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ソースチャンネル @NewLearnerChannel · Post #14708 · 9月9日

#APPLE 🍎Apple 2025 秋季发布会看些啥?—— 自留地 の 前瞻盘点 明天凌晨,一年一度的阿果秋季春晚又要来了。老规矩,结合此前种种爆料和信息,我们一起来盘点一下今年可能的看点 📱iPhone 17 系列 - A19 系列处理器 - 推出全新 Air 系列,主打 5.5mm 超薄机身,配备「药丸」后摄模组,预计搭载 12GB RAM、Apple C1 调制解调器和 6.6 英寸显示屏 - Air 首发或暂无国行,因其大概率仅支持 eSIM,需等 eSIM 政策落地 - Pro 系列将采用半玻璃半铝的设计,其中玻璃区域用于 MagSafe 充电,后背还将采用巨大摄影头模组 - Pro 系列有望搭载 A19 Pro 处理器,以及全 48MP 后置三摄 / 最高 8 倍光学变焦 - Pro 机型将提供橙色、深蓝色、灰色、白色和黑色机型 - 数字版将迎来 6.3 英寸显示屏、A19 处理器以及「小药丸」后摄模组,有望带来 ProMotion 功能 - 将采用均热板等手段,进一步改善 iPhone 散热问题 📸 今年升级的亮点,我觉得除了推出轻薄 SKU 取代了 Plus 系列之外,依然是影像。随着国产 Android 品牌以及三星等竞品的不断发力,光学长焦等手机相机体验越来越好,Apple 这几年感受到了压力。去年使得 Pro 和 Pro Max 在影像功能上做到了对等,今年很高兴看到模组增大的同时,有新的功能和变化 像素提升、光学倍数增加,都是我们喜闻乐见的,拍演唱会等场景可以排上大用场。但是,正如我去年说的那样,我们也应该拥有一个「专业模式」来充分发挥这些硬件的实力。此外,对于日常用的中焦焦段的选择,Apple 应该有自己的思考 🧠 去年以为 Apple Intelligence 会在过去的这一年大展拳脚,但其实 Apple 还是在做底层的框架协议,至于落地一直传闻想要通过合作或者收购其他 LLM 来实现。我能理解 Apple 站到了一个十字路口,下一步选择很重要。但去全球化日益明显的今天,Apple Intelligence 在各国的落地也受到诸多法律和监管方面阻碍 从我个人的角度来看,对 Apple Intelligence 的需求也不是太强烈,日常主要还是以电脑使用为主。因此,今年也不排除会继续选择国行。最后,eSIM 或许是接下来一年每个人都要考虑的问题,如果新机真的大规模砍掉双 nano-SIM 卡,变为单卡 + eSIM 的模式,应该怎么处理自己目前的多卡问题 ⌚️Apple Watch 系列 - Apple Watch Ultra 3 将搭载全新 S11 芯片,并支持 5G 网络连接,保留卫星通信功能,略微增大屏幕尺寸 - Apple Watch Series 11 预计延续 Series 10 的设计语言 - Apple Watch SE 3 也可能获得升级,重点是升级芯片 - 目前尚不清楚是否会引入血压监测功能 🎧AirPods - AirPods Pro 3 有望在下半年发布 - 有望取消背部的传统实体配对按键,同时为充电盒正面引入触控操作区 - 耳机盒将变得更小 - 引入心率监测、体温监测等健康功能 - 实时翻译功能可能无法随硬件首发一同提供 之前通过 AC+ 更换的越南产 AirPods Pro 一代,已经快要罢工了,因此我迫切地等待第三代的发布 👀 今年的传闻大致如上所述,期待 iPad 和 Mac 更新的朋友或需要等更迟一些的发布会了。随着年龄增长,逐渐发现即便如 Apple 这样的品牌,也不能做对、做好每一件事,黄金时期的发展掩盖了很多问题,一旦停滞进入瓶颈期便暴露无遗。不管怎样,我还是很怀念那个爆料没有这么发达、发布会还是实时直播的年代 🔗 附上一些国内外媒体长文前瞻:Bloomberg | 9to5Mac | MacRumors | The Verge | sspai * 以上所有前瞻信息来自网络和爆料人,均在早晚报出现过,不一一列举来源。请以最终发布会结果为准,欢迎大家届时进群 @NewlearnerGroup 和我们一同观看 🍿️ 频道:@NewlearnerChannel

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Borkena

@borkena · Post #5593 · 2025/12/21 16:33

ኢትዮጵያን እንደ ዩጎዝላቪያ ሳይበትኗት እንታደጋት! (ዶ/ር አክሎግ ቢራራ) https://youtu.be/zzOEyMG2V1g?si=5S9RKRlf9BTaurwt#Ethiopia#analysis

Repositorio data science

@repo_science · Post #3999 · 2024/01/22 11:01

#TimeSeries#Analysis#Python ⌚️ Forecasting Models and Time Series for Business in Python Time Series Analysis in Python. Demand Planning & Business Forecasting. Forecast with 6 Models: Prophet, ARIMA & More. ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----

Daily Channels

@dailychannels · Post #6767 · 2026/03/23 01:00

Channel: Propheta Indicator Signals Members: ~2.5K 💢 Username: @propheta_indicator Description: 😎 WE MILK THE EXCHANGES! 🔥 Reviews & Results - @propheta_reviews 📊 Performance Reports - @propheta_reports 🤖 Get Access - @ProphetaAccountBot Contact us: @propheta_help 🏷 Tags: #crypto_fx_trading #crypto#trading#signals#analysis#news https://lve.to/4rck4ca4c6

Venezuelanalysis

@venanalysis · Post #1850 · 2025/01/11 21:16

The Venezuelanalysis staff gathered to discuss the recent events surrounding Maduro’s third presidential term inauguration and the challenges ahead. The topics included an update on the situation on the ground, María Corina Machado's (fake?) arrest and the US response. Click to watch: https://venezuelanalysis.com/video/venezuelas-maduro-presidential-inauguration-recap-and-lookahead/ #Livestream#Analysis#Venezuela#PresidentialInauguration

Daily Channels

@dailychannels · Post #5943 · 2025/03/26 13:00

Channel: Bitcoin Trading Nicole Members: ~20.77K 💢 Username: @bitcointradingnicole Description: Nicole Bitcoin Trading is a place to be, where experts calls are backedup with sound Technical analysis. t.me/PayoutProof t.me/BitcoinAlgoPumps t.me/CryptoTradingNicole For VIP & Pump Contact: @NicoleCrypto 🏷 Tags: #crypto_fx_trading #bitcoin#trading#crypto#analysis#investing https://telegramchannels.me/channels/bitcointradingnicole

Daily Channels

@dailychannels · Post #6000 · 2025/04/11 01:00

Channel: Crypto Trading Signals ✅ Members: ~8.19K 💢 Username: @binancefuturetrading Description: Who are we? We are a group of professional traders who focus mainly on crypto publicity projects and crypto Trading. 🏷 Tags: #crypto_fx_trading #cryptocurrency#bitcoin#trading#analysis#investments https://telegramchannels.me/channels/binancefuturetrading

djangoproject

@djangoproject · Post #336 · 2017/05/09 05:24

https://dzone.com/articles/pyflakes-passive-checker There are several code #analysis tools for Python. The most well known is pylint. Then there’s pychecker and now we’re moving on to #pyflakes. The pyflakes project is a part of something known as the Divmod Project. Pyflakes doesn’t actually execute the code it checks, unlike #pychecker. Of course, #pylint also doesn’t execute the code. Regardless, we’ll take a quick look at it and see how pyflakes works and if it’s better than the competition.

Crypto M - Crypto News

@CryptoM · Post #64526 · 2026/04/09 06:14

🚀 Polymarket Traders' Earnings: Only 0.015% Achieve $5,000 Monthly Crypto analyst Andrey Sergeenkov's recent analysis reveals that a mere 0.015% of Polymarket traders managed to earn at least $5,000 monthly for four consecutive months. According to NS3.AI, the study examined trading data spanning from April 2024 to April 1, 2026, highlighting the challenges faced by traders in achieving consistent profitability on the platform. #Polymarket#Crypto#Trading#Earnings#Profitability#Analysis#NS3AI

Data Analytics

@sqlspecialist · Post #1644 · 2025/05/23 18:46

✨The STAR method is a powerful technique used to answer behavioral interview questions effectively. It helps structure responses by focusing on Situation, Task, Action, and Result. For analytics professionals, using the STAR method ensures that you demonstrate your problem-solving abilities, technical skills, and business acumen in a clear and concise way. Here’s how the STAR method works, tailored for an analytics interview: 📍 1. Situation Describe the context or challenge you faced. For analysts, this might be related to data challenges, business processes, or system inefficiencies. Be specific about the setting, whether it was a project, a recurring task, or a special initiative. Example: “At my previous role as a data analyst at XYZ Company, we were experiencing a high churn rate among our subscription customers. This was a critical issue because it directly impacted revenue.”* 📍 2. Task Explain the responsibilities you had or the goals you needed to achieve in that situation. In analytics, this usually revolves around diagnosing the problem, designing experiments, or conducting data analysis. Example: “I was tasked with identifying the factors contributing to customer churn and providing actionable insights to the marketing team to help them improve retention.”* 📍 3. Action Detail the specific actions you took to address the problem. Be sure to mention any tools, software, or methodologies you used (e.g., SQL, Python, data #visualization tools, #statistical#models). This is your opportunity to showcase your technical expertise and approach to problem-solving. Example: “I collected and analyzed customer data using #SQL to extract key trends. I then used #Python for data cleaning and statistical analysis, focusing on engagement metrics, product usage patterns, and customer feedback. I also collaborated with the marketing and product teams to understand business priorities.”* 📍 4. Result Highlight the outcome of your actions, especially any measurable impact. Quantify your results if possible, as this demonstrates your effectiveness as an analyst. Show how your analysis directly influenced business decisions or outcomes. Example: “As a result of my analysis, we discovered that customers were disengaging due to a lack of certain product features. My insights led to a targeted marketing campaign and product improvements, reducing churn by 15% over the next quarter.”* Example STAR Answer for an Analytics Interview Question: Question: *"Tell me about a time you used data to solve a business problem."* Answer (STAR format): 🔻*S*: “At my previous company, our sales team was struggling with inconsistent performance, and management wasn’t sure which factors were driving the variance.” 🔻*T*: “I was assigned the task of conducting a detailed analysis to identify key drivers of sales performance and propose data-driven recommendations.” 🔻*A*: “I began by collecting sales data over the past year and segmented it by region, product line, and sales representative. I then used Python for #statistical#analysis and developed a regression model to determine the key factors influencing sales outcomes. I also visualized the data using #Tableau to present the findings to non-technical stakeholders.” 🔻*R*: “The analysis revealed that product mix and regional seasonality were significant contributors to the variability. Based on my findings, the company adjusted their sales strategy, leading to a 20% increase in sales efficiency in the next quarter.” Hope this helps you 😊

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