#Data cleaning techniques

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Effective Data Cleaning: Essential Techniques for Data Hygiene

Data cleaning is a crucial part of any data analysis process. Ensuring that your data is accurate, consistent, and reliable can significantly impact the quality of insights drawn from it. Without proper data cleaning, your analysis could lead to faulty conclusions and potentially costly errors. In this article, we will explore key data cleaning techniques and offer practical steps on how to clean data effectively.

The first step in maintaining good data hygiene is identifying inconsistencies and errors in the dataset. This includes handling missing values, incorrect formats, and duplicate records. For instance, incomplete entries can skew analysis, so it’s important to either remove or fill in these gaps. Using imputation methods, where feasible, allows data scientists to estimate missing values based on other data points.

One of the most fundamental data cleaning tasks is removing duplicate entries. Duplicate data can lead to inflated analysis results, especially when working with large datasets. Software tools and scripts can easily detect and eliminate duplicate records, ensuring that the dataset remains as concise and accurate as possible.

Another key technique involves standardizing data formats. For example, dates or addresses might be formatted differently across records, which can confuse any analytical models or systems. By setting standard formats, you ensure that all data is uniform, making it easier to analyze and process efficiently.

Data cleaning techniques also involve correcting typos and errors in categorical data. A common example is the inconsistency of labels or values within a column, which can distort analysis. By standardizing the values and correcting misspellings, you can enhance the reliability of your dataset. Automation tools can help identify common errors and fix them systematically.

Once the data has been cleaned, it's important to ensure its integrity over time. Implementing regular checks for data hygiene can help maintain clean datasets and prevent issues from cropping up in future analyses. For instance, periodic reviews of new data inputs and a strong data governance framework can keep your data pristine and ready for use.

When considering how to clean data, it’s crucial to use a combination of manual and automated processes. For basic tasks like identifying and removing duplicates, automated scripts can save considerable time. However, some aspects of data cleaning, such as detecting outliers or interpreting contextual inconsistencies, may still require a human touch. Combining both approaches ensures the best possible results.

In conclusion, data cleaning is a vital part of preparing your data for analysis. By applying effective data cleaning techniques, ensuring regular data hygiene, and knowing how to clean data efficiently, you can ensure the integrity of your datasets and generate more accurate insights from your data. Regular cleaning practices will ultimately lead to better decision-making and more reliable outcomes.

[greatonlinetrainingsposts]

How Do You Use a SAS Tutorial to Learn Data Cleaning Techniques?

Before you start analyzing data, it's important to understand how clean your dataset is. If your data has missing values, duplicate entries, or inconsistent formatting, it can throw off your entire analysis. Even the most advanced model won’t work well if the data going into it is flawed.

That’s where SAS programming comes in. When you follow a SAS tutorial, you’re not just learning how to write code—you’re learning how to think through data problems. A good tutorial explains what each step does and why it’s important.

Here’s how to use a SAS tutorial to build your data cleaning skills, step by step.

1. Start by Inspecting the Data

The first thing most SAS tutorials will show you is how to explore and inspect your dataset. This helps you understand what you’re working with.

You’ll learn how to use:

PROC CONTENTS to see the structure and metadata

PROC PRINT to view the raw data

PROC FREQ and PROC MEANS to check distributions and summaries

As you review the outputs, you’ll start spotting common problems like:

Too many missing values in key variables

Numbers stored as text

Values that don’t make sense or fall outside expected ranges

These early steps help you catch red flags before you go deeper.

2. Learn How to Handle Missing Data

Missing data is very common, and a good SAS tutorial will show you a few ways to deal with it.

This includes:

Using IF conditions to identify missing values

Replacing them with zeros, averages, or medians

Removing variables or rows if they’re not useful anymore

The tutorial might also explain when to fill in missing data and when to just leave it out. Real-world examples from healthcare, marketing, or finance help make the decisions easier to understand.

3. Standardize and Format Your Data

A lot of data comes in messy. For example, dates might be stored in different formats or categories might use inconsistent labels like "M", "Male", and "male".

With SAS programming, you can clean this up by:

Converting dates using INPUT and PUT functions

Making text consistent with UPCASE or LOWCASE

Recoding values into standardized categories

Getting your formatting right helps make sure your data is grouped and analyzed correctly.

4. Remove Duplicate Records

Duplicate records can mess up your summaries and analysis. SAS tutorials usually explain how to find and remove duplicates using:

PROC SORT with the NODUPKEY option

BY group logic to keep the most recent or most relevant entry

Once you understand the concept in a tutorial, you’ll be able to apply it to more complex datasets with confidence.

5. Identify Outliers and Inconsistencies

Advanced tutorials often go beyond basic cleaning and help you detect outliers—data points that are far from the rest.

You’ll learn techniques like:

Plotting your data with PROC SGPLOT

Using PROC UNIVARIATE to spot unusual values

Writing logic to flag or filter out problem records

SAS makes this process easier, especially when dealing with large datasets.

6. Validate Your Cleaning Process

Cleaning your data isn’t complete until you check your work. Tutorials often show how to:

Re-run summary procedures like PROC MEANS or PROC FREQ

Compare row counts before and after cleaning

Save versions of your dataset along the way so nothing gets lost

This step helps prevent mistakes and makes sure your clean dataset is ready for analysis.

Why SAS Programming Helps You Learn Faster

SAS is great for learning data cleaning because:

The syntax is simple and easy to understand

The procedures are powerful and built-in

The SAS community is active and supportive

Whether you're a beginner or trying to improve your skills, SAS tutorials offer a strong, step-by-step path to learning how to clean data properly.

Final Thoughts

Learning data cleaning through a SAS tutorial doesn’t just teach you code—it trains you to think like a data analyst. As you go through each lesson, try applying the same steps to a dataset you’re working with. The more hands-on experience you get, the more confident you’ll be.

If you want to improve your data analysis and make better decisions, start by getting your data clean. And using SAS programming to do it? That’s a smart first move.

[matchdatapro]

Data Cleaning Techniques: Ensuring Accuracy and Quality in Your Data

In today's data-driven world, the accuracy and quality of data are crucial for informed decision-making, business insights, and operational efficiency. However, raw data is often incomplete, inconsistent, and riddled with errors. This is where data cleaning comes into play. Data cleaning, also known as data cleansing or scrubbing, involves identifying and rectifying errors, inaccuracies, and inconsistencies in data. Employing effective data cleaning techniques can significantly enhance the reliability of your datasets. In this article, we'll explore some of the most widely used data cleaning techniques that ensure data accuracy and quality.

Removing Duplicate Data

Duplicate records are a common issue in large datasets, and they can lead to misleading results or skewed analyses. Removing duplicate entries ensures that each record in the dataset is unique and accurate.

Technique:

Use tools or database queries to identify and remove repeated rows or entries.

Cross-check fields to confirm if multiple records are truly duplicates or unique cases.

Handling Missing Data

Missing data is another frequent problem in datasets. Leaving these gaps can cause issues during analysis, but simply deleting them may lead to loss of valuable information. Various techniques can be used to address missing data, depending on the nature of the dataset.

Techniques:

Imputation: Replace missing values with estimated values, such as the mean, median, or mode of a column.

Deletion: Remove rows with missing data if the impact on the dataset is minimal.

Flagging: Mark missing data points for further investigation or review.

Standardizing Data Formats

Inconsistent formats can create confusion and errors in data analysis. This issue often arises in fields like dates, addresses, or currency values, where variations in how information is entered may lead to inconsistencies.

Techniques:

Convert dates to a single, standardized format (e.g., YYYY-MM-DD).

Standardize text fields by ensuring proper capitalization, spelling, and abbreviations.

Normalize numeric fields to ensure uniformity in units of measurement.

Addressing Outliers

Outliers are data points that deviate significantly from the rest of the dataset. They can distort analytical models or hide meaningful patterns. Identifying and addressing these outliers is an essential part of data cleaning.

Techniques:

Z-score Method: Calculate how many standard deviations a data point is from the mean. Points beyond a set threshold can be flagged as outliers.

Box Plot Method: Use box plots to visually identify and remove extreme outliers.

Truncation: Replace extreme outliers with maximum or minimum threshold values that are within a reasonable range.

Validating Data Accuracy

Ensuring data accuracy means cross-checking information to ensure that the data in the dataset is correct and corresponds to the source or truth.

Techniques:

Cross-referencing with External Sources: Verify your data against reliable external databases, such as customer records, government databases, or industry benchmarks.

Consistency Checks: Run validation rules to identify any data that doesn’t match the expected format, values, or structure.

Data Normalization

Normalization is a technique used to reduce redundancy and improve data integrity. It involves organizing data into a structured format, ensuring that relationships between data points are properly represented.

Techniques:

Convert data into a consistent scale, such as percentages or ratios, to simplify comparisons.

Ensure consistent naming conventions across related fields, such as customer names or product codes.

Filtering Unwanted Data

Unwanted data refers to irrelevant or outdated records that do not contribute to the analysis or decision-making process. Removing this data helps in maintaining the dataset's relevance and quality.

Techniques:

Apply filters to remove data that falls outside of the necessary timeframes or categories.

Archive old data that is no longer needed but might be required for future reference.

Conclusion

Data cleaning is a critical step in preparing datasets for accurate and insightful analysis. By using techniques like removing duplicates, handling missing data, standardizing formats, addressing outliers, validating accuracy, normalizing data, and filtering unwanted information, you can ensure that your data is of the highest quality. Clean data leads to better decisions, more accurate analytics, and enhanced business performance.

For more info visit here:- data cleanup tools

[cybersuccesss]

Elevate Your Data Science Skills with Data Science Course in Pune

Success in the rapidly evolving field of data science hinges on one key factor: quality data. Before diving into more complex machine learning algorithms and detailed analysis, starting with a clean data set is important. At The Cyber Success Institute, our Data Science Course in Pune emphasizes mastering these core skills, equipping you with the expertise to handle data efficiently and drive impactful results. These basic data cleaning steps, known as data wrangling and preprocessing, are necessary to process raw data in sophisticated ways that support accurate analysis and prediction to hone these basic skills to process data thoroughly and prepare amazing results A resource that gives you essential knowledge.

Transform Your Career with The Best Data Science Course at Cyber Success

Data wrangling, or data manging, is the process of transforming and processing raw data from its often messy origin into a more usable form. This process involves preparing, organizing, and enhancing data to make it more valuable for analysis and modeling. Preprocessing, which is less controversial, focuses primarily on preparing data for machine learning models to normalize, transform, and scale them to improve performance

At the Cyber ​​Success Institute, we understand that strong data disputes are the cornerstone of any data science project. Our Data Science Course in Pune offers hands-on training in data wrangling and pre-processing, enabling you to effectively transform raw data into actionable insights.

Discover Data Cleaning Excellence with The Best Data Science Course at Cyber Success

The data management process involves preparing, organizing, and enhancing the data to make it more valuable for analysis and modeling. Less controversial preprocessing focuses on data preparation for machine learning models to ensure performance data quality will directly affect the accuracy and reliability of machine learning models The information is well suited and ensures insights are accurate and useful. This helps to identify hidden patterns and saves time during sample development and subsequent analysis. At Cyber ​​Success Institute, we focus on the importance of data security requirements so we prepare you and your employees to ensure that your data is always up to date. Our Data Science Course in Pune offers hands-on training in data wrangling and pre-processing, enabling you to effectively transform raw data into actionable insights. Basic Steps in Data Management and Preprocessing,

Data cleaning: This first and most important step includes handling missing values, eliminating inconsistencies, and eliminating redundant data points. Effective data cleaning ensures that the dataset is reliable, it is accurate and ready for analysis.

Data conversion: Once prepared, the data must be converted to usable form. This may involve converting categorical variables into numeric ones using techniques such as one-hot encoding or label encoding. Normalization and standardization are used to ensure that all factors contribute to the equality of the model, with no feature dominating due to scale differences make sure you are prepared to handle a variety of data environments.

Feature Engineering: Feature engineering is the process of creating new features from existing data to better capture underlying patterns. This may involve forming interactive phrases, setting attributes, or decomposing timestamps into more meaningful objects such as "day of the week" or "hour of the day".

Data reduction: Sometimes data sets can have too many or too many dimensions, which can lead to overqualification or computational costs. Data reduction techniques such as principal component analysis (PCA), feature selection, and dimensionality reduction are essential to simplify data sets while preserving valuable information Our Data Science Classes in Pune with Placement at the Cyber ​​Success Institute provide valuable experience in data reduction techniques to help you manage large data sets effectively.

Data integration and consolidation: Often, data from multiple sources must be combined to obtain complete data. Data integration involves combining data from databases or files into a combined data set. In our Data Science Course in Pune, you will learn how to combine different types of data to improve and increase the relevance and depth of research.

Why Choose Cyber Success Institute for Data Science Course in Pune?

The Cyber ​​Success Institute is the best IT training institute in Pune, India, offering the best data science course in Pune with Placement assistance, designed to give you a deep understanding of data science from data collection to preprocessing to advanced machine learning. With hands-on experience, expert guidance and a curriculum that is up to date with the latest industry trends, you will be ready to become a data scientist

Here are the highlights of the data science course at Cyber ​​Success Institute, Pune:

Experienced Trainers: Our data science expert trainers bring a wealth of experience in the field of data science, including advanced degrees, industry certifications, strong backgrounds in data analytics, machine learning, AI, and hands-on experience in real-world projects to ensure students learn Entrepreneurs who understand business needs.

Advanced Curriculum: Our Data Science Course in Pune is well structured to cover basic and advanced topics in data science, including Python programming, statistics, data visualization, machine learning, deep learning natural language processing and big data technology.

Free Aptitude Sessions: We believe that strong analytical and problem-solving skills are essential in data science. To support technical training, we offer free aptitude sessions that focus on developing logical reasoning, statistical analysis and critical thinking.

Weekly Mock Interview Sessions: To prepare you for the job, we conduct weekly mock interview sessions that simulate real-world interview situations. These sessions include technical quizzes on data science concepts, coding problems, and behavioral quizzes to build student confidence and improve interview performance.

Hands-on Learning: Our Data Science Course in Pune emphasizes practical, hands-on learning. You will work on real-world projects, data manipulation, machine learning model development, and applications using tools such as Python and Tableau. This approach ensures a deep and practical understanding of data science, preparing them for real job challenges.

100% Placement Assistance: We provide comprehensive placement assistance to help you start your career in data science. This includes writing a resume, preparing for an interview, and connecting with potential employers.

At Cyber Success, our Data Science Course in Pune ensures that students receive a well-rounded education that combines theoretical knowledge with practical experience. We are committed to helping our students become skilled, confident and career-ready data scientists.

Conclusion:

Data management and preprocessing are the unsung heroes of data science, transforming raw data into powerful insights that shape the future. At Cyber Success Institute, our Data Science Course in Pune will teach you the technical skills and it will empower you to lead the data revolution. With immersive, hands-on training, real-world projects, and mentorship from industry experts, we prepare you to harness data’s full potential and drive meaningful impact. Joining Cyber Success Institute, it’s about becoming part of a community committed to excellence and innovation. Start your journey here, master the art of data science with our Data Science Course in Pune, and become a change-maker in this rapidly growing field. Elevate your career, lead with data, and let Cyber Success Institute be your launchpad to success. Your future in data science starts now!

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[mitsde123]

A Beginner’s Guide to Data Cleaning Techniques

Data is the lifeblood of any modern organization. However, raw data is rarely ready for analysis. Before it can be used for insights, data must be cleaned, refined, and structured—a process known as data cleaning. This blog will explore essential data-cleaning techniques, why they are important, and how beginners can master them.

[pitlanepeach]

Radio Silence | Chapter Forty

Lando Norris x Amelia Brown (OFC)

Series Masterlist

Summary — Order is everything. Her habits aren’t quirks, they’re survival techniques. And only three people in the world have permission to touch her: Mom, Dad, Fernando.

Then Lando Norris happens.

One moment. One line crossed. No going back.

Warnings — Autistic!OFC, pregnancy, strong language, slight smut, a bit of general anxiety.

Notes — Welcome to Miami!!!!!

2024 (Miami—Imola)

The McLaren garage was quiet in that early-morning lull before the chaos. Screens still black. Tyres covered. Mechanics nursing coffees and stretching into the day. Amelia stood just inside the halo of overhead lights, hands on her hips, watching her car, her car, come alive in pieces.

The floor gleamed with fresh resin. The side-pods were lean, smooth, seamless in their curvature. The front wing was finally the right spec; the airflow data had confirmed it. The new floor geometry played nicer with the updated rear suspension. The whole package, finally cohesive.

It had taken months of pushing. Quiet conversations. Brutal ones. Drawings on the back of napkins, pacing in her kitchen at 2am. And it was all here now, carbon and copper and logic made real.

She didn’t say anything at first. Just circled the car slowly, one hand brushing against the wing mirror, the leading edge of the nose, the curve of the intake. Reverent, almost.

Tom stood a few feet back, sipping from a thermal mug. He was always nearby at the moment; watching and learning. “Looks different,” he said.

Amelia nodded. “This is the car I designed from the beginning. No compromises. No shortcuts.” She crouched beside the floor, fingers tracing the sculpted undercut, the exact shape she’d fought for. “We’ve been patch-working upgrades onto old foundations. But this; this is a clean slate. It’s mine. Finally.”

“So it’s ready?” He asked.

She looked up at him, eyes sharp. “Yeah. It’s ready to win.”

Lando ducked into the garage then, still in joggers and a hoodie, yawning around a protein bar. He caught her eye, then stopped mid-step. “Holy shit.”

Amelia nodded.

He stepped closer, hands in his pockets. Studied the car with wide eyes, taking in every minor adjustment, every small change that’d somehow made the entire car look different. Meaner.

“It looks fast.” He breathed.

“It is.”

He turned toward her, something quiet in his expression. “You happy?”

Amelia didn’t blink. “I’m relieved. Now it’ll do exactly what I designed it to do.”

Oscar wandered in a moment later, eyebrows lifting when he saw the chassis. “Oh shit, this the final spec?”

“The one I promised you both,” Amelia muttered.

Oscar grinned, circling the nose. “Looks like a weapon.”

Amelia hummed. “That’s because it is. All the patchwork’s gone. This weekend, you’ll both be driving the car I built for you from the ground up.”

Tom, now beside her, tapped his pen against his notebook. “You going to name it?”

Amelia looked at him like he’d grown two heads. “It already has a name — and that name has my initials in it anyway. Why would I give it another name?”

Oscar shrugged. “I name my chassis something new every weekend.”

“That’s because you’re weird.” She told him.

But later, when they were running race simulations and Lando had slipped out for media, she sat alone beside Oscar’s car, one hand resting lightly on the side-pod. Just for a second. And under her breath, too soft for anyone to hear: “Don’t let me down.”

Because it was all here now; her vision, her work, her legacy in motion.

And in Miami, for the first time all year, she was finally going to see her car on track.

—

Even in Miami, the F1 Academy paddock felt smaller. Tighter-knit. Less spectacle, more steel. It reminded Amelia of the early days she’d watched on flickering TV screens—before race suits were tailored, before engineers had agents. When she’d been three feet tall and already knew more about car setup than most of the men working on them.

She walked beside Susie, the low hum of tyre warmers and generators buzzing faintly underfoot. The air smelled like brake dust and fuel. It smelled like home.

“You don’t get much spare time,” Susie said, glancing down at the curve of Amelia’s bump beneath her papaya hoodie. “So thanks for making this one count.”

“I wouldn’t miss it,” Amelia said, eyes scanning the compact garages. “These girls are the future of motorsport.”

A mechanic rolled a jack across their path. A knot of young drivers stood nearby, still in their fireproofs, talking fast, voices tight with nerves.

Susie called one over. “Chloe. Come here a sec.”

Chloe Chambers jogged over, ponytail bouncing, already grinning like she knew exactly who Amelia was.

“Amelia Norris,” Susie said, pride softening her voice. “Meet Chloe. One of our brightest. She’s been dying to pick your brain.”

Chloe stuck out a hand, eyes wide. “I’ve watched every onboard from Oscar since you started working with him. And you basically built this year’s McLaren, right?”

Amelia glanced at the hand, winced, then gave a small shrug. “Built it. Argued over it. Cried about it once or twice. So—yes.”

Chloe lit up, dropped her hand like she didn’t even register the rejection. “I want to do what you do. I mean—I want to drive first. But also understand the car. Maybe even design one. Someday.”

Amelia's smile tugged sideways, something more serious behind it. “Then don’t let anyone tell you to choose. You don’t have to.”

A few more girls wandered over—Doriane, Abbi, Maya. One asked if it was true she’d rewritten part of the ride height algorithm in the middle of the night, thanks to pregnancy nausea.

“It’s true,” she said dryly. “Wouldn’t recommend it. I couldn’t stand the smell of carbon fibre for three days.”

They laughed, young, high, unfiltered, and something eased in her chest. She didn’t feel like a figurehead here. Not a myth. Just one of them. Older, yes. Blunter, definitely. But still part of it.

“Do you still get nervous?” One asked. “Being Oscar’s engineer?”

“No,” Amelia said. “But sometimes, I get… quiet before an upgrade. Or a tough strategy call. But I trust the hours I put in. That’s how you survive in this job—you trust the work, then you trust yourself.”

They asked for a photo. She said yes.

Afterwards, stepping back into the heat and light, Amelia felt something shift beneath her ribs. Not the baby. Something else.

“These girls,” she murmured. “They’re so—”

“Ready,” Susie finished. “They just need someone to show them what’s possible.”

Amelia looked down at her belly. The baby kicked once, low and firm. She wondered—would her daughter want this one day? The speed. The noise. The risk.

Would she want her to?

She didn’t know.

But she knew this: she wanted the door to be open. And she wanted it to stay that way.

“Well,” Amelia said, eyes back on the track. “Let’s make sure the road stays clear.”

Susie nodded, a quiet kind of promise in her voice. “That’s exactly why we’re here.”

—

The room was dark.

Not pitch-black—just enough light from the closed blinds to trace the edges of things. A spare media suite deep in the team hospitality unit, soundproofed from the bustle outside. Cold air whispered from the vents overhead.

Amelia sat curled up on the floor, back against the wall, knees drawn to her chest. Her hoodie sleeves were pulled down over her hands. In her lap, she twisted the stim toy between her fingers: click, roll, flip, snap. Again. Again. Again.

Her morning had unravelled in that invisible way it sometimes did. Nothing catastrophic—just too many voices, too many schedule changes, someone touching her shoulder without warning. The wrong texture on the cutlery at breakfast. The wrong smell in the paddock. She’d swallowed it all down with a brittle smile until she couldn’t anymore. Now the inside of her head felt raw and overlit, and only silence helped.

Click. Roll. Flip. Snap.

The door opened.

Soft, slow. No bright light flooding in. Just a narrow slice of hallway glow and a silhouette. Lando.

He didn’t say anything. He just stepped inside, closed the door again behind him. Let the dark settle. He moved quietly, then sat beside her, legs stretched out, shoulder to shoulder with hers.

A beat later, the door creaked again. Oscar this time.

She didn’t look up, but she knew him by the shape of his walk, the subtle way he moved like he was trying not to wake a sleeping cat. He settled on her other side, crossed-legged, just close enough to touch but not quite.

Nobody spoke.

Amelia kept clicking. Rolling. Flipping. Snapping.

And slowly, her breathing evened out.

Lando reached over and gently brushed his fingers across the back of her hand. She didn’t flinch. Didn’t pull away. She let him. Then let her head tilt sideways until it rested lightly on his shoulder.

Oscar stayed quiet, respectful in that way he always was with her—like he got it, even if he didn’t always understand. He just existed beside her, like a grounding point.

The toy made a soft clack as she turned it over again, her fingers finding the rhythm she liked best. The baby shifted inside her, low and firm. She exhaled slowly.

They weren’t talking. They weren’t asking her what she needed. They just were. Present. Patient. Steady.

It hit her, then, with quiet force: how deeply she was loved. Just… for being.

She blinked hard. One tear, maybe two. Nothing dramatic. Just the kind that came when the pressure released, even just a little.

Click. Roll. Flip. Snap.

Lando rested a hand on her hip, tracing soft circles on the red, itchy stretch marks. Oscar leaned his head against the wall, eyes closed, humming something tuneless under his breath.

Amelia let the dark hold all three of them.

And she knew that soon, she’d feel okay again.

—

Amelia had gone out for air.

That was the plan, anyway—just ten quiet minutes away from the structured chaos of media day. No cameras, no questions. Just walking, hoodie on, head down, hands in her pockets.

But somewhere along the paddock hospitality row, she saw them—six or seven VIP fans lingering near the McLaren garage, lanyards bright, eyes wide, trying not to look starstruck and failing. Most of them were young women. One had a notebook. Another had made her own earrings out of mini DRS wings. A third was nervously adjusting the hem of her papaya windbreaker.

They saw her before she could disappear.

“Hi—sorry—Amelia?”

She could’ve smiled and nodded and kept walking. Instead, she stopped. “Yes,” she said. “Hello. You’re not supposed to be standing there. You’ll block the tyre trolleys.”

One of them blurted, “You’re, like… kind of our hero.”

Amelia blinked at them. “Why?”

Which made them all laugh awkwardly.

“I mean,” the DRS earring girl said, “you built the car. Everyone knows it. You’re the reason we’re consistently getting podiums again.”

“That’s not entirely true,” Amelia said bluntly. “But thank you.”

The girl with the notebook held it out. “Could I maybe ask you a few questions? Just for fun?”

Amelia glanced around. There was a patch of artificial turf by the hospitality tents where a drinks cooler sat forgotten. No cameras. No execs. No schedule.

“Fine,” she said. “But I want to sit down. And I want something to eat.”

Fifteen minutes later, Amelia was cross-legged on a grassy patch, a fizzy drink in one hand and a half-eaten granola bar in the other, surrounded by a semicircle of fascinated girls. Someone had scrounged up crisps and trail mix from a hospitality unit. It was, essentially, a picnic.

She’d taken a napkin and a pen and was now drawing vortex flows and side-pod shapes in clean, confident lines, explaining how turbulent air off the front wing could be used as a tool, not just a nuisance.

“People always think air is the enemy,” she said. “It’s not. It’s a language. And if you understand what it’s saying, the car will behave for you.”

Someone gasped. Someone else scribbled furiously. One girl offered Amelia a gummy bear, which she accepted without breaking eye contact from the diagram.

“Do you… want your daughter to be an engineer too?” One asked, softly.

Amelia paused. “I want her to believe that she can be anything she wants to be.”

That was when Lando found her.

He was coming from an interview and nearly missed the scene entirely. Then he spotted her—Amelia, sitting in the middle of the grass like a camp counsellor or a pre-school teacher, surrounded by fans who all looked like they were in total and utter awe of her.

Oscar arrived seconds later. “Is this… what’s going on?”

“I think it’s a cult,” Lando whispered. “My wife has created a cult and she is their leader.”

One of the girls spotted them and nudged the others. The whole circle turned.

“Oh. Hi,” Amelia said, gesturing vaguely to them. “They asked me about ground effect. I got carried away.”

Lando sat down beside her without a word. Oscar followed, grabbing a crisp from the communal bowl like this was all perfectly normal.

“We’re learning,” Oscar said solemnly. “Let’s not interrupt the professor, Lando.”

One of the girls burst into laughter. Amelia handed her the napkin diagram and grinned.

And there, in the middle of a media day she’d meant to escape, Amelia Norris held court not to journalists or executives; but to the next generation. Bright-eyed. Hungry to learn. Eager to belong.

—

Later, Lando slipped an arm around Amelia’s shoulders.

“So,” he said, voice light but steady, “when our daughter’s old enough, do we risk teaching her about vortex generators and having her build a wind tunnel in our bathroom?”

Amelia rolled her eyes, resting her head against his chest. “Who knows? She might put us all out of a job.”

He laughed softly. “She’ll definitely get your brains.”

“And your stubbornness.” She gave him a sidelong look. “And adrenaline addiction.”

“Great combo.”

They walked slowly back toward the garage.

“Can I ask you something?”

“Anything.”

“If she wanted to race,” Amelia started, her hand moving instinctively to her hip, “would you want that for her?”

Lando scrunched his nose, bit his lip. “God. Uh…” He paused, searching her eyes. “I’d be worried. Not happy about it, but if it’s what she wanted, I’d make it happen.”

She studied him. “You’d make it happen even if it made you unhappy?”

“Worried,” he corrected gently. “Worried sick, probably. I’ve crashed, seen the worst of it. You know how dangerous this sport is. Would you be okay with it?”

She shrugged. “I’d tell her the risks, the stats. Karting? Sure. But racing professionally… I don’t know.” She hesitated, voice quieter. “I don’t know.”

Lando cupped her cheek. “It’s okay not to know yet.”

“I don’t know,” she repeated, staring into his eyes as panic fluttered beneath her skin. “Why don’t I know? I should.”

He pulled her close, voice low. “It doesn’t work like that, baby. I’m sorry.”

She sniffled, clutching his shirt. “Parenting is already hard and she isn’t even born yet.”

“Yeah,” Lando agreed, with a shaky kind of inhale. “Yeah.”

—

Amelia sat on the couch in their hotel room, fiddling with her stim toy, brow furrowed. The past few weeks had been… confusing. She knew about pregnancy hormones, but this sudden surge in her sex drive? That was new and confusing territory.

Lando entered the room, carrying a glass of water. He caught her eye and smiled, but there was a flicker of something (nervousness?) in his gaze.

“You okay?” He asked, voice a bit higher than usual.

Amelia bit her lip. “Can I ask you something?”

He nodded quickly, almost too quickly.

“Is it… normal to suddenly want sex all the time? Like, nonstop?” Her voice was blunt but uncertain. ‘I’m nervous to look it up in-case weird stuff comes up.”

Lando’s face flushed, and he scratched the back of his neck, looking anywhere but at her. “Uh, yeah. Totally normal. Second trimester… hormones and all that.” He cleared his throat. “Not that I’m complaining.”

Amelia blinked, surprised by his sudden heat.

Lando shifted closer, cheeks still pink. “I mean, it’s… well, you’re pretty irresistible right now.”

She raised an eyebrow. “Irresistible?”

He swallowed hard. “Yeah. So, uh… we can make you feel better, if you want?”

Before she could respond, he leaned in, brushing his lips lightly against hers. The kiss was soft but full of promise, and Amelia’s heart sped up in that familiar way; equal parts surprise and warmth.

When they parted, Lando grinned sheepishly. “You want to?”

Amelia stared at him. “Yeah. Now. And then again a few more times. And tomorrow morning before we go to the track.”

He stared at her for a beat before he smiled wide, sharp little fangs and all.

—

Amelia lay awake.

Her head rested on Lando’s chest, his hand soft against the curve of her belly. His breathing was slow, steady, familiar. She could feel the faint shift of it under her cheek.

She stared at the ceiling, fingers tracing idle circles over the sheets.

She hadn’t expected to want him like that. Not with this body — not now, not so much. And yet…

Flashes of the night flickered across her mind like bright sparks.

Lando’s laugh, half-muffled against her neck.

His voice, rough, whispering, “You sure? You’re sure?”

The way he’d kissed the inside of her wrist every time.

Her hoodie halfway off, clumsily caught around her elbows.

The sound she made when he touched her lower back — sharp, surprised.

His thumb brushing gently over her bump, reverent. “Hi, baby,” he’d whispered, “Your mum’s kind of a goddess.”

She blushed in the dark just thinking about it.

But what stuck with her most wasn’t the heat — it was how seen she felt. How known. How safe.

She’d spent most of her life learning to translate herself for the world. She thought that’s what relationships would always have to be — filtering, explaining, shrinking things down.

But with Lando, she had never once had to do that.

He read the pauses in her voice like she would read telemetry. Felt her silences without trying to explain. Met her confusion with patience, not pity. Anticipated the needs she hadn’t even decoded herself yet.

She tilted her head, studying him in the quiet.

She hadn’t just fallen in love with him all those year ago.

She’d grown into love with him — steady, real, elemental.

And somehow, impossibly, he kept giving her more reasons to love him even more.

She pressed a kiss to his chest, so soft he didn’t stir.

Then closed her eyes, finally ready to sleep.

—

The bathroom lights were aggressively bright for how little sleep Amelia had gotten.

She was perched on the closed toilet lid, sleep-shirt inside out, bump resting on her thighs, and a toothbrush in her mouth. Her phone leaned against a half-used roll of toilet paper on the counter, and Pietra’s face filled the screen, already smirking.

“You look like you’ve been run over,” Pietra said with wide eyes.

Amelia spat into the sink. “I had sex for four hours straight last night.”

Pietra choked on her iced coffee. “Good morning, mami.”

Amelia shrugged like she was reporting on tyre deg. “Hormones.”

“Second trimester hitting like DRS on the main straight, huh?”

She nodded seriously. “It’s physiological. There’s blood flow redistribution and heightened sensitivity in—”

“Stop,” Pietra laughed. “You can’t do the engineering breakdown of your sex life.”

Amelia grinned, a little proud. “I definitely can. Do you want to see my graphs?”

“No graphs.Please. No vibes. How’s Lando coping?”

“Hydrated. Exhausted. Still asleep,” she said, brushing through her tangled hair. “He kept making these noises like he couldn’t believe what was happening.”

Pietra chuckled. “Yeah, he’s down bad for you, my girl.”

“I know,” Amelia said. “He, like, kept kissing my wrist.”

“Amelia. Please.”

“No, like he held it and did it twice.”

There was a pause.

Pietra blinked slowly. “That’s so sweet.”

“He made me feel like myself again.” She flushed.

Pietra was quiet, her smile gentler now. “Because you are.”

Amelia nodded once. “He’s also half-worried that our daughter might invent a bathtub wind tunnel.”

“Oh God,” Pietra said, grinning again. “That little girl is going to make him go grey. I hope she cuts up her dolls and builds a diffuser from their severed limbs.”

“She won’t have dolls.” Amelia said dryly. “She’ll have CFD software.” Even though her tone was flat, the twitch of her lips betrayed her joke.

Pietra laughed. Amelia finished tying her hair into a low, slightly messy ponytail. A streak of sunlight cut through the window, warming the tiles beneath her feet.

“I should go,” she said. “Track walk in forty-five minutes.”

“Tell Lando I said ‘well done’.”

Amelia rolled her eyes. “No. That’s weird.”

“You love me anyway!”

Amelia ended the call and stared at herself in the mirror for a second.

Messy. Flushed. A little wild-looking.

Entirely herself.

And deeply, deeply loved.

—

The heat shimmered off the asphalt in waves, the whole paddock buzzing with anticipation. Miami was loud, chaotic, full of pastel shirts and bass-heavy DJ sets; but the McLaren garage felt like a storm waiting to break.

Amelia had one hand on Oscar’s halo as he settled into the car. Focused. Calm. Starting fourth on the grid. It was a good starting position, but they both knew it wasn’t going to be an easy climb through the field — if they even managed to keep their position into turn one.

“Conditions are fine. Brakes might take a while to come in. Let the tyres come to you.”

Oscar looked up at her, half-grinning under his visor. “And if I don’t?”

“I’ll scream at you over the radio for being annoying and not listening to me.”

He laughed. “As usual.”

She patted the car once, stepped back, and moved to her tiny little thrown-together desk just as Lando passed her on his way to climb into his car. His hand grabbed her back. Their eyes met. He gave her a look; small, private, thrilling. The kind of look that said: I think today is the day.

She nodded once. Just once.

She’d believed in him for years now — since before Sochi, since before he’d even been given the full-time McLaren seat.

He was capable of incredible things. 

—

The first 20 laps were a blur of strategy juggling and telemetry surges. Amelia was locked into Oscar’s race; managing his energy deployment, traffic, undercut threats.

He was driving sharp. But something wasn’t sticking.

A slow pit stop on Lap 32 killed their momentum. They dropped back into traffic. She clenched her jaw, recalculated in seconds, called Plan C.

“Ducky, don’t lose steam. We’re still in this for good points. Head down.”

“Copy,” he said, clipped. Frustrated, but fighting.

But further up the field, Lando was flying.

And then there was the safety car.

Chaos. All improper preparation and garages rushing.

And then Lando exited the pits. And he hadn’t just made up a few positions — he’d taken the lead.

The garage erupted. Amelia nearly stood up from her station. She felt it before the numbers confirmed it — Lando was about to win his first Grand Prix.

She could barely breathe.

—

Oscar crossed the line P6. Solid points. Not what they hoped for, but not failure.

But Lando…

Lando held off Max for the last five laps like his life depended on it. No mistakes. Just pure, blistering pace and nerves of steel.

And then—

“Lando Norris. That’s P1. You are a Formula One race winner!”

Will’s words cracked through the comms.

The garage exploded.

Amelia didn’t move.

She sat frozen, one hand over her mouth, the other gripping the edge of the console like it would float her back to earth.

He’d done it.

Finally.

No more self-doubt. No more what-ifs.

Lando won.

Her husband, who stayed up with her until 3am looking at ride height data; had won.

And he did it in the car she built for him.

"We did it, Will. Amelia — baby, we did it. We did it!" He said over the radio.  

The first race it was fully her spec — and sure, they’d gotten ‘lucky’ with the safety-car, but luck was insubstantial. His pace said it all.

He’d won. And he’d won by a mile.

—

The moment she found him in Parc Ferme, still helmeted, still breathless, still shocked, she ran.

Not far; just to the holding area, where only a few people were allowed. But she was McLaren’s lead engineer. She was also his wife.

She had every right.

He turned and saw her and the helmet came off in one swoop.

His face was flushed, eyes red-rimmed, disbelieving.

She launched into his arms and he caught her without hesitation, arms around her waist, face buried in her shoulder.

“I can’t believe it,” he whispered. “I won. I fucking won, baby.”

“I can believe it,” she said, steady and breathless. “I knew it was coming. How long have I told you that this would happen for you? You’ve been driving like a winner all year, Lando.”

He kissed her, fast, messy, barely containing the wild joy in him. “Tell me you saw the move on Max.”

“I saw it. It was amazing.”

He laughed against her neck, giddy and stunned and vibrating with relief. “I did it, Amelia.”

“You did.” She leaned into him, eyes pricking with tears. “I am so, so proud of you. So proud.”

—

They went to a few parties. Smaller ones. Danced together — Lando being celebrated in exactly the way he deserved.

He hadn’t been all to keen on the idea of his visibly pregnancy wife going into the Miami nightclub, but she’d insisted they go. Even just for a little while.

Oscar and Lando stayed close — like bodyguards. Max was no better, hovering, constantly bringing her water. It was sweet. It was nice to still be involved in the celebrations.

His trophy sat on their hotel room table.

Lando was in the shower, singing Queen, completely off-key.

Amelia sat on the bed in one of his t-shirts, one hand on her belly, the other tracing the MCL38-AN etched into the side of the silver.

Their daughter kicked.

She smiled. “Your dad,” she whispered, “is a Formula One race winner.”

—

They touched down just before dawn, Heathrow still hushed in early morning fog. Amelia’s body ached with the kind of deep exhaustion that only adrenaline can leave behind; but her hand never left Lando’s.

He’d won. That wasn’t going to stop echoing in her head any time soon.

By the time they got to his parents’ house, the sky had cracked open with gentle rain. The front door opened before they even rang the doorbell.

His mum pulled him into a tight hug, burying her face in his chest. His dad hovered behind, proud and misty-eyed in the quiet way he always was. There were champagne flutes already out in the kitchen, a cake someone had clearly stayed up late decorating — “P1, Finally!” scrawled in sugar icing.

But what caught Amelia off guard was how his mum hugged her too.

Carefully, because of the bump. But tightly. Fully. Without hesitation.

“We were watching,” she said, her voice warm in Amelia’s ear. “I’ve never screamed so loud in my life. He wouldn’t have gotten here without you, you know?”

Amelia blinked. Didn’t know what to say to that. Just squeezed her hand and nodded.

—

Later, in the quiet of Lando’s childhood bedroom, Amelia lay curled into his side beneath soft, over-washed sheets. The walls were still plastered with old racing posters, a few crooked photos of karting days — a little shrine to where it all began.

The trophy was on the dresser.

Not a glass cabinet, not a pedestal. Just… sitting there. Like it belonged next to a lava lamp and a stack of F1 magazines from 2009.

Amelia snorted at the sight of it. “You really just plonked it there?”

“It’s weird, right?” Lando said, his voice drowsy. “Feels like it should be… more. But also not. I don’t know.”

“It’s exactly right,” she said. “It belongs where you started.”

He looked over at her. Tucked a strand of hair behind her ear. “You okay?”

She nodded. Then, after a moment, “It’s strange. Everyone talks about how hard it is to get here. To win. To be part of something like this. But nobody tells you how hard it is to… stop. To come down from it. To believe that it’s real.”

He didn’t answer right away. Just pulled her closer, hand on her belly. “She’s gonna know,” he said softly. “Our daughter. She’s going to grow up knowing this is possible. Because she’ll have you. And she’ll have me too.”

“You,” Amelia said firmly, “are going to be her favourite person.”

He flushed, kissed her shoulder. “You’re both my favourite.”

—

Breakfast was a chaotic, sweet mess. His younger cousins had come by with orange balloons and mini trophies made of Lego. His grandmother insisted on touching Amelia’s belly and declared, in full authority, that the baby would be born with racing boots on already.

Someone pulled out a bottle of something sparkling, and Lando looked like he might cry for the tenth time in 48 hours.

Amelia stepped outside with her tea, just for a moment. The garden smelled like damp grass and daffodils.

Lando came out after her, wrapping his arms around her from behind, nose pressed into her neck.

“We really did it,” he murmured.

“You did.”

“No,” he said. “We.”

She leaned back into him, eyes fluttering shut.

For once, she didn’t argue.

—

The highly sought after private clinic was tucked behind a row of converted barns; all soft wood beams and white walls, the kind of place that smelled faintly of lavender and sterilised plastic. Quiet. Private. No waiting rooms. No fluorescent lights.

It had taken Amelia weeks to agree to in-person visits. Not because she didn’t trust the care, but because the idea of new faces, new spaces, new sounds — it made her skin hum in the wrong way.

But this midwife, Fiona, had been patient. Kind. Spoken to her over the phone like Amelia wasn’t strange or fragile or complicated. Just… herself. And today, for the first time, they were meeting in real life.

Amelia sat in the softly-lit consultation room, sleeves pulled over her knuckles, while Lando leaned back in the chair beside her, fingers loosely linked with hers.

The door opened, and Fiona stepped in; mid-forties maybe, silver at her temples, Doc Martens under a midi skirt. Exuding a calm energy.

“Hello, Amelia,” she said with a small smile. “It’s good to finally meet you properly.”

Amelia blinked at her. “You don’t sound as tall as you do on the phone.”

Fiona laughed, delighted. “That’s a first. Most people say I sound shorter.”

Lando grinned. “She’s very good at spatial audio. It’s… sort of freaky.”

Amelia elbowed him lightly. “It’s not freaky. It’s useful.”

“I know, baby,” he said, kissing her hair.

Fiona sat, not rushing. Just matching the room to Amelia’s pace.

“Shall we talk through everything slowly?” She offered. “We’ll do the checkup, listen to baby’s heartbeat if you’re feeling up for it — and then talk about next steps. I’ve got your notes printed exactly how you like them. Font size 13, double spaced.”

That surprised a smile out of Amelia. “You remembered.”

“Of course I did.”

—

Fiona talked her through every step before touching her. Let Amelia guide where the Doppler went. Gave her control.

The heartbeat came through — fast and steady and perfect.

Lando stared at the screen like it was made of gold.

“There she is,” he murmured. “There’s our girl.”

Amelia stared at the graph. “Still sounds like a horse galloping.”

“Strong horse,” Fiona said. “Very healthy.”

They spent another fifteen minutes going over nutrition changes, sleeping positions, birth plans. Fiona never pushed. Never filled silence with filler words. Just waited.

“You’re very good at this,” Amelia said finally. “I don’t like many people.”

Fiona smiled gently. “That means a lot. Thank you.”

—

They stepped back out into the quiet spring air, a softness between them.

Lando opened the car door for her, waiting until she was settled before getting in himself. He looked over at her, one hand finding hers on the armrest.

“I like her,” he said.

“I don’t hate her,” Amelia replied, which was even better.

“You did so well,” he added softly. “I’m really proud of you.”

She glanced at him. “Why?”

“Because I know how much it costs you to do things that feel uncertain,” he said. “And you still showed up for her. For our daughter.”

Amelia’s eyes prickled, caught off guard by the depth in his voice.

“She deserves someone better than me, sometimes,” she whispered.

“No,” he said firmly. “She’s getting someone more brilliant, more brave, more herself than anyone could hope for.”

She kissed him. “Okay. Take me to get some chicken, please?”

—

The kitchen was full of soft light and the smell of roast chicken and rosemary potatoes. There were too many voices, too many overlapping stories, the occasional clink of cutlery — but somehow, it didn’t overwhelm Amelia the way it usually did. Maybe it was the dimmer switch Lando had installed last year. Maybe it was the way he kept checking in with her from across the room. Or maybe… maybe it was just the peace that came from knowing her daughter was still tucked safe inside her, heartbeat strong.

Dinner was warm.

They passed around the scan print-outs — Lando sliding them carefully across the table. His mum teared up a little at the clearest one, where the outline of a tiny face and curled fingers was visible.

“She’s so beautiful already,” Cisca whispered.

“She looks like an angry shrimp,” Amelia said flatly, which made Adam chuckle into his wine.

“An angry shrimp with a big Norris head,” Lando added.

“Oi,” Adam said. “Watch it.”

“She’s got Amelia’s precision, though,” Lando added, turning the scan toward his dad. “Perfect symmetry in the profile. Look at that jawline. Look.”

“She’s 38 centimetres long, Lando,” Amelia said, eyebrows raised. “She’s still just a smudge.”

He shrugged, grinning. “Let me have this.”

��

Cisca topped up everyone’s water and gently set her glass down. “Have you two thought much about… the birth yet? Or after? What it’ll look like, who you want with you, where?”

Amelia nodded immediately, already sliding her phone from the edge of her placemat. “Yes. I’ve got it all planned.”

She pulled up a bullet-pointed note, clean and colour-coded. “I’ll be labouring at home for as long as is medically safe, with Fiona monitoring. Then transferring to the birth centre — the one with the adjustable light panels and hydrotherapy. I’ve selected a playlist that aligns with optimal relaxation frequencies, and Lando will be coached on pressure-point guidance in case I don’t want verbal input. We’ll have backup bags packed and pre-positioned in the car by Week 37.”

The table went still for a moment. Not unkind. Just… a bit awed.

“And after?” Adam asked gently.

“Fiona will do at-home checks. I’ll be off work technically, but I’ll still be supporting Oscar’s data remotely if we’re out of hospital. I’m going to stay with my mum in Woking. Sleep will be rotational in the first two weeks depending on Lando’s schedule, but my mum had already agreed to step in. Breastfeeding is Plan A, bottle Plan B. I have a spreadsheet.”

There was a quiet pause.

Then Cisca reached over the table, her hand warm as it closed gently over Amelia’s. “That all sounds wonderful, my darling. But, and this is only a but, if it doesn’t go exactly the way you’ve planned, don’t panic,” she said. Her voice was soft but certain. “Sometimes babies decide to do things their own way.”

Amelia didn’t flinch from the contact — rare for her. She just looked at Cisca’s hand, and then at her face. “I know that,” she said, a little stiffly. “Logically.”

“But knowing it logically isn’t the same as feeling okay when it happens,” Cisca said gently.

Amelia looked down at the scan photo in front of her. Then quietly, almost like a confession, “I want to do it right. I want her to feel safe from the second she arrives.”

“She will,” Lando said, reaching for her hand under the table. “Because she’ll have you.”

—

The door was already open before they even made it up the path.

“There she is!” Zak’s voice boomed from the hallway as Amelia climbed out of the car, Lando trailing behind with his hand protectively on the small of her back.

Tracey appeared right behind him, dish towel still slung over her shoulder. “Let her breathe, Zak, Jesus.”

Amelia barely had time to blink before she was enveloped in one of her mother’s trademark, over-long hugs — all vanilla perfume and chaotic warmth.

“I can’t believe how much she’s grown,” Tracey murmured, hands sliding down to press lightly at Amelia’s bump. “My granddaughter’s in there, that’s crazy.”

“She’s the size a watermelon,” Amelia said, dry. “A big watermelon. But still.”

Lando grinned. “Not for long. She’s growing every day.”

Zak clapped a hand on his son-in-law’s shoulder. “Still wrapping my head around the fact that you’re gonna be a dad, son.”

“Same,” Lando replied with a breathy laugh.

—

The Browns’ home was bigger than you might expect, but still carried the energy of a family who talked over each other and left laundry on stair banisters. The TV was on in the background playing a re-run of some F1 docuseries, and Zak had already pulled out a bottle of strawberry alcohol-free wine.

“No, Dad,” Amelia said, waving him off. “No bubbles. I’ll get heartburn.”

“I’ve got ginger beer!” Tracey called from the kitchen. “And saltines!”

Amelia drifted toward the fireplace, fingers brushing over old framed photos. There was one of her as a little girl with a screwdriver in one hand. Another of Zak holding her on his shoulders at the Silverstone track.

She stared at that one for a beat too long.

“You okay, kiddo?” Zak asked gently, appearing beside her.

She didn’t look up. “Yeah. Just remembering.”

“You’d sit on the garage floor with the brake calipers,” Zak said, fond. “You used to name them.”

“They needed names. They had personalities.”

“You said one was ‘grumpy and over-torqued.’ You were five.”

She let out a tiny laugh.

—

Dinner was loud. American-style pot roast, mashed potatoes, green beans drowning in butter. Tracey refilled everyone’s drinks every ten minutes. Zak told old stories about testing sessions Amelia had half-forgotten.

Later, Amelia found a quiet spot in her childhood bedroom, lights dimmed, the duvet still vaguely smelling of fabric softener. Lando leaned against the doorframe, watching her brush her fingers over an old model car she’d built with Zak when she was nine.

“You okay, baby?” He asked.

She nodded. “Yeah. I’m nervous to be staying here again, after having the baby. I wish we could just… have her in Monaco and disappear for a few months.” She frowned. “We didn’t plan our timing very well, did we? You’ll be mid-season, and Oscar won’t have me there, and—“

Lando crossed to her and wrapped his arms around her from behind, resting his chin on her shoulder.“Hey. Hey, calm down, baby. I think that you’re exactly where you’re supposed to be,” he murmured. “You’ll want your mum, yeah? She’ll be able to help you adjust without being overbearing.”

She hummed against his chest, her hands closing around his shirt. “What if you’re not here when it happens?”

He was quiet for a beat. “I’ll come home as soon as possible, baby. I promise.”

“I don’t want you to miss a single session.” She said, hotly. “But I want you with me all the time and I can’t have both, can I?”

“No, baby. I’m sorry.”

“It’s fine.” He murmured. “It’s fine, baby.”

—

Amelia stood at the edge of the test platform, squinting at the flow viz spread across the prototype floor. She wasn’t officially here to work, just visiting. Just dropping in. Just… checking the numbers. Seeing the model. Touching the damn tunnel wall like it could somehow speak to her.

“It’s still bleeding airflow here,” she muttered to herself, pointing at the front of the floor, just under the bargeboard curve. “Boundary layer’s detaching early.”

“Still better than Ferrari’s design,” someone mumbled behind her.

“Low bar,” she shot back.

She didn’t look up. Her fingers danced automatically across the control screen. Toggling split channel overlays, flipping between computational fluid dynamics layers. She could feel her heartbeat syncing with the faint thrum of the tunnel, her mind slotting into gear like it always had.

Until she felt someone step beside her, too quietly for a regular engineer.

“Amelia,” Oscar said softly, hands in his hoodie pockets. “Hey.”

She blinked, her brain still five seconds behind in aero-language.

He glanced at the setup, then at her bump, then back to her face. “Did you… sleep at all last night?” He asked.

“I took a nap on Lando’s thigh for twenty-three minutes in the car,” she said.

Oscar huffed. “Very normal. Very healthy.”

She turned back to the airflow sim. “This isn’t right. The adjustment from the Miami spec — it’s throwing off drag balance on the mid-straight.”

“Amelia.”

She didn’t answer this time. Just kept muttering corrections under her breath, lips moving like she was translating a language no one else could see.

Oscar stepped closer, then placed one hand gently on her wrist — not to stop her, just to connect.“You’ve been here for hours. You can come back to this later,” he said.

“I don’t know how to be here without doing something.”

“I know,” Oscar said. “But we’re not racing this week. And you’re allowed to just… exist in this space without trying to fix every tiny issue that you see.”

Amelia looked at him. Her mouth opened, then shut again. He didn’t push. Just stood with her in the quiet hum of the room, solid and calm.

Eventually, she whispered, “My brain’s too loud when I stop.”

“Then let me help you turn the volume down,” Oscar said simply. “C’mon. Let’s go sit by the lake for a bit.”

—

They ended up outside with two mugs of ginger tea that Oscar had somehow convinced catering to let them take out of the dining hall. Amelia sat with her feet up on the bench edge, dress stretched over her bump, breathing slower now.

She watched the fountain spray in silence for a few minutes before saying, “Thanks.”

“For the tea?”

“For not treating me like I’m fragile,” she said. “But also not treating me like I’m a machine.”

Oscar smiled sideways. “You’re a human. A terrifyingly brilliant, data-possessed human. But still.”

She let out a tired laugh and leaned her head briefly on his shoulder. “Don’t tell Lando I had a moment.”

“Alright,” he said. “It’ll stay between us and the ducks.”

She smiled. “My ducky and my ducks — conspiring together. Cute.”

He rolled his eyes.

—

The morning sun hit the Emilia-Romagna pit lane with a sharpness that reminded Amelia of why she loved racing. Clean, brutal light cutting through the lingering coolness of dawn.

She stood just inside the garage, eyes scanning telemetry streams on her iPad, but her mind elsewhere. This was her second-to-last race before maternity leave. A strange mix of accomplishment and anticipation knotted inside her.

Lando caught her eye across the garage, giving a small thumbs-up. She returned the gesture with a faint smile.

Oscar approached, carrying his helmet. “Ready?” He asked.

“Of course I am.”

—

During a quiet moment before qualifying, Amelia slipped out from behind the pit wall to find Lando.

He reached for her hand, squeezing it lightly. “You okay?”

She nodded. “I’m okay. Just… thinking about how this is all starting to feel a bit too much like a goodbye for my liking.”

He brushed a stray strand of hair behind her ear. “We’ll hold the fort. You’ll be back before you know it. You don’t need to worry.”

Her eyes softened. “I know. But it feels… weird.”

He held her. Kissed her. “You’ll be fine, baby.”

—

The race was intense. Strategy calls fired rapidly, tyres switching, gaps closing. Amelia’s voice came calm and precise over the radio, guiding Oscar through every corner, every lap.

When the checkered flag finally waved, Oscar finished fourth — solid, but just off the podium. Amelia exhaled, a complex wave of pride and bittersweet acceptance washing over her.

Lando’s race had been even more intense; a nail-biting late charge from Lando, a nail-bitingly close finish between him and Max.

They’d take second.

But she could see it. Hear it.

Her husband had enjoyed winning. And he was hungry for more.

—

Back in the garage, the team gathered around the screens replaying Lando’s brilliant win at Miami — a reminder of the highs to come. Amelia let herself smile, feeling the warmth of the team around her.

Lando slipped an arm around her waist. “Only one more weekend to go,” he murmured.

She leaned into him. “Yeah.”

Tom gave them a nervous smile. “I feel ready to take the reins. Do you think I’m ready?”

“As ready as you could possibly be.” Amelia told him.

Oscar laughed a bit. “I feel like I’m being passed between my divorced parents.”

Amelia rolled her eyes at him. “You’re ridiculous, ducky.”

NEXT CHAPTER

[itsnesss]

Hi lovely! I was wondering if you could do a lando norris x reader in the Miami gp 24' (based on the dts episode of him) where he is starting to have some self doubt because he is having a hard time beating max in the race so the McLaran team brings reader to talk to lando through the headsets/radio while he's racing and she encourages him to win but also says that other people's opinions about him shouldn't matter to him. And after all he ends up winning the race and reader is the first person lando finds after winning for the first time. Tyy

𝐦𝐢𝐚𝐦𝐢 𝐯𝐢𝐜𝐭𝐨𝐫𝐲 | lando norris × fem!reader

summary | lando, full of self-doubt during the 2024 miami gp, hears your voice over the team radio. your words push him to fight harder, he overtakes max and wins his first race

warnings | emotional vulnerability / self-doubt, slight angst, fluff, comfort, intense racing tension

word count | 1.4 k

🖇 more ln4 🖇 f1 masterlist

The Miami sun bore down fiercely on the circuit, illuminating every curve and inch of asphalt. The 2024 Grand Prix had kicked off with full intensity, and you were stationed at McLaren’s control center, watching with your heart in your throat as Lando fought on the track.

From the moment the race began, the battle for victory seemed destined to be a constant duel between him and Max Verstappen, the relentless champion.

But something about Lando worried you. Through the radio communications, you could sense a subtle change in his voice, a small crack that hadn’t been there before. He sounded less sure of himself, as if that spark that had always made him shine on the track was starting to flicker.

"Everything okay out there?" you asked calmly, trying to project confidence.

"I’m... I don’t know, not sure I can do it this time," he replied, a hint of doubt in his voice. "Max is too strong. I don’t know how I’m going to get past him."

You knew Lando was an incredible driver, capable of pure moments of genius. But you also knew that the pressure of facing a rival like Max could make even the strongest start to waver.

"Listen to me, Lando," you said, trying to make your voice both firm and comforting. "You have something Max doesn’t. It’s not just speed or technique. It’s you. Your heart. Your courage. Don’t let anyone’s opinion make you doubt that. You’re not what others say, you’re what you know you’re worth."

There was a moment of silence, then you heard him take a deep breath. You knew your words were reaching him, that they were starting to sink in.

The race continued, and with each lap, the tension rose. Lando seemed to be fighting not only Max, but also that inner voice whispering that maybe he wasn’t enough.

But you were there, on that invisible radio channel, reminding him he wasn’t alone. That someone believed in him someone who knew he could do it.

"Lando, focus on Sector 3. You’ve got pace, you can catch him on the straight. You have DRS."

The engineer’s voice was clear, but deep down, all he wanted was to hear yours again. Amid the heat, the speed, and the pressure, your voice had become his only anchor.

You came back on the comms, on direct order from the team principal. "Lando, listen to me. Breathe. You’ve done this before. You’re more than a stat or a podium. You brought yourself here. No one else."

From inside his cockpit, with his hands clenched on the wheel and his visor fogged from the heat, Lando closed his eyes for a second. Not enough to lose control but enough to let your words reach him.

"Don’t let Max live in your head," you continued, that mix of firmness and tenderness only you knew how to use. "He doesn’t live there. You do. Remember why you started. Remember who you are. Not to beat him... but because you never give up."

And then, something changed.

The next sector was clean, precise. Pure art on wheels. The gap shrank lap by lap. The pit wall erupted with data and strategies, but Lando wasn’t listening to the noise anymore. He was only listening to you.

On lap 54 of 57, he made his move. Aggressive, but smart. He tucked into the slipstream and, coming out of turn 11, he had him: DRS activated, he dove down the inside and

he passed him.

"Let’s go, Lando, you did it!" you shouted over the intercom, forgetting all protocol. You weren’t part of the technical crew, but in that moment, you were everything he needed.

"Thanks to you," he replied, voice breaking, barely audible beneath the helmet. "You have no idea how much I needed that..."

The final laps were the longest of his life. Not because of difficulty but because of restraint. He wanted to scream, cry, see you.

The team buzzed, fans went wild. Final corner. Final breath. Checkered flags.

"P1. Lando Norris. P1."

For the first time in his career, he crossed the line first, not by accident, not by luck. By merit. By fight.

And when the car stopped at the pit line, and he removed his helmet through tears and ragged breaths, he didn’t look for his engineer or his team boss.

He looked for you.

Mechanics surrounded him, applauding, lifting him onto shoulders while camera flashes exploded from all directions. But he barely registered their faces. It was all noise, confusion, and overwhelming celebration.

Until his eyes found you in the crowd.

You were there, headset hanging around your neck, walking quickly toward him, eyes shining with emotion and pride. You didn’t wear a race suit or technical gear, but you were more a part of the team than anyone.

Lando didn’t think. He broke free from the arms congratulating him, from the cameras trying to capture him. He ran to you as if the real finish line was exactly where you stood.

And you moved too because you knew what was coming.

You met halfway, right in front of the pit lane barrier. He wrapped you in an embrace so tight it nearly lifted you off the ground. His body trembled—not from physical effort, but from the emotional release he’d held in for 57 laps.

"You did it..." you whispered, burying your face in his neck, feeling the heat radiating from his race suit.

"No. We did," he replied, his voice cracking. "I couldn’t have without you. Really. Hearing you... saved me."

Slowly, you pulled back, just enough to look him in the eyes. His face was streaked with sweat and tears, still tense from the intensity but his gaze was clear. Free.

"Lando, win or lose, that doesn’t define who you are. People are always going to talk. But I see you. I always have."

He smiled. Not the usual media smile, or the cocky driver one. A real smile. Raw. Completely human.

"I promised myself that if I won… you’d be the first person I’d hug. And look at us. I didn’t let myself down."

He kissed your forehead, and for a second, the world disappeared. No roaring engines. No screaming fans. Just him, you, and the certainty that the day wasn’t about the trophy.

...

Drops of champagne still sparkled in his hair as Lando stepped down from the podium, the trophy in one hand, and that impossible smile still painted across his face. The British anthem still echoed through Miami’s loudspeakers, and you watched from the paddocksurrounded by media, crew, and curious onlookers. Everyone wanted a piece of that moment. His moment.

But not you. You just wanted to be with him. In silence. No cameras. No noise.

After the press conference, the photos with the team, and congratulations from drivers who finally saw him as more than just McLaren’s friendly kid, he slipped away.

He found you next to the hospitality unit, alone, a bottle of water in hand and your headset already packed away. Lando didn’t say a word. He just walked toward you slowly and, once close enough, set the trophy down and pulled you into his arms.

This time, the embrace wasn’t about euphoria. It was about relief. Intimacy. Belonging.

"Can we hide from the world for a while?" he whispered in your ear.

You nodded without a word, taking his hand.

You climbed into one of the team’s private rooms the one he used between sessions. No luxury. Just a couch, a ceiling fan, and soft sunset light filtering through the blinds. He stripped off his race suit down to his waist, leaving only his sweat-soaked black shirt, his neck still red from the heat.

You sat on the couch, and he dropped beside you, resting his head on your lap.

"You know something?" he murmured, eyes tired but joyful. "During that final lap, I wasn’t thinking about Verstappen. Or the trophy. I was thinking about how you’d look at me if I won."

Your fingers began gently combing through his damp hair, lowering his heart rate more than any cooling system ever could. "And how am I looking at you now?"

"Like I’m worth it. Not for winning. Just… for being me."

You smiled, lowering your gaze to meet his. "You’ve always been worth it. The rest is just... the consequence."

He slowly sat up, leaning in. His hands took yours, warm and soft. "Today, I felt like a champion. But with you… I always feel invincible."

And then he kissed you. Not a quick one. Not one stolen between pit stops. A deep kiss, honest, tasting of victory and salt. Of unspoken promises, clearly understood. Of staying together, through every race, every doubt, every lap.

Because the real finish line was never the checkered flag.

It was finding each other at the end.

[dandelionsresilience]

Dandelion News - February 22-28

Like these weekly compilations? Tip me at $kaybarr1735 or check out my Dandelion Doodles! (This month’s doodles will be a little delayed since I wasn’t able to work on them throughout the month)

1. City trees absorb much more carbon than expected

“[A new measurement technique shows that trees in LA absorb] up to 60% of daytime CO₂ emissions from fossil fuel combustion in spring and summer[….] Beyond offering shade and aesthetic value, these trees act as silent workhorses in the city’s climate resilience strategy[….]”

2. #AltGov: the secret network of federal workers resisting Doge from the inside

“Government employees fight the Trump administration’s chaos by organizing and publishing information on Bluesky[…. A group of government employees are] banding together to “expose harmful policies, defend public institutions and equip citizens with tools to push back against authoritarianism[….]””

3. An Ecuadorian hotspot shows how forests can claw back from destruction

“A December 2024 study described the recovery of ground birds and mammals like ocelots, and found their diversity and biomass in secondary forests was similar to those in old-growth forests after just 20 years. [… Some taxa recover] “earlier, some are later, but they all show a tendency to recover.””

4. Over 80 House Democrats demand Trump rescind gender-affirming care ban: 'We want trans kids to live'

“[89 House Democrats signed a letter stating,] "Trans young people, their parents and their doctors should be the ones making their health care decisions. No one should need to ask the President’s permission to access life-saving, evidence-based health care." "As Members of Congress, we stand united with trans young people and their families.”“

5. Boosting seafood production while protecting biodiversity

“A new study suggests that farming seafood from the ocean – known as mariculture – could be expanded to feed more people while reducing harm to marine biodiversity at the same time. […] “[… I]t’s not a foregone conclusion that the expansion of an industry is always going to have a proportionally negative impact on the environment[….]””

6. U.S. will spend up to $1 billion to combat bird flu, USDA secretary says

“The USDA will spend up to $500 million to provide free biosecurity audits to farms and $400 million to increase payment rates to farmers who need to kill their chickens due to bird flu[….] The USDA is exploring vaccines for chickens but is not yet authorizing their use[….]”

7. An Innovative Program Supporting the Protection of Irreplaceable Saline Lakes

“[… T]he program aims to provide comprehensive data on water availability and lake health, develop strategies to monitor and assess critical ecosystems, and identify knowledge gaps to guide future research and resource management.”

8. EU to unveil ​‘Clean Industrial Deal’ to cut CO2, boost energy security

“The bold plan aims to revitalize and decarbonize heavy industry, reduce reliance on gas, and make energy cheaper, cleaner, and more secure. […] By July, the EU said it will ​“simplify state aid rules” to ​“accelerate the roll-out of clean energy, deploy industrial decarbonisation and ensure sufficient capacity of clean-tech manufacturing” on the continent.”

9. Oyster Restoration Investments Net Positive Returns for Economy and Environment

“Researchers expect the restored oyster reefs to produce $38 million in ecosystem benefits through 2048. “This network protects nearly 350 million oysters[….]” [NOAA provided] $14.9 million to expand the sanctuary network to 500 acres by 2026 […] through the Bipartisan Infrastructure Law.”

10. Nations back $200 billion-a-year plan to reverse nature losses

“More than 140 countries adopted a strategy to mobilize hundreds of billions of dollars a year to help reverse dramatic losses in biodiversity[….] A finance strategy adopted to applause and tears from delegates, underpins "our collective capacity to sustain life on this planet," said Susana Muhamad[….]”

February 15-21 news here | (all credit for images and written material can be found at the source linked; I don’t claim credit for anything but curating.)

[literaryvein-reblogs]

Writing Notes: Self-Editing

Take a Break Before Editing

One of the most effective self-editing techniques is to distance yourself from your writing before diving into the editing process. After completing your draft, give yourself some time away from the text – a few hours, a day, or even longer if possible. This break provides a fresh perspective, allowing you to approach your work with a more critical eye.

Read Aloud

Engage your auditory senses by reading your work aloud. This not only helps identify grammatical errors and awkward phrasing but also allows you to assess the overall flow and rhythm of your writing. Awkward sentences are more apparent when heard.

Focus on One Element at a Time

To avoid feeling overwhelmed during the self-editing process, concentrate on specific elements in each round. Start by checking for grammatical errors and punctuation, then move on to sentence structure, coherence, and finally, style. This systematic approach ensures a thorough examination of your writing.

Add Dimensions

After you are finished with your first draft, flip to the beginning and start anew. As you write and edit more of your story, you may add different aspects to a character that might need to be mentioned in a section you already edited. You might add a part of the plot that should be alluded to earlier in your book.

Fill in the Gaps

Re-reading your first draft might reveal plot holes that will be addressed via revisions. It may expose logical inconsistencies that must be buttressed with enhanced detail. If you, as the author, know a lot of details about a character’s backstory, make sure your reader does as well.

Mend Character Arcs

Audiences want engaging plots, but they also want detailed characters who undergo change during the events of a story. Use a second draft to make sure that your main character and key supporting characters follow consistent character arcs that take them on a journey over the course of the story. If your story is told through first person point of view (POV), this will be even more important as it will also affect the story’s narration.

Track the Pacing of your Story

Find ways to space out your story points so that every section of your novel is equally compelling and nothing feels shoehorned in.

Clean up Cosmetic Errors

When some first time writers think of the editing process, they mainly think of corrections to grammar, spelling, syntax, and punctuation. These elements are certainly important but such edits tend to come toward the end of the process. Obviously no book will go out for hard copy publication without proofreading for typos and grammatical errors, but in the early rounds of revising, direct most of your energy toward story and character. If you consider yourself a good writer who simply isn’t strong on elements like spelling, grammar, and punctuation, consider hiring an outside proofreader to help you with this part of the writing process.

Inject Variety

The best novels and short stories contain ample variety, no matter how long or short the entire manuscript may be. Look for ways to inject variety into your sentence structure, your narrative events, your dialogue, and your descriptive language. You never want a reader to feel like s/he’s already read a carbon copy of a certain scene from a few chapters back.

Check for Consistency

Consistency is key to maintaining a professional and polished tone in your writing. Ensure that your language, formatting, and style choices remain consistent throughout your piece. Inconsistencies can distract the reader and diminish the overall impact of your work.

Eliminate Redundancies

Effective communication is concise and to the point. During the self-editing phase, be vigilant in identifying and eliminating redundancies. Repetitive phrases and unnecessary words can dilute your message and hinder clarity.

Verify Facts and Information

If your writing incorporates facts, figures, or data, double-check the accuracy of your information. Providing accurate and up-to-date information enhances your credibility as a writer. Cross-referencing your sources during the self-editing process ensures the reliability of your content.

Consider Your Audience

Keep your target audience in mind during the self-editing process. Ensure that your language, tone, and examples are tailored to resonate with your intended readership. This step is crucial for creating a connection with your audience and enhancing the overall impact of your writing.

Utilise Editing Tools

Take advantage of the various editing tools available to writers. Spell and grammar checkers, and style guides can serve as valuable companions during the self-editing journey. However, remember that these tools are aids, not substitutes, for your critical evaluation.

Seek Feedback

Engage with others to gain fresh perspectives on your writing. Peer reviews or feedback from mentors can offer valuable insights that you might have overlooked. Embrace constructive criticism and use it to refine your work further.

Be Ruthless with Revisions

Effective self-editing requires a degree of ruthlessness. Don’t be afraid to cut or rewrite sections that do not contribute to the overall strength of your piece. Trim excess words, tighten sentences, and ensure that every element serves a purpose.

Sources: 1 2 3 4 ⚜ More: Writing Notes & References ⚜ On Editing

[eirianerisdar]

I’m already seeing the discourse start online so let’s be clear. Ferrari’s brakes were shit the ENTIRE race. Charles wasn’t the only one being told to lift. He caught up to Carlos a lot in the second stint because Carlos’s brakes were nearly on fire, too. Carlos’s brakes didn’t clean up until like 15-20 laps from the end. Sky kept talking about how both of them were being told to lift and coast. It’s arguable whether Charles’s brakes could have survived him pushing more to stay ahead of Lando but without detailed brake data, we won’t know.

Either way you understand why it was frustrating for Charles - the car didn’t give him what he needed to stay in 2nd and it was this or his brakes catching fire. When it’s not about technique but about equipment it always hurts more.

[phagodyke]

meeting w my boss went fine she never tells me when things are supposed to fail so I always worry when I get dodgy looking results... 😭😭

nothing going right at work today aouuugh

[willowwindss]

100 Inventions by Women

LIFE-SAVING/MEDICAL/GLOBAL IMPACT:

Artificial Heart Valve – Nina Starr Braunwald

Stem Cell Isolation from Bone Marrow – Ann Tsukamoto

Chemotherapy Drug Research – Gertrude Elion

Antifungal Antibiotic (Nystatin) – Rachel Fuller Brown & Elizabeth Lee Hazen

Apgar Score (Newborn Health Assessment) – Virginia Apgar

Vaccination Distribution Logistics – Sara Josephine Baker

Hand-Held Laser Device for Cataracts – Patricia Bath

Portable Life-Saving Heart Monitor – Dr. Helen Brooke Taussig

Medical Mask Design – Ellen Ochoa

Dental Filling Techniques – Lucy Hobbs Taylor

Radiation Treatment Research – Cécile Vogt

Ultrasound Advancements – Denise Grey

Biodegradable Sanitary Pads – Arunachalam Muruganantham (with women-led testing teams)

First Computer Algorithm – Ada Lovelace

COBOL Programming Language – Grace Hopper

Computer Compiler – Grace Hopper

FORTRAN/FORUMAC Language Development – Jean E. Sammet

Caller ID and Call Waiting – Dr. Shirley Ann Jackson

Voice over Internet Protocol (VoIP) – Marian Croak

Wireless Transmission Technology – Hedy Lamarr

Polaroid Camera Chemistry / Digital Projection Optics – Edith Clarke

Jet Propulsion Systems Work – Yvonne Brill

Infrared Astronomy Tech – Nancy Roman

Astronomical Data Archiving – Henrietta Swan Leavitt

Nuclear Physics Research Tools – Chien-Shiung Wu

Protein Folding Software – Eleanor Dodson

Global Network for Earthquake Detection – Inge Lehmann

Earthquake Resistant Structures – Edith Clarke

Water Distillation Device – Maria Telkes

Portable Water Filtration Devices – Theresa Dankovich

Solar Thermal Storage System – Maria Telkes

Solar-Powered House – Mária Telkes

Solar Cooker Advancements – Barbara Kerr

Microbiome Research – Maria Gloria Dominguez-Bello

Marine Navigation System – Ida Hyde

Anti-Malarial Drug Work – Tu Youyou

Digital Payment Security Algorithms – Radia Perlman

Wireless Transmitters for Aviation – Harriet Quimby

Contributions to Touchscreen Tech – Dr. Annette V. Simmonds

Robotic Surgery Systems – Paula Hammond

Battery-Powered Baby Stroller – Ann Moore

Smart Textile Sensor Fabric – Leah Buechley

Voice-Activated Devices – Kimberly Bryant

Artificial Limb Enhancements – Aimee Mullins

Crash Test Dummies for Women – Astrid Linder

Shark Repellent – Julia Child

3D Illusionary Display Tech – Valerie Thomas

Biodegradable Plastics – Julia F. Carney

Ink Chemistry for Inkjet Printers – Margaret Wu

Computerised Telephone Switching – Erna Hoover

Word Processor Innovations – Evelyn Berezin

Braille Printer Software – Carol Shaw

⸻

HOUSEHOLD & SAFETY INNOVATIONS:

Home Security System – Marie Van Brittan Brown

Fire Escape – Anna Connelly

Life Raft – Maria Beasley

Windshield Wiper – Mary Anderson

Car Heater – Margaret Wilcox

Toilet Paper Holder – Mary Beatrice Davidson Kenner

Foot-Pedal Trash Can – Lillian Moller Gilbreth

Retractable Dog Leash – Mary A. Delaney

Disposable Diaper Cover – Marion Donovan

Disposable Glove Design – Kathryn Croft

Ice Cream Maker – Nancy Johnson

Electric Refrigerator Improvements – Florence Parpart

Fold-Out Bed – Sarah E. Goode

Flat-Bottomed Paper Bag Machine – Margaret Knight

Square-Bottomed Paper Bag – Margaret Knight

Street-Cleaning Machine – Florence Parpart

Improved Ironing Board – Sarah Boone

Underwater Telescope – Sarah Mather

Clothes Wringer – Ellene Alice Bailey

Coffee Filter – Melitta Bentz

Scotchgard (Fabric Protector) – Patsy Sherman

Liquid Paper (Correction Fluid) – Bette Nesmith Graham

Leak-Proof Diapers – Valerie Hunter Gordon

FOOD/CONVENIENCE/CULTURAL IMPACT:

Chocolate Chip Cookie – Ruth Graves Wakefield

Monopoly (The Landlord’s Game) – Elizabeth Magie

Snugli Baby Carrier – Ann Moore

Barrel-Style Curling Iron – Theora Stephens

Natural Hair Product Line – Madame C.J. Walker

Virtual Reality Journalism – Nonny de la Peña

Digital Camera Sensor Contributions – Edith Clarke

Textile Color Processing – Beulah Henry

Ice Cream Freezer – Nancy Johnson

Spray-On Skin (ReCell) – Fiona Wood

Langmuir-Blodgett Film – Katharine Burr Blodgett

Fish & Marine Signal Flares – Martha Coston

Windshield Washer System – Charlotte Bridgwood

Smart Clothing / Sensor Integration – Leah Buechley

Fibre Optic Pressure Sensors – Mary Lou Jepsen

[canmom]

oh no she's talking about AI some more

to comment more on the latest round of AI big news (guess I do have more to say after all):

chatgpt ghiblification

trying to figure out how far it's actually an advance over the state of the art of finetunes and LoRAs and stuff in image generation? I don't keep up with image generation stuff really, just look at it occasionally and go damn that's all happening then, but there are a lot of finetunes focusing on "Ghibli's style" which get it more or less well. previously on here I commented on an AI video model generation that patterned itself on Ghibli films, and video is a lot harder than static images.

of course 'studio Ghibli style' isn't exactly one thing: there are stylistic commonalities to many of their works and recurring designs, for sure, but there are also details that depend on the specific character designer and film in question in large and small ways (nobody is shooting for My Neighbours the Yamadas with this, but also e.g. Castle in the Sky does not look like Pom Poko does not look like How Do You Live in a number of ways, even if it all recognisably belongs to the same lineage).

the interesting thing about the ghibli ChatGPT generations for me is how well they're able to handle simplification of forms in image-to-image generation, often quite drastically changing the proportions of the people depicted but recognisably maintaining correspondence of details. that sort of stylisation is quite difficult to do well even for humans, and it must reflect quite a high level of abstraction inside the model's latent space. there is also relatively little of the 'oversharpening'/'ringing artefact' look that has been a hallmark of many popular generators - it can do flat colour well.

the big touted feature is its ability to place text in images very accurately. this is undeniably impressive, although OpenAI themeselves admit it breaks down beyond a certain point, creating strange images which start out with plausible, clean text and then it gradually turns into AI nonsense. it's really weird! I thought text would go from 'unsolved' to 'completely solved' or 'randomly works or doesn't work' - instead, here it feels sort of like the model has a certain limited 'pipeline' for handling text in images, but when the amount of text overloads that bandwidth, the rest of the image has to make do with vague text-like shapes! maybe the techniques from that anthropic thought-probing paper might shed some light on how information flows through the model.

similarly the model also has a limit of scene complexity. it can only handle a certain number of objects (10-20, they say) before it starts getting confused and losing track of details.

as before when they first wired up Dall-E to ChatGPT, it also simply makes prompting a lot simpler. you don't have to fuck around with LoRAs and obtuse strings of words, you just talk to the most popular LLM and ask it to perform a modification in natural language: the whole process is once again black-boxed but you can tell it in natural language to make changes. it's a poor level of control compared to what artists are used to, but it's still huge for ordinary people, and of course there's nothing stopping you popping the output into an editor to do your own editing.

not sure the architecture they're using in this version, if ChatGPT is able to reason about image data in the same space as language data or if it's still calling a separate image model... need to look that up.

openAI's own claim is:

We trained our models on the joint distribution of online images and text, learning not just how images relate to language, but how they relate to each other. Combined with aggressive post-training, the resulting model has surprising visual fluency, capable of generating images that are useful, consistent, and context-aware.

that's kind of vague. not sure what architecture that implies. people are talking about 'multimodal generation' so maybe it is doing it all in one model? though I'm not exactly sure how the inputs and outputs would be wired in that case.

anyway, as far as complex scene understanding: per the link they've cracked the 'horse riding an astronaut' gotcha, they can do 'full glass of wine' at least some of the time but not so much in combination with other stuff, and they can't do accurate clock faces still.

normal sentences that we write in 2025.

it sounds like we've moved well beyond using tools like CLIP to classify images, and I suspect that glaze/nightshade are already obsolete, if they ever worked to begin with. (would need to test to find out).

all that said, I believe ChatGPT's image generator had been behind the times for quite a long time, so it probably feels like a bigger jump for regular ChatGPT users than the people most hooked into the AI image generator scene.

of course, in all the hubbub, we've also already seen the white house jump on the trend in a suitably appalling way, continuing the current era of smirking fascist political spectacle by making a ghiblified image of a crying woman being deported over drugs charges. (not gonna link that shit, you can find it if you really want to.) it's par for the course; the cruel provocation is exactly the point, which makes it hard to find the right tone to respond. I think that sort of use, though inevitable, is far more of a direct insult to the artists at Ghibli than merely creating a machine that imitates their work. (though they may feel differently! as yet no response from Studio Ghibli's official media. I'd hate to be the person who has to explain what's going on to Miyazaki.)

google make number go up

besides all that, apparently google deepmind's latest gemini model is really powerful at reasoning, and also notably cheaper to run, surpassing DeepSeek R1 on the performance/cost ratio front. when DeepSeek did this, it crashed the stock market. when Google did... crickets, only the real AI nerds who stare at benchmarks a lot seem to have noticed. I remember when Google releases (AlphaGo etc.) were huge news, but somehow the vibes aren't there anymore! it's weird.

I actually saw an ad for google phones with Gemini in the cinema when i went to see Gundam last week. they showed a variety of people asking it various questions with a voice model, notably including a question on astrology lmao. Naturally, in the video, the phone model responded with some claims about people with whatever sign it was. Which is a pretty apt demonstration of the chameleon-like nature of LLMs: if you ask it a question about astrology phrased in a way that implies that you believe in astrology, it will tell you what seems to be a natural response, namely what an astrologer would say. If you ask if there is any scientific basis for belief in astrology, it would probably tell you that there isn't.

In fact, let's try it on DeepSeek R1... I ask an astrological question, got an astrological answer with a really softballed disclaimer:

Individual personalities vary based on numerous factors beyond sun signs, such as upbringing and personal experiences. Astrology serves as a tool for self-reflection, not a deterministic framework.

Ask if there's any scientific basis for astrology, and indeed it gives you a good list of reasons why astrology is bullshit, bringing up the usual suspects (Barnum statements etc.). And of course, if I then explain the experiment and prompt it to talk about whether LLMs should correct users with scientific information when they ask about pseudoscientific questions, it generates a reasonable-sounding discussion about how you could use reinforcement learning to encourage models to focus on scientific answers instead, and how that could be gently presented to the user.

I wondered if I'd asked it instead to talk about different epistemic regimes and come up with reasons why LLMs should take astrology into account in their guidance. However, this attempt didn't work so well - it started spontaneously bringing up the science side. It was able to observe how the framing of my question with words like 'benefit', 'useful' and 'LLM' made that response more likely. So LLMs infer a lot of context from framing and shape their simulacra accordingly. Don't think that's quite the message that Google had in mind in their ad though.

I asked Gemini 2.0 Flash Thinking (the small free Gemini variant with a reasoning mode) the same questions and its answers fell along similar lines, although rather more dry.

So yeah, returning to the ad - I feel like, even as the models get startlingly more powerful month by month, the companies still struggle to know how to get across to people what the big deal is, or why you might want to prefer one model over another, or how the new LLM-powered chatbots are different from oldschool assistants like Siri (which could probably answer most of the questions in the Google ad, but not hold a longform conversation about it).

some general comments

The hype around ChatGPT's new update is mostly in its use as a toy - the funny stylistic clash it can create between the soft cartoony "Ghibli style" and serious historical photos. Is that really something a lot of people would spend an expensive subscription to access? Probably not. On the other hand, their programming abilities are increasingly catching on.

But I also feel like a lot of people are still stuck on old models of 'what AI is and how it works' - stochastic parrots, collage machines etc. - that are increasingly falling short of the more complex behaviours the models can perform, now prediction combines with reinforcement learning and self-play and other methods like that. Models are still very 'spiky' - superhumanly good at some things and laughably terrible at others - but every so often the researchers fill in some gaps between the spikes. And then we poke around and find some new ones, until they fill those too.

I always tried to resist 'AI will never be able to...' type statements, because that's just setting yourself up to look ridiculous. But I will readily admit, this is all happening way faster than I thought it would. I still do think this generation of AI will reach some limit, but genuinely I don't know when, or how good it will be at saturation. A lot of predicted 'walls' are falling.

My anticipation is that there's still a long way to go before this tops out. And I base that less on the general sense that scale will solve everything magically, and more on the intense feedback loop of human activity that has accumulated around this whole thing. As soon as someone proves that something is possible, that it works, we can't resist poking at it. Since we have a century or more of science fiction priming us on dreams/nightmares of AI, as soon as something comes along that feels like it might deliver on the promise, we have to find out. It's irresistable.

AI researchers are frequently said to place weirdly high probabilities on 'P(doom)', that AI research will wipe out the human species. You see letters calling for an AI pause, or papers saying 'agentic models should not be developed'. But I don't know how many have actually quit the field based on this belief that their research is dangerous. No, they just get a nice job doing 'safety' research. It's really fucking hard to figure out where this is actually going, when behind the eyes of everyone who predicts it, you can see a decade of LessWrong discussions framing their thoughts and you can see that their major concern is control over the light cone or something.

[matchdatapro]

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[lollystocks]

Therapy for the Dead and Buried

A Danny Phantom x The Bright Sessions Crossover

DP Crossover Angst Week Day 6 - Runaway

Summary: Alone and in hiding, Danny is sent to mandatory therapy. It's a bit... strange. And unusual.

Notes: First chapter of a multific! Should be relatively friendly to those unfamiliar with The Bright Sessions, as it's mostly Danny's POV.

AO3

“New patient. Session one. Male, seventeen, no known history of psychological counseling. Referred by school for ‘antisocial behavior’, but no examples given, and strong comments were made about his, quote… ‘unsettling vibes.’ Condition unknown.”

-

It was a very ordinary-looking room.

Danny wasn't sure what he'd been expecting, but "boring" hadn't really occurred to him.

The office of Dr. Bright was reasonably spacious, with pure white walls and a thick baby blue carpet. A single sash window overlooked the park, and before it sat a laminate desk - almost certainly IKEA - with precisely organized trays of papers and stationery. No photos or trinkets adorned it. Not even a Newton's cradle, disappointingly.

Towards the center of the room sat two small sofas - firm looking, upholstered in dark blue vinyl. The hospital type, designed for ease of cleaning up bodily fluids. Plump-looking cushions softened their corners. A low coffee table sat between them, sporting a small succulent and a large box of tissues.

Danny had chosen the sofa which faced the window and door, with his back to the blank wall. He got the impression that he'd made the wrong choice, somehow. He didn't give a shit.

The doctor was looking at him, one manicured eyebrow just a micrometer higher than the other. The silence stretched on, awkwardly.

"Um. Sorry. Could you repeat the question, please?"

"Of course. I asked if you knew why you were here, James?"

Danny stared out of the window, into the cloudy sky. There were many ways to answer that question. Classic shrink tactic, probably, to suss out his brain. Most of the answers that came to mind were smartassery - because this is where your office is. Because the bus brought me here. Because of human evolution. Because I'd get kicked out of my school if I didn't come.

What impression did he want to give her? Who did Danny James want to be now? What was most useful to him?

He looked at the doctor's face. "Because people are unsettled by me. I can't help it, but they are. And they want me to stop. Unsettling them, that is. And you're meant to teach me, like, body language techniques or something."

Doctor Bright settled into the sofa a little, like a question had been answered, or a data point obtained. She smoothed the creaseless paper in her lap.

"And what makes you think that?"

"The whole, 'James, there's clearly something deeply fucking wrong with you, and it's freaking out your classmates. Get help,' thing kinda clued me in, Doc."

"I assume you're paraphrasing."

"I'm not, actually. F-bomb and everything. Scout's honor."

"I'm surprised that your principal would use such language with you, James. That must have been disconcerting."

Danny stared at her. That was an unexpected response. "You saying you believe me? That he said that?"

"I do, James. My job here isn't to be a skeptic, or to 'find out the truth'. I'm here to listen, offer advice, and help you learn some skills and techniques to redirect your own behavior and mentality as you wish." The doctor adjusted her glasses. "So yes, James, I believe you. And as your therapist, I will believe whatever you tell me in this room, no matter how... outlandish, you may feel it is. That is my job here."

Danny couldn't help but smile at that, just a little. "That's a sweet sentiment Doctor, genuinely, but you can't mean that seriously. You must get all sorts of compulsive liars or straight-up crazies through here, there's no way you just decide to believe them all."

"Let me rephrase, then. While it's true that many of my patients will tell me things that they know not to be true, I find it best to start from a place of belief. If I decide, after getting to know them, that they are in fact serially lying to me, or are mistaken, I adjust accordingly. But until I can know that? I believe them."

"So if a crackhead told you they could fly. You'd just believe them?"

"I would, yes. Up and until I come to the irrefutable conclusion that they are lying or mistaken. Does that surprise you?"

Danny scoffed. "Yeah, that surprises me. It's nuts. There's no way you can do your job properly like that."

Doctor Bright smiled. "I've found it works best. For one thing, any patients I get through this door will come to learn that, no matter how strange or unusual it may be, they can tell me. I will not judge them, or turn them away, or have them committed."

There was a pause.

"So. You want me to tell you how ' strange and unusual' I am."

"No, James. I want you to tell me whatever you wish to tell me. This is an introductory session, I just want to get to know you."

"Specifically, you want me to tell you outlandish things about myself. Things no one else would believe. Things that make others scared of me."

"James, I merely-"

"Nope. Bye. Tell Principal Khan I failed at therapy, I guess."

He grabbed his backpack, and left.

-

“End of session one. Patient left abruptly.”

Chapter 2 here

Masterpost here

[darkmaga-returns]

The Social Security Administration has removed 12.3 million people whose birth dates say they are over 120 years old from the rolls, preventing fraud and abuse at the agency.

The clean-up was achieved following an investigation by President Donald Trump’s Department of Government Efficiency (DOGE).

DOGE’s team used then-senior advisor Elon Musk’s advanced algorithms and data collection techniques to identify people on the rolls who should have been marked as “deceased.”

In a post on X, DOGE revealed that inaccurate data has now been updated, with millions of dead people removed.

“After 11 weeks, @SocialSecurity has finished this major cleanup initiative: ~12.3M individuals aged 120+ have now been marked as deceased,” the agency wrote.

The initiative began in March.

It was not clear whether any money was going to the 120+ year individuals on the list.

However, the inclusion of false data could lead to fraud if hackers obtained the information of dead people still listed as being alive.

There were some names that still had to be verified, as they had two or more differing birth dates in the system.