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A feature rich Open Source Core Electronic Medical Record for small medical providers developed with Plone/ Python/ Zope. Core EMR functionalities: Patient History, Past Visits, Rx, Health Maint., Allergies, Labs, Vitals, Notes, and Procedures.
l3lang is a persistent Python-like language and the l3gui provides a
worksheet-like interface where computed values can be examined via
simple selection and scripts can be assembled as structures.
Design by Contract allows a programmer to document a function/class with statements describing behavior. Metacontract implements the PEP-0316 related to this feature for the Python language using meta classes.
VRT is a Virtual Reality Toolkit.
It is planned to be OS-independent, easy-to-use, full-feature VR platform.
It integrates 3D engine, physical engine and AI engine.
CloudZero automates the collection, allocation, and analysis of your infrastructure and AI spend to uncover waste and improve unit economics.
CloudZero is the leader in proactive cloud cost efficiency. We enable engineers to build cost-efficient software without slowing down innovation. CloudZero's next-generation cloud cost optimization platform automates the collection, allocation, and analysis of cloud costs to uncover savings opportunities and improve unit economics. We are the only platform that enables companies to understand 100% of their operational cloud spend and take an engineering-led approach to optimizing that spend. CloudZero is used by industry leaders worldwide, such as Coinbase, Klaviyo, Miro, Nubank, and Rapid7.
Pony is an object-relational mapper. The most interesting feature of Pony is its ability to write queries to the database using generator expressions. Pony works with entities which are mapped to a SQL database. Using generator syntax for writing queries allows the user to formulate very eloquent queries. It increases the level of abstraction and allows a programmer to concentrate on the business logic of the application.
Following is an example of a query in Pony:
select(p for p in...
MTCC (Model-Driven
Test Case Construction) in an approach to the construction of acceptance tests by domain experts for testing system families based on feature models. MTCC is applied to the application domain of Digital Libraries.