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 The previous README file is available [here](doc/developer.rst).
 
 Work in progress documentation <http://redpitaya.readthedocs.io/en/latest/index.html>.
+
+# CALL FOR DEVELOPERS
+
+Our internal development is shifting toward [Jupyter](http://jupyter.org/).
+Jupyter with a set of Python modules provides a great tool for quick prototyping:
+1. UIO drivers written directly in Python,
+2. dynamic and interactive data visualization with [bokeh](http://bokeh.pydata.org),
+3. data storage and processing with [numpy](http://www.numpy.org/), [scipy](https://www.scipy.org/), ...
+4. source code management with Git and [GitHub](https://github.com/blog/1995-github-jupyter-notebooks-3),
+5. and under development is support for device tree overlays and FPGA manager.
+
+## 1. UIO (Userspace IO) drivers in Python
+Drivers for FPGA memory mapped peripherals can be written directly in Python using
+[mmap](https://docs.python.org/2/library/mmap.html), [ctypes](https://docs.python.org/3/library/ctypes.html)
+and [numpy](http://www.numpy.org/).
+[UIO description in a device tree](https://github.com/RedPitaya/RedPitaya/blob/mercury/jupyter/experiments/mercury.dts)
+in combination with [UDEV rules](https://github.com/RedPitaya/RedPitaya/blob/mercury/OS/debian/overlay/etc/udev/rules.d/10-redpitaya.rules#L7)
+provides named UIO devices as `/dev/uio/name`.
+Each device can be mapped into memory space and locked separately with
+[mmap](https://docs.python.org/2/library/mmap.html) and
+[fcntl](https://docs.python.org/3.6/library/fcntl.html).
+Register sets can be written using Python ctypes and/or
+[numpy.dtpye](https://docs.scipy.org/doc/numpy/reference/generated/numpy.dtype.html).
+Python offers language features for the creation of elegant APIs.
+
+## 2. Dynamic and interactive data visualization
+As a base [Matplotlib](http://matplotlib.org/) provides a vast array of features for data visualization.
+With the addition of [bokeh](http://bokeh.pydata.org) (JavaScript based library) visualization become dynamic
+with good frame rates (up to about 16fps depending on data size).
+There are widget libraries available for making interactive applications.
+
+## 3. Data storage and digital signal processing
+Python makes it easy to store data into a file, since the application is also written in Python,
+it easy to access data from various processing changes not just a filtered output.
+A simple data logger for example can write data file onto the SD card,
+the file can be later loaded onto a PC using the Jupyter file browser.
+Python is interpreted in Jupyter, which makes it slow.
+Fortunately most data processing can be done on arrays with dedicated libraries.
+Numpy and Scipy provide processing functions for a great spectrum of applications.
+They are well optimized although not very fast on the ZYNQ ARM CPU.
+Python wrappers can be written around optimized DSP libraries like [Ne10](https://projectne10.github.io/Ne10/).
+Processing can also be offloaded to the FPGA, project [PYNQ](https://github.com/Xilinx/PYNQ/) is making progress there.
+
+## 4. Git and GitHub
+Applications can be developed directly on the board and edited in the Jupyter editor.
+Git is now installed on SD card images.
+In combination with Github it provides a great tool for version control,
+publishing and distribution of Python applications and libraries.
+TODO: under Welcome instructions for Git SSH keys, maybe they should be created at first boot and displayed.
+
+## 5. Device tree overlays and FPGA manager
+Device tree overlays are already supported on our current 4.4 based kernel.
+We are working on a kernel 4.9 based version, which would also support FPGA manager.
+FPGA manager enables loading a FPGA bitstream with an overlay,
+which enables proper loading and unloading of kernel drivers (GPIO, LED, XADC, DMA, ...)
+needed by the overlay.
+Most of our problems are related to backward compatibility.