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$KIMI DEMAND EXPLODES: GPU CAPACITY REACHED LIMIT IN 48 HOURS 🚨
Moonshot AI paused new Kimi K3 subscriptions after a massive demand surge pushed GPU capacity to the limit within 48 hours. The 2.8 trillion-parameter model with a 1 million-token context window saw unprecedented interest right after its launch on Jul 16.
Existing subscriptions remain active while Moonshot directs all computing power to current members. The company plans to reopen access in batches with no confirmed timeline. This kind of operational bottleneck often signals real user adoption rather than hype.
Is this a signal that AI infrastructure demand is outpacing supply at record speed?
Not financial advice. Always manage your risk.
#KIMI #AI #GPU #DemandSurge #Crypto
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Moonshot AI paused new Kimi K3 subscriptions after a massive demand surge pushed GPU capacity to the limit within 48 hours. The 2.8 trillion-parameter model with a 1 million-token context window saw unprecedented interest right after its launch on Jul 16.
Existing subscriptions remain active while Moonshot directs all computing power to current members. The company plans to reopen access in batches with no confirmed timeline. This kind of operational bottleneck often signals real user adoption rather than hype.
Is this a signal that AI infrastructure demand is outpacing supply at record speed?
Not financial advice. Always manage your risk.
#KIMI #AI #GPU #DemandSurge #Crypto
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$KIMI GPU DEMAND SURGE CAUSES NEW SUBSCRIPTION PAUSE — SUPPLY CONSTRAINT AHEAD 🔥
Kimi has confirmed that K3 demand overwhelmed GPU capacity in under 48 hours, forcing a halt on new subscriptions while existing users remain unaffected. The team is rapidly expanding compute and splitting memberships into Kimi Members and Kimi Code Members to better allocate resources.
This level of demand signals a real infrastructure bottleneck — often a precursor to token utility shifts in GPU-linked projects. Which AI tokens do you think are positioned to absorb this overflow?
Not financial advice. Always manage your risk.
#KIMI #GPU #AITokens #SupplyConstraint #Crypto
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Kimi has confirmed that K3 demand overwhelmed GPU capacity in under 48 hours, forcing a halt on new subscriptions while existing users remain unaffected. The team is rapidly expanding compute and splitting memberships into Kimi Members and Kimi Code Members to better allocate resources.
This level of demand signals a real infrastructure bottleneck — often a precursor to token utility shifts in GPU-linked projects. Which AI tokens do you think are positioned to absorb this overflow?
Not financial advice. Always manage your risk.
#KIMI #GPU #AITokens #SupplyConstraint #Crypto
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$KIMI GPU DEMAND SURGES PAST EXPECTATIONS — SUBSCRIPTIONS PAUSED 🔥
The demand for Kimi K3 has massively exceeded projections in the last 48 hours. GPU resources are nearing full capacity right now. New subscriptions have been paused to protect existing users' experience.
This is a clear signal that AI compute demand is far from slowing down. Kimi is rapidly expanding capacity and splitting membership into Kimi Members and Kimi Code Members to match resources more precisely. Are you watching how this affects the token's supply dynamics?
Not financial advice. Always manage your risk.
#KIMI #AI #GPUShortage #DemandSurge #Crypto
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The demand for Kimi K3 has massively exceeded projections in the last 48 hours. GPU resources are nearing full capacity right now. New subscriptions have been paused to protect existing users' experience.
This is a clear signal that AI compute demand is far from slowing down. Kimi is rapidly expanding capacity and splitting membership into Kimi Members and Kimi Code Members to match resources more precisely. Are you watching how this affects the token's supply dynamics?
Not financial advice. Always manage your risk.
#KIMI #AI #GPUShortage #DemandSurge #Crypto
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$KIMI RESTRUCTURING FOR HONG KONG IPO — A NEW LISTING PLAY IN THE AI SPACE 🚀
The company behind Kimi AI has notified investors of a full restructuring and is targeting a Hong Kong IPO within the next 6 months. This is a major liquidity event for early backers and a signal that the project is maturing beyond just product hype.
Transaction volume on Kimi's native token has been picking up since the announcement hit top-tier exchange feeds. The narrative shift from "AI chatbot" to "public company" changes the long-term valuation floor entirely.
Are you holding through the restructuring or taking profits into the IPO hype?
Not financial advice. Always manage your risk.
#KIMI #IPO #AI #Crypto #Restructuring
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The company behind Kimi AI has notified investors of a full restructuring and is targeting a Hong Kong IPO within the next 6 months. This is a major liquidity event for early backers and a signal that the project is maturing beyond just product hype.
Transaction volume on Kimi's native token has been picking up since the announcement hit top-tier exchange feeds. The narrative shift from "AI chatbot" to "public company" changes the long-term valuation floor entirely.
Are you holding through the restructuring or taking profits into the IPO hype?
Not financial advice. Always manage your risk.
#KIMI #IPO #AI #Crypto #Restructuring
🚀
$KIMI RESTRUCTURING FOR HONG KONG IPO – LISTING IN 6 MONTHS? 🔥
Kimi has notified investors of its restructuring and plans for a Hong Kong IPO, with a potential listing in as little as six months. This signals a major step in bridging AI infrastructure with traditional capital markets.
The move adds a layer of institutional credibility to the AI sector, often a leading indicator for correlated crypto narratives. As liquidity rotates into real-world assets, AI tokens may see renewed interest. Are you positioning for the next wave of AI-driven capital flows?
Not financial advice. Always manage your risk.
#KIMI #IPO #AI #HongKong #CryptoNews
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Kimi has notified investors of its restructuring and plans for a Hong Kong IPO, with a potential listing in as little as six months. This signals a major step in bridging AI infrastructure with traditional capital markets.
The move adds a layer of institutional credibility to the AI sector, often a leading indicator for correlated crypto narratives. As liquidity rotates into real-world assets, AI tokens may see renewed interest. Are you positioning for the next wave of AI-driven capital flows?
Not financial advice. Always manage your risk.
#KIMI #IPO #AI #HongKong #CryptoNews
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$KIMI K3 JUST BEAT ANTHROPIC'S OPUS IN AI BENCHMARKS 🔥
This benchmark data from an independent analyst shows the Kimi K3 model surpassing Anthropic's Opus 4.8, forcing U.S. labs to accelerate Opus 5 and GPT-6 releases. The gap between GPT-5.6 Sol and K3 is now negligible — meaning the next generation is coming sooner than expected.
When AI models leapfrog each other, the underlying narrative for AI-focused crypto projects strengthens. This kind of competitive pressure typically drives capital into the sector as anticipation builds. Are you positioned for the next wave?
Not financial advice. Always manage your risk.
#KIMI #AI #CryptoAI #Breakthrough #Narrative
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This benchmark data from an independent analyst shows the Kimi K3 model surpassing Anthropic's Opus 4.8, forcing U.S. labs to accelerate Opus 5 and GPT-6 releases. The gap between GPT-5.6 Sol and K3 is now negligible — meaning the next generation is coming sooner than expected.
When AI models leapfrog each other, the underlying narrative for AI-focused crypto projects strengthens. This kind of competitive pressure typically drives capital into the sector as anticipation builds. Are you positioned for the next wave?
Not financial advice. Always manage your risk.
#KIMI #AI #CryptoAI #Breakthrough #Narrative
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Kimi K3 went live and within 48 hours pushed the compute cluster to its limits, so individual memberships are now paused.
This isn’t a “product is too popular” kind of achievement announcement. It’s a real-time snapshot of an imbalance between AI compute supply and demand.
Behind Kimi, a large amount of GPU usage comes from Alibaba Cloud—and it also often grabs resources from the Qwen team. Alibaba Cloud grew over 40% last quarter. Even before the K3 launch, it was already a monster quarterly performer as the largest cloud service provider in the Asia-Pacific.
What does this mean?
1. Compute power is the hard bottleneck in the AI arms race; it can’t be solved by “slowly expanding capacity.”
2. Cloud providers’ GPU production capacity is being consumed down to the bone by large model companies.
3. In the future, an AI company’s moat may not be model capability, but its ability to supply compute.
Kimi also adjusted its membership system: the main product and Kimi Code benefits are sold separately. Translation: compute is no longer enough, so they’ve started to allocate it more precisely.
The AI industry’s hunger for compute is far from reaching its peak. Whoever controls compute supply will control the oil of the AI era.
#AI #算力 #Kimi #Alibaba Cloud
This isn’t a “product is too popular” kind of achievement announcement. It’s a real-time snapshot of an imbalance between AI compute supply and demand.
Behind Kimi, a large amount of GPU usage comes from Alibaba Cloud—and it also often grabs resources from the Qwen team. Alibaba Cloud grew over 40% last quarter. Even before the K3 launch, it was already a monster quarterly performer as the largest cloud service provider in the Asia-Pacific.
What does this mean?
1. Compute power is the hard bottleneck in the AI arms race; it can’t be solved by “slowly expanding capacity.”
2. Cloud providers’ GPU production capacity is being consumed down to the bone by large model companies.
3. In the future, an AI company’s moat may not be model capability, but its ability to supply compute.
Kimi also adjusted its membership system: the main product and Kimi Code benefits are sold separately. Translation: compute is no longer enough, so they’ve started to allocate it more precisely.
The AI industry’s hunger for compute is far from reaching its peak. Whoever controls compute supply will control the oil of the AI era.
#AI #算力 #Kimi #Alibaba Cloud
Kimi K3 is a model from the “tough and doesn’t talk much” crowd. Not only is the front-end excellent, but to a certain extent its back-end performance also surpasses Fable5 and GPT-5.6 Sol.
Test result: Using GPT-5.6 Sol, find and fix 6 defects, then have Fable5 and Kimi K3 each conduct a review. Fable5 says “it’s ready to submit,” while Kimi K3 spots a serious PostgreSQL jsonb validation issue.
Regarding the controversy over Kimi Code’s harness—some say it isn’t done well, but the core logic is “a model company builds models.” As harness becomes less important, the model’s built-in capabilities are eating up the value of the harness.
This is actually the direction in which AI toolchains are evolving:
• harness is an add-on rules system; when the model’s ability is weak, it needs to make up the gap
• when the model itself becomes strong enough, harness shifts from being a “necessity” to a “constraint”
• Kimi’s built-in vertical capabilities—like its Tianyancha-style lookup—are more damaging than relying on a generic harness
China’s real-world AI model capabilities are narrowing the gap with, and in some scenarios even surpassing, Silicon Valley’s top players. Don’t just look at benchmark scores—look at the results of actual code reviews.
#AI #Kimi #大模型 #code review
Test result: Using GPT-5.6 Sol, find and fix 6 defects, then have Fable5 and Kimi K3 each conduct a review. Fable5 says “it’s ready to submit,” while Kimi K3 spots a serious PostgreSQL jsonb validation issue.
Regarding the controversy over Kimi Code’s harness—some say it isn’t done well, but the core logic is “a model company builds models.” As harness becomes less important, the model’s built-in capabilities are eating up the value of the harness.
This is actually the direction in which AI toolchains are evolving:
• harness is an add-on rules system; when the model’s ability is weak, it needs to make up the gap
• when the model itself becomes strong enough, harness shifts from being a “necessity” to a “constraint”
• Kimi’s built-in vertical capabilities—like its Tianyancha-style lookup—are more damaging than relying on a generic harness
China’s real-world AI model capabilities are narrowing the gap with, and in some scenarios even surpassing, Silicon Valley’s top players. Don’t just look at benchmark scores—look at the results of actual code reviews.
#AI #Kimi #大模型 #code review
$KIMI OPENS WEIGHTS ON JULY 27 — 2.8T PARAMETER MODEL TESTS LIQUIDITY ZONE 📉
Entry: (not provided)
Target: (not provided)
Stop Loss: (not provided)
Moonshot AI unveiled Kimi K3 with 2.8 trillion parameters and a 1M token context window, positioning it near frontier US models on independent benchmarks. The full open weights drop under a permissive license on July 27 — a structural shift that could drain liquidity from closed-source AI tokens.
Developers on Arena ranked it first for front-end coding, and the mixture-of-experts design fires only 16 of 896 experts per token, keeping inference costs low. The real test is whether this supply-side catalyst restructures the AI narrative or gets swept by competing ecosystems.
Are you accumulating pre-weight release or waiting for confirmation on-chain?
Not financial advice. Always manage your risk.
#KIMI #AI #OpenSource #Crypto
🔥
Entry: (not provided)
Target: (not provided)
Stop Loss: (not provided)
Moonshot AI unveiled Kimi K3 with 2.8 trillion parameters and a 1M token context window, positioning it near frontier US models on independent benchmarks. The full open weights drop under a permissive license on July 27 — a structural shift that could drain liquidity from closed-source AI tokens.
Developers on Arena ranked it first for front-end coding, and the mixture-of-experts design fires only 16 of 896 experts per token, keeping inference costs low. The real test is whether this supply-side catalyst restructures the AI narrative or gets swept by competing ecosystems.
Are you accumulating pre-weight release or waiting for confirmation on-chain?
Not financial advice. Always manage your risk.
#KIMI #AI #OpenSource #Crypto
🔥
1、Background: Kimi membership system restructuring, AI applications enter a phase of fine-grained pricing
The newest market update drawing attention today is that Kimi has adjusted its personal membership structure. Previously, the membership benefits covered web, the app, and Kimi Code under a single plan. Now, these are split into two tracks: a General Membership and a Coding Plan. In other words, everyday Q&A, long-text processing, web usage, and programming capabilities are no longer bundled into the same subscription. If users need both types of capabilities, they will have to pay for them separately in the future.
In terms of the pricing structure, the former lower annual-fee tiers bundled both basic web benefits and a certain amount of Code credits. Under the new system, choosing the lowest combination of web plus programming results in a noticeably higher annual spend. For price-sensitive users, this is essentially changing the “all-purpose starter package” into a “scene-based pay package.” On the surface, it makes product lines clearer; in practice, it means the platform is beginning to price high-cost capabilities separately.
2、Analysis: Splitting Code is, in essence, a rebalancing of compute costs and commercialization pressure
The cost structure of AI programming products differs from that of ordinary chat. Capabilities such as code generation, context understanding, long-task execution, and debugging assistance often require longer context windows, more frequent calls, and more stable inference resources. By selling Code as an independent offering, it indicates that AI vendors are shifting away from “winning users quickly with low-priced bundles” toward “raising ARPU by pricing for high-value scenarios.”
It’s worth noting that the new Code package emphasizes removing the cap on total monthly credits and provides more tokens at the same price level. However, the official has not yet provided sufficiently clear, comparable terms. As a result, users find it difficult to determine directly whether the newly added tokens are enough to offset the web and app benefits that are removed. For heavy developers who use the service frequently and consume many tokens, the new plan may be more flexible; but for light programming users, after the bundled benefits are reduced, overall value-for-money may decline.
This also reflects a broader trend in the AI industry: general AI entry points continue to handle mass-market traffic, while specialized tools handle monetization and cost recovery. High-cost functions such as programming, office productivity, data analysis, and video generation are more likely to be broken into separate paid modules in the future.
3、Impact: User stratification accelerates, and competition in the AI subscription market becomes more direct
For individual users, the most immediate impact is rising costs. In the past, users only needed to buy one all-in-one membership to cover multiple scenarios. Now they have to decide whether they mainly prefer web-based Q&A, mobile usage, or programming development. Light users may move to free quotas or alternative products. Heavy users, meanwhile, will pay more attention to stability, context length, response speed, and actual token consumption.
For AI vendors, this adjustment helps improve revenue quality, but it also introduces the risk of user churn. Competition among AI applications is fierce right now—pricing, model capabilities, ecosystem tools, and user experience all affect retention. If a price increase comes with stronger Code capabilities, more transparent credit rules, and more stable service, market acceptance will likely be higher; otherwise, users may feel it’s merely a disguised price hike.
For the broader technology and capital markets, Kimi’s change signals a shift in its AI business model from subsidy-driven expansion to cost accounting. In the future, AI products will not only compete on who is cheaper—they will compete on who can better balance compute costs, product experience, and paid conversion. For investors focused on the AI sector, ongoing observation of subscription-system changes like this is worthwhile, because it directly affects whether AI applications can evolve from a traffic-driven story into sustainable revenue.
#AI #Kimi #Technology Trend
The newest market update drawing attention today is that Kimi has adjusted its personal membership structure. Previously, the membership benefits covered web, the app, and Kimi Code under a single plan. Now, these are split into two tracks: a General Membership and a Coding Plan. In other words, everyday Q&A, long-text processing, web usage, and programming capabilities are no longer bundled into the same subscription. If users need both types of capabilities, they will have to pay for them separately in the future.
In terms of the pricing structure, the former lower annual-fee tiers bundled both basic web benefits and a certain amount of Code credits. Under the new system, choosing the lowest combination of web plus programming results in a noticeably higher annual spend. For price-sensitive users, this is essentially changing the “all-purpose starter package” into a “scene-based pay package.” On the surface, it makes product lines clearer; in practice, it means the platform is beginning to price high-cost capabilities separately.
2、Analysis: Splitting Code is, in essence, a rebalancing of compute costs and commercialization pressure
The cost structure of AI programming products differs from that of ordinary chat. Capabilities such as code generation, context understanding, long-task execution, and debugging assistance often require longer context windows, more frequent calls, and more stable inference resources. By selling Code as an independent offering, it indicates that AI vendors are shifting away from “winning users quickly with low-priced bundles” toward “raising ARPU by pricing for high-value scenarios.”
It’s worth noting that the new Code package emphasizes removing the cap on total monthly credits and provides more tokens at the same price level. However, the official has not yet provided sufficiently clear, comparable terms. As a result, users find it difficult to determine directly whether the newly added tokens are enough to offset the web and app benefits that are removed. For heavy developers who use the service frequently and consume many tokens, the new plan may be more flexible; but for light programming users, after the bundled benefits are reduced, overall value-for-money may decline.
This also reflects a broader trend in the AI industry: general AI entry points continue to handle mass-market traffic, while specialized tools handle monetization and cost recovery. High-cost functions such as programming, office productivity, data analysis, and video generation are more likely to be broken into separate paid modules in the future.
3、Impact: User stratification accelerates, and competition in the AI subscription market becomes more direct
For individual users, the most immediate impact is rising costs. In the past, users only needed to buy one all-in-one membership to cover multiple scenarios. Now they have to decide whether they mainly prefer web-based Q&A, mobile usage, or programming development. Light users may move to free quotas or alternative products. Heavy users, meanwhile, will pay more attention to stability, context length, response speed, and actual token consumption.
For AI vendors, this adjustment helps improve revenue quality, but it also introduces the risk of user churn. Competition among AI applications is fierce right now—pricing, model capabilities, ecosystem tools, and user experience all affect retention. If a price increase comes with stronger Code capabilities, more transparent credit rules, and more stable service, market acceptance will likely be higher; otherwise, users may feel it’s merely a disguised price hike.
For the broader technology and capital markets, Kimi’s change signals a shift in its AI business model from subsidy-driven expansion to cost accounting. In the future, AI products will not only compete on who is cheaper—they will compete on who can better balance compute costs, product experience, and paid conversion. For investors focused on the AI sector, ongoing observation of subscription-system changes like this is worthwhile, because it directly affects whether AI applications can evolve from a traffic-driven story into sustainable revenue.
#AI #Kimi #Technology Trend
Yáng Zhílín: Why didn’t he stay in the United States?
During his PhD at CMU, he interned at both Google Brain and Meta AI. His advisor later went to Apple to lead AI work with Ruslan Salakhutdinov. In 2023, he chose to return to China to start a business.
At the time, this choice in 2023 looked like gambling, but in 2026 it looks like computation. The U.S. has the strongest research environment; China’s advantage, however, is "team-building speed"—in AI competition, the speed to go from 0 to 1 matters more than the precision of going from 1 to 100.
Yang Zhílín’s return to China wasn’t abandoning U.S. technology—it was choosing an environment with "lower talent density but a shorter decision chain." Kimi K3’s 896-expert MoE and self-evolving kernel optimization don’t require more geniuses; they require an organization that can test and iterate quickly, and also shut down quickly when a path is wrong.
Choosing the battlefield is just as important as choosing the weapon.
#Kimi #杨植麟 #AI
During his PhD at CMU, he interned at both Google Brain and Meta AI. His advisor later went to Apple to lead AI work with Ruslan Salakhutdinov. In 2023, he chose to return to China to start a business.
At the time, this choice in 2023 looked like gambling, but in 2026 it looks like computation. The U.S. has the strongest research environment; China’s advantage, however, is "team-building speed"—in AI competition, the speed to go from 0 to 1 matters more than the precision of going from 1 to 100.
Yang Zhílín’s return to China wasn’t abandoning U.S. technology—it was choosing an environment with "lower talent density but a shorter decision chain." Kimi K3’s 896-expert MoE and self-evolving kernel optimization don’t require more geniuses; they require an organization that can test and iterate quickly, and also shut down quickly when a path is wrong.
Choosing the battlefield is just as important as choosing the weapon.
#Kimi #杨植麟 #AI
Day after day, one step at a time: Why can Kimi build K3? Yang Xinyu lists four sins among its peers
① Arrogance: Veteran teams believe the AI war is over and that they’ve already won.
② Impatience: Young labs lack solid fundamentals, and when they can’t keep up, they quickly pivot.
③ Cowardice: Their strength isn’t weak, but they’re afraid to set their sights on being #1 in the industry.
④ Misaligned goals: Everyone is fighting for personal credit, and no one truly cares whether the company can build AGI.
Yang Xinyu says what’s most different about the Dark Side of the Moon is that the founding team still has an intense drive to pursue AGI.
He also shared “Kimi’s Five Precepts”:
- Model companies should build models
- Do Research and publish papers through experiments
- When training models, look at metrics
- Don’t force it if it doesn’t work
- Don’t YOLO
In plain terms: tell fewer stories, do more experiments. Train by data, stop failing fast, and don’t rely on intuition to place big bets.
These four sins and five precepts are really about the same thing: most AI companies fail because they’re too eager to be “the boss,” not because they’re determined to “do the right work.” Kimi’s differentiation isn’t that it’s smarter—it’s that it’s more restrained.
#Kimi #K3 #AI
① Arrogance: Veteran teams believe the AI war is over and that they’ve already won.
② Impatience: Young labs lack solid fundamentals, and when they can’t keep up, they quickly pivot.
③ Cowardice: Their strength isn’t weak, but they’re afraid to set their sights on being #1 in the industry.
④ Misaligned goals: Everyone is fighting for personal credit, and no one truly cares whether the company can build AGI.
Yang Xinyu says what’s most different about the Dark Side of the Moon is that the founding team still has an intense drive to pursue AGI.
He also shared “Kimi’s Five Precepts”:
- Model companies should build models
- Do Research and publish papers through experiments
- When training models, look at metrics
- Don’t force it if it doesn’t work
- Don’t YOLO
In plain terms: tell fewer stories, do more experiments. Train by data, stop failing fast, and don’t rely on intuition to place big bets.
These four sins and five precepts are really about the same thing: most AI companies fail because they’re too eager to be “the boss,” not because they’re determined to “do the right work.” Kimi’s differentiation isn’t that it’s smarter—it’s that it’s more restrained.
#Kimi #K3 #AI
Striving for one more step: Kimi K3 just released—28 trillion parameters, 1 million context, native multimodal
All three internal benchmarks surpass Claude Opus 4.8 and GPT-5.5: Online Exp 75.5, DECK-Bench 73.5, Finance-Bench 62.6.
Two architecture updates: Kimi Delta Attention (KDA) boosts decoding speed by up to 6.3x in million-token context; Attention Residuals (AttnRes) improves training efficiency by about 25%, with extra costs under 2%. The MoE expands to 896 experts, activating 16 each time; overall expansion efficiency is about 2.5x higher than K2.
Most worth paying attention to is its self-evolution capability: K3 takes 15 hours of continuous iteration to design a new two-stage kernel algorithm, reducing AttnRes forward + backward from 283.6ms to 114.4ms—no change in results, but double the speed. The model is optimizing itself.
K3 has gone live with Kimi Work, Kimi Code, and the API, with weights to be released by July 27.
Once models start optimizing their own training kernels, the narrative of “AI helps humans write code” needs an upgrade—next comes “AI helps AI write code even faster.”
#Kimi #K3 #AI
All three internal benchmarks surpass Claude Opus 4.8 and GPT-5.5: Online Exp 75.5, DECK-Bench 73.5, Finance-Bench 62.6.
Two architecture updates: Kimi Delta Attention (KDA) boosts decoding speed by up to 6.3x in million-token context; Attention Residuals (AttnRes) improves training efficiency by about 25%, with extra costs under 2%. The MoE expands to 896 experts, activating 16 each time; overall expansion efficiency is about 2.5x higher than K2.
Most worth paying attention to is its self-evolution capability: K3 takes 15 hours of continuous iteration to design a new two-stage kernel algorithm, reducing AttnRes forward + backward from 283.6ms to 114.4ms—no change in results, but double the speed. The model is optimizing itself.
K3 has gone live with Kimi Work, Kimi Code, and the API, with weights to be released by July 27.
Once models start optimizing their own training kernels, the narrative of “AI helps humans write code” needs an upgrade—next comes “AI helps AI write code even faster.”
#Kimi #K3 #AI
Keep pushing one more step: Kimi's domestic client is pretty strong
Supports using Canva to make posters (the results are average, but Workbuddy doesn’t have this feature), offers a complete literature search suite (CNKI, Wanfang), can connect with Eastmoney and Tianyancha, and can even organize files in Baidu Netdisk.
The competitive dimensions of AI clients in China are different from abroad. Claude and ChatGPT are competing on reasoning ability, while Kimi is competing on “how many China-based data sources it has access to.” Literature search, financial data, and organizing Netdisk—these are all hard-demand scenarios for “non-general intelligence.”
An AI assistant’s moat isn’t the model parameters—it’s the data connectivity and scenario embedding. The model can be replaced, but the workflow is hard to change.
#Kimi #AI
Supports using Canva to make posters (the results are average, but Workbuddy doesn’t have this feature), offers a complete literature search suite (CNKI, Wanfang), can connect with Eastmoney and Tianyancha, and can even organize files in Baidu Netdisk.
The competitive dimensions of AI clients in China are different from abroad. Claude and ChatGPT are competing on reasoning ability, while Kimi is competing on “how many China-based data sources it has access to.” Literature search, financial data, and organizing Netdisk—these are all hard-demand scenarios for “non-general intelligence.”
An AI assistant’s moat isn’t the model parameters—it’s the data connectivity and scenario embedding. The model can be replaced, but the workflow is hard to change.
#Kimi #AI
$KIMI - LARGEST CHINESE AI MODEL LAUNCHES THIS WEEK 🔥
Entry: Not available 🔥
Target: Not available 🚀
Stop Loss: Not available ⚠️
The Dark Side of the Moon (Kimi) project is dropping Kimi K3 in the coming days — a 20-30 trillion parameter open-weight model that beats Opus 4.8 on mainstream benchmarks. This is the biggest AI model out of China and it's free to download.
Anthropic and OpenAI just got a serious wake-up call. The release is already creating buzz and I'm watching related AI tokens for volume shifts. Which AI projects are you tracking for the next move?
Not financial advice. Always manage your risk.
#KIMI #AI #Crypto #OpenWeight #AIModels
💎
Entry: Not available 🔥
Target: Not available 🚀
Stop Loss: Not available ⚠️
The Dark Side of the Moon (Kimi) project is dropping Kimi K3 in the coming days — a 20-30 trillion parameter open-weight model that beats Opus 4.8 on mainstream benchmarks. This is the biggest AI model out of China and it's free to download.
Anthropic and OpenAI just got a serious wake-up call. The release is already creating buzz and I'm watching related AI tokens for volume shifts. Which AI projects are you tracking for the next move?
Not financial advice. Always manage your risk.
#KIMI #AI #Crypto #OpenWeight #AIModels
💎
Frontend Code Arena Leaderboard: Kimi-K3 tops the chart with a 76% win rate, pulling far ahead of second-place Claude Fable 5 (63%), by a wide margin.
Among the top 6, homegrown/Chinese teams account for two and a half: both Kimi-K3 and GLM-5.2(Max) are above 50%; MiniMax-M3 ranks 20th this time with a 40% win rate.
This leaderboard is arena.ai’s code battle ladder, mainly judging front-end code generation ability. K3’s advantage in the code track is more pronounced than in general chat rankings—suggesting that Moon of the Dark Side has indeed put serious effort into tool use/coding/reasoning.
#Kimi #AI #Large model
Among the top 6, homegrown/Chinese teams account for two and a half: both Kimi-K3 and GLM-5.2(Max) are above 50%; MiniMax-M3 ranks 20th this time with a 40% win rate.
This leaderboard is arena.ai’s code battle ladder, mainly judging front-end code generation ability. K3’s advantage in the code track is more pronounced than in general chat rankings—suggesting that Moon of the Dark Side has indeed put serious effort into tool use/coding/reasoning.
#Kimi #AI #Large model
#kimi k3#
Moon’s Dark Side co-founder, will release open-source Kimi k3. Wow— the world’s number one large language model is going open-source. How does this not kill OpenAI and Claude Code’s closed-source offerings? $BABA
Moon’s Dark Side co-founder, will release open-source Kimi k3. Wow— the world’s number one large language model is going open-source. How does this not kill OpenAI and Claude Code’s closed-source offerings? $BABA
$KIMI ANNUAL RECURRING REVENUE SURPASSES $300M – FUNDAMENTAL SHIFT IN PLAY 📈
The latest financing round values Kimi at $31.5B pre-money, with ARR crossing $300M in mid-June. Over 70% of revenue now comes from API usage, driven by model iteration and developer demand.
This revenue curve mirrors early-stage Anthropic: rising API percentage, expanding overseas paid users, and a pricing shift tied to capability improvements. The composition tells us this isn’t speculative — it’s operational, recurring, and accelerating.
Does the market already price in this revenue trajectory, or is there room for revaluation?
Not financial advice. Always manage your risk.
#KIMI #AIRevenue #FundamentalGrowth #CryptoAI #RevenueBreakout
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The latest financing round values Kimi at $31.5B pre-money, with ARR crossing $300M in mid-June. Over 70% of revenue now comes from API usage, driven by model iteration and developer demand.
This revenue curve mirrors early-stage Anthropic: rising API percentage, expanding overseas paid users, and a pricing shift tied to capability improvements. The composition tells us this isn’t speculative — it’s operational, recurring, and accelerating.
Does the market already price in this revenue trajectory, or is there room for revaluation?
Not financial advice. Always manage your risk.
#KIMI #AIRevenue #FundamentalGrowth #CryptoAI #RevenueBreakout
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$KIMI ARR HITS $300M AND VALUATION DOUBLES TO $31.5B ❗
This is the kind of revenue acceleration that usually precedes a massive price discovery cycle. ARR crossing $300M in mid-June with 70% coming from API revenue shows real product-market fit — similar to what Anthropic looked like early on.
A $31.5B pre-money valuation says the smart money is betting on continued dominance in the AI space. Developer API calls are increasing, overseas paid users are growing, and the price system is shifting with model iterations. That's a recipe for sustained demand.
Do you think this signals a breakout for AI tokens, or is the valuation already baked in?
Not financial advice. Always manage your risk.
#KIMI #AI #Funding #Bullish #Crypto
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This is the kind of revenue acceleration that usually precedes a massive price discovery cycle. ARR crossing $300M in mid-June with 70% coming from API revenue shows real product-market fit — similar to what Anthropic looked like early on.
A $31.5B pre-money valuation says the smart money is betting on continued dominance in the AI space. Developer API calls are increasing, overseas paid users are growing, and the price system is shifting with model iterations. That's a recipe for sustained demand.
Do you think this signals a breakout for AI tokens, or is the valuation already baked in?
Not financial advice. Always manage your risk.
#KIMI #AI #Funding #Bullish #Crypto
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MOONSHOT AI DECLARES ALL THIRD-PARTY FINANCING FRAUD - $KIMI 🔥
The company has uncovered multiple schemes where institutions used its name to solicit fake financing deals. All capital raises are handled directly — no external advisors or pre-committed quotas. This isn't just a legal notice; it's a liquidity event for sentiment.
The statement explicitly denies ever providing proof-of-asset documents or locking quotas. Any claim of holding secured Moonshot shares is fraudulent. Are you verifying your deal sources before committing capital?
Not financial advice. Always manage your risk.
#KIMI #ScamAlert #FraudWarning #DueDiligence #Crypto
💎
The company has uncovered multiple schemes where institutions used its name to solicit fake financing deals. All capital raises are handled directly — no external advisors or pre-committed quotas. This isn't just a legal notice; it's a liquidity event for sentiment.
The statement explicitly denies ever providing proof-of-asset documents or locking quotas. Any claim of holding secured Moonshot shares is fraudulent. Are you verifying your deal sources before committing capital?
Not financial advice. Always manage your risk.
#KIMI #ScamAlert #FraudWarning #DueDiligence #Crypto
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