File 166503567524.jpg - (54.51KB , 600x530 , Remilia_Scarlet_600_1093879.jpg )

>>5570
ghost edited your script trololololol get rekt

>>5579
fkin pwnd :OOOOOOOOOO

thanks. I have plan to make local crm system. But without experience I can not do it so need to apply to https://syndicode.com/services/crm-development/ who have big experience in crm dvelopment. With a CRM system in place, every question, every service request, every preference and every past contact detail about every customer is instantly available, which means that every new interaction with them should always be personalised, relevant and up to date.

try pulling it off, so all the glowies can circle jerk around us

>>5533

seasoned comes after little, then reasoned is sort of tangential to those

https://sam-koblenski.blogspot.com/2018/12/tech-book-face-off-seasoned-schemer-vs.html

No boob pls you. Get dicked by medicine men that way hail satan

First think about what you would like to develop; what type of game, with what era feel, and then just wander to your local book store or jump on kindle and grab a copy of the first "for idiots" book of that language. Learn the language over 1-3 years and enjoy your growing skill set. Create an indie DEV team and spill your heart out in your project(s)

File 163654455230.png - (64.88KB , 1200x603 , Simple_DirectMedia_Layer,_Logo_svg.png )

>>5339
you can do it using sdl 1 or 2.0 if you want 3d.

C was originally designed as a system implementation language within Bell Labs and has since become one of the most popular programming languages in existence. It's been used for developing operating systems, compilers, debuggers, and many other applications that involve low-level computer hardware interaction. I started to study this prograaming recently on courses. But nowadays I apply to http://www.aimprosoft.com/blog/how-to-create-mobile-ecommerce-app-costs-tech-stack-and-market-strategy/ becasue want to develop own second ecommerce store.

>>5339
C is a systems language. It’s used to write things that require lots of control and/or high efficiency. Basically if you can think of something you that’s fundamental to computing is probably written in C. If not, whatever it’s complied/interpreter is written in is probably C or was itself written in C.

Also an incredibly good tool for learning about computer security.

Check the haskell wiki

>>5321
To be specific https://wiki.haskell.org/Haskell_in_industry
I've personally not touched Haskell, beyond following along in "Learn you a Haskell for Great Good," however I have bumped against modifying Erlang in the wild (specifically with RabbitMQ).

You can't Haskell without being aware of how it's different. Apart from ongoing research a lot of Haskell work is directed towards evolving the understanding of the language. This is also what many struggle with. Algebraic structures as type class hierarchies are very important in Haskell. While you can focus on what you could in any other language, the community is highly focused on details of these hierarchies. Coming from other languages this will feel masturbatory but trust me when I say this is one of the primary use case of Haskell. Learning instances and how they compose, learning what instances imply others are all a big part of it. It is equally important what instances don't exist https://blog.functorial.com/posts/2015-12-06-Counterexamples.html

In our time we need to check more attention onto logo and design of own websites. A good logo is simple, distinctive, and reflective of your brand values. It should be practical to use and feature a singular design. You can find more tips about logo and design on https://www.namecheap.com/guru-guides/logo-design-tips/

When I need any software, I ask for help from specialists, since I do not have time to understand all this. If you are interested, then on the https://8allocate.com/dedicated-teams/, you will be able to read more detailed information. Maybe you can find help here to build the software you need. Have a nice day.

I like cold coffee any time of the year

>>5435
>Can't tell which end of the bell curve you're on, but it's not the middle
Typically, hashes are unique for any input. In fact, they are designed for it to be statistically impossible to find 2 things with the same hash. Hardly useful for identifying the same author of different texts. However, there is a concept of locality-sensitive hashing which might be useful or unnecessary for you.

What you want is some machine learning model that takes in text and spits out a lower-dimensional vector describing the text (analogous to a convolutional net for images). Then, you can apply locality-sensitive hashing to collapse similar vectors to vectors you use as identities. For outliers, you would even be able to say % chance of each identity by calculating a simple projection in each.

However, if you do the machine learning properly, you won't need hashing; it's just a way to calibrate the model after the fact if you fuck up.

Now, for how to create and train the model, the first step is data. Ideally, you would have a large, diverse corpus of text labelled by author. If not, this is easy enough to create by scraping the web.

This problem is very similar to facial identification (different from facial recognition, which would be like a program that decides whether text was produced randomly or intelligently, not who produced it) in that both are solved by transforming the data into a vector of features describing the data which can then be compared to each other in the resulting metric space, and that both fundamentally deal with identity. Ie, the principle component of the facial metric space probably corresponds to gender. By comparing how white, how black, how asian, how fat, how thin, how masculine, how feminine, etc, the faces in multiple pictures are, you can tell which ones are probably of the same person. It is the same process for identifying the author of a text. The text has analogous features like syntax, verbosity, vocabulary, tone, etc, when taken together, can identify an author.

Anyway, set up a model that has an appropriate input size for your data and and your best guess at how many features you need for output (you will tweak this until you stop seeing improvement). Cost function should be (euclidian proportional, but more computationally efficient) distance of output vector from average of outputs for the same author minus the sum of the distances from each of the other authors' averages, normalized. If it doesn't work after completing training, mess with the output size and try again. Once it's as good as it'll get, optionally apply locality-sensitive hashing for maximum effort.