For centuries, ships have followed the same maritime route between Asia and Europe. Now, China wants to open a completely different route across the top of the world. As Arctic ice retreats, Beijing is turning a once-impenetrable frozen frontier into a potential new trade corridor.And the prize is enormous.&nbsp;A route that could slash sailing times between China and Europe, reduce dependence on crowded maritime chokepoints, and give China a new strategic link to the western markets.&nbsp;China calls it the “Ice Silk Road.” &nbsp;And if the route proves commercially viable, it could do more than shorten a voyage. It could redraw the map of global shipping.&nbsp;According to a Financial Times report, Chinese container shipping company Sea Legend is preparing to launch a regular service through the Arctic, linking Ningbo on China’s east coast with Felixstowe in Britain.The weekly service will travel along Russia’s northern coastline through the northern sea route.&nbsp;Sea Legend chose the name “Ice Silk Road” as a modern reference to the ancient Silk Road that connected China with Europe.The route could cut the usual 40-day sailing time between the two ports by roughly half. Interest in the North Sea route is growing. The route recorded 23 transits last summer, up from 15 in 2024. Climate change is helping open the passage.&nbsp;&nbsp;Research from the University of Southampton found that peak winter Arctic Sea ice fell 5.8 percent between 2024 and 2025, the largest decline on record. And the strategic attraction is clear.&nbsp;&nbsp;Ships can potentially avoid major chokepoints such as the Strait of Hormuz and the Bab al-Mandab Strait, where security threats have repeatedly disrupted shipping since February this year.&nbsp;&nbsp;&nbsp;

The Arctic boom is also driving demand for specialized vessels.Maritime data shows that 167 ice-class ships were built last year the highest number in more than a decade. At least another 164 are due to be built this year. The United States is also aiming to increase its presence in the Arctic, directly challenging Moscow and Beijing.Russia already dominates the region with more than 40 icebreakers and a growing military footprint. In October last year, U.S. President Donald Trump signed an agreement with Finnish President Alexander Stubb to expand America’s fleet of icebreakers. Icebreakers are specialized vessels designed to cut through ice-covered waters, ensuring safe navigation for other ships. Under the deal, the U.S. Coast Guard will acquire up to eleven cutting-edge icebreakers to strengthen national security in the Arctic. The Arctic region covers nearly one-sixth of the earth’s landmass. And the region is estimated to hold nearly 20 percent of the world’s undiscovered oil and gas reserves, making it one of the most resource-rich and politically sensitive zones on earth. But the Arctic remains an unforgiving environment. Crews must navigate shifting ice and extreme weather, sometimes without reliable GPS. A vessel can become trapped if icebreaker support is unavailable. And unlike major global shipping lanes, the Arctic has limited infrastructure for emergencies. Salvage, firefighting, medical assistance, and rescue can be extremely difficult when something goes wrong.For China, the ice silk road offers speed and strategic reach. For global shipping, it represents something far bigger, the possibility of a new trade highway emerging from a rapidly changing Arctic.

Chinese container shipping company Sea Legend is preparing to launch a regular service through the Arctic, linking Ningbo on China’s east coast with Felixstowe in Britain. The weekly service will travel along Russia’s northern coastline through the northern sea route.

For centuries, ships have followed the same maritime route between Asia and Europe. Now, China wants to open a completely different route across the top of the world. As Arctic ice retreats, Beijing is turning a once-impenetrable frozen frontier into a potential new trade corridor. And the prize is enormous. A route that could slash sailing times between China and Europe, reduce dependence on crowded maritime chokepoints, and give China a new strategic link to the western markets. China calls it the “Ice Silk Road.” And if the route proves commercially viable, it could do more than shorten a voyage. It could redraw the map of global shipping. According to a Financial Times report, Chinese container shipping company Sea Legend is preparing to launch a regular service through the Arctic, linking Ningbo on China’s east coast with Felixstowe in Britain. The weekly service will travel along Russia’s northern coastline through the northern sea route. Sea Legend chose the name “Ice Silk Road” as a modern reference to the ancient Silk Road that connected China with Europe. The route could cut the usual 40-day sailing time between the two ports by roughly half. Interest in the North Sea route is growing. The route recorded 23 transits last summer, up from 15 in 2024. Climate change is helping open the passage. Research from the University of Southampton found that peak winter Arctic Sea ice fell 5.8 percent between 2024 and 2025, the largest decline on record. And the strategic attraction is clear. Ships can potentially avoid major chokepoints such as the Strait of Hormuz and the Bab al-Mandab Strait, where security threats have repeatedly disrupted shipping since February this year.

Chinese container shipping company Sea Legend is preparing to launch a regular service through the Arctic, linking Ningbo on China’s east coast with Felixstowe in Britain. The weekly service will travel along Russia’s northern coastline through the northern sea route.

Why China’s ‘Ice Silk Road’ could rewrite global shipping | Gravitas

A Long March 7A carrier rocket carrying the ChinaSat-4B communication satellite exploded and broke apart in a fireball roughly 85 seconds after lifting off from the Wenchang Space Launch Site in China's southern Hainan province.

China's Long March 7A rocket explodes after Hainan launch

Taiwan is rapidly transforming its military strategy for a possible Chinese invasion. Drawing lessons from Ukraine’s drone warfare, Taipei is expanding fleets of aerial drones and unmanned attack boats designed to locate, track and strike invading forces.

During the 10-day Han kuang exercises, Taiwan is testing how these systems could operate alongside missiles and conventional weapons against a Chinese amphibious assault. Taiwan also aims to eventually produce up to 100,000 drones a month. But can cheap, mass-produced unmanned weapons really slow down China's vastly larger military?


Taipei tests war readiness with tanks, artillery & anti-aircraft

Taiwan is rapidly transforming its military strategy for a possible Chinese invasion. Drawing lessons from Ukraine’s drone warfare, Taipei is expanding fleets of aerial drones and unmanned attack boats designed to locate, track and strike invading forces.

During the 10-day Han kuang exercises, Taiwan is testing how these systems could operate alongside missiles and conventional weapons against a Chinese amphibious assault. Taiwan also aims to eventually produce up to 100,000 drones a month. But can cheap, mass-produced unmanned weapons really slow down China's vastly larger military?

Taiwan military prepares a drone nightmare for a Chinese invasion

A Chinese laboratory has released a coding model that performs within striking distance of the best available system and costs roughly one per cent as much to run.&nbsp;The Release&nbsp;DeepSeek officially released DeepSeek-V4-Flash-0731 on July 31, moving the model from preview into production and publishing its weights on Hugging Face the same day.&nbsp;The benchmark results are close to the frontier. On SWE-bench Verified — a test measuring whether a model can resolve real software issues drawn from open-source repositories — V4 Flash scored 79.0 per cent, within 1.8 percentage points of Claude Opus 4.7's 80.8 per cent. On LiveCodeBench it reached 91.6 per cent, one-tenth of a point behind Gemini 3.1 Pro.&nbsp;The Price Gap Is The Story&nbsp;V4 Flash costs roughly $0.09 to $0.14 per million input tokens and $0.18 to $0.28 per million output tokens.&nbsp;Anthropic's Claude Opus 4.8 costs $5 per million input tokens and $25 per million output. That is approximately 30 times more expensive on input and close to 90 times more on output — a difference of roughly 99 per cent for comparable output on coding tasks.&nbsp;The architecture explains part of it. V4 Flash has 284 billion total parameters but activates only about 13 billion per token, using a mixture-of-experts design that routes each token to the subset of the model relevant to it. Serving costs scale with the parameters actually engaged, not the parameters stored — which is how a large model can be cheap to run.&nbsp;What It Does To Everyone Else's Pricing&nbsp;This lands in the middle of the sharpest price collapse the AI industry has seen.&nbsp;In July alone: OpenAI's GPT-5.6 family launched from $1 per million tokens, then cut its lower tiers by up to 80 per cent on July 30, taking GPT-5.6 Luna to 20 cents per million input tokens. Meta entered the paid market for the first time with Muse Spark 1.1 at $1.25 and $4.25. Moonshot AI released Kimi K3's full weights for free download on July 27. Anthropic retreated on Fable 5 pricing after three deadline extensions under competitive pressure.&nbsp;The going rate for frontier-class capability has fallen by roughly an order of magnitude in a month, and V4 Flash pushes the floor lower again.&nbsp;The Caveats Worth Keeping&nbsp;Two qualifications belong on any account of these numbers.&nbsp;Benchmark scores are not the same as production reliability. SWE-bench Verified measures resolution of curated software issues; it does not capture how a model behaves across a long, messy engineering task with ambiguous requirements. Independent testing has found V4 Flash strong but not uniformly interchangeable with more expensive models, and at least one detailed comparison concluded the cheaper model was not the right choice despite the cost gap.&nbsp;And a near-miss on a benchmark is not a tie. Within two points at the top of a difficult evaluation still means losing, and for organisations where the failure cost of a wrong code change is high, the gap may be worth ninety times the price.&nbsp;What It Establishes Regardless&nbsp;The strategic point holds even with those caveats. The assumption underpinning frontier laboratory economics has been that the best models command premium prices because nothing else comes close.&nbsp;V4 Flash, alongside Kimi K3's free weight release and Qwen3.8-Max's autonomy claims, is evidence that something else now comes reasonably close — and at a price that makes the comparison unavoidable for any company running these systems at volume. That is a harder problem for the American labs than any single benchmark result, because it is not about capability. It is about what capability is worth once it is no longer scarce.

DeepSeek released V4 Flash on July 31, scoring 79.0% on SWE-bench Verified — within 1.8 points of Claude Opus 4.7 — at roughly $0.09-0.14 per million input tokens against Claude Opus 4.8's $5. It activates 13 billion of 284 billion parameters per token.

A Chinese laboratory has released a coding model that performs within striking distance of the best available system and costs roughly one per cent as much to run. The Release DeepSeek officially released DeepSeek-V4-Flash-0731 on July 31, moving the model from preview into production and publishing its weights on Hugging Face the same day. The benchmark results are close to the frontier. On SWE-bench Verified — a test measuring whether a model can resolve real software issues drawn from open-source repositories — V4 Flash scored 79.0 per cent, within 1.8 percentage points of Claude Opus 4.7's 80.8 per cent. On LiveCodeBench it reached 91.6 per cent, one-tenth of a point behind Gemini 3.1 Pro. The Price Gap Is The Story V4 Flash costs roughly $0.09 to $0.14 per million input tokens and $0.18 to $0.28 per million output tokens. Anthropic's Claude Opus 4.8 costs $5 per million input tokens and $25 per million output. That is approximately 30 times more expensive on input and close to 90 times more on output — a difference of roughly 99 per cent for comparable output on coding tasks. The architecture explains part of it. V4 Flash has 284 billion total parameters but activates only about 13 billion per token, using a mixture-of-experts design that routes each token to the subset of the model relevant to it. Serving costs scale with the parameters actually engaged, not the parameters stored — which is how a large model can be cheap to run. What It Does To Everyone Else's Pricing This lands in the middle of the sharpest price collapse the AI industry has seen. In July alone: OpenAI's GPT-5.6 family launched from $1 per million tokens, then cut its lower tiers by up to 80 per cent on July 30, taking GPT-5.6 Luna to 20 cents per million input tokens. Meta entered the paid market for the first time with Muse Spark 1.1 at $1.25 and $4.25. Moonshot AI released Kimi K3's full weights for free download on July 27. Anthropic retreated on Fable 5 pricing after three deadline extensions under competitive pressure. The going rate for frontier-class capability has fallen by roughly an order of magnitude in a month, and V4 Flash pushes the floor lower again. The Caveats Worth Keeping Two qualifications belong on any account of these numbers. Benchmark scores are not the same as production reliability. SWE-bench Verified measures resolution of curated software issues; it does not capture how a model behaves across a long, messy engineering task with ambiguous requirements. Independent testing has found V4 Flash strong but not uniformly interchangeable with more expensive models, and at least one detailed comparison concluded the cheaper model was not the right choice despite the cost gap. And a near-miss on a benchmark is not a tie. Within two points at the top of a difficult evaluation still means losing, and for organisations where the failure cost of a wrong code change is high, the gap may be worth ninety times the price. What It Establishes Regardless The strategic point holds even with those caveats. The assumption underpinning frontier laboratory economics has been that the best models command premium prices because nothing else comes close. V4 Flash, alongside Kimi K3's free weight release and Qwen3.8-Max's autonomy claims, is evidence that something else now comes reasonably close — and at a price that makes the comparison unavoidable for any company running these systems at volume. That is a harder problem for the American labs than any single benchmark result, because it is not about capability. It is about what capability is worth once it is no longer scarce.

DeepSeek officially released V4 Flash on July 31, a coding model that scores 79.0 per cent on SWE-bench Verified — within 1.8 points of Claude Opus 4.7 — while costing roughly $0.09 to $0.14 per million input tokens against Claude Opus 4.8's $5. It activates just 13 billion of its 284 billion parameters per token.

DeepSeek's new coding model costs 99% less than Claude and scores within two points of it

The company that owns TikTok is attempting to build the largest AI model China has ever produced, and it has reportedly been told to do it without copying anyone.&nbsp;The Scale&nbsp;According to reporting by the Financial Times on August 7, citing three people familiar with the project, ByteDance is pre-training a model with as many as 10 trillion parameters.&nbsp;For comparison, Moonshot AI's Kimi K3 — released as open weights on July 27 and described at the time as the largest open-weight model in history — has 2.8 trillion parameters. Alibaba's Qwen3.8-Max has 2.4 trillion. ByteDance's model would be more than three times either of them, and would place it among the largest systems being built anywhere in the world.&nbsp;The model uses a mixture-of-experts architecture, meaning only a fraction of its parameters activate for any given input. The effort is reported to require approximately 30,000 GPUs and three to six months of continuous pre-training, run by ByteDance's Seed AI team of around 2,000 staff.&nbsp;That timeline is the detail worth marking: three to six months from now places results, or problems, before the end of 2026.&nbsp;The Instruction Not To Distill&nbsp;The most consequential element of the report is not the parameter count. It is that founder Zhang Yiming has reportedly directed the Seed AI team to pursue genuine technical breakthroughs rather than relying on distillation from Western AI systems.&nbsp;Distillation — training a model to imitate the outputs of a more capable one — is precisely what the White House accused Moonshot AI of doing last month. On July 22, science and technology policy director Michael Kratsios publicly alleged Moonshot had conducted large-scale covert distillation of Anthropic's Fable model to build Kimi K3. Treasury Secretary Scott Bessent warned that ‘sanctions and Entity List designations will be on the table,’ writing that ‘open source is not open season on American IP.’&nbsp;Against that backdrop, an explicit internal directive to avoid distillation reads as a strategic position rather than a technical preference. A Chinese laboratory building a frontier model entirely from its own training runs is considerably harder to accuse of theft — and considerably harder to sanction.&nbsp;Whether the instruction is followed, and whether outside parties accept that it was, are separate questions that a finished model's weights would help answer.&nbsp;Why ByteDance Specifically&nbsp;ByteDance is not the obvious candidate for China's largest model. DeepSeek, Alibaba, Moonshot and Zhipu have been the names on leaderboards — in mid-July, six of the top ten models on independent rankings were Chinese, including all of the top five.&nbsp;But ByteDance has two advantages that matter at this scale. It has data at a volume few companies anywhere can match, from a recommendation system serving over a billion users. And it has the capital to commit 30,000 GPUs to a single training run for half a year without needing that run to produce revenue.&nbsp;The 2,000-person Seed AI team is also unusually large for a single research organisation, comparable to the headcount of entire frontier laboratories in the United States.&nbsp;What It Signals&nbsp;The broader significance is about where scale competition sits now. Western laboratories have moved noticeably toward efficiency — smaller active-parameter counts, aggressive price cuts, distillation of their own large models into cheaper ones. OpenAI cut prices up to 80 per cent on its lower tiers in July. DeepSeek's V4 Flash reaches near-frontier coding performance while activating only 13 billion of its 284 billion parameters.&nbsp;ByteDance is going the other way, betting that raw scale still has room. Ten trillion parameters is a wager that the returns to size have not yet flattened — which, if it pays off, resets the competition for everyone, and if it does not, will have been an expensive six months of GPU time.

ByteDance is pre-training an AI model of up to 10 trillion parameters, the FT reported August 7 — over three times Kimi K3's size. It needs ~30,000 GPUs and three to six months, with founder Zhang Yiming reportedly directing the team to avoid distilling from Western models.

The company that owns TikTok is attempting to build the largest AI model China has ever produced, and it has reportedly been told to do it without copying anyone. The Scale According to reporting by the Financial Times on August 7, citing three people familiar with the project, ByteDance is pre-training a model with as many as 10 trillion parameters. For comparison, Moonshot AI's Kimi K3 — released as open weights on July 27 and described at the time as the largest open-weight model in history — has 2.8 trillion parameters. Alibaba's Qwen3.8-Max has 2.4 trillion. ByteDance's model would be more than three times either of them, and would place it among the largest systems being built anywhere in the world. The model uses a mixture-of-experts architecture, meaning only a fraction of its parameters activate for any given input. The effort is reported to require approximately 30,000 GPUs and three to six months of continuous pre-training, run by ByteDance's Seed AI team of around 2,000 staff. That timeline is the detail worth marking: three to six months from now places results, or problems, before the end of 2026. The Instruction Not To Distill The most consequential element of the report is not the parameter count. It is that founder Zhang Yiming has reportedly directed the Seed AI team to pursue genuine technical breakthroughs rather than relying on distillation from Western AI systems. Distillation — training a model to imitate the outputs of a more capable one — is precisely what the White House accused Moonshot AI of doing last month. On July 22, science and technology policy director Michael Kratsios publicly alleged Moonshot had conducted large-scale covert distillation of Anthropic's Fable model to build Kimi K3. Treasury Secretary Scott Bessent warned that ‘sanctions and Entity List designations will be on the table,’ writing that ‘open source is not open season on American IP.’ Against that backdrop, an explicit internal directive to avoid distillation reads as a strategic position rather than a technical preference. A Chinese laboratory building a frontier model entirely from its own training runs is considerably harder to accuse of theft — and considerably harder to sanction. Whether the instruction is followed, and whether outside parties accept that it was, are separate questions that a finished model's weights would help answer. Why ByteDance Specifically ByteDance is not the obvious candidate for China's largest model. DeepSeek, Alibaba, Moonshot and Zhipu have been the names on leaderboards — in mid-July, six of the top ten models on independent rankings were Chinese, including all of the top five. But ByteDance has two advantages that matter at this scale. It has data at a volume few companies anywhere can match, from a recommendation system serving over a billion users. And it has the capital to commit 30,000 GPUs to a single training run for half a year without needing that run to produce revenue. The 2,000-person Seed AI team is also unusually large for a single research organisation, comparable to the headcount of entire frontier laboratories in the United States. What It Signals The broader significance is about where scale competition sits now. Western laboratories have moved noticeably toward efficiency — smaller active-parameter counts, aggressive price cuts, distillation of their own large models into cheaper ones. OpenAI cut prices up to 80 per cent on its lower tiers in July. DeepSeek's V4 Flash reaches near-frontier coding performance while activating only 13 billion of its 284 billion parameters. ByteDance is going the other way, betting that raw scale still has room. Ten trillion parameters is a wager that the returns to size have not yet flattened — which, if it pays off, resets the competition for everyone, and if it does not, will have been an expensive six months of GPU time.

ByteDance is pre-training an AI model of up to 10 trillion parameters, the Financial Times reported on August 7 — more than three times the size of Moonshot AI's Kimi K3 and larger than any Chinese system built to date. The effort requires around 30,000 GPUs and three to six months of continuous training, with founder Zhang Yiming reportedly directing the team to avoid distilling from Western models.

ByteDance is training a 10 trillion parameter model and says it won't copy the west to do it

Taiwan has conducted a major live-fire military drill simulating a Chinese assault, testing its forces’ ability to respond to a potential attack. The exercise comes amid heightened tensions across the Taiwan Strait and growing concerns over China’s military pressure on the island.

The drills come amid rising tensions across the Taiwan Strait as China continues to increase military pressure through frequent air and naval patrols around the island. The Han Kuang exercise is considered Taiwan's most important annual defense drill and reflects its efforts to strengthen combat readiness against possible aggression.


Taiwan forces throttle internet, test new drones in military exercise

Taiwan forces conduct 10-day annual military exercise

Typhoon Dolphin has made landfall on China’s eastern coast, bringing powerful winds and torrential rain as authorities prepare for widespread flooding and landslides.

China’s National Meteorological Center forecast sustained winds of up to 162 kmph before the typhoon gradually weakens as it moves inland. The storm has already caused major disruptions across eastern China, with nearly 1,300 flights cancelled at Shanghai’s two main airports and another 270 flights cancelled at Hangzhou airport.

Rail services across parts of the Yangtze River Delta have also been suspended as a precaution. Authorities have ordered vessels back to port, halted ferry services and restricted offshore and construction activities.


Typhoon Dolphin Hits China: Force 10 winds hit Fujian, 30,000 given aid

Typhoon Dolphin has made landfall on China’s eastern coast, bringing powerful winds and torrential rain as authorities prepare for widespread flooding and landslides.

China’s National Meteorological Center forecast sustained winds of up to 162 kmph before the typhoon gradually weakens as it moves inland. The storm has already caused major disruptions across eastern China, with nearly 1,300 flights cancelled at Shanghai’s two main airports and another 270 flights cancelled at Hangzhou airport.

Rail services across parts of the Yangtze River Delta have also been suspended as a precaution. Authorities have ordered vessels back to port, halted ferry services and restricted offshore and construction activities.

Typhoon Dolphin hits China: Force 10 winds hit Fujian, 30,000 given aid 

Taiwan has conducted a nuclear, biological and chemical (NBC) decontamination exercise as part of its ongoing annual Han Kuang military drills. The exercise, held in Hualien on Taiwan’s east coast, simulated a chemical weapons attack targeting military personnel and armored vehicles.

Troops practiced identifying contaminated areas, detecting hazardous substances and decontaminating personnel, tanks and armored vehicles. The drill is part of Taiwan’s broader effort to strengthen military readiness and prepare for a range of potential threats.

The Han Kuang exercises are Taiwan’s largest annual military drills and are taking place across the island through August 14. The exercises have attracted heightened attention amid growing tensions between Taiwan and China and discussions surrounding Beijing’s 2027 military readiness goals.


Taiwan holds NBC decontamination drill amid rising China tensions 

The artificial intelligence build-out has consumed so much of the world's memory chip supply that the largest Western PC manufacturers are now buying from the Chinese suppliers Washington spent years trying to keep out of their supply chains.&nbsp;What Changed&nbsp;Acer, Asus and HP have started using limited quantities of DRAM from ChangXin Memory Technologies, China's leading maker of dynamic random access memory, in their notebook computers. Dell has been weighing the same move.&nbsp;For context on why that is notable: CXMT and its domestic peer Yangtze Memory Technologies, China's leading NAND flash manufacturer, have been treated for years as strategic competitors that American policy sought to constrain rather than as routine suppliers to Western brands.&nbsp;The Cause Is AI, Not Policy&nbsp;The shift is not the product of a diplomatic thaw. It is arithmetic.&nbsp;AI data centre construction is expected to absorb roughly 70 per cent of all memory chip production in 2026. Modern AI accelerators are memory-bound — a processor computes only as fast as memory can supply it data — which has made high-bandwidth memory and DRAM the binding constraint on AI infrastructure, and made memory manufacturers the beneficiaries of an unprecedented demand surge.&nbsp;DRAM costs have tripled in the past eighteen months. Samsung has warned the shortage could persist into 2028 and has locked up to 70 per cent of its memory capacity in long-term agreements. When the incumbent suppliers are sold out years forward and prices have tripled, a PC manufacturer with laptops to ship has a narrow set of options.&nbsp;What It Means For CXMT&nbsp;The scale of the windfall is visible in the numbers. CXMT estimated its net profit for the first half of 2026 at between 52 billion and 58 billion yuan — an increase of as much as 2,530 per cent year-on-year.&nbsp;Both CXMT and YMTC are undertaking their most aggressive capacity expansions to date, with equipment installation planned for the second half of 2026 and production starting in 2027. That timing matters: the additional supply arrives precisely when the shortage is forecast to be at its most acute, positioning Chinese manufacturers to capture share at the moment Western buyers have the fewest alternatives.&nbsp;The Strategic Irony&nbsp;American technology policy over the past several years has aimed at limiting Chinese firms' access to advanced semiconductors and reducing Western dependence on Chinese suppliers. The AI boom driven largely by American companies has now produced a shortage severe enough to push Western manufacturers toward those same suppliers.&nbsp;The mechanism is straightforward. AI infrastructure spending — the roughly $740 billion in combined 2026 capital expenditure from Amazon, Alphabet, Meta and Microsoft — bids memory away from consumer electronics. Consumer manufacturers cannot match data centre prices. The supply that remains available at consumer price points is increasingly Chinese.&nbsp;It is the same dynamic visible in the United States banning imports of new Chinese humanoid robots in July while Chinese manufacturers shipped the majority of the world's humanoids. Policy can restrict what enters a market. It has a harder time changing who has the capacity to build things when demand outruns domestic supply.&nbsp;The Consumer End&nbsp;For buyers, the immediate effect is price. Memory is one of the largest cost components in a laptop, and tripled DRAM costs pass through to retail. Chinese memory is currently the mechanism by which some of that increase is being absorbed rather than passed on in full.&nbsp;The AI infrastructure boom has been discussed almost entirely in terms of data centres, capital expenditure and model capability. This is the point where it reaches an ordinary consumer purchase — and reshapes a supply chain that governments had spent years attempting to steer in the opposite direction.

Acer, Asus and HP have begun using DRAM from China's CXMT in notebooks as AI data centres absorb 70% of memory chip production and DRAM prices triple in 18 months. CXMT's first-half profit is up as much as 2,530% year-on-year.

The artificial intelligence build-out has consumed so much of the world's memory chip supply that the largest Western PC manufacturers are now buying from the Chinese suppliers Washington spent years trying to keep out of their supply chains. What Changed Acer, Asus and HP have started using limited quantities of DRAM from ChangXin Memory Technologies, China's leading maker of dynamic random access memory, in their notebook computers. Dell has been weighing the same move. For context on why that is notable: CXMT and its domestic peer Yangtze Memory Technologies, China's leading NAND flash manufacturer, have been treated for years as strategic competitors that American policy sought to constrain rather than as routine suppliers to Western brands. The Cause Is AI, Not Policy The shift is not the product of a diplomatic thaw. It is arithmetic. AI data centre construction is expected to absorb roughly 70 per cent of all memory chip production in 2026. Modern AI accelerators are memory-bound — a processor computes only as fast as memory can supply it data — which has made high-bandwidth memory and DRAM the binding constraint on AI infrastructure, and made memory manufacturers the beneficiaries of an unprecedented demand surge. DRAM costs have tripled in the past eighteen months. Samsung has warned the shortage could persist into 2028 and has locked up to 70 per cent of its memory capacity in long-term agreements. When the incumbent suppliers are sold out years forward and prices have tripled, a PC manufacturer with laptops to ship has a narrow set of options. What It Means For CXMT The scale of the windfall is visible in the numbers. CXMT estimated its net profit for the first half of 2026 at between 52 billion and 58 billion yuan — an increase of as much as 2,530 per cent year-on-year. Both CXMT and YMTC are undertaking their most aggressive capacity expansions to date, with equipment installation planned for the second half of 2026 and production starting in 2027. That timing matters: the additional supply arrives precisely when the shortage is forecast to be at its most acute, positioning Chinese manufacturers to capture share at the moment Western buyers have the fewest alternatives. The Strategic Irony American technology policy over the past several years has aimed at limiting Chinese firms' access to advanced semiconductors and reducing Western dependence on Chinese suppliers. The AI boom driven largely by American companies has now produced a shortage severe enough to push Western manufacturers toward those same suppliers. The mechanism is straightforward. AI infrastructure spending — the roughly $740 billion in combined 2026 capital expenditure from Amazon, Alphabet, Meta and Microsoft — bids memory away from consumer electronics. Consumer manufacturers cannot match data centre prices. The supply that remains available at consumer price points is increasingly Chinese. It is the same dynamic visible in the United States banning imports of new Chinese humanoid robots in July while Chinese manufacturers shipped the majority of the world's humanoids. Policy can restrict what enters a market. It has a harder time changing who has the capacity to build things when demand outruns domestic supply. The Consumer End For buyers, the immediate effect is price. Memory is one of the largest cost components in a laptop, and tripled DRAM costs pass through to retail. Chinese memory is currently the mechanism by which some of that increase is being absorbed rather than passed on in full. The AI infrastructure boom has been discussed almost entirely in terms of data centres, capital expenditure and model capability. This is the point where it reaches an ordinary consumer purchase — and reshapes a supply chain that governments had spent years attempting to steer in the opposite direction.

Acer, Asus and HP have begun putting DRAM from China's CXMT into notebook computers, with Dell considering it, as AI data centres absorb an estimated 70 per cent of all memory chip production in 2026 and DRAM prices triple in 18 months. CXMT's first-half profit is up as much as 2,530 per cent year-on-year.

'AI took it all': The world's biggest PC makers are now buying Chinese memory chips

China's next-generation "artificial sun" has achieved a major breakthrough, sustaining temperatures of 100 million degrees for 1,000 seconds. Scientists believe this fusion technology could revolutionize clean energy and bring humanity closer to unlimited power. 

Fusion named a key priority in China's 2026-2030 national plan

Alibaba says its new model worked on a software project for more than sixteen consecutive days without a human intervening — and that the first thing it did was build the tools it would need to do the work.&nbsp;The Claim&nbsp;Qwen3.8-Max, Alibaba's new flagship, is a mixture-of-experts model with 2.4 trillion total parameters, of which roughly 95 billion activate during any given inference. It carries a one-million-token context window.&nbsp;The headline demonstration is the autonomy run. According to Alibaba, the model worked continuously for over 16 days on a software engineering project, beginning from an empty repository and producing an open-sourced framework called oh-my-cli. What makes the claim notable is not the duration alone but what the model built during it: its own coding harness, with a state machine, a dispatcher and a testing loop — the scaffolding that would normally be supplied to an AI agent by the engineers deploying it.&nbsp;In other words, before writing the software it had been asked to write, the model constructed the apparatus it needed to keep itself working coherently across a span of days. It committed code to GitHub throughout.&nbsp;Why Duration Is The Hard Part&nbsp;Long-horizon autonomy has been the persistent weakness of AI coding systems. Models are strong at bounded tasks — write this function, fix this bug, refactor this module — and degrade over extended sequences. Context is lost, earlier decisions are contradicted, errors compound silently, and the system drifts from the original objective without noticing.&nbsp;Sixteen days of coherent work on a single project, if it holds up to outside examination, is a substantial jump rather than an incremental one. It is the difference between an assistant that helps a developer and a system that completes a project.&nbsp;The Sceptical Read&nbsp;That last conditional matters, and the reaction has not been uniformly admiring.&nbsp;The performance figures are vendor-reported. Alibaba conducted the run, defined the task, and published the result. There is no independent replication of a 16-day autonomous session, and the nature of the demonstration makes verification difficult — an outside party would need to run an equivalent project under equivalent conditions to test the claim.&nbsp;The New Stack characterised the release as ‘an API business model wearing an open source jacket,’ a pointed reference to the gap between open-weight positioning and the commercial reality of how the model is actually accessed and monetised.&nbsp;The industry also has recent reason for caution about vendor benchmark claims. Meta's former chief AI scientist, Yann LeCun, confirmed earlier this year that Llama 4's headline results had been assembled by training multiple checkpoints and selecting the best score on each individual benchmark — publishing a composite no single model had achieved.&nbsp;The Competitive Picture&nbsp;Qwen3.8-Max continues a pattern that has defined this quarter. Chinese laboratories briefly held six of the top ten positions on independent model leaderboards in mid-July, including all of the top five. Moonshot AI released Kimi K3's full weights for free download on July 27 — at 2.8 trillion parameters, the largest open-weight model ever published — and OpenAI cut prices on its lower-cost models by up to 80 per cent three days later.&nbsp;Alibaba's entry adds a distinct claim to that competition. Kimi K3 competed on scale and price. Qwen3.8-Max is competing on autonomy — the ability to be left alone with a task for weeks. If the demonstration is representative rather than a carefully staged best case, it is the more commercially consequential capability of the two.&nbsp;That is the question outside researchers will now try to answer, and the model being available is what makes answering it possible.

Alibaba's Qwen3.8-Max, a 2.4-trillion-parameter model, reportedly ran autonomously for over 16 days — starting from an empty repository and building its own coding harness with state machine, dispatcher and testing loop. The claim is vendor-reported and unverified independently.

Alibaba says its new model worked on a software project for more than sixteen consecutive days without a human intervening — and that the first thing it did was build the tools it would need to do the work. The Claim Qwen3.8-Max, Alibaba's new flagship, is a mixture-of-experts model with 2.4 trillion total parameters, of which roughly 95 billion activate during any given inference. It carries a one-million-token context window. The headline demonstration is the autonomy run. According to Alibaba, the model worked continuously for over 16 days on a software engineering project, beginning from an empty repository and producing an open-sourced framework called oh-my-cli. What makes the claim notable is not the duration alone but what the model built during it: its own coding harness, with a state machine, a dispatcher and a testing loop — the scaffolding that would normally be supplied to an AI agent by the engineers deploying it. In other words, before writing the software it had been asked to write, the model constructed the apparatus it needed to keep itself working coherently across a span of days. It committed code to GitHub throughout. Why Duration Is The Hard Part Long-horizon autonomy has been the persistent weakness of AI coding systems. Models are strong at bounded tasks — write this function, fix this bug, refactor this module — and degrade over extended sequences. Context is lost, earlier decisions are contradicted, errors compound silently, and the system drifts from the original objective without noticing. Sixteen days of coherent work on a single project, if it holds up to outside examination, is a substantial jump rather than an incremental one. It is the difference between an assistant that helps a developer and a system that completes a project. The Sceptical Read That last conditional matters, and the reaction has not been uniformly admiring. The performance figures are vendor-reported. Alibaba conducted the run, defined the task, and published the result. There is no independent replication of a 16-day autonomous session, and the nature of the demonstration makes verification difficult — an outside party would need to run an equivalent project under equivalent conditions to test the claim. The New Stack characterised the release as ‘an API business model wearing an open source jacket,’ a pointed reference to the gap between open-weight positioning and the commercial reality of how the model is actually accessed and monetised. The industry also has recent reason for caution about vendor benchmark claims. Meta's former chief AI scientist, Yann LeCun, confirmed earlier this year that Llama 4's headline results had been assembled by training multiple checkpoints and selecting the best score on each individual benchmark — publishing a composite no single model had achieved. The Competitive Picture Qwen3.8-Max continues a pattern that has defined this quarter. Chinese laboratories briefly held six of the top ten positions on independent model leaderboards in mid-July, including all of the top five. Moonshot AI released Kimi K3's full weights for free download on July 27 — at 2.8 trillion parameters, the largest open-weight model ever published — and OpenAI cut prices on its lower-cost models by up to 80 per cent three days later. Alibaba's entry adds a distinct claim to that competition. Kimi K3 competed on scale and price. Qwen3.8-Max is competing on autonomy — the ability to be left alone with a task for weeks. If the demonstration is representative rather than a carefully staged best case, it is the more commercially consequential capability of the two. That is the question outside researchers will now try to answer, and the model being available is what makes answering it possible.

Alibaba has launched Qwen3.8-Max, a 2.4-trillion-parameter model that the company says ran autonomously for more than 16 consecutive days — starting from an empty repository, building its own coding framework complete with state machine, dispatcher and testing loop, and committing to GitHub throughout. The claim is Alibaba's own, and not all reaction has been favourable.

An AI coded alone for 16 days and built its own tools to do the job

Taiwan has scrambled its ageing French-made Mirage fighter jets during the annual Han Kuang war games, demonstrating how the aircraft could be rapidly re-armed and returned to combat during a conflict with China. Crews at Hsinchu air base showcased the rapid attachment of Magic II and MICA air-to-air missiles, while ground teams rehearsed repairing bomb- and missile-damaged runways and taxiways. The 10-day exercises are focused on strengthening Taiwan's readiness for a potential Chinese attack.

Taiwan steps up military, civilian preparedness

In a firm counter-move against China’s periodic cartographic aggression, the Government of India has formally added 27 specific sites and geographical features in Arunachal Pradesh to the official Survey of India maps. Developed in close coordination with the Arunachal Pradesh state government, this initiative officially standardises the indigenous names of these locations to enhance public awareness and reinforce India's absolute sovereignty over the region.

India names 27 sites in Arunachal Pradesh

China's crude oil imports climbed 22 per cent in July from June's near decade low, as refiners moved to snap up cheap barrels during a brief reopening of the Strait of Hormuz. Though the total volume remains well below the pre-war levels, according to data from China's General Administration of Customs. China, the largest importer of Crude oil, brought roughly 35.73 million metric tons of crude last month, equivalent to roughly 8.41 million barrels per day, marking a sharp increase from June. China's imports in June saw a decline amid the global crisis and weak domestic demand, as well as an export curb Beijing imposed to safeguard its energy security. Even with the recent surge, it still remains 24.3 per cent lower than a year ago.The positive growth of imports can be traced to a period of relative ease in June, when the Strait of Hormuz partially reopened, and Brent crude slid into the $70 a barrel. Ye Lin, vice president at Rystad Energy, said Chinese buyers moved opportunistically during that stretch, locking in discounted cargoes that are still arriving on Chinese shores. Ye added that the wave of purchases is expected to keep flowing through August, and should help ease Beijing's restrictions on refined-product exports while nudging refinery run rates modestly higher as margins recover. Separate tracking from Vortexa put China's seaborne imports at 7.1 million barrels a day in July, with non-Iranian Middle Eastern supply rising by roughly 1 million barrels a day from June, a sign that Chinese refiners are also diversifying away from the discounted Iranian and Russian barrels they leaned on earlier in the conflict.Also Read: <a href="https://www.wionews.com/india-news/modi-sukhbir-badal-meeting-bjp-sad-alliance-punjab-2027-1786101731538">PM Modi meets Sukhbir Badal, fuels talk of BJP-SAD reunion in Punjab</a><h2>How Beijing's buying strategy eased pressure on global oil markets</h2>When Iran closed the Hormuz, roughly 20 million barrels per day of oil were taken out of the global market; that is huge. The global oil market produces 100 million barrels per day, with zero slack in the system, so losing 20 million bpd means 20 per cent of the oil-consuming world was going to power out. It was a global calamity, but there were countermeasures in place. The Saudi East- West pipeline, which was pumping 2.8 million bpd prior to the conflict, started adding 7 million bpd at maximum capacity, and a further 1.5-1.8 million bpd was added by the Abu Dhabi oil pipeline. With the Saudi East-West pipeline and the Abu Dhabi oil pipeline in Saudi Arabia and the UAE, respectively, putting back roughly 7 million barrels per day into the system, bypassing the Hormuz, reducing the shortfall significantly. Still, it's a 13 million bpd shortfall. The Strategic Petroleum Reserve released by the IEA's 32 members put back roughly 1.4 million barrels per day over 120 days.&nbsp;But China, as the world's largest crude importer, did not scramble to meet the demand in the open market. China managed the aftermath of a war that disrupted the global oil markets. According to sources like Kpler and Bloomberg, Beijing spent much of the spring drawing down a stockpile estimated at close to 1.4 billion barrels, thus meeting roughly 5 million bpd from its domestic inventory and alternative energy sources. A manoeuvre analysts credited with helping keep global oil prices from spiralling past $200 a barrel, reducing the demand to balance the shortfall of supply. Bloomberg columnist Javier Blas has argued the episode effectively turned China into the oil market's first "swing importer," giving Beijing outsized influence over global prices by choosing when to buy and when to sit out.

China's oil stockpile drawdown and cautious buying strategy helped ease pressure on global crude markets after the Strait of Hormuz disruption.

China's crude oil imports climbed 22 per cent in July from June's near decade low, as refiners moved to snap up cheap barrels during a brief reopening of the Strait of Hormuz. Though the total volume remains well below the pre-war levels, according to data from China's General Administration of Customs. China, the largest importer of Crude oil, brought roughly 35.73 million metric tons of crude last month, equivalent to roughly 8.41 million barrels per day, marking a sharp increase from June. China's imports in June saw a decline amid the global crisis and weak domestic demand, as well as an export curb Beijing imposed to safeguard its energy security. Even with the recent surge, it still remains 24.3 per cent lower than a year ago. The positive growth of imports can be traced to a period of relative ease in June, when the Strait of Hormuz partially reopened, and Brent crude slid into the $70 a barrel. Ye Lin, vice president at Rystad Energy, said Chinese buyers moved opportunistically during that stretch, locking in discounted cargoes that are still arriving on Chinese shores. Ye added that the wave of purchases is expected to keep flowing through August, and should help ease Beijing's restrictions on refined-product exports while nudging refinery run rates modestly higher as margins recover. Separate tracking from Vortexa put China's seaborne imports at 7.1 million barrels a day in July, with non-Iranian Middle Eastern supply rising by roughly 1 million barrels a day from June, a sign that Chinese refiners are also diversifying away from the discounted Iranian and Russian barrels they leaned on earlier in the conflict. Also Read: PM Modi meets Sukhbir Badal, fuels talk of BJP-SAD reunion in Punjab How Beijing's buying strategy eased pressure on global oil markets When Iran closed the Hormuz, roughly 20 million barrels per day of oil were taken out of the global market; that is huge. The global oil market produces 100 million barrels per day, with zero slack in the system, so losing 20 million bpd means 20 per cent of the oil-consuming world was going to power out. It was a global calamity, but there were countermeasures in place. The Saudi East- West pipeline, which was pumping 2.8 million bpd prior to the conflict, started adding 7 million bpd at maximum capacity, and a further 1.5-1.8 million bpd was added by the Abu Dhabi oil pipeline. With the Saudi East-West pipeline and the Abu Dhabi oil pipeline in Saudi Arabia and the UAE, respectively, putting back roughly 7 million barrels per day into the system, bypassing the Hormuz, reducing the shortfall significantly. Still, it's a 13 million bpd shortfall. The Strategic Petroleum Reserve released by the IEA's 32 members put back roughly 1.4 million barrels per day over 120 days. But China, as the world's largest crude importer, did not scramble to meet the demand in the open market. China managed the aftermath of a war that disrupted the global oil markets. According to sources like Kpler and Bloomberg, Beijing spent much of the spring drawing down a stockpile estimated at close to 1.4 billion barrels, thus meeting roughly 5 million bpd from its domestic inventory and alternative energy sources. A manoeuvre analysts credited with helping keep global oil prices from spiralling past $200 a barrel, reducing the demand to balance the shortfall of supply. Bloomberg columnist Javier Blas has argued the episode effectively turned China into the oil market's first "swing importer," giving Beijing outsized influence over global prices by choosing when to buy and when to sit out.

China's oil stockpile drawdown and cautious buying strategy helped ease pressure on global crude markets after the Strait of Hormuz disruption.

How China helped prevent a global oil shock during Hormuz crisis

The United States has announced fresh trade measures targeting polysilicon, a critical raw material used to manufacture semiconductors and solar panels.


Trump sets 15% tariff & import price floors on polysilicon

China's electric vehicle market is entering a new phase after years of explosive growth, aggressive government support, and intense price competition. The world's largest EV market helped reshape the global auto industry, but fresh data suggests the momentum may be slowing. July sales missed expectations despite continued discounts from major automakers, raising concerns about demand, profitability, and the future direction of the sector. As manufacturers navigate market saturation, competition, and changing consumer trends, investors and industry leaders are closely watching what comes next.


China's EV price war shows signs of fatigue

 India’s textile industry is entering a decisive growth phase as global sourcing patterns shift away from China, creating a multi-year opportunity that could reshape supply chains and consumer markets worldwide. While brokerage firm Nuvama highlights the investment case, the broader story is structural: global brands are actively diversifying manufacturing bases amid rising costs, geopolitical risks, and supply chain disruptions linked to China.


India textiles gain from China shift as brands diversify supply Chains

China’s EV boom appears to be entering a new phase. July sales data signals the market is starting to slow down. After years of rapid growth, demand is cooling amid price wars and competition. Analysts say subsidies ending and market saturation are key factors.


China’s EV boom enters a new phase, July EV sales signals market slow down 

Pakistan's ex-Federal Board of Revenue Chairman Shabbar Zaidi blamed China for his country's economic crisis. This comes amid reports that only 38 out of 90 projects envisaged under the China-Pakistan Economic Corridor have been completed so far. China remains Pakistan's biggest creditor.

Ex-Top Pak Official Blames Economic Woes on China: Here's How CPEC Has Been a Failure

The recent abduction and murder of a member of a global Chinese mafia syndicate by his rivals in Sri Lanka have raised concerns of a possible major national security threat, with law enforcement agencies uncovering various methods used by such criminals to abuse the island nation’s visa system.


Colombo: The new gateway for Chinese mafia

Japan's 2026 defense white paper brands China as its greatest strategic challenge, highlighting regional security risks, military expansion near Taiwan, and deeper ties with Russia.


The White Paper identifies China as Japan's strategic challenge 

Giant panda Qiao Qiao celebrated his 15th birthday with a special feast at a zoo in Tianjin, China. Affectionately known as "Master Qiao" by his fans, the beloved panda enjoyed a carefully prepared spread of birthday treats as visitors gathered to mark the milestone.

China: Giant panda celebrates 15th birthday with 'bamboo cake' in China

YouTube Description

China is doubling down on its manufacturing-led economic strategy despite growing pressure from the United States and Europe. As trade tensions persist and major negotiations loom, Beijing has signaled that it has no intention of making significant changes to its economic model.

With the European Union seeking progress on long-standing trade disputes and Washington continuing to scrutinize Chinese industrial policies, the debate over global trade imbalances is intensifying. Why is China standing firm, and what could this mean for international markets, supply chains, and future trade relations? Here's a closer look.

Xi's economic playbook remains unchanged

China has conducted joint naval and air combat drills around the disputed Scarborough Shoal in the South China Sea, further escalating tensions in one of the world's most contested maritime regions.

The exercises, announced by the People's Liberation Army (PLA) Southern Theater Command, were described as a test of combat readiness and a demonstration of China's ability to defend what it calls its maritime rights and interests.

The drills took place around Scarborough Shoal, known as Huangyan Dao in China and Bajo de Masinloc in the Philippines. The reef lies approximately 220 kilometres west of Luzon and falls within the Philippines' Exclusive Economic Zone (EEZ) under international law.

China-Philippines Dispute Deepens | PLA Conducts Joint Naval And Air Exercises

Japan has officially launched its new National Intelligence Bureau (NIB), upgrading the older Cabinet Intelligence and Research Office (CIRO). Led by director Kazuya Hara with a staff of roughly 730, the agency has the legal power to force different government ministries to share security and intelligence information.

Takaichi: First step in the country's intellignce reform

Tensions in West Asia have surged again after Iran launched a surprise strike on a U.S. military base in Jordan, ending days of relative calm and signaling what analysts believe is a major shift in Tehran's military strategy.

Unlike previous exchanges, Iran struck first instead of waiting for another U.S. attack. Experts suggest Tehran may now prefer a controlled, low-intensity conflict over a prolonged ceasefire that leaves sanctions and economic isolation unchanged.

The conflict is also reshaping global energy markets, with rising oil prices potentially benefiting countries like Russia, while China continues to strengthen its position in global energy and diplomacy.

Meanwhile, the human cost continues to rise. Reports indicate that 16 Indian nationals, including several seafarers, have lost their lives amid the ongoing regional conflict.

Iran Launches Surprise Strike on U.S. Base in Jordan | West Asia War Escalates | US- Iran War 

Tensions in West Asia continue to simmer despite the absence of a full-scale war, as Iran weighs military pressure against escalating regional risks. From surprise strikes to sanctions, blockades, and shifting strategic calculations, the conflict is entering a new phase with far-reaching consequences for regional stability and global security.

Sanction & Blockades: Why a ceasefire may not suit Tehran

Chinese state media has released footage showing a Type 052D destroyer launching the YJ-20 hypersonic anti-ship missile for the first time. Previously associated with larger cruisers, the deployment of the so-called "carrier-killer" missile on standard destroyers significantly expands China's maritime strike capabilities and strengthens its naval deterrence.

China unleashes hypersonic carrier killer weapon

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