| • Feature freeze for Go 1.7 announced
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| • Binary-only packages now allowed in Go
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| • Peter Bourgon updates his "Go Best Practices" talk from 2014 to 2016
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| • Discussion of impostor syndrome and code review by Brad and Andrew
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| • Upcoming GopherCon presentation on Gopher data science by Daniel Whitenack
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| • GAFKA: a Go tool suite for managing KAFKA clusters
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| • ChatOps and microservices with Micro and ChatOps bots
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| • UNIK: compiling apps into unikernels
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| • Micro framework as an ecosystem for microservices
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| • Security benefits of unikernels (reduced attack surface)
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| • Benefits and limitations of unikernels
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| • Data science definition and process
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| • Industry applications and drivers behind data science advancements (commerce, advertising)
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| • Use cases for data science in business processes and engineering
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| • Open-source tools and frameworks for data science (R, Python, etc.)
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| • Future replacements for current data science tools in the Go language
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| • Shift in community towards accepting multiple languages for data science tasks
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| • Discussion of various programming languages used in data science, including R, Python, JavaScript, Go, and Java Scala
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| • Introduction of Pachyderm as an interesting project for big data workflows
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| • Exploration of Go libraries for data science, such as Gonum and Gota
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| • Portability of Python libraries to Go for data processing tasks
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| • Efficiency of writing custom code in Go versus relying on dependencies
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| • Discussion of the "A little copying is better than a little dependency" mindset in Go development
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| • Advice on getting started with data science in Go
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| • Importance of mindset when approaching data science in Go
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| • Resources for learning data science in Go (Peter's resources, Dave Cheney's)
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| • Jupyter kernel for Go and interactive data exploration
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| • Existing projects that demonstrate data science capabilities in Go (InfluxDB, Pachyderm)
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| • Live demo of data science process using only Go at GopherCon
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| • Go notebooks and interactivity with Jupyter
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| • Brief overview of Jupyter and its ecosystem
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| • Jupyter notebook and Go kernel
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| • Carlisia's experience setting up Jupyter and using the Go kernel for note-taking and development
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| • Discussion of Vim Go and Neovim as tools for improving development workflow
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| • Daniel Whitenack mentions Vim Go and Neovim, and thanks Fatih Arslan for his work on these projects
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| • Erik St. Martin talks about Nvim and its benefits over regular Vim
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| • Promoting GopherCon and encouraging listeners to attend and participate in panels with speakers |
