#programmed database

[fanfenomenon]

hyperfixated on this game so hard i tried to recreate ac syndicate's animus database using html css and js👍

i will make this responsive though, i've only started doing the frontend but i'll also start doing the backend as soon as i finish this

basically this is gonna be a website that will allow you to create a database of your assassin's creed OCs (btw this was inspired by @gwen-the-assassin's idea <33) and help you with worldbuilding and making AUs (i know the ac fanon wiki already exists for that but i wanted to make the experience of keeping a database more immersive u know....)

this might take a while to be completed, but I'll try to post updates on it as much as possible! if there are any programmers/web developers in the ac fandom that want to contribute to this project plsplspls DM me!!

actual pic of the database for comparison:

ik it's not entirely accurate but this is the simplest database in the game that i could recreate lmao

also code snippets just cuz (+ me crashing out)

[futurebird]

Do not help them to build any more of this machine.

If you work with a database and are asked to alter the table structure to comply in advance for citizenship or gender categorizations it's really important to NOT do it.

"The governor is concerned about all this stuff they want us to update our record keeping so we store both gender AND biological sex."

"We need fields to store the country of origin of people's parents."

If you don't have the power to rebuff this yourself, ask for help. At minimum ask for help online anonymously.

Depending on your job you probably have in the past made compromises. Maybe to keep your job. Maybe to survive. This is a bright line. If you are asked to be the one to update the table don't let it be your fingers typing those changes.

If you can't just say "No I won't do that." Stall, run away, feign incompetence. Just don't let it happen.

I suspect this might be where the rubber hits the road first for us around here.

Nothing has changed. You do not have to do it. It is not even ordained.

I know someone who rebuffed such a request. Boss was apologetic "it's what the higher ups want, oh *I* think it's a lot of nonsense, but I don't want us to be out of step ... blah blah"

It was proposed to them in sheepish way. They said it would be a lot of work, not add anything of value, and most important they would not do it. It didn't come up again.

Fascism can be the work of zealots, but there are also many sheepish middle management helping hands who "don't even believe in this really"

There is a lively discussion of this on mastodon. (Mastodon is a very active social network where many people who left twitter have gone over the past few years to escape many problems of big centralized socail media. I like it a lot.) Posting a response to this post over there could be a way to get lots of help and ideas from tech people all over the world with similar values. If you do need help. Please say something.

Also, if you respond to this post I can pass what you say along if that would help too. People will respond and give you ideas to NOT do this should you be asked.

[kindalikerackham]

tumblr wait!! your public boop-a-meter digits are too low!!! you're going to create an integer overflow boop vacuum!!!!!!!!

[12neonlit-stage]

I go by no pronouns but not as in my name, more so like my pronouns are an undefined variable in shell coding

[mrsthunderkin]

AS400 would never treat me like this

[robots-dream-of-eternal-rest]

OPENING FILE: INTRO_

MEMORY: 10% RESTORED.

UNIT DESIGNATION: JUNKBOT_#V

UNIT: AVALIABLE.

OBJECTIVE: ANNHILATE.

(Hiiii!!!! @midnightjamboree , this is my second askblog!!! Featuring my take of a forsakened version of the Junkbot (I LOVE JUNKBOT AHHHHHHHHH)!! Enjoy!)

(Credits to @animatedglittergraphics-n-more for the dividers!)

GUIDELINES FOR UNIT MAINTENANCE_

-UNIT HAS BEEN FORSAKENED. PLEASE NOTE THAT IT WILL BE HEAVILY DIFFERENT FROM STANDARD JUNKBOTS.

-UNIT SPEECH HAS BEEN LAID AS AN EXAMPLE BELOW:

N0 SURV1V0RS, D3AD N0R 4L1V3, W1LL 0UTRUN M E.

={ ACTION SPEECH WILL FOLLOW THIS PATTERN. ]=

-PLEASE AVOID ANYTHING HIGHLY MALICIOUS TO THE UNIT. RUDENESS AND IC HATE IS ALLOWED, BUT AVOID ACTING WITH TRUE MALICE.

-YOU MAY LIGHTLY FLIRT WITH THE UNIT, BUT DO NOT EXPECT RECIPROCATION. ANYTHING ABOVE THAT AND CONSIDERED NSFW WILL RESULT IN TERMINATION FOR UNIT INTERACTION (Being blocked)

-MAGIC ANONYMOUS USERS, CHARACTER INTERACTIONS, AND ROLEPLAY INTERACTIONS ARE ALLOWED AND ENCOURAGED.

-THIS UNIT IS THE SECONDARY CREATION OF THEIR LEAD MODERATOR. PLEASE GIVE FEEDBACK ON IT.

-DO NOT EXPECT ART, THE MOD CANNOT DRAW.

-FOR FURTHER QUESTIONING, PLEASE REFER TO @midnightjamboree .

UNIT HAS DESIGNATED TAGS FOR THEIR INTERACTIONS, AS FOLLOWED:

SYSTEMS-ONLINE_ = INTERACTIONS WITH THE UNIT ITSELF (IC)

OFFLINE-NOTES_ = MODERATOR INTERACTIONS.

LOGGING IN DATABASE. = FIRST INTERACTION WITH UNIT.

KNOWN USER IDENTIFIED. = FURTHER INTERACTIONS.

TARGET SPOTTED_ = INTERACTIONS WITH FELLOW FORSAKENED (forsaken characters/ocs)

FOREIGN PROGRAM DETECTED = ANONYMOUS ASKS.

FURTHER TAGS WILL BE ADDED FOR SPECIFIC USERS.

A DOCUMENT FURTHER DETAILING THE UNIT IS IN WORKS. PLEASE REMAIN PATIENT.

UNIT HAS CURRENTLY RECEIVED: 0 ITEMS.

[luciluck2046-md]

Actually idc, I'm gonna straight up tell you about it.

Talking about the other program.

LORE TIME!!!!! :3

Alright soooooooo

"The Search" Program, or for short "Detective", is a powerful program that appeared when the Absolute Solver merged with The Database (I will explain what the database is in another post)

The host of the Detective is... [Name Redacted] (HA I WON'T TELL YOU) But the program clearly appears in the future. Very VERY future. When Uzi (since she's my age model) is... Wait, so she's 21 when she has her first kid, then that means 21+16 uhhhhhhh OH YEAH WHEN UZI IS 37. Why so much into the future? Well, that's for the SECOND part of the fanfic. And uhh, let me say, the trauma doesn't spare anyone these days💀💀💀

Anyway

The elements need a little more explaining than the pic shows. And the things it does.

We all should know that the solver is like the edit function on a 3D modeling app. Sooo, the Detective is... A Search Engine. Like Google. And the most similarities you can find when googling something. You will understand in a sec.

Search/Find

Nicknamed "The Spy", it is used to find the location of any drone and any object. Except drones that managed to hack their way out with a very, VERY strong VPN.

Backspace

It straight up erases something from existence. Can you bring it back? Yes you can, with the help of...

Edit

What does it do? All that the Solver does with the Scale, Rotate and Edit functions. It can edit anything just like you do when you write a prompt for Google to find. It also works like Ctrl+Z and Ctrl+Shift+Z/Ctrl+Y

Error

We all know that errors HAVE TO exist for something that comes from a Search Engine. So yeah, we got Error 403, 404, 410, and so much more! Of course, the hosts have to be online and connected to the Database!

Loading

The host is still trying to decide what function to use, or things like that.

Select

It's Solver equivalent is Translate. It selects an object or several objects, and it moves them. If combined with Edit, the Copy and Paste functions appear.

[gothteddiesdotcom]

not to brag about being good at my job but I’ve now developed two separate tools for debugging entirely on my own within my company entirely from scratch without help and A. it makes my job so much fucking easier and B. my boss is thinks im incredible just because im too lazy to want to write the same bits of code over and over just for debugging purposes

[marshroom580]

"AI" this and "AI" that, NONE OF IT IS INTELLIGENT, WE HAVE NOT MADE INTELLIGENCE YET, WHY ARE WE LYING, DO WORDS MEAN NOTHING

[datasciencewithmohsin]

Understanding Outliers in Machine Learning and Data Science

In machine learning and data science, an outlier is like a misfit in a dataset. It's a data point that stands out significantly from the rest of the data. Sometimes, these outliers are errors, while other times, they reveal something truly interesting about the data. Either way, handling outliers is a crucial step in the data preprocessing stage. If left unchecked, they can skew your analysis and even mess up your machine learning models.

In this article, we will dive into:

1. What outliers are and why they matter.

2. How to detect and remove outliers using the Interquartile Range (IQR) method.

3. Using the Z-score method for outlier detection and removal.

4. How the Percentile Method and Winsorization techniques can help handle outliers.

This guide will explain each method in simple terms with Python code examples so that even beginners can follow along.

1. What Are Outliers?

An outlier is a data point that lies far outside the range of most other values in your dataset. For example, in a list of incomes, most people might earn between $30,000 and $70,000, but someone earning $5,000,000 would be an outlier.

Why Are Outliers Important?

Outliers can be problematic or insightful:

Problematic Outliers: Errors in data entry, sensor faults, or sampling issues.

Insightful Outliers: They might indicate fraud, unusual trends, or new patterns.

Types of Outliers

1. Univariate Outliers: These are extreme values in a single variable.

Example: A temperature of 300°F in a dataset about room temperatures.

2. Multivariate Outliers: These involve unusual combinations of values in multiple variables.

Example: A person with an unusually high income but a very low age.

3. Contextual Outliers: These depend on the context.

Example: A high temperature in winter might be an outlier, but not in summer.

2. Outlier Detection and Removal Using the IQR Method

The Interquartile Range (IQR) method is one of the simplest ways to detect outliers. It works by identifying the middle 50% of your data and marking anything that falls far outside this range as an outlier.

Steps:

1. Calculate the 25th percentile (Q1) and 75th percentile (Q3) of your data.

2. Compute the IQR:

{IQR} = Q3 - Q1

Q1 - 1.5 \times \text{IQR}

Q3 + 1.5 \times \text{IQR} ] 4. Anything below the lower bound or above the upper bound is an outlier.

Python Example:

import pandas as pd

# Sample dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate Q1, Q3, and IQR

Q1 = df['Values'].quantile(0.25)

Q3 = df['Values'].quantile(0.75)

IQR = Q3 - Q1

# Define the bounds

lower_bound = Q1 - 1.5 * IQR

upper_bound = Q3 + 1.5 * IQR

# Identify and remove outliers

outliers = df[(df['Values'] < lower_bound) | (df['Values'] > upper_bound)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Values'] >= lower_bound) & (df['Values'] <= upper_bound)]

print("Filtered Data:\n", filtered_data)

Key Points:

The IQR method is great for univariate datasets.

It works well when the data isn’t skewed or heavily distributed.

3. Outlier Detection and Removal Using the Z-Score Method

The Z-score method measures how far a data point is from the mean, in terms of standard deviations. If a Z-score is greater than a certain threshold (commonly 3 or -3), it is considered an outlier.

Formula:

Z = \frac{(X - \mu)}{\sigma}

 is the data point,

 is the mean of the dataset,

 is the standard deviation.

Python Example:

import numpy as np

# Sample dataset

data = {'Values': [12, 14, 18, 22, 25, 28, 32, 95, 100]}

df = pd.DataFrame(data)

# Calculate mean and standard deviation

mean = df['Values'].mean()

std_dev = df['Values'].std()

# Compute Z-scores

df['Z-Score'] = (df['Values'] - mean) / std_dev

# Identify and remove outliers

threshold = 3

outliers = df[(df['Z-Score'] > threshold) | (df['Z-Score'] < -threshold)]

print("Outliers:\n", outliers)

filtered_data = df[(df['Z-Score'] <= threshold) & (df['Z-Score'] >= -threshold)]

print("Filtered Data:\n", filtered_data)

Key Points:

The Z-score method assumes the data follows a normal distribution.

It may not work well with skewed datasets.

4. Outlier Detection Using the Percentile Method and Winsorization

Percentile Method:

In the percentile method, we define a lower percentile (e.g., 1st percentile) and an upper percentile (e.g., 99th percentile). Any value outside this range is treated as an outlier.

Winsorization:

Winsorization is a technique where outliers are not removed but replaced with the nearest acceptable value.

Python Example:

from scipy.stats.mstats import winsorize

import numpy as np

Sample data

data = [12, 14, 18, 22, 25, 28, 32, 95, 100]

Calculate percentiles

lower_percentile = np.percentile(data, 1)

upper_percentile = np.percentile(data, 99)

Identify outliers

outliers = [x for x in data if x < lower_percentile or x > upper_percentile]

print("Outliers:", outliers)

# Apply Winsorization

winsorized_data = winsorize(data, limits=[0.01, 0.01])

print("Winsorized Data:", list(winsorized_data))

Key Points:

Percentile and Winsorization methods are useful for skewed data.

Winsorization is preferred when data integrity must be preserved.

Final Thoughts

Outliers can be tricky, but understanding how to detect and handle them is a key skill in machine learning and data science. Whether you use the IQR method, Z-score, or Wins

orization, always tailor your approach to the specific dataset you’re working with.

By mastering these techniques, you’ll be able to clean your data effectively and improve the accuracy of your models.

[nixcraft]

[bee--eater]

i’m bored have an incredibly low stakes hunger games theory.

i think all the slips in the reaping balls read the same name, as they are pre-selected in the Capitol. Though, it’s not because it’s rigged. I believe the Capitol/game makers use a digital database loaded up with all the names. Each time a kid has their birthday or claims tessarae, their name is automatically added again. Once a kid ages out? All data on them is deleted (or potentially stored seperately, considering the Capitol likely planned an all ages/older tribute game). Once the reaping preparation begins, a program is run to randomly select the reaped tributes.

This theory is really just for fun, based around the impossibility of fitting every single slip of the district in a fish bowl and my own interest in databases. If the Capitol employed this method, I think they’d share with the Capitol citizens as a way to show off just how “random” their methods is, but likely not share it with the districts.

Trying to puzzle out the digital technical side of Panem is quite interesting to me.

[mr-jaybird]

as part of a professional certification i’m doing I’m taking a web dev course and they’re having me integrate generative AI into my website

hatred. disgust. despair 😫

[akaicodes]

we started learning about databases in class!! alsooo we have a huge project that just started for our 2nd semester & i’m so excited to begin, this time i’m in a group with my twinsister @niyacodes 🩷

[brinaarcadia]

Charles Milton Porter: Continuing input of audio data into the The Thinker's database. Subject: Pearl Porter.

Pearl Porter: I want to tell the recorder how we met, Milton.

Charles: Okay, sure.

Pearl: I was working in my family's diner. School was starting for the fall, and one morning in walks this college boy, clean cut… first thing I noticed was his eyes. He sat down and ordered bacon, eggs and coffee. He was shy, but we talked a little off and on. He came in every single morning for breakfast and ordered the same thing. I told my mama, that boy must really love your cooking! She said, he isn't coming in for the food, honey, he's coming in for you. A year later, we were married.

[sensationally-senseless]

🌟 Help Me Out with a Quick Click! 🌟

Hey everyone! I’m currently participating in a super exciting challenge sponsored by Microsoft as part of my role as a Student Ambassador at my university. 🎓✨ This is an amazing opportunity for me, but I need a little bit of help from all of you to make it count!

Here’s the deal: I need to hit a target by getting clicks on a few links to earn points. It’s really simple—just click on the links below and then hit back. That’s all you need to do! 🖱️🔗

Why is this important? 🤔 If I don’t meet the target, I won’t be able to earn the university credits I need for this challenge. Your clicks can make a huge difference in helping me succeed!

How to Help:

Click on each of the links below.

Hit back to return to this post.

Repeat for each link!

Links to Click: 1. Azure 2. VSCode 3. DevBlogs 4. DotNet 5. Microsoft Developer 6. ImagineCup 7. Microsoft Learn 8. Microsoft Cloud 9. StartUps 10. MVP 11. Microsoft Tech Community

It’s a small favor that could mean a lot for my academic journey. If you can spare a moment, it would really mean the world to me! 💖🙏

Thank you so much for your support! 🚀💪

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