#sql queries for practice

[tpointtech1]

Essential SQL Queries to Boost Your Data Skills

[thedbahub]

Navigating MAXDOP in SQL Server 2022: Best Practices for Multi-Database Environments

In the realm of SQL Server management, one of the pivotal settings that database administrators must judiciously configure is the Maximum Degree of Parallelism (MAXDOP). This setting determines the number of processors that SQL Server can use for the execution of a query. Proper configuration of MAXDOP is essential, especially in environments with multiple databases and a limited number of CPU…

[chappydev]

Future of LLMs (or, "AI", as it is improperly called)

Posted a thread on bluesky and wanted to share it and expand on it here. I'm tangentially connected to the industry as someone who has worked in game dev, but I know people who work at more enterprise focused companies like Microsoft, Oracle, etc. I'm a developer who is highly AI-critical, but I'm also aware of where it stands in the tech world and thus I think I can share my perspective. I am by no means an expert, mind you, so take it all with a grain of salt, but I think that since so many creatives and artists are on this platform, it would be of interest here. Or maybe I'm just rambling, idk.

LLM art models ("AI art") will eventually crash and burn. Even if they win their legal battles (which if they do win, it will only be at great cost), AI art is a bad word almost universally. Even more than that, the business model hemmoraghes money. Every time someone generates art, the company loses money -- it's a very high energy process, and there's simply no way to monetize it without charging like a thousand dollars per generation. It's environmentally awful, but it's also expensive, and the sheer cost will mean they won't last without somehow bringing energy costs down. Maybe this could be doable if they weren't also being sued from every angle, but they just don't have infinite money.

Companies that are investing in "ai research" to find a use for LLMs in their company will, after years of research, come up with nothing. They will blame their devs and lay them off. The devs, worth noting, aren't necessarily to blame. I know an AI developer at meta (LLM, really, because again AI is not real), and the morale of that team is at an all time low. Their entire job is explaining patiently to product managers that no, what you're asking for isn't possible, nothing you want me to make can exist, we do not need to pivot to LLMs. The product managers tell them to try anyway. They write an LLM. It is unable to do what was asked for. "Hm let's try again" the product manager says. This cannot go on forever, not even for Meta. Worst part is, the dev who was more or less trying to fight against this will get the blame, while the product manager moves on to the next thing. Think like how NFTs suddenly disappeared, but then every company moved to AI. It will be annoying and people will lose jobs, but not the people responsible.

ChatGPT will probably go away as something public facing as the OpenAI foundation continues to be mismanaged. However, while ChatGPT as something people use to like, write scripts and stuff, will become less frequent as the public facing chatGPT becomes unmaintainable, internal chatGPT based LLMs will continue to exist.

This is the only sort of LLM that actually has any real practical use case. Basically, companies like Oracle, Microsoft, Meta etc license an AI company's model, usually ChatGPT.They are given more or less a version of ChatGPT they can then customize and train on their own internal data. These internal LLMs are then used by developers and others to assist with work. Not in the "write this for me" kind of way but in the "Find me this data" kind of way, or asking it how a piece of code works. "How does X software that Oracle makes do Y function, take me to that function" and things like that. Also asking it to write SQL queries and RegExes. Everyone I talk to who uses these intrernal LLMs talks about how that's like, the biggest thign they ask it to do, lol.

This still has some ethical problems. It's bad for the enivronment, but it's not being done in some datacenter in god knows where and vampiring off of a power grid -- it's running on the existing servers of these companies. Their power costs will go up, contributing to global warming, but it's profitable and actually useful, so companies won't care and only do token things like carbon credits or whatever. Still, it will be less of an impact than now, so there's something. As for training on internal data, I personally don't find this unethical, not in the same way as training off of external data. Training a language model to understand a C++ project and then asking it for help with that project is not quite the same thing as asking a bot that has scanned all of GitHub against the consent of developers and asking it to write an entire project for me, you know? It will still sometimes hallucinate and give bad results, but nowhere near as badly as the massive, public bots do since it's so specialized.

The only one I'm actually unsure and worried about is voice acting models, aka AI voices. It gets far less pushback than AI art (it should get more, but it's not as caustic to a brand as AI art is. I have seen people willing to overlook an AI voice in a youtube video, but will have negative feelings on AI art), as the public is less educated on voice acting as a profession. This has all the same ethical problems that AI art has, but I do not know if it has the same legal problems. It seems legally unclear who owns a voice when they voice act for a company; obviously, if a third party trains on your voice from a product you worked on, that company can sue them, but can you directly? If you own the work, then yes, you definitely can, but if you did a role for Disney and Disney then trains off of that... this is morally horrible, but legally, without stricter laws and contracts, they can get away with it.

In short, AI art does not make money outside of venture capital so it will not last forever. ChatGPT's main income source is selling specialized LLMs to companies, so the public facing ChatGPT is mostly like, a showcase product. As OpenAI the company continues to deathspiral, I see the company shutting down, and new companies (with some of the same people) popping up and pivoting to exclusively catering to enterprises as an enterprise solution. LLM models will become like, idk, SQL servers or whatever. Something the general public doesn't interact with directly but is everywhere in the industry. This will still have environmental implications, but LLMs are actually good at this, and the data theft problem disappears in most cases.

Again, this is just my general feeling, based on things I've heard from people in enterprise software or working on LLMs (often not because they signed up for it, but because the company is pivoting to it so i guess I write shitty LLMs now). I think artists will eventually be safe from AI but only after immense damages, I think writers will be similarly safe, but I'm worried for voice acting.

[shumw4y]

"Here are some quick, practical SQL learning resources that will help you get comfortable without overwhelming you:

1. Codecademy - SQL for Beginners

Why: Interactive lessons and hands-on exercises.

What you'll learn: Basics like SELECT, WHERE, JOINs, and aggregation (SUM, COUNT, AVG).

Link: Codecademy - SQL

2. W3Schools - SQL Tutorial

Why: A great reference for looking up syntax and examples.

What you'll learn: SQL fundamentals and queries with examples that are easy to try in a browser.

Link: W3Schools SQL Tutorial

3. SQLBolt

Why: Short, hands-on lessons that help you practice writing queries immediately.

What you'll learn: Data filtering, sorting, and combining tables with JOINs.

Link: SQLBolt

4. Khan Academy - Intro to SQL

Why: Beginner-friendly and focused on the basics, plus you can do exercises along the way.

What you'll learn: Selecting, filtering, sorting, and JOINs, with examples.

Link: Khan Academy SQL

5. LeetCode - SQL Practice

Why: More challenging, with real-world SQL problems you can solve.

What you'll learn: Advanced queries, subqueries, and more complex data manipulations.

Link: LeetCode SQL"

[biopractify]

How to Transition from Biotechnology to Bioinformatics: A Step-by-Step Guide

Biotechnology and bioinformatics are closely linked fields, but shifting from a wet lab environment to a computational approach requires strategic planning. Whether you are a student or a professional looking to make the transition, this guide will provide a step-by-step roadmap to help you navigate the shift from biotechnology to bioinformatics.

Why Transition from Biotechnology to Bioinformatics?

Bioinformatics is revolutionizing life sciences by integrating biological data with computational tools to uncover insights in genomics, proteomics, and drug discovery. The field offers diverse career opportunities in research, pharmaceuticals, healthcare, and AI-driven biological data analysis.

If you are skilled in laboratory techniques but wish to expand your expertise into data-driven biological research, bioinformatics is a rewarding career choice.

Step-by-Step Guide to Transition from Biotechnology to Bioinformatics

Step 1: Understand the Basics of Bioinformatics

Before making the switch, it’s crucial to gain a foundational understanding of bioinformatics. Here are key areas to explore:

Biological Databases – Learn about major databases like GenBank, UniProt, and Ensembl.

Genomics and Proteomics – Understand how computational methods analyze genes and proteins.

Sequence Analysis – Familiarize yourself with tools like BLAST, Clustal Omega, and FASTA.

🔹 Recommended Resources:

Online courses on Coursera, edX, or Khan Academy

Books like Bioinformatics for Dummies or Understanding Bioinformatics

Websites like NCBI, EMBL-EBI, and Expasy

Step 2: Develop Computational and Programming Skills

Bioinformatics heavily relies on coding and data analysis. You should start learning:

Python – Widely used in bioinformatics for data manipulation and analysis.

R – Great for statistical computing and visualization in genomics.

Linux/Unix – Basic command-line skills are essential for working with large datasets.

SQL – Useful for querying biological databases.

🔹 Recommended Online Courses:

Python for Bioinformatics (Udemy, DataCamp)

R for Genomics (HarvardX)

Linux Command Line Basics (Codecademy)

Step 3: Learn Bioinformatics Tools and Software

To become proficient in bioinformatics, you should practice using industry-standard tools:

Bioconductor – R-based tool for genomic data analysis.

Biopython – A powerful Python library for handling biological data.

GROMACS – Molecular dynamics simulation tool.

Rosetta – Protein modeling software.

🔹 How to Learn?

Join open-source projects on GitHub

Take part in hackathons or bioinformatics challenges on Kaggle

Explore free platforms like Galaxy Project for hands-on experience

Step 4: Work on Bioinformatics Projects

Practical experience is key. Start working on small projects such as:

✅ Analyzing gene sequences from NCBI databases ✅ Predicting protein structures using AlphaFold ✅ Visualizing genomic variations using R and Python

You can find datasets on:

NCBI GEO

1000 Genomes Project

TCGA (The Cancer Genome Atlas)

Create a GitHub portfolio to showcase your bioinformatics projects, as employers value practical work over theoretical knowledge.

Step 5: Gain Hands-on Experience with Internships

Many organizations and research institutes offer bioinformatics internships. Check opportunities at:

NCBI, EMBL-EBI, NIH (government research institutes)

Biotech and pharma companies (Roche, Pfizer, Illumina)

Academic research labs (Look for university-funded projects)

💡 Pro Tip: Join online bioinformatics communities like Biostars, Reddit r/bioinformatics, and SEQanswers to network and find opportunities.

Step 6: Earn a Certification or Higher Education

If you want to strengthen your credentials, consider:

🎓 Bioinformatics Certifications:

Coursera – Genomic Data Science (Johns Hopkins University)

edX – Bioinformatics MicroMasters (UMGC)

EMBO – Bioinformatics training courses

🎓 Master’s in Bioinformatics (optional but beneficial)

Top universities include Harvard, Stanford, ETH Zurich, University of Toronto

Step 7: Apply for Bioinformatics Jobs

Once you have gained enough skills and experience, start applying for bioinformatics roles such as:

Bioinformatics Analyst

Computational Biologist

Genomics Data Scientist

Machine Learning Scientist (Biotech)

💡 Where to Find Jobs?

LinkedIn, Indeed, Glassdoor

Biotech job boards (BioSpace, Science Careers)

Company career pages (Illumina, Thermo Fisher)

Final Thoughts

Transitioning from biotechnology to bioinformatics requires effort, but with the right skills and dedication, it is entirely achievable. Start with fundamental knowledge, build computational skills, and work on projects to gain practical experience.

Are you ready to make the switch? 🚀 Start today by exploring free online courses and practicing with real-world datasets!

[pentesttestingcorp]

Protect Your Laravel APIs: Common Vulnerabilities and Fixes

API Vulnerabilities in Laravel: What You Need to Know

As web applications evolve, securing APIs becomes a critical aspect of overall cybersecurity. Laravel, being one of the most popular PHP frameworks, provides many features to help developers create robust APIs. However, like any software, APIs in Laravel are susceptible to certain vulnerabilities that can leave your system open to attack.

In this blog post, we’ll explore common API vulnerabilities in Laravel and how you can address them, using practical coding examples. Additionally, we’ll introduce our free Website Security Scanner tool, which can help you assess and protect your web applications.

Common API Vulnerabilities in Laravel

Laravel APIs, like any other API, can suffer from common security vulnerabilities if not properly secured. Some of these vulnerabilities include:

>> SQL Injection SQL injection attacks occur when an attacker is able to manipulate an SQL query to execute arbitrary code. If a Laravel API fails to properly sanitize user inputs, this type of vulnerability can be exploited.

Example Vulnerability:

$user = DB::select("SELECT * FROM users WHERE username = '" . $request->input('username') . "'");

Solution: Laravel’s query builder automatically escapes parameters, preventing SQL injection. Use the query builder or Eloquent ORM like this:

$user = DB::table('users')->where('username', $request->input('username'))->first();

>> Cross-Site Scripting (XSS) XSS attacks happen when an attacker injects malicious scripts into web pages, which can then be executed in the browser of a user who views the page.

Example Vulnerability:

return response()->json(['message' => $request->input('message')]);

Solution: Always sanitize user input and escape any dynamic content. Laravel provides built-in XSS protection by escaping data before rendering it in views:

return response()->json(['message' => e($request->input('message'))]);

>> Improper Authentication and Authorization Without proper authentication, unauthorized users may gain access to sensitive data. Similarly, improper authorization can allow unauthorized users to perform actions they shouldn't be able to.

Example Vulnerability:

Route::post('update-profile', 'UserController@updateProfile');

Solution: Always use Laravel’s built-in authentication middleware to protect sensitive routes:

Route::middleware('auth:api')->post('update-profile', 'UserController@updateProfile');

>> Insecure API Endpoints Exposing too many endpoints or sensitive data can create a security risk. It’s important to limit access to API routes and use proper HTTP methods for each action.

Example Vulnerability:

Route::get('user-details', 'UserController@getUserDetails');

Solution: Restrict sensitive routes to authenticated users and use proper HTTP methods like GET, POST, PUT, and DELETE:

Route::middleware('auth:api')->get('user-details', 'UserController@getUserDetails');

How to Use Our Free Website Security Checker Tool

If you're unsure about the security posture of your Laravel API or any other web application, we offer a free Website Security Checker tool. This tool allows you to perform an automatic security scan on your website to detect vulnerabilities, including API security flaws.

Step 1: Visit our free Website Security Checker at https://free.pentesttesting.com. Step 2: Enter your website URL and click "Start Test". Step 3: Review the comprehensive vulnerability assessment report to identify areas that need attention.

Screenshot of the free tools webpage where you can access security assessment tools.

Example Report: Vulnerability Assessment

Once the scan is completed, you'll receive a detailed report that highlights any vulnerabilities, such as SQL injection risks, XSS vulnerabilities, and issues with authentication. This will help you take immediate action to secure your API endpoints.

An example of a vulnerability assessment report generated with our free tool provides insights into possible vulnerabilities.

Conclusion: Strengthen Your API Security Today

API vulnerabilities in Laravel are common, but with the right precautions and coding practices, you can protect your web application. Make sure to always sanitize user input, implement strong authentication mechanisms, and use proper route protection. Additionally, take advantage of our tool to check Website vulnerability to ensure your Laravel APIs remain secure.

For more information on securing your Laravel applications try our Website Security Checker.

[lunacoding]

SQL GitHub Repositories

I’ve recently been looking up more SQL resources and found some repositories on GitHub that are helpful with learning SQL, so I thought I’d share some here!

Guides:

s-shemee SQL 101: A beginner’s guide to SQL database programming! It offers tutorials, exercises, and resources to help practice SQL

nightFuryman SQL in 30 Days: The fundamentals of SQL with information on how to set up a SQL database from scratch as well as basic SQL commands

Projects:

iweld SQL Dictionary Challenge: A SQL project inspired by a comment on this reddit thread https://www.reddit.com/r/SQL/comments/g4ct1l/what_are_some_good_resources_to_practice_sql/. This project consists of creating a single file with a column of randomly selected words from the dictionary. For this column, you can answer the various questions listed in the repository through SQL queries, or develop your own questions to answer as well.

DevMountain SQL 1 Afternoon: A SQL project where you practice inserting querying data using SQL. This project consists of creating various tables and querying data through this online tool created by DevMountain, found at this link https://postgres.devmountain.com/.

DevMountain SQL 2 Afternoon: The second part of DevMountain’s SQL project. This project involves intermediate queries such as “practice joins, nested queries, updating rows, group by, distinct, and foreign key”.

[adhdnursegoat]

Episode 2

Word Count: 9.2k

Content Warning: none right now

Pairing: Edward Nashton X OC Romy Winslow

Setting: Pre-Arkham Origins; 2013

─── [ sequence: loading ] ───

Tuesday, December 18th, 2012

Something isn’t right.

Edward narrowed his eyes at the screen, the onyx and emerald glow casting hard shadows across his face, deepening the lines of ever-present ire. The dataset sprawled before him, tangled, disorganized, and inefficient—a perfect mirror of the Gotham City Police Department itself. 

For years, the GCPD’s reputation for sloppy documentation had been almost impressive in its own way, as if this endless mess were some grand tradition they upheld out of sheer spite for change. Crime logs scrawled hastily, half-formed incident reports lost in the shuffle of physical files, a scattering of disjointed data without a semblance of order or care. And now, all of it had fallen to him.

The so-called “cybercrime division” was practically a joke before he arrived, a name slapped on an old, cluttered storage room. Its single, flickering fluorescent light buzzed overhead like a dying insect; its lone, wheezing computer, so ancient it sounded like it was about to take off the first time he powered it on. It had taken him months to convince the precinct to let him install even basic equipment, months of tolerating the grinding fan and a monitor that crackled whenever he turned it on. He had even bought and collected his own equipment to help do their job for them.

But now, he had slowly, painstakingly transformed the place, pulling it from the brink of irrelevance.

He was the GCPD’s cybercrime division. And, if he were honest, he’d rather it be this way.

The first task had been nothing short of brutal, a punishment only someone as patient—or as obsessively thorough—as him could withstand. He had spent weeks, months even, combing through stacks of paper files that had yellowed with age, pulling arrest records, crime logs, and incident reports from years past, each entry a piece of Gotham’s history filed with indifference and half-hearted effort.

But that was just the beginning.

Once the data had been extracted and uploaded into a digital system, Edward moved to the next step: cleaning it. He combed through each entry, scrubbing it clean of mistakes, standardizing formats, deleting duplicates, and filling in the blanks left by years of neglect. It was an endless process, every correction a small battle against the chaos that had festered there long before his arrival. The work had been like sculpting—he chipped away at it, day by day, until the rough edges began to take shape.

With the groundwork set, he had turned his attention to the architecture itself. The system he was building would become Gotham’s digital skeleton, a structure capable of supporting and, eventually, predicting the city’s crimes. He designed SQL databases from the ground up, creating logical tables for every critical piece of data: incident types, time of day, locations, affiliations, every detail that could build a comprehensive picture of Gotham’s criminal underworld. Each table was linked, connected, and cross-referenced in ways that only he fully understood.

He wrote queries that could pull up crime histories, correlate locations, and flag patterns—all in the blink of an eye. Every inch of it had been optimized, refined, and customized, honed to be faster, sharper, and more intuitive than anything the department had ever seen. It was a framework only he knew how to navigate, the kind of code that would baffle even the most tech-savvy officer.

But this was Gotham.

Data alone wasn’t enough; the system needed security—a wall strong enough to withstand the city’s relentless forces. He had spent countless nights implementing layer upon layer of protection, configuring firewalls, building encryption protocols so complex that even he would struggle to undo them. Each file, each report, each encrypted string had become a piece of his fortress. He was transforming this forgotten room into a stronghold, its walls fortified against any threat that dared to infiltrate. Only he held the keys, and only he knew which locks he’d installed.

Then the real work had begun.

Once he had established a patent data flow in the system, he had started layering in more complex tools—predictive algorithms and crime prediction models that mapped Gotham’s streets like veins, arteries pulsing with the city’s crime. He had used regression analysis to find trends, drawing connections between crimes that no one else had even considered. He mapped crime incidents to temporal and spatial data, forming a pattern that gave him a lens into Gotham’s soul. 

But the GCPD couldn’t understand raw numbers—not the way he did. They needed visuals, pretty pictures, something digestible for their mushy minds. So he had built dashboards and reports, simple yet elegant, that displayed his work in colorful heat maps, time-series analyses, and relational charts. Even Gotham’s least tech-savvy officers could click through the data now, though they hardly knew what they were looking at. But Edward did. He could track hotspots, watch the swell of crime ebbing and flowing unlike anyone else.

Each day, as the system grew, he had refined it further. He ran diagnostics, tweaked scripts, and checked logs to ensure there were no breaches, no unexpected bugs. Every piece of data was backed up, replicated on secure servers, ready to be restored at a moment’s notice if Gotham’s chaos took a swipe at his work. And if it did, he would be prepared. Because this was more than a job; this was his creation, his legacy.

With every keystroke, every security protocol, every predictive model, he built a machine that made Gotham’s chaos readable, its patterns decipherable, and its secrets… well, not so secret.

Until a few days ago, his work had seemed routine—a necessary but unglamorous role. But then something unusual had caught his attention: a pattern in the officer response logs.

Every month, he reviewed the logs. It was a habit, part of his meticulous nature. Until recently, there had been nothing unexpected. But now, a repeated anomaly had begun to emerge. Certain neighborhoods showed response times that were curiously high, particularly in cases involving specific types of violent crimes—kidnappings, assaults, even homicides. In other areas, responses to similar crimes were fast, efficient, predictable. Yet, in these particular zones, it was as if time slowed.

He had noticed response times of fifteen, even twenty minutes, where they would typically average around five.

It was subtle, barely noticeable at first. Most people would have brushed it off as a glitch or user error. But Edward Nashton was not most people—and “user error” was not in his personal vocabulary.

“What if…” he muttered, pulling up a fresh SQL query and setting filters for crimes tagged as high-priority in those specific neighborhoods. His fingers flew across the keyboard as he added parameters, refining the search.

SELECT Neighborhood, AVG(Response_Time) AS Avg_Response 

FROM Incident_Reports 

WHERE Crime_Type = 'High-Priority' 

GROUP BY Neighborhood;

The query ran, and Edward leaned forward, his glasses catching the glow of the screen as rows of data populated in rapid succession. A comparison of average response times across all The data stared back at him, validating his suspicions. The averages for these neighborhoods were well outside the norm. Frowning, he created a quick bar chart to visualize the data, and there it was—a spike in response times, glaringly obvious, almost like a neon sign begging for someone to notice.

What’s more, the pattern seemed to correlate with the involvement of certain officers. He drilled down further, narrowing the logs to responses where these outlier times were recorded, and sure enough, the same handful of officers’ IDs kept appearing. At least three officers, in particular, showed up again and again, logged as the responding parties in incidents with suspiciously delayed responses:

Edison, James

Hartley, Jack

Murphy, Curtis

Edward leaned back, his lips twitching to the side in a faint sneer. Gotham’s filth didn’t just rest on its streets—it was deeply embedded within the very department meant to protect it. This pattern wasn’t accidental. The slow responses weren’t random errors; they were deliberate, selectively applied.

For the first time in months, Edward felt the rush of excitement he’d been craving since joining the GCPD. This wasn’t just data compilation or trend analysis anymore. He had uncovered something substantial, something buried, waiting to be unearthed. It wasn’t just about numbers; this was a deeper, darker game involving the very people entrusted with Gotham’s safety.

This wasn’t merely an inconsistency. It was corruption, plain and simple, hiding in the numbers. And if there was one thing Edward Nashton excelled at, it was peeling back layers to expose the truth lurking beneath.

The screen flickered faintly, his cursor hovering over rows of data as his mind picked apart the patterns, noticing every inconsistency, every shred of deception. This wasn’t an error or some accidental miscalculation. No, what he saw here was intentional—something deliberate and dark slipping under the radar, a clear thread of corruption woven into the fabric of Gotham’s police force.

If anyone could expose it, could tug at the threads until it unraveled into undeniable truth, it was him. The thought sent a thrill down his spine, a familiar surge of satisfaction that came with knowing he was on the verge of something significant.

Bing!

The sharp notification broke his concentration, dragging his attention to the corner of his monitor where an email preview appeared. Edward’s expression shifted, his lips pressing tight as he read the sender’s name: Commissioner Gillian B. Loeb. A scowl formed before he could stop it, his eyes narrowing behind his glasses. 

“come 2 my office”

The words glared at him. No punctuation, no capitalization—shorthand, as if Loeb couldn’t be bothered with even a semblance of respect. The sheer laziness grated on Edward, adding another layer to his already simmering disdain. Commissioner Loeb might as well have stomped down to his desk and demanded his presence with the same lack of decorum, and Edward doubted he would have been as irked. His lip curled, the faintest twitch of irritation betraying his thoughts.

Edward didn’t have friends here—never had. He didn’t linger by the watercooler, didn’t care for small talk, and had no interest in the routine camaraderie his coworkers indulged in. Loeb, however, wasn’t just a minor irritant like the rest. No, Loeb sat proudly at the top of a list of people Edward preferred to avoid—a list with its own special level of contempt reserved just for him. Loeb’s greed, his smug superiority, the way he flaunted his power as though it were untouchable—it all disgusted Edward. But he wasn’t foolish enough to ignore him.

He drew in a slow breath, pushing back the annoyance as he removed his glasses, his thumb and forefinger pressing firmly against the bridge of his nose. The tightness settling behind his eyes was familiar, a strain born from hours spent at the monitor. He rubbed at it, hoping to ease the creeping fatigue. Forcing himself to release a sigh, he closed his eyes briefly, letting the weight of the task at hand wash over him, clearing his thoughts.

Edward’s eyes flicked back to the fresh data on his screen, teeming with unspoken implications. He could go now, take this to Loeb, drop the details in his lap, and watch the Commissioner squirm. But… no. Not yet. If there was anything he’d learned, it was that timing was everything, and he wanted this case to be “pretty” and clean—undeniable.

With a quiet sigh, he finally pushed back from the desk, his legs and back groaning in protest. The human body wasn’t built for this kind of work, not the endless hours hunched over monitors and squinting at screens. He stretched, lifting his arms until he felt the crack in his shoulders, then rolled his neck, savoring the sharp pop that released some of the tension.

After a final look around his cramped, shadow-filled corner of the storage room, he made his way to the door. The space was dark and dank, with stacks of old case files and barely-functioning equipment shoved into every corner. He’d been asking for more space since the day he arrived, but as long as he remained the sole member of the “cybercrime division,” there was no point—not according to the people holding the budget. He could already imagine their dismissive words, the laughter as they shrugged him off. Why upgrade the closet for one man?

When he opened the door, a different kind of darkness hit him. GCPD’s main floor was lit by the harsh hue of fluorescent lights, casting an unnatural pallor over everything. The grime felt omnipresent, tinging every surface with a layer of wear that no amount of scrubbing could erase. The entire precinct pulsed like a spastic nerve, alive with chaotic energy.

He stepped out, crossing to the bustling bullpen. The layout was predictable—three levels stacked atop one another like a fortress of bureaucracy. A sublevel housed the detained. The main level, where he stood now, held the bullpen at its center, filled with two rows of desks paired off in clusters. Corridors stretched out on the east and west sides of the building, leading to file and evidence rooms, interrogation suites, and break areas.

Officers strolled by with coffee in hand, their conversations blending into the background noise. Detectives leaned against desks, swapping stories and laughing loud enough to be heard across the room. Secretaries rushed from one end of the bullpen to the other, arms stacked with paperwork or balancing phones against their shoulders. Above, the second and third levels housed offices for secretaries and various divisions, their windows glowing faintly in the overhead light.

And above it all, perched on the second-level landing like a throne, was the Commissioner’s office. It loomed over the precinct, a constant reminder of who held power there.

Edward shoved his hands into his pockets, his stride unfaltering, gaze fixed straight ahead. As he wove through the bustling bullpen, the familiar hum of GCPD’s endless chatter faded into a low buzz, a background noise he had long since learned to ignore. He didn’t belong here—not with these people, not with their idle gossip and endless banter. He was here to work, nothing more. And most of the time, they respected that, leaving him alone, unnoticed in the corners of the precinct.

“Dracula has risen!”

Most of the time.

Edward gritted his teeth, his jaw tightening as he caught the grating laughter ringing from behind him. He didn’t break stride, didn’t turn—just kept moving, his hands shoved deep into his pockets, shoulders hunched slightly as if to shield himself from the attention. Just keep moving. He had mastered the art of appearing unbothered, of letting these low-effort taunts roll off him. But Hartley’s voice, dripping with smug familiarity, broke through, just loud enough to draw the attention of a few nearby officers who exchanged knowing looks.

“Naaaashton!” the voice called, drawing out the syllables with exaggerated cheer, as if addressing an old friend. Edward could practically feel the man’s self-satisfied smirk boring into the back of his head. “I’m always surprised to see you out in the sun. More surprised when you don’t burn.”

It was the kind of comment he had grown used to, the small digs Hartley loved to throw his way whenever he passed by. Hartley, with his false bravado and ignorance parading as wit, never missed a chance to turn Edward into the precinct’s punchline.

Officer Jack Hartley—the poster boy of stereotypical “All-American” masculinity, with cobalt eyes and sandy hair, tall and built like he was carved out of an idealized gym catalog, complete with a bulky torso that fanned out into broad shoulders and arms that tapered down in a ‘V’ like an oversized Dorito. A man who would be lost without his badge to wave around and his flexed biceps, displaying that questionable tribal tattoo spiraling down one arm.

Edward kept moving, eyes trained straight ahead, but he allowed himself a sidelong glance, just enough to see Hartley’s smirk and the dumb faces around him. He could feel the heat of their attention, their eyes eagerly watching for his reaction. This time, he didn’t stay silent.

“Hartley,” he replied, his voice sharp and controlled. “I’m always surprised to see you haven’t been fired for your incompetence.”

There was a beat of silence. Edward didn’t stop to savor it, but he caught the reaction—the flicker of embarrassment in Hartley’s expression, the slight widening of his eyes before the scowl settled in. A few snickers rippled through the nearby officers, a sound that only deepened Hartley’s frown. His cheeks flushed slightly, the kind of reaction that Hartley, a man who considered himself untouchable, never expected to feel.

“Oh, you’re a real comedian, aren’t you, Nashton?” Hartley muttered, his voice barely audible now, laced with a gruff edge, the forced comeback of someone unprepared for a response.

Edward didn’t dignify it with another verbal reply. But, to answer the question— no. He wasn’t a comedian. He hated jokes. He only spoke truth. The words, the tiny prick of retaliation, had already done their work, striking just the right note to unsettle Hartley without so much as breaking his stride. He allowed himself to savor it for only a second, a brief and private victory that curled ever so slightly at the corner of his mouth. He knew it was minor, a passing exchange that no one would remember by the end of the day—but that small reminder, that assertion of his own superiority, was more than enough. For Edward, it wasn’t about showing off; it was about reminding himself, and everyone around him, that he was sharper, quicker, and not someone who could be so easily dismissed.

As he steadied his pace toward Loeb’s office, his thoughts drifted to the people around him, each one of them blending into the other like dumb lumps of flesh. Idiots—all of them. The entire precinct was an echo chamber of mediocrity, swollen with officers who took pride in their badges but lacked even a shred of real intellect. They sat at their desks, shuffling papers, swapping jokes, indulging in the hollow camaraderie of shared ignorance. They had no ambition, no hunger for knowledge, no desire to see past the routines they repeated day after day. They were just bodies filling space, a backdrop against which his mind and his skills blazed brighter by contrast.

Each step up the stairs only solidified his distaste. Every click of his shoes against the metal felt like a declaration, a rhythm that reminded him he was alone in a sea of self-satisfied drones. None of them measured up. None of them could measure up. Hartley’s lazy jeers, the way he flexed as if it made him someone important, the way he reveled in the pointless antics of the bullpen—these were the people tasked with keeping Gotham safe. It would have been laughable if it weren’t so tragic.

His eyes stayed fixed ahead, not sparing a single glance back at the bullpen. He had no reason to look, no interest in indulging the officers’ empty stares or their shared smirks. They were beneath him, irrelevant to his purpose, and the thought only strengthened his resolve as he approached Loeb’s office.

When he reached the landing, Edward straightened, pulling himself up to his full height, his fingers brushing over the door handle. He spared no glances to the bullpen below as he entered the Commissioner’s office and shut the door behind him with a soft click. 

The room was a display of power—ornate but garish, every detail chosen for intimidation rather than taste. Heavy mahogany furniture dominated the space, the Commissioner’s oversized desk an imposing centerpiece cluttered with papers and a gleaming nameplate. The walls were lined with plaques and framed commendations, their polished surfaces reflecting the faint light from a brass floor lamp in the corner. A thick, dark green carpet muffled Edward’s steps as he moved further inside, the smell of old leather and cigar smoke lingering in the air like a stain. Behind Loeb, floor-to-ceiling windows framed the grimy skyline of Gotham, their blinds half-drawn, letting in just enough gray light to make the space feel oppressive rather than bright. The office was a monument to its occupant’s ego—a fortress designed to remind anyone who entered exactly who held the power here.

The old man, standing at the windows, barely glanced over his shoulder to see Edward enter. “Sit.”

Edward frowned but did as he was told. Then he waited. And waited. And waited some more. Loeb’s stance, hands clasped firmly behind his back, suggested authority—or, more precisely, a performance of it. Edward couldn’t tell if the Commissioner was actually observing anything down on the street or merely pretending to do so, basking in his own bloated sense of importance. The stance, the imperious tone, the refusal to even acknowledge him face-to-face—every detail screamed a carefully curated aura of authority. Loeb stood as if by habit, a fossil of bureaucratic pomposity, clinging to a legacy of hollow power.

The man himself was almost a caricature, the embodiment of the department’s rot. His body strained against his uniform, seams puckered and pulled tight around his frame. The cap on his head dug visibly into his pallid skin, leaving an indentation along his brow, a mark of fluid retention only emphasized by the puffiness of his jowls. Loeb was thick-necked, with sagging skin that folded around his face in a way that resembled a bulldog’s. The clubbed fingers clasped at his back gave away years of heart strain, his slow circulation, and unchecked lifestyle, further evident in the labored rise and fall of his shoulders. He was an uncomfortable-looking man, like a worn-out relic forced into a role it no longer fit.

Edward glanced at his watch.

At last, the coot deigned to speak.

“Nashton,” the Commissioner quipped, “you’ll be getting a student.” His tone brooked no argument.

Gillian Loeb finally turned from the window, taking heavy, unhurried steps toward the desk, his movements sluggish, a body too tired to fully lift its feet from the floor. The scuffing of his shoes against the linoleum was maddeningly loud in the otherwise silent office, each step punctuated by his labored breath—a rasping sound that filled the room, making his presence that much harder to ignore. He reached his desk, his eyes narrowing just enough to convey irritation, perhaps at the exertion of moving across the room. With a relieved huff, he lowered himself into the worn red leather chair behind his desk, and it groaned under his weight, the sound of old leather and strained springs filling the air.

Edward resented being voluntold for anything, especially by a man who likely couldn’t navigate a basic search engine. But what choice did he have? Loeb’s words, dripping with condescension, only served to deepen Edward’s frown. He shifted in the stiff wooden chair opposite the Commissioner’s desk. He crossed his arms, fingers digging into his elbows as he suppressed the urge to roll his eyes. The impatience was barely masked—an edge to his expression that spoke volumes to anyone perceptive enough to notice. Loeb, of course, was not.

Then, the Commissioner began his speech, one that had likely been rehearsed, perhaps at his morning mirror. His voice rolled through the room, slow and full, each word dragging as he introduced the “exciting new work-study program.” Edward’s eyes flickered, resisting the urge to visibly wince as Loeb stressed the importance of “investing in someone’s future with the GCPD.” It was predictable, even painfully so, and Edward could practically see through Loeb’s words to the core of it: this so-called initiative was just a thinly veiled scheme, some tax break or budget cut disguised as a benefit to the community.

He was not naïve. He didn’t need the specifics to understand how the department operated. The GCPD’s funding, already stretched thin, had likely prompted this decision. The idea of a “program” that would cost them next to nothing while earning them goodwill with Gotham’s public was probably irresistible to the old bureaucrat. With students desperate for experience, the department could add another set of hands—hands they wouldn’t even have to pay. To Loeb, it was a flawless plan.

Edward’s leg bounced lightly as Loeb continued, the man oblivious to his impatience. Loeb droned on about the value of “real-world experience,” his words as empty as the promises they contained. Edward had read enough department memos and budget drafts to know the truth. This wasn’t about nurturing young talent or providing mentorship. It was about creating a self-serving “opportunity” that the GCPD could tout in press releases.

Loeb, meanwhile, was fully immersed in his monologue, clasping his hands as he expounded upon the program’s “benefits.” There was a look of smug satisfaction on his face, as if he were certain Edward should be grateful for the “honor” of mentoring this student. Edward could feel his jaw clenching, the tension in his arms building as he listened to the Commissioner pontificate about the duty of guiding someone who “could be the future of Gotham’s finest.”

Finally, Loeb paused, and Edward seized the chance to speak., his voice level, measured. “And this ‘student’ is supposed to assist me?”

“Yes, precisely.”

“I highly doubt they would be of any assistance, Commissioner.” Edward had a difficult time barring the condescension in his voice.

“You should be thankful.” Loeb narrowed his beady brown eyes at him. “Think of it as… additional help. Someone who can shoulder some of the workload.”

The Commissioner said it as if he were doing him a favor. Pfft. Edward knew better. He wasn’t being given a protégé; he was being saddled with an amateur who would inevitably fumble through tasks, leaving him to clean up the mess. More work—that’s what this was. The idea of a student trying to “help” in his field felt like a bad joke. He had spent a year refining his division—every system, every dataset was his creation. The thought of letting some kid handle even a fraction of it filled him with a quiet dread, like watching someone try to operate a complex machine without understanding a single gear.

Loeb shifted in his chair, taking Edward’s silence as agreement. “The youth these days, Nashton. They’re the future, and we have a duty to mold them. The department sees this as an investment. Someone to eventually join your endeavors full time.”

Edward’s jaw tightened. Investment? He couldn’t help but smirk slightly at the absurdity. Loeb had no real idea what Edward did, no real grasp of the complexity his work required. In Loeb’s mind, a student could simply step in and soak up skills like a sponge. But Edward knew better. To him, this wasn’t an investment; it was a hindrance, a risk of inefficiency, and the last thing he needed.

But with Loeb’s expectant gaze bearing down on him, he understood the futility of voicing his concerns. The decision had been made, probably long before he was even called into this office. He wasn’t being given a choice—he was being told to fall in line.

“We’ve got some candidates lined up. You narrow it down, and we’ll finalize it.”

Loeb pushed a stack of russet-colored folders toward him, and Edward suppressed a sigh as he unfurled his arms, grabbed the stack, and flipped open the first file. The pages were full of redacted lines—names, ages, and even genders all neatly blacked out. He rolled his eyes. There were pages of transcripts, an accompanying essay (which he was not going to read), academic achievements, extracurriculars, and sanitized letters of recommendation, none of which told him anything interesting.

Edward felt the familiar dull boredom creep in.

He eyed the first profile, scanning each line with a growing sense of irritation. Harvard, it read in bold letters, as if the word alone signified worth. Straight As, a laundry list of commendations from professors who probably barely knew this student beyond the name printed on their assignments. It was the kind of profile built from legacy admissions, expensive prep schools, and connections more valuable than skill. Every accolade, every honor felt manufactured, the result of privilege rather than grit or true intelligence. This was the sort of person whose future had been paid for, gift-wrapped, and delivered to them on a silver platter. A pawn that had been moved through life’s chessboard with no actual understanding of the game.

Edward flipped to the next file, another profile reeking of the same glossy, untarnished perfection: a prestigious background, impeccable grades, extracurriculars that spoke more to showmanship than substance. His lip curled, an almost imperceptible twist of disdain. What use was someone like this to him? He didn’t need another pre-packaged prodigy, the type who had been endlessly praised but never challenged, the kind who breezed through academia without ever truly understanding what it meant to think, to analyze, to push limits. He needed someone who had actually had to work for something, who had seen struggle, who understood what it meant to build something from scratch—someone with the kind of determination that couldn’t be bought.

These files in front of him represented everything he despised about the world: the hollow merit of titles, the pretense of excellence. It was the kind of privilege that relied on appearances rather than substance, and it left a sour taste in his mouth. He flipped through each one with growing impatience, each page a carbon copy of the last, all polished to an empty sheen that hid any real substance.

His gaze sharpened as he closed another file. What he wanted, if he was to have an assistant, was someone with actual mettle. Someone with grit, someone who hadn’t had everything handed to them. The kind of candidate who could be taught something beyond the regurgitated lessons of privilege. Edward’s jaw tightened as he tossed the files back onto the desk before grabbing another file near the bottom of the stack.

When he opened this one, he cocked a brow. Something caught his eye.

There was an entry—a two-month juvenile record attached to a high school transcript from their junior year. Edward’s interest piqued immediately. He leaned back in the chair, letting the file rest in his fingers as he read the details. The record noted a hacking incident: unauthorized access to school servers to alter grades. He almost chuckled, finding this much more intriguing than the immaculate résumés of Ivy League candidates.

The report stated they had felt their grades were given unfairly and decided to take matters into their own hands. It was an act of rebellion, yes, but also one of precision and calculation. They hadn’t sabotaged the system—they had simply revised their grades without damaging any other records or erasing traces of the hack. There was a comment from a principal decrying the act as undermining the school’s “integrity” and a record of a lengthy expulsion hearing. Yet, despite this incident, there were a handful of letters from teachers who seemed reluctant to give up on them.

He read further, finding notes on their turnaround at their senior year and at Gotham City Community College. After high school, it seemed no other institution had wanted to take a chance on them, except for this one. But instead of coasting through, they had thrived—joining the debate team, earning honors, and eventually transferring to Gotham University. Now they were a college senior majoring in computer science with a minor in criminal justice.

As he skimmed through the final notes, Edward smirked. This work-study tied directly into their capstone project—a predictive AI programmed to determine when and where crimes were more likely to occur. It was a smart move, one that showed ambition and resilience. They were not another cookie-cutter success story from an Ivy League—they were someone who had clawed their way out of a mess, took risks, and kept climbing. Whoever they were, they were far more intriguing than the other candidates. He didn’t need some entitled, bougie fraternity brat who would think they were smarter than him.

He closed the file with a soft pat, already deciding. He flicked it onto the desk with an air of indifference and slid to a stop in front of Loeb. “This one,” he said flatly.

The Commissioner picked up the folder, his thick fingers fumbling with the dry edges as he peeled it open. His brow furrowed deeper as he read, and he shot Edward a wary look over the papers. “This one? The one with the juvie record? Are you sure?”

Edward’s expression remained cool, detached. “It’s either this one or none at all,” he replied without missing a beat.

Loeb stared at him for a moment, rubbing his jaw, clearly weighing his options. After a long pause, he sighed and tossed the file back on the desk with a resigned grunt. “Fine,” he muttered. “They’ll be here after the holidays.”

─── [ sequence: loading ] ───

In under a month’s time, Edward Nashton found himself caught off guard.

It was not often he was caught off guard, and he did not like it.

He was hunched over his workstation, eyes narrowed as he sifted through lines of encrypted data. It was after lunch, during which he had remained in his space, still working, forgoing eating as he normally did. His office, if one could call it that, was a windowless space in a back corner of the GCPD headquarters, dimly lit and reeking of stale coffee and burnt-out ambition. It was crammed with outdated computers and stacks of scattered papers, the sort of place where Edward thrived in isolation. He was so absorbed in his task that when the door opened and a knock sounded on the doorframe, he muttered, “Yes?” without looking up, already bracing himself for another mundane IT request—misguided souls thinking that the "computer guy" could fix the printer.

But then an unfamiliar voice responded.

“Excuse me? Are you Mr. Edward Nashton?”

It was not the tone he expected—there was no hint of impatience or condescension, which he had grown accustomed to when people sought him out. The voice was feminine, with an even pitch, its calm, smokey cadence infiltrating the monotony of his work. It was an unobtrusive sound, yet so unusual to his ears that he was compelled to see who it belonged to. He looked up. He froze.

A girl was standing at the doorway, her fingers resting lightly on the doorframe as if unsure whether to fully step inside. He had not even heard the door open.

Edward frowned.

His first impression of her was one of dissonance—a sharp, almost unsettling contrast between her and the office she had just entered. The grimy, worn-down precinct felt even darker with her in it, as if the dingy fluorescent lights themselves were suddenly more aware of their inadequacy.

She was beautiful—irritatingly so. Her long, sleek dark hair fell like silk curtains, parted perfectly down the middle, framing her face with an effortless elegance that didn’t belong anywhere near the GCPD. Her eyes, lined meticulously with dark, precise wings, were fixed on him with a hint of amusement. There was a different energy to her, one that felt deliberate, almost as though she knew exactly how out of place she looked and was inviting him to react. He barely realized how long he held her gaze.

With a faint scowl, he forced himself to look away, taking in the rest of her with a detached, analytical eye. Her lavender blazer dress caught what little light there was, gold buttons glinting as they drew a subtle line down her figure. The hem stopped just short of professional modesty, skirting the edge of propriety with a cut that was as tailored as it was daring. She had a designer bag slung over her shoulder, a fuzzy purple notebook and a gray-and-pink plaid winter coat clutched in the same hand, and she was only one chihuahua short of being GCPD’s own Elle Woods.

This office hadn’t seen anything like her, and by the looks of it, she was fully aware of that fact. For a moment, he wondered if she was mocking the precinct in her own way, challenging the drab confines of the facility with something so polished, so perfectly styled. 

His thoughts were cut short by the sound of her clearing her throat, and his eyes snapped back to hers. He realized with sudden embarrassment that she had caught him staring. Worse, she was smirking—her lips shiny and curved in an almost mocking acknowledgment of his mistake.

“Yes,” he said stiffly, clearing his own throat in a failed attempt to reestablish control. “And who might you be?”

“I’m your student, Romy. Romy Winslow.”  Her half-lidded eyes seemed to smolder in the low lighting.

“Student?” Edward repeated, the word coming out more as a question than he intended.

“Yeah,” she nodded. “Like, they told you, right?”

“Of course,” Edward grumbled, scrambling to regain some semblance of authority. He wasn’t used to feeling unprepared, especially not in his own domain.

He did not like when Romy pursed her shiny lips and narrowed her eyes. “You forgot, didn’t you?” she pressed, a teasing lilt to her voice.

Edward’s back straightened, jaw tightening. “You will soon find that I forget nothing, girl,” he quipped. “I’m merely intrigued by your—” he gestured vaguely at her—“appearance. Are you sure your silly little head didn’t get confused? Got lost on your way to a sorority luncheon?”

Romy blinked. She checked her smartwatch, then looked back at him and tilted her head, the innocent confusion in her eyes seeming a little too thoughtful to be genuine. “No… The Greek Meet isn’t until Saturday.”

He frowned.

Oh, she was definitely fucking with him.

Soon, her pink lips pursed in a slight pout, and she glanced down at herself. “Is it too much?”

As she turned to the side, Romy casually modeled her silhouette, the lavender fabric clinging to her form in a way that was both tasteful and tantalizing. The movement drew Edward’s attention, his gaze instinctively tracing her figure. He couldn’t help but follow the curve of her form, from her shoulders that tapered elegantly down to the delicate arch of her spine, and finally to her shapely backside, perfectly showcased by the tailored fit of the dress. He resented that his gaze followed the lines of her legs, made even longer by the gray knee-high, heeled boots she had chosen.  Each line was accentuated with precision.

She caught his eye again, her expression playful yet somehow earnest. “I thought it was just the right amount of business meets pleasure.”

Edward cleared his throat. “Not quite what I was talking about,” he muttered, his gaze darting away in an attempt to collect his thoughts.

“What did you mean then?” Romy asked as she stepped further into the room. She glanced around, her nose wrinkling slightly at the sight of the meticulously stacked boxes of files, outdated monitors, and blinking fluorescent lights. “This is the GCPD Cybercrime Division?” she asked in an offhand manner. “This looks very—” she wriggled her fingers at the general space “—humble.” Though she smiled, it was clear she was struggling to be polite.

“I mean that I did not expect someone so— soft.” He glanced around the area, grimacing at the— as she called it—‘humble’ surroundings. “It is what it is.”

“You mean you didn’t expect a girl?”

“Yes,” he admitted, refusing to dance around it.

“Well,” she said with a shrug, “guess we both had false expectations of the situation, Mr. Nashton.”

Edward felt the frustration building, both at himself and at Romy’s unsettling confidence. “And what exactly did you expect?” he retorted, his eyebrow cocking. “Quantico?”

She smirked, but the movement was subtle, a brief twitch at the corner of her lips. “No.” Her fingers traced over the edge of a dusty computer monitor, her almond-shaped nails—a soft mint green—making the action seem delicate. “But, like,  maybe I expected something a little more contemporary than this, I suppose.”

He bristled at the unintentional insult to his sanctuary of cobbled-together tech that he had spent the better part of a year collecting to upgrade this dump. He found himself oddly off-balance, grappling with the realization that he had expected someone completely different. Someone less refined, more—unpolished. But here she was, her demeanor perfectly maintained in a lavender blazer dress, with the confidence of someone used to catching others off guard.

He did not like it. He did not like how she acted. He did not like how she talked. He did not like what she said. He did not like how she looked. He did not like her.

Edward sat behind his uncluttered desk, arms folded as he leaned back in his creaky chair, eyes narrowing at her. “The GCPD still does not see the full benefit of a cybercrime division,” he said, his voice laced with a bitterness that hinted at more than just professional frustration. He was used to his work being sidelined, his expertise disregarded by those who should know better. Her arrival was yet another inconvenience in a long line of offenses. “These bald apes are content to remain in the twentieth century.”

Trailing closer, she soon sat in a nearby chair, setting her belongings on a table crowded with equipment. “Quite the shame,” she replied, crossing one leg over the other as she settled into the seat he did not offer her to sit in. “I was hoping to gain some valuable expertise before graduating. I wanted to work here in fact.” There’s a glimmer of amusement in her eyes and her voice holds a polite, measured tone.  “My professors said you are brilliant.”

Smug satisfaction settled in his chest. 

“I am.” Edward’s lip curled ever so slightly, and he straightened, giving her a half-lidded look. 

Romy looked at him for a moment before speaking. “They said you were difficult too.”

“Who’s they?’”

“Duncan and Hadley.”

Edward’s eyes narrowed at the mention of his old professors, the faint smugness that had crept into his expression now sharpening into something colder, more cutting. He studied her with a slow, deliberate gaze. This close, he can finally see her eyes—a moss green

“Duncan and Hadley,” he repeated, his tone laced with disdain. “Duncan—let me guess—still regurgitating decades-old theories as if they’re groundbreaking revelations? And Hadley…” He sneered faintly, his lip curling. “Hadley’s what happens when tenure protects the incompetent. Is he still using Windows XP?”

“Unfortunately… They had strong opinions about you as well,” Romy remarked lightly, looking at her nails in an absent minded manner.

“I’m sure they did,” Edward replied smoothly, sitting forward now, his elbows resting on his desk as he leveled her with a pointed look. “Professors like them always do when confronted with someone who doesn’t just color outside their precious lines but redraws the entire picture. Of course, to them, that’s ‘difficult.’”

Her lips quirked at one side and she rested her chin on her hand, watching him with an amused air. “Then it seems I made the right decision to come to you.”

“While it would undoubtedly be an honor for you to work with someone of my genius firsthand,” Edward continued, his voice dripping with confidence as he narrowed his gaze at her, “you won’t stand a chance.”

Romy merely tilted her head, watching him with an expression of calm intrigue, seemingly unbothered by the sharp bite of his words. It unnerved him more than he cared to admit. He wasn’t used to this feeling, least of all in his own space.

“I’m used to people underestimating me, Mr. Nashton.”

“My estimations are always accurate,” he continued, his voice sharper now. He sighed giving her a bored look. “Let’s cut to it, I suppose.” He let one of his hands rest on the desk. “You will only get in my way. I don’t want to waste my time or my breath educating you on something that will likely go in one ear and out the other.” He tapped his fingers against the tabletop in a measured way, his voice cold. “You are to sit, stay, and not move. Don’t touch anything else. You can watch, and maybe, just maybe , you might be graced with a touch of my intellect... One would only be so lucky to have someone of my caliber rub off on them.”

Before Romy responded, there was a slight twitch of her perfectly plucked brow. “... Do you like to rub off on people, Mr. Nashton?”

He blinked, absorbing what she had just said. Rub off, he thought dryly. Clever, very clever. But what really stopped him wasn’t the phrasing; it was the look in her eyes—a knowing, steady gaze that held him longer than it should. There was a flicker of challenge there, of cool confidence, that made him shift in his seat, uncomfortable under the weight of that steady, unflinching stare.

“You know exactly what I mean, girl,” Edward snapped. He fixed Romy with a squint. “I can see you are going to be quite the pain in my ass, aren’t you?”

Romy’s lips twitched as she considered him with sharp eyes. “Oh, no, not at all,” she lilted. “I’m actually trying to make a good impression.”

He watched as she relaxed her slender hands on the arms of the chair, mint green nails clicking once on the wood. Then, when she crossed her legs, it was a slow movement. His attention flicked to her shapely thighs, noting how the lavender hem of her dress raised slightly with the movement. His frown deepened, brows knitting together, and then he looked back at her easy gaze.

“And how do you plan on doing that?” he asked.

Her eyes flicked across his face, and she hummed thoughtfully, obviously thinking about her answer. Then, a slow smirk stretched across her shiny, plush lips, and those young eyes of hers glittered with amusement. She clicked her tongue. “By being quiet, submissive, and obedient…”

Immediately, Edward felt the heat rise, an unbidden flush creeping up his neck and settling under his collar. He resented it, and his jaw tightened in frustration. She leaned back in the chair, her lips curling into that slow, deliberate smirk, and something glittered in her gaze. The subtle bite to her lip—did she even realize she was doing it?—and the way she settled back, so at ease, as if she were testing him, watching to see how he’d react. It was maddening. There was no reason to let a stranger, much less a student, get under his skin.

He kept his tone even, measured. “I have a hard time believing that,” he said with forced calm. “You are already disrupting my workflow by being here. I don’t have the time or interest to indulge anyone’s… antics.”

“Antics?” Romy repeated. “So, like, you assume I’m here to waste your time? That I won’t take this seriously?”

Edward smirked. “Well, if it looks like a duck and talks like a duck,” he chided, not at all masking the disdain in his voice.

Her smile sharpened. “Except when it’s a unicorn,” she simpered, lashes fluttering as she peered at him through half-lidded eyes. “Is that it, Mr. Nashton? Is it because I’m not some acne-riddled, snot-nose, basement incel?” She tilted her head to the side, her long black hair shifting with the movement, and she narrowed her gaze. “Is it because I’m pretty… ?”

The question struck him off balance. He realized he’d been observing every inch of her carefully put-together appearance, struggling to reconcile it with the notion that Commissioner Loeb thought it fit to place her here with him. But Loeb had been unaware of the candidates as well. The disconnect irritated him, the softness of her expression and the sharpness of her words stirring something hot in his chest.

“Listen, little girl,” he sneered, mustering every ounce of cold detachment, “I don’t know what game you’re trying to play, but I’m not the one to challenge.”

Romy’s smile widened, the look in her eyes unmistakably daring. “Oh, I don’t know about that,” she said, letting her voice dip playfully. “You seem like exactly the kind of man to enjoy a good challenge.” She tapped a nail thoughtfully on the wooden chair arm. “Or am I wrong?”

“Challenges are acceptable,” Edward said, his lips twitching as though considering a smile, though his gaze remained guarded. “But only those that actually require intellect. Challenges that flex the mind… not distractions.”

“So, that’s what you see me as? A distraction?” Romy tilted her chin up, looking at him with that gaze that made her look so cool. It only grated on his nerves. “I’ll make sure to cover my shoulders and hide my bra straps then.”

Edward’s eyes narrowed. He opened his mouth to retort, but she was faster, leaning in with a look that was half-sweet, half-mischievous. “Unless, of course…” she purred, “a little distraction is exactly what you need. Maybe it would loosen you up.”

“Loosen up?” he echoed, his voice edged with forced calm. “I don’t need to loosen up. I need focus and productivity, two qualities I have a hard time believing you possess.”

“I have plenty of focus.” She settled back in her chair, unabashedly grinning at his obvious discomfort. “I’m sure we’ll make a… productive team, Mr. Nashton.”

He exhaled slowly, trying to maintain his composure. “You’re insufferably confident, aren’t you?”

“Pot meet kettle,” she replied breezily, gesturing in a casual manner, clearly unbothered by his barbs. “So… are you ready to be impressed, or are we going to keep up the foreplay?”

Edward rolled his eyes then shifted and spun back to his computer. “ Fine,” he said tightly. “You want to prove yourself? Then start by doing exactly what I tell you, without the smart commentary, Ms. Winslow.” He made movements to bring up his work, his fingers tapping away at the keyboard.

She shifted to the side, her eyes gleaming with a playful challenge as she retrieved a sleek laptop from her purse. “Yes, Mr. Nashton, sir.”

His fingers stalled over the keyboard, his usual fluidity momentarily broken. A shiver ran down his spine, slithering low. It made him grit his teeth.

With a deep inhale and an exasperated sigh, he settled into his work, typing with the familiar, precise rhythm he was known for. While he maintained perfect focus, he couldn’t shake the uncomfortable feeling of having someone in his space. He worked alone. He had never had to precept anyone. He was not a teacher. He didn’t have the patience nor the desire for it. Professors had tried setting him up to tutor during his time in college—it hadn’t worked out as they thought it would. It had taken only one time to make someone cry for them to decide teamwork might not be something for him.

He felt it inevitable: Romy would say something completely idiotic; he would correct her; it would hurt her puny little feelings; she would cry; she would quit; and he would never have to hear from her again.

All he had to do was bide his time. He could be patient… when he wanted to be.

But, as much as it stung to admit, Romy surprised him. She was quiet—perfectly quiet, almost too quiet—and she seemed wholly absorbed in what he was doing. It was almost like she didn’t exist.

The minutes stretched, long and quiet, with nothing but the soft hum of computers and the steady beat of typing filling the air. Twenty minutes slipped into thirty, and then an hour, and still, she remained there, intently focused. The steadiness of her gaze as it flickered between her screen, his screen, and his hands—the unwavering attention she devoted to each click, each keystroke—was almost unnerving. There was something in the way she was present, so completely engaged, that felt oddly invasive. And yet, she wasn’t disruptive. She didn’t give any more snarky quips. She didn’t sigh in boredom. She didn’t ask questions or interrupt with idle conversation, simply watching, occasionally typing, the rhythm of her own keystrokes echoing his in a strange, synchronized cadence.

But it was the sound of her nails that really got to him. Each click of the keys under her fingers was punctuated by the sharper snap of those mint-colored acrylics atop them, a sound somehow distinct from the natural clack of a keyboard. It wasn’t irritating—not yet—but he sensed the potential. It was the kind of sound that, over time, could likely chip away at his concentration, like Chinese water torture, each click burrowing into his awareness with grating persistence.

Every now and then, Edward risked a glance at Romy, expecting to catch her on her phone or zoned out, ready to dismiss the task at hand. But she stayed. She was observant, her posture straight, fingers poised and ready, and she took in every word, every glance he spared her, without saying a thing—only a simple nod here and there in respectful acknowledgment. 

The hours slipped by faster than usual, her silence still unbroken. Edward leaned back, cracking his knuckles and flexing his fingers, savoring the temporary reprieve. But as he shifted, his eyes caught movement—Romy, standing right in front of his desk.

He jolted, a sharp intake of breath betraying his surprise. He hadn’t even heard her move.

“ What?” he snapped, his voice tight. “What do you want, girl?”

She blinked, glancing at her watch with maddening calm. “Time to go home.”

It was only then that he noticed the bag slung over her arm and the paper she was holding out. He scowled, snatching it briskly, his lips pulling into a tight, displeased line. A time log. Of course. With a resigned sigh, he grabbed his pen and scribbled his name and initials before shoving it back at her.

She glanced down at the sheet and grimaced. “You have terrible handwriting.”

“Get out,” he gritted, his flat look doing nothing to mask his irritation. He didn’t need her critique on top of everything else.

“Alright. See you tomorrow, Mr. Nashton,” she chuckled, her tone airy, carrying that infuriating undercurrent of amusement, as though his opinion of her couldn’t matter less. Then she spun on her heel and tossed a languid wave over her shoulder, twiddling her mint-colored acrylics.

“Unfortunately.”

Then, the door clicked shut behind her, leaving the office mercifully quiet and empty. Edward leaned back in his chair. Finally, he had his silence. But it wasn’t the victory he’d hoped for.

His gaze flicked toward the empty chair she’d occupied, a faint scowl tugging at the corners of his mouth. This was only the beginning. She’d be back tomorrow, and the day after that, and every Wednesday, Thursday, and Friday after that until the semester ended.

Edward’s jaw tightened at the thought, the weight of it pressing down on him like a slowly closing trap. She wasn’t just a nuisance; she was a disruption, a thorn in his side he couldn’t pull out, no matter how much he wanted.

Fifteen weeks and two days of this. Of her.

With a sharp exhale, he turned back to his monitors, forcing his attention onto the scrolling lines of data. He didn’t have time to dwell on irritations. He had work to do, and she was gone for the day. That was enough.

It would have to be.

[matrixmasterassembly]

A Beginner’s Guide to Data Science: Skills and Tools You Need

Data science is transforming industries, offering tools and insights to solve complex problems. As a beginner, understanding the essential skills and tools can set you on the right path to success.

Key Skills to Start Your Journey To excel in data science, begin with foundational skills like:

Programming Languages: Python and R are essential for data manipulation and analysis.

Data Visualization: Tools like Tableau and matplotlib simplify the presentation of insights.

Machine Learning Basics: Learning algorithms such as regression and clustering is a great starting point.

Tools Every Beginner Should Know Starting with the right tools can make your learning process smoother:

Python and Jupyter Notebooks: Ideal for coding and data visualization.

SQL: Essential for querying and managing data stored in databases.

Tableau: A powerful tool for creating interactive and engaging dashboards.

Explore more about the essential skills and tools for data science beginners on Matrix Masters Assembly.

If you're ready to build your first data science project, learn how data visualization tools like Tableau and coding in Python can simplify complex data analysis. Discover practical steps to get started at Matrix Masters Assembly’s comprehensive guide.

[uthra-krish]

Exploring Data Science Tools: My Adventures with Python, R, and More

Welcome to my data science journey! In this blog post, I'm excited to take you on a captivating adventure through the world of data science tools. We'll explore the significance of choosing the right tools and how they've shaped my path in this thrilling field.

Choosing the right tools in data science is akin to a chef selecting the finest ingredients for a culinary masterpiece. Each tool has its unique flavor and purpose, and understanding their nuances is key to becoming a proficient data scientist.

I. The Quest for the Right Tool

My journey began with confusion and curiosity. The world of data science tools was vast and intimidating. I questioned which programming language would be my trusted companion on this expedition. The importance of selecting the right tool soon became evident.

I embarked on a research quest, delving deep into the features and capabilities of various tools. Python and R emerged as the frontrunners, each with its strengths and applications. These two contenders became the focus of my data science adventures.

II. Python: The Swiss Army Knife of Data Science

Python, often hailed as the Swiss Army Knife of data science, stood out for its versatility and widespread popularity. Its extensive library ecosystem, including NumPy for numerical computing, pandas for data manipulation, and Matplotlib for data visualization, made it a compelling choice.

My first experiences with Python were both thrilling and challenging. I dove into coding, faced syntax errors, and wrestled with data structures. But with each obstacle, I discovered new capabilities and expanded my skill set.

III. R: The Statistical Powerhouse

In the world of statistics, R shines as a powerhouse. Its statistical packages like dplyr for data manipulation and ggplot2 for data visualization are renowned for their efficacy. As I ventured into R, I found myself immersed in a world of statistical analysis and data exploration.

My journey with R included memorable encounters with data sets, where I unearthed hidden insights and crafted beautiful visualizations. The statistical prowess of R truly left an indelible mark on my data science adventure.

IV. Beyond Python and R: Exploring Specialized Tools

While Python and R were my primary companions, I couldn't resist exploring specialized tools and programming languages that catered to specific niches in data science. These tools offered unique features and advantages that added depth to my skill set.

For instance, tools like SQL allowed me to delve into database management and querying, while Scala opened doors to big data analytics. Each tool found its place in my toolkit, serving as a valuable asset in different scenarios.

V. The Learning Curve: Challenges and Rewards

The path I took wasn't without its share of difficulties. Learning Python, R, and specialized tools presented a steep learning curve. Debugging code, grasping complex algorithms, and troubleshooting errors were all part of the process.

However, these challenges brought about incredible rewards. With persistence and dedication, I overcame obstacles, gained a profound understanding of data science, and felt a growing sense of achievement and empowerment.

VI. Leveraging Python and R Together

One of the most exciting revelations in my journey was discovering the synergy between Python and R. These two languages, once considered competitors, complemented each other beautifully.

I began integrating Python and R seamlessly into my data science workflow. Python's data manipulation capabilities combined with R's statistical prowess proved to be a winning combination. Together, they enabled me to tackle diverse data science tasks effectively.

VII. Tips for Beginners

For fellow data science enthusiasts beginning their own journeys, I offer some valuable tips:

Embrace curiosity and stay open to learning.

Work on practical projects while engaging in frequent coding practice.

Explore data science courses and resources to enhance your skills.

Seek guidance from mentors and engage with the data science community.

Remember that the journey is continuous—there's always more to learn and discover.

My adventures with Python, R, and various data science tools have been transformative. I've learned that choosing the right tool for the job is crucial, but versatility and adaptability are equally important traits for a data scientist.

As I summarize my expedition, I emphasize the significance of selecting tools that align with your project requirements and objectives. Each tool has a unique role to play, and mastering them unlocks endless possibilities in the world of data science.

I encourage you to embark on your own tool exploration journey in data science. Embrace the challenges, relish the rewards, and remember that the adventure is ongoing. May your path in data science be as exhilarating and fulfilling as mine has been.

Happy data exploring!

[govindhtech]

Gemini Code Assist Enterprise: AI App Development Tool

Introducing Gemini Code Assist Enterprise’s AI-powered app development tool that allows for code customisation.

The modern economy is driven by software development. Unfortunately, due to a lack of skilled developers, a growing number of integrations, vendors, and abstraction levels, developing effective apps across the tech stack is difficult.

To expedite application delivery and stay competitive, IT leaders must provide their teams with AI-powered solutions that assist developers in navigating complexity.

Google Cloud thinks that offering an AI-powered application development solution that works across the tech stack, along with enterprise-grade security guarantees, better contextual suggestions, and cloud integrations that let developers work more quickly and versatile with a wider range of services, is the best way to address development challenges.

Google Cloud is presenting Gemini Code Assist Enterprise, the next generation of application development capabilities.

Beyond AI-powered coding aid in the IDE, Gemini Code Assist Enterprise goes. This is application development support at the corporate level. Gemini’s huge token context window supports deep local codebase awareness. You can use a wide context window to consider the details of your local codebase and ongoing development session, allowing you to generate or transform code that is better appropriate for your application.

With code customization, Code Assist Enterprise not only comprehends your local codebase but also provides code recommendations based on internal libraries and best practices within your company. As a result, Code Assist can produce personalized code recommendations that are more precise and pertinent to your company. In addition to finishing difficult activities like updating the Java version across a whole repository, developers can remain in the flow state for longer and provide more insights directly to their IDEs. Because of this, developers can concentrate on coming up with original solutions to problems, which increases job satisfaction and gives them a competitive advantage. You can also come to market more quickly.

GitLab.com and GitHub.com repos can be indexed by Gemini Code Assist Enterprise code customisation; support for self-hosted, on-premise repos and other source control systems will be added in early 2025.

Yet IDEs are not the only tool used to construct apps. It integrates coding support into all of Google Cloud’s services to help specialist coders become more adaptable builders. The time required to transition to new technologies is significantly decreased by a code assistant, which also integrates the subtleties of an organization’s coding standards into its recommendations. Therefore, the faster your builders can create and deliver applications, the more services it impacts. To meet developers where they are, Code Assist Enterprise provides coding assistance in Firebase, Databases, BigQuery, Colab Enterprise, Apigee, and Application Integration. Furthermore, each Gemini Code Assist Enterprise user can access these products’ features; they are not separate purchases.

Gemini Code Support BigQuery enterprise users can benefit from SQL and Python code support. With the creation of pre-validated, ready-to-run queries (data insights) and a natural language-based interface for data exploration, curation, wrangling, analysis, and visualization (data canvas), they can enhance their data journeys beyond editor-based code assistance and speed up their analytics workflows.

Furthermore, Code Assist Enterprise does not use the proprietary data from your firm to train the Gemini model, since security and privacy are of utmost importance to any business. Source code that is kept separate from each customer’s organization and kept for usage in code customization is kept in a Google Cloud-managed project. Clients are in complete control of which source repositories to utilize for customization, and they can delete all data at any moment.

Your company and data are safeguarded by Google Cloud’s dedication to enterprise preparedness, data governance, and security. This is demonstrated by projects like software supply chain security, Mandiant research, and purpose-built infrastructure, as well as by generative AI indemnification.

Google Cloud provides you with the greatest tools for AI coding support so that your engineers may work happily and effectively. The market is also paying attention. Because of its ability to execute and completeness of vision, Google Cloud has been ranked as a Leader in the Gartner Magic Quadrant for AI Code Assistants for 2024.

Gemini Code Assist Enterprise Costs

In general, Gemini Code Assist Enterprise costs $45 per month per user; however, a one-year membership that ends on March 31, 2025, will only cost $19 per month per user.

Read more on Govindhtech.com

[olibr08]

Unlock Success: MySQL Interview Questions with Olibr

Introduction

Preparing for a MySQL interview requires a deep understanding of database concepts, SQL queries, optimization techniques, and best practices. Olibr’s experts provide insightful answers to common mysql interview questions, helping candidates showcase their expertise and excel in MySQL interviews.

1. What is MySQL, and how does it differ from other database management systems?

Olibr’s Expert Answer: MySQL is an open-source relational database management system (RDBMS) that uses SQL (Structured Query Language) for managing and manipulating databases. It differs from other DBMS platforms in its open-source nature, scalability, performance optimizations, and extensive community support.

2. Explain the difference between InnoDB and MyISAM storage engines in MySQL.

Olibr’s Expert Answer: InnoDB and MyISAM are two commonly used storage engines in MySQL. InnoDB is transactional and ACID-compliant, supporting features like foreign keys, row-level locking, and crash recovery. MyISAM, on the other hand, is non-transactional, faster for read-heavy workloads, but lacks features such as foreign keys and crash recovery.

3. What are indexes in MySQL, and how do they improve query performance?

Olibr’s Expert Answer: Indexes are data structures that improve query performance by allowing faster retrieval of rows based on indexed columns. They reduce the number of rows MySQL must examine when executing queries, speeding up data retrieval operations, and optimizing database performance.

4. Explain the difference between INNER JOIN and LEFT JOIN in MySQL.

Olibr’s Expert Answer: INNER JOIN and LEFT JOIN are SQL join types used to retrieve data from multiple tables. INNER JOIN returns rows where there is a match in both tables based on the join condition. LEFT JOIN returns all rows from the left table and matching rows from the right table, with NULL values for non-matching rows in the right table.

5. What are the advantages of using stored procedures in MySQL?

Olibr’s Expert Answer: Stored procedures in MySQL offer several advantages, including improved performance due to reduced network traffic, enhanced security by encapsulating SQL logic, code reusability across applications, easier maintenance and updates, and centralized database logic execution.

Conclusion

By mastering these MySQL interview questions and understanding Olibr’s expert answers, candidates can demonstrate their proficiency in MySQL database management, query optimization, and best practices during interviews. Olibr’s insights provide valuable guidance for preparing effectively, showcasing skills, and unlocking success in MySQL-related roles.

[amparol12]

Unveiling Expertise: An Interview with Dr. Elon, a Database Homework Help Maestro (+9 Years of Experience)

Welcome to the insightful world of database management! In our exclusive interview today, we bring you face-to-face with Dr. Elon, a seasoned expert in the realm of database homework help, boasting an impressive track record of over nine years. Dr. Elon is not just a name; it's a synonym for proficiency and passion in navigating the intricate landscapes of SQL homework assistance.

Q1: Dr. Elon, could you share a bit about your journey and what inspired you to delve into the world of database homework help?

Dr. Elon: Absolutely. My journey began with a profound interest in database systems during my academic years. As I delved deeper, I realized the challenges students faced in grasping SQL concepts. This realization ignited my passion for aiding them in mastering the art of database management.

Q2: With over nine years of experience, you've witnessed the evolution of database technology. How has this experience shaped your approach to helping students with SQL homework?

Dr. Elon: It's been an exciting ride. Over the years, I've seen technology leap forward. This experience has allowed me to adapt my teaching methods, ensuring students are not just acquainted with the fundamentals but are also well-prepared for the dynamic, real-world applications of SQL.

Q3: "Help with SQL homework" is a common plea from students. What, in your opinion, makes SQL assignments particularly challenging, and how do you assist students in overcoming these challenges?

Dr. Elon: SQL assignments often involve intricate queries and a deep understanding of database design. The challenge lies in translating theoretical knowledge into practical application. I guide students through this process, emphasizing hands-on practice and offering step-by-step assistance tailored to their learning pace.

Q4: Can you share an experience where you witnessed a student's "aha" moment while working on their SQL homework, and what strategies do you employ to foster such breakthroughs?

Dr. Elon: Oh, absolutely. There was a student struggling with a complex JOIN operation. Through patient guidance and breaking down the problem into manageable parts, the student suddenly grasped the concept. It's about building confidence through small victories and celebrating those "aha" moments.

Q5: The field of database management is ever-evolving. How do you ensure your assistance aligns with the latest trends and technologies, keeping students ahead of the curve?

Dr. Elon: Continuous learning is at the core of my approach. I stay abreast of the latest industry trends, incorporating relevant updates into my teaching materials. This ensures that students not only understand the foundational principles but are also prepared for the current demands of the field.

Conclusion:

Our interview with Dr. Elon, a stalwart in database homework help, has unveiled a wealth of experience and insights. From inspiring journeys to overcoming SQL challenges, Dr. Elon's expertise has proven instrumental in guiding students toward mastering the intricacies of database management. If you find yourself uttering the words "help with SQL homework," rest assured, Dr. Elon is the beacon of knowledge you've been seeking on your academic journey.

[vivekavicky12]

The Ever-Evolving Canvas of Data Science: A Comprehensive Guide

In the ever-evolving landscape of data science, the journey begins with unraveling the intricate threads that weave through vast datasets. This multidisciplinary field encompasses a diverse array of topics designed to empower professionals to extract meaningful insights from the wealth of available data. Choosing the  Top Data Science Institute can further accelerate your journey into this thriving industry. This educational journey is a fascinating exploration of the multifaceted facets that constitute the heart of data science education.

Let's embark on a comprehensive exploration of what one typically studies in the realm of data science.

1. Mathematics and Statistics Fundamentals: Building the Foundation

At the core of data science lies a robust understanding of mathematical and statistical principles. Professionals delve into Linear Algebra, equipping themselves with the knowledge of mathematical structures and operations crucial for manipulating and transforming data. Simultaneously, they explore Probability and Statistics, mastering concepts that are instrumental in analyzing and interpreting data patterns.

2. Programming Proficiency: The Power of Code

Programming proficiency is a cornerstone skill in data science. Learners are encouraged to acquire mastery in programming languages such as Python or R. These languages serve as powerful tools for implementing complex data science algorithms and are renowned for their versatility and extensive libraries designed specifically for data science applications.

3. Data Cleaning and Preprocessing Techniques: Refining the Raw Material

Data rarely comes in a pristine state. Hence, understanding techniques for Handling Missing Data becomes imperative. Professionals delve into strategies for managing and imputing missing data, ensuring accuracy in subsequent analyses. Additionally, they explore Normalization and Transformation techniques, preparing datasets through standardization and transformation of variables.

4. Exploratory Data Analysis (EDA): Unveiling Data Patterns

Exploratory Data Analysis (EDA) is a pivotal aspect of the data science journey. Professionals leverage Visualization Tools like Matplotlib and Seaborn to create insightful graphical representations of data. Simultaneously, they employ Descriptive Statistics to summarize and interpret data distributions, gaining crucial insights into the underlying patterns.

5. Machine Learning Algorithms: Decoding the Secrets

Machine Learning is a cornerstone of data science, encompassing both supervised and unsupervised learning. Professionals delve into Supervised Learning, which includes algorithms for tasks such as regression and classification. Additionally, they explore Unsupervised Learning, delving into clustering and dimensionality reduction for uncovering hidden patterns within datasets.

6. Real-world Application and Ethical Considerations: Bridging Theory and Practice

The application of data science extends beyond theoretical knowledge to real-world problem-solving. Professionals learn to apply data science techniques to practical scenarios, making informed decisions based on empirical evidence. Furthermore, they navigate the ethical landscape, considering the implications of data usage on privacy and societal values.

7. Big Data Technologies: Navigating the Sea of Data

With the exponential growth of data, professionals delve into big data technologies. They acquaint themselves with tools like Hadoop and Spark, designed for processing and analyzing massive datasets efficiently.

8. Database Management: Organizing the Data Universe

Professionals gain proficiency in database management, encompassing both SQL and NoSQL databases. This skill set enables them to manage and query databases effectively, ensuring seamless data retrieval.

9. Advanced Topics: Pushing the Boundaries

As professionals progress, they explore advanced topics that push the boundaries of data science. Deep Learning introduces neural networks for intricate pattern recognition, while Natural Language Processing (NLP) focuses on analyzing and interpreting human language data.

10. Continuous Learning and Adaptation: Embracing the Data Revolution

Data science is a field in constant flux. Professionals embrace a mindset of continuous learning, staying updated on evolving technologies and methodologies. This proactive approach ensures they remain at the forefront of the data revolution.

In conclusion, the study of data science is a dynamic and multifaceted journey. By mastering mathematical foundations, programming languages, and ethical considerations, professionals unlock the potential of data, making data-driven decisions that impact industries across the spectrum. The comprehensive exploration of these diverse topics equips individuals with the skills needed to thrive in the dynamic world of data science. Choosing the best Data Science Courses in Chennai is a crucial step in acquiring the necessary expertise for a successful career in the evolving landscape of data science.

[rosieblogstuff]

This sounds a lot like baseless speculation, probably due to a lack of knowledge about how practical and possible it likely is to actually remove content from a data stream regardless of who reblogs it.

All reblog chains maintain a record of where a post originated. That's easy to see because original post's blog name is right there then you look at a reblog. It's not hard to strip all content from all blogs with certain settings out of a data dump.

This is basic basic basic database query stuff. I'm not a software engineer, I'm a technical writer, but even I could probably right a good enough SQL query to accomplish this feat. I'm absolutely certain the actual engineers who run Tumblr can easily accomplish this.

PSA TO ANYONE WHO DOESNT EVEN MAKE ART, DISABLE AI DATA COLLECTION ON YOUR BLOG

ART REBLOGGED TO AN ACCOUNT WITH THIS ENABLED WILL ALLOW MIDJOURNEY TO USE THE ORIGINAL POSTERS WORKS WITHOUT THEIR CONSENT

[pentesttestingcorp]

SQL Injection in RESTful APIs: Identify and Prevent Vulnerabilities

SQL Injection (SQLi) in RESTful APIs: What You Need to Know

RESTful APIs are crucial for modern applications, enabling seamless communication between systems. However, this convenience comes with risks, one of the most common being SQL Injection (SQLi). In this blog, we’ll explore what SQLi is, its impact on APIs, and how to prevent it, complete with a practical coding example to bolster your understanding.

What Is SQL Injection?

SQL Injection is a cyberattack where an attacker injects malicious SQL statements into input fields, exploiting vulnerabilities in an application's database query execution. When it comes to RESTful APIs, SQLi typically targets endpoints that interact with databases.

How Does SQL Injection Affect RESTful APIs?

RESTful APIs are often exposed to public networks, making them prime targets. Attackers exploit insecure endpoints to:

Access or manipulate sensitive data.

Delete or corrupt databases.

Bypass authentication mechanisms.

Example of a Vulnerable API Endpoint

Consider an API endpoint for retrieving user details based on their ID:

from flask import Flask, request import sqlite3

app = Flask(name)

@app.route('/user', methods=['GET']) def get_user(): user_id = request.args.get('id') conn = sqlite3.connect('database.db') cursor = conn.cursor() query = f"SELECT * FROM users WHERE id = {user_id}" # Vulnerable to SQLi cursor.execute(query) result = cursor.fetchone() return {'user': result}, 200

if name == 'main': app.run(debug=True)

Here, the endpoint directly embeds user input (user_id) into the SQL query without validation, making it vulnerable to SQL Injection.

Secure API Endpoint Against SQLi

To prevent SQLi, always use parameterized queries:

@app.route('/user', methods=['GET']) def get_user(): user_id = request.args.get('id') conn = sqlite3.connect('database.db') cursor = conn.cursor() query = "SELECT * FROM users WHERE id = ?" cursor.execute(query, (user_id,)) result = cursor.fetchone() return {'user': result}, 200

In this approach, the user input is sanitized, eliminating the risk of malicious SQL execution.

How Our Free Tool Can Help

Our free Website Security Checker your web application for vulnerabilities, including SQL Injection risks. Below is a screenshot of the tool's homepage:

Upload your website details to receive a comprehensive vulnerability assessment report, as shown below:

These tools help identify potential weaknesses in your APIs and provide actionable insights to secure your system.

Preventing SQLi in RESTful APIs

Here are some tips to secure your APIs:

Use Prepared Statements: Always parameterize your queries.

Implement Input Validation: Sanitize and validate user input.

Regularly Test Your APIs: Use tools like ours to detect vulnerabilities.

Least Privilege Principle: Restrict database permissions to minimize potential damage.

Final Thoughts

SQL Injection is a pervasive threat, especially in RESTful APIs. By understanding the vulnerabilities and implementing best practices, you can significantly reduce the risks. Leverage tools like our free Website Security Checker to stay ahead of potential threats and secure your systems effectively.

Explore our tool now for a quick Website Security Check.