A year ago, ZServer was one of the last Zope parts that did not run on Python 3. The idea was to use WSGI instead. I wondered why Python 3 would not be possible. So I tried porting it. It worked. And I added websocket support. You can use this to automatically push pages from Plone to Gatsby. Or show a notification when a page needs review, via content rules.

I replaced the old Medusa server with Twisted. ZServer was optionally using Twisted years ago, but this was ripped out, and I don't know why. But I got it working again. So for HTTP and webdav we have a Twisted http server and thread pool with an async FileSender, an AsyncIO/uvloop when avilable, standard ZConfig configuration and logging, And HTTP/2 works as well.

Bonus: websockets with PubSub messaging. It uses the Autobahn Twisted WebSocket protocol. ZeroMQ PubSub cockets using IPC-socket. The connection autosubscribes to local events. The server publishes a guarded event, for example indicating that an event is restricted to Editors.

One problem at this moment with the wsgi setup using waitress, is that the requests are limited to the number of wsgi threads, so server large files can become problematic. There are probably ways around this, but ZServer does not have this problem.

Status:

• My ZServer fork is at https://github.com/datakurre/ZServer. See mostly branch datakurre/master.
• You will need a special branch of plone.recipe.zope2instance as well.
• Plus collective.wsevents, plonectl, collective.taskqueue.

Upstream:

• In Zope 4, WebDav was made dependent on ZServer, by mistake?
• Zope 5 assumes ZServer and WebDav no longer exist.

Remaining steps:

• Get it upstream or not?
• Eliminate dead code.
• QA: tests, documentation
• Release it.
• Restore wsgi=off on Python 3.

I would be interested to hear when others are interested in using this. It is better if more people use this.

I am an introvert from Finland, so I did not ask the Zope people, or visited the Zope sprints.

Jens: There was no real reason to drop ZServer, except that no one has tried it because the code was so very old. Now you got it working, let's get this into Zope.

See the slides.

It is hard to see what part of machine learning is over hyped, and what part is actually useful.

Basis of ML (Machine Learning) is to get input and transform it to output. The ML gets a picture of a duck, and it gives as answer "duck". That is image recognition. You train the ML with images that you already label. And then you give it an image, and hope it gives a good answer. Same: from English to Japanese text. From an fMRI scan to an image visualized.

Simpler example: solar panel. Input: time of day, output: how much generated power. You would train this with data. The ML would turn this into a function that gives an approximately good answer.

Simplest model: linear regression. Turn the data into a function like this:

a * X + b

No line through the training points will be perfect. You find the line that minimizes the average error. So the ML uses linear regression to find a and b.

In our example, we try to guess the amount of mojitos we sell, based on the number of passengers that a daily boat brings to our beach bar.

But this may also depend on the temperature, or the number of sharks. With two variables you would not have a line, but a plane. With n, you would get an n-dimensional shape. You have n inputs, and give a weight to each input, and add them.

This is about numbers. For image recognition, you don't get a number as output. But we can apply a function to the result, and get a number between 0 and 1 that gives a likelyhood. For an image we may have 0.02 certainty that it is a cat, and 0.9 that it is a duck, so our answer will be the highest: a duck. Translated to the mojitos: what is the likelyhood that my bar breaks even?

This system with weights and a reducer function is called a perceptron. With a short Python program I got 90 percent accuracy on the standard NDIST test. We can do better.

We look at neural networks. This is basically: mash two perceptrons together. Or more. We are finding a function that approximates the data, and reduce the error iteratively.

What would I say is deep learning? Why has it grown?

1. Neural networks with many layers...
2. ... trained with a lot of data...
3. ... with specialized architectures.

We helped Facebook with neural networks by tagging our photos. We gave them training data!

A lot of engineering is going into deep learning.

Generative Adversarial Networks: GANs. Example: a horse discriminator. Train the system with images of horses and others, and it should answer with: yes or no horse.

Other part: horse generator. Randomly generate images for feeding to the horse discriminator. You train this by saying: you did or did not manage to trick the discriminator. And you try to get better. I trained this a night, and after about a million iterations, I got pictures that are not quite horses, there is something wrong, but they are amazingly close.

Is Volto compatible with Guillotina? Not 100 percent. Question is how we keep the APIs in sync. If people figure out a shared generic API that works with both Plone and Guillotina as backend, this can work.

How do you migrate? You can migrate from Plone 4.3 to Volto. We did that with transmogrifier. Biggest problem is moving composite pages, like cover, to the Volto blocks. In a post migration step, you can fix things up.

Why Semantic UI? Rob researched all the popular ones, concentrating on how easy it is to theme or override stuff. Semantic UI makes this easy. You derive from a theme, and only override some parts.

For add-ons, how do you keep the backend part in sync with the frontent part? You don't. You use Python packages in the backend and node packages in the frontend. There will probably be a lot of packages that are only in the backend or only in the frontend. You may need to be careful in the frontend so that it can work with several versions of the backend of this add-on.

TypeScript has won the JavaScript wars. Will you support that? We don't want to support two ways of doing the same thing. So if we switch to TypeScript, we want to stop using ES6, also in the documentation. Same for class based versus function based.

Did you check accessibility? Yes. We have a static code checker ALM that helped us fix issues. Also the Cyprus tool. But we also do manual checks. Plone Foundation is trying to get funding for an audit.

Frontend is nice, but it needs a backend, so let's talk about that. I will talk about plonecli, plone.api, Plone snippets for VS Code and plone.restapi.

You should use plonecli. It saves a lot of time for boring stuff. It helps you to create a product and enhance it step by step. It can cleanup and create a fresh virtualenv with a specific Python version and requirements and run buildout.

It has sub templates, for example to add a content type to your package. Now a sub template for a restapi service. Also one for upgrade steps, adding an upgrade profile per upgrade step.

We create a structure and files, and if you don't use some parts you can ignore or remove them. We will check if you have a clean git status before, and ask to commit, and afterwards we automatically commit.

You have good test coverage right from the start. All features added by plonecli have at least basic test coverage. You only write the tests for your own code. We make a tox environment to test different Plone and Python versions.

You can configure plonecli/mr.bob to your taste, changing the default answers or ignoring questions in a .mrbob file.

It is extensible, you can write your own custom bobtemplate packages and register them for the plonecli.

Visual Studio Code: snippets for Plone

There are snippets for schemas, fields, registry xml.

Use plone.api. It makes add-on code much easier to understand, without arcane incantations.

Ideas for the future.

• plonecli:
  • add option to set Interface for view, viewlet.
  • REST API sub templates for de/serializer.
  • Graphical UI (ncurses-like) to make selecting options easier.
• VS Code: plone.api autocompletion. VS Code is not so smart with namespaces. And it does not know buildout. You can use a buildout recipe that helps here though. Or use the generated zopepy script as your Python.

I would love more contributions. Don't be shy. There is no grand jury who makes decisions. Publish your own bobtemplates. Improve the VS Code snippets, or for other editors. Meet me at the sprints.

Question: is there a bobtemplate to create a new bobtemplate?

Answer: No. Should be possible.