CareerOps is a local-first, AI-assisted job search automation platform. It scans 100+ ATS job boards and aggregators (Greenhouse, Lever, Ashby, Workday, iCIMS, PeopleSoft, 55+ providers) through a standardized provider abstraction, evaluates each posting against the user's CV by delegating to AI coding CLIs (Claude Code, Codex, Gemini, OpenCode, etc.), emits structured A–H evaluation reports with 1–5 fit scores, generates ATS-optimized CV PDFs and cover letters, and tracks the full application lifecycle in a Markdown/TSV data store. It ships three surfaces: a Node.js CLI core (~150 .mjs modules), a Next.js 16 web dashboard, and a Go Bubble Tea TUI. All data remains on the user's machine; nothing is ever auto-submitted—a human presses send. The evaluation, scoring, and pattern-analysis features (funnel velocity, repost detection, calibration against outcomes) are product features, not research artifacts, so the repo is classified as an application despite these analytical components. Architecturally, this is a monorepo spanning three runtimes that share a flat-file data layer coordinated by cross-process file locks (SHA256-derived lock directories compatible between Go and Node). Its standout engineering includes a provider registry pattern for ATS integrations, a plugin engine with mandatory human-in-the-loop enforcement, capability-fenced CLI spawning for AI agents (read-only/web-search-only modes with per-CLI fencing probes), atomic write-with-backup for every user-layer file, and a 400+ file test suite including byte-level parity tests that keep web-side algorithm mirrors aligned with core logic, SSRF-hardening tests across all providers, and cross-platform contention tests. The repo also demonstrates unusually mature governance: plugin registration with pinned SHAs, direction gates in CI, localization drift checks for 16 languages, and a self-updater with rollback. Weaknesses: the Go/Node split duplicates tracker parsing logic (mitigated by shared header-alias tables but still a maintenance burden), parsing is regex-heavy over markdown tables with many special-case bugs, and rapid feature expansion has produced visible bloat—dozens of AI-provider-specific eval scripts, many localized mode trees, and extensive one-off migration scripts. The external ecosystem (ATS site markup, AI CLI permission models) shifts constantly, requiring the active dead-board pruning and fencing probes visible in the codebase. Overall this is a well-crafted, heavily tested local-first application with several genuinely reusable security and concurrency patterns, held back from exceptional by its dual-runtime duplication and feature sprawl.
This repository provides a LaTeX document class (`awesome-cv.cls`) for creating professionally formatted CVs, résumés, and cover letters. The class offers a clean, semantic markup system with configurable sections for personal information, work experience, education, skills, honors, publications, and more. It includes a rich set of styling commands, color themes, and support for Font Awesome icons and modern fonts (Source Sans 3, Roboto). The repository also ships example documents (cv.tex, resume.tex, coverletter.tex) with sample content demonstrating all features. The project is maintained with CI/CD via GitHub Actions to compile PDF examples on each push, and it provides both a Makefile-based build and a Docker workflow for reproducible compilation. Its primary value is as a reusable LaTeX template that can be customized for job applications.
This repository is a minimalist CV (resume) web application built with Next.js 16, React 19, TypeScript, and Tailwind CSS. It serves as a personal portfolio site that renders resume data from a single TypeScript file into a print-friendly layout. The architecture follows Next.js App Router conventions with server-side rendering and static generation. Key features include a command menu for quick navigation, responsive design, dark mode support, SEO-optimized structured data (JSON-LD), and a production-ready Docker setup. The codebase demonstrates strong engineering practices with clear separation of concerns, reusable UI components, and attention to accessibility and performance.
ResumeLM is an AI-powered resume builder application built with Next.js 15, React 19, and TypeScript. It provides a SaaS platform for creating, editing, and optimizing resumes for tech job applications, featuring AI-assisted content generation, resume tailoring for specific job applications, ATS optimization, and cover letter generation. The application uses Supabase for authentication and data storage, Stripe for subscription management, and integrates with multiple AI providers (OpenAI, Anthropic, Google, etc.) via OpenRouter or direct API keys. The architecture follows a modular structure with separate concerns for authentication, dashboard, resume editor, admin panel, and settings, leveraging server actions for data mutations and a robust AI abstraction layer for model selection and usage tracking.
This repository is a Hugo-based starter template for building an academic CV website, part of the HugoBlox ecosystem. It provides pre-configured project structure, sample content (blog posts, publications, projects, courses, events, slides), and CI/CD workflows for deployment to GitHub Pages or Netlify. The codebase is primarily configuration (Hugo, YAML, Go modules, npm packages) and Markdown content, with minimal custom layout code (a single partial for a GitHub button). Its primary value is as a boilerplate for researchers and academics to quickly spin up a personal portfolio site. While it contains no complex application logic, it demonstrates solid engineering practices through well-structured configuration, modern build tooling (Tailwind CSS v4, Pagefind search), and reusable CI/CD patterns.
The resume-cli is a command-line tool for managing JSON Resume files. It provides commands to initialize, validate, export (to HTML/PDF), and serve a resume locally. The codebase follows a modular structure with separate modules for each command (init, validate, export, serve) and shared utilities for input handling, schema loading, validation, and theme-based rendering. The application uses the commander package for CLI argument parsing, relies on external themes for rendering, and supports multiple input formats (JSON, YAML, directory via quaff). Despite being archived per the README (development moved to a monorepo), the code demonstrates clean separation of concerns, proper error handling, and test coverage for core functionalities.