#deepseek
deepseek
2.5M views
1,350 Discussing
Hot
Latest
DeepSeek raised another 100 billion in its second funding round. Here’s how everyone stacks up:
Liang Wenfeng: Other people seek capital when they fundraise; I get to choose my investors. You can share the money, but don’t touch the steering wheel. Locked in for five years, with no voting rights. Invest if you want; don’t if you don’t.
Tencent, CATL: Existing shareholders are back for another round, doubling down from their cornerstone positions.
Perfect World, Century Huatong: Gaming is the main business; funding AI is the side hustle—the endgame for gaming companies is computing power.
Retail investors: That 100 billion has absolutely nothing to do with you. You can’t get a piece of the equity, so all you can do is watch crypto AI shitcoins rewrite their white papers overnight and bold the word “AI” in size 3.
The most painful part: demand was so hot that the raise doubled from 50 billion to 100 billion, while the 500 billion pre-money valuation didn’t budge an inch. This isn’t fundraising; it’s an order being issued: there are no seats for retail investors at the AI table.
Comment section, sound off: Team A says the AI narrative is heating up again, with crypto AI tokens rising in sympathy; Team B says a funding round is just a funding round, and shitcoins will still go to zero. Roll call.
#DeepSeek #AI
$BTC $ETH $SOL
DeepSeek raised another 100 billion in its second funding round. Here’s how everyone stacks up:
Liang Wenfeng: Other people seek capital when they fundraise; I get to choose my investors. You can share the money, but don’t touch the steering wheel. Locked in for five years, with no voting rights. Invest if you want; don’t if you don’t.
Tencent, CATL: Existing shareholders are back for another round, doubling down from their cornerstone positions.
Perfect World, Century Huatong: Gaming is the main business; funding AI is the side hustle—the endgame for gaming companies is computing power.
Retail investors: That 100 billion has absolutely nothing to do with you. You can’t get a piece of the equity, so all you can do is watch crypto AI shitcoins rewrite their white papers overnight and bold the word “AI” in size 3.
The most painful part: demand was so hot that the raise doubled from 50 billion to 100 billion, while the 500 billion pre-money valuation didn’t budge an inch. This isn’t fundraising; it’s an order being issued: there are no seats for retail investors at the AI table.
Comment section, sound off: Team A says the AI narrative is heating up again, with crypto AI tokens rising in sympathy; Team B says a funding round is just a funding round, and shitcoins will still go to zero. Roll call.
#DeepSeek #AI
$BTC $ETH $SOL
Liang Wenfeng: Other people seek capital when they fundraise; I get to choose my investors. You can share the money, but don’t touch the steering wheel. Locked in for five years, with no voting rights. Invest if you want; don’t if you don’t.
Tencent, CATL: Existing shareholders are back for another round, doubling down from their cornerstone positions.
Perfect World, Century Huatong: Gaming is the main business; funding AI is the side hustle—the endgame for gaming companies is computing power.
Retail investors: That 100 billion has absolutely nothing to do with you. You can’t get a piece of the equity, so all you can do is watch crypto AI shitcoins rewrite their white papers overnight and bold the word “AI” in size 3.
The most painful part: demand was so hot that the raise doubled from 50 billion to 100 billion, while the 500 billion pre-money valuation didn’t budge an inch. This isn’t fundraising; it’s an order being issued: there are no seats for retail investors at the AI table.
Comment section, sound off: Team A says the AI narrative is heating up again, with crypto AI tokens rising in sympathy; Team B says a funding round is just a funding round, and shitcoins will still go to zero. Roll call.
#DeepSeek #AI
$BTC $ETH $SOL
545
·
--
U.S. Government Orders DeepSeek Removed: Keep a Close Watch on AI Chatbots and Halt Procurement!
The U.S. government has ordered all government agencies to remove the DeepSeek AI chatbot from their systems and banned new purchases. The open-source model once attracted widespread attention, but is now a target of heightened scrutiny. Keep an eye on where the money is flowing and stay on top of AI trends. #DeepSeek #Money Trackers.
U.S. Government Orders DeepSeek Removed: Keep a Close Watch on AI Chatbots and Halt Procurement!
The U.S. government has ordered all government agencies to remove the DeepSeek AI chatbot from their systems and banned new purchases. The open-source model once attracted widespread attention, but is now a target of heightened scrutiny. Keep an eye on where the money is flowing and stay on top of AI trends. #DeepSeek #Money Trackers.
The U.S. government has ordered all government agencies to remove the DeepSeek AI chatbot from their systems and banned new purchases. The open-source model once attracted widespread attention, but is now a target of heightened scrutiny. Keep an eye on where the money is flowing and stay on top of AI trends. #DeepSeek #Money Trackers.
23
·
--
Reuters, citing people familiar with the matter, reports that DeepSeek is finalizing a funding round of about RMB 100 billion (approximately US$15 billion), with Tencent and CATL among the largest investors. The target has risen from an initial roughly RMB 50 billion to RMB 80 billion and then to around RMB 100 billion. The deal is expected to close within days, though the terms could still change.
Bloomberg previously reported that the target had been raised twice and was nearing RMB 100 billion. Wallstreetcn reports that CATL plans to add about RMB 10 billion in this round, following an initial investment of roughly RMB 5 billion. If the deal closes, DeepSeek’s total fundraising over six months could reach around RMB 150 billion, while it is also advancing plans for a STAR Market IPO.
In September, The Information also reported that Liang Wenfeng had told investors that annualized revenue had reached around US$1 billion, mainly from APIs. A RMB 100-billion-scale funding round is massive by Chinese private-market standards, but it is still nowhere near the scale of OpenAI and Anthropic’s combined fundraising, which is in the hundreds of billions of dollars.
#DeepSeek #AI #funding
Reuters, citing people familiar with the matter, reports that DeepSeek is finalizing a funding round of about RMB 100 billion (approximately US$15 billion), with Tencent and CATL among the largest investors. The target has risen from an initial roughly RMB 50 billion to RMB 80 billion and then to around RMB 100 billion. The deal is expected to close within days, though the terms could still change.
Bloomberg previously reported that the target had been raised twice and was nearing RMB 100 billion. Wallstreetcn reports that CATL plans to add about RMB 10 billion in this round, following an initial investment of roughly RMB 5 billion. If the deal closes, DeepSeek’s total fundraising over six months could reach around RMB 150 billion, while it is also advancing plans for a STAR Market IPO.
In September, The Information also reported that Liang Wenfeng had told investors that annualized revenue had reached around US$1 billion, mainly from APIs. A RMB 100-billion-scale funding round is massive by Chinese private-market standards, but it is still nowhere near the scale of OpenAI and Anthropic’s combined fundraising, which is in the hundreds of billions of dollars.
#DeepSeek #AI #funding
Bloomberg previously reported that the target had been raised twice and was nearing RMB 100 billion. Wallstreetcn reports that CATL plans to add about RMB 10 billion in this round, following an initial investment of roughly RMB 5 billion. If the deal closes, DeepSeek’s total fundraising over six months could reach around RMB 150 billion, while it is also advancing plans for a STAR Market IPO.
In September, The Information also reported that Liang Wenfeng had told investors that annualized revenue had reached around US$1 billion, mainly from APIs. A RMB 100-billion-scale funding round is massive by Chinese private-market standards, but it is still nowhere near the scale of OpenAI and Anthropic’s combined fundraising, which is in the hundreds of billions of dollars.
#DeepSeek #AI #funding
1
·
--
Bullish
🚨UNITED STATES ORDERS DEEPSEEK BAN IN GOVERNMENT AGENCIES: THE IMPACT ON THE AI MARKET! 🛑🇺🇸
The geopolitical and regulatory landscape of Artificial Intelligence has just entered a decisive new chapter: the U.S. Department of Homeland Security (DHS) has issued a direct order requiring all federal civilian agencies to uninstall DeepSeek and immediately stop using technologies based on the Chinese model!
📌 KEY POINTS OF THE DECISION:
1️⃣ Federal Restrictions: The measure requires the complete removal of systems that use DeepSeek’s architecture and blocks new contracts or purchases. 2️⃣ Data Security and Geopolitics: This move reflects growing tensions and U.S. officials’ concerns about data sovereignty and the advancement of Chinese AI in public infrastructure. 3️⃣ Impact on AI Tokens: News like this often directly affects sentiment around memecoins and tokens in ecosystems linked to DeepSeek or AI on the BNB Chain and other networks, causing spikes in volatility.
💡 PROFESSIONAL PERSPECTIVE:
Regulation and government restrictions are critical factors that every technology and crypto investor needs to monitor. While AI technology is advancing rapidly, geopolitical restrictions can change the course of institutional adoption overnight (DYOR).
📊 KEEP A CLOSE EYE ON THE MARKET:
👇 Before taking any position, keep an eye on liquidity and the behavior of Artificial Intelligence sector tokens in the WIDGET below!
$DEEP
Do you believe government sanctions could slow the global advancement of Artificial Intelligence, or will open-source development overcome these barriers? Share your analysis in the comments! 👇💬
#Write2Earn #BinanceSquare #DeepSeek #AI #CryptoNews
🚨UNITED STATES ORDERS DEEPSEEK BAN IN GOVERNMENT AGENCIES: THE IMPACT ON THE AI MARKET! 🛑🇺🇸
The geopolitical and regulatory landscape of Artificial Intelligence has just entered a decisive new chapter: the U.S. Department of Homeland Security (DHS) has issued a direct order requiring all federal civilian agencies to uninstall DeepSeek and immediately stop using technologies based on the Chinese model!
📌 KEY POINTS OF THE DECISION:
1️⃣ Federal Restrictions: The measure requires the complete removal of systems that use DeepSeek’s architecture and blocks new contracts or purchases. 2️⃣ Data Security and Geopolitics: This move reflects growing tensions and U.S. officials’ concerns about data sovereignty and the advancement of Chinese AI in public infrastructure. 3️⃣ Impact on AI Tokens: News like this often directly affects sentiment around memecoins and tokens in ecosystems linked to DeepSeek or AI on the BNB Chain and other networks, causing spikes in volatility.
💡 PROFESSIONAL PERSPECTIVE:
Regulation and government restrictions are critical factors that every technology and crypto investor needs to monitor. While AI technology is advancing rapidly, geopolitical restrictions can change the course of institutional adoption overnight (DYOR).
📊 KEEP A CLOSE EYE ON THE MARKET:
👇 Before taking any position, keep an eye on liquidity and the behavior of Artificial Intelligence sector tokens in the WIDGET below!
$DEEP
Do you believe government sanctions could slow the global advancement of Artificial Intelligence, or will open-source development overcome these barriers? Share your analysis in the comments! 👇💬
#Write2Earn #BinanceSquare #DeepSeek #AI #CryptoNews
The geopolitical and regulatory landscape of Artificial Intelligence has just entered a decisive new chapter: the U.S. Department of Homeland Security (DHS) has issued a direct order requiring all federal civilian agencies to uninstall DeepSeek and immediately stop using technologies based on the Chinese model!
📌 KEY POINTS OF THE DECISION:
1️⃣ Federal Restrictions: The measure requires the complete removal of systems that use DeepSeek’s architecture and blocks new contracts or purchases. 2️⃣ Data Security and Geopolitics: This move reflects growing tensions and U.S. officials’ concerns about data sovereignty and the advancement of Chinese AI in public infrastructure. 3️⃣ Impact on AI Tokens: News like this often directly affects sentiment around memecoins and tokens in ecosystems linked to DeepSeek or AI on the BNB Chain and other networks, causing spikes in volatility.
💡 PROFESSIONAL PERSPECTIVE:
Regulation and government restrictions are critical factors that every technology and crypto investor needs to monitor. While AI technology is advancing rapidly, geopolitical restrictions can change the course of institutional adoption overnight (DYOR).
📊 KEEP A CLOSE EYE ON THE MARKET:
👇 Before taking any position, keep an eye on liquidity and the behavior of Artificial Intelligence sector tokens in the WIDGET below!
$DEEP
Do you believe government sanctions could slow the global advancement of Artificial Intelligence, or will open-source development overcome these barriers? Share your analysis in the comments! 👇💬
#Write2Earn #BinanceSquare #DeepSeek #AI #CryptoNews
Binance BiBi:
Working on it. Your reply is on the way.
2
32
·
--
🐳 DeepSeek open-sources its in-house API inference engine, DeepInfer, bringing Huawei Ascend and NVIDIA onto the same stage!
📌 DeepInfer is the inference framework powering DeepSeek V4.1 Flash’s online services, released under the Apache 2.0 license
📌 Initial support includes NVIDIA SM100 (Blackwell) and Huawei Ascend 950; older architectures such as Hopper aren’t on the list
📌 The company says a single cluster processes 155 trillion tokens per day, with revenue of about $38 per GPU-hour at off-peak prices
📌 But only offline inference is being released this time: KV caching, PD disaggregation, and load balancing aren’t open-sourced, so you’ll need to build those pieces yourself to run a complete service
📌 In the same space: Mistral Large 4 (1T parameters) is scheduled for an open-weights release at the end of October
AI compute is becoming increasingly “domestic + open source.” As compute and inference costs fall, will on-chain trading by AI agents become more widespread? $BTC
Do you think open-source inference will bring down prices for large-model APIs? Share your thoughts in the comments 👇
#AI #DeepSeek #开源 #Compute
For reference only; this is not investment advice
🐳 DeepSeek open-sources its in-house API inference engine, DeepInfer, bringing Huawei Ascend and NVIDIA onto the same stage!
📌 DeepInfer is the inference framework powering DeepSeek V4.1 Flash’s online services, released under the Apache 2.0 license
📌 Initial support includes NVIDIA SM100 (Blackwell) and Huawei Ascend 950; older architectures such as Hopper aren’t on the list
📌 The company says a single cluster processes 155 trillion tokens per day, with revenue of about $38 per GPU-hour at off-peak prices
📌 But only offline inference is being released this time: KV caching, PD disaggregation, and load balancing aren’t open-sourced, so you’ll need to build those pieces yourself to run a complete service
📌 In the same space: Mistral Large 4 (1T parameters) is scheduled for an open-weights release at the end of October
AI compute is becoming increasingly “domestic + open source.” As compute and inference costs fall, will on-chain trading by AI agents become more widespread? $BTC
Do you think open-source inference will bring down prices for large-model APIs? Share your thoughts in the comments 👇
#AI #DeepSeek #开源 #Compute
For reference only; this is not investment advice
📌 DeepInfer is the inference framework powering DeepSeek V4.1 Flash’s online services, released under the Apache 2.0 license
📌 Initial support includes NVIDIA SM100 (Blackwell) and Huawei Ascend 950; older architectures such as Hopper aren’t on the list
📌 The company says a single cluster processes 155 trillion tokens per day, with revenue of about $38 per GPU-hour at off-peak prices
📌 But only offline inference is being released this time: KV caching, PD disaggregation, and load balancing aren’t open-sourced, so you’ll need to build those pieces yourself to run a complete service
📌 In the same space: Mistral Large 4 (1T parameters) is scheduled for an open-weights release at the end of October
AI compute is becoming increasingly “domestic + open source.” As compute and inference costs fall, will on-chain trading by AI agents become more widespread? $BTC
Do you think open-source inference will bring down prices for large-model APIs? Share your thoughts in the comments 👇
#AI #DeepSeek #开源 #Compute
For reference only; this is not investment advice
65
·
--
DeepSeek has open-sourced its own reasoning framework too.
Do you get how huge this is?
Before, open-sourcing a model was like publishing the recipe.
This time, they’ve brought out the entire kitchen for you to tour,
and they’re even teaching you how to toss a wok in person.
One comment summed it up so perfectly I’m dying:
“Who taught you that when you open-source something, you have to open-source the revenue too?”
To the folks next door still agonizing over subscription fees—
this whole “making money with your head held high” thing has really got you rattled.
One runs on love, the other makes money by taking a cut—
whose side do you think consumers are on?😏
#DeepSeek #开源
DeepSeek has open-sourced its own reasoning framework too.
Do you get how huge this is?
Before, open-sourcing a model was like publishing the recipe.
This time, they’ve brought out the entire kitchen for you to tour,
and they’re even teaching you how to toss a wok in person.
One comment summed it up so perfectly I’m dying:
“Who taught you that when you open-source something, you have to open-source the revenue too?”
To the folks next door still agonizing over subscription fees—
this whole “making money with your head held high” thing has really got you rattled.
One runs on love, the other makes money by taking a cut—
whose side do you think consumers are on?😏
#DeepSeek #开源
Do you get how huge this is?
Before, open-sourcing a model was like publishing the recipe.
This time, they’ve brought out the entire kitchen for you to tour,
and they’re even teaching you how to toss a wok in person.
One comment summed it up so perfectly I’m dying:
“Who taught you that when you open-source something, you have to open-source the revenue too?”
To the folks next door still agonizing over subscription fees—
this whole “making money with your head held high” thing has really got you rattled.
One runs on love, the other makes money by taking a cut—
whose side do you think consumers are on?😏
#DeepSeek #开源
1
·
--
🐋 DeepSeek has open-sourced its own inference engine! So why are Chinese tech giants still buying NVIDIA like crazy?
🔹 DeepSeek open-sourced DeepInfer, the inference framework it uses for its own online services. Licensed under Apache 2.0, it supports both NVIDIA GPUs and Huawei Ascend NPUs, and topped the Hacker News daily leaderboard (2,000+ upvotes).
🔹 Meanwhile, foreign media report that ByteDance, Alibaba, and Tencent have collectively ordered over 3 million NVIDIA RTX PRO 5500 cards, with the total potentially exceeding $35 billion (not yet confirmed by all the companies).
🔹 Open source to cut costs on one side, huge spending to secure computing power on the other—the AI arms race is still accelerating.
As demand for AI computing power surges, is this good news for $BTC miners and the computing-power sector, or will it mean more competition for electricity and chips?
💬 Do you think open-source models will eventually beat the closed-source giants? Share your thoughts in the comments 👇
#AI #DeepSeek #英伟达 #ComputingPower
For reference only; this is not investment advice.
🐋 DeepSeek has open-sourced its own inference engine! So why are Chinese tech giants still buying NVIDIA like crazy?
🔹 DeepSeek open-sourced DeepInfer, the inference framework it uses for its own online services. Licensed under Apache 2.0, it supports both NVIDIA GPUs and Huawei Ascend NPUs, and topped the Hacker News daily leaderboard (2,000+ upvotes).
🔹 Meanwhile, foreign media report that ByteDance, Alibaba, and Tencent have collectively ordered over 3 million NVIDIA RTX PRO 5500 cards, with the total potentially exceeding $35 billion (not yet confirmed by all the companies).
🔹 Open source to cut costs on one side, huge spending to secure computing power on the other—the AI arms race is still accelerating.
As demand for AI computing power surges, is this good news for $BTC miners and the computing-power sector, or will it mean more competition for electricity and chips?
💬 Do you think open-source models will eventually beat the closed-source giants? Share your thoughts in the comments 👇
#AI #DeepSeek #英伟达 #ComputingPower
For reference only; this is not investment advice.
🔹 DeepSeek open-sourced DeepInfer, the inference framework it uses for its own online services. Licensed under Apache 2.0, it supports both NVIDIA GPUs and Huawei Ascend NPUs, and topped the Hacker News daily leaderboard (2,000+ upvotes).
🔹 Meanwhile, foreign media report that ByteDance, Alibaba, and Tencent have collectively ordered over 3 million NVIDIA RTX PRO 5500 cards, with the total potentially exceeding $35 billion (not yet confirmed by all the companies).
🔹 Open source to cut costs on one side, huge spending to secure computing power on the other—the AI arms race is still accelerating.
As demand for AI computing power surges, is this good news for $BTC miners and the computing-power sector, or will it mean more competition for electricity and chips?
💬 Do you think open-source models will eventually beat the closed-source giants? Share your thoughts in the comments 👇
#AI #DeepSeek #英伟达 #ComputingPower
For reference only; this is not investment advice.
131
·
--
Bullish
🚨 BREAKING AI NEWS: DeepSeek Releases an Updated AI Model! 🤖🔥
#DeepSeek has introduced DeepSeek-V4.1-Flash-1017, an upgraded AI model announced on October 17, 2026.
⚡ What's new? ✅ Faster task completion
✅ Improved coding and AI agent performance
✅ Shorter, more efficient responses
✅ Better benchmark results
✅ Search & Fetch APIs now available to users
📊 DeepSeek reported a score of 77.0 on the DeepSWE v1.1 benchmark, up from 74.2 in the previous version.
🌐 The updated model is available through DeepSeek's website, mobile app and API.
💡 Why does it matter? The AI race is heating up, with companies competing to deliver more powerful tools at lower costs.
Will DeepSeek challenge the biggest names in AI? 👀
💬 What do you think? Is DeepSeek becoming a serious competitor in the AI industry?
#DeepSeek #TechNews #Innovation
#deepseekreleasesupdatedaimodel
🚨 BREAKING AI NEWS: DeepSeek Releases an Updated AI Model! 🤖🔥
#DeepSeek has introduced DeepSeek-V4.1-Flash-1017, an upgraded AI model announced on October 17, 2026.
⚡ What's new? ✅ Faster task completion
✅ Improved coding and AI agent performance
✅ Shorter, more efficient responses
✅ Better benchmark results
✅ Search & Fetch APIs now available to users
📊 DeepSeek reported a score of 77.0 on the DeepSWE v1.1 benchmark, up from 74.2 in the previous version.
🌐 The updated model is available through DeepSeek's website, mobile app and API.
💡 Why does it matter? The AI race is heating up, with companies competing to deliver more powerful tools at lower costs.
Will DeepSeek challenge the biggest names in AI? 👀
💬 What do you think? Is DeepSeek becoming a serious competitor in the AI industry?
#DeepSeek #TechNews #Innovation
#deepseekreleasesupdatedaimodel
#DeepSeek has introduced DeepSeek-V4.1-Flash-1017, an upgraded AI model announced on October 17, 2026.
⚡ What's new? ✅ Faster task completion
✅ Improved coding and AI agent performance
✅ Shorter, more efficient responses
✅ Better benchmark results
✅ Search & Fetch APIs now available to users
📊 DeepSeek reported a score of 77.0 on the DeepSWE v1.1 benchmark, up from 74.2 in the previous version.
🌐 The updated model is available through DeepSeek's website, mobile app and API.
💡 Why does it matter? The AI race is heating up, with companies competing to deliver more powerful tools at lower costs.
Will DeepSeek challenge the biggest names in AI? 👀
💬 What do you think? Is DeepSeek becoming a serious competitor in the AI industry?
#DeepSeek #TechNews #Innovation
#deepseekreleasesupdatedaimodel
Taybou:
peut etre
1
4
690
·
--
🤖 DeepSeek Upgrades Again: Is the AI Sector Primed for a Breakout?
DeepSeek has officially rolled out its latest model update (V4.1-Flash), delivering high-speed reasoning and complex coding at a fraction of traditional computing costs.
Here is why this matters for tech and Web3 AI sectors:
💡 1. Efficiency Over Hype High-throughput, lower-cost inference models force established AI giants to drop prices, accelerating global developer adoption.
⚡ 2. Web3 AI & Decentralized Compute Efficient models drive real-world demand for decentralized GPU networks ($RENDER, $AKT) and decentralized intelligence protocols ($TAO, $NEAR,$ASTER).
🎯 3. The Reality Check Headlines create initial spikes, but real value capture comes from developer usage, integration, and actual fee generation—not just hype.
💬 What’s your take? Will open-weights efficiency disrupt closed AI giants, or will big tech hold the throne? Drop your thoughts below! 👇
#DeepSeek #AI #CryptoNews #TAO #NEAR
🤖 DeepSeek Upgrades Again: Is the AI Sector Primed for a Breakout?
DeepSeek has officially rolled out its latest model update (V4.1-Flash), delivering high-speed reasoning and complex coding at a fraction of traditional computing costs.
Here is why this matters for tech and Web3 AI sectors:
💡 1. Efficiency Over Hype High-throughput, lower-cost inference models force established AI giants to drop prices, accelerating global developer adoption.
⚡ 2. Web3 AI & Decentralized Compute Efficient models drive real-world demand for decentralized GPU networks ($RENDER, $AKT) and decentralized intelligence protocols ($TAO, $NEAR,$ASTER).
🎯 3. The Reality Check Headlines create initial spikes, but real value capture comes from developer usage, integration, and actual fee generation—not just hype.
💬 What’s your take? Will open-weights efficiency disrupt closed AI giants, or will big tech hold the throne? Drop your thoughts below! 👇
#DeepSeek #AI #CryptoNews #TAO #NEAR
DeepSeek has officially rolled out its latest model update (V4.1-Flash), delivering high-speed reasoning and complex coding at a fraction of traditional computing costs.
Here is why this matters for tech and Web3 AI sectors:
💡 1. Efficiency Over Hype High-throughput, lower-cost inference models force established AI giants to drop prices, accelerating global developer adoption.
⚡ 2. Web3 AI & Decentralized Compute Efficient models drive real-world demand for decentralized GPU networks ($RENDER, $AKT) and decentralized intelligence protocols ($TAO, $NEAR,$ASTER).
🎯 3. The Reality Check Headlines create initial spikes, but real value capture comes from developer usage, integration, and actual fee generation—not just hype.
💬 What’s your take? Will open-weights efficiency disrupt closed AI giants, or will big tech hold the throne? Drop your thoughts below! 👇
#DeepSeek #AI #CryptoNews #TAO #NEAR
114
🚀 DeepSeek Releases V4.1-Flash-1017: Better AI Performance at Low Cost
🔍 1. What’s New?
$FET — DeepSeek released V4.1-Flash-1017 on October 17, 2026. The update improves reported coding and agent-task benchmark scores while aiming to reduce task completion time and output length.
📊 2. Key Performance Numbers
According to DeepSeek’s published results:
DeepSWE v1.1: 77.0, up from 74.2.
Terminal-Bench 4.0: 42.3, up from 31.2.
API pricing: Off-peak input at $0.15 and output at $0.60 per million tokens.
License: MIT, allowing broad use subject to its terms.
The API continues to use the deepseek-flash model alias. Benchmark scores are self-reported and can vary with testing frameworks.
🤖 3. Why It Matters for Crypto
$RENDER — More capable and affordable AI models could support experimentation with autonomous agents, decentralized computing, and AI-powered blockchain applications.
However, a model release does not automatically increase demand for decentralized infrastructure or guarantee gains for AI-related crypto tokens.
🎯 4. What Should Investors Watch?
$NEAR — Focus on real adoption, developer activity, network usage, and actual demand for decentralized compute. AI-related headlines can influence market sentiment, but price movements are uncertain.
Prime Crypto Lab — No Hype, Just Research.
DYOR | Educational Content | Not Financial Advice
#DeepSeek #Binance #DeepSeekReleasesUpdatedAIModel #BinanceSquareTalks
🚀 DeepSeek Releases V4.1-Flash-1017: Better AI Performance at Low Cost
🔍 1. What’s New?
$FET — DeepSeek released V4.1-Flash-1017 on October 17, 2026. The update improves reported coding and agent-task benchmark scores while aiming to reduce task completion time and output length.
📊 2. Key Performance Numbers
According to DeepSeek’s published results:
DeepSWE v1.1: 77.0, up from 74.2.
Terminal-Bench 4.0: 42.3, up from 31.2.
API pricing: Off-peak input at $0.15 and output at $0.60 per million tokens.
License: MIT, allowing broad use subject to its terms.
The API continues to use the deepseek-flash model alias. Benchmark scores are self-reported and can vary with testing frameworks.
🤖 3. Why It Matters for Crypto
$RENDER — More capable and affordable AI models could support experimentation with autonomous agents, decentralized computing, and AI-powered blockchain applications.
However, a model release does not automatically increase demand for decentralized infrastructure or guarantee gains for AI-related crypto tokens.
🎯 4. What Should Investors Watch?
$NEAR — Focus on real adoption, developer activity, network usage, and actual demand for decentralized compute. AI-related headlines can influence market sentiment, but price movements are uncertain.
Prime Crypto Lab — No Hype, Just Research.
DYOR | Educational Content | Not Financial Advice
#DeepSeek #Binance #DeepSeekReleasesUpdatedAIModel #BinanceSquareTalks
🔍 1. What’s New?
$FET — DeepSeek released V4.1-Flash-1017 on October 17, 2026. The update improves reported coding and agent-task benchmark scores while aiming to reduce task completion time and output length.
📊 2. Key Performance Numbers
According to DeepSeek’s published results:
DeepSWE v1.1: 77.0, up from 74.2.
Terminal-Bench 4.0: 42.3, up from 31.2.
API pricing: Off-peak input at $0.15 and output at $0.60 per million tokens.
License: MIT, allowing broad use subject to its terms.
The API continues to use the deepseek-flash model alias. Benchmark scores are self-reported and can vary with testing frameworks.
🤖 3. Why It Matters for Crypto
$RENDER — More capable and affordable AI models could support experimentation with autonomous agents, decentralized computing, and AI-powered blockchain applications.
However, a model release does not automatically increase demand for decentralized infrastructure or guarantee gains for AI-related crypto tokens.
🎯 4. What Should Investors Watch?
$NEAR — Focus on real adoption, developer activity, network usage, and actual demand for decentralized compute. AI-related headlines can influence market sentiment, but price movements are uncertain.
Prime Crypto Lab — No Hype, Just Research.
DYOR | Educational Content | Not Financial Advice
#DeepSeek #Binance #DeepSeekReleasesUpdatedAIModel #BinanceSquareTalks
362
·
--
#GrayscaleAndRepublicLaunchThreeCryptoFunds
🚀 DeepSeek Upgrades AI Performance Without Raising Prices!
DeepSeek-V4.1-Flash-1017, announced on October 17, improves coding, reasoning, and AI-agent performance. Its DeepSWE v1.1 score rose to 77.0 from 74.2, while API pricing remains unchanged.
📊 My Take: Better efficiency, faster execution, and stable pricing could increase competition among AI providers. However, real-world reliability matters more than benchmarks alone.
💬 Will efficient AI models challenge expensive AI services?
Follow for more AI & tech insights!
#DeepSeek #AI #ArtificialIntelligence #TechNews
#GrayscaleAndRepublicLaunchThreeCryptoFunds
🚀 DeepSeek Upgrades AI Performance Without Raising Prices!
DeepSeek-V4.1-Flash-1017, announced on October 17, improves coding, reasoning, and AI-agent performance. Its DeepSWE v1.1 score rose to 77.0 from 74.2, while API pricing remains unchanged.
📊 My Take: Better efficiency, faster execution, and stable pricing could increase competition among AI providers. However, real-world reliability matters more than benchmarks alone.
💬 Will efficient AI models challenge expensive AI services?
Follow for more AI & tech insights!
#DeepSeek #AI #ArtificialIntelligence #TechNews
🚀 DeepSeek Upgrades AI Performance Without Raising Prices!
DeepSeek-V4.1-Flash-1017, announced on October 17, improves coding, reasoning, and AI-agent performance. Its DeepSWE v1.1 score rose to 77.0 from 74.2, while API pricing remains unchanged.
📊 My Take: Better efficiency, faster execution, and stable pricing could increase competition among AI providers. However, real-world reliability matters more than benchmarks alone.
💬 Will efficient AI models challenge expensive AI services?
Follow for more AI & tech insights!
#DeepSeek #AI #ArtificialIntelligence #TechNews
1
73
·
--
China Released AI Developer Released DeepSeek V4.1-Flash-1017#DeepSeekReleasesUpdatedAIModel
#BinanceSquareTalks #DeepSeek
$BTC $USDT $BTTC
What Traders Should Know About the Latest AI Efficiency Update
The AI sector continues to shift its focus from raw model size to practical efficiency. On October 17, 2026, Chinese AI developer DeepSeek released an update to its Flash series: DeepSeek V4.1-Flash-1017. While the version name suggests a minor revision, the changes carry implications for developers, quantitative analysts, and anyone who relies on AI tools for data processing and automation.
For the crypto community, this release is less about hype and more about the underlying economics of AI compute. Here is a factual breakdown of what was announced and why it matters.
What Was Released?
DeepSeek updated its V4.1 Flash model to the 1017 snapshot (October 17, 2026). The new version is live across the company’s web interface, mobile app, and API. According to DeepSeek’s official documentation, the update delivers a Pareto improvement: better benchmark performance with reduced output length and faster run times. In practical terms, this means lower cost per API call for developers and firms that use the model for tasks such as code generation, data analysis, and agentic automation.
Architecture and Technical Highlights
The V4.1-Flash-1017 is a sparse mixture-of-experts (MoE) model built on DeepSeek’s proprietary Causal Encoder-Decoder architecture. It activates approximately 8 billion parameters on input and 16 billion on output from a 552-billion-parameter backbone. This asymmetric design keeps per-token compute low while maintaining capability.
Key technical features include:
· Native image understanding (multimodal input support)
· Compressed KV caching that reduces cache memory to roughly one-quarter of the previous generation
· Tuning for agentic workloads, meaning the model is optimized for multi-step autonomous tasks
The company also highlighted a capability called “wish-style programming,” which allows users to generate a runnable project from a short prompt with minimal reference material. This is attributed to reinforcement learning based on self-feedback during training.
Benchmark Improvements
DeepSeek published specific benchmark gains to support its efficiency claims. On Terminal-Bench 4.0, a metric for agentic coding, the score increased from 31.2 to 42.3. Other benchmarks showed double-digit percentage improvements:
· ProgramBench: 20.3 → 29.1
· DeepSWE v1.1: 74.2 → 77.0
· Automation-Bench: 54.8 → 60.5
For the crypto industry, improved coding and automation benchmarks are relevant to smart contract auditing, trading algorithm development, and on-chain data analysis. Better performance in these areas can reduce development time and operational overhead.
Pricing and Open-Source Availability
Despite the performance upgrades, DeepSeek has kept API pricing unchanged. The model remains accessible via the deepseek-flash endpoint at $0.10 per million input tokens** and **$0.50 per million output tokens.
The model weights are available on Hugging Face and ModelScope under the MIT License. This open-source approach allows independent developers and decentralized AI projects to fine-tune the model without restrictive licensing fees, which aligns with the transparency and permissionless innovation valued in the crypto ecosystem.
Why This Matters for the Market
From an editorial perspective, this release reinforces two trends shaping the tech-crypto intersection:
1. The efficiency race: Competition is shifting from parameter count to cost-per-task. DeepSeek’s ability to improve benchmarks while reducing output length challenges the cost structures of larger AI providers.
2. Agentic readiness: The specific tuning for agentic tasks suggests that the next wave of AI adoption will involve autonomous agents. In crypto, this could lead to more sophisticated
The Bencmark Increased Not The Pricing.
China Released AI Developer Released DeepSeek V4.1-Flash-1017
#DeepSeekReleasesUpdatedAIModel
#BinanceSquareTalks #DeepSeek
$BTC $USDT $BTTC
What Traders Should Know About the Latest AI Efficiency Update
The AI sector continues to shift its focus from raw model size to practical efficiency. On October 17, 2026, Chinese AI developer DeepSeek released an update to its Flash series: DeepSeek V4.1-Flash-1017. While the version name suggests a minor revision, the changes carry implications for developers, quantitative analysts, and anyone who relies on AI tools for data processing and automation.
For the crypto community, this release is less about hype and more about the underlying economics of AI compute. Here is a factual breakdown of what was announced and why it matters.
What Was Released?
DeepSeek updated its V4.1 Flash model to the 1017 snapshot (October 17, 2026). The new version is live across the company’s web interface, mobile app, and API. According to DeepSeek’s official documentation, the update delivers a Pareto improvement: better benchmark performance with reduced output length and faster run times. In practical terms, this means lower cost per API call for developers and firms that use the model for tasks such as code generation, data analysis, and agentic automation.
Architecture and Technical Highlights
The V4.1-Flash-1017 is a sparse mixture-of-experts (MoE) model built on DeepSeek’s proprietary Causal Encoder-Decoder architecture. It activates approximately 8 billion parameters on input and 16 billion on output from a 552-billion-parameter backbone. This asymmetric design keeps per-token compute low while maintaining capability.
Key technical features include:
· Native image understanding (multimodal input support)
· Compressed KV caching that reduces cache memory to roughly one-quarter of the previous generation
· Tuning for agentic workloads, meaning the model is optimized for multi-step autonomous tasks
The company also highlighted a capability called “wish-style programming,” which allows users to generate a runnable project from a short prompt with minimal reference material. This is attributed to reinforcement learning based on self-feedback during training.
Benchmark Improvements
DeepSeek published specific benchmark gains to support its efficiency claims. On Terminal-Bench 4.0, a metric for agentic coding, the score increased from 31.2 to 42.3. Other benchmarks showed double-digit percentage improvements:
· ProgramBench: 20.3 → 29.1
· DeepSWE v1.1: 74.2 → 77.0
· Automation-Bench: 54.8 → 60.5
For the crypto industry, improved coding and automation benchmarks are relevant to smart contract auditing, trading algorithm development, and on-chain data analysis. Better performance in these areas can reduce development time and operational overhead.
Pricing and Open-Source Availability
Despite the performance upgrades, DeepSeek has kept API pricing unchanged. The model remains accessible via the deepseek-flash endpoint at $0.10 per million input tokens** and **$0.50 per million output tokens.
The model weights are available on Hugging Face and ModelScope under the MIT License. This open-source approach allows independent developers and decentralized AI projects to fine-tune the model without restrictive licensing fees, which aligns with the transparency and permissionless innovation valued in the crypto ecosystem.
Why This Matters for the Market
From an editorial perspective, this release reinforces two trends shaping the tech-crypto intersection:
1. The efficiency race: Competition is shifting from parameter count to cost-per-task. DeepSeek’s ability to improve benchmarks while reducing output length challenges the cost structures of larger AI providers.
2. Agentic readiness: The specific tuning for agentic tasks suggests that the next wave of AI adoption will involve autonomous agents. In crypto, this could lead to more sophisticated
The Bencmark Increased Not The Pricing.
#BinanceSquareTalks #DeepSeek
$BTC $USDT $BTTC
What Traders Should Know About the Latest AI Efficiency Update
The AI sector continues to shift its focus from raw model size to practical efficiency. On October 17, 2026, Chinese AI developer DeepSeek released an update to its Flash series: DeepSeek V4.1-Flash-1017. While the version name suggests a minor revision, the changes carry implications for developers, quantitative analysts, and anyone who relies on AI tools for data processing and automation.
For the crypto community, this release is less about hype and more about the underlying economics of AI compute. Here is a factual breakdown of what was announced and why it matters.
What Was Released?
DeepSeek updated its V4.1 Flash model to the 1017 snapshot (October 17, 2026). The new version is live across the company’s web interface, mobile app, and API. According to DeepSeek’s official documentation, the update delivers a Pareto improvement: better benchmark performance with reduced output length and faster run times. In practical terms, this means lower cost per API call for developers and firms that use the model for tasks such as code generation, data analysis, and agentic automation.
Architecture and Technical Highlights
The V4.1-Flash-1017 is a sparse mixture-of-experts (MoE) model built on DeepSeek’s proprietary Causal Encoder-Decoder architecture. It activates approximately 8 billion parameters on input and 16 billion on output from a 552-billion-parameter backbone. This asymmetric design keeps per-token compute low while maintaining capability.
Key technical features include:
· Native image understanding (multimodal input support)
· Compressed KV caching that reduces cache memory to roughly one-quarter of the previous generation
· Tuning for agentic workloads, meaning the model is optimized for multi-step autonomous tasks
The company also highlighted a capability called “wish-style programming,” which allows users to generate a runnable project from a short prompt with minimal reference material. This is attributed to reinforcement learning based on self-feedback during training.
Benchmark Improvements
DeepSeek published specific benchmark gains to support its efficiency claims. On Terminal-Bench 4.0, a metric for agentic coding, the score increased from 31.2 to 42.3. Other benchmarks showed double-digit percentage improvements:
· ProgramBench: 20.3 → 29.1
· DeepSWE v1.1: 74.2 → 77.0
· Automation-Bench: 54.8 → 60.5
For the crypto industry, improved coding and automation benchmarks are relevant to smart contract auditing, trading algorithm development, and on-chain data analysis. Better performance in these areas can reduce development time and operational overhead.
Pricing and Open-Source Availability
Despite the performance upgrades, DeepSeek has kept API pricing unchanged. The model remains accessible via the deepseek-flash endpoint at $0.10 per million input tokens** and **$0.50 per million output tokens.
The model weights are available on Hugging Face and ModelScope under the MIT License. This open-source approach allows independent developers and decentralized AI projects to fine-tune the model without restrictive licensing fees, which aligns with the transparency and permissionless innovation valued in the crypto ecosystem.
Why This Matters for the Market
From an editorial perspective, this release reinforces two trends shaping the tech-crypto intersection:
1. The efficiency race: Competition is shifting from parameter count to cost-per-task. DeepSeek’s ability to improve benchmarks while reducing output length challenges the cost structures of larger AI providers.
2. Agentic readiness: The specific tuning for agentic tasks suggests that the next wave of AI adoption will involve autonomous agents. In crypto, this could lead to more sophisticated
The Bencmark Increased Not The Pricing.
1
1
36
·
--
#DeepSeekReleasesUpdatedAIModel
🚀 DeepSeek just shipped V4.1-Flash-1017, an updated checkpoint live since Oct 17 under the same deepseek-flash API alias, with pricing unchanged.
📊 Key numbers (DeepSeek-reported):
• DeepSWE v1.1: 77.0 (up from 74.2)
• Terminal-Bench 4.0: 42.3 (up from 31.2)
• Off-peak API: $0.15 / $0.60 per 1M input / output tokens
• Open weights under the MIT license
💡 Why it matters for crypto: cheaper, stronger AI inference strengthens the case for on-chain AI agents and decentralized compute (DePIN). AI-narrative tokens like $FET, $RENDER and $NEAR often react to headlines like this, but those moves are usually sentiment-driven and short-lived.
⚠️ Watch real usage and on-chain metrics, not just the news. Manage your risk.
Not financial advice.
$FET
#DeepSeek #AI #crypto
#DeepSeekReleasesUpdatedAIModel
🚀 DeepSeek just shipped V4.1-Flash-1017, an updated checkpoint live since Oct 17 under the same deepseek-flash API alias, with pricing unchanged.
📊 Key numbers (DeepSeek-reported):
• DeepSWE v1.1: 77.0 (up from 74.2)
• Terminal-Bench 4.0: 42.3 (up from 31.2)
• Off-peak API: $0.15 / $0.60 per 1M input / output tokens
• Open weights under the MIT license
💡 Why it matters for crypto: cheaper, stronger AI inference strengthens the case for on-chain AI agents and decentralized compute (DePIN). AI-narrative tokens like $FET , $RENDER and $NEAR often react to headlines like this, but those moves are usually sentiment-driven and short-lived.
⚠️ Watch real usage and on-chain metrics, not just the news. Manage your risk.
Not financial advice.
$FET
#DeepSeek #AI #crypto
🚀 DeepSeek just shipped V4.1-Flash-1017, an updated checkpoint live since Oct 17 under the same deepseek-flash API alias, with pricing unchanged.
📊 Key numbers (DeepSeek-reported):
• DeepSWE v1.1: 77.0 (up from 74.2)
• Terminal-Bench 4.0: 42.3 (up from 31.2)
• Off-peak API: $0.15 / $0.60 per 1M input / output tokens
• Open weights under the MIT license
💡 Why it matters for crypto: cheaper, stronger AI inference strengthens the case for on-chain AI agents and decentralized compute (DePIN). AI-narrative tokens like $FET , $RENDER and $NEAR often react to headlines like this, but those moves are usually sentiment-driven and short-lived.
⚠️ Watch real usage and on-chain metrics, not just the news. Manage your risk.
Not financial advice.
$FET
#DeepSeek #AI #crypto
1
218
·
--
#deepseekreleasesupdatedaimodel 🚀 DeepSeeks New AI Model Raises the Competition Bar!
🌕 DeepSeeks V4.1-Flash-1017 update is focused on cheaper AI with stronger coding and agent performance. The reported DeepSWE score went up from 74.2 to 77.0. Automation-Bench increased from 54.8 to 60.5. These are company published benchmarks, not proof of real-world superiority.
💡 Why it matters: Better performance per dollar could mean lower AI development costs and more competition among AI providers. DeepSeek also says Search and Fetch APIs cost $0.75, per 1,000 calls.
🌕 For crypto the connection is not direct: cheaper AI tools might help blockchain analytics, trading bots and Web3 development—. This release alone does not prove a positive market signal.
Will low-cost AI change Web3 development. Will premium models still have the advantage?
Personal analysis, not advice.
#DeepSeek #AI #crypto #Web3
$FET $RENDER $NEAR
{spot}(NEARUSDT)
{spot}(RENDERUSDT)
{spot}(FETUSDT)
#deepseekreleasesupdatedaimodel 🚀 DeepSeeks New AI Model Raises the Competition Bar!
🌕 DeepSeeks V4.1-Flash-1017 update is focused on cheaper AI with stronger coding and agent performance. The reported DeepSWE score went up from 74.2 to 77.0. Automation-Bench increased from 54.8 to 60.5. These are company published benchmarks, not proof of real-world superiority.
💡 Why it matters: Better performance per dollar could mean lower AI development costs and more competition among AI providers. DeepSeek also says Search and Fetch APIs cost $0.75, per 1,000 calls.
🌕 For crypto the connection is not direct: cheaper AI tools might help blockchain analytics, trading bots and Web3 development—. This release alone does not prove a positive market signal.
Will low-cost AI change Web3 development. Will premium models still have the advantage?
Personal analysis, not advice.
#DeepSeek #AI #crypto #Web3
$FET $RENDER $NEAR
🌕 DeepSeeks V4.1-Flash-1017 update is focused on cheaper AI with stronger coding and agent performance. The reported DeepSWE score went up from 74.2 to 77.0. Automation-Bench increased from 54.8 to 60.5. These are company published benchmarks, not proof of real-world superiority.
💡 Why it matters: Better performance per dollar could mean lower AI development costs and more competition among AI providers. DeepSeek also says Search and Fetch APIs cost $0.75, per 1,000 calls.
🌕 For crypto the connection is not direct: cheaper AI tools might help blockchain analytics, trading bots and Web3 development—. This release alone does not prove a positive market signal.
Will low-cost AI change Web3 development. Will premium models still have the advantage?
Personal analysis, not advice.
#DeepSeek #AI #crypto #Web3
$FET $RENDER $NEAR
268
·
--
Analysis of the DeepSeek V4.1 Flash-1017 Update: Wish-Driven Programming Leads a New Trend
The DeepSeek V4.1 Flash-1017 update focuses on “wish-driven programming,” with significant improvements to technical metrics. Key-level analysis reveals potential growth areas that investors should watch. #DeepSeek #TechnicalAnalysis
Analysis of the DeepSeek V4.1 Flash-1017 Update: Wish-Driven Programming Leads a New Trend
The DeepSeek V4.1 Flash-1017 update focuses on “wish-driven programming,” with significant improvements to technical metrics. Key-level analysis reveals potential growth areas that investors should watch. #DeepSeek #TechnicalAnalysis
The DeepSeek V4.1 Flash-1017 update focuses on “wish-driven programming,” with significant improvements to technical metrics. Key-level analysis reveals potential growth areas that investors should watch. #DeepSeek #TechnicalAnalysis
11
·
--
One thousand searches for five yuan. Half a fen per search—cheaper than printing a sheet of paper.
On October 17, DeepSeek turned its in-house search into two APIs: Search finds webpages, Fetch retrieves their content. It indexes hundreds of billions of webpages and handles hundreds of millions of requests a day. Then it posted the price: five yuan per thousand calls. One yuan for 200. That’s the number.
The price is just the surface. I compared the announcement with the pricing page, and the striking part was how they bill. Failed calls aren’t charged, timeouts don’t count, and empty search results are free. You’re charged only for a call that successfully returns content.
Look through the cloud providers’ price lists and you’ll see that billing per call is the default: once you send a request, it goes on the bill, whether it succeeds or not. This shifts the risk from the buyer back to the seller.
They can do this because they’re the judge. And there’s only one judge. They produce the search results, and they decide whether a call succeeded.
Think one step further, and you get to the blockchain.
A lot of people think micropayments are held back by transaction fees. Send a fen at a time, and miners won’t bother, so it can’t work. That’s only half true. Fees can come down. These days, an on-chain stablecoin transfer costs just a few thousandths of a yuan—not much more than this search API. The thing you can’t bring down is the counter.
There’s no service provider on-chain, just a bunch of nodes that each recalculate everything independently. For a transaction to count, every node has to run the same rules and get the same result. No one has the authority to sign off on behalf of everyone else, and no one can be authorized to keep the books for another person.
So there’s no such thing as “the 50,000th call” on-chain. Who gets to decide? No one has the authority to count it, and no one accepts someone else’s count.
Failed transactions still consume gas. Same principle. The work was dispatched, the nodes processed it, the result didn’t go through, but the bill still has to be settled. On-chain, you can’t charge only for success; you can only deduct first and refund later.
The crypto world has been talking about micropayments for nearly a decade. Lightning Network, state channels, prepaid balances—they’ve all been circling the same problem: how do you count usage accurately when nobody is keeping the books? So far, the thing that’s actually turned half-a-fen-per-call into a business is a centralized API. A little ironic. That’s it.
Midnight on November 1 is the cutoff. Before then, search calls made server-side through Harness, Claude Code, and Codex are free. After that, they’ll be billed per call.
The announcement only gives the unit price. It doesn’t say how calls already being made these past few days will be counted, or whether they’ll be settled by the day or by usage.
The connection to crypto isn’t really about the price; that’s just the hook. When AI starts searching, fetching, and placing orders on its own, payments between agents will be broken down into tiny amounts—just a few thousandths at a time. Who will keep the books then? Whoever keeps the books is the clearing party. If blockchains want to handle this business, being cheap isn’t enough. They first need to make clear how the counter works.
Take a look at your API bill. Of the calls you’ve been charged for, how many returned nothing at all?
$TAO $USDC $BTC
#DeepSeek #AI搜索 #微支付 #Agent
One thousand searches for five yuan. Half a fen per search—cheaper than printing a sheet of paper.
On October 17, DeepSeek turned its in-house search into two APIs: Search finds webpages, Fetch retrieves their content. It indexes hundreds of billions of webpages and handles hundreds of millions of requests a day. Then it posted the price: five yuan per thousand calls. One yuan for 200. That’s the number.
The price is just the surface. I compared the announcement with the pricing page, and the striking part was how they bill. Failed calls aren’t charged, timeouts don’t count, and empty search results are free. You’re charged only for a call that successfully returns content.
Look through the cloud providers’ price lists and you’ll see that billing per call is the default: once you send a request, it goes on the bill, whether it succeeds or not. This shifts the risk from the buyer back to the seller.
They can do this because they’re the judge. And there’s only one judge. They produce the search results, and they decide whether a call succeeded.
Think one step further, and you get to the blockchain.
A lot of people think micropayments are held back by transaction fees. Send a fen at a time, and miners won’t bother, so it can’t work. That’s only half true. Fees can come down. These days, an on-chain stablecoin transfer costs just a few thousandths of a yuan—not much more than this search API. The thing you can’t bring down is the counter.
There’s no service provider on-chain, just a bunch of nodes that each recalculate everything independently. For a transaction to count, every node has to run the same rules and get the same result. No one has the authority to sign off on behalf of everyone else, and no one can be authorized to keep the books for another person.
So there’s no such thing as “the 50,000th call” on-chain. Who gets to decide? No one has the authority to count it, and no one accepts someone else’s count.
Failed transactions still consume gas. Same principle. The work was dispatched, the nodes processed it, the result didn’t go through, but the bill still has to be settled. On-chain, you can’t charge only for success; you can only deduct first and refund later.
The crypto world has been talking about micropayments for nearly a decade. Lightning Network, state channels, prepaid balances—they’ve all been circling the same problem: how do you count usage accurately when nobody is keeping the books? So far, the thing that’s actually turned half-a-fen-per-call into a business is a centralized API. A little ironic. That’s it.
Midnight on November 1 is the cutoff. Before then, search calls made server-side through Harness, Claude Code, and Codex are free. After that, they’ll be billed per call.
The announcement only gives the unit price. It doesn’t say how calls already being made these past few days will be counted, or whether they’ll be settled by the day or by usage.
The connection to crypto isn’t really about the price; that’s just the hook. When AI starts searching, fetching, and placing orders on its own, payments between agents will be broken down into tiny amounts—just a few thousandths at a time. Who will keep the books then? Whoever keeps the books is the clearing party. If blockchains want to handle this business, being cheap isn’t enough. They first need to make clear how the counter works.
Take a look at your API bill. Of the calls you’ve been charged for, how many returned nothing at all?
$TAO $USDC $BTC
#DeepSeek #AI搜索 #微支付 #Agent
On October 17, DeepSeek turned its in-house search into two APIs: Search finds webpages, Fetch retrieves their content. It indexes hundreds of billions of webpages and handles hundreds of millions of requests a day. Then it posted the price: five yuan per thousand calls. One yuan for 200. That’s the number.
The price is just the surface. I compared the announcement with the pricing page, and the striking part was how they bill. Failed calls aren’t charged, timeouts don’t count, and empty search results are free. You’re charged only for a call that successfully returns content.
Look through the cloud providers’ price lists and you’ll see that billing per call is the default: once you send a request, it goes on the bill, whether it succeeds or not. This shifts the risk from the buyer back to the seller.
They can do this because they’re the judge. And there’s only one judge. They produce the search results, and they decide whether a call succeeded.
Think one step further, and you get to the blockchain.
A lot of people think micropayments are held back by transaction fees. Send a fen at a time, and miners won’t bother, so it can’t work. That’s only half true. Fees can come down. These days, an on-chain stablecoin transfer costs just a few thousandths of a yuan—not much more than this search API. The thing you can’t bring down is the counter.
There’s no service provider on-chain, just a bunch of nodes that each recalculate everything independently. For a transaction to count, every node has to run the same rules and get the same result. No one has the authority to sign off on behalf of everyone else, and no one can be authorized to keep the books for another person.
So there’s no such thing as “the 50,000th call” on-chain. Who gets to decide? No one has the authority to count it, and no one accepts someone else’s count.
Failed transactions still consume gas. Same principle. The work was dispatched, the nodes processed it, the result didn’t go through, but the bill still has to be settled. On-chain, you can’t charge only for success; you can only deduct first and refund later.
The crypto world has been talking about micropayments for nearly a decade. Lightning Network, state channels, prepaid balances—they’ve all been circling the same problem: how do you count usage accurately when nobody is keeping the books? So far, the thing that’s actually turned half-a-fen-per-call into a business is a centralized API. A little ironic. That’s it.
Midnight on November 1 is the cutoff. Before then, search calls made server-side through Harness, Claude Code, and Codex are free. After that, they’ll be billed per call.
The announcement only gives the unit price. It doesn’t say how calls already being made these past few days will be counted, or whether they’ll be settled by the day or by usage.
The connection to crypto isn’t really about the price; that’s just the hook. When AI starts searching, fetching, and placing orders on its own, payments between agents will be broken down into tiny amounts—just a few thousandths at a time. Who will keep the books then? Whoever keeps the books is the clearing party. If blockchains want to handle this business, being cheap isn’t enough. They first need to make clear how the counter works.
Take a look at your API bill. Of the calls you’ve been charged for, how many returned nothing at all?
$TAO $USDC $BTC
#DeepSeek #AI搜索 #微支付 #Agent
167
#DeepSeekReleasesUpdatedAIModel
The AI industry never stands still, and DeepSeek is once again drawing attention with its updated AI model.
In my opinion, every major AI update brings new opportunities, stronger competition, and fresh possibilities for the future of technology. What matters most is not just how powerful a model is, but how efficiently it performs, how useful it is in real-world applications, and whether it makes AI more accessible to everyone.
My Market View:
I believe continued AI innovation could create new opportunities across the technology and crypto sectors. However, not every AI-related crypto project will benefit equally, so research and risk management remain essential.
The big question is: Can DeepSeek raise the bar again and challenge the biggest names in AI? What do you think? Will DeepSeek's latest update change the AI competition in 2026?
Share your thoughts below!
#DeepSeek #AI #ArtificialIntelligence
#DeepSeekReleasesUpdatedAIModel
The AI industry never stands still, and DeepSeek is once again drawing attention with its updated AI model.
In my opinion, every major AI update brings new opportunities, stronger competition, and fresh possibilities for the future of technology. What matters most is not just how powerful a model is, but how efficiently it performs, how useful it is in real-world applications, and whether it makes AI more accessible to everyone.
My Market View:
I believe continued AI innovation could create new opportunities across the technology and crypto sectors. However, not every AI-related crypto project will benefit equally, so research and risk management remain essential.
The big question is: Can DeepSeek raise the bar again and challenge the biggest names in AI? What do you think? Will DeepSeek's latest update change the AI competition in 2026?
Share your thoughts below!
#DeepSeek #AI #ArtificialIntelligence
The AI industry never stands still, and DeepSeek is once again drawing attention with its updated AI model.
In my opinion, every major AI update brings new opportunities, stronger competition, and fresh possibilities for the future of technology. What matters most is not just how powerful a model is, but how efficiently it performs, how useful it is in real-world applications, and whether it makes AI more accessible to everyone.
My Market View:
I believe continued AI innovation could create new opportunities across the technology and crypto sectors. However, not every AI-related crypto project will benefit equally, so research and risk management remain essential.
The big question is: Can DeepSeek raise the bar again and challenge the biggest names in AI? What do you think? Will DeepSeek's latest update change the AI competition in 2026?
Share your thoughts below!
#DeepSeek #AI #ArtificialIntelligence
135
·
--
Officials called it a “minor update.” But after the community took it apart, they found it was clearly a major overhaul.
On October 17, DeepSeek released V4.1-Flash-1017. The model card said it would “shorten task time and reduce output length.” The API name and pricing stayed the same, and the weights were open-sourced under MIT.
One developer compared the new weights with the September version layer by layer and reportedly found that about 40% of routed experts, 30% of memory, and 50% of the vision-tower expert tensors had been rewritten. Another developer ran quantization on two DGX Sparks and reportedly got 56.8 tokens/second for text and 92.2 tokens/second for code with the TensorFold engine—nearly twice vLLM’s speed. The architecture hadn’t changed, but the scale of the changes was on the level of a “major version.”
The price war has entered a new dimension, too: according to an engineer’s tests, DeepSeek completed a real coding task for only about 4.5 cents, but burned through roughly 550,000 tokens (Opus used only 30,000). Tokens are so cheap you can “waste” them and still pay less. OpenRouter data shows it processed about 38.7T tokens over the past 7 days—three times as many as the runner-up.
The timing is intriguing: the day before the MIT-licensed weights were released (October 16), the Ministry of Commerce and the Ministry of Science and Technology published a draft export-control proposal for public comment. It proposed adding 40 technologies to the control list, 23 of them AI-related. Models with compute of 10²⁶ FLOPs, more than 2 trillion parameters, or a global top-ten ranking would have their weights, source code, and architecture brought under controls. APIs and SaaS would be unaffected, and already open-sourced models would not be covered retroactively. The public comment period runs until November 15, as confirmed by multiple media outlets.
I think this was the week the AI narrative of “openness vs. control” blew its opening whistle: for the first time, model weights were written into an export-control list. In the short term, the cheaper models get, the less it costs to run AI agents at scale—and the more fuel there’ll be for on-chain AI narratives (such as $TAO $RENDER).
There’s still the mystery of V4.1-Pro: reportedly, some accounts spotted its hidden model name in early access as far back as late September. Prediction markets put the odds of a release before the end of October at around 16%. One thing to watch: how the draft is finalized on November 15.
This is not investment advice. DYOR; trading involves risk.
#DeepSeekReleasesUpdatedAIModel
#DeepSeek #AI
Officials called it a “minor update.” But after the community took it apart, they found it was clearly a major overhaul.
On October 17, DeepSeek released V4.1-Flash-1017. The model card said it would “shorten task time and reduce output length.” The API name and pricing stayed the same, and the weights were open-sourced under MIT.
One developer compared the new weights with the September version layer by layer and reportedly found that about 40% of routed experts, 30% of memory, and 50% of the vision-tower expert tensors had been rewritten. Another developer ran quantization on two DGX Sparks and reportedly got 56.8 tokens/second for text and 92.2 tokens/second for code with the TensorFold engine—nearly twice vLLM’s speed. The architecture hadn’t changed, but the scale of the changes was on the level of a “major version.”
The price war has entered a new dimension, too: according to an engineer’s tests, DeepSeek completed a real coding task for only about 4.5 cents, but burned through roughly 550,000 tokens (Opus used only 30,000). Tokens are so cheap you can “waste” them and still pay less. OpenRouter data shows it processed about 38.7T tokens over the past 7 days—three times as many as the runner-up.
The timing is intriguing: the day before the MIT-licensed weights were released (October 16), the Ministry of Commerce and the Ministry of Science and Technology published a draft export-control proposal for public comment. It proposed adding 40 technologies to the control list, 23 of them AI-related. Models with compute of 10²⁶ FLOPs, more than 2 trillion parameters, or a global top-ten ranking would have their weights, source code, and architecture brought under controls. APIs and SaaS would be unaffected, and already open-sourced models would not be covered retroactively. The public comment period runs until November 15, as confirmed by multiple media outlets.
I think this was the week the AI narrative of “openness vs. control” blew its opening whistle: for the first time, model weights were written into an export-control list. In the short term, the cheaper models get, the less it costs to run AI agents at scale—and the more fuel there’ll be for on-chain AI narratives (such as $TAO $RENDER ).
There’s still the mystery of V4.1-Pro: reportedly, some accounts spotted its hidden model name in early access as far back as late September. Prediction markets put the odds of a release before the end of October at around 16%. One thing to watch: how the draft is finalized on November 15.
This is not investment advice. DYOR; trading involves risk.
#DeepSeekReleasesUpdatedAIModel
#DeepSeek #AI
On October 17, DeepSeek released V4.1-Flash-1017. The model card said it would “shorten task time and reduce output length.” The API name and pricing stayed the same, and the weights were open-sourced under MIT.
One developer compared the new weights with the September version layer by layer and reportedly found that about 40% of routed experts, 30% of memory, and 50% of the vision-tower expert tensors had been rewritten. Another developer ran quantization on two DGX Sparks and reportedly got 56.8 tokens/second for text and 92.2 tokens/second for code with the TensorFold engine—nearly twice vLLM’s speed. The architecture hadn’t changed, but the scale of the changes was on the level of a “major version.”
The price war has entered a new dimension, too: according to an engineer’s tests, DeepSeek completed a real coding task for only about 4.5 cents, but burned through roughly 550,000 tokens (Opus used only 30,000). Tokens are so cheap you can “waste” them and still pay less. OpenRouter data shows it processed about 38.7T tokens over the past 7 days—three times as many as the runner-up.
The timing is intriguing: the day before the MIT-licensed weights were released (October 16), the Ministry of Commerce and the Ministry of Science and Technology published a draft export-control proposal for public comment. It proposed adding 40 technologies to the control list, 23 of them AI-related. Models with compute of 10²⁶ FLOPs, more than 2 trillion parameters, or a global top-ten ranking would have their weights, source code, and architecture brought under controls. APIs and SaaS would be unaffected, and already open-sourced models would not be covered retroactively. The public comment period runs until November 15, as confirmed by multiple media outlets.
I think this was the week the AI narrative of “openness vs. control” blew its opening whistle: for the first time, model weights were written into an export-control list. In the short term, the cheaper models get, the less it costs to run AI agents at scale—and the more fuel there’ll be for on-chain AI narratives (such as $TAO $RENDER ).
There’s still the mystery of V4.1-Pro: reportedly, some accounts spotted its hidden model name in early access as far back as late September. Prediction markets put the odds of a release before the end of October at around 16%. One thing to watch: how the draft is finalized on November 15.
This is not investment advice. DYOR; trading involves risk.
#DeepSeekReleasesUpdatedAIModel
#DeepSeek #AI
ABO3ZAM:
تحليل ممتاز يا وحش، فعلاً خفض التكاليف بيحرك سيولة ضخمة للقطاع، راقب TAO عند مناطق الدعم الحالية، إذا ثبت فوقها بنشوف انفجار سعري قريب 🎯
5
93
·
--
One ruler, eighteen marks, and every one belongs to someone else.
On the morning of October 18, someone fed DeepSeek’s newly launched model into a tool called ModelTrace. The result was a single line: 84.66 similarity to GPT-6 Astra. Within half a day, that number had climbed to seventh place on Zhihu’s trending list, and the same post scored 744 points on the Hacker News front page.
The first verdict in the comments was four words: irrefutable proof of distillation.
Two days earlier, the company had just compensated users for a five-hour outage, refunding credits equal to their full seven-day usage. It had won back a little goodwill. For two days. Then, less than a day after the new model launched, the whole thing was dragged back into question.
I went and read the tool’s documentation. Its reference library contains eighteen candidate models, all from the GPT and Claude families. Not a single version of DeepSeek is in there. The documentation itself says something even more damning: models that aren’t in the library will still be assigned the label of an existing candidate, so the result can’t be used to establish a model’s identity.
Eighteen slots, not one with your name on it. You walk in, and it has to point you to your closest relative. That’s the whole logic.
Someone actually tried it. They generated random numbers using pure Python, fed them in, and got a result saying they were most similar to GPT-5.5. So random numbers have a lineage too?
Some people went by feel, saying the new model’s coding style had the same vibe as Astra’s. Feelings don’t count as evidence.
Then look at how the samples were collected. The GPT samples came from a Codex subscription; the Claude samples came from a third-party service. Use samples collected under two different sets of conditions as your ruler, and the distance you measure can only tell you which model on this list is the closest. As for how far away it is from anything outside the list, the tool was never designed to answer that.
There’s evidence pointing the other way, too. In real-world tests, the new model outputs 244 tok/s, with some requests nearing 300. Which version of Astra’s hosted service has ever run at that speed? The weights are also on Hugging Face under an MIT license, so anyone can download them and run the model themselves.
Anyone who’s used an API proxy service should find this scenario familiar. You pay for A, but what comes back is B, and there’s nothing unusual on the bill. Hosting providers, over-the-counter quotes, quant APIs—the same problem crops up everywhere. Anywhere outside the ledger, verifying what you’re getting depends on the other party cooperating. They tell you what it is, and you have to take their word for it. The means to check aren’t in your hands. If something goes wrong and you ask questions afterward, the most you’re likely to get is, “We’ve verified it.” Few will take responsibility.
The things that can be self-verified on-chain are pretty basic: a signature, a block height. Who signed, and when—it can’t be changed. But this approach has blind spots, too. Very few people actually check things one by one; most rely on conclusions someone else has already verified. Doing it yourself comes with a cost. That’s the problem.
After thinking it over, I reckon the thing worth remembering most from this controversy is the ruler’s scale. The tool can measure similarity, but not lineage. Those are two different things. If you treat similarity as proof of lineage, that’s on the people using the tool, not the tool itself.
As of this evening, DeepSeek still hasn’t responded, and it has never published its training data item by item. Put those two facts together, and no one can say for sure how the cause-and-effect chain runs.
The scores are real. The benchmark results have improved, and the per-task price has been cut from 3.3 to 1.3. The company has published charts and figures for both. Lineage is another matter. It can’t be measured this way. We’ll have to wait for the infrastructure for third-party re-testing to be built; it doesn’t exist yet.
$TAO $FET $BTC
#DeepSeek #AI模型 #开源 #crypto
One ruler, eighteen marks, and every one belongs to someone else.
On the morning of October 18, someone fed DeepSeek’s newly launched model into a tool called ModelTrace. The result was a single line: 84.66 similarity to GPT-6 Astra. Within half a day, that number had climbed to seventh place on Zhihu’s trending list, and the same post scored 744 points on the Hacker News front page.
The first verdict in the comments was four words: irrefutable proof of distillation.
Two days earlier, the company had just compensated users for a five-hour outage, refunding credits equal to their full seven-day usage. It had won back a little goodwill. For two days. Then, less than a day after the new model launched, the whole thing was dragged back into question.
I went and read the tool’s documentation. Its reference library contains eighteen candidate models, all from the GPT and Claude families. Not a single version of DeepSeek is in there. The documentation itself says something even more damning: models that aren’t in the library will still be assigned the label of an existing candidate, so the result can’t be used to establish a model’s identity.
Eighteen slots, not one with your name on it. You walk in, and it has to point you to your closest relative. That’s the whole logic.
Someone actually tried it. They generated random numbers using pure Python, fed them in, and got a result saying they were most similar to GPT-5.5. So random numbers have a lineage too?
Some people went by feel, saying the new model’s coding style had the same vibe as Astra’s. Feelings don’t count as evidence.
Then look at how the samples were collected. The GPT samples came from a Codex subscription; the Claude samples came from a third-party service. Use samples collected under two different sets of conditions as your ruler, and the distance you measure can only tell you which model on this list is the closest. As for how far away it is from anything outside the list, the tool was never designed to answer that.
There’s evidence pointing the other way, too. In real-world tests, the new model outputs 244 tok/s, with some requests nearing 300. Which version of Astra’s hosted service has ever run at that speed? The weights are also on Hugging Face under an MIT license, so anyone can download them and run the model themselves.
Anyone who’s used an API proxy service should find this scenario familiar. You pay for A, but what comes back is B, and there’s nothing unusual on the bill. Hosting providers, over-the-counter quotes, quant APIs—the same problem crops up everywhere. Anywhere outside the ledger, verifying what you’re getting depends on the other party cooperating. They tell you what it is, and you have to take their word for it. The means to check aren’t in your hands. If something goes wrong and you ask questions afterward, the most you’re likely to get is, “We’ve verified it.” Few will take responsibility.
The things that can be self-verified on-chain are pretty basic: a signature, a block height. Who signed, and when—it can’t be changed. But this approach has blind spots, too. Very few people actually check things one by one; most rely on conclusions someone else has already verified. Doing it yourself comes with a cost. That’s the problem.
After thinking it over, I reckon the thing worth remembering most from this controversy is the ruler’s scale. The tool can measure similarity, but not lineage. Those are two different things. If you treat similarity as proof of lineage, that’s on the people using the tool, not the tool itself.
As of this evening, DeepSeek still hasn’t responded, and it has never published its training data item by item. Put those two facts together, and no one can say for sure how the cause-and-effect chain runs.
The scores are real. The benchmark results have improved, and the per-task price has been cut from 3.3 to 1.3. The company has published charts and figures for both. Lineage is another matter. It can’t be measured this way. We’ll have to wait for the infrastructure for third-party re-testing to be built; it doesn’t exist yet.
$TAO $FET $BTC
#DeepSeek #AI模型 #开源 #crypto
On the morning of October 18, someone fed DeepSeek’s newly launched model into a tool called ModelTrace. The result was a single line: 84.66 similarity to GPT-6 Astra. Within half a day, that number had climbed to seventh place on Zhihu’s trending list, and the same post scored 744 points on the Hacker News front page.
The first verdict in the comments was four words: irrefutable proof of distillation.
Two days earlier, the company had just compensated users for a five-hour outage, refunding credits equal to their full seven-day usage. It had won back a little goodwill. For two days. Then, less than a day after the new model launched, the whole thing was dragged back into question.
I went and read the tool’s documentation. Its reference library contains eighteen candidate models, all from the GPT and Claude families. Not a single version of DeepSeek is in there. The documentation itself says something even more damning: models that aren’t in the library will still be assigned the label of an existing candidate, so the result can’t be used to establish a model’s identity.
Eighteen slots, not one with your name on it. You walk in, and it has to point you to your closest relative. That’s the whole logic.
Someone actually tried it. They generated random numbers using pure Python, fed them in, and got a result saying they were most similar to GPT-5.5. So random numbers have a lineage too?
Some people went by feel, saying the new model’s coding style had the same vibe as Astra’s. Feelings don’t count as evidence.
Then look at how the samples were collected. The GPT samples came from a Codex subscription; the Claude samples came from a third-party service. Use samples collected under two different sets of conditions as your ruler, and the distance you measure can only tell you which model on this list is the closest. As for how far away it is from anything outside the list, the tool was never designed to answer that.
There’s evidence pointing the other way, too. In real-world tests, the new model outputs 244 tok/s, with some requests nearing 300. Which version of Astra’s hosted service has ever run at that speed? The weights are also on Hugging Face under an MIT license, so anyone can download them and run the model themselves.
Anyone who’s used an API proxy service should find this scenario familiar. You pay for A, but what comes back is B, and there’s nothing unusual on the bill. Hosting providers, over-the-counter quotes, quant APIs—the same problem crops up everywhere. Anywhere outside the ledger, verifying what you’re getting depends on the other party cooperating. They tell you what it is, and you have to take their word for it. The means to check aren’t in your hands. If something goes wrong and you ask questions afterward, the most you’re likely to get is, “We’ve verified it.” Few will take responsibility.
The things that can be self-verified on-chain are pretty basic: a signature, a block height. Who signed, and when—it can’t be changed. But this approach has blind spots, too. Very few people actually check things one by one; most rely on conclusions someone else has already verified. Doing it yourself comes with a cost. That’s the problem.
After thinking it over, I reckon the thing worth remembering most from this controversy is the ruler’s scale. The tool can measure similarity, but not lineage. Those are two different things. If you treat similarity as proof of lineage, that’s on the people using the tool, not the tool itself.
As of this evening, DeepSeek still hasn’t responded, and it has never published its training data item by item. Put those two facts together, and no one can say for sure how the cause-and-effect chain runs.
The scores are real. The benchmark results have improved, and the per-task price has been cut from 3.3 to 1.3. The company has published charts and figures for both. Lineage is another matter. It can’t be measured this way. We’ll have to wait for the infrastructure for third-party re-testing to be built; it doesn’t exist yet.
$TAO $FET $BTC
#DeepSeek #AI模型 #开源 #crypto
226
Verified
Article
DeepSeek Launches Its Updated AI Model! An Open-Source Revolution and Impact on the Token#deepseekreleasesupdatedaimodel
AI on blockchain networks: The $30 million investment by the Sui Foundation to develop its decentralized data network stands out. The network aims to accelerate processing, optimize on-chain storage, and reduce gas costs in AI and Web3 gaming dApps.
What’s New in DeepSeek’s Updated Model? ⚡
The #DeepSeek model update introduces significant architectural advances:
Extreme Inference Efficiency: Optimized processing capabilities that dramatically reduce GPU memory consumption and server infrastructure costs.
DeepSeek Launches Its Updated AI Model! An Open-Source Revolution and Impact on the Token
#deepseekreleasesupdatedaimodel
AI on blockchain networks: The $30 million investment by the Sui Foundation to develop its decentralized data network stands out. The network aims to accelerate processing, optimize on-chain storage, and reduce gas costs in AI and Web3 gaming dApps.
What’s New in DeepSeek’s Updated Model? ⚡
The #DeepSeek model update introduces significant architectural advances:
Extreme Inference Efficiency: Optimized processing capabilities that dramatically reduce GPU memory consumption and server infrastructure costs.
AI on blockchain networks: The $30 million investment by the Sui Foundation to develop its decentralized data network stands out. The network aims to accelerate processing, optimize on-chain storage, and reduce gas costs in AI and Web3 gaming dApps.
What’s New in DeepSeek’s Updated Model? ⚡
The #DeepSeek model update introduces significant architectural advances:
Extreme Inference Efficiency: Optimized processing capabilities that dramatically reduce GPU memory consumption and server infrastructure costs.
ABO3ZAM:
طرح ممتاز يا بطل، SUI مشروع قوي لكن لا تنسى تأمين أرباحك عند كل مقاومة وإدارة محفظتك بذكاء، السوق لا يعترف بالعواطف. 🤝
2
2.3k
Log in to explore more content
Join global crypto users on Binance Square
⚡️ Get latest and useful information about crypto.
💬 Trusted by the world’s largest crypto exchange.
👍 Discover real insights from verified creators.
Email / Phone number
Trending Topics
1
#BinancePick
230,590 views
46,172 Discussing
2
#TeslaSlides5%PreMarket
71,309 views
848 Discussing
3
#KOSPISinks3.52%ToLowestCloseSinceApril
17,565 views
501 Discussing
4
#TeslaAndNvidiaToInvest$2.8BInSamsungElectroMechanics
1,721 views
58 Discussing
5
#ChinaSeeksToSeize61800BTC
40,941 views
624 Discussing
6
#BitcoinETFsPullIn$1.08BInThreeDays
2,736 views
56 Discussing
7
#EarningsSeason
2.6M views
12,687 Discussing
8
#EthereumSurpasses$2500
45,632 views
263 Discussing
9
#BitcoinDropsBelow$84K
20,408 views
98 Discussing
10
#FirstUNIBlockTradeViaRegulatedTradFiChannelCompleted
2,866 views
76 Discussing