Reactive Resume is a full-stack, open-source resume builder and job application tracker. It provides a rich web interface for creating, editing, and managing resumes with real-time preview, multi-format export (PDF, DOCX, Markdown, JSON), and ATS compatibility analysis. The system includes an AI-powered assistant for resume tailoring and cover letter drafting, an MCP server for LLM integration, and a semantic CSS stylesheet system for fine-grained PDF styling. The architecture is a monorepo with a React frontend (TanStack Router, Zustand, Lingui i18n), a Node.js API layer (oRPC, Drizzle ORM, PostgreSQL), and dedicated packages for PDF generation, DOCX export, authentication, and UI components. Key entry points include the resume builder interface, public resume sharing pages, an AI agent chat, and a job application board with stage tracking and CSV import/export. External dependencies include PostgreSQL, Redis, AI provider APIs (OpenAI, Anthropic, Gemini), and cloud storage (Vercel Blob or S3). The codebase is well-structured with extensive unit and integration tests, a reusable UI component library, and a modular package architecture that supports self-hosting via Docker or Vercel.
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.
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.