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Source channel @olddriverGDstudy · Post #53 · Mar 24

#知识#接吻 第一式:舔吻 用舌舔对方的上下唇,让对方感受舌部味蕾舔掠的感觉,注意要保持唾液的充分,如果唾液太少,干燥的舔吻会有不舒服的感觉。 第二式:咬吻 用牙齿轻咬对方的唇,但别咬的太用力,以免受伤喔! 第三式:吸吻 轻轻的吸吮对方的唇部;可用自己的唾液轻抹在对方的唇部,然后吸吮干净。 第四式:推动吻 把舌伸进对方口中,让舌与舌互相推放,男生力气应放小,以免女生疼痛;这种互推吻可形成快感。 第五式:吸舌吻 以你的唇含住他的舌,轻轻的吸吮对方的舌头,动作宜缓慢而轻柔,勿过于仓促。 第六式:齿龈吻 用舌探索对方的牙及牙龈的内外两侧,以刺激口内粘膜为目的。动作要仔细,慢,轻柔的介于碰触与不碰触之间,以产生一种特殊的亲密感。 第七式:滑动吻 用舌尖稍用力的舔对方的舌部内侧,由里向外滑舔。 第八式:舔舌吻 双方以舌对舌互舔,以用舌尖为主,不用唇。 第九式:嚼食之吻 咬住对方的舌头,似欲吞食般的吻;请小心别用力过火,只是假装而已。想像对方的舌头是好吃的东西,又咬又舔又吸的想吞进肚子里去。 第十式:律动之吻 以舌在对方的口中,有节奏律动般的的绕着对方的舌尖,画圈似的舔吻。 第十一式:深喉咙吻 将舌深入对方的喉咙重舔。重压,是霸道占有般的吻;这是一种颇不舒服的吻法,但还是有乐在其中的人。 第十二式:热情之吻 将自己的舌把对方的舌包卷于口中,上下左右回旋翻动,用放肆的旋动来增加快感,虽嫌粗鲁但颇具挑战性,是接吻高手必备的技巧之一。 第十三式:甘泉之吻 利用两唇相接时……以舌将自己的唾液渡入对方口中,并吸食对方的唾液。适用于两情相悦且身体健康的爱侣,会觉入口之唾液为琼浆玉液般,世间独有。

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Data Science Jobs

@datasciencejobs · Post #3203 · 03/30/2026, 12:04 PM

#vacancy#вакансия#job#работа#Data_Scientist#DS Senior Data Scientist 📍 Location: Serbia, Armenia (We are ready to discuss other countries as well) 🏢 Remote work is possible 💶 Payment terms are open to discussion from 3500 € and up About the product At FlameTree, we are building a platform for creating AI agents that help businesses scale customer support, lead follow-up, and sales across multiple communication channels — both inbound and outbound. Our AI agents work with knowledge bases, communicate in real time, and drive conversions across messaging platforms. The platform supports 150+ languages and integrates with WhatsApp, email, and web applications, offering strong security and high scalability for business growth. 🎯Responsibilities: • Design and develop the core agent layer responsible for orchestrating interactions with LLMs • Build and maintain complex conversational logic: state machines, agent workflows, and orchestration pipelines • Control and shape LLM behavior: prompt design, structured outputs, deterministic flows • Manage conversational context: memory, history, token limits, and degradation strategies • Ensure reliability and predictability on top of inherently non-deterministic models • Implement resilient integrations with LLM providers (timeouts, retries, fallbacks, multi-provider strategies) • Optimize latency and cost (streaming, batching, caching, token efficiency) • Debug complex production issues (inconsistent outputs, race conditions, state loss) • Contribute to system architecture: clear boundaries between agents, backend, and real-time components • Build observability around LLM pipelines (prompt/response logging, tracing, quality metrics) 🎯Requirements: • 5+ years of backend development experience with strong Python skills (async, architecture, performance) • Proven production experience with LLMs (not side projects): understanding of limitations, cost, and behavior • Experience building agent-based systems or complex orchestration logic (state machines, pipelines) • Ability to make LLM behavior predictable (structured outputs, schema validation, guardrails) • Strong debugging skills in non-deterministic systems • Deep understanding of API integrations (timeouts, retries, idempotency, backpressure) • Experience optimizing latency and throughput in production systems • Solid Docker experience and understanding of production environments • Ability to make architectural decisions independently and take ownership • Strong engineering mindset: writing maintainable, scalable, production-grade code 🎯Nice to Have: • Experience with multi-agent systems, tool/function calling • Experience with local LLMs (Ollama, vLLM, GPU inference) • Experience with real-time / voice systems • LLM observability (prompt tracing, evals, quality metrics) • Cost optimization at scale for LLM usage 🎯What Makes This Role Interesting: • You will work on the core intelligence layer of the product — not just integrations • Real production challenges: high load, low latency, reliability requirements • Direct impact on system architecture and technical decisions • Fast execution cycle — minimal bureaucracy • Engineering-driven approach to LLMs (reliability, control, metrics — not just prompt tinkering) • Strong engineering team focused on building real systems, not prototypes 🎯Who This Role Is NOT For: • Candidates without real production experience with LLMs • Engineers relying only on frameworks without understanding underlying mechanics • Developers without experience in high-load or latency-sensitive systems • People focused on quick hacks rather than building reliable systems 📩 If you want to join a team where everything is fast, exciting, and truly about AI — drop a message: https://t.me/Irene_Bakaeva!

Data Science Jobs

@datasciencejobs · Post #1327 · 02/16/2023, 08:01 AM

#vacancy#parttime#Data_Scientist#Python#NLP Мы ищем академического директора для магистерской программы Искусственный интеллект в области лингвистики (компьютерная лингвистика), реализуемой совместно с Томским государственным университетом. Каким мы видим идеального кандидата? - Senior Data Scientist в области NLP (Natural Language Processing) и выше с опытом работы в этом грейде от 5 лет; - Опыт в найме junior и middle-специалистов; - Понимание стандартов профессии и актуального профиля компетенций специалиста, требуемого на рынке труда; - Опыт работы в компаниях, лидирующих на российском или зарубежном рынке в выбранной индустрии; - Активный участник сообщества, опыт выступления на конференциях, митапах (или их организация) будет преимуществом. Предлагаем: - Удаленная парт-тайм работа до 20 часов в месяц. - Возможность реализовывать свои идеи и влиять на IT-индустрию/ - Ежемесячный гонорар, привязанный к количеству новых студентов (по типу роялти). - Крутая команда с сильной экспертизой в сфере EdTech. - Укрепление вашего личного бренда. - Бесплатное обучение на любом курсе образовательной группы SkillFactory: в школе дата-профессий и программирования SkillFactory, школе дизайна Contented. С полным ТЗ можно ознакомиться по ссылке- https://docs.google.com/document/d/11yE4ycHg_oZLRmfRD936yhVISWmtw3A1chxWUI-qe0Q/edit?usp=sharing Контакт для связи- @anika_kor

GitHub Trends

@githubtrending · Post #15438 · 01/26/2026, 11:30 AM

#python#agents#ai#ai_engineer#ai_engineering#copilot#data_science#data_scientist#generative_ai#gpt#machine_learning#ml_engineer#ml_engineering#openai AI Data Science Team is a free Python library with AI agents that speed up your data work 10X by handling loading, cleaning, visualization, EDA, feature engineering, modeling, and SQL tasks. Its flagship AI Pipeline Studio app creates visual, reproducible pipelines you can run with Streamlit after easy install (Python 3.10+, OpenAI or Ollama). This saves you hours on repetitive jobs, boosts accuracy, and lets you focus on insights and business results. https://github.com/business-science/ai-data-science-team