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AI NEWSLatest AI NewsArticleDeepSeek V4 Official Version Tentatively Scheduled for August 3rd: Silicon Flow Price Hike Exposed, API Can Now Answer New QuestionsPublished in Latest AI NewsTime :Jul 31, 2026Read :4minuteDeepSeek V4’s official version was delayed after its gradual testing started in mid-July, and the official has not yet announced the exact release date. However, multiple new clues point to August 3rd being a key node - the Silicon Valley Flow platform first exposed the secret.The platform announced that it will raise the cache hit price of DeepSeek V4 Pro model from the current 0.1 yuan per million Tokens to 1.0 yuan starting on August 3rd. Such an abrupt price increase can only be reasonably explained by the imminent release of the official version of DeepSeek V4; otherwise, it is hard to explain the motivation behind the Silicon Valley Flow’s actions.APIs become ‘smarter’ ahead of time, frequent service fluctuations confirm the gradual testingAnother key signal comes from the developer community’s test verification. Several netizens found that some DeepSeek V4-related APIs can now correctly answer the release dates of models such as Claude Opus 4.5 and Sonnet 4.5, which were previously unable to be answered without internet access. The tests were conducted through APIs rather than web chats, and not all users could reproduce them, which is a typical feature of gradual testing with phased releases.Recently, DeepSeek services have frequently experienced instability, which also indirectly confirms that the team is continuously conducting small-scale tests and rolling back changes to refine stability. Since mid-July, DeepSeek has been continuously iterating, and the external expectation is that the official version’s performance should at least reach the level between GLM 5.2 and Claude Opus 4.8.Competitors are not idle: OpenAI cuts prices by 80%, GLM increases pricesThe pressure on DeepSeek V4 is not limited to the technical side. OpenAI announced today that the GPT-5.6 series has been reduced by up to 80%, and the cost-effectiveness after the price cut is almost comparable to DeepSeek V4 Pro, combined with multi-modal advantages, the title of the most cost-effective model has already been taken by OpenAI. In contrast, the latest subscription price of GLM has increased by 2 to 3 times in China, making the cost-effectiveness advantage of GPT even more prominent. Under this differentiated situation where competitors are cutting prices to capture the market and raising prices to increase revenue, whether DeepSeek V4 can regain ground with performance may be answered on August 3rd.

AI NEWSLatest AI NewsArticleMoon Shadow Kimi Secures $3.5 Billion in Series F Funding, Valuation Rises to $35 Billion, Series G Financing Has Already Started in AdvancePublished in Latest AI NewsTime :Jul 29, 2026Read :2minuteKimi, a subsidiary of Moonshot, has completed its Series F funding round, with the amount exceeding 3.5 billion USD, and the post-money valuation has risen to 35 billion USD. Due to the subscription volume being more than three times the original target, this funding round was closed ahead of schedule, with the enthusiasm of the funds closing earlier than planned.The pace is even tighter ahead. The Series G (pre-IPO) round, originally scheduled to start in August, has already begun early, with the pre-money valuation raised to 50 billion USD. From the conclusion of the Series F round to the start of the Series G round, there was almost no pause — the capital’s pursuit of the company is accelerating by the month. When one round of financing closes early due to oversubscription and the next round quickly follows, Kimi’s value in the primary market is climbing along a steep curve.

AI NEWSLatest AI NewsArticleWorkBuddy Major Update: ‘Human-Machine Dual Writing’ Launches, the Office of the AI Era Has Arrived!Published in Latest AI NewsTime :Jul 30, 2026Read :9minuteOn July 30, Tencent WorkBuddy released a major version update (V5.3.5): in collaboration with Tencent Docs, it officially launched the “human-AI dual-writing” collaborative editing capability for the Agent era. The new version allows users to directly collaborate with AI within the same file when opening Word, Excel, PPT, Markdown, and other document formats, addressing pain points such as multi-role collaboration in “human-AI hybrid writing” teams.From the traditional “human-file interaction” of conventional Office, to “human-human collaboration” in online documents, to “human-AI task completion” in the AI era, the launch of this feature in WorkBuddy has made AI the first time an “add-on” in office collaboration become a collaborator within the same document. It also achieved, for the first time in the industry, “human-human, human-AI, AI-AI” multi-end synchronized collaboration, driving Office into an AI-native collaboration era.AI Enters Documents: From “Help You Write” to “Write with You"Previously, users needed to switch between chat windows and Office software when using AI for work. To get AI to generate content, they had to copy and paste it into the document; to ask AI to modify existing materials, they had to re-describe the background and revise the entire text; when working in teams, it was difficult to collaborate in real-time around the same document with the same AI partner. Although AI could generate content, it was hard to truly integrate into the user’s ongoing workflow.With this upgrade, WorkBuddy now directly integrates AI capabilities into the document editing environment. After opening a Tencent Docs or local Office file in WorkBuddy, users can select text, table areas, or PPT pages in the original file and use natural language to tell AI what task needs to be completed. For example, selecting a market analysis report to optimize its expression; circling a set of Excel data to have AI analyze it and generate charts; specifying a PPT page to have AI adjust its structure and content; marking blank areas in a report to have AI add relevant sections.AI-generated content appears directly in the original file, where users can continue to edit or add comments. AI will perceive and complete the modifications in real time. After AI completes the edits, users can take over and continue writing at any time. This means that AI is no longer just a “side panel assistant,” but becomes a true collaborator in the creation process.Three Core Upgrades Make AI Transition from “Auxiliary Add-on” to “Third Colleague"The core of this upgrade is to give AI more document operation capabilities and context awareness similar to real collaborators, which are mainly reflected in three aspects:First, it covers more files and scenarios, supporting full human-AI co-writing.Relying on Tencent Docs’ editing capabilities, the latest version of WorkBuddy supports mainstream office formats such as Markdown, Word, Excel, and PPT, and covers both local Office files and online Tencent Docs. Whether it is a thesis, proposal, lesson plan, technical documentation, consulting report, or operational weekly reports, pitch materials, and data analysis, users can complete creation, editing, and collaboration in the same workflow.Second, selecting and modifying is precise and efficient.Users can select paragraphs, data areas, or PPT canvases and let AI make changes only within the specified range, keeping the rest of the layout and content unchanged. For example, in Excel, you can circle thousands of rows of data and let AI perform a pivot analysis, generate trend charts, or batch correct errors and apply complex formulas. Switching to PPT, you can let AI automatically layout the analysis conclusions and charts in a blank area. For structured documents like proposals and lesson plans, you can select the areas to be filled and let AI complete them according to the template.Third, multiple people plus AI, real-time collaboration without interruption.AI-generated content can be uploaded to Tencent Docs with one click, allowing team members to jointly edit the same document across different terminals, as well as collaborate with the same AI partner. All changes are synchronized in real time, and the document context remains intact. From personal writing to team collaboration, to multiple people working on the same task with AI together, human collaboration, human-AI collaboration, and AI-AI collaboration are seamlessly integrated within the same document.From the first generation solving individual local office work, to the second generation solving multi-person collaboration through online documents, as AI agents are deeply integrated into enterprise productivity, office software is moving towards the third stage. By enabling “human-human, human-AI, AI-A

AI NEWSLatest AI NewsArticleConcerns about Microsoft’s Cloud: Growth Deceleration and Leverage Risks Behind Billions in RevenuePublished in Latest AI NewsTime :Jul 31, 2026Read :3minuteIn today’s cloud services market, the arms race among major players is accelerating rapidly. Microsoftachieved its first annual revenue of over $100 billion in the just-completed fiscal year, with a year-over-year growth of 43%. However, this performance does not guarantee peace of mind, as its business growth faces challenges and is even surpassed bywith an astonishing growth rate of 82%.The reason behind this lies in the different paths both giants have chosen in terms of technical approaches and business returns. Google, with its complete ecosystem of self-developed models and chips, has significantly increased its cloud operating profit margin from 20.7% to 35.6%, doubling its profits. In contrast, Microsoft, whose Maia and Cobalt chips are still in early stages, currently relies heavily on commercial chips from NVIDIA. The reselling stack model not only thins its profit structure but also puts more pressure on its computing cost.At the same time, Microsoft’s large order backlog has drawn deep scrutiny from the market. A significant portion of its $67.8 billion in contract backlogs depends on continuous investment from a single client. This highly concentrated client structure and the cycle of capital market lending have raised concerns about potential risks in infrastructure.Against the backdrop of intensifying industry competition, major cloud providers continue to increase their investments. How to find a balance between aggressive computing power arms race and stable financial health will become key to reshaping the future cloud landscape.

AI NEWSLatest AI NewsArticleMegMind Open Sources ForgeStencil: Dual Agent Automatically Optimizes Over 100 Industrial Software in One Week with Zero Human InterventionPublished in Latest AI NewsTime :Aug 4, 2026Read :8minuteEvery breakthrough in modern industry and cutting-edge research is backed by a large-scale high-performance computing software, whose core often lies in a computationally intensive and unavoidable computational pattern - Stencil computation. Whether it’s weather forecasting, seismic exploration, electromagnetic simulation, or material simulation, the essence is to repeatedly update the entire grid using weighted combinations of grid point neighbors. This kind of computation is memory-intensive and constrained by memory bandwidth, often consuming a large portion of the application’s runtime, and its speed directly determines the efficiency of key industrial and scientific software in a country.Previously, optimizing Stencil to the physical limits of hardware could only be done by extremely scarce HPC experts. Although compilers, code generators, and automated tuning systems can automatically generate code and search parameters, the optimization algorithms themselves still required manual design and integration. When scenarios, shapes, or hardware changed, they had to be manually re-integrated, always failing to scale. Now this situation has been broken open - Mianbi Intelligence, together with the OpenBMB open-source community, has released the world’s first AI optimization system for Stencil, ForgeStencil, which supports automatic research and deployment, and is directly open-sourced.According to disclosures, ForgeStencil completed the automatic optimization of over 100 real industrial and scientific computing software within just one week, reducing the optimization time per application to hours, increasing R&D efficiency by about 100 times, and scaling with computing power. This means that for the first time, Stencil optimization is free from human constraints, making scalability possible.Its most disruptive aspect is the dual Agent-driven, zero-human intervention approach. Humans simply need to throw in the source code of the application to be optimized. From automatic analysis, hotspot localization, kernel forging, to operator replacement, correctness verification, and integration back into the original application, no experts are involved in decision-making throughout the process. The system consists of two parts: Kernel Agent and App Agent. The former is the scientist responsible for operator forging, independently researching and synthesizing high-performance kernels, building specialized operator matrices for mainstream Stencil types, various shapes, and precision requirements, translating mathematical expressions into elegant code that approaches the hardware limit. The latter is the pragmatic engineer, crafting tailored solutions for each real application, identifying hotspots, establishing GPU baselines, verifying integrations, and finally conducting end-to-end evaluation using the application’s own test cases.In the operator competition, ForgeStencil invited well-known frameworks like Halide, Devito, EBISU, DRStencil, and FlashFFTStencil to compete on equal terms: under the same fp32 precision, it achieved a geometric mean acceleration of 2.35 times; under mixed fp16 precision, it gained an additional 1.95 times speedup. Even on the notoriously difficult variable-coefficient Stencil with all shapes, it still achieved a geometric mean acceleration of 1.34 times compared to the best baseline.Even more impressive is that it delves into real industrial scenarios, not just idealized benchmarks. In mainstream scientific software and authoritative benchmarks, about 42% of the optimizations directly correspond to real industrial production: the hypre structured multigrid solver library was accelerated by 3.86 times, minisweep nuclear reactor neutron transport was accelerated by 5.78 times, gprMax and FDTD electromagnetic simulations were accelerated by 2.47 times, RTM reverse time migration for oil and gas seismic imaging was accelerated by 1.81 times, Total’s minimod seismic mini-app was accelerated by 1.22 times, QuantLib bond pricing was accelerated by 1.82 times, non-Cartesian MRI reconstruction was accelerated by 2.45 times, and digital breast tomosynthesis back-projection was accelerated by 1.63 times. It directly tackles real production loads from oil companies, medical equipment, and meteorological departments.Compared to human experts who keep their knowledge locked in their minds and are hard to share, ForgeStencil allows a large number of parallel agents to share a common knowledge base, enabling real-time exchange of experience and achieving intelligent collective synchronization and evolution. This is another achievement from Mianbi Intelligence based on the Forge Engineering software engineering paradigm, following the ForgeTrain release in May. It moves from self-genera