrupt #SoftwareDevelopment," published on @EnterpriseAI_ buff.ly/2NHu4iJ
"Clean Code... Why bother?" buff.ly/2PLplPT via @pauxdsantamaria, Co-Founder of @uppernauts 1. Your teammates will thank you 2. Think about your future self 3. Messy code tends to get messier 4. Faster decision making 5. Reduce repeated code 6. It feels great!
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Paper Review: "Import2vec - Learning Embeddings for Software Libraries" by Hugo Mougard, Senior Machine Learning Engineer at source{d} buff.ly/2Nj4uBF #MLonCode
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Learn how to "get the product feeling great, the #Engineering team feeling productive and proud of delivering a high-quality product, pumping out features while keeping the bugs down" by former @facebook and @YouTube, @radoshi buff.ly/2PQXDRC 💻 💡 🎓🐜
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We analyzed all the @TensorFlow git repositories with source{d} EE to extract interesting insights for the Tensorflow community. Check out our summary here: buff.ly/2WLufhk View the dashboard of the entire analysis here: buff.ly/33gih1p #MLonCode
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Paper review: "code2seq - generating sequences from structured representations of code" by source{d} Engineer, @seoul_engineer buff.ly/2pFIbx7 #MLonCode
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Multi-GPU #DeepLearning at source{d} Learn how we solved several problems in order to train neural networks with @Tensorflow 2.0 on several local GPUs in our ML cluster. buff.ly/2CfvNq4 #MLonCode
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Top 4 metrics to measure your #Software Delivery Performance 1. Change Lead Time 2. Deployment Frequency 3. Change Failure Rate 4. Mean Time to Restore (MTTR) buff.ly/36y1WHz #EngineeringObservability
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🎓 #DeepLearning on #Dataframes with @PyTorch "The goal of this post is to lay out a framework that could get you up and running with deep learning predictions on any dataframe using PyTorch and Pandas." via Mike Chaykowsky buff.ly/36tEuuV
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