# Productivity İlgili Makaleler

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

This is How God Karpathy Uses Claude?

Andrej Karpathy, a prominent figure in AI, has reportedly joined Anthropic, leading to a noticeable decrease in his open-source contributions and social media activity. A document claiming to be his personal "CLAUDE.md" file—a set of instructions for the Claude AI to follow within a specific codebase—has been circulating online. While its authenticity is unverified, the content aligns closely with Karpathy's publicly shared principles on effective AI-assisted programming. The document outlines key rules for AI coding assistants, emphasizing the importance of reading existing code thoroughly before writing new code to maintain consistency. It advises against over-engineering, advocating for simple, surgical modifications that match the project's existing style. Other guidelines include clarifying assumptions upfront, writing meaningful tests, thoughtful debugging, and carefully considering dependencies. The core message is that these principles help prevent common AI coding failures, such as introducing unnecessary abstractions, style drift, or making invisible architectural decisions. The community has noted that even experts like Karpathy require detailed instructions to guide AI effectively, akin to managing a junior developer. A related GitHub repository, "andrej-karpathy-skills," which encapsulates these ideas, is reported to significantly reduce Claude's code error rate. Ultimately, the advice stresses that the best CLAUDE.md is tailored to one's own tech stack and coding practices.

marsbit3 saat önce

This is How God Karpathy Uses Claude?

marsbit3 saat önce

The Creators of ChatGPT No Longer Use ChatGPT for Work

The creators of ChatGPT are increasingly moving away from using it themselves, shifting their primary AI tool to AI agents like Codex. As of June 2026, Codex handles 99.8% of OpenAI's weekly output tokens—a dramatic rise from less than 10% just ten months prior. This transition, which accelerated around September, marks a fundamental change: from single-turn chatbot queries to delegating entire, long-running tasks to autonomous agents. The shift began with engineers but quickly spread company-wide. By April 2026, non-technical departments like legal, finance, and HR had adopted Codex as their primary tool, with personnel in these roles now generating over 85% of their output tokens through it. Notably, growth among non-developer users has far outpaced that of developers. Codex has evolved from a coding assistant into a general workflow agent. It can autonomously manage complex, multi-step processes—from implementation and debugging to testing—running for hours without human intervention. Heavy users at OpenAI can now delegate over 60 hours of agent runtime per day. The model underpinning Codex, GPT-5.5, not only executes tasks but also optimizes its own infrastructure, demonstrating improved efficiency. This report signals a broader shift in knowledge work: the key differentiator is becoming the scale and complexity of tasks users are willing to fully delegate to AI agents.

marsbit22 saat önce

The Creators of ChatGPT No Longer Use ChatGPT for Work

marsbit22 saat önce

Goldman Sachs Revisits the State of the AI Boom: Strong Earnings Will Override Valuation Concerns Until the Investment Cycle Peaks, Volatility to Rise Further

Goldman Sachs discusses the current AI investment boom, arguing it is not a simple repeat of the 1999-2000 bubble. Unlike the dot-com era, current valuations are not wildly out of control because earnings expectations are rising in tandem with stock prices. The key issue is that market prices have already priced in substantial optimism, making them sensitive to any narrative shifts. The core risk has shifted from "valuation bubble" to "earnings bubble." While strong profits from companies in semiconductors, cloud infrastructure, and data centers currently outweigh valuation concerns, this is dependent on the ongoing capital expenditure cycle. The true test will be whether these robust earnings can be sustained after the investment cycle peaks. Current market prices imply optimistic assumptions, such as faster AI adoption, higher productivity gains, and a larger share of economic profits going to capital owners. The AI boom may also be masking relative weakness in the non-AI sectors of the economy. As a result, market volatility is expected to rise. Goldman recommends that investors consider maintaining exposure while adding downside protection, such as put options, to manage potential drawdowns.

marsbit06/24 04:07

Goldman Sachs Revisits the State of the AI Boom: Strong Earnings Will Override Valuation Concerns Until the Investment Cycle Peaks, Volatility to Rise Further

marsbit06/24 04:07

Chips, Open-Source Models, and $50 Trillion: Joe Tsai Reassesses Alibaba Once Again

Alibaba Executive Chairman Joe Tsai recently outlined the company's comprehensive AI strategy in a public discussion. He believes AI represents a massive opportunity, estimating its potential economic impact at up to $50 trillion, stemming from the automation of human intelligence and productivity. Tsai detailed Alibaba's four-layer investment approach across the AI stack: starting from the chip level, moving to cloud infrastructure (Alibaba Cloud), then the model layer with its open-source Qwen model, and finally applications within its vast digital ecosystem (e-commerce, logistics, etc.). The company avoids the energy layer due to China's efficient infrastructure. This broad strategy is designed to ensure Alibaba captures value regardless of where it ultimately concentrates in the AI value chain. He dismissed concerns about an AI investment bubble, pointing to the enormous $50 trillion opportunity. While acknowledging U.S. cloud giants' higher capital expenditure, he argued Chinese firms, including Alibaba (funded by its cash-generative e-commerce core), need to invest more in AI infrastructure. A key theme was technological sovereignty. Tsai positioned open-source models like Qwen as a solution for companies, especially in Europe, seeking independence from proprietary U.S. models and greater data privacy control. He contrasted this with the trend of U.S. giants keeping their models closed-source. Tsai highlighted Alibaba's collaborations with European manufacturers like Bosch and Siemens, using AI for design and quality control. He concluded with an optimistic vision of AI agents enhancing productivity, ultimately freeing up human time for leisure, family, and experiences like live entertainment.

marsbit06/22 07:51

Chips, Open-Source Models, and $50 Trillion: Joe Tsai Reassesses Alibaba Once Again

marsbit06/22 07:51

Beyond the Model Lies the Harness: Deepseek Enters the Arena, Why Has the Main Battlefield of China's AI Competition Shifted?

In mid-to-late May 2026, Deepseek internally established a new Harness team focused on code agent products, internally benchmarked against Anthropic's Claude Code. This move, marked by the formula "Model + Harness = Agent" in their job postings, signals a major shift in China's AI competition: the main battlefield is transitioning from developing large models to building toolchains and achieving workplace integration. Deepseek's direct involvement in Harness development aims to secure control over interface design and training data feedback loops, moving beyond open-sourcing powerful models. Harness, the runtime infrastructure for AI agents, handles everything beyond model reasoning—task orchestration, tool calling, context management, safety checks, and error recovery. It is crucial because agent products are not just outputs of model capability but also training grounds for it. Real-world task failures recorded by Harness can feed back into model training, creating a flywheel effect. Engineering Harness is more critical than optimizing prompts, as poor context management or error handling can drastically reduce agent success rates in multi-step, real-world scenarios. This shift is not isolated. Other major Chinese tech companies are also pursuing differentiated toolchain strategies. Tencent leverages its enterprise ecosystem (WeChat Work, Tencent Cloud) to build connectors for organizational-level AI collaboration and complex task delivery. Alibaba focuses on lowering automation barriers on the web with a front-end, browser-based GUI Agent framework, PageAgent. This diversification shows the industry recognizes that success lies not in a perfect general agent, but in vertically focused solutions built with robust engineering. The trend is validated by overseas success, such as Poland's Viktor, an AI coworker on Slack achieving $20M ARR by autonomously executing complex, multi-step tasks. This proves a shift in enterprise willingness to pay—from "AI-assisted generation" to "AI-autonomous execution." As Harness matures to provide safety guards and reliability, AI transitions from a human-supervised intern to an independent outsourcer. The competition now faces key engineering challenges: preventing "token explosion" through intelligent context compression, and building "thick frameworks" with features like sandbox isolation and checkpoint recovery for enterprise-grade stability. Geopolitical restrictions on tools like Claude Code further create a significant market vacuum for domestic solutions like Deepseek's Harness. For enterprises and developers, the focus must shift from comparing model benchmarks to evaluating a vendor's engineering capabilities, error recovery mechanisms, context management, and ecosystem compatibility when choosing AI products and platforms.

marsbit06/22 06:05

Beyond the Model Lies the Harness: Deepseek Enters the Arena, Why Has the Main Battlefield of China's AI Competition Shifted?

marsbit06/22 06:05

1996 or 1999? Walsh's First Test is 'How to View AI'

"1996 or 1999? Wall's First Big Test Is 'How to View AI'" Federal Reserve Chairman Wall's initial challenge is not whether to raise or cut rates, but a more fundamental judgment: what kind of boom is the current AI boom? This will determine the Fed's policy path and define his legacy. Economics is split between two opposing views, according to reporter Nick Timiraos. One sees imminent productivity gains that will increase supply and cool inflation, allowing the Fed to hold steady. The other argues that while productivity benefits are distant, demand shocks are here now, and waiting for data confirmation risks missing the intervention window, forcing sharper rate hikes later. Wall has signaled a leaning toward the first view, echoing 1996-era Alan Greenspan, who embraced strong, productivity-driven growth without fear of inflation. However, Wall faces a different macro environment than Greenspan did, with tariff pressures, expanding fiscal deficits, and diminishing globalization benefits, which could force more significant inflation pressures even if AI benefits materialize. Wall's logic, expressed before taking office, is that AI-driven productivity gains won't show in official data for years. If the Fed waits for confirmation, it might mistakenly tighten policy and choke off the very growth that could suppress inflation. This argues for using forward-looking narratives over lagging data. Chicago Fed President Austan Goolsbee presents a key counter-argument. He distinguishes between expected and unexpected productivity booms. A widely anticipated boom, like the current AI wave, can cause people to spend future wealth gains in advance, overheating the economy before productivity actually rises, thus requiring preemptive rate hikes. He cites rising costs for AI data centers as evidence of such overheating. Fed Governor Christopher Waller offers a rebuttal to Goolsbee, noting the "expected spending" mechanism only works if people can borrow against future income, which many households cannot do due to borrowing constraints. Wall also faces a paradox related to his desire to reduce the Fed's use of "forward guidance" (pre-announcing policy moves). This practice was established in 1999 when Greenspan began signaling hikes to avoid market shocks. If the economy follows a less optimistic path, Wall may be forced to choose between using the guidance he wants to abolish or risking market volatility by staying silent. The ultimate question defining Wall's first major test remains: Is this 1996 or 1999?

marsbit06/20 07:53

1996 or 1999? Walsh's First Test is 'How to View AI'

marsbit06/20 07:53

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

No Sales Team, $20 Million in Revenue: How Did AI Employee Viktor Win Over 30,000 Companies?

The AI employee Viktor, developed by a team with DeepMind background, has achieved $20 million in annual revenue without a traditional sales team, serving over 30,000 companies. Its core innovation lies in positioning itself as a "Tier 3 AI Coworker" capable of "end-to-end execution and delivery of results," moving beyond the "draft and wait for human completion" model of typical AI assistants. Users can simply mention Viktor in Slack or Microsoft Teams using natural language commands, and it autonomously performs tasks like pulling sales data from a CRM, generating reports, or even cross-tool operations like creating board meeting PPTs by aggregating data from six different sources. Key to its growth is a pure Product-Led Growth (PLG) model, eliminating complex implementation cycles and per-seat licensing. Instead, it charges based on task credits or consumption, lowering the trial barrier with a $100 free credit offer and no credit card required. This enabled viral, bottom-up adoption within organizations. Viktor's interaction paradigm removes the barrier of prompt engineering, allowing non-technical employees to delegate complex workflows seamlessly. It also features proactive, automated task execution (e.g., overnight bookkeeping, scheduled reports) based on triggers, effectively embedding AI as an automated "process layer" within business operations. However, its expansion into Microsoft Teams—a platform with 320 million users—highlights challenges. Large enterprises require stringent IT compliance, security reviews (e.g., SOC 2), and governance, potentially hindering the frictionless, user-driven adoption that succeeded in Slack. Additionally, the "black box" nature of its autonomous decision-making raises concerns about operational risks, data integrity, and the need for robust audit logs and permission controls. Balancing efficiency gains with security and trust remains a critical hurdle for Viktor and similar AI agents aiming to become core enterprise infrastructure.

marsbit06/19 10:55

No Sales Team, $20 Million in Revenue: How Did AI Employee Viktor Win Over 30,000 Companies?

marsbit06/19 10:55

If the AI Bubble Is Already Bursting, Who Will Truly Survive?

If the AI Bubble is Bursting, Who Will Remain? The debate over an AI bubble is intensifying, with figures like Ray Dalio warning of high levels and Jensen Huang seeing immense, early-stage opportunity. Both views hold truth: a speculative bubble in capital markets likely exists, mirroring the dot-com era, but the underlying technological shift is real and transformative. History shows that while bubbles burst—wiping out overvalued companies and speculative capital—they often leave behind critical physical and digital infrastructure. The dot-com bust, for instance, eliminated many firms but left the global fiber optic networks and data centers that enabled the rise of Amazon, Netflix, and cloud computing. Today's massive AI infrastructure investments (projected at trillions by 2030) in data centers, power, cooling, and GPUs may follow a similar path, creating the foundation for future applications. A key divergence from past bubbles is the "Jevons Paradox" effect in AI. As the cost of AI inference has plummeted by over 99.7% since 2023, enterprise spending on AI has skyrocketed. Cheap "tokens" have unlocked vast, previously uneconomical use cases, moving AI from simple chatbots into core business workflows—code generation, legal document review, scientific simulation, and financial analysis. The market is now in a phase of self-correction, weeding out superficial "API-wrapper" startups, but this cleansing process strengthens the ecosystem. The long-term trajectory is clear. The value is gradually shifting from capital expenditure (CapEx) on hardware to operational expenditure (OpEx) on transformative applications. As AI becomes a utility, the winners will be firms that deeply integrate it to solve vertical industry problems in law, healthcare, finance, and manufacturing. The泡沫 will recede, but the foundational shift towards an AI-powered era across all sectors is irreversible. The underlying productive force of AI contains no bubble.

marsbit06/15 04:42

If the AI Bubble Is Already Bursting, Who Will Truly Survive?

marsbit06/15 04:42

If the AI Bubble Is Already Bursting, Who Will Truly Remain?

**Summary: If the AI Bubble is Bursting, What Will Remain?** The debate around an AI bubble is intensifying, with figures like Ray Dalio warning of high valuations while Jensen Huang sees immense opportunity. This echoes the dot-com bubble, which saw massive wealth destruction but ultimately left behind critical infrastructure like undersea cables and broadband, enabling future giants like Amazon and Netflix. Similarly, today's AI boom involves trillions invested in data centers, power, cooling, and GPUs, while application-layer revenue remains comparatively modest. This investment-disparity signals a bubble. However, the core technological progress is real and accelerating. AI inference costs have plummeted by over 99.7% since 2023, making intelligence increasingly cheap and accessible. This cost collapse is unlocking vast new demand. Instead of reducing spending, enterprises are tripling their AI cloud expenditure. Cheap "tokens" enable AI to move beyond simple chatbots into complex workflows—automating code writing, legal document review, financial analysis, and scientific research. This follows "Jevons's paradox": improved efficiency leads to greater total consumption. The market is now undergoing a necessary purification, weeding out "API-wrapper" startups with no real moat. The deeper evolution involves a shift from capital expenditure (CapEx) on infrastructure to operational expenditure (OpEx) on value-creation in applications. While hardware vendors currently profit most, long-term value will migrate to AI-native firms solving vertical industry problems. Ultimately, a market correction will cleanse speculative excess but will not reverse the AI+ trend. The massive physical and algorithmic infrastructure being built will endure, becoming a cheap, utility-like foundation. Just as the internet became indispensable to all industries post-2000, AI is poised to empower and redefine every sector, moving society irreversibly toward an intelligence-augmented era. The bubble may burst, but the underlying productive momentum is solid.

链捕手06/15 04:35

If the AI Bubble Is Already Bursting, Who Will Truly Remain?

链捕手06/15 04:35

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