2025-11-22 00:00:30
Bill Simmons and Kyle Brandt dive into John Hughes’s 1985 classic “Weird Science,” unpacking all the iconic ’80s vibes—from Anthony Michael Hall’s awkward teen energy to Kelly LeBrock’s bombshell arrival. Expect a deep (and delightfully off-color) look at the movie’s blend of sex, drugs, rock ’n’ roll and sci-fi gadgetry, plus plenty of nerdy trivia and behind-the-scenes anecdotes.
Producers Craig Horlbeck, Chia Hao Tat and Eduardo Ocampo keep the conversation rolling while State Farm pops in to remind you about bundle savings. Subscribe to The Ringer channels for more movie breakdowns, and follow them across YouTube, Twitter, Facebook and Instagram for all the pop-culture goodness.
Watch on YouTube
2025-11-21 23:56:52
Design patterns provide reusable solutions to common software problems. Here’s a concise recap with actual Java / Spring framework examples — not generic ones.
Ensures only one instance exists.
/ Real Example:
Calendar calendar = Calendar.getInstance();
Runtime runtime = Runtime.getRuntime();
Used in logging, DB connections, cache managers.
Creates objects without exposing logic.
// Example: Java Collections
List<String> list = List.of("A", "B"); // Java 9 Factory
NumberFormat format = NumberFormat.getInstance();
Spring Example: BeanFactory.getBean("beanName")
Best for creating complex objects with many fields.
// Example: StringBuilder
String result = new StringBuilder()
.append("Hello ")
.append("World")
.toString();
Also used in Lombok: @Builder
Create objects by cloning.
// Example: Object clone()
Employee cloned = (Employee) employee.clone();
Useful when object creation is costly.
Converts one interface to another.
// Example: InputStreamReader adapts InputStream to Reader.
Reader reader = new InputStreamReader(inputStream);
Used when integrating legacy systems.
Adds functionality at runtime.
// Example: I/O Streams
BufferedInputStream bis = new BufferedInputStream(new FileInputStream("file.txt"));
Also in Spring: HandlerInterceptor, Filter
Provides a simplified interface.
// Example: JDBC
Connection conn = DriverManager.getConnection(url);
// JDBC hides complex driver logic!
Also: HibernateTemplate, RestTemplate
Controls access to an object.
// Example: Java Dynamic Proxy (used in Spring AOP)
Proxy.newProxyInstance(...)
Used for authentication, logging, lazy loading.
Notifies when state changes.
// Example: PropertyChangeListener (Java Beans)
button.addActionListener(...);
Spring Example: ApplicationEventPublisher
Different algorithms, same interface.
// Example: Comparator
Collections.sort(list, (a, b) -> a.compareTo(b));
Spring: AuthenticationStrategy, RetryStrategy
Defines skeleton → subclasses override steps.
// Example: HttpServlet.doGet() / doPost()
abstract class HttpServlet {
protected void doGet() {}
}
Spring JDBC Template follows this pattern.
Encapsulates a request as an object.
// Example: Runnable
ExecutorService.exec(new MyTask());
Used in scheduling, undo operations.
Sequential access without exposing structure.
// Example:
Iterator<String> itr = list.iterator();
| Pattern | Real Java Example |
|---|---|
| Singleton | Runtime.getRuntime() |
| Factory | NumberFormat.getInstance() |
| Builder | StringBuilder |
| Adapter | InputStreamReader |
| Decorator | BufferedInputStream |
| Facade | JDBC (DriverManager) |
| Proxy | Spring AOP / Dynamic Proxy |
| Observer | ActionListener |
| Strategy | Comparator |
| Template Method | HttpServlet |
| Command | Runnable |
2025-11-21 23:48:37
While building my ConversaAI web app, I started experimenting with a local AI model using Ollama, running the 𝗚𝗲𝗺𝗺𝗮 𝟯 (𝟭𝗕) model, a “lightweight” 𝟴𝟭𝟱 𝗠𝗕 model.
According to the documentation, a system with 8 GB RAM can handle models up to 7B parameters, so I expected a smooth run since my system has:
• CPU: Intel i7-8650U @ 1.9 GHz
• RAM: 16 GB
• OS: Windows 11 Pro
• GPU: NVIDIA MX130
But in practice… it’s a different story.
Every time model started generating a longer response,
• CPU jumped from 𝟭𝟬 % to 𝟲𝟬 %
• GPU (NVIDIA MX130) hit 𝟵𝟱% 𝘂𝘁𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻.
• Memory climbed to 𝟴.𝟱 𝗚𝗕 / 𝟭𝟲 𝗚𝗕
• Fans roared like a jet engine. 🌀
…and then the model simply stopped generating mid-response. No error. No crash message.
A few seconds later, the fan also slowed down again.
👇 Here’s the before-and-during-generating-longer-response comparison of hardware utilization.
At first, I thought something broke.
But the real reason was far more interesting.
Even at 1B parameters, the model performs 𝗯𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗽𝗲𝗿 𝘁𝗼𝗸𝗲𝗻.
Every generated word triggers 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝗺𝗮𝘁𝗿𝗶𝘅 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. The GPU handles the heavy computation, while the CPU manages data flow, scheduling, and memory movement. Meanwhile, RAM and VRAM temporarily hold weights, activations, and intermediate states.
In simple terms:
• The 𝗚𝗣𝗨 does intense math.
• The 𝗖𝗣𝗨 coordinates everything.
• 𝗥𝗔𝗠/𝗩𝗥𝗔𝗠 hold model weights and activations.
• The 𝗳𝗮𝗻 is the system’s way of saying, “I’m working really hard.”
When GPU utilization peaks (95%), it produces heat rapidly.
Once temperature crosses a safe limit, 𝘁𝗵𝗲𝗿𝗺𝗮𝗹 𝘁𝗵𝗿𝗼𝘁𝘁𝗹𝗶𝗻𝗴 activates, a safety mechanism that slows performance to protect the hardware.
This throttling explains why the AI response halted and fan speed decreased shortly after.
📽️ Watch the video below that explains this situation.
Hardware strain during model inference
“Small” in AI doesn’t mean “light” for consumer hardware.
A quick comparison:
• My GPU (MX130) → ~0.4 TFLOPS
• Modern AI GPU (RTX 4090) → ~82 TFLOPS
A gap of nearly 𝟮𝟬𝟬×.
So, even though my laptop is capable of running local inference, it has trouble maintaining it, particularly when the model needs to continue executing billions of operations per second for longer outputs.
Running AI locally isn’t just about generating text. It teaches you how your CPU, GPU, RAM, and thermal system manage the workload of modern AI models in real time.
Now, every time my laptop fan spins up, I know it’s just trying to think really hard. 😅
Did you ever run into a similar issue? If yes, how did you tackle it?
If you found this post useful or learned something new, drop a ❤️ and share your thoughts in the comments. I’d love to hear your experience.
Feel free to reach out on
2025-11-21 23:37:23
Introduction
At My MobApp Studio, we are preparing to launch a new mobile app. To make the most strategic decision possible, we must understand the Google Play Store ecosystem: market size, category performance, pricing dynamics, and the potential opportunities for our new product.
This report summarizes the results of a full exploratory data analysis (EDA) conducted on the Google Play Store dataset. The study follows the structure of a scientific experiment:
Assumptions
Methodology
Data cleaning & preparation
Experiments & visualizations
Insights
Conclusions & next steps
All analyses were performed using Python in Jupyter Notebook, utilizing functions such as load_dataset(), print_summarize_dataset(), clean_dataset(), and various histogram, heatmap, and scatter plot utilities.
🧪 1. Assumptions
Before examining the data, we established the following assumptions:
Download count is a proxy for market demand — higher installs indicate stronger user interest
Category popularity influences competition and revenue potential — saturated categories may be harder to penetrate
Paid apps represent a smaller but more valuable segment — fewer downloads but higher revenue per user
Family category is critical due to its broad user age range and parental purchasing power
Price impacts installs and must be analyzed per category to understand willingness to pay
Google Play Store metadata is sufficiently reliable for high-level strategic analysis
🧹 2. Data Preparation & Cleaning
We applied the clean_dataset() pipeline to ensure data quality:
Removed duplicates
Converted Reviews, Installs, Price, and Rating to numeric types
Standardized Size measurements to MB
Parsed Android version strings
Filled missing values with medians (numerical) or cleaned strings (categorical)
Converted dates into datetime format
Ensured all statistical functions operate on consistent data types
After cleaning, the dataset was ready for rigorous analysis.
📈 3. Experiments & Visualizations
3.1. Most Popular Paid Apps in the Family Category
Goal: Identify which paid Family apps attract the most installs.
Result: The majority of top-performing paid Family apps belong to education and creativity sub-genres. Paid Family apps generally have moderate install numbers, but the leaders stand out sharply due to niche audience demand and strong brand reputation.
3.2. Most Popular Genres Within Paid Family Apps
We aggregated installs per genre for paid Family apps only.
Result: The pie chart highlights:
Education dominates the paid Family segment with the largest share
Creativity and Simulation is the second most popular genre
Other genres represent relatively small fractions
This shows parents are willing to pay premium prices for educational content that benefits their children's development.
3.3. Installations per Category
We created a summary table of total installs by category.
Key insights:
Communication, Social, Tools, Video Players, and Entertainment are the largest categories by total installs
Lifestyle, Beauty, Events, and Parenting are significantly smaller markets
This helps identify where consumer demand is most concentrated
3.4. Market Distribution: Installs per Category
This pie chart visually expresses each category's share of total downloads across the entire Play Store.
Main observation: Just a handful of categories control the majority of consumer attention. Targeting a high-share category requires stronger differentiation and marketing to stand out from established players.
3.5. Mean Price per Category
We computed the average paid-app price per category to understand pricing dynamics.
Key findings:
Finance, Lifestyle, and Productivity categories have the highest-priced apps
Family, Education, and Entertainment apps remain competitively priced with lower averages
This means pricing strategy depends heavily on your target vertical and audience expectations
3.6. Most Expensive Apps per Category
For each category, we extracted the single most expensive app to identify pricing outliers.
Interesting results:
Some niche categories contain surprisingly expensive apps, with prices reaching up to $399 in rare cases
Business, Medical, and Finance categories often feature premium-priced apps due to professional audiences willing to pay for specialized tools
Most consumer-facing categories have maximum prices under $20
📊 4. Correlation & Statistical Exploration
Using histograms, correlation matrix heatmaps, and scatter matrices, we explored relationships between variables.
Key observations:
Installs correlate positively with Reviews — unsurprising, as bigger apps naturally receive more feedback
Price has negative correlation with Installs — higher prices reduce download volume
Size has weak or no correlation with Ratings — users don't penalize larger apps if quality is high
Rating distribution is heavily skewed toward 4.0+ — most successful apps maintain high quality standards
This supports the idea that pricing and marketing matter more than technical attributes like file size.
🧠 5. Key Insights
Market Size
Total downloads across the dataset reach into the billions
Paid apps represent less than 10% of the dataset but bring significant revenue potential
The market is massive but highly concentrated in a few dominant categories
By Category
Top demand categories: Communication, Social, Tools, Video Players, Entertainment
Top revenue-potential paid categories: Finance, Productivity, Medical
Emerging opportunities: Health & Fitness, Education
Family Category
Paid Family apps are dominated by Education-focused content
Parents are especially willing to pay for learning and creativity apps
Top-paid Family apps still have moderate install ranges compared to free alternatives
Pricing Strategy
Avoid overpricing in traditionally low-price categories (Family, Education, Games)
High-price opportunities exist in productivity, medical, and professional tool niches
Free-to-paid conversion through freemium models shows strong results in most categories
📌 6. Conclusion
This experiment allowed us to understand the Google Play market from a data-driven perspective:
The market is massive and highly concentrated — success requires strategic category selection
Category choice influences both visibility and revenue potential — different categories have different dynamics
For a Family-focused app, success relies on delivering educational value — parents prioritize learning
For a profit-focused app, Finance or Productivity may offer higher returns — professional users pay premium prices
Given My MobApp Studio's existing strengths in design and software engineering, we are well positioned to create a polished, user-friendly app tailored to one of the high-potential categories identified in this analysis.
2025-11-21 23:36:20
In an era of information overload and overcomplicated tools, QuickJot offers a refreshing alternative: a minimalist micro-note network that enables instant idea sharing through nothing more than a 6-character key. No accounts. No authentication barriers. No friction.
QuickJot demonstrates that powerful solutions don't require complex infrastructure or significant investment. Built entirely on free-tier cloud services, it serves as both a practical tool and a proof-of-concept for developers exploring cost-effective full-stack architecture.
Live Application: quickjot-kqo6.onrender.com
GitHub Repository: github.com/hejhdiss/QuickJot
License: GPLv3 (Open Source)
Modern note-taking and sharing tools often introduce unnecessary complexity:
These barriers make quick information sharing cumbersome. QuickJot eliminates this friction entirely.
QuickJot's approach is elegantly simple:
CB7R0m)This key-based system transforms notes into portable, instantly accessible information packets that can be shared across devices, teams, or individuals without any authentication overhead.
QuickJot utilizes modern cloud infrastructure to deliver a fast, reliable experience:
Frontend & Backend
Database & Hosting
QuickJot's design embodies several core principles:
The interface removes all non-essential elements, creating a distraction-free writing environment that lets users focus solely on their content.
By eliminating authentication requirements, QuickJot becomes immediately accessible to anyone, anywhere, on any device with a web browser.
The 6-character key system provides a perfect balance between memorability, shareability, and uniqueness for a micro-note platform.
Open-source under GPLv3, the entire codebase is available for learning, modification, and improvement by the developer community.
QuickJot proves that simple tools can be remarkably powerful. In a landscape dominated by feature-bloated applications, it demonstrates that focused functionality often serves users better than complexity.
QuickJot demonstrates that meaningful applications can be built with smart architectural choices and creative problem-solving.
QuickJot represents more than a note-taking tool—it's a statement about modern software development:
✓ Simplicity scales: Focused applications can serve users effectively without endless features
✓ Cloud platforms enable innovation: Modern infrastructure makes deployment accessible
✓ Open source enables learning: Transparent codebases accelerate developer education
✓ Minimal friction maximizes utility: Removing barriers increases adoption and usefulness
Developer: Muhammed Shafin P
GitHub: @hejhdiss
Concept Creator: Vazeerudheen
Instagram: @vazeerudheen_
Experience the simplicity of key-based micro-notes:
🌐 Try QuickJot: quickjot-kqo6.onrender.com
💻 Explore the Code: github.com/hejhdiss/QuickJot
📱 Follow Development:
QuickJot demonstrates that innovation doesn't always mean adding more features—sometimes it means removing everything unnecessary. By focusing on a single use case and executing it perfectly, QuickJot delivers a tool that's immediately useful and refreshingly simple.
In a world of complexity, QuickJot offers clarity. In a market of closed platforms, it offers open collaboration.
QuickJot: Share ideas with nothing but a key.
Open Source • Built for Everyone
2025-11-21 23:34:51
Reliable Plumbing Solutions in Bolton: Farworth Plumbing delivers expert bathroom and shower installations, radiator fitting, leak detection, and rapid pipe repairs. Skilled engineers ensure quality workmanship, efficient systems, and dependable service across the region—always ready to help.