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Home » Fantasia Film Festival » Inside Out 2018 Review: "What Keeps You Alive" (2018) ★★★
Inside Out 2018 Review: "What Keeps You Alive" (2018) ★★★
Posted by Ulkar Alakbarova in Fantasia Film Festival, Festival Coverage, LGBT Film Festival, Movie Reviews // 0 Comments
Horror genre is like blood that circulates in the cinematic world and if it stops flowing, cinema can simply die with it. This is why it's important to appreciate the genre in a way the viewer after seeing it in cinema is left fully satisfied with whatever he was offered. Written and directed by Colin Minihan, "What Keeps You Alive" manages to grab the horror genre, shake it like a milkshake and turn it into a ridiculously funny comedy which, may not sound that way though, is a big compliment that must go to the entire cast and crew. And I really mean it.
Without further ado, the film follows a married lesbian couple, Jackie and Jules, who are eagerly anticipating the arrival of the weekend to celebrate their one-year anniversary. However, Jackie's mind quickly switches from sane to psychopath mode that turns the weekend into a cat-and-mouse game from which it will be hard to predict who will come out alive.
Jackie has a secret from her childhood which Jules knows nothing about. And when Jackie shares what her father told her about killing something which kept her alive, which is the story of a killed bear, it makes the entire film intriguing to watch. At one point with a very serious expression on Jackie's face, she hands the knife over to Jules asking her to keep it if the dark side of Jackie decides to wake up the same way it happened back when she was a little girl when she had to kill a bear in self defence.
Strange or not, the same knife the viewer will be seeing throughout the movie like an additional lead actor but with no lines to deliver. But overall, what you will be left to watch is the most outstanding fight between two women where one will try to kill its target while the other one stubbornly refuses to die. And despite the plot itself being so ridicilously dull, at some point you will give up struggling with what's going on between the women and watch them try to oversmart the other. And all this is extremely entertaining, funny and even scary, if you wish. But it's indeed never boring.
In conclusion, the best thing about "What Keeps You Alive" is how the filmmaker/director distances his film from the general concept of horror cinema and adds more art and creativity to it, making it absolutely unique and somewhat impossible to digest. But whatever the writer wanted to tell us through his film never mattered, as the result of it is much bigger and astounding even though it had no logical explanation to provide whatsoever.
But with films like this, I guess, entertainment is what comes first, and for the actors it was like as if they were members of a big orchestra where both Hannah Emily Anderson and Brittany Allen were superbly in sync with the characters they portrayed as if they were a part of them. As if it was for both of them the only way to keep themselves alive….
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My name is Ulkar Alakbarova. A wonderful name, is it not? I come from the exotic land of Azerbaijan but now I live in the hub of Hollywood North – Toronto, Canada. How lucky I am to be able to enjoy and indulge in my favourite avocation of watching movies. Movies of every genre are screened here in Toronto. There are black and white classics, avant garde, independently produced, and of course, newly released movies for all to enjoy. Let me share this treasure of entertainment with you. Read my thoughts and opinions then let me know if you agree.


Soft Cover. First Soft Cover Edition. 203pp. The first collection of critical works on the celebrated novelist and diarist, the friend and peer of Henry Miller, Lawrence Durrell and Antonin Artaud. So ftcover Book, Near Fine, light shelf wear outside, else all Very Clean. FAST SERVICE: ALL ORDERS WITH PAYMENT RECEIVED BY 3:00pm PST, M-F, SHIPPED SAME DAY!!! BOOKS WITH DJs COME WITH FREE CLEAR PROTE CTIVE MYLAR COVER!


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Homenews6 popular and top nft games
6 Popular and Top NFT Games
One of the most exciting gaming developments on the blockchain horizon is the non-fungible token, which promises a new and exciting order. Top NFT games have begun to take shape powered by the transparency, security, and speed of blockchain technology, giving gamers a chance to be even more immersed in never-before experiences. In this article, we'll go over some of the top NFT games available today.
But first, it's time to learn about what NFTs are and what these special tokens can do for your favourite games.
NFT games let players do more than just stand back and watch the show, by allowing them to play an active role in its narrative and events. These games offer a great opportunity to kick-start game development on blockchain networks by anchoring game assets to decentralized economies. This is made possible because games can now be launched on blockchains and residents will be encouraged to play.
These tokens are distinguishable and tamper-proof while preserving the rarity and uniqueness of some in-game items so that they can be valued at a higher price than other less rare token items.
Players now own their game assets as a result of changing the gaming model from "pay-to-win" to "play-to-earn". The players now pick between one of three strategies for successfully owning their game assets: to create NFTs and/or breed new characters, purchase digital items on native markets, or unlock and earn and new and special items.
Integrating NFTs with popular game themes, these titles are leading the charge on the global scene. Here are some must-play games that implement NFT integration to elevate play.
List of must-play Top NFT Games
Axie is a game that brings us all of the nostalgic feels of Pokémon while adding blockchain twists to make gameplay more challenging & exciting. Players collect virtual pets, called Axies, with their own genetic imprint for competitive battles with other players, where the weakness and strength of Axies is passed down to offspring. To cash in on these battles cost a certain amount, which can be paid with a token that the developer has issued called Axis Infinity Shard (AXS).
Playing Axie Infinity is easy, you just have to purchase 3 Axies, assign one as your Stable Master for breeding, one as Champion to defend your Stable Master, and one as Scrounger to collect Jewels. However, this game is Ethereum-based, so there are many hoops through which you would need to jump before actually beginning playing.
Alien World
The game of Alien Worlds is a team-based multiplayer space exploration game. Teams are responsible for exploring unknown planets using complex algorithms to discover workable planets with the best opportunity of survival. Unlike other games, this one give players something they can do more of or find ways of increasing the number of Trilium they have available, by spending less time competing against each other.
Players of the game Alien Worlds will find augmented reality, where they complete in-game quests by building and assembling NFTs of individual gameplay. By electing for their six counselors, players of the game can make key decisions on how the game is developed.
Sorare — Fantasy Football
Sorare is a Blockchain-based trading card game that has players collecting or buying cards to compete in competitions. As of October, almost 200 clubs have been officially licensed as Sorare users as the timeline goes on. To name a few renowned names Paris Saint-Germain, Liverpool, Real Madrid, and Bayern Munich are included.
The four rarity categories for cards are commons, rare, super-rare and unique. You start with a deck of random commons but complete certain onboarding tasks to receive a deck of rare cards. Purchasing a deck at the very least is necessary to progress up their division leagues and attain a prize.
Sandbox 3D
Sandbox 3D is an open NFT game platform to trade your specific voxel objects. The Sandbox toolkit sets you up with building pieces, biome settings, landscapes, textures and music, so you can get designing without needing to worry about the complexities of coding.
Sandbox 3D is an open-world game where you can manipulate various objects by calculating how they interact. Sandbox also has extensive tools for designing the models and selling the models for a profit on different markets. It also allows players to create their own games.
Sandbox 3D creates a lush virtual world for gamers to use SAND tokens as the native currency. With this ERC-20 coin you can buy a number of products on the Sandbox3D marketplace – from stunning in-game assets to physical cards. In addition, you can earn LAND tokens, Sandbox 3D's NFT token which has high value.
Battle of Guardian
Battle of Guardians is a fighting game that brings blockchain-generated high-quality graphics to PC gamers. This fighting game allows players to battle with AIs in multiple realms for more NFTs, which can be used with Battle of Guardians even on your mobile device.
The Battle of Guardian intends to foster a fun, entertaining gaming environment with long-term potential. A future of virtual reality has yet to be born, but each new game released is backed by the Battle of Guardian. All are based on the blockchain, enabling customization with non-fungible tokens, unique graphics, and an economy full of rewards.
Not all gamers like to purchase gameplay with money. Games like Guardian, which contribute less than 10% of the global gaming market are popular because of their reliance on sociality, collectiveness, and the community.
Crypto Kitties
The game blockchain-fied cats, allowing users to buy, trade, and breed these felines. The game is built on the Ethereum network and was published by Dapper Labs in November 2017. It's indisputable that CryptoKitties brought developers over $120 million in the games first two months, but its success will depend on expanding user base after all.
Collecting digital cats has captivated many people in the past year, due to the game flooding on the Ethereum network. In CryptoKitty, players can buy a new one in the marketplace or combine two existing ones to make a new, unique one. If you're not sure what kind of cat you want, you could try looking at completed breeding in the Marketplace to see how that will turn out.
Breeding unlocks rare attributes and allows you to earn rewards through your collection of digital cats. You can also collect them on KittyVerse, where they participate in catfights. You can even do puzzles with other players on this platform.
The worlds of online gaming and NFTs have merged to the point where they are currently strongly intertwined within the gaming industry. People are not just enjoying the all-new gaming universes thoroughly, but also concurrently participating in its growth and development ahead.
With the impressive increase in the adoption of cryptocurrency, more than 300 million people are already using these digital currencies and the numbers are only growing.
A study suggests that millennials are the biggest blog post audience, with 38% of them being between 21-38 years old. Interestingly, education has not been found to be a significant predictor of whether people blog or not.
These NFT games are becoming increasingly popular, due to their ability to provide monetary benefits. As the gaming industry become increasingly commoditized, cryptocurrency may have a role to play.
It has the scarcity needed for investment, proven ownership rights, and the ability to transfer gaming items or assets between players that would otherwise be unavailable. With the growing interest from billionaire investors and retail traders, cryptos have been taken more seriously by large media companies.
Most of these NFT games are being developed in 2021 and a few of them are already available to play. More immersive games will be out by 2022 when we can expect features such as "non-finite" resource models and augmented reality. Developers who start sooner will have the means to acquire an early edge. They can remove middlemen and "seamlessly" integrate into the future we never thought we could become accustomed to! So, get ready for NFT games and start funding your NFT wallets.
The news of Sorare, a French firm, selling football trading cards as non-fungible tokens ...

redpajama_set_name: RedPajamaCommonCrawl


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Dec 02, 2015 • By Monte Olmsted
Annie's coming soon to schools
For years, Annie's has sought to enter the U.S. K-12 school market. But the company found it difficult to establish sales connections with food service directors, develop a deep understanding of the school market and gather resources to reformulate products to meet the USDA's nutritional guidelines for schools.
That all changed when Annie's became part of the General Mills family last year.
So, beginning in January, the brand behind Bernie the Bunny will debut in schools with two varieties of its reformulated Bunny Grahams snack crackers.
"The shifting food landscape is happening beyond the grocery stores and home," says John Foraker, president of Annie's. "Annie's is just as passionate as parents and education workers are about having clean food options at school. The opportunity to offer our great tasting, organic, kid and parent approved snacks in the cafeteria is just one way we are trying to reinforce our mission to spread goodness." "We're excited to have Organic Honey Bunny Grahams and Friends Bunny Grahams in schools," says Sarah Raney, an Annie's marketing associate. "We think Annie's can boost the quality of school nutrition with a snack that has a clean label and is made with simple ingredients."
Once General Mills acquired Annie's last fall, the two companies sought to grow the Annie's portfolio into new channels and food categories. Annie's entered the soup category and traditional cookie category in July. And early next year, Annie's plans to introduce an organic yogurt.
Within months of the acquisition, teams from Annie's and General Mills worked to get Annie's inside U.S. schools – a channel where our Convenience & Foodservice (C&F) team has excelled.
Well in advance of when the stricter nutritional rules when into effect in the 2013-14 school year, the C&F team had converted its existing K-12 food products to meet the USDA's nutritional guidelines that require more whole grains, less fat, sugar and sodium, and fewer calories, says Lynn Choi Perrin, a senior food scientist.
"What we accomplished became a huge advantage for us in the K-12 channel," Lynn says.
General Mills then shared that knowledge with the Annie's team, which chose its popular Honey Bunny Grahams and Friends Bunny Grahams to become the brand's first products in schools. Both varieties will be available in single serve 1.25 ounce snack pouches. C&F already had a similar product in schools: Nature Valley Crisps, a cookie-like biscuit with 16 grams of whole grain per serving.
"We shared with the Annie's team the formulas from our existing K-12 products to give them a sense in how to achieve the USDA's whole grain requirement," says Lynn.
As a result, Annie's doubled the amount of whole grain in each pouch of Bunny Grahams so that it reached 16 grams per serving.
Joselynne Fynboh, a marketing manager in C&F, says having Bunny Grahams available in schools represents a win for General Mills, Annie's and K-12 food service operators.
"We understand that food service operators can increase perception of the menu by adding trusted brands such as Nature Valley and Annie's," says Joselynne. "This will help them achieve their goal of increasing lunch participation."
Post Tags: annie's, Convenience & Foodservice, convenience and foodservice


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text:
Global Trailer
Trailer Design
Lucidity: The quest for value
From the November 2013 issue.
Generating value is important to Taiwanese company, Lucidity, a company supremely focused on maximising its market potential.
There is no denying that profit is important for any commercial organisation. It is the oxygen of a company; it keeps the organisation alive and allows it to sustain and grow. But businesses around the world increasingly search for an alternative way to measure success. One option is to look at success from a value creation perspective – given that creating value is arguably the most important objective of any company, regardless of industry, cultural background or size. But value is also the hardest variable to define. How does a business create value? And, does the product or service it is offering create value in return, helping the organisation execute its strategy, realise its vision and eventually fulfil its mission?
One example of a corporation with a strong value generation focus is Taiwanese lighting specialist Lucidity. In the course of a global expansion strategy, the company from Taiwan attached great importance to cultivating a value creation process that would benefit every entity involved in the supply chain.
In that sense, Lucidity has adapted quite a contemporary perspective on success management, one that is approaching the value creation concept from a networking side, which is not necessarily based on market capitalisation, net present value of expected dividend streams or share price anymore.
According to Toby Hatch, Senior Principal, Performance Management Applications at IT giant Oracle, looking at value creation from a shareholder perspective may be the most tangible approach, but not necessarily the only one.
"But because something is easy to measure doesn't mean that it is the right measure of success."
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Tabanca, N.; Demirci, B.; Can Baser, K.H.; Aytac, Z.; Ekici, M.; Khan, S.I.; Jacob, M.R.; Wedge, D.E., Chemical Composition and Antifungal Activity of Salvia macrochlamys and Salvia recognita Essential Oils, J. Agric. Food Chem., 2006, 54, 18, 6593-6597, https://doi.org/10.1021/jf0608773 .
115 matching species were found.


309 Room (Business Center), CentralBuilding, NSTDA Thailand Science Park.
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With my forthcoming cruise around the Antarctic Peninsula as the excuse, a few days ago I started posting some scans of slides dragged from the ancient past, when I spent two summer field seasons in Antarctica on British and Norwegian science programmes.
I've got something like 1500 slides from those trips, a mix of Kodachrome 25, Kodachrome 64, and Ektachrome (100, I think). About 250 I had selected around 15 years ago, and stored in archival boxes. The rest, some of which I've barely glanced at, are in the "rejects" folder. Many are in poor condition, having suffered fungus attacks. A large proportion are badly exposed, badly composed, heavily vignetted, or out of focus. Usually all of these. But some are interesting - to me at least, from a number of points of view. They show how I took photographs when I had no real idea of what phtography was about. Sometimes they are of some merit, but mostly they show that I was trying to tell stories to people back home, to capture atmospheres, moods and colours. There's no real sense that I had any concept of "landscape photography" as an aim in itself.
From a technical point of view, I'm benefitting from a lot more experience in scanning. I have had attempts at scanning selections in the past, in particular about 6 years ago, when I published a small book, but now I have a fully colour managed Kodachrome calibrated, Silverast HDR workflow, and I can use Silverfast 8 HDR. I started scanning on my Canoscan 9000F flatbed, but eventually switched to the Minolta film scanner. Even though the benefit with some of these slides is minimal, and I lose the 64bit HDRi option, the ability to auto focus, set the focus point, or fully manual focus on the Minolta is a significant benefit for extracting the finest detail.


text:
Q: Best way to check function arguments? I'm looking for an efficient way to check variables of a Python function. For example, I'd like to check arguments type and value. Is there a module for this? Or should I use something like decorators, or any specific idiom?
def my_function(a, b, c):
    """An example function I'd like to check the arguments of."""
    # check that a is an int
    # check that 0 < b < 10
    # check that c is not an empty string

A: The most Pythonic idiom is to clearly document what the function expects and then just try to use whatever gets passed to your function and either let exceptions propagate or just catch attribute errors and raise a TypeError instead. Type-checking should be avoided as much as possible as it goes against duck-typing. Value testing can be OK – depending on the context.
The only place where validation really makes sense is at system or subsystem entry point, such as web forms, command line arguments, etc. Everywhere else, as long as your functions are properly documented, it's the caller's responsibility to pass appropriate arguments.

A: You can use Type Enforcement accept/returns decorators from
PythonDecoratorLibrary
It's very easy and readable:
@accepts(int, int, float)
def myfunc(i1, i2, i3):
    pass

A: There are different ways to check what a variable is in Python. So, to list a few:

*

*isinstance(obj, type) function takes your variable, obj and gives you True is it is the same type of the type you listed.

*issubclass(obj, class) function that takes in a variable obj, and gives you True if obj is a subclass of class. So for example issubclass(Rabbit, Animal) would give you a True value

*hasattr is another example, demonstrated by this function, super_len:

def super_len(o):
    if hasattr(o, '__len__'):
        return len(o)

    if hasattr(o, 'len'):
        return o.len

    if hasattr(o, 'fileno'):
        try:
            fileno = o.fileno()
        except io.UnsupportedOperation:
            pass
        else:
            return os.fstat(fileno).st_size

    if hasattr(o, 'getvalue'):
        # e.g. BytesIO, cStringIO.StringI
        return len(o.getvalue())

hasattr leans more towards duck-typing, and something that is usually more pythonic but that term is up opinionated.
Just as a note, assert statements are usually used in testing, otherwise, just use if/else statements.

A: I did quite a bit of investigation on that topic recently since I was not satisfied with the many libraries I found out there.
I ended up developing a library to address this, it is named valid8. As explained in the documentation, it is for value validation mostly (although it comes bundled with simple type validation functions too), and you might wish to associate it with a PEP484-based type checker such as enforce or pytypes.
This is how you would perform validation with valid8 alone (and mini_lambda actually, to define the validation logic - but it is not mandatory) in your case:
# for type validation
from numbers import Integral
from valid8 import instance_of

# for value validation
from valid8 import validate_arg
from mini_lambda import x, s, Len

@validate_arg('a', instance_of(Integral))
@validate_arg('b', (0 < x) & (x < 10))
@validate_arg('c', instance_of(str), Len(s) > 0)
def my_function(a: Integral, b, c: str):
    """an example function I'd like to check the arguments of."""
    # check that a is an int
    # check that 0 < b < 10
    # check that c is not an empty string

# check that it works
my_function(0.2, 1, 'r')  # InputValidationError for 'a' HasWrongType: Value should be an instance of <class 'numbers.Integral'>. Wrong value: [0.2].
my_function(0, 0, 'r')    # InputValidationError for 'b' [(x > 0) & (x < 10)] returned [False]
my_function(0, 1, 0)      # InputValidationError for 'c' Successes: [] / Failures: {"instance_of_<class 'str'>": "HasWrongType: Value should be an instance of <class 'str'>. Wrong value: [0]", 'len(s) > 0': "TypeError: object of type 'int' has no len()"}.
my_function(0, 1, '')     # InputValidationError for 'c' Successes: ["instance_of_<class 'str'>"] / Failures: {'len(s) > 0': 'False'}

And this is the same example leveraging PEP484 type hints and delegating type checking to enforce:
# for type validation
from numbers import Integral
from enforce import runtime_validation, config
config(dict(mode='covariant'))  # type validation will accept subclasses too

# for value validation
from valid8 import validate_arg
from mini_lambda import x, s, Len

@runtime_validation
@validate_arg('b', (0 < x) & (x < 10))
@validate_arg('c', Len(s) > 0)
def my_function(a: Integral, b, c: str):
    """an example function I'd like to check the arguments of."""
    # check that a is an int
    # check that 0 < b < 10
    # check that c is not an empty string

# check that it works
my_function(0.2, 1, 'r')  # RuntimeTypeError 'a' was not of type <class 'numbers.Integral'>
my_function(0, 0, 'r')    # InputValidationError for 'b' [(x > 0) & (x < 10)] returned [False]
my_function(0, 1, 0)      # RuntimeTypeError 'c' was not of type <class 'str'>
my_function(0, 1, '')     # InputValidationError for 'c' [len(s) > 0] returned [False].

A: This checks the type of input arguments upon calling the function:
def func(inp1:int=0,inp2:str="*"):

    for item in func.__annotations__.keys():
        assert isinstance(locals()[item],func.__annotations__[item])

    return (something)

first=7
second="$"
print(func(first,second))

Also check with second=9 (it must give assertion error)

A: Edit: as of 2019 there is more support for using type annotations and static checking in Python; check out the typing module and mypy. The 2013 answer follows:

Type checking is generally not Pythonic. In Python, it is more usual to use duck typing. Example:
In you code, assume that the argument (in your example a) walks like an int and quacks like an int. For instance:
def my_function(a):
    return a + 7

This means that not only does your function work with integers, it also works with floats and any user defined class with the __add__ method defined, so less (sometimes nothing) has to be done if you, or someone else, want to extend your function to work with something else. However, in some cases you might need an int, so then you could do something like this:
def my_function(a):
    b = int(a) + 7
    c = (5, 6, 3, 123541)[b]
    return c

and the function still works for any a that defines the __int__ method.
In answer to your other questions, I think it is best (as other answers have said to either do this:
def my_function(a, b, c):
    assert 0 < b < 10
    assert c        # A non-empty string has the Boolean value True

or
def my_function(a, b, c):
    if 0 < b < 10:
        # Do stuff with b
    else:
        raise ValueError
    if c:
        # Do stuff with c
    else:
        raise ValueError

Some type checking decorators I made:
import inspect

def checkargs(function):
    def _f(*arguments):
        for index, argument in enumerate(inspect.getfullargspec(function)[0]):
            if not isinstance(arguments[index], function.__annotations__[argument]):
                raise TypeError("{} is not of type {}".format(arguments[index], function.__annotations__[argument]))
        return function(*arguments)
    _f.__doc__ = function.__doc__
    return _f

def coerceargs(function):
    def _f(*arguments):
        new_arguments = []
        for index, argument in enumerate(inspect.getfullargspec(function)[0]):
            new_arguments.append(function.__annotations__[argument](arguments[index]))
        return function(*new_arguments)
    _f.__doc__ = function.__doc__
    return _f

if __name__ == "__main__":
    @checkargs
    def f(x: int, y: int):
        """
        A doc string!
        """
        return x, y

    @coerceargs
    def g(a: int, b: int):
        """
        Another doc string!
        """
        return a + b

    print(f(1, 2))
    try:
        print(f(3, 4.0))
    except TypeError as e:
        print(e)

    print(g(1, 2))
    print(g(3, 4.0))

A: Normally, you do something like this:
def myFunction(a,b,c):
   if not isinstance(a, int):
      raise TypeError("Expected int, got %s" % (type(a),))
   if b <= 0 or b >= 10:
      raise ValueError("Value %d out of range" % (b,))
   if not c:
      raise ValueError("String was empty")

   # Rest of function

A: One way is to use assert:
def myFunction(a,b,c):
    "This is an example function I'd like to check arguments of"
    assert isinstance(a, int), 'a should be an int'
    # or if you want to allow whole number floats: assert int(a) == a
    assert b > 0 and b < 10, 'b should be betwen 0 and 10'
    assert isinstance(c, str) and c, 'c should be a non-empty string'

A: In this elongated answer, we implement a Python 3.x-specific type checking decorator based on PEP 484-style type hints in less than 275 lines of pure-Python (most of which is explanatory docstrings and comments) – heavily optimized for industrial-strength real-world use complete with a py.test-driven test suite exercising all possible edge cases.
Feast on the unexpected awesome of bear typing:
>>> @beartype
... def spirit_bear(kermode: str, gitgaata: (str, int)) -> tuple:
...     return (kermode, gitgaata, "Moksgm'ol", 'Ursus americanus kermodei')
>>> spirit_bear(0xdeadbeef, 'People of the Cane')
AssertionError: parameter kermode=0xdeadbeef not of <class "str">

As this example suggests, bear typing explicitly supports type checking of parameters and return values annotated as either simple types or tuples of such types. Golly!
O.K., that's actually unimpressive. @beartype resembles every other Python 3.x-specific type checking decorator based on PEP 484-style type hints in less than 275 lines of pure-Python. So what's the rub, bub?
Pure Bruteforce Hardcore Efficiency
Bear typing is dramatically more efficient in both space and time than all existing implementations of type checking in Python to the best of my limited domain knowledge. (More on that later.)
Efficiency usually doesn't matter in Python, however. If it did, you wouldn't be using Python. Does type checking actually deviate from the well-established norm of avoiding premature optimization in Python? Yes. Yes, it does.
Consider profiling, which adds unavoidable overhead to each profiled metric of interest (e.g., function calls, lines). To ensure accurate results, this overhead is mitigated by leveraging optimized C extensions (e.g., the _lsprof C extension leveraged by the cProfile module) rather than unoptimized pure-Python (e.g., the profile module). Efficiency really does matter when profiling.
Type checking is no different. Type checking adds overhead to each function call type checked by your application – ideally, all of them. To prevent well-meaning (but sadly small-minded) coworkers from removing the type checking you silently added after last Friday's caffeine-addled allnighter to your geriatric legacy Django web app, type checking must be fast. So fast that no one notices it's there when you add it without telling anyone. I do this all the time! Stop reading this if you are a coworker.
If even ludicrous speed isn't enough for your gluttonous application, however, bear typing may be globally disabled by enabling Python optimizations (e.g., by passing the -O option to the Python interpreter):
$ python3 -O
# This succeeds only when type checking is optimized away. See above!
>>> spirit_bear(0xdeadbeef, 'People of the Cane')
(0xdeadbeef, 'People of the Cane', "Moksgm'ol", 'Ursus americanus kermodei')

Just because. Welcome to bear typing.
What The...? Why "bear"? You're a Neckbeard, Right?
Bear typing is bare-metal type checking – that is, type checking as close to the manual approach of type checking in Python as feasible. Bear typing is intended to impose no performance penalties, compatibility constraints, or third-party dependencies (over and above that imposed by the manual approach, anyway). Bear typing may be seamlessly integrated into existing codebases and test suites without modification.
Everyone's probably familiar with the manual approach. You manually assert each parameter passed to and/or return value returned from every function in your codebase. What boilerplate could be simpler or more banal? We've all seen it a hundred times a googleplex times, and vomited a little in our mouths everytime we did. Repetition gets old fast. DRY, yo.
Get your vomit bags ready. For brevity, let's assume a simplified easy_spirit_bear() function accepting only a single str parameter. Here's what the manual approach looks like:
def easy_spirit_bear(kermode: str) -> str:
    assert isinstance(kermode, str), 'easy_spirit_bear() parameter kermode={} not of <class "str">'.format(kermode)
    return_value = (kermode, "Moksgm'ol", 'Ursus americanus kermodei')
    assert isinstance(return_value, str), 'easy_spirit_bear() return value {} not of <class "str">'.format(return_value)
    return return_value

Python 101, right? Many of us passed that class.
Bear typing extracts the type checking manually performed by the above approach into a dynamically defined wrapper function automatically performing the same checks – with the added benefit of raising granular TypeError rather than ambiguous AssertionError exceptions. Here's what the automated approach looks like:
def easy_spirit_bear_wrapper(*args, __beartype_func=easy_spirit_bear, **kwargs):
    if not (
        isinstance(args[0], __beartype_func.__annotations__['kermode'])
        if 0 < len(args) else
        isinstance(kwargs['kermode'], __beartype_func.__annotations__['kermode'])
        if 'kermode' in kwargs else True):
            raise TypeError(
                'easy_spirit_bear() parameter kermode={} not of {!r}'.format(
                args[0] if 0 < len(args) else kwargs['kermode'],
                __beartype_func.__annotations__['kermode']))

    return_value = __beartype_func(*args, **kwargs)

    if not isinstance(return_value, __beartype_func.__annotations__['return']):
        raise TypeError(
            'easy_spirit_bear() return value {} not of {!r}'.format(
                return_value, __beartype_func.__annotations__['return']))

    return return_value

It's long-winded. But it's also basically* as fast as the manual approach. * Squinting suggested.
Note the complete lack of function inspection or iteration in the wrapper function, which contains a similar number of tests as the original function – albeit with the additional (maybe negligible) costs of testing whether and how the parameters to be type checked are passed to the current function call. You can't win every battle.
Can such wrapper functions actually be reliably generated to type check arbitrary functions in less than 275 lines of pure Python? Snake Plisskin says, "True story. Got a smoke?"
And, yes. I may have a neckbeard.
No, Srsly. Why "bear"?
Bear beats duck. Duck may fly, but bear may throw salmon at duck. In Canada, nature can surprise you.
Next question.
What's So Hot about Bears, Anyway?
Existing solutions do not perform bare-metal type checking – at least, none I've grepped across. They all iteratively reinspect the signature of the type-checked function on each function call. While negligible for a single call, reinspection overhead is usually non-negligible when aggregated over all calls. Really, really non-negligible.
It's not simply efficiency concerns, however. Existing solutions also often fail to account for common edge cases. This includes most if not all toy decorators provided as stackoverflow answers here and elsewhere. Classic failures include:

*

*Failing to type check keyword arguments and/or return values (e.g., sweeneyrod's @checkargs decorator).

*Failing to support tuples (i.e., unions) of types accepted by the isinstance() builtin.

*Failing to propagate the name, docstring, and other identifying metadata from the original function onto the wrapper function.

*Failing to supply at least a semblance of unit tests. (Kind of critical.)

*Raising generic AssertionError exceptions rather than specific TypeError exceptions on failed type checks. For granularity and sanity, type checking should never raise generic exceptions.

Bear typing succeeds where non-bears fail. All one, all bear!
Bear Typing Unbared
Bear typing shifts the space and time costs of inspecting function signatures from function call time to function definition time – that is, from the wrapper function returned by the @beartype decorator into the decorator itself. Since the decorator is only called once per function definition, this optimization yields glee for all.
Bear typing is an attempt to have your type checking cake and eat it, too. To do so, @beartype:

*

*Inspects the signature and annotations of the original function.

*Dynamically constructs the body of the wrapper function type checking the original function. Thaaat's right. Python code generating Python code.

*Dynamically declares this wrapper function via the exec() builtin.

*Returns this wrapper function.

Shall we? Let's dive into the deep end.
# If the active Python interpreter is *NOT* optimized (e.g., option "-O" was
# *NOT* passed to this interpreter), enable type checking.
if __debug__:
    import inspect
    from functools import wraps
    from inspect import Parameter, Signature

    def beartype(func: callable) -> callable:
        '''
        Decorate the passed **callable** (e.g., function, method) to validate
        both all annotated parameters passed to this callable _and_ the
        annotated value returned by this callable if any.

        This decorator performs rudimentary type checking based on Python 3.x
        function annotations, as officially documented by PEP 484 ("Type
        Hints"). While PEP 484 supports arbitrarily complex type composition,
        this decorator requires _all_ parameter and return value annotations to
        be either:

        * Classes (e.g., `int`, `OrderedDict`).
        * Tuples of classes (e.g., `(int, OrderedDict)`).

        If optimizations are enabled by the active Python interpreter (e.g., due
        to option `-O` passed to this interpreter), this decorator is a noop.

        Raises
        ----------
        NameError
            If any parameter has the reserved name `__beartype_func`.
        TypeError
            If either:
            * Any parameter or return value annotation is neither:
              * A type.
              * A tuple of types.
            * The kind of any parameter is unrecognized. This should _never_
              happen, assuming no significant changes to Python semantics.
        '''

        # Raw string of Python statements comprising the body of this wrapper,
        # including (in order):
        #
        # * A "@wraps" decorator propagating the name, docstring, and other
        #   identifying metadata of the original function to this wrapper.
        # * A private "__beartype_func" parameter initialized to this function.
        #   In theory, the "func" parameter passed to this decorator should be
        #   accessible as a closure-style local in this wrapper. For unknown
        #   reasons (presumably, a subtle bug in the exec() builtin), this is
        #   not the case. Instead, a closure-style local must be simulated by
        #   passing the "func" parameter to this function at function
        #   definition time as the default value of an arbitrary parameter. To
        #   ensure this default is *NOT* overwritten by a function accepting a
        #   parameter of the same name, this edge case is tested for below.
        # * Assert statements type checking parameters passed to this callable.
        # * A call to this callable.
        # * An assert statement type checking the value returned by this
        #   callable.
        #
        # While there exist numerous alternatives (e.g., appending to a list or
        # bytearray before joining the elements of that iterable into a string),
        # these alternatives are either slower (as in the case of a list, due to
        # the high up-front cost of list construction) or substantially more
        # cumbersome (as in the case of a bytearray). Since string concatenation
        # is heavily optimized by the official CPython interpreter, the simplest
        # approach is (curiously) the most ideal.
        func_body = '''
@wraps(__beartype_func)
def func_beartyped(*args, __beartype_func=__beartype_func, **kwargs):
'''

        # "inspect.Signature" instance encapsulating this callable's signature.
        func_sig = inspect.signature(func)

        # Human-readable name of this function for use in exceptions.
        func_name = func.__name__ + '()'

        # For the name of each parameter passed to this callable and the
        # "inspect.Parameter" instance encapsulating this parameter (in the
        # passed order)...
        for func_arg_index, func_arg in enumerate(func_sig.parameters.values()):
            # If this callable redefines a parameter initialized to a default
            # value by this wrapper, raise an exception. Permitting this
            # unlikely edge case would permit unsuspecting users to
            # "accidentally" override these defaults.
            if func_arg.name == '__beartype_func':
                raise NameError(
                    'Parameter {} reserved for use by @beartype.'.format(
                        func_arg.name))

            # If this parameter is both annotated and non-ignorable for purposes
            # of type checking, type check this parameter.
            if (func_arg.annotation is not Parameter.empty and
                func_arg.kind not in _PARAMETER_KIND_IGNORED):
                # Validate this annotation.
                _check_type_annotation(
                    annotation=func_arg.annotation,
                    label='{} parameter {} type'.format(
                        func_name, func_arg.name))

                # String evaluating to this parameter's annotated type.
                func_arg_type_expr = (
                    '__beartype_func.__annotations__[{!r}]'.format(
                        func_arg.name))

                # String evaluating to this parameter's current value when
                # passed as a keyword.
                func_arg_value_key_expr = 'kwargs[{!r}]'.format(func_arg.name)

                # If this parameter is keyword-only, type check this parameter
                # only by lookup in the variadic "**kwargs" dictionary.
                if func_arg.kind is Parameter.KEYWORD_ONLY:
                    func_body += '''
    if {arg_name!r} in kwargs and not isinstance(
        {arg_value_key_expr}, {arg_type_expr}):
        raise TypeError(
            '{func_name} keyword-only parameter '
            '{arg_name}={{}} not a {{!r}}'.format(
                {arg_value_key_expr}, {arg_type_expr}))
'''.format(
                        func_name=func_name,
                        arg_name=func_arg.name,
                        arg_type_expr=func_arg_type_expr,
                        arg_value_key_expr=func_arg_value_key_expr,
                    )
                # Else, this parameter may be passed either positionally or as
                # a keyword. Type check this parameter both by lookup in the
                # variadic "**kwargs" dictionary *AND* by index into the
                # variadic "*args" tuple.
                else:
                    # String evaluating to this parameter's current value when
                    # passed positionally.
                    func_arg_value_pos_expr = 'args[{!r}]'.format(
                        func_arg_index)

                    func_body += '''
    if not (
        isinstance({arg_value_pos_expr}, {arg_type_expr})
        if {arg_index} < len(args) else
        isinstance({arg_value_key_expr}, {arg_type_expr})
        if {arg_name!r} in kwargs else True):
            raise TypeError(
                '{func_name} parameter {arg_name}={{}} not of {{!r}}'.format(
                {arg_value_pos_expr} if {arg_index} < len(args) else {arg_value_key_expr},
                {arg_type_expr}))
'''.format(
                    func_name=func_name,
                    arg_name=func_arg.name,
                    arg_index=func_arg_index,
                    arg_type_expr=func_arg_type_expr,
                    arg_value_key_expr=func_arg_value_key_expr,
                    arg_value_pos_expr=func_arg_value_pos_expr,
                )

        # If this callable's return value is both annotated and non-ignorable
        # for purposes of type checking, type check this value.
        if func_sig.return_annotation not in _RETURN_ANNOTATION_IGNORED:
            # Validate this annotation.
            _check_type_annotation(
                annotation=func_sig.return_annotation,
                label='{} return type'.format(func_name))

            # Strings evaluating to this parameter's annotated type and
            # currently passed value, as above.
            func_return_type_expr = (
                "__beartype_func.__annotations__['return']")

            # Call this callable, type check the returned value, and return this
            # value from this wrapper.
            func_body += '''
    return_value = __beartype_func(*args, **kwargs)
    if not isinstance(return_value, {return_type}):
        raise TypeError(
            '{func_name} return value {{}} not of {{!r}}'.format(
                return_value, {return_type}))
    return return_value
'''.format(func_name=func_name, return_type=func_return_type_expr)
        # Else, call this callable and return this value from this wrapper.
        else:
            func_body += '''
    return __beartype_func(*args, **kwargs)
'''

        # Dictionary mapping from local attribute name to value. For efficiency,
        # only those local attributes explicitly required in the body of this
        # wrapper are copied from the current namespace. (See below.)
        local_attrs = {'__beartype_func': func}

        # Dynamically define this wrapper as a closure of this decorator. For
        # obscure and presumably uninteresting reasons, Python fails to locally
        # declare this closure when the locals() dictionary is passed; to
        # capture this closure, a local dictionary must be passed instead.
        exec(func_body, globals(), local_attrs)

        # Return this wrapper.
        return local_attrs['func_beartyped']

    _PARAMETER_KIND_IGNORED = {
        Parameter.POSITIONAL_ONLY, Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD,
    }
    '''
    Set of all `inspect.Parameter.kind` constants to be ignored during
    annotation- based type checking in the `@beartype` decorator.

    This includes:

    * Constants specific to variadic parameters (e.g., `*args`, `**kwargs`).
      Variadic parameters cannot be annotated and hence cannot be type checked.
    * Constants specific to positional-only parameters, which apply to non-pure-
      Python callables (e.g., defined by C extensions). The `@beartype`
      decorator applies _only_ to pure-Python callables, which provide no
      syntactic means of specifying positional-only parameters.
    '''

    _RETURN_ANNOTATION_IGNORED = {Signature.empty, None}
    '''
    Set of all annotations for return values to be ignored during annotation-
    based type checking in the `@beartype` decorator.

    This includes:

    * `Signature.empty`, signifying a callable whose return value is _not_
      annotated.
    * `None`, signifying a callable returning no value. By convention, callables
      returning no value are typically annotated to return `None`. Technically,
      callables whose return values are annotated as `None` _could_ be
      explicitly checked to return `None` rather than a none-`None` value. Since
      return values are safely ignorable by callers, however, there appears to
      be little real-world utility in enforcing this constraint.
    '''

    def _check_type_annotation(annotation: object, label: str) -> None:
        '''
        Validate the passed annotation to be a valid type supported by the
        `@beartype` decorator.

        Parameters
        ----------
        annotation : object
            Annotation to be validated.
        label : str
            Human-readable label describing this annotation, interpolated into
            exceptions raised by this function.

        Raises
        ----------
        TypeError
            If this annotation is neither a new-style class nor a tuple of
            new-style classes.
        '''

        # If this annotation is a tuple, raise an exception if any member of
        # this tuple is not a new-style class. Note that the "__name__"
        # attribute tested below is not defined by old-style classes and hence
        # serves as a helpful means of identifying new-style classes.
        if isinstance(annotation, tuple):
            for member in annotation:
                if not (
                    isinstance(member, type) and hasattr(member, '__name__')):
                    raise TypeError(
                        '{} tuple member {} not a new-style class'.format(
                            label, member))
        # Else if this annotation is not a new-style class, raise an exception.
        elif not (
            isinstance(annotation, type) and hasattr(annotation, '__name__')):
            raise TypeError(
                '{} {} neither a new-style class nor '
                'tuple of such classes'.format(label, annotation))

# Else, the active Python interpreter is optimized. In this case, disable type
# checking by reducing this decorator to the identity decorator.
else:
    def beartype(func: callable) -> callable:
        return func

And leycec said, Let the  @beartype bring forth type checking fastly: and it was so.
Caveats, Curses, and Empty Promises
Nothing is perfect. Even bear typing.
Caveat I: Default Values Unchecked
Bear typing does not type check unpassed parameters assigned default values. In theory, it could. But not in 275 lines or less and certainly not as a stackoverflow answer.
The safe (...probably totally unsafe) assumption is that function implementers claim they knew what they were doing when they defined default values. Since default values are typically constants (...they'd better be!), rechecking the types of constants that never change on each function call assigned one or more default values would contravene the fundamental tenet of bear typing: "Don't repeat yourself over and oooover and oooo-oooover again."
Show me wrong and I will shower you with upvotes.
Caveat II: No PEP 484
PEP 484 ("Type Hints") formalized the use of function annotations first introduced by PEP 3107 ("Function Annotations"). Python 3.5 superficially supports this formalization with a new top-level typing module, a standard API for composing arbitrarily complex types from simpler types (e.g., Callable[[Arg1Type, Arg2Type], ReturnType], a type describing a function accepting two arguments of type Arg1Type and Arg2Type and returning a value of type ReturnType).
Bear typing supports none of them. In theory, it could. But not in 275 lines or less and certainly not as a stackoverflow answer.
Bear typing does, however, support unions of types in the same way that the isinstance() builtin supports unions of types: as tuples. This superficially corresponds to the typing.Union type – with the obvious caveat that typing.Union supports arbitrarily complex types, while tuples accepted by @beartype support only simple classes. In my defense, 275 lines.
Tests or It Didn't Happen
Here's the gist of it. Get it, gist? I'll stop now.
As with the @beartype decorator itself, these py.test tests may be seamlessly integrated into existing test suites without modification. Precious, isn't it?
Now the mandatory neckbeard rant nobody asked for.
A History of API Violence
Python 3.5 provides no actual support for using PEP 484 types. wat?
It's true: no type checking, no type inference, no type nuthin'. Instead, developers are expected to routinely run their entire codebases through heavyweight third-party CPython interpreter wrappers implementing a facsimile of such support (e.g., mypy). Of course, these wrappers impose:

*

*A compatibility penalty. As the official mypy FAQ admits in response to the frequently asked question "Can I use mypy to type check my existing Python code?": "It depends. Compatibility is pretty good, but some Python features are not yet implemented or fully supported." A subsequent FAQ response clarifies this incompatibility by stating that:

*

*"...your code must make attributes explicit and use a explicit protocol representation." Grammar police see your "a explicit" and raise you an implicit frown.

*"Mypy will support modular, efficient type checking, and this seems to rule out type checking some language features, such as arbitrary runtime addition of methods. However, it is likely that many of these features will be supported in a restricted form (for example, runtime modification is only supported for classes or methods registered as dynamic or 'patchable')."

*For a full list of syntactic incompatibilities, see "Dealing with common issues".  It's not pretty. You just wanted type checking and now you refactored your entire codebase and broke everyone's build two days from the candidate release and the comely HR midget in casual business attire slips a pink slip through the crack in your cubicle-cum-mancave. Thanks alot, mypy.

*A performance penalty, despite interpreting statically typed code. Fourty years of hard-boiled computer science tells us that (...all else being equal) interpreting statically typed code should be faster, not slower, than interpreting dynamically typed code. In Python, up is the new down.

*Additional non-trivial dependencies, increasing:

*

*The bug-laden fragility of project deployment, especially cross-platform.

*The maintenance burden of project development.

*Possible attack surface.

I ask Guido: "Why? Why bother inventing an abstract API if you weren't willing to pony up a concrete API actually doing something with that abstraction?" Why leave the fate of a million Pythonistas to the arthritic hand of the free open-source marketplace? Why create yet another techno-problem that could have been trivially solved with a 275-line decorator in the official Python stdlib?
I have no Python and I must scream.

A: def someFunc(a, b, c):
    params = locals()
    for _item in params:
        print type(params[_item]), _item, params[_item]

Demo:
>> someFunc(1, 'asd', 1.0)
>> <type 'int'> a 1
>> <type 'float'> c 1.0
>> <type 'str'> b asd

more about locals()

A: If you want to check **kwargs, *args as well as normal arguments in one go, you can use the locals() function as the first statement in your function definition to get a dictionary of the arguments.
Then use type() to examine the arguments, for example whilst iterating over the dict.
def myfunc(my, args, to, this, function, **kwargs):
    d = locals()
    assert(type(d.get('x')) == str)
    for x in d:
        if x != 'x':
            assert(type(d[x]) == x
    for x in ['a','b','c']:
        assert(x in d)

    whatever more...

A: If you want to do the validation for several functions you can add the logic inside a decorator like this:
def deco(func):
     def wrapper(a,b,c):
         if not isinstance(a, int)\
            or not isinstance(b, int)\
            or not isinstance(c, str):
             raise TypeError
         if not 0 < b < 10:
             raise ValueError
         if c == '':
             raise ValueError
         return func(a,b,c)
     return wrapper

and use it:
@deco
def foo(a,b,c):
    print 'ok!'

Hope this helps!

A: This is not the solution to you, but if you want to restrict the function calls to some specific parameter types then you must use the PROATOR { The Python Function prototype validator }. you can refer the following link. https://github.com/mohit-thakur-721/proator

A: def myFunction(a,b,c):
"This is an example function I'd like to check arguments of"
    if type( a ) == int:
       #dostuff
    if 0 < b < 10:
       #dostuff
    if type( C ) == str and c != "":
       #dostuff

redpajama_set_name: RedPajamaStackExchange


text:
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Home Education Spotivity Mobile App Marks the "X" for Teens in Search for Meaningful...
Spotivity Mobile App Marks the "X" for Teens in Search for Meaningful After-School Activities
Chicago, IL, May 30, 2019 — The free Spotivity mobile application is happy to announce that they have launched 3 new additional program categories – Jobs, Internships, and Camps – and grew their number of searchable program categories by 50% to a total of nine engagement categories. Spotivity has also implemented an updated UX design to make onboarding and app navigation a more seamless process.
The Additional Categories (called "buckets") have increased the searchable Chicago focused database to over 2,600 programs (up from 1,900 previously). These items (Jobs, Internships, and Camps) will focus more heavily on summer related activities and have been launched to coincide with the conclusion of the 2018-19 school year. The present searchable database now stands at 9 and include the following categories:
1. Sports. Sports programs provide teens access to unique ways of engaging in physical activities. A sport of choice not only helps improve physical strength and endurance but also helps relieve tension and anxiety.
2. Camps. Camps provide teens organized and focused engagement opportunities to try new activities in the context of group dynamics. Camps range from academic to fitness and from education to mentoring.
3. Volunteerism. Volunteer opportunities provide a great way to enhance one's self-esteem and level of satisfaction – while also supporting Civic Engagement and Community Involvement.
4. Arts. Art programs are a useful way to hone or participate in activities related to all aspects of artistic expression. It helps in the creative expansion of those artistically inclined while also providing access for those who wish to venture into the world of creativity.
5. Mentoring. Receiving mentorship is an effective development method for an individual looking for a window into a future world, which they may want to belong, and need guidance to navigate next steps.
6. Education. Tutoring can help teens invest in themselves for better academic performance or gaining necessary training on a subject of choice.
7. Internships. Gaining hands-on experience is integral for teens and via Internships the participant learns real-time insight into the working environment and is allowed to wrestle with the concepts of productivity, collaboration, and work deliverables.
8. Health. Health is a broad category of interrelated dimensions of an individual's wellbeing. Spotivity allows teens to find varied health-related engagement opportunities (mental, emotional, and physical).
9. Jobs. Jobs provide valuable life skills along with a heightened focus on personal responsibility – all the while giving the teen the power of an earned wage.
Spotivity's goal is simple: more teens in more programs more of the time. To accomplish this we not only need to give the end-user good information but also provide supportive measures that make engagement meaningful. Spotivity is a double-sided marketplace tool, looking to support both the users looking for programs (teens and their parents/guardians) and the programs looking to serve this need. After the Chicago beta, Spotivity plans to expand to the surrounding suburbs and then targeted additional markets on its way to becoming a first-of-its-kind nation-wide tool.
"More than half of the 40 million American children between the ages of 13-17 aren't engaged in an after-school activity," says Spotivity founder Montana Butsch. "For many of them, the time after school, on weekends and over vacations signals boredom and risk. This means they miss out on activities that may cultivate talent, lift self confidence, increase engagement with school, and decrease the likelihood of risky or self-destructive behavior. With Spotivity, we expect to change that ratio, so more teens find an accessible pathway to pursue meaningful activities and develop their passions," he continues.
Butsch created Spotivity to help teens and parents make both better-informed decisions and remove luck from decision process. Not only does it provide greater visibility of available options, but it also provides solutions to
barriers-to-entry such as transport logistics support, community building via user forums, and digital badging to foster encouragement. Spotivity's dynamic mapping tool overlays student interest, program offerings, and transportation networks in a manner that supports a personal itinerary for each member.
In partnership with Utah State University Human Development and Family Studies Institute, in addition to project work with Loyola Quinlan School of Business, OTCR of University of Illinois, Robert Morris University, Columbia College, INIGO Communications of Loyola University, and Designation Digital Design, spotivity created a novel points-based system that encourages engagement by rewarding via social recognition. Users will be able to check-in to their activities on their phone, share their progress with their peers, see their ranking between different use groups, and use a built-in motivational system that encourages both parents and program leaders to create incentive matrixes, all to further bolster program engagement and familial involvement.
Spotivity also addresses the baseline needs of the program provider. This element is greatly informed by Butsch's experience as the founder of nationally-recognized, inner-city rowing/mentoring organization Chicago Training Center.
"The hardest thing to achieve with any program is a depth and breadth of the participant base," Butsch says. "Spotivity provides a tremendous free participant identification platform while also supporting an affordable and easy to use back-end platform for the Premium Program administrator to engage their current group of program participants."
Services will continually roll out on the app and on the web. The first Premium Program subscriptions will be available by Q2 2019 when we conclude the integration of the merchant tool.
About Spotivity
Chicago-based Spotivity is spearheaded by 1871 member Montana Butsch. The app currently lists over 2,600 programs across 9 different categories called "buckets" in metropolitan Chicago. Founder Montana Butsch started his involvement in afterschool when he founded the non-profit Chicago Training Center in 2006. Spotivity recently generated support from local angel investors and is an Illinois Angel Tax Credit program member. Spotivity was developed in partnership with Algoworks Solutions Inc. Recent media attention has come from American Innovation, Coffee with Closers, 1871, Oxford University, Loyola Academy and 30sec Mom.
For more information, please visit https://www.spotivity.com/ or call (312) 342 5241. Follow Spotivity on social media at:
https://www.pinterest.com/spotivityapp/
https://www.instagram.com/spotivityapp/
https://twitter.com/SpotivityApp
https://www.facebook.com/spotivityapp/
https://www.linkedin.com/company/spotivity/
App is available on both iTunes and Google Play:
https://itunes.apple.com/us/app/spotivity/id1417312664
https://play.google.com/store/apps/details?id=com.spotivity
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A fine Victorian silver novelty mustard pot, London 1848 by Charles and George Fox. In the form of and owl chased with feathers and inset with red glass eyes. Complete with a mustard spoon with a mouse finial that rests beneath the owl's beak. 3 5/8" (9cm) high, 6.1oz.