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#announcements
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2020-04-08
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Candid03:04:29

Joker v0.15.3 https://github.com/candid82/joker/releases/tag/v0.15.3 (fixes a bug that causes bash to misbehave when exiting from repl with (exit), also enables bash-like tab completion in repl)

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kelveden10:04:30

It has been out for a while now and is something we've been using in production for some time but we've never really pushed it in the community much. But in the hope that it proves useful to folks: https://github.com/ovotech/ring-jwt.

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Jakub Holý17:04:16

Just the thing I was about to start looking for :)

kelveden20:04:09

We've also just released https://github.com/ovotech/duct.middleware.ring-jwt with wrappers for Integrant and Duct.

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chrisn15:04:20

http://tech.ml.dataset is now at beta version 2.0. It is a dataframe-like library and now includes supports for joins and the majority of the java.time datetime api. Column types are autodetected from data in most cases and can overridden in multiple ways. Once loaded you have the full power of the tech.datatype library to transform and clean data into whatever format you wish. It is also very easy to take this data and apply some simple linear regression and categorical inference on it (predict one column from a set of other columns). Some features: * Joins work in index space making them subsecond for even very large datasets. * Full datetime support including a set of 'packed' date time datatypes that are stored as 32 or 64 bit integers but can be operated on and print like their date time analogues. * Support for reading tsv, csv, xlsx, xls, and gzipped varieties of those. * Support for writing tsv, csv, and gzipped varieties of those. * Strings are loaded into variable width string tables so if for instance you have a categorical variable with 5 categories each entry will be stored in a byte. * Column datatype autodetection with a clear system for overriding and specifying the desired column types. * Memory efficient - data is stored in primitive arrays, dates are packed, and strings are stored in string tables. This means you can work with data many times larger in than before and stay in Clojure. It also means that sorting, group-by, filtering, and joining a table by a column is extremely fast. All of these operations are done in index space so they are both fast and if they result in an expansion of the datatset then you only pay for the indexes required but not the duplication of the data. https://github.com/techascent/tech.ml.dataset

clj 48
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bringe22:04:24

Calva now has a debugger! It utilizes cider-nrepl and VS Code’s debugger extension API. This work has been done as part of the Clojurists Together Q1 2020 funding period. Many thanks to them for allowing us the time to focus on adding this feature, and many thanks to the cider-nrepl creators and maintainers for providing great foundational functionality for Clojure tooling. Features: * Set breakpoints with #break * Instrument functions with #dbg * Continue to next breakpoint * Evaluate code in the debug context * See variable values in the debugger side pane Upcoming Features: * Step over form * Step into form * Step out of form * See structured variables in the debugger side pane (currently maps and collections are just shown as strings) See the ClojureVerse post here: https://clojureverse.org/t/calva-gets-a-debugger/5751 See the documentation here: https://calva.io/debugger/

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parrot 8