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Ollama: Resume Tailoring CLI
Learn a CLI workflow that scrapes job descriptions, uses local Ollama AI to generate YAML resume overlays, and compiles final PDFs with Typst.
It’s a CLI for managing resume submissions to jobs and using AI to tailor parts of my resume based on the job description. There’s a workflow that involves:
- Scrape JD into markdown
- Prompt AI to generate a resume overlay rewrite based on JD
- Manual review of overlay
- Compilation of resume from YAML to PDF using typst
It uses YAML heavily, resume data is stored in YAML, role-specific YAML overlays to rewrite sections and then there’s a singular YAML file that acts as a repository to keep track of roles as they flow through the process, some CLI commands to update statuses.
- GPT-4GPT-4 is OpenAI’s large multimodal model: it processes both text and image inputs, delivering human-level performance on complex professional and academic benchmarks.This is OpenAI’s latest milestone in scaling deep learning: a large multimodal model accepting both text and image inputs. It demonstrates a significant capability leap over its predecessor, scoring in the top 10% on a simulated bar exam (GPT-3.5 scored in the bottom 10%). The model handles nuanced instructions and long-form content, supporting context windows up to 32,768 tokens (32K model). This capacity allows processing up to 25,000 words in a single, complex prompt. GPT-4 is engineered for enhanced reliability, steerability, and advanced reasoning across diverse tasks.
- LangChainThe open-source framework for building and deploying reliable, data-aware Large Language Model (LLM) applications.LangChain is the essential framework for engineering LLM-powered applications: it simplifies connecting models (like GPT-4 or Claude) to external data, computation, and APIs. The platform provides a modular set of components—Chains, Agents, Tools, and Memory—allowing developers to quickly build complex workflows like Retrieval-Augmented Generation (RAG) pipelines and sophisticated conversational agents. Its Python and JavaScript libraries, combined with LangChain Expression Language (LCEL), offer a standardized interface for rapid prototyping and moving applications to production with confidence.
- OpenAI APIOpenAI API: Your direct gateway to cutting-edge AI models (GPT-4o, DALL-E 3, Whisper), enabling scalable, multimodal intelligence integration into any application.The OpenAI API provides authenticated, programmatic access to a powerful suite of generative AI models. Developers leverage REST endpoints and official libraries (Python, Node.js) to integrate capabilities like advanced text generation (GPT-4o), image creation (DALL-E 3), and speech-to-text transcription (Whisper). This platform is engineered for scale, supporting millions of daily requests for tasks from complex reasoning to real-time customer support agents, ensuring your application gets reliable, state-of-the-art intelligence.
- PythonPython: The high-level, general-purpose language built for readability, powering everything from web backends to advanced machine learning models.Python is the high-level, general-purpose language prioritizing clear, readable syntax (via significant indentation), ensuring rapid development for any team . Its ecosystem is massive: use it for robust web development with frameworks like Django and Flask, or leverage its power in data science with libraries such as Pandas and NumPy . The Python Package Index (PyPI) provides thousands of community-contributed modules, offering immediate solutions for tasks from network programming to GUI creation . The language is actively maintained by the Python Software Foundation (PSF), with the stable release currently at Python 3.14.0 (as of November 2025) .
- DockerDocker is the open-source platform that packages applications and dependencies into standardized, portable containers for consistent execution across any environment.Docker is the industry-standard containerization platform, enabling developers to build, ship, and run applications efficiently. It uses the Docker Engine (the core runtime) to create lightweight, isolated environments called containers: these units bundle an application’s code, libraries, and configuration. This self-contained approach guarantees consistency, eliminating the 'it works on my machine' problem across development, testing, and production environments (local workstations, cloud, or on-premises). Docker debuted in 2013 and now serves over 20 million developers monthly, simplifying complex workflows like CI/CD and microservices architecture by leveraging tools like Docker Hub for image sharing and Docker Compose for multi-container applications.
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