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I'm looking for a developer to create a Telegram bot for our clothing store. The essence: the manager sends the bot a regular photo of the product (on a hanger, on a table) — the bot generates a ready gallery of professional shots for the product card on the website/marketplace using AI. What the bot should be able to do Receive 1–4 photos of the product in the Telegram chat. Automatically recognize the product (category, cut, male/female) — without manual description. Generate a gallery of ~6 shots: product on a model: studio front, back, dynamic pose; product shots: flat lay, close-up detail, ghost mannequin; lifestyle shot. Consistent model — the same person in all shots of the gallery and between galleries (brand face). Several shooting styles to choose from (for example: streetwear, casual, sport, old money). Output format: JPG 4:5, files with numbering (-01, -02…) — ready for upload. The ability to regenerate a single shot or change the style without regenerating the entire gallery. Access only for our team (allowlist), daily limits per user. Accounting for AI API costs: show the price before generation, keep statistics. Technical expectations Image generation: Gemini (Nano Banana) / DALL·E / Flux or similar — please suggest an option with justification of quality and cost per shot. The bot must operate 24/7 on VPS (auto-restart, turnkey deployment). Secrets via .env, code delivered to us (repository). What we will provide Product photos for testing, wishes regarding brand styling. What I expect in the response Cost and timeline estimate. Approximate cost of one gallery. From and To

A chatbot is needed for booking clients in the beauty sector (Instagram/Telegram) on SendPulse or n8n: Collecting name and phone number Choosing a service through buttons (list of 3-5 options) Final message with booking details

Auto-notification system for inventory replenishment analytics. On 8n8. Small automation for e-commerce. Once a day, the system calculates the sales velocity for each product and sends a message in Telegram indicating when to reorder (with the recommended quantity) and how many days the remaining stock will last. What we do: Data retrieval: KeyCRM API (order history) + stock/orders from Rozetka (API or export) Storage in SQLite on my VPS (Contabo, Linux) Calculation of average sales, days of stock, reorder point ABC analysis: prioritization of which product to replenish first (class A — main by revenue — more important) Daily Telegram bot + cron job All thresholds (lead time, safety stock, target stock) — in the config

A system is needed for the automatic processing of large volumes of information using AI. I am currently using my own prompt in Claude. It works well with small texts, but with larger volumes, the neural network may skip parts, stop before the end, or report completion even though the material has not been fully processed. A system is needed that can accept a book of 200+ pages, several books, PDFs, DOCX, TXT, subtitles, transcripts of audio and video, or another large array of information and automatically process it from start to finish. One of the main formats for the result for each meaningful sentence: English version. •••••••••••••••••••••••••••• Russian version. •••••••••••••••••••••••••••• English version. •••••••••••••••••••••••••••• English version. •••••••••••••••••••••••••••• Russian version. •••••••••••••••••••••••••••• English version. Each meaningful line is output four times in English and two times in Russian in the specified order. The translation must accurately convey the meaning. Short sentences can be combined, and long ones can be divided into complete meaningful lines. The format must be customizable: the number of English repetitions and translations, their order, languages, separator, line length, main prompt, and saving different templates. For example, instead of English–Russian–English–English–Russian–English, any other sequence can be chosen. If the document cannot be processed in one request, the program should automatically split it into internal blocks, send them to Claude, ChatGPT, Gemini, or another model, check the result, repeat problematic parts, and combine everything into one file. The user should not have to manually copy text for four pages. It is necessary to check that no sentence, paragraph, or meaningful fragment is missed, that there are no duplicates between blocks, and that the structure corresponds to the template. The check should not rely solely on the neural network's assertion. Audio processing The system should also process large archives of audio, such as a Telegram channel with 300 broadcasts of about one hour each. It is preferable to automatically download audio or accept the entire archive, recognize speech, and process everything without manual involvement. A structured summary should be created for each broadcast: topic, main thoughts, important facts, examples, recommendations, and conclusions. Greetings, advertisements, conversational filler, and meaningless repetitions are removed, but useful information is retained. After processing, not only separate notes are needed, but also one comprehensive readable document where the information is organized by topics. If a topic was discussed in different broadcasts, the materials are gathered into one section, duplicates are removed, and links to the original recordings are preserved. The results should include: — a summary for each broadcast; — a general thematic document; — search by words and topics; — connection of conclusions with the original audio; — saving progress and continuing after errors. I can already perform most of these actions myself, but only on a small scale and manually. Therefore, I am also open to considering a more efficient way to download and convert audio to text if the contractor offers a solution better than what I currently use. What to include in the response Please write: — how the system will be implemented and what product I will receive; — which AI and speech recognition models will be used; — how completeness is checked and how omissions and repetitions are excluded; — whether prompts, the number of translations, repetitions, and their order can be changed; — whether it is possible to download materials from Telegram; — the cost of development, API, timelines, and the price for further improvements. The main goal is a universal system that processes large books and hundreds of hours of audio without manual involvement, does not lose information, and delivers a finished result strictly according to the chosen template.

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