Develop an AI agent that autonomously executes user scenarios in the Chrome browser, identifies discrepancies with expected behavior, and formats them as bug reports in [Google Docs / Notion / Jira / Markdown in the repository]. How it should work: 1. Launch: [manually by button / scheduled once a day / automatically on each stage deployment]. 2. The agent opens Chrome, logs into a test account, and executes scenarios from the list (we will provide the list — [N] items at the start, format is free: in plain human language). 3. At each step, it checks the result: whether the expected element appeared, whether the data is correct, if there are any errors in the console and failed network requests, and whether the layout is broken. 4. At the end of the run, it generates a report: executed scenarios, found defects, for each defect — reproduction steps, expected and actual behavior, screenshot, severity, link to video or trace of the run Who can take this on and possibly even from existing repos on GitHub? Who is ready to start?
need someone who can create solutions in Pocket Option create a bot strategy algorithm waiting for a specialist to join the team. for a reasonable price
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
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.
We specialize in obtaining grants for existing companies that plan investments. We came up with the idea of creating/connecting available AI models to search for companies that are planning or are already in the process of investing and to offer them grants for this purpose (we mainly deal with grants for the purchase of machinery and equipment). The AI would search available websites and registries to find investors. Then, it would also handle the automation of mailing to these companies and would filter out companies that: - fit into the lead categories (I think, 5-6 parameters); - replied to the email and are interested in the grant; Of course, additional suggestions are welcome; I am also considering Voice Calling and SMS sending. I would like to emphasize that I am not only interested in finding LARGE or very LARGE companies, but also smaller ones, for example, employing 8-50 people. And not just large cities, but also smaller towns. Sample target for testing: SUBCARPATHIAN For micro, small, and medium-sized enterprises (SMEs) and large enterprises from the Subcarpathian Voivodeship that belong to the security and defense industry. An enterprise from the security and defense industry is understood as an enterprise that: develops or manufactures products, technologies, or systems intended for military applications, related to national security, critical infrastructure protection, or citizen safety or provides services supporting security and defense systems (e.g., in the field of cybersecurity) or conducts research and development in the area of military technologies and security systems and which achieves a significant share of revenue from the sale of products, technologies, systems, or services in the area of security and defense, i.e., above 5% of total net revenue (cumulatively over the last 3 closed accounting periods). Support available for companies that conduct business in an organized and continuous manner in the Subcarpathian Voivodeship for a period of no less than 24 months counting back from the date of the call for applications. We have proposals for websites to search. Ultimately, the program would prepare a daily summary of such investors for us. Can someone help us with this? I read that it is possible, but I don't have time to deal with it myself :)
It is necessary to develop a service based on Claude or another suitable AI model that can automatically find competitor companies, contact them via email and phone, gather necessary information, and enter the results into a single table. Main task of the service The user specifies a specific request, for example: - to find out the price of a certain type of meat; - to find out the price of a specific type of wood; - to clarify the cost of a product or service; - to check the availability of a product; - to find out the delivery times; - to get the terms of cooperation. After that, the system should: 1. Find suitable companies. 2. Collect their contact details. 3. Send them personalized emails. 4. Call the companies using an AI bot. 5. Get answers to the questions asked. 6. Save all results in Google Sheets or another table. For each new task, the user should be able to change search queries, selection criteria, email text, and phone call script. Stage 1. Company database collection Stage 2. Email distribution and response collection Stage 3. AI calling of companies --- Important technical requirements - Ability to use Claude for generating emails, analyzing responses, and managing dialogue. - Ability to replace the AI model without a complete system overhaul. - Integration with Google Sheets. - Integration with Gmail or another email service. - Integration with an AI telephony service. - History of all emails, calls, and changes. - Ability to stop or pause a task. - Control of expenses for calls, emails, and AI requests. - Limitation on the number of calls per day. - Protection against resending emails and making repeated calls to the same company. - Compliance with legislation on phone calls, recording conversations, email distributions, and the use of personal data. --- Expected result As a result, there should be a service in which the user creates a task, specifies what information needs to be obtained, selects geography and sources, after which the system independently: 1. Collects a database of companies. 2. Finds contact details. 3. Sends personalized emails. 4. Calls companies according to the specified script. 5. Analyzes responses. 6. Forms a single table with results. 7. Shows from which source each piece of information was obtained. For the first discussion with the developer, it is also worth asking to separately estimate the cost and timelines for MVP, automatic parsing, AI telephony, and monthly infrastructure.
Hello everyone! There are currently many sellers on the market selling claude tokens through proxies. I need to test different sellers and understand which API works most reliably for coding claude code. You will need to authenticate through the terminal in claude code and monitor the stability of the API (run it under load) and find the most optimal one. Who can take this on right now?
I'm looking for a performer to build an AI-agent system that automates the marketing and sales pipeline: from content generation to lead segmentation in CRM and hypothesis analytics. Below are the tasks grouped by functional purpose. How many services/agents will be in the final architecture and on which stack is up to you, based on your own experience. The main thing is that the solution covers all the tasks below, is functional, maintainable, scalable, and must have a convenient mechanism for review/approval of results by a person before publication or launching ads. Block 1. Content Generation (Multichannel Copywriting)One or several generating modules that produce texts for various formats based on project materials (course programs, interviews, broadcasts): Landing page texts Email sequences: warming chains, newsletters Posts in Telegram bot/channel Advertising texts for Meta, including variants for different hypotheses Content plan and posts for Facebook / Instagram / Telegram Blog articles (including based on transcribed videos — see Block 3)Block 2. Production of Final MaterialsTransforming the finished text into a final artifact ready for publication: Lead magnets — layout and assembly of a ready PDF (checklists, guides) with Canva/Figma integration for editing Meta advertising creatives — static visuals with resizes for campaign formats Landing pages on Framer — page structure, CMS filling, assembly of a ready page for launch.Block 3. Video → Blog Transcription of videos (broadcasts, interviews, workshops) Cutting the transcript into articles that lead into the funnel Publication in the blog on the main site (there is currently no blog — possibly a blog section needed on Framer CMS; open question: can the system create it itself, or is this a separate task)Block 4. Integration with Meta Advertising Cabinet Uploading finished texts and creatives to the cabinet Tagging ads/campaigns by hypothesesBlock 5. CRM and Lead Routing Automatic segmentation of leads in the Telegram bot (attended/did not attend the event, funnel branch, offer) Transferring segments to CRM Auto-tagging new leads in CRM when entering the funnel (through registration)Block 6. Hypothesis Analytics Formation of a table/dashboard of hypotheses: costs/results for each funnel, lead magnet, creative. Periodic AI analysis with recommendations: what to scale, what to stop Evaluation of landing page conversion ratesWhat We Expect in the Response Architecture Vision — how would you break these 6 blocks into agents/services, on which stack (orchestration, generation, data storage). Portfolio/Cases — examples of similar automation systems (marketing, leads, content generation). Estimated assessment of timelines and costs, preferably step by step. Questions about the Terms of Reference, if something is unclear.
We are looking for an experienced Python/AI developer (or a small team) to create an AI service based on a Telegram bot. The project automates the acceptance of orders from masters and service centers on an active B2B marketplace for spare parts for mobile devices and electronics (catalog of ~30,000 SKUs). What the bot should do:Multimodal input: accept text, voice messages (Whisper API), and photos of parts/markings (GPT-4o Vision), recognize nomenclature, revisions, and part numbers.Smart search: match masters' jargon (battery, accumulator, display, original) with official names through vector search (Vector DB).Interactive UI: when there are several quality options (Original, High Copy, etc.) — group them into one message, display inline checkboxes with prices and stock, recalculate the cart "on the fly".Cascading clarifications (Slot Filling): when faced with an ambiguous request, clarify parameters step by step (model → color → revision).Order processing: automatically send the completed JSON order package to the website via API after confirming the cart.Stack: Python (FastAPI), Aiogram 3.x, OpenAI API (GPT-4o + Whisper), Qdrant, Docker + docker-compose. Implementation period: 5–7 weeks.⚠️ Mandatory condition for response: To filter out auto-responses from spam bots, please indicate in your message: which embedding model you will choose for vector semantic search across 30,000 technical SKUs and why? Responses with template text that do not answer this question will be automatically rejected.
Counting of finished products and peopleNon-conforming products. Based on a high-performance line, ensure accurate counting of throughput, workforce participation, identification of non-conformities, and context of stops/reconfigurations, providing ready analytical information.
I'm looking for a person (not a bot) who understands AI agents and knows how to build them. By AI agent, I mean: processing input data, making a request to a 1x LLM or similar AI model, potentially requesting MCP or similar, potentially requesting a RAG system, processing output data And experience should include working with multiple agents that also self-check, pass data to each other, etc. Understanding of RAG systems is also required. Experience in automating marketing projects is preferable, for example: search for the latest topics using deep research on sources 1,2,3.... compare them with what has been found before and remove duplicates. Also compare with already published articles analyze the found news in relation to the necessary context search for and assess the importance of these topics create a report for the user create a sample post for LinkedIn, Instagram, X in the style that was previously used (and check)
Task Description: Development of MCP Server for 1C EcosystemGeneral Goal To develop an intermediary layer (MCP Server) that will allow LLM agents to safely interact with the 1C information base. This will enable users to receive reports, create documents, and analyze data through a chat interface using natural language.Functional Capabilities (Tools) The server must provide AI models with a set of tools for performing the following actions: Data Reading: Searching for counterparties, obtaining stock balances, sampling product prices. Analytics: Generating management reports in text or tabular form. Actions: Creating drafts of documents (Customer Order, Invoice), changing task statuses. Metadata: Obtaining the structure of objects (what fields are in the "Employees" directory) so that the model understands what it is working with.Main Implementation Stages API Design in 1C: Preparation of HTTP services in the 1C extension that will accept requests from the MCP server. Setting up authorization (Basic or Bearer token). Development of MCP Server: Defining the input parameter schema for tools (JSON Schema). Mapping requests from MCP to OData calls or HTTP requests to 1C. Security and Limitations: Restricting access rights (Read-only by default). Limits on the volume of returned data (to avoid "crashing" the model's context window with a huge table). Testing: Connecting the server to Claude Desktop or another MCP client. Checking scenarios: "How much of the product 'Brick' is left in the main warehouse?" or "Create a draft invoice for LLC 'Vector' for 5 monitors".Expected Result A working executable file or service that registers in the MCP client configuration. When a request is entered in the chat, the AI automatically calls the necessary tool, queries 1C, and provides a structured response based on real data from the system. Important Note: The main value of this solution lies in the transition from manual report generation to the concept of "Talk to your ERP" (talking to your ERP system).