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Whats inside OpenAI’s Stargate megafactory? – Sam Altman

OpenAI's Stargate megafactory in Abilene, Texas is a $500 billion global bet on the future of AI, built in partnership with industry giants like SoftBank and Oracle.
Stargate houses over 400,000 GPUs spread across 1,200 acres, working day and night to train the next generation of AI models.
The scale and ambition of Stargate exemplify the rapid evolution of the tech industry, leaving a profound impact on individuals like Anurup Millan, a developer and entrepreneur.
Stargate offers a glimpse into a highly connected future where technology and humanity are intertwined, reflecting the fast-paced nature of AI advancement.

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Javacodegeeks

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Docker Compose Keep Container Running Example

Docker containers exit because they are designed to run a single primary process, which is specified either by the CMD or ENTRYPOINT in the Dockerfile, or by the command provided at runtime.
Reasons why a Docker container might exit include main process completion, failed health checks, process termination, empty or invalid command, and lack of a foreground process.
Running an infinite loop in a Docker container or using the tail -f command on a file such as /dev/null can keep a container alive by maintaining an active process.
Allocating a TTY or using the sleep infinity command are additional methods to ensure containers stay running in Docker Compose.
Verification of container status can be done using commands like docker ps, docker logs , and docker exec -it /bin/sh.
This article provides strategies to keep Docker containers running in scenarios where no long-running service is present, beneficial for debugging sessions or development environments.
Techniques discussed include simulating interactive sessions, executing indefinite sleep states, and using infinite loops to maintain container uptime.
Overall, understanding how Docker containers function and implementing appropriate strategies can ensure containers remain active when orchestrated with Docker Compose.

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Medium

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You Are Misusing Interfaces in Go - Architecture Smells: Wrong Abstractions

Using interfaces in Go is essential for following SOLID principles and abstracting implementation details from the caller/client side.
The use of interfaces becomes necessary when abstracting packages from one another and for testing the caller/client side independently of external dependencies.
It is emphasized that putting an interface in front of every struct definition may not be the best practice and should be done thoughtfully after considering the need for abstraction and testing requirements.
The best practice for using interfaces in Go involves asking critical questions about the necessity of abstraction, direction of dependencies, and testing needs before deciding where interfaces should be implemented.

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Python Blogs

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Top 5 Strategies To Outsmart Hackers

Creating strong passwords with a mix of numbers, letters, and symbols is crucial to fend off hackers' attacks.
Implementing Two-Factor Authentication (2FA) adds an extra layer of security by requiring both a password and a unique code.
Being cautious of phishing scams can prevent hackers from tricking individuals into revealing sensitive information.
Regularly updating all software, including operating systems and applications, is essential to patch vulnerabilities that hackers can exploit.
Using security tools like antivirus software, firewalls, VPNs, and ad blockers can provide additional protection against cyber threats.

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Medium

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The Power of Digital Minimalism: Why Less Screen Time Means More Life…

Digital minimalist phones are gaining popularity as people seek clarity and control in a hyperconnected world.
These pared-down devices focus on calling, texting, and basic productivity to offer a break from constant notifications and distractions.
Minimalist phones help users reclaim time and attention by providing stripped-down interfaces and limited features.
Studies suggest that excessive screen time can lead to anxiety, depression, and sleep issues, making digital detox a beneficial practice for improving mental well-being.

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Dev

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🚀 Top 6 PDF Viewers for React.js Developers in 2025

When working with React.js, displaying PDFs can range from simple to complex, depending on needs.
Selecting a PDF viewer depends on factors like project requirements, ease of integration, and customization options.
Open source options like PDF.js and react-pdf offer basic PDF rendering and easy integration for small to mid-sized applications.
Paid options like React PDF and React PDF Viewer provide more functionalities tailored for React projects, including customization and advanced features.
Hybrid options like PDF.js Express and Syncfusion React PDF Viewer offer a mix of free and paid features for scalable PDF capabilities.
Consider factors like React integration, feature strengths, and limitations when choosing a PDF viewer for your project.
Always review documentation and support to ensure the selected library aligns with your project's scope, future needs, and budget.
React PDF is recommended for its flexible, powerful features suitable for various React.js applications.
Different libraries cater to different needs, with open source options being budget-friendly and paid options offering more advanced features.
It's essential to assess each library's strengths, weaknesses, and compatibility with your project before making a decision.

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Self-Learning-Java

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How to rename a column in BigQuery?

BigQuery supports renaming columns using the ALTER TABLE RENAME COLUMN statement.
To rename a column in BigQuery, use the syntax: ALTER TABLE `project.dataset.table` RENAME COLUMN old_column_name TO new_column_name;
Important considerations include the impact on dependent objects, limitations on renaming nested fields, and restrictions on renaming partitioning and clustering columns.
Best practices for column renaming in BigQuery involve backing up data, updating dependencies, testing changes, and reviewing documentation.

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Self-Learning-Java

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How to Add a New Column to an Existing Table in BigQuery?

Adding a new column to an existing table in BigQuery is a common task when evolving your data schema. BigQuery allows schema updates like adding new columns without requiring a full table rewrite.
You can use the ALTER TABLE statement to add a new column. For example, to add a new column named 'new_column_name' with data type STRING to a table, you can execute: ALTER TABLE `my_project.my_dataset.my_table` ADD COLUMN new_column_name STRING;
To demonstrate, you can create an employee table with columns like employee_id, first_name, last_name, email, hire_date, and department. Afterward, you can add a new column 'salary' of type DOUBLE to the employee table using ALTER statement.
Alternatively, you can also add a new column by using the 'Edit schema' option in the BigQuery UI. Simply click on 'Add field', provide the field name, type, Mode, and description, and then save the changes to reflect the new column in the table schema.

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Self-Learning-Java

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Efficient Schema Design in BigQuery

BigQuery performs best with denormalized schema designs that avoid JOIN operations and store all necessary information in a single table.
Normalized schemas like star and snowflake schemas used in traditional relational databases are not as efficient in BigQuery.
Denormalization in BigQuery leads to faster query execution by eliminating costly JOIN operations.
The benefits of denormalization in BigQuery include distributed processing, better query performance, and reduced query complexity.
Some queries like ORDER BY or DISTINCT may still require data shuffling, impacting performance in BigQuery.
Partitioning and clustering tables can reduce shuffling and optimize queries in BigQuery.
Utilizing nested and repeated columns in BigQuery allows for efficient relationship maintenance without extensive JOINs.
The UNNEST function in BigQuery flattens arrays stored in columns, making it easier to query nested data.
BigQuery recommends denormalization for improved performance but highlights the importance of considering storage costs.
Optimizing operations like ORDER BY and DISTINCT, partitioning tables, and using nested fields are key strategies for efficient schema design in BigQuery.

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Self-Learning-Java

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Introduction to Auto Schema Detection in BigQuery

BigQuery provides an auto schema detection feature for CSV and JSON files.
Auto schema detection analyzes the first up to 100 rows to assign data types based on sample values.
Limitations include inaccuracies based on sample data and inconsistent types for columns with varied formats.
Best practices involve manually defining schemas for data consistency and verifying inferred schemas before querying.

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Self-Learning-Java

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Saving and Sharing Queries in BigQuery

BigQuery allows users to save and share queries for future use and collaboration.
To save a query, open the BigQuery Query Editor, type the query, execute it, and click on the 'Save query' button.
When saving a query, provide a name, choose the region for storage, and click 'Save' to store it.
Saved queries can be accessed under the Queries section, and can be shared by managing permissions or copying the shareable link.

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Self-Learning-Java

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Prometheus: Ignore specific labels while matching

Prometheus allows you to use the ignoring keyword to exclude specific labels while matching metrics in PromQL queries.
By using ignoring, you can compare or combine metrics that differ only by certain labels such as mode, without considering those specific labels.
This feature is useful when you want to perform operations on metrics with shared labels but differing by certain specific labels.
By applying ignoring(mode), you can ignore the mode label and focus on matching other common labels, enabling you to derive insights from metrics more effectively.

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Self-Learning-Java

331

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Logical/Set Binary Operators in Prometheus (AND, OR, UNLESS)

Prometheus uses logical/set binary operators for set-based operations on instant vectors.
Operators like AND (Intersection) return time series present in both sides.
Operators like OR (Union) return time series present in either side.
Operators like UNLESS (Complement) return time series on the left side only, not on the right.

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Dev

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Detecting When a Sticky Element Becomes Sticky

Sticky headers changing style as you scroll can be seen on Luma's site, adding a polished feel to the UI.
Detection of when a sticky element becomes sticky is not straightforward, as there is no built-in way to do so using CSS or JavaScript events.
The author explores replicating the sticky header effect using the IntersectionObserver API, a lightweight and widely supported solution that provides opportunities for various interactions.
While container queries are a promising future option for detecting sticky elements, they are currently only available in Chrome, leaving IntersectionObserver as a practical choice for now.

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Self-Learning-Java

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Comparison Binary Operators in Prometheus

Prometheus comparison binary operators help filter or compare metrics based on conditions for triggering alerts, setting thresholds, or refining queries.
Operators work on Scalar to Scalar, Scalar to Vector, and Vector to Vector with label matching rules.
Available operators include == (equal to), != (not equal to), > (greater than), < (less than), >= (greater or equal), <= (less or equal).
These operators are crucial for filtering data, setting thresholds, customizing alerts, and comparing metrics with different sources or labels.

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