# Пов'язані статті щодо Productivity

Центр новин HTX надає останні статті та поглиблений аналіз на тему "Productivity", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

Gold Sachs Issues Statement on Upcoming Fed Interest Rate Decisions! 'In September...'

Goldman Sachs analyst Robert Kaplan stated that the Federal Reserve's decision to leave interest rates unchanged in July was "absolutely correct." He emphasized that policymakers should analyze incoming data with transparency leading up to the September meeting, avoiding rigid policy guidance due to the growing complexity of inflation factors. Kaplan highlighted that various forces currently exert both upward and downward pressure on inflation. While significant AI infrastructure investment, tariffs, labor supply constraints, and rising oil prices could increase inflationary pressure, the application of AI could also boost productivity, lower costs, and accelerate disinflation. Therefore, the Fed must assess economic developments comprehensively. He also commented on Fed Chair Warsh's upcoming speech at the Jackson Hole symposium, suggesting that a clear explanation of the July rate decision would help markets better understand the Fed's policy approach. Kaplan added that the recent rise in long-term U.S. Treasury yields is primarily driven by structural supply-demand imbalances from persistent U.S. budget deficits, not directly by Fed policy.

cryptonews.ru12 год тому

Gold Sachs Issues Statement on Upcoming Fed Interest Rate Decisions! 'In September...'

cryptonews.ru12 год тому

Overnight, GPT-5.6 Sol Was Accelerated 14x by OpenAI

OpenAI, in collaboration with chipmaker Cerebras, has unveiled a limited preview of an "Ultrafast Mode" for its flagship GPT-5.6 Sol model. This new service tier reportedly achieves output speeds of up to 750 tokens per second—a 14x increase over the standard mode's baseline of ~53 tokens/s—without any loss in quality. Key to this acceleration is Cerebras's wafer-scale architecture (WSE-3), which houses model parameters entirely in on-chip SRAM to eliminate the memory bandwidth bottlenecks typical of traditional GPU clusters. In benchmark testing on the challenging "Humanity's Last Exam" (HLE), GPT-5.6 Sol in Ultrafast Mode answered all 2500 questions in 11 hours and 11 minutes, compared to over 78 hours for a competitor model, while maintaining similar accuracy. The speed boost also translated to a 5.6x faster end-to-end performance on the GDP-Val benchmark for economically valuable knowledge work. OpenAI highlights several potential applications for such rapid inference, including real-time event response and reliability analysis, dynamic financial research and security, complex customer support, interactive shopping assistance, and accelerated research and experimentation workflows that enable multiple iterative cycles within a single workday. This advancement may allow users to deploy the highest-tier models for tasks previously requiring slower secondary models, significantly compressing multi-step agent workflows from hours to minutes.

marsbit18 год тому

Overnight, GPT-5.6 Sol Was Accelerated 14x by OpenAI

marsbit18 год тому

Analyzing the Impact of AI on Economic Growth and Productivity

**Title: Analyzing AI's Impact on Economic Growth and Productivity** This article examines three contrasting views on AI's influence on economic growth and productivity. **The Optimistic View** posits that AI, especially through automating R&D ("recursive self-improvement"), could dramatically accelerate growth, even triggering a technological "singularity" with explosive, potentially infinite, economic expansion. **The Moderate/Mainstream View** acknowledges AI's productivity benefits but emphasizes significant real-world constraints that could limit its impact. These include: limited cost savings per task, structural ceilings on which jobs and industries are "exposed" to AI, adoption bottlenecks (e.g., compute, energy, regulatory hurdles), and the "weak link" effect where non-automatable tasks cap overall gains. Consequently, the realized AI dividend may be far lower than optimistic projections, with estimates typically ranging from 0.1% to 1.3% annual productivity growth. **The Pessimistic View** stems from two strands. The first aligns with the moderate view but applies extremely conservative assumptions about task exposure and efficiency gains, yielding minimal projected impact. The second introduces a demand-side critique: if AI primarily replaces rather than augments labor, it could depress labor's share of income, weaken consumer demand, and create a "demand trap" that ultimately stifles growth, unless offset by redistribution policies. **The authors' assessment** is nuanced: * **Short-term (1-2 years):** AI will support growth primarily through investment spending, not significant productivity gains. * **Medium-term (3-5 years):** Three potential paths emerge based on AI demand and bottleneck severity: 1. **"Optimistic Path":** High demand, few bottlenecks. Rapid productivity gains but risk of major job displacement and social conflict without redistribution. 2. **"Moderate Path" (most likely):** High demand but significant, surmountable bottlenecks. Leads to moderate productivity gains, financial market volatility (K-shaped returns), and sectoral job losses. 3. **"Pessimistic Path":** Low demand or severe bottlenecks. Minimal productivity and growth impact, triggering financial market corrections but allowing a smoother societal transition with less labor disruption. * **Long-term:** AI holds potential for a major productivity revolution and prosperity. The conclusion stresses that no path is smooth. Technologically "optimistic" outcomes could be socially detrimental, while "pessimistic" technological diffusion might be more socially stable. Policymakers must monitor developments and prepare balanced responses to manage economic, financial, and social sustainability.

marsbit07/31 10:46

Analyzing the Impact of AI on Economic Growth and Productivity

marsbit07/31 10:46

Feishu Has Become Doubao

This may be the most important day in the ten-year history of Feishu. On July 30, ByteDance announced a major restructuring via internal email. Feishu, an independent operation for a decade, will no longer function separately and will lose its independent sales and marketing teams. Its product team is being integrated into Doubao AI, and its Go-to-Market strategy merges into the Volcano Engine's To B system. This move signals a strategic pivot, not a failure of Feishu. The era where Feishu sought to be an all-encompassing super-app for productivity is ending. AI has become the dominant logic. Industry trends show a shift from building platform "entrances" to embedding AI directly into workflows, as seen with Microsoft Copilot and Google's Gemini. Data reveals the driver: over 90% of Feishu's new clients in Q2 2026 purchased its AI products. Enterprises are now buying integrated AI solutions, not standalone software. By integrating Feishu's deep enterprise workflow scenarios, Doubao evolves from a consumer-facing chatbot into a powerful enterprise productivity engine. This gives Doubao a critical asset: real-world business application. Concurrently, ByteDance established the "Creative Services Platform," unifying its previously fragmented sales fronts (Feishu, Doubao, Volcano Engine) into a single customer-facing entity. This represents ByteDance's first genuine shift to thinking like a true To B company, organizing around client needs rather than internal product silos. The restructuring underscores that AI is no longer just a business line but foundational infrastructure. Doubao is becoming the new organizational core, mirroring TikTok's past ascendancy. This demonstrates a profound shift: AI is beginning to reshape corporate structures, determining resource allocation and strategic priorities. ByteDance is likely just the first of many companies to undergo such an AI-driven reorganization.

marsbit07/30 10:37

Feishu Has Become Doubao

marsbit07/30 10:37

Walsh: 2% Inflation Target Unwavering, Maintain Independence, Focus on AI Transformation (Full Text Attached)

Fed Chair Warsh emphasized an unwavering commitment to the 2% inflation target, stating there is "no soft target" and that the FOMC is united on this goal. He indicated that if high inflation persists, interest rate hikes would likely be part of the solution. The Fed voted 9-3 to maintain the benchmark rate at 3.5%-3.75%. Warsh announced a significant shift in policy communication, moving away from forward guidance. He noted that market rates have risen between meetings, reflecting a tightening of financial conditions as participants now react more to direct economic data rather than Fed signals. On AI, Warsh highlighted a nearly 20% annual growth rate in high-tech capital expenditures related to artificial intelligence. He acknowledged its potential to boost productivity and supply but also its role in driving up prices for certain sectors like chips and AI infrastructure. The Fed is assessing whether these price pressures will remain sector-specific or spread more broadly. Warsh stressed the Fed's independence, asserting it will not deviate from its mandate due to external pressures. He framed the current decision not as a "pause" but as a period of rigorous review of economic conditions, with future actions to be data-dependent.

marsbit07/30 00:58

Walsh: 2% Inflation Target Unwavering, Maintain Independence, Focus on AI Transformation (Full Text Attached)

marsbit07/30 00:58

Those Who Use AI to 'Quickly Finish the Job' Are Ruining the Workplace

Some people are using AI tools to quickly produce superficially complete work, like reports, proposals, or performance reviews, just to "check a box." This results in "Workslop" – workplace AI output that looks polished but lacks factual verification, professional judgment, and real value. The core problem is the shifting of work costs: while the generator saves time, the receiver must spend hours verifying information, correcting errors, and deciphering vague conclusions. This undermines team efficiency and trust. Examples include a manager using AI for vague, unactionable employee feedback, an analyst generating weekly reports with misleading AI-interpreted data, and a team producing extensive project documents with no one able to justify the underlying assumptions. The issue isn't AI itself, but how it's used. It amplifies existing work habits. Responsible users treat AI as an assistant for drafting and organizing, then add firsthand verification and judgment. Those merely looking to "hand in" work delegate responsibility to the AI and their colleagues. Ultimately, the value of work should be judged not by its speed or polish, but by whether it provides new facts, clear judgments, and an accountable owner. AI cannot understand context, make critical decisions, or bear the consequences – those remain irreplaceably human responsibilities.

marsbit07/29 08:11

Those Who Use AI to 'Quickly Finish the Job' Are Ruining the Workplace

marsbit07/29 08:11

Claude Designer Lags Behind Engineers, Fires Back by Creating a Million-User Tool

The article tells the story of Nate Parrott, a designer at Anthropic who created Claude Design as a side project to keep pace with his engineering teammates. When Anthropic released Opus 4.5 in November 2025, the two engineers on Parrott's Claude Code team significantly increased their output using the new AI capabilities. Parrott, the sole designer, found himself struggling to match their speed, becoming a bottleneck in the workflow. To catch up, he began experimenting in his spare time. He initially tried prompting Claude to generate designs from text descriptions and screenshots, with limited success. His breakthrough came when he shifted focus from asking Claude to "design" to asking it to generate HTML. He realized HTML could be a rich visual canvas for creating everything from slides and interactive prototypes to full web pages. He built a simple interface with a chat panel on the left and a live HTML preview on the right. The key to making the output useful was incorporating Anthropic's brand system—fonts, colors, assets, and design principles—into the prompts. This ensured generated designs were immediately on-brand. He shared an internal prototype with his team, and other product designers quickly adopted it for creating clickable prototypes, a task traditionally requiring manually drawing every state. The project's "official" turning point came during an Anthropic Labs offsite, where Parrott noticed many attendees were using his tool to build presentation slides on the fly, sometimes right before their turn to speak. This organic adoption convinced the Labs team to formally staff the project, turning the side project into a real product. Claude Design is positioned as a "pre-production" tool for visual communication and exploration—handling slides, landing pages, PDFs, emails, and social media graphics. It integrates with tools like Canva, Adobe, and Vercel. Its core value is accelerating the early stages of design: exploring directions, building consensus, and establishing systems. For actual production code, Anthropic still recommends Claude Code. The story highlights how AI disrupts workflows unevenly and how individuals can respond by building new tools to create their own advantages. Parrott's tool, born from necessity, eventually gained over a million users in its first week.

marsbit07/28 00:41

Claude Designer Lags Behind Engineers, Fires Back by Creating a Million-User Tool

marsbit07/28 00:41

With One Sentence, It Built a 9-Person Travel Website; Altman: The Name ChatGPT Work Was Too Small

OpenAI CEO Sam Altman demonstrates the new capabilities of ChatGPT Work by using it to plan a long weekend trip for himself and eight friends. With a single spoken command on his phone, ChatGPT Work leveraged Altman's chat history to generate three travel ideas, built a full-stack website for the group to discuss and decide on options, and even drafted a Gmail message for him to send. Altman and other OpenAI executives, including Greg Brockman, suggest the "Work" name undersells its scope, positioning it more as a personal AGI assistant. The article details the underlying features enabling this, such as the Sites function for creating interactive web applications, Plugins for connecting to external services like Gmail and Google Drive, and Scheduled Tasks for automating recurring actions. It highlights a shift from ChatGPT merely providing answers to autonomously executing complex, multi-step projects across both work and personal life, with users primarily responsible for setting goals, reviewing outputs, and approving key actions. The integration across web, desktop, and mobile allows the smartphone to act as a "remote control" for these AI agents. However, the technology still requires human oversight for permissions, sensitive data, and final approvals.

marsbit07/27 13:01

With One Sentence, It Built a 9-Person Travel Website; Altman: The Name ChatGPT Work Was Too Small

marsbit07/27 13:01

ChatGPT Voice Enters the Desktop Arena: You Talk, a Team of AIs Get to Work

Recently, AI pioneer Andrej Karpathy shared his preferred method for interacting with AI: using voice input to convey complex, unstructured thoughts. Instead of carefully typing out requests, he simply speaks freely for minutes at a time. He finds that AI excels at untangling these "stream-of-consciousness" monologues, returning clearer, more organized outputs than the original spoken input. This approach, he argues, improves "mind-merging" with the model by providing high-bandwidth context. Following this trend, OpenAI has integrated advanced voice capabilities into the desktop version of ChatGPT for Work and Codex scenarios, available globally for macOS and Windows. Powered by the new GPT-Live model, the feature allows real-time, interruptible conversation—users can speak to start tasks, check progress, manage multiple AI agents simultaneously, and change directions mid-task. The system can leverage project context, connected documents, calendars, and communication tools to continue unfinished work. Industry leaders like Elon Musk and Sam Altman have also emphasized the shift toward voice, noting that typing is a bottleneck for conveying complex intent to increasingly capable AI agents. Speech offers a much higher throughput, allowing users to dump extensive context quickly, even if it's messy, letting the AI handle the structuring. Beyond mere input, OpenAI's desktop voice feature enables project management through speech. Users can prioritize tasks, coordinate multiple agents, and receive audio or visual notifications on task completion or blockers. This positions ChatGPT not just as a conversational tool but as an "AI work operating system"—a central hub where users act as managers orchestrating a team of AI assistants to handle tasks across files, codebases, and software on the desktop, the primary workspace for complex work. The core insight is that by removing the input bottleneck, voice interaction allows humans to focus mental energy on higher-level decision-making, while delegating execution to an ever-present team of AI agents.

marsbit07/27 10:42

ChatGPT Voice Enters the Desktop Arena: You Talk, a Team of AIs Get to Work

marsbit07/27 10:42

Claude Doesn't Submit Code Directly After Writing It: Runs 4 Skills for Self-Check, Fixes Issues, Then Comes Back to You

Claude No Longer Submits Code Directly: 4 Self-Check Skills to Run Before Coming Back to You AI already writes code, but the burden of reviewing it still falls on you. To address this, Anthropic has built a "verification loop" into Claude Code. After writing code, Claude now runs four self-check skills before delivering the work: * `/code-review`: Finds potential bugs and provides review feedback. * `/simplify`: Cleans up the diff, removing redundant or over-complex implementations to reduce future maintenance costs. * `/verify`: Performs end-to-end validation, actually running the application to confirm the feature works, not just appears to. * `/design`: Used only for UI changes; cross-references the implementation against the project's DESIGN.md file. This loop extends the AI agent's workflow from "gather context → execute" to "gather context → execute → auto-verify → fix → re-verify." It tackles the new bottleneck in AI-assisted development: the speed of verifying code now outpaces human review. These skills are built on Claude Code's existing verification foundation (like running apps and using linters). Teams can create their own custom verification skills by documenting their repetitive manual checks in plain language as Markdown files. Verification can be triggered at four levels: manually (Standalone), embedded in a task, chained with other skills, or automatically on every PR (On every PR). The shift signifies that competition in AI programming is moving from code generation to robust verification and self-correction. Well-built verification loops allow AI agents to run longer and more autonomously with less human supervision. Skills, which encapsulate team knowledge and workflows, are becoming a cross-vendor standard, meaning a team's efficiency gap will depend less on the AI model and more on their investment in these automated workflows and verification mechanisms.

marsbit07/27 10:23

Claude Doesn't Submit Code Directly After Writing It: Runs 4 Skills for Self-Check, Fixes Issues, Then Comes Back to You

marsbit07/27 10:23

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