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FC_Long_Bull
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FC_Long_Bull

a crypto enthusiast who stumbled into the wild world of blockchain. Like many beginner's
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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.
Is Bitcoin Approaching a Buy Zone? by Shaurya Malwa CoinDeskAccording to a fresh @CoinDesk report by Editor Shaurya Malwa, Bitcoin may be inching toward historically significant accumulation territory — but we're not quite there yet. BTC currently trades about 21% above its realized price, meaning most holders are still in profit. That gap has compressed rapidly from a ~120% premium in late 2024 down to 21% now — a fast-moving compression phase, but not a completed market reset. (CoinDesk) On-chain indicators show no capitulation event and no broad institutional surge in demand. This suggests a deeper pullback toward the realized price near $54,000 may be needed to truly mirror past cycle bottoms. (CoinDesk) In short: Bitcoin is closer to its "buy zone" than it's been in three years, but the capitulation that typically precedes those bottoms hasn't happened yet. By Shaurya Malwa CoinDesk $BTC #BitcoinPrices # {spot}(BTCUSDT)

Is Bitcoin Approaching a Buy Zone? by Shaurya Malwa CoinDesk

According to a fresh @CoinDesk report by Editor Shaurya Malwa, Bitcoin may be inching toward historically significant accumulation territory — but we're not quite there yet.
BTC currently trades about 21% above its realized price, meaning most holders are still in profit. That gap has compressed rapidly from a ~120% premium in late 2024 down to 21% now — a fast-moving compression phase, but not a completed market reset. (CoinDesk)
On-chain indicators show no capitulation event and no broad institutional surge in demand. This suggests a deeper pullback toward the realized price near $54,000 may be needed to truly mirror past cycle bottoms. (CoinDesk)
In short: Bitcoin is closer to its "buy zone" than it's been in three years, but the capitulation that typically precedes those bottoms hasn't happened yet.
By Shaurya Malwa CoinDesk
$BTC #BitcoinPrices #
Done
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AI Researcher_01
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Bitroot曹哥
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Bullish
科技向善,Bitroot不止于快!并行公链架构兼容EVM生态,更联动AI技术打造智能计算底座,从跨境支付提速降费到实体资产数字化,用技术赋能实体经济。透明可溯的特性+持续优化的协议,让每一次创新都扎根实用,和千万开发者一起,解锁区块链更多可能#巨鲸动向 #ETH走势分析 #加密市场观察
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just comments on:morning
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不好说
不好说
慢就是快227
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Bullish
这些人工智能,你在用那个人工人工智能炒币比特币?炒狗狗币,炒以太坊?#DOGE $SHIB
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Whale韭阴针鲸0628
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穿越回2009年,手把手拿下创世期比特币
Follow ➕ like and forwardتابع ➕ أعجبني وأرسل
2009年的比特币,还只是中本聪论文里的“数字实验品”,全网没几个玩家,没有交易所、没有价格锚点,买币的核心不是“交易”,而是挖币,储存则是和“代码、私钥”死磕的原始操作。
第一步:搞定比特币的获取——挖矿是唯一途径
2009年没有任何比特币交易所,想买币都找不到渠道,普通人获取比特币的唯一方式就是挖矿,而且当时挖矿门槛极低,一台普通家用电脑就能搞定。
1. 准备工具
找一台能正常运行的电脑(Windows或Linux系统都可以),不需要高端显卡,2009年的主流配置(比如酷睿2处理器、2G内存)完全够用。
2. 下载挖矿程序
打开浏览器,访问比特币官方网站(bitcoin.org),下载比特币核心客户端(Bitcoin Core) 的早期版本。这个客户端既是钱包,也是挖矿工具,还能同步整个比特币区块链。
3. 开始挖矿
安装客户端后,启动程序并同步区块链数据——2009年的区块链刚起步,数据量极小,几分钟就能同步完成。
客户端会自动生成一个挖矿节点,你的电脑会参与比特币网络的记账和哈希运算。当时的挖矿难度几乎为零,平均每10分钟左右就能挖出一个区块,每个区块奖励50枚比特币(后来每4年减半)。
挖到币后,比特币会自动存入客户端内置的钱包地址里,这就是你第一批“创世级”比特币。
第二步:储存比特币——守住私钥就是守住一切
2009年的比特币储存,没有纸钱包教程、没有硬件钱包,全靠手动操作,核心原则只有一个:备份好私钥,远离联网风险。
1. 找到私钥文件
比特币核心客户端的私钥,以文件形式储存在电脑的指定目录里:
◦ Windows系统:C:\Users\[你的用户名]\AppData\Roaming\Bitcoin\wallet.dat
◦ Linux系统:~/.bitcoin/wallet.dat
这个wallet.dat文件就是你的“币仓”,里面包含了所有钱包地址的私钥信息。
2. 安全备份私钥文件
◦ 首先断开电脑网络,防止黑客远程窃取文件。
◦ 复制wallet.dat文件,备份到多个安全载体上:比如加密U盘、移动硬盘,甚至可以把文件内容用记事本打开,手抄关键字符(不建议,容易出错)。
◦ 备份的载体要分开存放,比如一个藏在家里保险柜,一个放在隐秘的亲友处,避免丢失或损坏。
3. 绝对禁忌
◦ 不要把wallet.dat文件传给任何人,也不要上传到网盘、邮箱等联网平台。
◦ 不要随意重装系统或格式化硬盘,否则没备份的话,比特币就彻底消失了。
◦ 2009年没有“找回密码”的功能,一旦忘记客户端的密码,或者丢失私钥文件,币就再也找不回来了。
第三步:如果想“买币”——只能找极客私下交易
2009年的比特币没有公开价格,直到2010年5月才出现第一笔比特币实物交易(1万枚比特币买了两个披萨)。如果实在不想挖矿,只能去早期比特币社区碰运气。
1. 找到交易圈子
注册比特币论坛(Bitcoin Forum) 账号,这是当时唯一的比特币玩家聚集地,里面全是技术极客。
2. 发布交易帖
发帖说明“想用现金购买比特币”,注明你的所在地和支付方式(比如银行转账、现金当面交易)。2009年的比特币几乎没有价值,大概率能以极低的价格(甚至几美分一枚)收到少量比特币。
3. 交易与提币
和卖家谈妥后,对方会用他的客户端给你发送比特币。你只需要把自己客户端的钱包地址发给对方,对方确认转账后,等待区块确认(几分钟),币就会到你的钱包里。
穿越者的终极提醒
1. 2009年的比特币没人关注,别指望靠它一夜暴富,耐心持有才是王道。
2. 备份wallet.dat文件时,一定要多备几份,哪怕过了十几年,这个文件依然能导入最新版比特币核心客户端,找回你的币。
3. 别轻易向身边人炫耀你在“挖数字金币”,2009年的人大概率会觉得你在搞传销或电脑病毒。
#BTC $BTC
victory
victory
Mr-Bullish
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54 years of Bangladesh.
Happy Victory Day Bangladesh 🇧🇩
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