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I have a stream of numerical information being pulled automatically from several news sites, and I need that raw output cleaned, coded, and delivered in a tidy, analysis-ready format. The web-scraping side is already set up; what’s missing is a solid routine that parses each scrape, identifies the key figures buried in the articles, applies consistent codes to them, and drops everything into a structured file (CSV or JSON works for me). You’ll receive: • the current scraping script and a sample of the raw dump • a field dictionary showing how each number should be labeled or categorised I’m expecting your returned script (Python preferred—BeautifulSoup/Scrapy plus pandas is perfect, but use what you like) along with the final processed dataset and a brief read-me so I can rerun or extend the pipeline later. Accuracy of the coding and reproducibility of results will be the benchmarks for sign-off.

I am launching a management consulting business targeting startups and data-driven companies. I have a strong research background and expertise in methodologies such as SWOT analysis, MECE Framework, Issue Trees, Hypothesis-Driven Problem Solving, Root Cause Analysis, Design Thinking, and data analysis using Excel, SQL, Python, and Power BI. I need a freelancer to help me create a LinkedIn outreach campaign that highlights these strengths and effectively attracts potential startup clients. The ideal freelancer will have experience in B2B marketing, LinkedIn lead generation, and crafting persuasive messaging for consulting services. I want the campaign to showcase my skills, problem-solving approach, and ability to support informed decision-making for business owners.

I need an experienced Python analyst who can jump in immediately and turn raw data into clear, actionable insights. The core focus is straightforward data analysis—no model-building or database overhaul this time—so I expect the work to centre on Pandas, NumPy and, where useful, basic visualisations in Matplotlib or Seaborn. The dataset itself will be supplied as soon as we start; I’m flexible on its format (CSV, JSON, or even a quick pull from MongoDB). What matters most is speed and accuracy: I’d like preliminary findings within the first 24 hours and the full deliverable finished inside two days. Deliverables (must-have): • A cleaned, well-documented dataset • A concise report (Jupyter Notebook or PDF) highlighting key statistics, trends and any anomalies you discover • Visualisations that make the insights easy to grasp at a glance I’m available for quick check-ins on Slack or Zoom to keep the turnaround tight. If you’ve got the bandwidth to start today and you’re confident with Python and Pandas, let’s get moving.

I need production-ready code that trades non-directional option straddle and strangle setups, with full adjustment logic, across both the Indian derivatives segment (index and every F&O-listed stock) and the main crypto pairs on leading exchanges. Development has to progress in parallel for two environments: plain-vanilla Python (for back-testing and automation via REST/WebSocket APIs) and a Quantower custom strategy module for live deployment. Core expectations • A single, clearly organised Python package that can: – Pull option chain data in real time, – Calculate Greek-based risk metrics, – Fire entry, partial exit, and adjustment orders according to parameters I will share, – Log trades to a CSV or database for later analysis. • A Quantower script mirroring the same logic, with parameter fields exposed in the UI so I can tweak strikes, time filters, and adjustment thresholds on the fly. • Brief but complete documentation and a quick video walkthrough showing both versions operating on demo data. The code must be modular enough that I can slot in new exchanges or brokers without touching the core strategy engine, and I should be able to turn individual legs on or off for testing. Clean, readable style (PEP-8) and sensible error handling are mandatory. If this is clear and you’ve already built exchange adapters or option algos before, let’s get started—I’d like to review an initial prototype within a week.

I need a Python automation script that runs smoothly on Windows 10/11. The aim is to remove tedious, repetitive steps from my workflow—whether that ends up being file or data manipulation, web scraping, simple system operations, or another Python-friendly task we identify together. Once we agree on the exact routine, I will share all supporting files, URLs, or process notes. Deliverables • A clean, well-commented .py script (Python 3.x) ready to execute on Windows without manual tweaks • A concise README with setup instructions and a list of required libraries (e.g., pandas, requests, BeautifulSoup, pyautogui, etc.) • Meaningful logging and graceful error handling so I can track what the script is doing and why I value straightforward code I can maintain myself later, so please keep the structure logical and modular. Let me know your proposed approach, turnaround time, and any clarifying questions so we can get started quickly.

我手头有一份 Arduino 草稿（.ino 文件），当前在编译阶段就报错，导致无法烧录到板子上。 我的首要目标是“修复错误”——也就是让代码在 Arduino IDE 中顺利通过编译，无任何报错或警告。主要场景是单片机控制，并不涉及 Python 或 C 代码，其余部分暂时无需改动。 具体期望： • 找出并修正所有编译错误，确保草稿可在最新稳定版 Arduino IDE 中成功编译。 • 如发现隐藏的逻辑缺陷，可在修复编译问题后顺便指出，但不强制要求功能扩展。 • 提供一份简短的变更记录，说明修改了哪些行、为何修改，方便我后续维护。 交付内容： 1. 已修复的 .ino 文件。 2. 变更记录（Markdown 或纯文本皆可）。 3. 如需额外库或版本说明，请一并附上安装指引。 我会在收到可正常编译的代码并确认运行无误后结项。期待与擅长 Arduino 调试的你合作，谢谢！

I’m ready to launch a polished, conversion-focused e-commerce site and want to get the build started right away. The store needs to project a premium look, load quickly on desktop and mobile, and give me everything required to manage inventory, orders, discounts, and basic SEO without touching code. Your job is to plan, design, and deliver the entire storefront—from the responsive UI and product catalogue to secure checkout, payment gateway integration, tax/shipping rules, and analytics hooks. I’m flexible on the underlying platform; whether you favour Shopify, WooCommerce, Magento, or a custom stack, show me why it fits a growing catalogue and how it will stay easy to maintain. Deliverables • Fully functional online store deployed to my domain • Branded, mobile-optimised theme with intuitive navigation and search • Integrated payments, shipping calculator, and automated order emails • Lightweight SEO setup (meta tags, clean URLs, schema where relevant) • Admin walkthrough plus concise documentation for future updates Acceptance Criteria • Sub-3-second page load times on GTmetrix • End-to-end checkout passes with a sandbox card • No critical Lighthouse accessibility errors Once the store is live and stable, I’ll circle back for a Python tool to automate Excel data processing, but the website comes first. Let’s get the shop open for business.

We are looking to hire 4 skilled developers to join our team. Below are the roles and requirements: 1. Software Engineer - Design, develop, and maintain scalable software applications - Write clean, efficient, and well-documented code - Collaborate with cross-functional teams to define and implement new features - Conduct code reviews and ensure best practices are followed - Proficiency in one or more languages such as Python, Java, C++, or JavaScript - Experience with RESTful APIs and microservices architecture 2. Cloud Engineer - Design, implement, and manage cloud infrastructure on AWS, Azure, or GCP - Automate cloud deployments using Infrastructure as Code (Terraform, CloudFormation) - Ensure high availability, scalability, and security of cloud environments - Monitor and optimize cloud costs and performance - Experience with Kubernetes, Docker, and CI/CD pipelines 3. AI/ML Engineer - Develop, train, and deploy machine learning and deep learning models - Work with large datasets to build predictive and analytical solutions - Integrate AI/ML models into production systems - Experience with frameworks such as TensorFlow, PyTorch, or Scikit-learn - Strong background in NLP, computer vision, or generative AI is a plus 4. Data Engineer - Build and maintain robust data pipelines and ETL processes - Design and manage data warehouses and data lakes - Work with large-scale distributed systems such as Apache Spark, Kafka, or Airflow - Collaborate with data scientists and analysts to support data needs - Experience with SQL, NoSQL databases, and cloud data services (BigQuery, Redshift, Snowflake) All candidates should have strong communication skills, the ability to work in an agile environment, and a passion for technology and innovation. Please include your portfolio, relevant experience, and expected rate in your proposal. Preferred Candidate Locations: We are specifically looking for developers based in the following countries or regions: - Turkey - Armenia - Bosnia and Herzegovina - Iran - Ukraine - Honduras - Latin America (LATAM) – particularly candidates from countries such as Bolivia, Nicaragua, Paraguay, El Salvador, and Guatemala We welcome talented developers from these regions who can offer competitive rates while delivering high-quality work. Please mention your country of residence in your proposal.

I’m ready to launch a polished, conversion-focused e-commerce site and want to get the build started right away. The store needs to project a premium look, load quickly on desktop and mobile, and give me everything required to manage inventory, orders, discounts, and basic SEO without touching code. Your job is to plan, design, and deliver the entire storefront—from the responsive UI and product catalogue to secure checkout, payment gateway integration, tax/shipping rules, and analytics hooks. I’m flexible on the underlying platform; whether you favour Shopify, WooCommerce, Magento, or a custom stack, show me why it fits a growing catalogue and how it will stay easy to maintain. Deliverables • Fully functional online store deployed to my domain • Branded, mobile-optimised theme with intuitive navigation and search • Integrated payments, shipping calculator, and automated order emails • Lightweight SEO setup (meta tags, clean URLs, schema where relevant) • Admin walkthrough plus concise documentation for future updates Acceptance Criteria • Sub-3-second page load times on GTmetrix • End-to-end checkout passes with a sandbox card • No critical Lighthouse accessibility errors Once the store is live and stable, I’ll circle back for a Python tool to automate Excel data processing, but the website comes first. Let’s get the shop open for business.

I’ve developed a Movie Recommendation system using Python, with core libraries such as random and pyttsx3. It’s a customized solution designed to suggest movies based on specific criteria. The system doesn’t rely on standard frameworks like Scikit-Learn or Pandas but instead focuses on a straightforward implementation leveraging Python's capabilities.

I already shoot and edit short-form social media videos; what I need now is a clean, reliable face-swap pipeline that drops a chosen face into those clips for pure entertainment. InsightFace is the preferred engine, but you’re welcome to weave in Wav2Lip or FaceFusion if that improves lip sync or realism. Here’s the flow I have in mind: I hand you a finished video (with its original audio track) plus one or more source images or a source video of the face that should appear. Your script ingests the material, applies detection, alignment, swapping and blending with InsightFace, keeps the original timing and audio intact, then renders a final MP4 that’s ready for immediate upload to TikTok, Reels and Shorts. Deliverables • Well-commented Python script or notebook that performs the full swap end-to-end • One-command setup guide (conda or Docker) listing every dependency and model checkpoint • Example output using my supplied test clip so I can compare before/after quality Acceptance criteria • Face stays locked to head movement with no noticeable jitter or colour mismatch • Lip movements remain synced to the existing audio throughout the clip • Processing time per 60-second 1080p video stays within a reasonable window on a single RTX-class GPU If something in the workflow can be optimised or automated further, feel free to suggest it—clarity and reproducibility are more important to me than chasing marginal gains.

Full-Stack AI Short-Form Video Clipping & Social Media SaaS like Vizard.ai , cliphi.com and klap Target Deliverable: Minimum Viable Product (MVP) with Advanced Admin & User Controls 1. ARCHITECTURE & TECH STACK PREFERENCES - Frontend: Next.js (App Router), React, Tailwind CSS, Shadcn/ui (optimized for desktop and mobile web viewports with local state persistence for Day/Night mode toggles). - Backend: Python (FastAPI or Django REST Framework) preferred due to native library support for video processing (FFmpeg, MoviePy, yt-dlp). - Database & Auth: Supabase (PostgreSQL) or Firebase. Must include secure email/password auth, Google OAuth, and integrated session management. 2. SYSTEM DATABASE SCHEMA & CORE MODELS Developers must implement a relational database structure containing these explicit models: - Profiles: (id, email, role ['user', 'admin'], credit_balance_minutes, stripe_or_payment_customer_id, theme_preference, created_at) - Pricing_Packages: (id, name, credit_amount_minutes, price_inr, price_usd, discount_percentage, is_featured, is_active) - Coupon_Codes: (id, code [unique], discount_percent, max_redemptions, current_redemptions, expires_at, is_active) - User_Subscriptions: (id, user_id, package_id, status, current_period_end) - Videos_Processed: (id, user_id, original_url, source_type ['upload', 'url'], status ['pending', 'transcribing', 'processing', 'completed', 'failed'], duration_seconds) - Generated_Clips: (id, video_id, user_id, clip_title, start_time_seconds, end_time_seconds, virality_score, storage_url_mp4, is_scheduled, scheduled_time, is_posted) - Newsletters_Sent: (id, subject, body_content, total_sent, created_at) 3. USER DASHBOARD & CORE FUNCTIONALITY - Ingestion Terminal: Support local drag-and-drop file uploads (MP4) and URL pasting (YouTube, Instagram, TikTok links using yt-dlp on the backend). - Automated Video Logic Mocking: Balance verification must happen before ingestion. If credits are sufficient, deduct appropriate minutes. For the MVP, provide a fully functioning interface connected to backend mocks that progress cleanly through transcription, timestamp evaluation, and composition. - Interactive Editing Studio: The dashboard must show generated clip outputs on a grid. Each card requires: a video playback frame, an editable text field for titles, an explicit 'Download MP4' action path that pulls directly from the cloud storage bucket, and a secondary scheduling layout. - Subtitle Customization Sidebar: Users must be able to change typography settings in real-time (fonts like Montserrat Bold, Impact; colors; text strokes) and toggle a rule to "Auto-add 3s trending random meme hook at video start." 4. MULTI-CURRENCY BILLING & GEOLOCATION DISPATCH - Implement an automated location-routing loop at the app level. If the client IP is from India, lock the workspace to INR (₹) and deploy Razorpay SDK infrastructure. For international traffic, route to USD ($) utilizing Paddle or Lemon Squeezy (acting as a Merchant of Record). - Build a dual-state credit interactive slider panel on the front page ranging from 300 minutes to 3,000 minutes. Fetch tiered configurations dynamically from the database table (applying a 20% discount above 1,000 credits, 40% above 2,500 credits). Includes a promo code redemption form that runs backend validations against active database coupons. 5. CONTENT CALENDAR & SOCIAL MEDIA QUEUE - Each clip card must have an option to pick a target date/time and select platform destination toggles (YouTube Shorts, Instagram Reels, TikTok). - Save these inputs into the `generated_clips` queue table and render a global visual "Content Calendar" view dashboard where the user can inspect, modify post times, or delete scheduled actions seamlessly. 6. MASTERS CONTROL OPERATIONS PORTAL (Gated at /admin) Create an administrative control workspace restricted strictly to users with the 'admin' role flags. It must contain separate UI modules for: - Packages & Inventory CRUD: Data tables to add, update, discount, or deactivate packaging tiers on the fly without updating codebase arrays. - Coupon Code Engine: UI to spawn code strings with custom maximum limits, expiration dates, and discount rates. - Wallet Overwrite Suite: Searchable client ledger allowing the admin to manually add/subtract minute token balances from profiles or override permissions. - In-App Alerts & Newsletter Broadcast: Rich text template view to dispatch mass messages. The execution must insert tracking records into `newsletters_sent` and push instant UI alerts to user account side-drawers. - Analytics Hub: Visual metric overview tracking Total Platform Revenue, Active Subscriptions, Total Videos Processed, and aggregate fleet remaining credits. 7. COMPLIANCE & SECURITY DELIVERABLES - The application backend must process payment updates using secure, signed webhook listener endpoints (not client-side success callback loops). - The global layout must implement responsive layout principles, smooth loading skeletons, custom toast feedbacks, and explicit legal page components (Terms of Service, Privacy Policy, Refund Policy) in the footer.

I need a secure, reliable web application built from the ground up with payment processing at its core. A Python-based stack is preferred—Django, Flask, or FastAPI are all acceptable—but I’m open to a C++ service layer if it improves performance for critical workflows. Key requirements • End-to-end payment gateway integration (Stripe, PayPal, or a comparable API) with tokenization, refund handling, and webhook support. • User-friendly front-end that guides customers through checkout and clearly displays transaction status. • REST-style API endpoints so the payment module can be reused by other services. • Solid error handling and logging; all sensitive data must be encrypted in transit and at rest. • Setup scripts or Docker files that let me launch the project locally and deploy to a cloud host with minimal steps. • Brief but clear documentation covering environment variables, build commands, and how to add new payment providers. Nice to have • Basic user authentication scaffolded in, ready to expand. • Unit tests for the payment workflow. I value clean, commented code and prompt communication. If you have previous examples of payment-enabled web apps, include a link or repo snapshot so I can gauge fit.

Hello, I am interested in your project. I have experience with Python and C++ development and would be happy to help you build an efficient and reliable solution. I look forward to working with you.

My Python-based market-scanner is connected to the Bybit exchange and is meant to flag when it’s time to buy or sell. Right now the side-detection logic is off on both ends—it labels potential longs as shorts and vice-versa. I need the detection algorithm reviewed, corrected, and stress-tested against live and historical Bybit data so that: • every true buy signal is tagged as such, • every true sell signal is tagged correctly, and • no extra false positives slip through. You’ll have direct access to the existing repository, which uses the Bybit REST/WebSocket APIs along with standard Python trading libraries (pandas, numpy, ta). Unit tests are already in place; feel free to extend them to cover edge cases you uncover while debugging. Deliver a clean, commented patch or pull request plus a brief summary of the changes and the test evidence that the scanner now behaves as intended.

I’m refreshing my “Hidden Village” Discord and want a cohesive, mystical-forest experience from the moment someone lands on the server through to their first conversation. All parts of the template—logo, banner, animated welcome & verification screen, channel layout, onboarding flow, and bot set-up—carry equal weight, so please give each element the same level of polish. Visual & motion design • Create a logo and banner that feel like an enchanted settlement hidden deep in ancient woods—think shimmering greens, soft glows, and subtle runic accents. • Design and lightly animate the welcome and verification screen so new members feel they are crossing into a living forest; gentle particle effects or ambient wildlife loops preferred. • Apply matching animated touches (e.g., looping canopy sway, firefly particles) to channel headers or category dividers without hurting load times. Server structure • Reorganise existing channels into intuitive sections (village centre, quests, campfire chat, traders’ lane, etc.) while keeping roles and permissions intact. • Deliver clear, visually branded onboarding guides (static PDF or embedded Discord pages) that explain roles, rules, and perks. Bots • Audit current bots, recommend additions, and configure each one. • Build one custom bot named “Treebeard” with a fitting avatar; basic duties are welcome-messages, role assignment, light moderation cues, and occasional lore-based fun facts. Style reference: Mystical forest—more ethereal than cartoonish, but not ultra-realistic. Subtle motion and soft textures are key. Milestones 1. Style board & rough layouts for sign-off 2. Static assets (logo, banner, onboarding materials) 3. Animation passes & bot prototype 4. Final integration on the live server, due no later than 25 June 2026 Deliverables are approved once they load correctly in Discord (desktop & mobile), match the agreed style board, and show no permission errors. Source files (PSD/AI/AE or equivalent) and bot code must be handed over at project close. Also need roles with names and onboarding

Özüm artıq elektronika mağazaları üçün hazır, tam funksional e-commerce sayt hazırlamışam. Texniki tərəfi tam mənə həvalədir: dizaynı, inteqrasiyaları və istənilən funksional əlavəni müştərinin tələb etdiyi kimi qısa müddətdə həyata keçirirəm. Sənə ehtiyacım olan hissə: • Potensial müştəriləri tapmaq və onlara həllimizi təqdim etmək • Danışıqları aparmaq, ehtiyaclarını dəqiqləşdirmək və satışa bağlamaq • Müştəri ilə ünsiyyəti əsasən WhatsApp və ya Telegram üzərindən, Azərbaycan dilində idarə etmək Qazanc modeli Başlanğıc satış qiyməti 500 AZN olaraq təklif edirəm. Müştəri daha yüksək məbləğə razılaşarsa, əldə olunan əlavə gəlirdən sənin payın da olacaq. Satış uğurla bağlandıqdan sonra saytın müştəriyə uyğunlaşdırılması və texniki dəstəyi tam mənim öhdəmə düşür. Səndən gözlədiyim nəticə ən azı bir ödənişli müştərinin cəlb olunması və proses boyunca davamlı geribildirimdir. Sayta öncədən baxmaq üçün: https://x-tech8.vercel.app Əgər bu əməkdaşlıq modelini uyğun görürsənsə, yaz və detalları razılaşdıraq!

I’m building a real-cash Ludo experience that runs just as well in a WebGL browser window as it does on Android. Players will stake and win USDT, so every deposit and payout must flow through a robust escrow smart contract. The contract should hold funds during the match, automatically release winnings, and connect to common Web3 wallets (MetaMask, Trust Wallet, etc.) for a friction-free on-ramp. Gameplay & rooms • Classic 4-player Ludo rules. • Public lobbies anyone can jump into plus invite-only private tables—users choose on the fly. • Live matchmaking, reconnection handling, and anti-cheat logic. Engagement layer • Global and friend-only leaderboards. • In-app referral system that tracks codes and rewards both parties. • Direct match-result posts to an official Telegram bot/channel for community buzz. Interface I need a medium level of visual customization: branded color palette, logo integration, and custom table backgrounds while leaving core Ludo mechanics untouched. Responsive layouts for desktop browsers, tablets, and a range of Android screen sizes are essential. Payments & security • Solidity (or comparable) USDT escrow contract, deployed and verified. • Clear on-chain audit trail for every game. • Server authority for physics/random seed to prevent client tampering. Deliverables 1. Unity (or equivalent) project with separate WebGL and Android builds. 2. Deployed, tested USDT escrow smart contract, source code included. 3. Backend match-manager with REST/Socket API. 4. Documentation: setup, build pipeline, and wallet integration guide. 5. Two-week post-launch bug-fix window. If you have shipped multiplayer WebGL or Android titles that handled real money, I’d love to see them. Let’s create a smooth, secure Ludo arena that players can trust with their USDT stakes.

لدي ملف مبيعات واحد بصيغة CSV يضم بيانات معاملات فقط، وأحتاج إلى تمريره بمرحلتين واضحتين. أولًا، أعوّل عليك لتنظيفه بطريقة منهجية: أريد حذف جميع الصفوف المكررة، تصحيح أي قيم خاطئة رُصدت في الحقول الرقمية أو النصية، ثم توحيد تنسيقات التواريخ والعملات بحيث يصبح الملف متناسقًا بالكامل. بعد حصولي على نسخة خالية من العيوب، أنتقل معك إلى المرحلة الثانية وهي التحليل الإحصائي. أحتاج إلى مؤشرات وصفية (متوسط، وسـط، انحراف معياري، إلخ)، جداول تلخّص حجم المبيعات والأصناف الأكثر تداولًا، وأي رؤى رقمية مهمة يمكن استنباطها من السجلات. سلّم لي في النهاية: • ملف CSV مُنظَّف جاهز للاستخدام • تقرير موجز (PDF أو ‎.xlsx) يضم الجداول الإحصائية والرسوم البيانية التوضيحية • ملف عمل أو كود (Excel / Python pandas / R حسب راحتك) حتى أتمكّن من تتبّع خطواتك مستقبلاً سأراجع العمل بناءً على خلوّ البيانات من التكرار، تطابق إجمالي القيم قبل وبعد التصحيح، واتساق تنسيق الحقول. إذا وجدت كل شيء منضبطًا ونتائج التحليل واضحة، يُعتمد التسليم فورًا.

Infra Engineer – SRE (Kubernetes) (INDIAN EMPLOYEE ONLY) About the Role We are seeking a skilled Site Reliability Engineer specializing in Kubernetes to join a Global Infrastructure team. This role is hands-on and critical to ensuring the stability, efficiency, and reliability of large-scale high-performance AI/ML clusters in data centers. The ideal candidate will bring expertise in system-level troubleshooting, AI cluster maintenance, and operational excellence to ensure maximum performance for infrastructure environments. Experience with large-scale infrastructure automation is considered a strong plus. Responsibilities * Design, implement, and maintain scalable AI/ML infrastructure solutions. * Proactively monitor GPU cluster health, performance, and troubleshoot issues across compute, accelerator, networking, and storage systems. * Automate deployment, configuration, and management of infrastructure resources. * Manage GPU node lifecycle workflows, including provisioning, scaling, maintenance, decommissioning, and upgrades. * Implement CI/CD pipelines for infrastructure deployment and orchestration. * Ensure security, compliance, and operational best practices across infrastructure environments. * Manage incident response related to infrastructure resources, including GPU, CPU, storage, and network components. * Handle customer provisioning requests for GPU resources, including onboarding, configuration, and troubleshooting. * Resolve customer service requests related to infrastructure and platform operations while maintaining high customer satisfaction. * Stay current with emerging GPU hardware and software technologies and integrate improvements where appropriate. * Support regional and international travel requirements to data center locations when necessary. Qualifications * Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field. * 3+ years of experience in data center operations, infrastructure engineering, systems engineering, or site reliability engineering. * Proven experience with infrastructure automation tools such as Terraform and Ansible. * Strong experience with Kubernetes and container orchestration technologies. * Familiarity with NVIDIA GPU Operator, NVIDIA Network Operator, CNI, CSI, and similar Kubernetes ecosystem tools. * Experience with job scheduling systems such as Slurm. * Strong Linux system administration skills. * Proficiency in scripting and automation using Python and Bash. * Experience with observability and monitoring platforms such as Prometheus, Grafana, and Loki. * Knowledge of GPU architectures, NVIDIA CUDA, NCCL, and AI/ML infrastructure is a strong advantage. * Strong troubleshooting and root-cause analysis skills with the ability to analyze logs, metrics, and system performance data. * Excellent communication, collaboration, and problem-solving abilities. Preferred Skills * Large-scale Kubernetes cluster operations. * AI/ML infrastructure and GPU cluster management. * Infrastructure-as-Code (IaC) and automation-first mindset. * Production incident management and reliability engineering. * Data center operations and hardware troubleshooting. * CI/CD platform design and implementation. Meeting every qualification is not required. Candidates with strong technical foundations, relevant experience, and a passion for building reliable large-scale infrastructure are encouraged to apply.

Redactor Especialista en Ingeniería y Ciencias Aplicadas En Asecor Academy estamos buscando un *Redactor Especialista Freelance en áreas de Ingeniería y Ciencias Aplicadas*, con experiencia comprobable en desarrollo de proyectos académicos, investigación, redacción técnica y asesoría metodológica. Buscamos una persona versátil, con capacidad para trabajar diferentes áreas de ingeniería, comprender requerimientos académicos y brindar orientación técnica de alto nivel. No buscamos únicamente un redactor, sino un perfil con mentalidad de asesor/tutor académico, capaz de analizar proyectos, proponer soluciones y desarrollar documentos técnicos con calidad profesional y suma velocidad. Que cuide la atención y comunicación con sus clientes asignados. Modalidad de trabajo * Freelancer / independiente. * Trabajo remoto. * Pago por proyecto asignado. * Disponibilidad según carga de proyectos (al menos 10 al mes, varía por temporada y complejidad). * Colaboración a largo plazo según desempeño. * Pago Mensual Perfil principal requerido Profesional con experiencia en una o varias de las siguientes áreas: Ingeniería y tecnología (prioridad alta) * Ingeniería de Sistemas. * Ingeniería Informática. * Ciencias de Datos. * Desarrollo de software. * Ingeniería de Computación. * Ingeniería de Software. * Ingeniería Electrónica. * Ingeniería Eléctrica. * Ingeniería Mecánica. * Ingeniería Industrial. * Ingeniería Química. * Ingeniería Civil. * Ingeniería Hidráulica. * Ingeniería Automotriz. * Ingeniería en Telecomunicaciones. * Logística y Transporte. * Ingeniería en Estructuras. * Geología * Ingeniería en minas/petróleo * Ingeniería en Manufactura/en procesos. Conocimientos técnicos altamente valorados Buscamos experiencia práctica en: * Desarrollo web. * Desarrollo móvil. * Programación. * Arquitectura de software. * Bases de datos y Análisis de Datos. * Inteligencia artificial aplicada. * Automatización. * Análisis de datos. * Machine Learning. * Estadística aplicada. * Modelamiento y simulación. * Sistemas embebidos. * Electrónica aplicada. Lenguajes/programas deseables pero no limitantes: * Python. * Java. * JavaScript. * C++. * C#. * SQL. * PHP. * PowerBi * Matlab * Frameworks modernos de desarrollo. * Herramientas de análisis de datos. Áreas secundarias valoradas También consideramos perfiles con experiencia en: * Ingeniería Agropecuaria. * Ingeniería Agroindustrial. * Agronomía. * Ciencia de alimentos. * Procesos industriales relacionados. Funciones principales El especialista será responsable de: * Desarrollar trabajos académicos de ingeniería. * Elaborar proyectos de grado, tesis, artículos, informes técnicos y documentos de investigación. * Analizar requerimientos del estudiante. * Proponer estructuras metodológicas y técnicas. * Desarrollar contenido especializado según normas académicas. * Apoyar al estudiante mediante orientación técnica. * Resolver observaciones o correcciones. * Aplicar herramientas de IA como apoyo para investigación y productividad. * Mantener comunicación profesional y organizada durante los proyectos. * Realizar procesos de cobranza en conjunto con el equipo designado en el área. Perfil ideal Buscamos alguien que: * Tenga experiencia realizando proyectos académicos. * Sepa investigar y desarrollar soluciones técnicas. * Sea rápido aprendiendo nuevos temas. * Tenga capacidad para trabajar diferentes ingenierías. * Sea organizado con entregas y tiempos (ejemplo, entrega de un capítulo en 2 días o menos; entrega de una tesis completa en una semana. No todos los proyectos son de solución rápida, algunos pueden durar un año). * Tenga criterio para detectar errores técnicos. * Pueda adaptarse a diferentes metodologías universitarias. Valoramos especialmente: * Experiencia como tutor, asesor académico o docente. * Experiencia profesional en el área técnica. * Manejo avanzado de herramientas digitales e IA. * Capacidad para explicar conceptos complejos de forma sencilla. Requisitos * Experiencia comprobable en proyectos académicos. * Excelente redacción técnica. * Manejo de normas académicas (APA u otras). * Capacidad investigativa. * Disponibilidad para trabajar por asignaciones. Preferiblemente: * Menor de 45 años. * Perfil activo profesionalmente. * Experiencia reciente en tecnología, desarrollo o ingeniería aplicada. Buscamos construir una relación profesional estable con especialistas de alto nivel que quieran formar parte de nuestro equipo.

We have an existing Python web application that is already developed and functional. We are looking for a Fresher or Junior Python Developer to help with deployment, bug fixing, testing, and minor enhancements to existing modules. Tech Stack Python (FastAPI) Supabase (PostgreSQL) HTML, CSS, JavaScript React/Next.js (if applicable) GitHub Cloud Hosting Scope of Work Deploy the application to production Fix existing bugs and issues Resolve API and database-related problems Test application workflows and functionality Make minor changes and improvements to existing modules Add small features based on requirements Improve UI/UX where required Provide support during deployment and testing Project Type Freelance / Remote Experience Required Fresher to 1 Years Experience Knowledge of Python web development Basic understanding of databases and deployment Familiarity with GitHub and version control Budget Fixed-budget project with the possibility of ongoing maintenance and future enhancement work. Project Note The application is already developed and operational. The work mainly involves deployment, bug fixing, testing, troubleshooting, and implementing small changes or enhancements in existing modules rather than building the application from scratch.

I want to automate my Instagram direct messages so that I can schedule them in advance and let the system send them out on a daily, weekly, or monthly cadence. One-time scheduling alone is not enough—I specifically need a recurring option that I can configure per campaign. Key points I have in mind: • Message scheduling must support daily, weekly, and monthly recurrence. • The solution should let me draft each message, choose the recipient(s), set the start date, and define how often it repeats. • A simple dashboard or script-based approach is fine as long as it is reliable and easy to update. • Compliance with Instagram’s current API or approved automation practices is essential so my account stays safe. I’m ready to get started immediately (SEGERA), so please outline how you would implement the scheduler, which language or framework you prefer (e.g., Python, Node.js, or a no-code tool that integrates with Instagram), and how quickly you can deliver a working version.

I need a custom web platform that lets Roblox players wager their own in-game collectibles against each other in a secure, provably-fair environment. The focus is entirely on Roblox betting—no sports or fiat gambling—so every feature must integrate cleanly with existing Roblox APIs, OAuth and the trade system. Core flow: a user signs in with their Roblox account, deposits a chosen item into the site’s bot inventory, joins or creates a betting pool (coin-flip, crash, roulette or similar mini-game mechanics are fine), and, if they win, immediately withdraws either the pooled items or the value equivalent back to their Roblox inventory. What I expect from you • A responsive website (desktop & mobile) with a slick, gamer-friendly UI • Real-time odds, automated item escrow, and instant payout logic • Provably fair randomisation (seed reveal / hash verification) • Admin dashboard for manual item management, user bans, and odds tweaking • Anti-duplication, rate-limit and trade-scam protection baked into the backend • Clean codebase in Node.js or Python (I’m flexible if you prefer a different modern stack) and a straightforward deployment guide Completion timeline: within one month from project start, including testing on a staging server and final hand-over to production. Once the live site successfully processes deposits, runs at least one full betting cycle without errors, and completes a test withdrawal, the job is done.

I need a developer who can take the SnapTrade API and turn it into a fully-functional copy trading platform. The scope is clear: a trader should be able to link an account, select whom to follow, and have every trade mirrored instantly on their own account, while I oversee the system from an admin dashboard. Core features to deliver • Real-time trade copying so follower accounts never lag behind the source account • Automated trade execution triggered directly through SnapTrade endpoints, with graceful handling of order fills, partial fills, and rejections User roles • Trader – the person copying trades, with simple onboarding, a live P/L view, and the ability to pause or resume copying • Admin – my role, where I can monitor system health, approve new trader accounts, view logs, and intervene if an order fails Architecture expectations The backend should lean on SnapTrade’s REST and WebSocket streams for low-latency updates. A lightweight, secure database for account keys and an audit-grade log of every order is essential. I’m flexible on language—Node.js, Python, Go, or whatever you prefer—as long as the final system is robust, documented, and deployable to a cloud host (AWS, DigitalOcean, or similar). Acceptance criteria 1. Connecting a live or paper SnapTrade account, authorising scopes, and persisting tokens securely. 2. Selecting a source account and confirming that a matching order appears on the follower account within two seconds. 3. Admin panel showing real-time order events and the status of each follower. 4. Clear README with setup steps and environment variables. If you have previous experience building trading or copy-trading infrastructure, especially with SnapTrade or similar broker-agnostic APIs, your insight will be invaluable. Let’s get this shipped and start copying trades in production.

AI / LLM Engineer Required – Maritime Safety Intelligence Platform Project Overview This is a maritime safety intelligence platform that collects, organizes, and analyzes maritime accident investigation reports from trusted investigation authorities and industry organizations. The data collection pipeline has already been developed and deployed. The existing system: * Continuously collects reports from multiple maritime sources. * Downloads and stores PDF reports. * Extracts initial metadata. * Stores data in a structured database. * Supports automated scheduled updates. The objective of this project is **NOT** to rebuild the pipeline. The objective is to build the **AI Intelligence Layer** that transforms investigation reports into structured, searchable, transparent maritime safety intelligence. This is **not** a chatbot. This is **not** a summarization project. This is an enterprise-grade document intelligence and knowledge extraction system. Project Objective Develop an AI-powered information extraction and intelligence engine capable of reading maritime accident investigation reports and converting them into structured, validated data. The solution must integrate with the existing Python pipeline and database. The AI must never fabricate information. Every extracted or generated field must remain fully transparent and traceable. --- Scope of Work 1. LLM-Based Information Extraction Extract structured information including (where available): * Incident title * Incident type * Date * Vessel information * Location * Weather conditions * Equipment involved * Sequence of events * Immediate causes * Root causes * Contributing factors * Human factors * Technical failures * Environmental factors * Regulatory issues * Injuries * Fatalities * Pollution * Property damage * Lessons learned * Corrective actions * Safety recommendations * Keywords Output must be structured JSON compatible with the existing database schema. Each field must include its confidence score. --- 2. Evidence-Based Extraction Every extracted value must include: * Confidence Score * Source Page Number(s) * Supporting Quote from the report * Extraction Status Example Root Cause Value: Bridge Resource Management Failure Evidence: Page 17 Quote: "Inadequate bridge resource management contributed to the grounding." Confidence: 99% Status: Official Report Information --- # 3. AI Intelligence Layer Many investigation reports do not explicitly contain sections called: * Lessons Learned * Corrective Actions * Human Factors * Root Causes Instead they contain: * Findings * Discussion * Conclusions * Analysis The AI should analyse these sections and derive structured intelligence. Examples: * Lessons Learned * Corrective Actions * Human Factors * Technical Factors * Procedural Factors * Environmental Factors * Keywords * Similar Incident Tags However... These fields must NEVER appear as official report information. Every AI-generated field must clearly display: Status: AI Generated Supporting Page Numbers Supporting Quotes Reasoning Confidence Score AI Model Version Generation Timestamp Example Lesson Learned Status: AI Generated Value: "Verify ballast sequence before commencing heavy lift operations." Supporting Evidence Page 24 Quote "The Chief Officer commenced ballast operations without verifying the ballast sequence." Reasoning Derived from investigation findings. Confidence 94% --- 4. Unsupported Information If information cannot be supported by the report, the AI must NOT invent an answer. Instead: Status: Not Supported Value: NULL Reason: Insufficient supporting evidence. Confidence: 0% 5. Human Review Workflow Every extracted field must automatically receive one of the following statuses: ✓ Official Report Information ✓ AI Generated ✓ Human Reviewed ✓ Human Approved ✓ Human Corrected ✓ Requires Human Review ✓ Not Supported Low-confidence or unsupported fields should automatically be flagged for human review. 6. Rule Engine Develop a transparent rule engine responsible for calculating: * Severity * Likelihood * Risk Score IMPORTANT The LLM must NOT generate these values. Instead, the rule engine should calculate them using configurable business rules based on extracted facts such as: * Fatalities * Injuries * Pollution * Vessel damage * Environmental impact * Operational consequences The dashboard must display: * Which rules were triggered * Why each score was assigned * Complete calculation history 7. Dashboard Develop an internal admin dashboard. The dashboard should allow users to: Incident Management * Browse incidents * Search incidents * Filter incidents * View original PDF * View structured data * View AI-generated intelligence * Reprocess reports * Edit records * Track processing status --- AI Review For every field display: * Value * Confidence Score * Status * Evidence Page Number(s) * Supporting Quote(s) * AI Reasoning * Human Review Status Users must immediately understand: What came directly from the report. What was generated by AI. What requires human review. Data Quality Dashboard Monitor: * Extraction success rate * Failed reports * Missing fields * Confidence score distribution * Validation errors * Pipeline health * Processing statistics Analytics Provide dashboards including: * Incidents by category * Vessel type * Root causes * Human factors * Technical failures * Geographic distribution * Lessons learned * Corrective actions * Safety recommendations * Risk score distribution 8. Audit Trail Every field must maintain: * Original extracted value * AI-generated value * Human modifications * Approval history * Confidence history * AI model version * Rule engine version * Processing timestamps No information should ever be overwritten without preserving previous versions. Technical Requirements Preferred technologies: * Python * FastAPI * React / Next.js * PostgreSQL (future) / SQLite (current) * OpenAI / Anthropic / Gemini or equivalent * Prompt Engineering * PDF Parsing * OCR (if required) * JSON Schema Generation * Git The solution must integrate with the existing Python pipeline. --- # Deliverables * AI Intelligence Layer * LLM extraction engine * Rule engine * Internal dashboard * API / processing module * Source code * Documentation * Installation guide * Unit tests * Sample outputs --- # Proposal Requirements Please include: 1. Examples of similar document intelligence or LLM extraction systems you have built. 2. Which LLM(s) you recommend and why. 3. How you will minimize hallucinations. 4. How you will evaluate extraction accuracy. 5. How you will implement evidence traceability (page numbers and supporting quotations). 6. How you will distinguish between official report information and AI-generated intelligence. 7. How you will implement confidence scoring and human review workflows. The ideal solution should prioritize accuracy, transparency, auditability, and maintainability over simply maximizing AI-generated content. Note: Do not bid if your expectations are out of the offered budget.

I am looking for an experienced Python Developer for a long-term collaboration. The ideal candidate should be reliable, detail-oriented, and capable of handling various Python-related tasks independently. Requirements: Strong knowledge of Python Experience with Web Scraping and Data Extraction API Integration and Automation Data Processing and Data Management Ability to troubleshoot and optimize existing scripts Good communication skills Ability to meet deadlines Preferred Skills: Selenium BeautifulSoup Scrapy Pandas Requests Flask or Django (optional) Database experience (MySQL, PostgreSQL, SQLite) What I Offer: Long-term work opportunities Multiple projects every month Clear requirements and communication Prompt payment for completed work Please include: Your Python experience Examples of previous projects Your hourly rate or fixed-price expectations Your availability I am looking for a trustworthy developer who can become a long-term partner for future projects.

I’m looking for an experienced trainer who can run engaging, hands-on sessions for university students and faculty eager to learn how AI agents work in real-world settings. Our focus is introductory to intermediate content, and I’m open to both code-based and no-code approaches so long as the concepts land clearly with an academic audience. You’ll deliver a already created curriculum that walks participants through practical use of the following tools (either you can build agent with code or no code both works) in the context of building, deploying and managing agents: • N8N for low-code orchestration • Vibe Coding for rapid prototyping • Claude Co-Work for collaborative AI workflows • Python foundations paired with LangChain and LangGraph for custom agent logic and integrations Sessions should weave in product-management thinking—showing how to translate a requirement into an agent specification, select the right stack, iterate, and measure success. Expect interactive demos, live walkthroughs and plenty of Q&A. What I consider a complete delivery: 1. Live delivery on-campus for the agreed number of hours. 2. A brief summary report capturing participant feedback and your improvement suggestions for the next run. If you have prior experience teaching these tools, or you’ve built production-grade agents yourself, let me know the details and share any sample decks or recordings. Do mention your city and location.

The new FvwmTabs has rewritten the classic Perl FVWM tabbing module in Python/Tk under the same name FvwmTabs. Everything works as expected—multiple tabbers, assigned tabber IDs, the right-click menu, auto-Swallow, and full socket/Tk interaction—but at the moment it still relies on a small helper called tabber_client.py. I now want the existing FvwmMFL module to replace that helper, so the final stack is loaded from .fvwm/config like this: ``` DestroyFunc StartFunction AddToFunc StartFunction + I Module FvwmMFL # will absorb tabber_client.py role + I ModulePath ${HOME}/.fvwm/modules:+ + I Module FvwmTabs ``` The finished script must behave exactly as FvwmTabs does today—no visual or functional regressions—while gaining the extra flexibility FvwmMFL already offers. Primary objective • Enhance functionality by eliminating the duplicate helper and letting FvwmMFL handle all client duties. Key improvements I expect • Cleaner, more reliable socket communication between FVWM, FvwmTabs, and any future helpers. • Room to add extra FVWM commands from within FvwmMFL; I’m not yet certain which ones, so keep the design open for straightforward extension. Scope of work 1. Preserve every current feature: multiple tabbers, assigned IDs, right-click popup, autoSwallow, etc. 2. Supply brief installation steps and a diff or short doc explaining any new command-line switches or FVWM functions you introduce. 3. Validate on a live FVWM session: spawning, docking, tab movement, menu actions, and graceful exit all function without errors. Acceptance criteria • Running StartFunction exactly as shown launches both modules with no stderr complaints. • FvwmTabs UI and behaviour are indistinguishable from the current release. • A test script or steps proving at least one extra FVWM command can be added later without code restructuring. If you know the FVWM internals and speak Python, I have uploaded a zip that includes all related files. The readme.txt describes the dependency, installation, and usage. Also, one screenshot.

I need a seasoned developer who can turn my ideas for an AI-driven trading engine into a production-ready solution that trades Stocks, Forex, and Cryptocurrency seamlessly from the same code-base. The core of the job is to code and fine-tune two complementary strategies—trend-following for capturing momentum and mean-reversion for exploiting pullbacks—then wire everything to real-time data feeds and broker/exchange APIs so orders are executed automatically. I already have broad rules in mind, but I’m counting on your expertise with Python, pandas, NumPy, scikit-learn (or an equivalent ML framework) and a solid back-testing toolkit such as Zipline, Backtrader or TradingView’s Pine Script for quick iteration. Risk management, position sizing, and logging must be built in from day one; think stop-loss, max drawdown guardrails, and configurable exposure caps that I can adjust without touching the source. High-level deliverables • Strategy logic for both trend-following and mean-reversion, coded and unit-tested • Historical back-test report with key metrics (Sharpe, max DD, win rate) plus a forward-test sandbox • Live trading module connected to at least one stock broker API, one forex broker API, and a major crypto exchange API • Simple dashboard or CLI that lets me start/stop strategies, adjust parameters, and monitor P&L in real time • Handover session and well-commented code so I can extend the system later The project will be considered complete once the system places live trades in all three markets, logs them correctly, and the back-test/forward-test results match the logic we agreed upon. If this sounds like your wheelhouse, let’s discuss the tech stack you prefer and map out milestones.