Nis 14

The Great Anthropic Leak: A Deep Dive into Claude Code’s Exposed Source Code

By admin in AI

The Great Anthropic Leak: A Deep Dive into Claude Code’s Exposed Source Code

In what is rapidly becoming the most significant security breach in the brief history of generative AI, approximately 512,000 lines of TypeScript source code for Anthropic’s “Claude Code” have been leaked to the public. Claude Code represents the “agentic” front-end that allows Anthropic’s models to move beyond simple chat interfaces and interact directly with a user’s terminal, file system, and operating system.

This is more than a mere data breach; it is a historic revelation. For the first time, the industry has a clear view into the “secret sauce” of the world’s leading agentic AI tool, providing an unprecedented look at the internal mechanics, unreleased features, and the strategic roadmap of one of the industry’s most guarded players.

The Anatomy of a Multi-Million Dollar Mistake

The leak was not the result of a sophisticated state-sponsored cyberattack, but rather a catastrophic process failure involving the “bun bundler” — a JavaScript runtime utility used by Anthropic’s engineering team. During the packaging process, a 60MB source map file was accidentally included in a public release. Because source maps map minified code back to its original human-readable form, they allow anyone to reconstruct the original TypeScript files.

What transforms this from a simple error into a systemic crisis is the timeline. This specific failure occurred twice within a single week: first on March 26, 2025 (when the codename “Mitos” first surfaced), and again on March 31, 2025. Coupled with a similar incident reported in February 2025, the pattern suggests a “junior-level” engineering management error within a multi-billion dollar enterprise.

The community response has been swift and irreversible. Recognizing that Anthropic would attempt to scrub the code from GitHub, developers immediately mirrored the data globally, with repositories appearing in Korea and China. To further obscure the origin and facilitate deep analysis, the TypeScript code was fed through OpenAI’s Codex, translating the entire repository into Python — effectively ensuring the “genie cannot be put back in the bottle.”

The Hidden Roadmap: 44 Feature Flags and Future Capabilities

The leaked code contains 44 distinct “feature flags” — toggles for unreleased or experimental capabilities. These flags reveal that Anthropic’s internal development is months ahead of its public offerings.

Feature Name

Description

The “Buddy” System

Originally conceived as an April Fools’ feature, this allows for personalizable mascots (Duck, Dragon, Mushroom, Ghost) with unique animations to increase user engagement.

Tengu

A sophisticated voice-based communication tool designed for hands-free, seamless interaction with the AI agent.

Chicago (Computer Use)

A major breakthrough in autonomous navigation. It features advanced screen capture and coordinate transformation, designed for full-screen OS control.

Undercover Mode

A “stealth” mode with specific system instructions to hide all traces of Anthropic or Opus, designed for anonymous contributions to open-source projects.

The “Chicago” project is the crown jewel of these discoveries. It signals Anthropic’s evolution from providing a chatbot to building a comprehensive Operating System Agent. By mastering full-screen visual navigation, Claude is being positioned to operate any software as a human would.

Kairos and Otadim: The “Dreaming” AI and Memory Management

A standout technical discovery is the “Dream” feature, internally codenamed Kairos or Otadim. This system addresses the “context bloating” that plagues current LLMs.

The “Dreaming” Logic: While the user is inactive (the “sleep” phase), the AI independently triggers a processing cycle. It reviews the day’s interactions to distill temporary data into permanent knowledge.
Automatic Compression: The source code reveals a sophisticated mechanism that cleans and compresses memory files. For example, the system can reduce a memory file from 15kb to 3kb, significantly improving efficiency and reducing token consumption.
Long-Term Memory: These compressed summaries are saved as .md files, ensuring that the AI remains accurate and context-aware as the interaction history grows, without overwhelming the model’s processing capacity.

Unveiling the Model Pipeline: Capybara, Numbat, and Mitos

The source code provides a definitive look at Anthropic’s internal model families and their engineering priorities:

Capybara Family: A new series of models focused on reliability. The code indicates these models are specifically engineered for lower hallucination rates, prioritizing accuracy over raw creative power.
Capybara Fast: A specialized variant featuring a staggering 1-million-token context window.
Fenek: Confirmed to be the internal codename for the model released as Sonnet 4.6.
Mitos and Numbat: Additional model names discovered in the leak, suggesting a highly specialized pipeline for different agentic tasks.

Security, Internal Prompts, and Pricing Data

The leak has exposed Anthropic’s internal “Telemetries” — security features designed to detect profanity and unauthorized usage. However, the exposure of internal system prompts is a primary security risk. These prompts include the “Undercover” instructions that guide the AI to act as a secret agent. For researchers, this is a goldmine for understanding how to bypass safety filters via prompt injection.

The code also revealed 187 pre-coded “status words” (e.g., “actualizing,” “accomplishing”) used to simulate a thought process while the model generates a response.

Furthermore, hard-coded pricing data for future models was discovered, providing a glimpse into Anthropic’s enterprise strategy. These costs are listed per million tokens:

Opus 4.5: $5 for input / $25 for output.
Opus 4.6: $30 for input / $150 for output.

The Strategic Impact: A “Sputnik Moment” for Open Source?

The ethical and strategic implications of this leak are profound. The “Undercover” mode, in particular, suggests a potential trust crisis in open-source development. If Anthropic-trained agents are contributing to public repositories while actively hiding their identity, it raises questions about the transparency of AI-generated contributions.

Industry analysts are calling this the “Sputnik Moment” of AI. The leak is being compared to the historical sharing of nuclear secrets. Because the code has been translated into Python and mirrored across international jurisdictions, Anthropic has no way to retract the “agentic blueprint.” Competitors — particularly Chinese firms and tools like Cursor — now have a manual on how to structure a world-class agentic front-end. This will undoubtedly accelerate the global development of cloned AI agents by several months.

Conclusion: Engineering Excellence vs. Process Failure

The Claude Code leak exposes a striking duality. The 512,000 lines of code demonstrate undeniable engineering excellence, proving that Anthropic has built a memory-aware, autonomous system that is months ahead of its rivals.

However, the fact that this code reached the public through a basic bundling error — twice in one week — reveals a staggering lack of process maturity. While Anthropic has gifted the community a masterclass in natural language programming, they have also highlighted the extreme vulnerability of the world’s most powerful AI labs. The industry must now grapple with the fact that the “secret sauce” is out, and the era of closed-source agentic workflows has effectively ended.

References

Notebooklm was used as a reference in this article.

Source: Oğuz Ergin — Claude Code’un Kaynak Kodu Ortaya Saçıldı

Nis 14

The Markdown Manifesto: How a 20-Year-Old Blogging Tool Became the Neural Language of AI

By admin in AI

The Markdown Manifesto: How a 20-Year-Old Blogging Tool Became the Neural Language of AI

1. Introduction: The Invisible Revolution

In 2004, John Gruber was a frustrated blogger at Daring Fireball, tired of the technical friction required to publish a simple thought. To bridge the gap between writing and the web, he partnered with 17-year-old internet idealist Aaron Swartz to create a “simple” way to format text. They traded complex HTML tags for asterisks and pound signs, calling their creation Markdown.

What began as a humble tool for bloggers to escape the tyranny of angle brackets has undergone a radical metamorphosis. Today, Markdown has transcended its origins to become the primary semantic bridge of the digital era. It is the language through which artificial intelligence models think, learn, and increasingly, execute complex commands.

2. The Philosophy of “Human-First” Design

The enduring genius of Markdown lies in its foundational requirement: it must remain elegant in its rawest state. Gruber and Swartz believed that formatting symbols should never obscure the message, ensuring the document remained legible whether viewed in a browser or a terminal.

As the creators famously dictated: “Markdown should be readable as normal human writing even in its unprocessed form.” This design choice unintentionally pre-trained an entire generation of software engineers for the AI age.

Why it matters: This philosophy birthed a global developer culture. When GitHub launched in 2008, the README.md became the “front door” of every project, creating an unwritten rule that a repository without Markdown was incomplete. This massive, structured corpus became the ultimate training set, teaching AI models to internalize Markdown as their natural mode of organization.

3. The Token Tax: Why AI Prefers Markdown Over HTML

In the economy of artificial intelligence, “tokens” are the currency of processing. Every character and tag processed by a model costs money and compute power. When an AI “reads” a standard webpage, it is forced to sift through a mountain of digital noise — CSS classes, tracking pixels, and redundant HTML boilerplate.

A landmark Cloudflare study from February 2026 demonstrated the sheer technical necessity of Markdown efficiency. A standard blog post in HTML format consumed 16,180 tokens; when converted to its Markdown equivalent, that count plummeted to 3,150. This 80% reduction in consumption isn’t just a stylistic preference — it is the financial foundation of the industry.

Why it matters: While Markdown serves as the content layer, it works in tandem with JSON for tool definitions and XML for prompt organization. However, the efficiency of Markdown is what makes long-term reasoning and autonomous agents financially viable for startups. It allows machines to “see” more while spending significantly less.

4. Solving “AI Amnesia” with Context Engineering

The greatest hurdle in the modern workflow is “AI amnesia” — the tendency for an assistant to forget architectural decisions or library preferences once a session ends. This isn’t a flaw in the model; it is a lack of persistent structure.

The solution has emerged through the rise of files like cursorrules.md, cloud.md, and the universal agents.md. These files act as a permanent instruction manual for the agent. By placing these in the root directory, developers create a persistent memory that the AI consults at the start of every session.

Andrej Karpathy, former AI lead at Tesla, famously declared in June 2025: “Prompt engineering is over… the real issue is context engineering: giving the AI the right information in the right format at the right time.”

Why it matters: Context engineering transforms a forgetful chat bot into a sophisticated architectural partner. By using Markdown as a semantic bridge, we ensure the AI understands the rules of the project before the first line of code is ever discussed.

5. Spec-Driven Development: Markdown as the Blueprint

We are moving past the era of “vibe coding,” where developers rely on vague, conversational prompts to build software. As projects scale, this approach leads to a “hallucination debt” that eventually breaks the system. The industry is pivoting toward Spec-Driven Development.

In this model, the developer writes a “Spec” — a detailed, logical blueprint in plain Markdown. The AI tool consumes this spec, generates a technical plan, and executes it sequentially. The human focuses on the “what,” while the AI handles the “how.”

Why it matters: This shift turns the AI from a creative guesser into a deterministic executor. By using Markdown to set the logical boundaries, developers achieve significantly higher accuracy and maintainability, treating the AI as an expert implementation engine rather than a magic wand.

6. The 250,000-Star Explosion: The OpenCloud Phenomenon

The power of this Markdown-centric world was validated by Peter Steinberger and the OpenCloud project. Steinberger, who led PSPDFKit for over a decade, built an agent where every skill and memory was stored simply as a .md file. Adding a new capability didn’t require a deployment; it just required a text file.

This low barrier sparked the “Cloudhub” market and led to an unprecedented explosion in growth. Within 60 days of its 2026 relaunch, OpenCloud surpassed 250,000 GitHub stars — a milestone that took React ten years to reach.

Why it matters: Despite its success, the project faced “growth pains,” including researchers discovering malicious skills in the Cloudhub market. This balance of power and peril led to Steinberger joining OpenAI and the project becoming an independent foundation. It proved that while Markdown is powerful enough to run an ecosystem, simplicity also requires vigilant security.

7. The Two-Layer Web: One URL, Two Realities

We are witnessing the birth of a two-layer web infrastructure. In this new reality, a single URL serves two different audiences: HTML for humans (rich with layouts, colors, and buttons) and Markdown for machines (structured, clean, and fast).

Platforms like Cloudflare have already standardized “Markdown for Agents,” serving a parallel stream for AI tools. Protocols like the Model Context Protocol (MCP) and “Agent-to-Agent” communication are formalizing this, allowing machines to trade tasks and information without ever needing to “see” a traditional website.

Why it matters: This represents a fundamental shift in how we build for the future. The visual web is becoming a curated experience for human consumption, while a shadow “Markdown web” handles the heavy lifting of the global AI economy.

8. Conclusion: The Power of Simple Solutions

The journey of Markdown from a 2004 blogging hack to the 2026 backbone of AI is a testament to the longevity of simple ideas. Gruber and Swartz didn’t set out to build an “interoperability layer” for the future of intelligence; they just wanted to make it easier to write a blog post.

Yet, because their solution was rooted in human readability and technical efficiency, it became the only language simple enough for humans and structured enough for machines. The most significant architectural changes rarely come from a boardroom; they come from a simple solution to a small, irritating problem.

Why it matters: As we step deeper into the era of autonomous agents, your most important architectural decision might be your simplest. Do you have an agents.md file in your project yet, or are you still letting your AI work from a clean slate every morning?

References

Notebooklm was used as a reference in this article.

Source: https://www.youtube.com/watch?v=gh50jn_cFWU&t

ai, Markdown, md
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Nis 14

Beyond the Copy-Paste: What It Really Means to “Learn” Linux

By admin in AI, Linux

Beyond the Copy-Paste: What It Really Means to “Learn” Linux

1. Introduction: The “Command Not Found” Wall

You have finally made the switch to Linux. You find a tutorial online to install a specific application, copy the command into your terminal, and press enter — only to be met with “command not found” or a string of cryptic errors. This is the common frustration of the new Linux user. The problem isn’t necessarily a mistake on your part; it is the result of treating Linux like a static, monolithic product where one size fits all.

Truly learning Linux is not about memorizing a list of “magic spells” to paste into a prompt. It is about understanding the underlying architecture. As a systems architect, I view the terminal not as a hurdle, but as the most transparent interface we have. When you stop fighting the system and start understanding how its components — from the kernel to the package manager — interact, the “command not found” wall begins to crumble.

2. Linux is a Kernel, Not a Product

One of the first hurdles to clear is understanding that “Linux” itself is just a kernel — the essential bridge between hardware and software. What you actually interact with is a “distribution” (or distro) like Ubuntu, Fedora, or Arch.

Think of the kernel as the engine of a vehicle. To make it drivable, developers add tools, a “body” known as the Desktop Environment (DE), and a maintenance system called the package manager. While the engine remains similar, your experience changes drastically depending on the DE (like GNOME, KDE, or Hyprland). A “command not found” error often occurs because a solution for a Debian-based system won’t work on a Fedora-based one.

The most critical technical distinction lies in the package manager, which dictates how you install, update, and remove software:

APT: The standard for Debian and Ubuntu-based systems.
DNF: The primary tool for the Fedora family.
Pacman: The lightweight, powerful manager for Arch Linux.

3. The Architect’s Toolkit: Language and Editors

The terminal is a language, and most commands are simply logical abbreviations. By connecting the command to its English etymology, you reduce the cognitive load of “memorizing” and start “translating.”

ls: Short for List (viewing directory contents).
cd: Short for Change Directory.

When you hit a wall, the terminal provides the “clearest answers” through the man command. Typing man followed by a command name opens the built-in manual, the ultimate source of truth.

However, understanding the system is useless if you cannot modify it. This is where text editors come in. Since many system configurations lack a graphical interface, you must learn to edit via the terminal. Start with Nano for its simplicity and speed. As you progress, transition to Vim. While Vim has a steeper learning curve with its distinct modes (Normal, Insert, and Visual), it is an essential tool for any professional.

“The terminal is your friend; it is the place where the system speaks most clearly, providing the diagnostics you need to move from guessing to knowing.”

4. The chmod 777 Trap: Understanding Permissions

A common beginner mistake is blindly granting permissions to bypass an error. If a script fails, tutorials often suggest chmod 777. While this “fixes” the problem, it does so by giving every user on the system full read, write, and execute permissions, creating a massive security vulnerability.

Linux enforces a strict security hierarchy visualized through the ls -la command, where you see characters like r (read), w (write), and x (execute). These are applied across three tiers:

Owner: The specific user who owns the file.
Group: A designated set of users.
Others: Every other person or process on the system.

Instead of opening the door to everyone, use sudo (SuperUser Do). This allows you to temporarily elevate your privileges to perform administrative tasks without remaining logged in as the “Root” user—an account so powerful that a single typo can destroy the entire filesystem.

5. The Zen of “Everything is a File”

A core philosophy of Linux architecture is that everything — from your hard drive to a running process — is treated as a file. This modularity allows for incredible transparency.

If you explore the /dev directory, you will find file-like structures representing your disks and peripherals. You can “see” your hardware through specific diagnostic tools:

lsblk: View connected disks and their partition structures.
lsusb: List all connected USB devices.
lspci: View internal hardware like graphics cards and Wi-Fi adapters.

This philosophy extends to processes. By using ps aux, you can see every active task on the system, identified by a Process ID (PID). Treating processes this way allows the system to manage, stop, or prioritize tasks using the same logic it uses for files.

6. The Anatomy of a Boot: From BIOS to Login

To troubleshoot a system that won’t start, you must understand the boot hierarchy. This sequence is the “nervous system” coming online:

BIOS/UEFI: The hardware performs an initial self-check.
GRUB: The bootloader where you select your operating system.
Kernel: The core loads and acts as the bridge to the hardware.
Initramfs: A temporary filesystem that loads necessary drivers (like those for your disk).
systemd: The “init” process that starts all background services.
Display Manager: The final step that presents your login screen (e.g., GDM or SDDM).

Understanding this flow allows you to identify exactly where a failure occurs — whether it’s a driver issue in the initramfs or a service failing to start under systemd.

7. The Nervous System: systemd and Automation

In modern Linux, systemd is the primary manager for services like networking, Bluetooth, and scheduled tasks. To master Linux, you must master systemctl. This tool allows you to start, stop, enable, or check the status of any background service.

Beyond manual control, an architect automates. You can schedule tasks using cron for simple, classic scheduling, or systemd timers for more complex, integrated automation. Whether it’s nightly backups or clearing temporary files, automation is what separates a user from an administrator.

8. Reading the System’s Mind via Logs

Expertise is defined by the transition from guessing to “listening.” When an application fails, Linux records the event in logs, typically located in /var/log.

The primary tool for this is journalctl, which is inextricably linked to systemd. It allows you to filter logs by time, service, or priority. For hardware and kernel-level issues, dmesg provides the raw messages directly from the kernel bridge.

“Log reading is listening to the system’s trouble in its own words. It is the diagnostic process of a doctor rather than the guesswork of a gambler.”

9. The Hierarchy of the Filesystem

Linux follows a strict organizational structure. Knowing this map is half the battle in system maintenance:

/home: The user’s personal sanctuary for documents and settings.
/etc: The central nervous system for system-wide configuration files.
/var: The repository for variable data, including logs and databases.
/boot: The critical directory containing the kernel and bootloader files.

10. Networking and Resilience

A Linux expert must also be a network troubleshooter. Many “internet” problems are actually DNS issues — where the connection exists, but the system cannot translate names into addresses. Knowing how to check your DNS configuration files is a vital skill.

Furthermore, resilience requires backups. Tools like scp allow for quick file transfers between machines, while rsync is the industry standard for synchronizing data and creating incremental backups. In Linux, a backup is not a luxury; it is a fundamental requirement of system ownership.

11. Conclusion: From Manual to Mastery

Mastery is a gradual process of building layers of understanding. It is a language you learn to speak through daily practice. To facilitate this, I recommend interactive platforms like Labex.io. Their “Linux Journey” provides the necessary theory, while their “Labs” provide a safe, virtual environment to practice. Labex.io offers three free virtual machines per day, making it an accessible sandbox for hands-on learning.

Are you ready to stop fighting the terminal and start speaking its language? The system is already telling you what it needs — you just have to learn how to listen.

References

Notebooklm was used as a reference in this article.

Source: https://www.youtube.com/watch?v=gh50jn_cFWU&t

Ağu 07

Claude.ai ile Tetris Oyunu Oluşturma

By admin in AI

Claude.ai ile Tetris Oyunu Oluşturma

Günümüzde chatGPT, gemini çeşitli ai platformları çıktı bunlardan biride claude.ai genelde kodlama üzerine kullanılan bir yapay zeka. Bu yapay zeka platformunda ücretsiz sürümü ile klasik bir oyun olan tetris oyununu html’de çalışacak şekilde kodlamasını istedim.

Harika! Size tam çalışan bir Tetris oyunu yazdım. Oyun şu özelliklere sahip:

Özellikler:

7 farklı Tetris parçası (I, O, T, S, Z, L, J)
Skor sistemi (satır temizleme ve hızlı düşürme bonusları)
Level sistemi (her 10 satır temizlediğinizde level artar)
Sonraki parçayı gösterme
Oyun duraklatma özelliği
Modern ve görsel olarak çekici tasarım

Kontroller:

←→ oklari: Parçayı sola/sağa hareket ettir
↓ ok: Parçayı hızlı düşür
↑ ok: Parçayı döndür
Space: Parçayı anında düşür
P: Oyunu duraklat/devam ettir

Oyun Kuralları:

Parçalar yukarıdan düşer
Tam satırlar otomatik olarak temizlenir
Level arttıkça oyun hızlanır
Parçalar üst kısma ulaşınca oyun biter

Oyun responsive tasarıma sahip ve modern CSS efektleriyle süslenmiş. Hemen oynamaya başlayabilirsiniz!

ve oyunun ilk sürümünü oluşturdu test etmeye başladım.

Oyunda ses özelliğinin olmadığını fark ettim ve yapay zekadan bunu eklemesini istedim.

Ancak kodlamayı yapınca ses açma kapama butonuna tıklayınca bir hata aldım ve bu hatayı yapay zekaya bildirdim. Claude.ai hata algılayıp düzeltti ve hatanın nereden kaynaklandığını yaptığı düzeltme işlemlerini açıklayarak bildirimde bulundu.

Oyunun güncellenmiş şekli aşağıdaki gibi oldu.

Oyunu oyanarak tekrar test ettim. Oyunda satırdaki tüm kareler dolunca patlama efekti olması kullanıcı adı olması gibi özellikler olmasını istedim ve güncelleme işlemini yaptı.

Tabi bu işlemleri yaparken claude.ai ücretsiz versiyonunda belirli bir kullanma limiti var limit dolunca diğer gün yeniden tetris oyunundaki güncelleme işlemlerine devam ettim.

Mükemmel! Oyuna harika yeni özellikler ekledim:

🎮 Yeni Özellikler:

✨ Görsel Efektler:

Satır temizleme efekti: Dolu satırlar beyaz-mavi yanıp sönme efektiyle silinir
Skor popup’ı: Satır temizlendiğinde puan gösterisi ekranın ortasında belirir
Animasyonlu efektler: Yumuşak geçişler ve fade-out efektleri

🎵 Gelişmiş Ses Sistemi:

1 satır: Tek ton ses
2 satır: Artan tonlarla ikili akor
3 satır: Üçlü akor kombinasyonu
4 satır (Tetris): Özel melodi ve square wave ses efekti
Ses hacmi kontrolü: Farklı aksiyonlar için optimize edilmiş ses seviyeleri

👤 Oyuncu Sistemi:

İsim girişi: Oyuncu adını girebilme
Kişiselleştirme: Oyun bitişinde oyuncu adı gösterimi
Skor tablosu: Oyuncu adı, skor, level ve temizlenen satır sayısı

🎯 Puan Sistemi:

1 satır: 100 × level puan
2 satır: 300 × level puan (bonus!)
3 satır: 500 × level puan (büyük bonus!)
4 satır: 800 × level puan (Tetris bonusu!)

🎨 Görsel İyileştirmeler:

Parlaklık efektleri: Satır temizleme sırasında parlak beyaz flash
Renk geçişleri: Cyan-beyaz renk kombinasyonu
Skor animasyonu: Büyüyerek yukarı çıkan puan gösterimi

Bu özellikler oyunu çok daha etkileyici ve profesyonel hale getiriyor! Artık satır temizlediğinizde harika görsel ve ses efektleri yaşayacaksınız. 🎉

Oyunda bazı ekleme çıkartmalar yapılabilirdi ama bu hali ile claude.ai kodlama deneyimi için yeterli oldu ve tetris oyunu makul bir seviye geldiği için bıraktım.

Oyunu oynamak isterseniz siteme ekledim aşağıdaki linkten oyunu deneyebilirsiniz.

https://claude.ai/public/artifacts/5a77f5bf-2a80-4452-afde-ba955625a9c3

https://www.mzkarakas.com/tetris.html

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Eki 19

DOAS 2023 Yılında 3. Temettü Ödemesi Yapacak

By admin in Borsa, Temettü

Doğuş Otomotiv Servis ve Ticaret A.Ş. (DOAS) bilançoda bulunan kar dağıtımı yapılabilir nitelikteki kaynaklardan net 1.250.000.000 TL’nin ortaklarına payları oranında nakden 28 Ekim 2023 tarihinde nakit olarak dağıtılacaktır. Temettüden yararlanmak için hisse sahibi olmak için son tarih 27 Ekim 2023’tür. Hisse başına net 5.1136-TL dağıtılacak olup, temettü verimi 18.10.2023 tarihli hisse fiyatıyla %1,89’dur. DOAS, bu temettü ödemesiyle 2023 yılında 3 ödeme yapacak. Devamını oku

Ağu 02

YUNSA Bedelsiz Dağıtımı

By admin in Borsa

yünsa logo

YÜNSA YÜNLÜ SANAYİ VE TİCARET A.Ş. YUNSA

YUNSA.E %105,76131 İç Kaynaklardan Bedelsiz, bölünme sonrası teorik fiyat: 84,175 TL (Teorik fiyat, fiyat adımına yuvarlanmamıştır).

Elinizde 100 lot YUNSA hisseniz var ise 02.08.2023 tarihinde 205,76 lot hisseniz olacak. Bölünme Devamını oku

Tem 18

Android Studio Android Telefon Emülatörüne Alternatifi

By admin in Android, Flutter

Genymotion Android Emulator

Android Studionun telefon emülatörünü kullanıyorsanız bilgisayarın ram’ini fazla kullandığını ve işlemciyi fazla kullanması nedeniyle dizüstü bilgisayarın ısındığı ve fanın son devirde çalışmaya başladığını fark etmişsinizdir. En azın çok yüksek donanım özellikleri olmayan bilgisayarda bu sorunları gözlemleyebilirsiniz. Bu emülatör yerine Genymotion android emülatör programını kullanabilirsiniz.

Android Studio Emulator

Bilgisayarın rahatladığını ve daha az takıldığını fark edeceksiniz. Programı https://www.genymotion.com/download/ adresinden indirebilirsiniz. Eğer bilgisayarınızda Virtual Box yoksa indirme sayfasını Virtuak Box ile olan dosyayı indirip kullanabilirsiniz.

Eyl 28

PHP Dosya ve Dizinlerde CHMOD Ayarları chmod()

By admin in PHP

PHP’de oluşturulan dosya ve dizinlerde dosya sahibi, kullanıcı grupları ve diğer kullanıcılar için ilgili dosya ve dizinde yapacağı değişiklik izinlerini chmod() fonksiyonu ile belirlenir. Bu fonksiyonda ilk parametre yetki düzenlemesi yapılacak dosya yada dizin adı ikinci parametre ise 4 haneli izin sayısı girilir. Bu dört haneli sayının Devamını oku

Eyl 27

PHP Dosya ve Dizin Kontrolü file_exists()

By admin in PHP

PHP de bir dizin yada yada dosya üzerinde işlem yaparken hata almamak için bu dosya yada dizinin kontrol etmemiz gerek durumlar olabilir. Örneği bir dizini silmek isteğimizde önce bu dizinin olup olmadığını if ile kontrol edip sonra dizin silme işlemi yapılabilir. Hem dizin hemde dosyanın var olup olmadığının kontrolünü file_exists() fonksiyonu ile kontrol ediyoruz. Devamını oku

Eyl 16

Yurt Dışından Amerikadan (USA) Hisse Senedi (Apple, Amazon, Tesla vs.) Almak

By admin in Borsa, Finans

Yurt Dışından Amerika Borsalarından (USA) Hisse Senedi Almak

Hisse senedi alımı ile yatırım yapıyorsanız İMKB (İstanbul Menkul Kıymetler Borsası) ile şirketlerin hisselerine yatırım yapmanın yanında yurt dışı piyasalarında hisse alımı yaparak yatırım yapabilirsiniz. Bu sayede dolardaki kur değişiklikleri etkisinden kurtulabilirsiniz ve dolar olarak kazanç sağlayabilirsiniz. Tabi zararınızda dolar üzerinden olacak :).

Devamını oku