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Source channel @olddriverGDstudy · Post #102 · Oct 18

游龙历险记 孔子云:食色性也。本人自然逃不出圣人所料。于是踏上了这条不归路。能看到这篇文章的估计都已经在此道初窥门径,我便不再规劝各位,望各位好自为之。以下我分享一下个人探索世界的经历,希望各位能从其中吸取教训,少上当,多开好车。 探索篇 人生初体验: 资源途径是朋友分享的专业招嫖软件,名为51品茶。一日恰逢休假,兴致大发,遂行动。QQ约好800/pp(上门)。到了宾馆之后给她拍房卡,发送手机号,坐等上门。约半小时后,人到。人图不一,想退货,奈何是个新手在小姐的忽悠下同意了(这个小姐外形也还行)。付钱开搞。服务非常简单,口硬了开干。态度奇差,一直玩手机。一炮结束后,大为扫兴,要求退钱。小姐没同意,说给推荐其他资源。让人走了,发消息不回。两百块没了。 事后反省: 招嫖软件上的基本都是代聊,鸡头,层层转包,八百最后到小姐手机可能只有四百。尽量不要通过软件找。根据另一次经历,推测出一个人软件发布资源,然后转给鸡头,鸡头联系小姐。对小姐不要心软,人图不一的全是代聊,直接拒绝。路费都不要给。这种小姐能拿到手的都非常少,不可能有好的体验。不要对小姐的人品抱有期待,和小姐的交易必须当面完成,人走账清。 人生再探索: 去找同学玩,同学介绍了一家洗浴中心,398半套,技师年纪偏大,服务一流。不满意的可以换,多换几个总能找到个还行的。熟人带着才有全套。 事后反省: 熟人带着可以搞大活,要么就装老嫖客,技师可以私聊带出来。级别翻倍。随便搞。 斗智斗勇篇 洗浴中心第二天,同学给了一个QQ号,加上之后网上选人。888/p,本人选了两个1600。留下联系方式和房卡。约好时间,时间到了之后让转账后小姐上楼。觉得号是同学给的诚信有保障,遂给888。转账后暴露,各种借口让付另一半,小姐没上楼。期间双方斗智斗勇,互相忽悠。我想让对面给我把钱转回来,对面忽悠我转剩下的一半。最终恼羞成怒,报上我的姓名,扬言砍我一只手,(猜测酒店前台泄露了我的信息)同时发来一段视频,西瓜刀寒光四射。本人放话:有种上来。同时戴上口罩开门跑路,110已经拨好,随时可打。 反省:任何时候都不要放松警惕,哪怕同学给的资源,不见小姐不付钱。面对卖淫团伙仙人跳威胁不要怂,他刚你更刚。报警挂嘴上。(报警流程有不熟悉的建议有机会找个小事试一下,一般会问一些信息,提前准备好,比如出警地点) 安魂舒缓篇 找同学玩回来,欲找个熟女安慰一下受惊的心灵。人来略坦,无奈大莱莱迷惑了我的双眼,上门后推荐闺蜜双飞,怦然心动。共计2400。无奈服务相当机车,身材走样,下面松垮垮,除了奶子可以,其余都不行。没射出来就软了。实在下不去鸡儿。 反省:不要相信鸡头嘴里熟女这种东西,玛德二十多的他说是学生,30多的他说是二十的,四五十的才是他们嘴里的熟女。再次强调不要在床上相信小姐任何话,这时候男人每个清醒的,要谈也是提上裤子以后。 同一个地方跌倒四次: 一日兴起,招嫖,谈好价格1000pp,人来看中,付钱后准备洗漱。小姐借口自己来之前已经洗漱过了,让我自行洗漱,于是洗漱,途中和小姐聊天,指挥我洗一下鸡儿,不然口的时候不卫生。遂用肥皂擦洗,泡沫正浓时,小姐夺路而逃。跑了。又一日兴起,约好后酒店等人敲门后端详良久,这特么不是上次跑路的那个小姐,遂激动指控,逼其退钱,无奈忘记堵门,又跑了。再一日兴起,来一未成年,吓我一哆嗦,赶紧换了一个,由于兴致大起,已经洗好澡等待,准备人来直接开干。来后小姐说已经洗过澡了,没多久,提枪上马,干到一半,小姐私处异味严重,大为影响兴致。某一日,兴致再起,欲探索酒店小卡片。打电话后,人来。500一次,没啥服务,催人,质量不行,隆胸,关键隆过以后也只有B-,还特么硬,我都不敢捏,害怕摸坏了。 反省:之所以是一个地方跌倒四次,是因为开房地点都在万达中心。怀疑此地有诈。各位谨慎。小姐来了以后一定要洗澡,不论她什么借口。一定要注意卫生。不健康不说,还特么影响兴致。如果洗澡前付了钱,就同时洗澡,要么洗澡之后付钱。针对上门小姐服务机车,不认真的情况,各位可以尝试事后付款。(这点要约之前就谈好,省的浪费时间),另外远离未成年,绝对不能精虫上脑。万一被抓就不是换个星球生活的事了 云南之行: 微信约好1600包夜,小姐来到后,外形颜值良好。遂付款开整态度良好。体验良好。两炮结束后,小姐借口上厕所,卫生间内偷偷穿戴整齐,趁机夺路而逃。一日游玩结束后,浑身酸痛,想洗个澡。打车告诉司机说去洗澡。无奈司机会错意,直接拉到一家养生馆,说有当地特色。于是体验一把。没有大活298,洗澡加按摩加轻色情服务,最后大飞机。技师相当漂亮。听话。云南少数民族农村的,后悔没加微信。 反省:包夜一定要谨慎小姐偷偷溜走,思来想去只有钱给一半这个办法,这种方法也得提前说好。省的浪费时间。养生馆的小姐姐,我怎么就没要微信呢。真特么后悔。 青岛之行: 是一家spa馆,只做特殊服务的那种,小姐质量超高,服务非常机车。1399打了个飞机摸了一下奶。 反省:不要让妹妹迷失了双眼啊,看到漂亮姐姐就付钱是可耻的。 门店会员: 一家我工作城市的足浴店,挺大的,技师日常上班三四十个。质量有好有差,不满意就换,服务分档次,1000的会员,3000的会员,10000的会员。我是3000的,3000的不给口,可以打奶炮。服务挺好,单次消费666,按摩,加胸推,调情之类的,不给口,不给日。 反省:足浴店的技师因为按摩脚丫子,稍有不慎就会沾染脚气,再摸你的蛋蛋,容易引起蛋蛋瘙痒,或者各种皮肤病。要谨慎啊,事后一定要用肥皂清洗自己的二弟,别图省事用纸擦擦了事。别问我怎么知道的。 大本营: 一个外围2000两小时,相当漂亮,服务温柔,身材也好。 反省:我怎么这么穷? 作者:王一 标签:#原创,#知识,#经验反省

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404 KIDS SEE GHOSTS (生产力之王版

@Isaiahsystem · Post #1520 · 05/11/2026, 09:59 AM

生产力全自动化技巧·贰 学会场景模型下沉。这要求给自己所有任务和模型能力对应分层,自动化场景模型要学会下沉,探索部分上升,最优化模型帕累托边界。它意味着要摸清模型能力边界,了解自己任务程度,对应配置。 现在我发现我沉淀下来的日常/工作全自动化场景,Codex 5.5 Medium/ Claude Sonnet 4.6 完全足够(国产模型也能兜底),速度快、成效好。过去我刻意耗费了很多 Tokens,Codex 5.5 xhigh Fast/ Opus 4.7 乱跑,现在已经逐步将场景模型转向收敛。 模型能力调用配置算个人认知、执行等综合行为能力投射。耗费是必要的,这是边界探索、能力投射、纯粹享受的过程。但某些任务思考过度是种数字毒素,对任务质量并不好,并非最优。 相关链接: 1. 生产力全自动化小技巧·壹 2. 没有护城河丨LLMs 帕累托前沿实时更新 #models

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Tesla News 🅥

@teslacn · Post #1044 · 04/22/2022, 03:05 PM

🗞️Tesla Model S Plaid racecar bares its fangs by overtaking hypercars in Laguna Seca 特斯拉Model S格子赛车在拉古纳赛卡赛道上超越超跑 📢频道💭群组🏷#ModelS

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Car News

@car_news · Post #570 · 09/26/2024, 01:01 PM

Amalgam company has released a miniature copy of 🚘12Cilindri for the price of Nissan Versa 🏎 The copy of Ferrari 12Cilindri is made in 1:8 scale (length about 60 centimeters). Amalgam designers achieve a high similarity with the original by using the original documentation and the help of the manufacturer's engineers. 📸 The attention to detail is astounding. The miniature leather interior is decorated with red contrast stitching, just like the full-size car. The level of accuracy is so high that the model can be mistaken for the real thing in the photos. ⏳ Each car takes about 300 hours to assemble by hand. In total, Amalgam will produce 119 coupe models and the same number of roadsters. 👍 As for the real Ferrari 12Cilindri, this car replaces the 812 Superfast, continuing the tradition of V12-powered grand tourers. 💸 The price of the model starts at $18,220. @CarsNews #Ferrari#sportscar#models

🌿 Xalqaro amaliy seminar: “Yashil” kelajak sari modellar va nazarlar “Yangi O‘zbekiston” universiteti HTWD Dresden (Germaniya), Myunxen texnika universiteti (Germaniya) va Nemis-Iordan (Iordaniya)universitetlaribilan hamkorlikda ikki kunlik xalqaro seminar tashkil etmoqda. Seminar-treyningda O‘zsanoatqurilishbank, Osiyo taraqqiyot banki va IT Park dan mutaxassislar va ekspertlar ishtirok etishadi. 📅 Sana: 2025-yil 5–6-noyabr 📍 Manzil: “Yangi O‘zbekiston” universiteti Seminar dasturi 5-noyabr kuni asosiy ma’ruzalar va panel muhokamasidan boshlanadi: ❕“Yashil biznes modellari va mas’uliyatli innovatsiyalar: global va mahalliy istiqbollar”. 6-noyabr kuni TUM International amaliy seminar o‘tkazadi: 💡“Ta’limdan harakatga: barqarorlik va tadbirkorlik ta’siri uchun ta’lim berish”. ‼️Ro‘yxatdan o‘tish 4-noyabrgacha davom etadi. 👉Seminarga ro‘yxatdan o‘tish 🔁English ➖➖➖➖➖➖➖➖➖➖ #workshop#greenfuture#models ⬇️Biz ijtimoiy tarmoqlarda: Veb-sayt | Telegram | Instagram | Facebook

djangoproject

@djangoproject · Post #193 · 10/31/2016, 12:33 PM

http://deeplearning.net/software/theano/tutorial/index.html#basics In terms of #algorithm availability, you can get plenty of algorithms out of the box with scikit-learn. And if you want to customize every detail of your #models, #Python has #Theano. In addition, Theano is easily configured to run on the #GPU, which gives you a cheap and easy way to get much higher speeds without having to change a single line of code or delve into performance details.

Data Science Jobs

@datasciencejobs · Post #2093 · 05/16/2024, 05:03 PM

#вакансия#middle#senior#python#dev#postgresql#ml#math#models#sql Всем привет! Мы Axenix (ex. Accenture) находимся в поисках Python Developer на проект разработки продукта In.Plan (платформа интегрированного планирования) 🔎 Мы ищем Middle/Senior Python Developer Формат работы: гибрид, удаленка (РФ/Армения/Казахстан) 💰 обсуждается после тех. интервью Чем предстоит заниматься: 🔸 Разработка решений для платформы интегрированного планирования производства 🔸 Участие в написании модулей прогнозирования спроса, календарного планирования и производственного планирования, модуля оптимизации складских запасов 🔸 Работа с PostgreSQL как источником данных. Задачи включают написание компонентов по чтению/записи данных из БД, обработке (очистка, проверка качества, сглаживание и т.д.) данных, формулировке задач мат. оптимизации для солверов, написание ML модулей для прогнозирования. Здорово, если ты: 🔹 Знаешь алгоритмы и структуры данных 🔹 Имеешь опыт коммерческой разработки на Python 🔹 Понимаешь и применял нотации Big O для оценки сложности алгоритмов 🔹 Знаешь паттерны ООП и принципы проектирования вычислительных решений 🔹 Имеешь опыт написания тестов, понимание отличий и целей разных типов тестов 🔹 Имеешь опыт работы с SQL базами данных 🔹 Знаешь Git Мы предлагаем: ▪️ Работу в Аккредитованной ИТ-компании ▪️ Конкурентоспособный уровень дохода, годовые бонусы и регулярное повышение по результатам Performance Review ▪️ Культуру непрерывного обучения: сертификация, online и offline обучение в России, менторство в профессиональном развитии; ▪️ ДМС с первого месяца работы, включая стоматологию, в лучших клиниках Москвы и МО для cотрудника и его семьи (жена/муж, дети до 18 лет); ▪️ Возможность оформления по ИП Контакт для связи: @masha_axenix

Data Analytics

@sqlspecialist · Post #1644 · 05/23/2025, 06:46 PM

✨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 😊