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Tuesday

  4. The Elec

    AI FinOps ①: Why Traditional FinOps Can't Control AI Costs

    A Harness.io survey of about 700 global enterprises shows that 67% of enterprises spend at least $250,000 per month on AI, and 73% of AI projects exceed budget because AI workload costs are hard to predict. The article points out four major reasons why traditional cloud FinOps fails in the AI environment: token-based billing, multi-model environments, rapid model iteration, and the acceleration of code generation and agentic workflows. The survey also shows that 57% of surveyed enterprises encourage employees to use AI without restrictions, and Harness.io estimates that 26% of monthly AI budgets are wasted as a result.

    A Harness.io survey of about 700 global enterprises shows that 67% of enterprises spend at least $250,000 per month on AI, and 73% of AI projects exceed budget because AI workload costs are hard to predict. The article points out four major reasons why traditional cloud FinOps fails in the AI environment: token-based billing, multi-model environments, rapid model iteration, and the acceleration of code generation and agentic workflows. The survey also shows that 57% of surveyed enterprises encou

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    #hot-signal#Industry news#English text is being prepared.
  5. Vapi Blog

    Vapi Appoints Jeff Liu as VP of Engineering

    Vapi announced that Jeff Liu has joined as Vice President of Engineering. He previously served as VP of Engineering at Turing for about five years and has also worked at LinkedIn and Google. Jeff Liu said his first priority is platform reliability and scalability to support high-impact product launches over the coming quarters. He believes Vapi is building the platform on which voice deployment depends, rather than a single application.

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    #company#Industry news#Speech

Monday

  8. Electronic Technology ApplicationSelected

    Morgan Stanley Releases 2027 AI Capital Expenditure Flow Chart: Nvidia Alone Captures Two-Thirds of S&P 500 Benefits

    Morgan Stanley strategists Nick Lentini, Mike Wilson and others released the report "Tracking the $1.6 Trillion AI Money Flow," which for the first time traces hyperscalers' $1.58 trillion in 2027 capital expenditure line by line to specific companies' revenue. The report says AI capital expenditure will drive about 25% of S&P 500 sales growth, of which about two-thirds, or roughly $690 billion, flows to Nvidia, while the other 499 constituents together account for about $339 billion. The report also notes that hyperscalers have disclosed more than $1.1 trillion in lease commitments not yet commenced, which are economically equivalent to debt financing, and projects that 2027 will be the year with the largest free cash flow gap for them.

    Morgan Stanley strategists Nick Lentini, Mike Wilson and others released the report "Tracking the $1.6 Trillion AI Money Flow," which for the first time traces hyperscalers' $1.58 trillion in 2027 capital expenditure line by line to specific companies' revenue. The report says AI capital expenditure will drive about 25% of S&P 500 sales growth, of which about two-thirds, or roughly $690 billion, flows to Nvidia, while the other 499 constituents together account for about $339 billion. The report

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    #chinese#Industry news#English text is being prepared.#Papers/Research

Sunday

  2. Tencent Cloud Developer Community

    A Guide to "Agent Engineering in Practice: Eight Lectures": Engineering Methods from Working Prototype to Production-Ready

    The Tencent Cloud Developer Community has published a guide to "Agent Engineering in Practice: Eight Lectures," centered on how to upgrade a prototype capable of calling models and tools into a system that can complete business tasks, explain failures, control costs, and iterate continuously. The series runs through two fictional teaching cases—a case-filing assistant Agent and a postoperative follow-up Agent—covering eight topics: task contracts, reasoning and collaboration, context and memory, reliable tool execution, observability and replay, evaluation and calibration, security boundaries, and production operations. The article emphasizes that a model being able to suggest the next step does not mean the system can deliver business results; the example fields, budgets, and figures are all teaching calculations and do not constitute legal advice.

    The Tencent Cloud Developer Community has published a guide to "Agent Engineering in Practice: Eight Lectures," centered on how to upgrade a prototype capable of calling models and tools into a system that can complete business tasks, explain failures, control costs, and iterate continuously. The series runs through two fictional teaching cases—a case-filing assistant Agent and a postoperative follow-up Agent—covering eight topics: task contracts, reasoning and collaboration, context and memory,

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    #hot-signal#Tutorials / practice#Agents#English text is being prepared.
  3. Shao Meng

    Stanford CS336 Lecture 10: Percy Liang on Inference Optimization

    Stanford CS336 Lecture 10 "Inference" is taught by Percy Liang, who uses a single mathematical thread to connect three layers of inference optimization—architecture, algorithms, and systems: at the architecture layer, GQA/MLA/CLA reduce dimensionality, local attention truncates context, and quantization, pruning, and distillation compress the KV cache and parameters; at the algorithm layer, speculative sampling achieves lossless acceleration; at the systems layer, continuous batching, paging, prefix sharing, and copy-on-write are introduced. The lecture notes are in executable form, with formulas derived step by step by sympy and verified with assertions. Percy believes that the KV cache is the structural root of the Transformer's unfriendliness to inference, and that architectures natively designed for inference (SSMs, linear attention, diffusion-style non-autoregressive generation) may offer the greatest room for improvement.

    Stanford CS336 Lecture 10 "Inference" is taught by Percy Liang, who uses a single mathematical thread to connect three layers of inference optimization—architecture, algorithms, and systems: at the architecture layer, GQA/MLA/CLA reduce dimensionality, local attention truncates context, and quantization, pruning, and distillation compress the KV cache and parameters; at the algorithm layer, speculative sampling achieves lossless acceleration; at the systems layer, continuous batching, paging, pr

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    #hot-signal#Tutorials / practice#Reasoning#English text is being prepared.

Friday