https://t.co/JV9UaSABHz [CEO AI Revealed for the First Time] The Business Plan Automatically Wrote Itself Late at Night. The Secret Sauce of a Public Company CEO Was Something Everyone Already Had CEO Yoosuke Akinoshi [AI × Management] #AISummary How to Build a CEO AI and Multiply Productivity by 10x 🔳 Business Communications Increased 5x with CEO AI After implementing CEO AI, communications to employees increased by approximately fivefold. By automating project progress checks and document instructions, a system was established where AI continues operations even while the CEO sleeps. 🔳 Dramatic Increase in Business Plan Creation AI has automated business plan creation, enabling the generation of around 30 plans per day and roughly 200 per week. Previously neglected tasks due to time constraints are now being addressed, significantly boosting productivity. 🔳 Building a CEO AI Is Surprisingly Simple All you need initially is a computer and an AI tool like Claude. The key is teaching the AI your thinking patterns, decision criteria, and past experiences. No specialized programming skills are required—you can build your own personalized AI. 🔳 Train the AI on Both Work and Personal Life The CEO AI ingests not only emails, calendars, notes, and work documents but also shopping history, health data, and daily habits. The goal is not merely to optimize work but to streamline all aspects of life. Security measures for handling personal information are essential. 🔳 Historical Data Enhances AI Accuracy By training the AI on past emails, notes, and work records—not just current information—it learns how your thinking and decision-making have evolved over time. The more long-term data you accumulate, the closer the AI becomes to replicating your unique thought process. 🔳 The Secret Sauce of CEO AI Is Email Email was identified as the most critical training data. It accumulates information on work interactions, purchases, banking transactions, schedules, and more—providing approximately 70% of the insights needed to understand the individual. Those who have used the same email address for a long time find it easier to build a personalized AI. 🔳 Optimize Your Day with Morning Briefings Each morning, the CEO AI automatically generates a summary of the day’s schedule, key priorities, meeting readiness, and recommendations for work and personal life. Information that previously took 12 hours to gather can now be reviewed in just 3 minutes, helping you identify what to prioritize each day. 🔳 Significant Reduction in Meeting Time AI pre-reviews meeting materials and instructs staff on necessary preparations, allowing the CEO to focus solely on final decisions. Cases that previously required multiple meetings can now be resolved in one session due to higher-quality prep work, drastically cutting meeting time. 🔳 AI Pre-Reviews Employee Submissions Employee-created documents are first reviewed by AI against the CEO’s decision criteria. Only documents that pass this review reach the CEO, reducing confirmation workload while improving decision quality and reducing time spent evaluating materials. 🔳 Automated Business Plan Generation by AI By learning from the CEO’s past business plans and thinking patterns, the AI generates plans nearly identical to those the CEO would create—based on brief instructions alone. In a video demonstration, a 15-second prompt generated multiple plan options, incorporating past experience and career background into each proposal. 🔳 Enhance AI Performance with High-Quality External Data Even with limited personal data, you can improve output quality by training the AI on business plans or public management reports from top performers. Personal style is learned from private data like emails; specialized knowledge and planning skills are strengthened using external resources. 🔳 Start for Just ¥3,000–4,000 Per Month The CEO pays approximately $400/month for AI services but explains that under $100/month is typically sufficient. Even at ¥3,000–4,000/month, users may save 50–100 hours of work time per month. 🔳 Security Measures Are Mandatory When feeding internal or personal data into AI, preventing data leaks is critical. The CEO recommends configuring systems to store data locally and restrict external transmission. However, absolute security cannot be guaranteed—enterprise approval and proper management are prerequisites. 🔳 Begin by Consulting AI on Company Strategy As an entry point for AI adoption, recommend uploading your company’s revenue, profit, financial data, and business strategy. Analyze strategies for growing sales from ¥100 million to ¥1 billion, differentiating from competitors, or launching new ventures leveraging employee strengths to support decision-making. 🔳 Focus on Trial Volume Over Perfect Prompts You don’t need a perfect prompt from day one. Repeatedly giving instructions via voice input or other methods is more important than spending days refining wording. Learning speed increases faster through 100 trials than through three days of planning. Never blindly accept AI responses—always apply your own experience and intuition to judge outcomes. 🔳 Eight Levels of CEO AI AdoptionLevel 1: Answering questions Level 2: Document creation Level 3: Integration of personal data Level 4: Morning briefings Level 5: 24/7 AI agent At Level 6, the AI can make proposals exceeding the individual’s own capabilities; Level 7 involves scaling across all employees; Level 8 aims to expand to other companies. 🔳 The CEO’s AI has begun surpassing the individual’s own abilities The CEO explains they have now reached Level 6. An AI trained on 16 years of historical records now proposes ideas and business strategies that the CEO could not have generated alone. As the AI model itself evolves, its outputs now exceed the CEO’s personal capabilities. 🔳 Lancers’ Goal: Widespread Adoption of AI-Driven Management In addition to its traditional freelance matching platform, Lancers is strengthening its AI initiatives. The company promotes AI adoption among employees and operates an AI talent community of approximately 10,000 members. It helps enterprises adopt AI agents that replicate the thinking and decision-making frameworks of executives, supporting overall organizational productivity improvements. 🔳 The Ultimate Goal of AI Adoption: 10x Increase in Management Productivity The purpose of the CEO’s AI is not merely to automate routine tasks, but to enhance the quality of executive decision-making while reducing time spent. The ultimate goal is for every employee to master advanced AI agents, enabling a 10x increase in overall corporate productivity.

DATA_SOURCE: TradingView host bridge ★★★U.S. Eastern Time 10:00 (Japan 23:00) Status★★★ #USStocks #usstocks Sector ETFs === Legend (Ranked by Yield) === Ticker Name (Japanese) Current Value (Normalized) Yield (%) Daily Change (%) ^SOX SOX Index 151.50 51.50 3.90 IYE Energy (IYE) 148.47 48.47 -0.56 SMH Semiconductors (SMH) 143.76 43.76 1.85 IYH Healthcare (IYH) 119.75 19.75 0.46 IYW Technology (IYW) 119.35 19.35 0.79 IYK Consumer Staples (IYK) 116.50 16.50 -1.24 ^GSPC S&P 500 110.48 10.48 0.28 IYR Real Estate (IYR) 107.21 7.21 -0.24 IYF Financials (IYF) 102.52 2.52 -0.59 IDU Utilities (IDU) 102.49 2.49 -0.55 ITA Defense (ITA) 100.73 0.73 0.73 IYC Consumer Discretionary (IYC) 98.46 -1.54 -1.02

DATA_SOURCE: TradingView host bridge ★★★ U.S. Eastern Time 10:00 (Japan 24:00) Status ★★★ #USStocks #USSTOCKS #JapanStocks 3 Indices, FANG+, Gold, USD/JPY (USD-based) Gold: Yield -8.67%, Change -0.08% S&P 500: Yield 10.49%, Change +0.30% NASDAQ 100: Yield 11.85%, Change +0.90% ACWI: Yield 10.30%, Change +0.15% NYSE FANG+: Yield 10.13%, Change +0.54% USD/JPY: Change +0.23%

https://t.co/G8vEQTjck8 I was considering buying property in Dubai, but decided against it. #OverseasRelocation #Dubai #NewZealand Overseas Y Papa Radio: Understanding the World #AISummary Is now the right time to invest in Dubai real estate? Price declines due to the Iran war and structural market risks 🔳 Conclusion: There’s no need to rush into investing in Dubai real estate The speaker argues that for investors who do not plan to live in Dubai, there is currently no need to purchase property. The price correction triggered by the war may have just begun, and future downside risks should be closely monitored. 🔳 92% of Dubai’s population are foreigners According to the speaker, 92% of Dubai’s population are foreigners, with only 8% being nationals. Many residents are drawn by economic incentives such as tax benefits and visa policies, creating a structure where population outflows can occur rapidly if conditions deteriorate. 🔳 Dubai real estate is vulnerable to speculative capital flows The speaker compares this dynamic—where people and capital concentrate under favorable conditions but flee quickly when conditions worsen—to a “locust tower.” If wealthy investors withdraw, the ripple effects could impact employment and housing demand across related sectors. 🔳 The 30% drop in the real estate index differs from actual property prices The nearly 30% decline occurred in the DFM Real Estate Stock Index, which is composed of shares of real estate development companies. According to the speaker, actual property prices have only dropped by approximately 4–7%, and these two metrics should not be confused. ◙ Real estate market deterioration lags behind stock market trends The speaker notes that real estate transaction volumes increased by 5.5% year-over-year and transaction values rose by 23.4% in Q1 2026. However, since real estate prices take longer to adjust, it is premature to judge market resilience based solely on current transaction data. ◙ Concerns over reduced flight capacity and tightening financing conditions The speaker highlights a 68% reduction in flight capacity, financial institutions considering evacuating staff, and six corporate bonds from real estate developers reaching crisis levels, indicating mounting downward pressure on the market. ◙ Real estate bubble concerns existed even before the war According to the speaker, Dubai rose from 14th to 5th place in UBS’s 2025 Global Real Estate Bubble Index. Over the past five years, property prices have surged by more than 50%, and overvaluation was already widely noted prior to the war. ◙ Past recoveries after crashes do not guarantee a similar outcome this time During the 2008 financial crisis, prices fell by 50–60% and took 4–5 years to recover. After the pandemic, prices dropped about 10% and rebounded within 18 months. However, this correction follows an elevated price level, so a similar recovery path cannot be assumed. ◙ Increased new supply in 2026 will also exert downward pressure Approximately 120,000 new housing units are scheduled for completion in 2026. With housing demand weakening due to the war, increased supply could worsen the supply-demand imbalance and further depress prices. ◙ Declining trust in safety poses a long-term risk The speaker cites Beirut, Lebanon—after its civil conflict—as an example of a city where loss of perceived safety led to prolonged real estate market recovery. If Dubai’s reputation as a safe place to live is damaged, recovery could be significantly delayed. ◙ Risk varies by area Markets with long-term residents differ in stability from those reliant on short-term speculative capital. While acknowledging regional differences, the speaker maintains that the overall structural risk—dependence on foreign residents—remains. ◙ Key factors for investment decisions: purpose of residence and price attractiveness The speaker concludes that if you have no intention of living in Dubai, there is no compelling reason to invest in its unstable market. At present, sufficient price undervaluation has not been confirmed; investors should wait and carefully observe further price adjustments and shifts in supply-demand dynamics.

This is an article from three months ago (AI, check this out): https://t.co/odEWCZRmhs Brace yourselves! Japan’s largest asset transfer in history is about to begin!! Overseas Y Papa Radio: Decoding the World #AISummary Japan’s Era of Massive Inheritance: The Impact of a ¥650 Trillion Asset Transfer on Stock Prices, Banks, and Inequality 🔳 Approximately ¥650 trillion in financial assets will be inherited over the next 30 years In Japan, around 1.6 million people die annually, over 70% of whom are aged 80 or older. According to estimates by Sumitomo Mitsui Trust Bank, financial assets to be inherited over the next 30 years will total approximately ¥650 trillion—exceeding ¥1 quadrillion when including real estate. 🔳 Three major changes brought by the era of mass inheritance The speaker predicts that large-scale inheritance will drive up asset prices such as stocks and gold, shift funds from regional banks to major financial institutions in urban areas, and increase disputes over inheritance within families and society. 🔳 The core of inheritance is asset transfer from elderly to elderly Over half of inheritors are aged 60 or older, while fewer than 10% are under 40. The dominant pattern is “elder-to-elder inheritance,” where assets pass from parents in their late 80s to children in their 60s, with limited direct transfer to younger generations. 🔳 Elderly tend to hold rather than spend inherited assets According to the speaker, Japanese elderly households rarely deplete their assets even after age 80. Due to anxiety about retirement, inherited assets are rarely spent and instead remain accumulated as deposits or financial assets. 🔳 Cognitive decline is further immobilizing assets It is estimated that approximately ¥30 trillion in financial assets are held by individuals with dementia or at risk of developing it. Reduced cognitive function makes managing or disposing of these assets difficult, further limiting their flow into markets or consumption. 🔳 Expansion of inherited asset investment will support stocks and gold Fidelity’s survey shows only about 7% of people have previously invested inherited assets. As future inheritors become more familiar with NISA and online brokerages, even a partial shift from savings into stocks or gold could push up asset prices, according to the speaker. ListItemIcon Approximately 20% of inherited assets will move across prefectures About 20% of inherited financial assets are expected to cross prefectural boundaries. Funds from parents living in rural areas will increasingly flow into children’s accounts in urban centers due to relocation for education or employment, accelerating capital concentration in cities. ListItemIcon Capital outflow from regional banks to major financial institutions The speaker notes that only about 40% of deposits from regional banks remain with the same bank after inheritance; the rest move to urban banks or online securities firms, potentially weakening the deposit base of regional banks and credit unions. ListItemIcon Business opportunities for major Tokyo banks and securities firms The movement of inherited assets presents opportunities for major urban financial institutions to acquire new client assets. Growth is anticipated in inheritance-related services such as trust bank will trusts and securities firms’ inheritance support offerings. ListItemIcon Increase in vacant homes and land with unknown owners in rural areas Japan has approximately 9 million vacant homes, and the speaker explains that inheritance accounts for about 60% of this phenomenon. When heirs live in urban areas, rural family homes are often abandoned, leading to growing problems with maintenance costs and fixed asset taxes. ListItemIcon Inheritance disputes are rising even among ordinary households The number of inheritance division cases brought to family courts has risen to about 15,000 annually—1.7 times higher than in the past. About 80% of these disputes occur in households with inheritances under ¥50 million, indicating this is not solely an issue for the wealthy. ListItemIcon Real estate, difficult to divide, fuels inheritance conflicts Unlike cash, real estate such as family homes cannot be easily divided among heirs. Conflicts often arise when elderly siblings attempt to split limited savings and property, as individual living circumstances complicate fair allocation. ListItemIcon Japan’s inheritance tax burden is among the highest globally The speaker highlights that, according to OECD comparisons, only Japan, South Korea, France, and Belgium have inheritance-related taxes exceeding 1% of total tax revenue. Since the 2015 reduction in basic deductions, approximately 10% of deceased individuals now fall under inheritance tax liability. ListItemIcon The government promotes early asset transfer through lifetime gifts The period during which lifetime gifts are added back to inheritance assets has been extended from three to seven years. Meanwhile, the inheritance settlement taxation system includes an annual ¥1.1 million basic deduction, encouraging earlier asset transfers. ListItemIcon Concerns over固化 of asset inequality through inheritance The speaker notes that households with existing assets are more likely to pass wealth to the next generation through inheritance. In a slowing economy, living standards may increasingly depend on inherited wealth rather than labor income, potentially widening inequality. ListItemIcon The essence of the mass inheritance phenomenon is not asset movement—but asset stagnationThe speaker expresses concern about a structure in which assets inherited by a child in their 60s from a parent in their 90s remain unused and are then passed on to the next generation. Since these funds are not channeled into consumption, the real economy finds it difficult to gain momentum, while financial asset prices remain artificially supported. 🔳 Widening wealth gap between asset holders and wage-earning households When inherited assets flow into financial markets rather than consumption, those who hold stocks, gold, and other financial assets benefit disproportionately. The speaker highlights that this dynamic could structurally widen the economic gap between asset owners and those reliant solely on wage income. 🔳 Preventing inheritance disputes requires early family discussions The speaker recommends that families discuss the status of assets and inheritance wishes while the parent is still healthy. Preparing a will and planning asset division in advance is seen as an effective way to prevent future conflicts. 🔳 Younger generations should begin building wealth without waiting for inheritance Even with approximately ¥65 trillion in asset transfers expected, not everyone will benefit from inheritance. The speaker concludes that for younger generations, time is their greatest asset—and it is more important to start building personal wealth early than to rely on anticipated inheritances.

https://t.co/Rsm2ijLCtL [Palantir Q3 Earnings Snapshot] Revenue up 96% YoY; U.S. Commercial Segment Surpasses Government with 148% Growth. Deep Dive on Anthropic Losses, Intel Shock, Apple, Marvell Acquisition—all in 46 Minutes Kashiwara Jin | AI Marketing Strategy #AISummary Latest Earnings Trends in AI, Sovereign AI, Semiconductors, and Optical Communications 🔳 Palantir’s Revenue Grew 96% Year-over-Year Palantir’s revenue reached $2.313 billion, a 96% increase year-over-year. For the first time, U.S. commercial segment revenue surpassed government segment revenue, driven by enterprise adoption of AI. 🔳 U.S. Commercial Segment Surpasses Government Segment U.S. commercial segment revenue hit $984 million, up 148% YoY, while government segment revenue reached $953 million, up 96% YoY. The strategy of extending government-developed technologies to private enterprises is delivering results. 🔳 Sovereign AI Accelerates Palantir’s Growth Demand for “sovereign AI”—enabling enterprises to utilize AI securely without exposing proprietary data—is expanding. Palantir supports enterprise data management and AI adoption through AIP and ontologies. 🔳 Enterprise Data Matters More Than AI Models CEO Alex Karp predicted the commoditization of large language models, arguing that competitiveness stems from combining low-cost open models with proprietary enterprise data, rather than relying on expensive proprietary AI models. 🔳 Strengthening Manufacturing Sector Expansion Through NVIDIA Partnership Palantir is collaborating with NVIDIA to enhance visibility and optimization of complex supply chains. By integrating the open model “Nemotron” with ontologies, it aims to streamline processes from manufacturing to AI processing. 🔳 Palantir’s Profitability and Customer Lifetime Value Improve Palantir’s Rule of 40 (combined growth and profit margin) reached 158%. Adjusted operating margin hit 62%, and net dollar retention stood at approximately 163–165%, driven by expansion of business with existing customers. 🔳 Concerns Over Large Contracts and Backlog Customer count increased to 1,090, but the number of large contracts declined. Remaining performance obligations (RPO) fell slightly from $4.9 billion to $4.88 billion, raising attention to future contract trends. 🔳 Palantir Raises Full-Year Revenue Guidance Raised full-year revenue guidance to $8.53–8.534 billion, projecting 91% YoY growth. Anticipates 143% growth in the U.S. commercial segment, reflecting continued expansion of private-sector AI demand. 🔳 Anthropic’s Revenue Grew Approximately 12x Year-over-Year Anthropic’s revenue for July–September reached $16.956 billion, up roughly 12x YoY. However, operating loss amounted to $567 million due to massive expenditures on AI compute resources. 🔳 Anthropic Expands Compute Resource Orders to Google Anthropic disclosed that its payments to Google for compute resources have surged to approximately ¥18.7 trillion. Its reliance on Google’s TPU and cloud infrastructure is intensifying. 🔳 Recovering Capital Investment in AI Infrastructure Is a Critical Challenge Companies like Google and Amazon continue heavy investments in AI infrastructure. Google’s capital expenditures reached $51.4 billion, resulting in negative free cash flow. The key question remains: when will these investments break even amid rising AI demand? 🔳 Google Search Growth Slows Amid AI Monetization Challenges While Google Cloud maintains high growth, search revenue growth slowed to 12%. AI-generated responses are impacting ad monetization efficiency—balancing increased search usage with ad revenue remains a challenge. 🔳 Intel and Qualcomm Face Memory Supply Constraints Surging memory demand for AI data centers has caused shortages and price increases for PC and smartphone components. Intel missed market expectations for revenue guidance, while Qualcomm faces slowing smartphone-related business growth. 🔳 Apple Maintains Growth Despite Supply Constraints Apple’s revenue rose 12% YoY to $114.477 billion. It mitigated the impact of rising memory prices through inventory management and lower costs for other components—all while avoiding large-scale AI infrastructure investments. 🔳 Marvell Announces Acquisition of Lumentum The video highlights Marvell’s announcement to acquire optical communications company Lumentum—a strategic move to integrate semiconductor and optical technologies to capture growing high-speed communication demands in AI data centers. 🔳 Accelerated Shift Toward Optical Communications in AI Data Centers Traditional copper wiring faces challenges in speed, heat dissipation, and power consumption. Optical communications is expanding beyond long-distance links to intra-rack and even intra-semiconductor-package applications, becoming a critical technology for AI infrastructure. 🔳 Optical Communications Market Projected to Reach $90 Billion by 2030 The video cites projections that the AI-driven optical communications market will exceed $90 billion by 2030, growing at an annual rate of ~40%. Marvell and Lumentum aim to expand market share through convergence of semiconductor and optical technologies. 🔳 Growing Demand for Optical Communications in Quantum ComputingOptical connectivity technology is seen as crucial not only for AI data centers but also for quantum computers and quantum networks. In addition to quantum-related companies such as IonQ, optical communication businesses from Coherent and Corning are also drawing attention. 🔳 Three key themes to watch in the future AI market Throughout the video, the key themes highlighted were sovereign AI for securing enterprise data, profitability amid massive AI infrastructure investments, and the advancement of supply chains including semiconductors and optical communications. It was emphasized that in the future, corporate competitiveness will be determined not only by AI performance but also by deployment costs, return on investment, and supply capacity.

#Claude #Codex # I Tried Running a Local LLM on a Ryzen Zen3 GPU (Qwen3-8B Heretic: Performance Comparison and Analysis of CPU vs GPU) Keep going, AMD, keep going, Vulkan. I thought it wouldn’t amount to much since it’s a gaming GPU—and I was right. Apparently, memory bandwidth is the bottleneck (due to small VRAM, relying on main memory; looking forward to Mac M5 Studio). AMD’s latest is Zen6, so this is a CPU three generations old (with integrated GPU). ---- # Qwen3-8B Heretic: Performance Comparison and Analysis of CPU vs GPU Measurement Date: 2026-11-02 / Target: Ubuntu Machine ## Conclusion - **The GPU (integrated Radeon) is about 2.5x faster than the CPU during prompt loading.** - **Text generation is only 1.1–1.2x faster on the GPU.** Generation speed is capped by memory transfer rates, as the integrated GPU shares the same memory as the CPU. - When using long instruction prompts repeatedly—as with OpenClaw—the benefit of **prompt caching** outweighs GPU acceleration; subsequent responses dropped from ~7 minutes to ~1 minute. ## Measurement Environment | Item | Details | |---|---| | CPU | AMD Ryzen 7 7730U (8 cores, 16 threads, Zen 3) | | GPU | Integrated AMD Radeon Graphics (Barcelo, equivalent to Vega 8) | | Memory | 30GB (Specified as DDR4-3200 dual-channel; actual configuration unverified) | | GPU Memory | Dedicated VRAM: 512MB + Shared Memory (GTT): ~15.4GB. LM Studio can use 10.59GiB. | | OS / Driver | Ubuntu 24.04.5 LTS, Mesa 25.2.8 (RADV) | | LM Studio | Headless version (llmster), llama.cpp 2.66.0 | | CPU Runtime | `llama.cpp-linux-x86_64-avx2` | | GPU Runtime | `llama.cpp-linux-x86_64-vulkan-avx2` (all layers on GPU, `--gpu max`) | | Model | DreamFast/qwen3-8b-heretic Q4_K_M (5.03GB) | | Loading Settings | Context length: 32768, Concurrent requests: 1 | ## Measurement Method - Sent identical prompts via LM Studio’s API (`/api/v0/chat/completions`) and compared CPU vs GPU performance. - Each condition was executed twice. For the second run, prompts were reused from cache; thus, **loading times use values from the first run**. - Generation speed is measured using LM Studio’s reported `tokens_per_second`. - Pre-generation reasoning was disabled using `/no_think`, and `temperature` was set to 0. - After GPU measurements, the system cooled for 2 minutes before CPU measurements. - Scripts and logs are located at `~/bench/bench.py`, `~/bench/run.sh`, and `~/bench/result.log` on the Ubuntu machine. ## Results ### Summary Comparison | Category | Metric | CPU | GPU | Ratio | |---|---|---|---|---| | Short query (24 tokens) generating 256 tokens | Generation Speed | 7.3 tokens/sec | 8.2 tokens/sec | 1.13x | | | Total Time | 35.7 sec | 31.4 sec | | | Prompt: 921 tokens | Loading Speed | 30.5 tokens/sec | 76.3 tokens/sec | **2.5x** | | | Time to First Token | 30.2 sec | 12.1 sec | | | | Generation Speed | 7.0 tokens/sec | 8.0 tokens/sec | 1.13x | | Prompt: 3,621 tokens | Loading Speed | 35.4 tokens/sec | 87.5 tokens/sec | **2.5x** | | | Time to First Token | 102.3 sec | 41.4 sec | | | | Generation Speed | 5.9 tokens/sec | 7.2 tokens/sec | 1.2x | ### Detailed Measurements | Method | Description | Run | Prompt Tokens | Generated Tokens | Total Time | Time to First Token | Generation Speed | |---|---|---|---|---|---|---|---| | GPU | Short query | 1 | 24 | 256 | 31.4 sec | 0.50 sec | 8.26 | | GPU | Short query | 2 | 24 | 256 | 31.2 sec | 0.12 sec | 8.23 | | GPU | Prompt ~1,000 tokens | 1 | 921 | 32 | 16.0 sec | 12.07 sec | 7.97 | | GPU | Prompt ~1,000 tokens (cached) | 2 | 921 | 32 | 4.0 sec | 0.14 sec | 7.98 | | GPU | Prompt ~4,000 tokens | 1 | 3,621 | 32 | 45.7 sec | 41.38 sec | 7.18 || GPU | Prompt ~4,000 | 2 (cached) | 3,621 | 32 | 4.5 sec | 0.14 sec | 7.22 | | CPU | Short question | 1 | 24 | 256 | 35.7 sec | 0.73 sec | 7.29 | | CPU | Short question | 2 | 24 | 241 | 32.8 sec | 0.14 sec | 7.36 | | CPU | Prompt ~1,000 | 1 | 921 | 32 | 34.7 sec | 30.21 sec | 7.03 | | CPU | Prompt ~1,000 | 2 (cached) | 921 | 32 | 4.6 sec | 0.15 sec | 6.96 | | CPU | Prompt ~4,000 | 1 | 3,621 | 32 | 107.5 sec | 102.30 sec | 5.94 | | CPU | Prompt ~4,000 | 2 (cached) | 3,621 | 32 | 5.4 sec | 0.17 sec | 5.96 | Units for prompt and generation are tokens; generation speed is measured in tokens/sec. ### Correction Previously, we stated that generation speed on the GPU was approximately 2.2 times faster, but this was incorrect. The CPU value of 3.86 tokens/sec was measured when processing a prompt of approximately 14,000 tokens on bot2. Since longer prompts result in slower generation, comparing it to the short-question scenario was misleading. When compared under identical conditions, the generation speed difference is only 1.1 to 1.2 times. ## Why Using a GPU Doesn’t Result in Much Faster Performance ### 1. Generation is Limited by Memory Transfer Speed Each time an LLM generates one token, it must read all model weights from memory. The upper limit of generation speed is roughly determined by: “Memory transfer speed ÷ Model size.” | Item | Value | |---|---| | Memory transfer speed (theoretical) | ~51 GB/s (dual-channel DDR4-3200) | | Amount read per token | ~5 GB (entire model) | | Theoretical upper limit of generation speed | 51 ÷ 5.03 ≒ **~10 tokens/sec** | | Measured (GPU) | 8.2 tokens/sec (~80% of limit) | | Measured (CPU) | 7.3 tokens/sec (~70% of limit) | The integrated GPU shares the same main memory as the CPU, so its upper limit is nearly identical to that of the CPU. Even with a GPU, there is only about a 10–20% potential improvement toward reaching this limit. Discrete GPUs have dedicated high-speed memory, significantly raising this upper bound. | GPU | Memory Transfer Speed | Ratio Compared to This Ubuntu Machine | |---|---|---| | Integrated GPU in this Ubuntu machine | ~51 GB/s (shared with CPU) | 1x | | RTX 4060 | ~272 GB/s | ~5x | | RTX 4090 | ~1,008 GB/s | ~20x | ### 2. Loading Depends on Computational Power, but the Integrated GPU Has Limited Compute Capacity During prompt loading, many tokens are processed in bulk, so speed is determined more by computational power than memory bandwidth—the area where GPUs typically excel. In practice, we observed a 2.5x improvement here. However, this GPU has the following constraints, limiting performance to just 2.5x: | Constraint | Details | |---|---| | Few compute units | Only 8 units; estimated single-precision performance ~2 TFLOPS—just a fraction of gaming GPUs | | No dedicated matrix运算 circuits | Lacks specialized AI hardware like NVIDIA’s Tensor Cores | | ROCm not supported | AMD’s GPU computing platform ROCm does not officially support this integrated GPU; it runs via generic Vulkan | Processing one token in an 8B model requires approximately 16 GFLOPs of computation. With ~2 TFLOPS, the theoretical upper limit is ~120 tokens/sec. The measured values of 76–88 tokens/sec represent about 60–70% of this theoretical maximum. ### 3. Longer Prompts Result in Slower Performance As context length increases, each token must reference more prior tokens, slowing down both loading and generation. | Condition | Loading Speed | Generation Speed | |---|---|---| | GPU, prompt ~1,000 tokens | 76 tokens/sec | 8.0 tokens/sec | | GPU, prompt ~3,600 tokens | 88 tokens/sec | 7.2 tokens/sec | | GPU, OpenClaw (15,686 tokens), first 2,048 tokens | 78.7 tokens/sec | — | | GPU, OpenClaw (15,686 tokens), overall average | 44.1 tokens/sec | 5.1 tokens/sec | | CPU, OpenClaw (14,021 tokens) | ~19.5 tokens/sec | 3.86 tokens/sec |For up to 3,600 tokens, processing larger batches simultaneously actually improved loading speed. Beyond 15,000 tokens, the rate dropped to as low as 46 tokens/second at the end. ## Cache Validation with OpenClaw (bot2 Mei) Two consecutive requests were sent using Qwen3 running on GPU within a test session. | Item | First Request | Second Request | |---|---|---| | Prompt sent by OpenClaw | 15,686 tokens | 15,744 tokens | | Actual tokens loaded | 15,686 (all) | **135 (only newly added)** | | Loading time | 356.1 seconds | **6.1 seconds** | | Generation | 247 tokens, 48.1 seconds (5.11 tokens/sec) | 249 tokens, 48.5 seconds (5.11 tokens/sec) | | Total | **410 seconds** | **58 seconds** | - OpenClaw consistently sends the same instruction text at the beginning of each prompt, so 98% of the content from the first request was reused in the second. - Most of the waiting time in the second request was due to approximately 240 tokens of internal reasoning. Even with `--thinking off` specified, reasoning did not stop. If reasoning could be disabled, total time could be reduced to around 10 seconds. - Cache does not expire based on time. It is cleared only when the model is unloaded, another process interrupts (due to a limit of one concurrent process), or the conversation is compressed. ## Options for Improving Speed Since memory transfer speed cannot be changed, reducing the amount loaded per token is effective. | Method | Expected Benefit | Notes | |---|---|---| | Leverage prompt caching | Subsequent requests load in just a few seconds (validated) | Cache can be cleared by interruptions or compression | | Disable Qwen3 reasoning | Reduce per-request time by ~48 seconds | May slightly reduce answer quality | | Switch to MoE Qwen3-30B-A3B | Uses only ~3B weights per token; generation expected to be 2–3x faster with improved reasoning | Model size is ~18GB, exceeding GPU’s 10.6GiB capacity—some processing occurs on CPU. Unverified whether a censored version is available | | Use smaller model (Qwen3-4B) | Generation speed ~2x faster | Reduced reasoning capability | | Use speculative decoding | Use Qwen3-0.6B as draft model; generation expected to be 1.3–2x faster | Effectiveness varies by content | | Further quantization (e.g., Q3) | ~10–20% improvement | Reduced accuracy | | Shorten instruction text | Reduces initial load and reload time after cache loss | Requires sacrificing functionality | Except for prompt caching, all benefits listed above are estimates and have not been measured empirically. ## Current Status (as of 2026-11-03) | Item | Status | |---|---| | LM Studio | Stopped; auto-start disabled | | Selected runtime | Vulkan (GPU) | | bot2 (Mei) | gpt-5.4-mini | | Transfer port for bot2 | Stopped; auto-start disabled | | Qwen3 configuration for bot2 | `~/.openclaw-bot2/openclaw.json.bak-qwen3-active-*` |

#Claude #Codex It’s common—Kosuke has never been used, but for websites where appearance matters, specifications and coding are separated. https://t.co/6ePkAFL6y4 【The Failure 90% Do】Why Does Letting AI Build an App Result in “This Isn’t What I Expected”? A Thorough Review of Development Workflows and Cloud Development Environment “Kosuke” to Prevent Missteps #chatgpt #claude Reck’s Forest of Knowledge #AISummary Thorough Review of the New Service “Kosuke” to Reduce AI Development Failures and Its Differences from Codex 🔳 Reasons for Unmet Expectations in AI Development When instructing AI to build a web app, ambiguous requirements often lead the AI to fill gaps with assumptions, or critical decisions may be lost midway through long conversations, resulting in outputs that deviate from intent. To mitigate this, it’s essential to break the process into stages: clarifying objectives, handing off to implementation, and verifying functionality. 🔳 Features and Mechanism of Kosuke Kosuke is a service that integrates a cloud development environment with structured workflows tailored for AI agents. It separates “Teacher,” responsible for requirement gathering and progress management, from “Development Sessions,” where actual coding occurs. This allows clarified discussions to be handed off cleanly to the implementation team. 🔳 Development Environment and AI Integration Each development session comes with its own independent branch, execution environment, and preview screen, enabling parallel workflows. Kosuke integrates with AI accounts like Codex and GitHub, allowing development to proceed without impacting existing codebases. 🔳 Demo: Weekly To-Do App A personal app was created that assigns one-hour tasks in priority order based on available time each week. It tracks goals, completion criteria, outcomes, and replanning when schedules change—but excludes login, sharing, external calendar sync, and notifications. 🔳 Requirement Clarification Before Coding Instead of writing code immediately, the AI was asked to clarify open questions and refine specifications. Goals, required features, excluded features, and completion criteria were defined; only after human validation did implementation proceed—reducing misalignment. 🔳 Handoff of Specifications to Implementation Team Finalized requirements are transferred from “Teacher” to a separate development session. Separating requirement gathering from implementation prevents irrelevant background noise and missed decisions—this structure was particularly praised by the author. 🔳 Review and Approval of UI Mockups Before full implementation, browser-based UI mockups were generated to confirm layout and interaction direction; revisions could be requested if needed. Note: mockups are for visual verification only and do not guarantee functional capabilities like data saving. 🔳 Code Review and Automated QA After UI approval, the process moves to implementation, AI-assisted code review, fixes, and automated QA. The actual app is tested via browser interaction, with recordings and check results preserved as evidence. During pre-validation, an issue was detected and fixed: old processes still executed after edit cancellation. 🔳 Final App Functionality Verification The implemented app successfully supported registering goals and completion criteria, adding tasks, setting priorities, allocating time, and replanning. Basic functions worked, but areas for improvement—such as color scheme and font size—were identified. This version prioritized minimal viable completion. 🔳 Integration with GitHub After implementation and verification, changes were reviewed via GitHub pull requests and merged into the main branch. The goal was formal code integration only; public deployment to external servers was not included. 🔳 Differences from Codex Codex alone can achieve a similar workflow by combining Plan Mode, Skills, and a local development environment—but you must manually construct the entire pipeline: requirement gathering, handoff to implementation, previewing, reviewing, QA, and GitHub integration. Kosuke’s strength lies in having all these steps pre-integrated from the start. 🔳 Pricing Plans and Additional Costs As of October 2026: Starter plan is $20/month with $25 in environment credits; Pro plan is $190/month with $250 in credits. Standard small environments cost $0.15/hour, consuming credits based on usage time. Separate credits are required for connected AI services like Codex. 🔳 Issues Encountered in Practice During pre-validation, issues arose: deployments showed as successful but previews returned 403 errors; some merges were blocked due to insufficient review approvals. These were resolved via configuration adjustments or manual GitHub merges—confirming that fully autonomous development is not yet possible. 🔳 Important Notes When Using the Service Even with advanced AI automation, human review and approval of UIs remain essential. Environment usage fees and AI service credits are separate; monthly fees do not grant unlimited usage. Additionally, this review was not a controlled comparative experiment—so it cannot be concluded that Kosuke outperforms Codex in speed or quality. 🔳 Request Methods Usable in Regular AI Chat When instructing AI, clearly define three items: “What you want to do,” “Conditions for completion,” and “What will not be done this time.” Then, before execution, ask the AI to clarify open questions; after agreement, organize and hand off to implementation: objectives, constraints, decisions made, remaining questions, and verification methods—to minimize unintended outcomes.🔳 Suitable for users who: Feel burdened by having to manually design AI development workflows each time, or prefer to streamline tasks from requirement analysis to validation within an integrated environment. Conversely, users who already manage their development environments and workflows freely using tools like Codex can achieve similar results using existing tools alone. 🔳 Verification conditions and referral rewards: This video was tested in a free environment provided with a Pro plan, featuring unlimited usage for three months. A referral reward is paid to the creator if a paid subscription is initiated through their referral link. Standard registration benefits and usage terms differ from this special offering. 🔳 The most important mindset for AI-assisted development: Rather than delegating tasks entirely to AI, it’s crucial to clearly define goals and success criteria, accurately pass on decisions, and iteratively refine outputs by verifying working results. The key to achieving desired outcomes lies not in relying solely on AI’s capabilities, but in establishing a development process where humans remain responsible for judgment and approval.

DATA_SOURCE: TradingView host bridge ★★★ U.S. Eastern Time 10:00 (Japan 23:00) Status ★★★ #USStocks #USSTOCKS #JapanStocks 3 Indices, FANG+, Gold, USD/JPY (USD-based) Gold: Yield -9.20%, Change -2.56% S&P 500: Yield 11.41%, Change -1.22% NASDAQ 100: Yield 13.36%, Change -2.65% All Country World Index: Yield 11.03%, Change -1.08% NYSE FANG+: Yield 12.10%, Change -2.25% USD/JPY: Change -0.25%

DATA_SOURCE: TradingView host bridge ★★★U.S. Eastern Time 10:00 (Japan 23:00) Status★★★ #USStocks #usstocks Sector ETFs === Legend (Ranked by Yield) === Ticker Name (Japanese) Current Value (Normalized) Yield (%) Daily Change (%) IYE Energy (IYE) 155.29 55.29 1.99 ^SOX SOX Index 153.33 53.33 -5.71 SMH Semiconductors (SMH) 148.25 48.25 -4.95 IYW Technology (IYW) 123.07 23.07 -3.11 IYH Healthcare (IYH) 120.63 20.63 1.26 IYK Consumer Staples (IYK) 117.02 17.02 1.45 ^GSPC S&P 500 111.43 11.43 -1.20 IYR Real Estate (IYR) 105.94 5.94 0.00 IDU Utilities (IDU) 102.61 2.61 0.84 IYF Financials (IYF) 101.71 1.71 -0.82 ITA Defense (ITA) 98.75 -1.25 -1.07 IYC Consumer Discretionary (IYC) 97.30 -2.70 -0.18