# AI Programming İlgili Makaleler

HTX Haber Merkezi, kripto endüstrisindeki piyasa trendleri, proje güncellemeleri, teknoloji gelişmeleri ve düzenleyici politikaları kapsayan "AI Programming" hakkında en son makaleleri ve derinlemesine analizleri sunmaktadır.

Just Now, Claude Fable 5 Took First Place Again

Claude Fable 5 has achieved a staggering 64% success rate on the MirrorCode leaderboard, significantly outperforming competitors like GPT-5.6 Sol (approx. 21%). The benchmark tests models' ability to fully reverse-engineer and reimplement 15 medium-to-large software projects (e.g., Unix tools, compilers) in an isolated environment, requiring a perfect 100% pass rate on all tests. Notably, Fable 5's performance remained remarkably consistent at 61% when using the niche language Ada, compared to 64% with Go. Given that Python training data is roughly 230x more abundant than Ada, this minimal 3-point drop suggests the model has moved beyond simple code memorization and can reason about and construct complete software projects from fundamental principles. The MirrorCode benchmark is extremely rigorous, allowing up to 100 billion tokens and 7 days of continuous runtime per task to achieve 100% correctness, with one run costing $2600 over 19 days. The results indicate that state-of-the-art AI is transitioning from a tool for writing code snippets to an agent capable of autonomously completing entire, sizable software projects when given clear specifications and automated verification. This shift will make code increasingly inexpensive while elevating the value of those who can precisely define problems and validate outcomes.

marsbitDün 08:31

Just Now, Claude Fable 5 Took First Place Again

marsbitDün 08:31

Who Makes the Best Use of Claude Code? The Answer Might Not Be Programmers

Claude Code Usage Report Summary (Based on ~400k sessions) Core Finding: In agentic programming with Claude Code, a clear division of labor has emerged: humans primarily decide *what* to build (planning decisions), while Claude decides *how* to build it (execution decisions). Key Insights: 1. **Effectiveness is not limited to programmers.** In code-generation tasks, success rates for users in non-technical fields (law, finance, management, research) are nearing those of software engineers. What matters most is the user's domain expertise and understanding of the problem to be solved. 2. **Domain expertise drives success and efficiency.** Sessions where users exhibited "expert" proficiency in the task's domain saw verified success rates double compared to "novice" sessions. Experts also delegated more work per instruction, with Claude executing more actions and producing more output. 3. **AI is amplifying, not replacing, domain knowledge.** Claude Code lowers the *implementation* barrier, not the *judgment* barrier. The value of knowing the "what" and "why" is increasing relative to just knowing the "how" to code. 4. **Usage is evolving.** Over a 7-month period (Oct '25 - Apr '26), the share of sessions for debugging halved, while use for software operations, data analysis, and non-code writing roughly doubled. The estimated economic value of typical tasks increased by ~25%. Conclusion: The data suggests coding agents are making programming background less critical for completing technical tasks. However, they reward and amplify deep domain understanding. The ability to successfully direct an AI agent stems more from mastery of a specific field than from coding skill itself. The primary gains come from being competent in a domain; deep specialization adds only marginal additional advantage. This may signal a shift where software creation becomes integrated into various professions.

marsbit06/20 02:03

Who Makes the Best Use of Claude Code? The Answer Might Not Be Programmers

marsbit06/20 02:03

Either Go Full-Stack or Get Out: The Calculations Behind xAI's $60 Billion Acquisition of Cursor

The article discusses xAI's $60 billion stock acquisition of Anysphere, the parent company of the AI coding tool Cursor, arguing that the core motivation is not market share but access to high-quality training data from its 7 million daily developers. It posits that to become a major AI player, a company must build a full-stack encompassing compute, model, and application layers. This thesis is illustrated by Anthropic's 540x revenue growth in 28 months, largely driven by its coding product, Claude Code, which captured 54% of the enterprise AI programming market. The author, a VC, contends that full-stack integration creates sustainable unit economics for model training and provides proprietary data for defensible competitive advantages, predicting a wave of model companies aggressively building or acquiring application-layer products. The central takeaway is that in an era where building products is 10x easier, ambition must be 10x greater to succeed.

marsbit06/18 09:41

Either Go Full-Stack or Get Out: The Calculations Behind xAI's $60 Billion Acquisition of Cursor

marsbit06/18 09:41

Cursor: Why Did It Board Elon Musk's Rocket?

SpaceX announced its first major acquisition after its historic IPO: a $60 billion all-stock deal to acquire AI programming startup Cursor (parent company Anysphere). Cursor is a popular AI coding assistant that allows developers to switch between models from OpenAI, Anthropic, Google, and others. Founded in 2022 by MIT graduates including CEO Michael Truell, Cursor saw explosive revenue growth, reaching a $4 billion annualized run rate by early 2026. However, its market share had declined as key supplier Anthropic launched its own competing product, Claude Code. Facing dependency risks, Cursor decided to build its own AI model, Composer, but lacked the necessary computing power. In April 2026, Cursor and SpaceX revealed a partnership and an option agreement: SpaceX could acquire Cursor for $60 billion post-IPO, or pay a breakup fee and provide substantial computing resources. After SpaceX's successful IPO, it exercised the option. The deal gives Cursor access to SpaceX's massive "Colossus" supercomputer, while SpaceX gains Cursor's strong foothold among elite software engineers to boost its AI capabilities, as Musk's xAI model Grok lags in programming. The acquisition aligns with SpaceX's broader AI and orbital data center ambitions, as Musk targets $1 trillion in revenue by 2030. For Truell, who once aimed to build an enduring independent company, joining SpaceX represents a monumental bet on an unprecedented scale.

marsbit06/17 04:14

Cursor: Why Did It Board Elon Musk's Rocket?

marsbit06/17 04:14

Tying Itself to SpaceX: Cursor's $60 Billion Rise

This article recounts the rapid rise of AI-powered coding startup Cursor and its 25-year-old MIT graduate CEO, Michael Truell. Launched in 2023, Cursor achieved explosive growth, reaching over 10 billion USD in revenue by late 2025. However, its journey highlights a central dilemma for AI application companies: dependence on foundational model providers. Cursor initially relied heavily on Anthropic's models but faced an existential threat when Anthropic launched its own competing coding tool, Claude Code. In response, Cursor declared an internal emergency in early 2026 and accelerated development of its own model, Composer. To secure the immense computing power needed, Truell struck a pivotal deal with Elon Musk's SpaceX in April 2026. The collaboration grants Cursor access to SpaceX's supercomputing resources for Composer, while SpaceX's Grok model benefits from Cursor's programming data. The agreement includes a potential 600 billion USD acquisition of Cursor by SpaceX later in the year, though a substantial termination fee is in place if the deal falls through. The story explores Cursor's intense, sometimes controversial hiring practices involving lengthy unpaid "work trials," its complex partnership-turned-rivalry with Anthropic, and its high-stakes gamble to ensure independence through the SpaceX alliance. The core question remains: will Cursor evolve into a defining, independent "generational" software company, or become a key piece in a tech giant's AI arsenal?

marsbit06/17 00:03

Tying Itself to SpaceX: Cursor's $60 Billion Rise

marsbit06/17 00:03

I Built Myself an Investment Workbench Using AI

For the past two weeks, I've been immersed in Vibe Coding—using AI to write code from natural language descriptions. This process has enabled me to quickly build functional tools that address long-standing personal ideas. Previously, I had many concepts but found execution too cumbersome. Key ideas included a unified dashboard for assets across US stocks, Crypto, HK stocks, and A-shares; a real-time alert system for price movements; an investment map visualizing sector relationships; and a tool to correlate prediction market bets with news and market data. Traditional development hurdles meant these often remained unrealized. Using AI (Codex, Claude Code, and DeepSeek API), I built four initial tools: 1. A **Cross-Market Asset Dashboard** showing total assets, daily P&L, and holdings by market, with added features for alerts and sector mapping. It's deployed locally for privacy. 2. A **Prediction Market (PM) Monitor** tracking bets on events (e.g., company valuations) and correlating probability shifts with news and market movements. I categorize bets by conviction to filter noise. 3. A **Simple Operations Backend** for managing my writing workflow (topics, progress, publishing). It's cloud-deployed for mobile access. 4. A **One-Click Formatting Tool** that automates converting drafts into various platform-specific formats, saving manual effort. While these tools are basic, they represent a significant shift: AI lowers the barrier to creating personalized systems. I believe individual investors can now feasibly build core systems for: * **Asset Observation** (tracking holdings and changes) * **Signal Monitoring** (watching for key market shifts) * **Sector Mapping** (understanding network relationships within a sector) * **Performance Review** (documenting rationale and outcomes) The power of Vibe Coding is its fast feedback loop. Ideas can be implemented, tested, and iterated on rapidly, turning "want-to-do" into "done." This marks the start of my new phase, where I'll share investment thoughts, tool tests, on-chain operations, and educational Web3 content.

marsbit06/16 06:22

I Built Myself an Investment Workbench Using AI

marsbit06/16 06:22

Founder of Baixing.com: My Fourteen Experience Points in Using Claude Code

Founder of Baixing.com: My Fourteen Claude Code Usage Experiences The author shares personal insights from using Claude Code. Key recommendations include: focusing deeply on one primary tool; mastering essential shortcuts like Control+G for the editor; utilizing voice input; starting projects with a structured PROJECT.md; defaulting to Claude agents; integrating with GitHub and Cloudflare for infrastructure; clearly separating human-written core files (like CLAUDE.md) from AI-generated content, and interacting with AI output only through queries; dragging various files (audio, video, screenshots) into the interface for clarification. He advises centralizing and version-controlling memory and skill files in git (e.g., ~/.claude/CLAUDE.md) to build a permanent, cumulative knowledge base across projects. Skills should be continuously refined and used to capture learnings. For complex tasks, using ultracode for dynamic workflows is recommended despite cost. Using git documentation as handoff between agents ensures task continuity without relying solely on context. Finally, he suggests treating Claude Code like a horse with its own path-finding abilities—setting goals and boundaries rather than micromanaging—viewing its autonomy as a feature, not a bug.

marsbit06/08 13:01

Founder of Baixing.com: My Fourteen Experience Points in Using Claude Code

marsbit06/08 13:01

Founder of Baixing.com: My Fourteen Claude Code Usage Experiences

Founder of Baixing.com Shares 14 Personal Tips for Using Claude Code The author outlines his personal, non-universal strategies for maximizing Claude Code. Key points include: focusing deeply on one primary tool (Claude Code) rather than constant comparison; mastering essential shortcuts for the editor and command line; utilizing voice input like HoldSpeak; starting projects with a structured PROJECT.md file; defaulting to Claude agents for most tasks; and leveraging integrations with GitHub and Cloudflare for build, deployment, and infrastructure. He emphasizes a clear separation between human and machine work: manually maintain a core CLAUDE.md file, and understand AI-generated content by asking the AI, not reading its raw code. Efficient communication involves dragging files (screenshots, audio, documents) directly into the interface. For knowledge management, he recommends a centralized, Git-synced memory system based on ~/.claude/CLAUDE.md to ensure permanence and avoid scattered project memories. Other practices include writing and continuously refining "Skills," using the expensive but reliable ultracode for complex dynamic workflows, and employing Git documentation as handoff points between agents. The overarching philosophy is to treat Claude Code like a horse (or a person) with its own pathfinding abilities—setting goals and boundaries rather than micromanaging every turn.

链捕手06/08 12:54

Founder of Baixing.com: My Fourteen Claude Code Usage Experiences

链捕手06/08 12:54

ChatGPT Might Be Disappearing Soon

OpenAI announced at its "Intelligence at Work" event that its coding assistant, Codex, will be fully integrated into the ChatGPT app within weeks. This move marks a strategic shift from a conversational AI (Chat) towards a unified "agentic" platform capable of execution. Codex, originally launched to compete with Anthropic's Claude Code, has grown rapidly to 5 million weekly active users, with 20% being non-developers like analysts and designers. Its enterprise revenue now constitutes 40% of OpenAI's total. The integration is the first step in creating a super-app combining ChatGPT (interface), Codex (execution engine), and the Atlas browser (web access). OpenAI also unveiled new Codex features: specialized Agent plugins for six professional roles, an "Annotations" tool for direct document editing, and a "Sites" function to turn work into shareable web apps. Internally, this reflects a power shift; the Codex team now leads core product strategy. While the ChatGPT brand remains for its vast user base, the platform's future is focused on autonomous agents that perform tasks, not just chat. The article notes that competition with Claude Code pushed OpenAI's development, with Codex competing on cost-effectiveness and accessibility rather than raw coding quality. It concludes that the essence of "ChatGPT" is evolving from a chatbot into an AI agent platform, with the name potentially becoming a legacy symbol of its original function.

marsbit06/03 23:52

ChatGPT Might Be Disappearing Soon

marsbit06/03 23:52

Topping GitHub's Trending, the Essential Guide for Claude Code Users

The CLAUDE.md file, trending on GitHub, is a project-level guide for Claude Code designed to dramatically improve its accuracy and efficiency. It addresses key issues like repetitive context explanations, unauthorized code changes, and forgotten decisions across sessions. By placing this plain-text file in a project root, Claude Code reads it automatically at the start of each session. The guide includes rules to eliminate redundant explanations, enforce strict behavioral constraints (e.g., no modifications outside the requested scope without confirmation), and establish a "memory" system using companion files like MEMORY.md and ERRORS.md to log past decisions and failures. It also locks in the project's specific tech stack to prevent inappropriate tool recommendations. Highlighted are four foundational rules from Andrej Karpathy that reportedly increased coding accuracy from 65% to 94%: always ask for clarity first, implement the simplest solution, never touch unrelated code, and explicitly flag uncertainties. The article quantifies significant weekly cost savings for developers and teams by eliminating wasted time on re-explaining context, rolling back unauthorized edits, and re-evaluating previously rejected solutions. The core message is that a small, upfront investment in creating a CLAUDE.md file leads to a more predictable, controlled, and cost-effective AI programming assistant.

marsbit05/18 09:38

Topping GitHub's Trending, the Essential Guide for Claude Code Users

marsbit05/18 09:38

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