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Source channel @olddriverGDstudy · Post #49 · Mar 24

江湖舔狗传 江湖者,江湖也! 各兄弟五湖四海汇聚一堂,为的是个情字,讲的是个义字,说的是个道理。 江湖上无数前辈好汉,忍饥挨饿,夜以继日,通宵达旦,上下求索,陷过无数的坑,踏破无数双鞋换得了有限的几个极品资源,未曾敢占为己有,而是毫无保留,无私公布奉献。 这一切为什么?为的是天下草根、屌丝们,不受仙人跳之苦,不遭各种骗费之难,不枉花了辛苦搬砖的银两盘缠,这是多么高尚的精神,多么高贵的品质啊! 江湖就是江湖,林子大了什么鸟儿都有,舔狗们也像病毒般出没,为害人间。这些禽兽毫无尊严、毫无底线,从溜须拍马、到阿谀奉承,从冷屁股到甜盘子全方位无死角。 舔狗,做着劝婊子从良的梦,抱着救风尘女子出火坑的“崇高”的性幻想,岂不知自己已是婊子口中的笑话! 江湖有江湖的规矩,江湖有江湖的原则,江湖有江湖的风貌,江湖有江湖的脾气。 我知舔狗是死不光的,这一车死光了,下一车还在路上。 但舔狗永远不过是个道具而已,又何必自作多情。 舔狗,你听,电话声已响起,你的钟到了!闭上臭嘴,滚出去把门关上! 作者:41秒哥 标签:#语录

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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