Frank
327e78b950
The vault was 155 pages / 31,210 lines, most of it measurement ledgers for payloads that no longer run. A throughput figure for a runtime nobody operates is not a record: AI moves faster than the vault, so the number is wrong before anyone reads it, and a wrong number in a runbook gets believed. Per the new purpose test in `AGENTS.md` → *Documentation Policy*, it is destroyed. Gone (`.AGENTTRASH/20261019-destruction/`, KILL-LIST.txt is the manifest): - the `frank-medium` endpoint and its five era pages, `frank-npu`, FastFlowLM, the ikllama/kingjones/dealign/frank-redo eras, the ling/swift15/freetoken/ubatch campaign pages, qwen3.8 spec-matrix and strix-optimizations, the two "Running Qwen3.6 …" pages; - the whole `Pipeline Parallelism/` folder (no live script passes a pipeline flag) and the multi-device/x4-pair/hybrid/offload experiment pages; - every vLLM page — no vLLM endpoint exists on this fleet; - `LLM/deployment.md` (`Architecture/Deployment Flow.md` is the test-referenced owner) and the external surveys (SGLang, Lemonade, llama.cpp forks/diff, self-hosted app census, the TTS survey) that were obsolete by construction; - `log.md`, 2,380 lines. Git holds the chronology; a causal law belongs to the page that owns the thing it governs, never to a diary. Survivors: 101 pages, 17,542 lines, 0 OKF violations, 0 unresolved links. Eras tables keep the window, payload, runtime, reason and rollback pointer; the rate columns are blanked. The live shapes stay verbatim — those numbers are what the deployer and `models.yml` consume.
215 lines
13 KiB
Markdown
215 lines
13 KiB
Markdown
---
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type: reference
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description: "Self-hosted job-search and resume tools: the landscape, local-LLM fit, and the recommended homelab stack."
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tags:
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- research
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- job-search
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- resume
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- self-hosted
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timestamp: 2026-09-22
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---
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# AI Job Search and Resume Tools
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Research brief for self-hostable (Docker/Podman) open-source applications in two
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categories: (a) **job search** — finding jobs, matching, tracking applications —
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and (b) **resume/CV formatting** — building, tailoring, and optimizing. Target
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environment: this homelab (2 Ubuntu GPU servers + 1 macOS mini, Podman/Quadlet,
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Caddy, local LLMs via Ollama/llama.cpp).
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## The Two Shapes
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The space split into two distinct shapes in 2026:
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1. **Web-app trackers** — Dockerized dashboards for tracking applications,
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resumes, and AI-assisted matching/tailoring (JobSync, offerPilot, JobNavigator
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on the job-search side; Reactive Resume, Resume Matcher, OpenResume, ResumeLM
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on the resume side). The natural homelab fit: one `docker compose up` (or
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Quadlet unit), data in a local volume, AI pointed at a local Ollama/llama.cpp
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endpoint.
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2. **CLI agent frameworks** — "bring your own AI coding CLI" tools that run the
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whole job search as local files + prompts (career-ops, MadsLorentzen/ai-job-search).
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Viral in 2026 (72k and 44k stars) and the strongest at *finding* jobs (portal
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scanning) and *tailoring* CVs, but not self-hosted web services — they run on
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your machine against a paid AI CLI (Claude Code, Codex, etc.).
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**Strongest self-hostable picks for this fleet:** JobSync (MIT, Ollama-as-default
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AI + MCP server), Resume Matcher (Apache-2.0, best dedicated tailoring tool)
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Reactive Resume (MIT, best pure resume builder, 43k stars). There is **no
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dominant self-hosted app for pure job *discovery*** at scale — that niche is
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JobNavigator (Docker, 126 stars) plus the CLI frameworks.
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## Job-Search Applications
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### career-ops — https://github.com/career-ops-hq/career-ops
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MIT, ~72,400 stars (verified 2026-09-22). Open-source AI job-search "command
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center" that runs inside any AI coding CLI (Claude Code, Codex, Copilot, Gemini
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CLI). Evaluates listings against your CV with a published five-dimension rubric,
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generates ATS-optimized per-role PDF resumes, drafts Greenhouse/Ashby/Lever
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application answers, scans 150+ career portals with zero API tokens, and tracks
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the pipeline in a Go terminal dashboard. Never auto-submits. Uses whichever AI
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CLI you already pay for; data is local Markdown/YAML you own. Not a server:
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`npx @santifer/career-ops init`.
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### ai-job-search (MadsLorentzen) — https://github.com/MadsLorentzen/ai-job-search
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MIT, ~43,600 stars. Claude Code framework: `/setup`, `/scrape`, `/rank`
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`/apply <url>` (fit evaluation + tailored LaTeX CV + cover letter + a reviewer
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agent), `/interview`, plus an ATS parseability check on generated PDFs. Portal
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skills ship for the Danish market but are swappable. Prereqs: Claude Code,
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Python 3.10+, Bun, a LaTeX distribution. **Caveat (README):** a GitHub fork is
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always *public* and `/setup` writes personal data into tracked files — use a
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private repo with this repo as `upstream`.
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### JobSync — https://github.com/Gsync/jobsync
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MIT, ~1,300 stars. The most complete self-hosted tracker: application tracker +
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monitoring dashboard, resume management (PDF/DOCX import with AI extraction)
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contacts, company pages, interview question bank, tasks/time tracking, and
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**automated job discovery** (scheduled automations track companies via job-board
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APIs and AI-match each listing). In-app AI assistant (resume review, job-match
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scoring, tailored cover letters) and a built-in **MCP server** (Claude Desktop
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can add jobs from chat). **Ollama is the default provider — fully local, no API
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key**; known-good `qwen3.5:9b`. Models must support tool calling + structured
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output. `git clone` + `docker compose up` → :3737. Env: `AUTH_SECRET`
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`ENCRYPTION_KEY` (encrypts stored API keys — set before adding keys, never
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change after), `NEXTAUTH_URL`, `MCP_ENABLED`. Treats pasted postings as untrusted
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text (prompt-injection defense).
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### offerPilot — https://github.com/offercontext/offerPilot
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AGPL-3.0, ~730 stars. Local-first AI job-application tracker (Chinese-first,
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English README). Pipeline by company/role/stage, resume versioning, JD-linked
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material generation, **mock interviews** (offline Whisper transcription in
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browser, audio never persisted), interview review, **offer comparison and
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salary-negotiation prep**, a "Pilot" AI assistant. Provider-agnostic (connect
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your own model service). Docker: `docker build -t offerpilot . && docker run
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--rm -p 127.0.0.1:8080:8080 -v offerpilot-data:/data offerpilot`. Will not
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auto-submit.
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### JobNavigator — https://github.com/vesaias/JobNavigator
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MIT, ~126 stars. The most aggressive *discovery* tool: scrapes career pages
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(Playwright + 11 ATS handlers) and job boards (JobSpy: LinkedIn, Indeed,
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ZipRecruiter, Google), dedupes, AI-scores jobs against your résumés, tailors
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résumés/cover letters (8+8 templates), auto-fills ATS forms, tracks applications
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with funnel/Sankey stats, auto-detects Gmail replies, sends Telegram alerts.
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Chrome extension for passive LinkedIn capture. AI: Claude/Codex/Antigravity
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CLIs, OpenAI, OpenRouter, **Ollama**. `git clone` + `docker compose up -d`
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(prebuilt GHCR images, pinnable via `JN_IMAGE_TAG`).
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## Resume / CV Applications
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### Reactive Resume — https://github.com/reactive-resume/reactive-resume
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MIT, ~43,300 stars. The most popular open-source resume builder: 15 templates,
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live preview, PDF/JSON/DOCX export, drag-and-drop, share links, JSON Resume
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import, passkeys/2FA, and an **ATS checker with optional AI review**.
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Provider-agnostic AI — OpenAI, Claude, Gemini, OpenRouter, **Ollama**, and any
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OpenAI-compatible endpoint (base URL + model); credentials encrypted
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server-side. `docker compose up -d` (PostgreSQL + optional SeaweedFS). Since
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v5.1.0 PDF generation is fully client-side (`@react-pdf/renderer`) — no
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Browserless/Chromium. No tracking/analytics by default; full export; one-click
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delete.
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### Resume Matcher — https://github.com/srbhr/Resume-Matcher
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Apache-2.0, ~28,500 stars. The dedicated *tailoring* tool: upload a master
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resume, paste a JD, get AI-tailored content with a review step, per-job
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templates, cover-letter + interview-prep generation, **resume scoring + keyword
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highlighting** against the JD, PDF export (4 templates). **Ollama (local)** plus
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OpenAI, Claude, Gemini, OpenRouter, DeepSeek (built on LiteLLM). Official
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Docker images: `docker run -p 3000:3000 -v resume-data:/app/backend/data
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ghcr.io/srbhr/resume-matcher:latest` (API at `/api`, health at `/api/v1/health`).
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**Ollama-in-Docker gotcha (README):** use `http://host.docker.internal:11434`
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not `localhost`.
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### OpenResume — https://github.com/xitanggg/open-resume
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AGPL-3.0, ~8,900 stars. Resume builder **and** parser. The builder produces an
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ATS-friendly design (Greenhouse/Lever compatible) with real-time PDF preview;
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the parser deterministically tests an existing resume's ATS readability.
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**No AI** — deliberately algorithmic (PDF.js + layout heuristics). Purely
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client-side (works offline, data never leaves the browser).
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### ResumeLM — https://github.com/olyaiy/resume-lm
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AGPL-3.0, ~326 stars. AI resume builder for tech jobs: base + tailored resumes,
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ATS scoring, AI bullet editing, cover-letter generator, job/application
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tracking. Cloud API providers only in practice (OpenAI, Claude, Gemini,
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DeepSeek, Groq) — no Ollama/local documented. Next.js 15 + Supabase + Redis.
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### rendercv (non-AI) — https://github.com/rendercv/rendercv
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MIT, ~17,600 stars. LaTeX-based YAML→PDF resume builder. No AI, but the
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strongest "ATS-safe by construction" formatter (single column, standard fonts)
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pairs well with any AI tailoring step.
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## Best Practices for a Homelab AI Resume/Job Pipeline
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**Local LLM vs API.**
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1. **Local-first for PII.** CVs carry name, contact, employment history, often
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salary. The apps that matter all support fully-local inference: JobSync
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(Ollama default), Resume Matcher (Ollama via LiteLLM), Reactive Resume
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(Ollama + any OpenAI-compatible endpoint), JobNavigator (Ollama). Point them
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at Ollama on the GPU server, or a llama.cpp `llama-server` OpenAI-compatible
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endpoint (Reactive Resume and Resume Matcher consume it via base-URL
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override).
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2. **Pick a model with tool calling + structured output** for JobSync — its
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assistant works by calling tools; a model without tool support "will reply
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with text instead of doing anything." JobSync's tested local model is
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`qwen3.5:9b`; anything 8B–32B with tool support is a reasonable bet.
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3. **Raise Ollama's context to ~16k** for resume/JD matching (the 4k default
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truncates long resume+JD prompts; don't go much higher).
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4. **Networking:** expose Ollama on the LAN (`OLLAMA_HOST`); inside Docker on
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the same host use `http://host.docker.internal:11434`.
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5. **Budget reality:** the CLI frameworks are the strongest at discovery +
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tailoring quality but require a paid AI CLI. For zero per-token cost and full
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privacy, the Docker apps + local Ollama is the way; expect somewhat weaker
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tailoring than frontier-API models.
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**Privacy & data handling.**
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1. **Treat job postings as untrusted input.** The best projects defend against
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prompt injection in JDs (JobSync scopes the assistant to the visible job;
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ai-job-search documents postings as untrusted). Keep a human review step
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before anything is sent.
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2. **Never auto-submit.** All the serious tools keep submission human by
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design. Mass auto-application burns standing with recruiters and ATS systems.
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3. **Don't fork the CLI frameworks publicly** for personal use (ai-job-search:
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forks are public, `/setup` writes personal data into tracked files).
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4. **Protect stored secrets.** JobSync: set `ENCRYPTION_KEY` (`openssl rand
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-base64 32`) *before* adding API keys. Reactive Resume: AI credentials are
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encrypted server-side.
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5. **Keep the service off the public internet.** These apps store sensitive PII.
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Bind to LAN / put behind the existing Caddy reverse proxy with auth (the
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fleet already runs Anubis+Authentik+Caddy — route through the same guard
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chain). offerPilot's own default is `127.0.0.1:8080`.
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6. **Back up the data volume.** JobSync has built-in zip backup/restore (excludes
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API keys by design). offerPilot and Resume Matcher keep everything in one
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volume — snapshot those.
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7. **Podman/Quadlet specifics:** all Docker-compose stacks map directly to
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Quadlet `.container`/`.network` units for boot persistence (fleet convention).
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Remember the published-port cutover gate (3737 JobSync, 3000 Resume Matcher,
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8080 offerPilot) and that Ollama must be reachable from the container network
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(published port or `host.containers.internal`).
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## Suggested Stack for This Fleet
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| Role | App | Why |
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|---|---|---|
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| Tracking + matching + discovery | **JobSync** (MIT) | Only self-hosted tracker with Ollama-as-default AI, job-board API discovery, MCP server, backup/restore |
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| Per-JD tailoring | **Resume Matcher** (Apache-2.0) | Master-resume workflow, scoring + keyword highlighting, single container, Ollama |
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| Resume building / ATS-safe PDFs | **Reactive Resume** (MIT) | 15 templates, ATS checker + AI review, JSON Resume import, client-side PDF |
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| (optional) deep discovery | **JobNavigator** (MIT) | Playwright scraping of career pages + boards, Telegram alerts |
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| (optional) CLI power mode | **career-ops** (MIT) | Best-in-class scoring rubric + portal scanning, if a paid AI CLI is acceptable |
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All five run comfortably on the existing GPU servers alongside Ollama; none
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requires more than a few hundred MB RAM.
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# Citations
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- career-ops: https://github.com/career-ops-hq/career-ops (MIT, ~72,386 stars) — site: https://career-ops.org
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- ai-job-search: https://github.com/MadsLorentzen/ai-job-search (MIT, ~43,609 stars)
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- JobSync: https://github.com/Gsync/jobsync (MIT, ~1,299 stars)
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- offerPilot: https://github.com/offercontext/offerPilot (AGPL-3.0, ~730 stars)
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- JobNavigator: https://github.com/vesaias/JobNavigator (MIT, ~126 stars)
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- Reactive Resume: https://github.com/reactive-resume/reactive-resume (MIT, ~43,301 stars) — AI guide: https://docs.rxresu.me/guides/using-ai ; self-hosting: https://docs.rxresu.me/self-hosting/docker
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- Resume Matcher: https://github.com/srbhr/Resume-Matcher (Apache-2.0, ~28,499 stars)
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- OpenResume: https://github.com/xitanggg/open-resume (AGPL-3.0, ~8,908 stars)
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- ResumeLM: https://github.com/olyaiy/resume-lm (AGPL-3.0, ~326 stars)
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- rendercv (non-AI): https://github.com/rendercv/rendercv (MIT, ~17,620 stars)
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- JobSync Ollama default + 16k context + ENCRYPTION_KEY: https://github.com/Gsync/jobsync/blob/main/README.md
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- Resume Matcher Ollama-in-Docker URL: https://github.com/srbhr/Resume-Matcher/blob/main/README.md
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- Reactive Resume client-side PDF since v5.1.0: https://github.com/reactive-resume/reactive-resume/blob/main/README.md
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