Machine learning

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're looking for an experienced Blockchain Full-Stack Developer to help build a next-generation Endless Runner Web3 game with on-chain gameplay mechanics, NFTs, and tokenized rewards. This is an opportunity to work on an innovative game that combines modern web technologies with blockchain infrastructure. You'll collaborate with game developers, backend engineers, and designers to build scalable smart contracts, Web3 integrations, and a seamless player experience.

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.

We are looking for a specialist or a team to implement AI solutions in the communication of the CT and MRI medical center. Requirements: integrate AI into telephony for handling incoming and cold calls; ensure natural Ukrainian-speaking communication with minimal pauses; integrate an AI assistant into the Binotel chat or propose an effective alternative; automate responses to inquiries, initial consultations, and scheduling for examinations. Important: the medical field requires accurate information gathering before CT or MRI, particularly regarding the examination area, preparation, referrals, and possible contraindications. The solution must be empathetic, professional, and allow for the transfer of complex cases to an operator. In your response, please send relevant cases, a description of the proposed solution, and estimated timelines. Experience in medical projects will be an advantage.

It is necessary to develop an application in MATLAB that can process images/videos, identify individual objects, analyze their characteristics, and, if necessary, use machine learning methods to automate the analysis. Desirable: confident knowledge of MATLAB; experience in Computer Vision / Image Processing; experience with Machine Learning;

Developer for creating AI bots - multi-agent system and automations in AI application. What is needed: proficiency in n8n; experience integrating OpenAI and other AI models via API; working with webhooks, REST API, and Supabase; ability to create reliable AI workflows and automations. Create multi-agent systems from scratch or with a framework AI Agents, MCP, RAG

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.

Good day, what is needed: 1. Conduct an analysis of the processes (personal of the manager) and the business. 2. Consider where (in which processes) AI assistants can be implemented. Where it will be appropriate and effective. 3. Develop a plan. We are engaged in the publication of scientific articles.

We are looking for a 3D GenAI Engineer / AI 3D Pipeline Developer We need to create a solution that can qualitatively generate 3D models from one or more images. It is important that this is not just a ready-made demo, but a clear and reproducible process: from the input image to a full-fledged 3D asset with mesh, geometry, textures, and the possibility of further use. What needs to be done: * test modern image-to-3D models and approaches; * determine which option is best suited for our task; * understand Trellis, Hunyuan3D, or similar solutions; * if necessary, use Gaussian Splatting in the 3D pipeline; * set up the conversion of Gaussian Splat / splat representation into a 3D mesh; * obtain usable geometry; * generate high-quality textures; * bring the result to a usable 3D asset state; * find the optimal balance between quality, generation speed, and pipeline complexity; * build a clear process that can be repeated for different images; * perform fine-tuning, LoRA, or other model adaptations for specific types of objects; * be able to work with ComfyUI: understand graphs, use the ComfyUI API, and write custom nodes if necessary. In your response, be sure to show a few examples in the format: input image and the resulting 3D model. The format does not matter: render, video, 3D viewer, GLB, OBJ, FBX, or Blender file. We are primarily interested in realistic objects: furniture, jewelry, interior items, decor, accessories, and other physical goods. Examples from anime or stylized characters are not suitable.

About the Project We are looking for an experienced AI Automation Engineer to design and build a secure, self-hosted AI platform that combines a local Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and multiple AI agents to automate business workflows. This is a hands-on engineering role for someone who has experience building production AI systems—not simply integrating ChatGPT APIs. The goal is to create a private AI ecosystem capable of securely indexing company knowledge, answering questions using cited sources, processing meeting transcripts, and automating internal business processes. Responsibilities You will be responsible for: Designing and deploying a locally hosted LLM on a VPS or dedicated server Building a secure RAG pipeline using frameworks such as LlamaIndex or similar Creating document ingestion pipelines supporting PDF (including OCR), DOCX, TXT, XLSX and meeting transcripts Implementing document indexing, metadata management, deduplication, and versioning Developing AI agents for: Meeting transcription processing Automatic meeting summaries Action item extraction Client knowledge retrieval Building APIs or a simple web interface for querying the knowledge base Ensuring strict client data isolation and permission controls Implementing source-cited responses to minimize hallucinations Optimizing system performance, scalability, and reliability Writing documentation and deployment guides Performing testing and security validation Required Skills Strong Python development experience Experience with LLM frameworks RAG architecture experience LlamaIndex, LangChain, or equivalent Vector databases (Qdrant, Chroma, Pinecone, Weaviate, FAISS, etc.) Local/open-source LLM deployment (Llama, Mistral, Gemma, DeepSeek, etc.) API development (FastAPI preferred) Docker Linux server administration VPS deployment Git Authentication and access control Experience with OCR pipelines Experience working with structured and unstructured documents Fluent English What We’re Looking For The ideal candidate: Has built production AI systems from the ground up Understands RAG best practices Can work independently Thinks like a software architect—not only a developer Writes clean, maintainable code Communicates clearly Can recommend the best technologies instead of simply following instructions Project Type Freelance / Contract Remote Milestone-based Long-term opportunity for future AI automation projects Please Include With Your Application Portfolio of similar AI/RAG projects Examples of local LLM or AI agent implementations Estimated timeline Estimated project cost Hourly or fixed-rate pricing

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)