Completely free open source software

Browse free and open source software that is completely free to use. Every tool listed here offers a free plan or is free of charge — no cost barriers to get started.

Wan 2.7

Wan 2.7 is a major all-around upgrade over Wan 2.6, built for creators who need more structure than plain text-to-video. It expands control across generation, reference conditioning, editing, and recreation so one workflow can move from idea to revision to remake. Use Wan 2.7 when you need clearer shot boundaries, stronger subject consistency, and instruction-driven changes without rebuilding every clip from scratch. It fits storyboards, ads, talking-head experiments, product demos, and video teams iterating fast.

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spacedrive

spacedrive is a free, open-source alternative to ExpanDrive . Spacedrive One file manager for all your devices and clouds. Powered by a Virtual Distributed File System, complete with apps for macOS, Windows, Linux, iOS and Android v2.spacedrive.com • Discord • Getting Started What is Spacedrive? Spacedrive is a cross-device data platform. Index files, emails, notes, and external sources. Search everything. Sync via P2P. Keep AI agents safe with built-in screening. Content identity — every file gets a BLAKE3 content hash. Same file on two devices produces the same hash. Spacedrive tracks redundancy and deduplication across all your machines. Cross-device — see all your files across all your devices in one place. Files on disconnected devices stay in the index and appear as offline. P2P sync — devices connect directly via Iroh/QUIC. No servers, no cloud, no single point of failure. Metadata syncs between devices. Files stay where they are. Cloud volumes — index S3, Google Drive, Dropbox, OneDrive, Azure, and GCS as first-class volumes alongside local storage. Nine views — grid, list, columns, media, size, recents, search, knowledge, and splat. QuickPreview for video, audio, code, documents, 3D, and images. Local-first — everything runs on your machine. No data leaves your device unless you choose to sync between your own devices. Is this a replacement for Finder or Explorer? No. Spacedrive sits above your OS file manager and adds capabilities Finder/Explorer lack: Portal across everything — search and browse files across local disks, external drives, NAS, cloud storage, and archived data sources from one interface. Operating surface for files — content identity, sidecars, derivative artifacts, rich metadata, sync, and cross-device awareness built into the core model. Embeddable and shareable — run it as a desktop app, headless server, hosted file service, or embed the interface and APIs into other products. AI-ready by design — indexing and analysis pipelines prepare data ahead of time instead of giving agents raw shell access. Safer access model — route AI and automation through structured APIs, permissions, and processing layers instead of direct file operations. You still use your OS for low-level file interactions. Spacedrive adds the cross-platform, cross-device, cloud-aware, and automation-friendly layer on top. Data Archival Spacedrive indexes external data sources via script-based adapters: Gmail, Apple Notes, Chrome bookmarks, Obsidian, Slack, GitHub, calendar events, contacts. Each source becomes a searchable repository alongside your files. Adapters are a folder with an adapter.toml manifest and a sync script in any language. If it reads stdin and prints lines, it works. Shipped adapters: Gmail, Apple Notes, Chrome Bookmarks, Chrome History, Safari History, Obsidian, OpenCode, Slack, macOS Contacts, macOS Calendar, GitHub. Spacebot Spacedrive integrates with Spacebot , an open source AI agent runtime. Spacebot runs as an optional separate process. Spacedrive provides the data, permission, and execution layer. Spacebot provides the intelligence. Each Spacebot instance pairs with one Spacedrive node as its home device. That node authenticates the agent, maintains the device graph, resolves permissions, and forwards operations to peer devices. Every device in your library can reach Spacebot through the paired node over P2P (Iroh/QUIC) without direct network access. One agent runtime serves your entire device fleet. When Spacebot spawns a worker, that worker can target any device in the library. File reads, shell commands, and operations proxy through Spacedrive to the target device. Talk to the agent from your phone while work executes on a server. Read files from a NAS, run commands on a workstation, report to a laptop — all in one task. Every operation passes through Spacedrive's permission system: which devices the agent can access, which paths are readable or writable, which operations are allowed, and which require human confirmation. The paired node resolves effective policy before forwarding. One security model, one audit surface across all devices and clouds. File System Intelligence Spacedrive adds intelligence to your filesystem by combining three layers: File intelligence — derivative data like OCR, transcripts, extracted metadata, thumbnails, previews, classifications, and sidecars. Directory intelligence — contextual knowledge attached to folders and subtrees ("active projects", "dormant archives", etc). Access intelligence — permissions and policy that apply across devices and clouds, routing agents through structured access instead of raw shell commands. When an agent navigates through Spacedrive, it receives the file listing, subtree context, effective permissions, and summaries. Users can explain how they organize their system. Agents can add attributed notes. Jobs generate summaries from structure and activity. The intelligence stays attached to the filesystem, not buried in temporary session memory. Safety Screening When enabled, every record passes through a safety pipeline before becoming searchable: Prompt Guard 2 — local classifier detects prompt injection in emails, messages, and documents before they enter the index. Trust tiers — authored content (your notes) gets balanced screening, external content (email inbox) gets strict screening. Quarantine system — flagged records excluded from AI agent queries, reviewable in desktop app. Content fencing — search results include trust metadata so agents know what's safe vs untrusted. No other local data tool screens indexed content before exposing it to AI. Architecture The core is built on four principles: Virtual Distributed Filesystem (VDFS) — files and folders become first-class objects with rich metadata, independent of physical location. Every file gets a universal address ( SdPath ) that works across devices. Content Identity System — adaptive hashing (BLAKE3 with strategic sampling for large files) creates a unique fingerprint for every piece of content. Enables deduplication, redundancy tracking, and content-based operations. Transactional Actions — every file operation can be previewed before execution. See space savings, conflicts, and estimated time, then approve or cancel. Operations become durable jobs that survive network interruptions and device restarts. Leaderless Sync — peer-to-peer synchronization without central coordinators. Device-specific data uses state replication. Shared metadata uses an HLC-ordered log with deterministic conflict resolution. The implementation is a single Rust crate with CQRS/DDD architecture. Every operation (file copy, tag create, search query) is a registered action or query with type-safe input/output that auto-generates TypeScript types for the frontend. Component Technology Language Rust Async runtime Tokio Database SQLite (SeaORM + sqlx) P2P Iroh (QUIC, hole-punching, local discovery) Content hashing BLAKE3 Vector search LanceDB + FastEmbed Cloud storage OpenDAL Cryptography Ed25519, X25519, ChaCha20-Poly1305, AES-GCM Media FFmpeg, libheif, Pdfium, Whisper Desktop Tauri 2 Mobile React Native + Expo Frontend React 19, Vite, TanStack Query, Tailwind CSS v4 Design system SpaceUI (shared component library) Type generation Specta spacedrive/ ├── core/ # Rust engine (CQRS/DDD) ├── apps/ │ ├── tauri/ # Desktop app (macOS, Windows, Linux) │ ├── mobile/ # React Native (iOS, Android) │ ├── cli/ # CLI and daemon │ ├── server/ # Headless server │ └── web/ # Browser client ├── packages/ │ ├── interface/ # Shared React UI │ ├── ts-client/ # Auto-generated TypeScript client │ ├── ui/ # Component library │ └── assets/ # Icons, images, SVGs ├── crates/ # Standalone Rust crates (ffmpeg, crypto, etc.) ├── adapters/ # Script-based data source adapters └── schemas/ # TOML data type schemas Getting Started Requires Rust 1.81+, Bun 1.3+, just , and Python 3.9+ (for adapters). git clone https://github.com/spacedriveapp/spacedrive cd spacedrive just setup # bun install + native deps + cargo config just dev-desktop # launch the desktop app (auto-starts daemon) just test # run all workspace tests Privacy & Security Spacedrive is local-first. Your data stays on your devices. End-to-End Encryption — all P2P traffic encrypted via QUIC/TLS At-Rest Encryption — libraries can be encrypted on disk (SQLCipher) No Telemetry — zero tracking or analytics Self-Hostable — run your own relay servers Data Sovereignty — you control where your data lives Optional cloud integration is available for backup and remote access, but it's never required. The cloud service runs unmodified Spacedrive core as a standard P2P device—no special privileges. Contributing Join Discord to chat with developers and community Contributing Guide Adapter Guide — write a data source adapter SpaceUI — shared design system (clone alongside Spacedrive to work on UI) License FSL-1.1-ALv2 — Functional Source License , converting to Apache 2.0 after two years.

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Site Search 360

Navigating your site content should be effortless. Help your visitors find exactly what they want with our fast and insightful on-site search engine.

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Visual Paradigm

Visual Paradigm Online offers a comprehensive solution to bring all your documents, presentations, PDFs, charts, and digital publications together under one roof. Bring all your documents, presentations, PDFs, charts and digital publications under one roof with Visual Paradigm Online. VP Online unites teams, goals and actions in one place.

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Tiny RDM

Tiny RDM is a modern lightweight cross-platform Redis desktop manager available for Mac, Windows, and Linux. Feature Super lightweight, built on Webview2, without embedded browsers (Thanks to Wails ). Provides visually and user-friendly UI, light and dark themes (Thanks to Naive UI and IconPark ). Multi-language support ( Need more languages ? Click here to contribute ). Better connection management: supports SSH Tunnel/SSL/Sentinel Mode/Cluster Mode/HTTP proxy/SOCKS5 proxy. Visualize key value operations, CRUD support for Lists, Hashes, Strings, Sets, Sorted Sets, and Streams. Support multiple data viewing format and decode/decompression methods. Use SCAN for segmented loading, making it easy to list millions of keys. Logs list for command operation history. Provides command-line mode. Provides slow logs list. Segmented loading and querying for List/Hash/Set/Sorted Set. Provide value decode/decompression for List/Hash/Set/Sorted Set. Integrate with Monaco Editor Support real-time commands monitoring. Support import/export data. Support publish/subscribe. Support import/export connection profile. Custom data encoder and decoder for value display ( Here are the instructions ).

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Bitly

Build stronger digital connections Use our URL shortener, QR Codes, and landing pages to engage your audience and connect them to the right information. Build, edit, and track everything inside the Bitly Connections Platform. Bitly’s Connections Platform is more than a free URL shortener, with robust link management software, advanced QR Code features, and a Link-in-bio solution.

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Meigen AI

Create AI videos from text or images, explore reusable prompts, and manage short-form creative workflows from one browser workspace.

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MikeCRM

MikeCRM is an online form builder, contact manager and market explorer, which affirmatively strengthens you with the ability to collect data, discover potential customers and win deals.

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Dating photos

Turn your real photos into natural, attractive dating profile pics with AI. Improve lighting, pose, and confidence to get more matches.

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career-ops

career-ops is a free, open-source alternative to Teal . English | Español | Deutsch | Français | Português (Brasil) | 한국어 | 日本語 | 简体中文 | 繁體中文 | Українська | Русский | Polski | Dansk | தமிழ் | العربية | हिन्दी | Türkçe I spent months applying to jobs the hard way. So I engineered the system I wish I had. Companies use AI to filter candidates. I just gave candidates AI to choose companies. Now it's open source. FEATURED IN 740+ job listings evaluated · 100+ personalized CVs · 1 dream role landed Also runs on any agent-skill-standard CLI. See Supported CLIs . What Is This career-ops ( career-ops.org , also known as careerops ) turns any AI coding CLI into a full job search command center. Instead of manually tracking applications in a spreadsheet, you get an AI-powered pipeline that: Evaluates offers with a structured evaluation -- blocks A-F scored across 5 weighted dimensions, plus block G, a separate posting-legitimacy assessment that never affects the 1-5 score Generates tailored PDFs -- ATS-optimized CVs customized per job description Scans portals automatically (Greenhouse, Ashby, Lever, company pages) Processes in batch -- evaluate 10+ offers in parallel with sub-agents Tracks everything in a single source of truth with integrity checks Researches companies and finds the right person to contact -- applications get you in the queue; research gets you a conversation Important: This is NOT a spray-and-pray tool. career-ops is a filter -- it helps you find the few offers worth your time out of hundreds. The system strongly recommends against applying to anything scoring below 4.0/5. Your time is valuable, and so is the recruiter's. Always review before submitting. career-ops is agentic: whichever AI coding CLI you choose navigates career pages with Playwright, evaluates fit by reasoning about your CV vs the job description (not keyword matching), and adapts your resume per listing. Heads up: the first evaluations won't be great. The system doesn't know you yet. Feed it context -- your CV, your career story, your proof points, your preferences, what you're good at, what you want to avoid. The more you nurture it, the better it gets. Think of it as onboarding a new recruiter: the first week they need to learn about you, then they become invaluable. Built by someone who used it to evaluate 740+ job offers, generate 100+ tailored CVs, and land a Head of Applied AI role. Read the full case study . The CareerOps Manifesto career-ops is the first reference implementation of the CareerOps Manifesto . read it. if it says what you believe, sign it. your signature becomes a commit. Features Feature Description Auto-Pipeline Paste a URL, get a full evaluation + PDF + tracker entry A-G Evaluation Role summary, CV match, level strategy, comp research, personalization, interview prep (STAR+R) -- plus a Block G posting-legitimacy check that flags scams and ghost jobs, and a Work-Auth signal that flags an explicit no-sponsorship JD as a hard blocker Interview Story Bank Accumulates STAR+Reflection stories across evaluations -- 5-10 master stories that answer any behavioral question Negotiation Scripts Salary negotiation frameworks, geographic discount pushback, competing offer leverage ATS PDF Generation Keyword-injected CVs with Space Grotesk + DM Sans design Cover Letter Generator Research-backed cover letters with keyword mirroring, four interactive angle prompts (why/problems/approach/tone), draft-in-chat approval gate, and A4 PDF via the same HTML + Playwright pipeline as CVs. Auto-drafts on every evaluation; complete and generate on demand via /career-ops cover Application Email Drafts Formal recruiter/referral/cold application emails from a report or pasted JD, with subject line, attachment checklist, source-backed fit points, and a profile-driven contact block. Draft-only -- career-ops never sends, submits, or clicks anything. Portal Scanner 100+ companies pre-configured (Anthropic, OpenAI, ElevenLabs, Retool, n8n...) + custom queries across Ashby, Greenhouse, Lever, Wellfound Funded Company Discovery Review-first company:funded command surfaces recently funded companies and source diagnostics from structured public feeds without editing your data Batch Processing Parallel evaluation with headless CLI workers ( claude -p / opencode run ) Dashboard TUI Terminal UI to browse, filter, and sort your pipeline Human-in-the-Loop AI evaluates and recommends, you decide and act. The system never submits an application -- you always have the final call Pipeline Integrity Automated merge, dedup, status normalization, health checks Interview Suite Time-blocked prep plans, practice sessions with feedback, post-interview debriefs ( interview/ ), and a company red-flag detector ( interview-redflag ) Offer Stage Contract reading companion -- clause walk plus a lawyer question list ( offer-prep ) -- and a desired/advertised/actual salary-gap analyzer ( salary-gap.mjs ) Follow-ups & Replies Follow-up cadence calculator and seeded reminders ( followup-cadence.mjs , followup-seed.mjs ); employer reply classification into tracker updates ( reply-watch ) Pattern Analysis Rejection patterns and per-ATS-channel advance rates ( analyze-patterns.mjs ), lifetime funnel stats ( stats.mjs ), repost/ghost-job detection ( detect-reposts.mjs ) Plugin System Opt-in integrations (Gmail, Notion, Apify + a community registry), disabled by default -- see docs/PLUGINS.md Beyond the CV Company research ( deep ) surfaces AI strategy, recent moves, engineering culture, and the angle your profile should take. Contact discovery ( contacto ) identifies the hiring manager, recruiter, or team peer worth reaching out to and drafts a ≤300-character LinkedIn message tuned to each contact type. Formal application email drafts ( email ) turn an evaluated report or pasted JD into a subject line, body, and attachment checklist without sending, submitting, or clicking anything. Applications get you in the queue; research gets you a conversation. Quick Start Fastest way — one command: npx @santifer/career-ops init 💡 npx ships with Node.js — it runs the installer once, without installing anything globally. No Node yet? Install it first. (Already using a Claude Code / Gemini / Codex CLI? Then you already have it.) This clones the latest release into ./career-ops and installs dependencies. Then: cd career-ops claude # or codex / qwen / opencode / agy / grok — open your AI CLI here On first launch, career-ops walks you through setup — your CV, profile and target roles — just by chatting. Nothing to edit by hand. Prefer to set it up manually? (git clone) git clone https://github.com/santifer/career-ops.git cd career-ops && npm install npx playwright install chromium # only needed for PDF generation # 2. Check setup npm run doctor # Validates all prerequisites # 3. Configure cp config/profile.example.yml config/profile.yml # Edit with your details cp templates/portals.example.yml portals.yml # Customize companies # 4. Add your CV # Create cv.md in the project root with your CV in markdown # 5. Open your AI CLI in this directory claude # or codex / opencode / qwen / agy / grok # Then ask your CLI to adapt the system to you: # "Change the archetypes to backend engineering roles" # "Translate the modes to English" # "Add these 5 companies to portals.yml" # "Update my profile with this CV I'm pasting" # 6. Start using # Paste a job URL or JD text to trigger auto-pipeline # If your CLI supports slash commands, use /career-ops (or its CLI-specific alias) # In Codex, ask for the same mode in plain language, e.g.: # "Run the career-ops scan mode" # "Run the career-ops pipeline mode for data/pipeline.md" # "Run the career-ops pdf mode for the latest evaluated role" # "Run the career-ops tracker mode and summarize the current statuses" Global install npm i -g @santifer/career-ops This installs the career-ops binary globally so you can run it directly instead of via npx . Unlike npx @santifer/career-ops init (which bootstraps a project directory), the global install gives you a persistent career-ops command available anywhere in your terminal. Which one should you use? npx @santifer/career-ops init — best for first use; creates a dedicated project folder. npm i -g @santifer/career-ops — best once you have a project folder and want to run career-ops commands directly. The system is designed to be customized by your AI coding CLI itself. Modes, archetypes, scoring weights, negotiation scripts -- just ask it to change them. It reads the same files it uses, so it knows exactly what to edit. See docs/SETUP.md for the full setup guide, docs/RUNNING_ON_A_BUDGET.md for instructions on running career-ops cheaply using custom or local models (and docs/FREE_TIER.md for running it at zero cost on Antigravity CLI's free tier), docs/AUTOMATION.md for scheduling recurring scans and a zero-token triage-to-shortlist recipe, docs/APPLY_AUTOFILL.md for details on the ATS auto-fill flow, and docs/FAQ.md for answers to common setup questions. Design principles live in ARCHITECTURE.md ; runtime flows in docs/ARCHITECTURE.md . Antigravity CLI Integration career-ops supports Antigravity CLI natively, the same way it supports Claude Code and OpenCode. All slash commands are available through the shared skill entrypoint, using the same modes/*.md evaluation logic. Google has transitioned consumer Gemini CLI access to Antigravity CLI. GEMINI.md is now a no-op compatibility guard so Antigravity does not duplicate the full project instructions when it reads both AGENTS.md and GEMINI.md . Native Antigravity CLI # 1. Run in the career-ops directory cd career-ops agy # 2. Use the unified /career-ops command with subcommands: /career-ops " Senior AI Engineer at Anthropic... " /career-ops pipeline /career-ops scan /career-ops pdf /career-ops tracker The skill is defined using the open standard in .agents/skills/career-ops/SKILL.md and symlinked/referenced for each supported CLI (e.g. .claude/ , .cursor/ , .qwen/ , .antigravitycli/ , .grok/ ). Codex Integration career-ops supports Codex through the same shared router, but the invocation model is different from CLIs that auto-register slash commands. For the full guide, see docs/CODEX.md . Interactive Codex cd career-ops codex Slash commands are not guaranteed in Codex. If /career-ops is unavailable, ask Codex to run the mode directly in plain language: Evaluate this JD with career-ops auto-pipeline: https://company.com/jobs/123 Run the career-ops scan mode and summarize new matches. Run the career-ops pipeline mode for data/pipeline.md. Run the career-ops pdf mode for the latest evaluated role. Run the career-ops tracker mode and summarize the current statuses. One-shot Codex ( codex exec ) codex exec " Evaluate this JD with career-ops auto-pipeline: https://company.com/jobs/123 " codex exec " Run career-ops scan mode in this repo and summarize new matches. " codex exec " Run career-ops pipeline mode for data/pipeline.md. " codex exec " Run career-ops pdf mode for the latest evaluated role. " codex exec " Run career-ops tracker mode and summarize the current statuses. " Grok Build CLI Integration career-ops supports Grok Build CLI natively, the same way it supports Claude Code and OpenCode. AGENTS.md is auto-loaded as project rules, and all slash commands are available through the shared skill entrypoint. Native Grok Build CLI # 1. Run in the career-ops directory cd career-ops grok # 2. Use the unified /career-ops command with subcommands: /career-ops " Senior AI Engineer at Anthropic... " /career-ops pipeline /career-ops scan /career-ops pdf /career-ops tracker For headless batch workers, use grok -p "prompt" (add --yolo to auto-approve tool executions). Standalone Gemini API Script (No CLI install needed) # 1. Get a free API key at https://aistudio.google.com/apikey cp .env.example .env # Edit .env, set GEMINI_API_KEY=your_key_here # 2. Install dependencies npm install # 3. Evaluate a job description node gemini-eval.mjs " We are looking for a Senior AI Engineer... " node gemini-eval.mjs --file ./jds/my-job.txt node agent-inbox.mjs add " ... " # queue a request for the next session npm run gemini:eval -- " JD text here " Free tier: Both options work without billing. Native CLI uses Google OAuth; the API script uses gemini-3.6-flash (rate limits are model- and tier-dependent; see Google AI docs for current quotas). Usage career-ops uses a shared command router. In CLIs that register slash commands, it looks like this: /career-ops → Show all available commands /career-ops {paste a JD} → Full auto-pipeline (evaluate + PDF + tracker) /career-ops scan → Scan portals for new offers /career-ops pdf → Generate ATS-optimized CV /career-ops cover → Cover letter generator (paste JD or /career-ops cover {slug}) /career-ops email → Formal application email draft (draft-only; never sends, submits, or clicks) /career-ops batch → Batch evaluate multiple offers /career-ops tracker → View application status /career-ops apply → Fill application forms with AI /career-ops outcome → Record application outcome & archive artifacts /career-ops pipeline → Process pending URLs /career-ops contacto → Find hiring manager / recruiter / peer + draft a ≤300-char LinkedIn message per contact type /career-ops deep → Generate a structured 6-axis research prompt (AI strategy, recent moves, culture, challenges, competitors, candidate angle) /career-ops training → Evaluate a course/cert /career-ops project → Evaluate a portfolio project Or just paste a job URL or description directly -- career-ops auto-detects it and runs the full pipeline. In Codex, slash commands are not guaranteed. Use the same mode names in a prompt instead, or call them from codex exec . How It Works You paste a job URL or description │ ▼ ┌──────────────────┐ │ Archetype │ Classifies: LLMOps / Agentic / PM / SA / FDE / Transformation │ Detection │ └────────┬─────────┘ │ ┌────────▼─────────┐ │ A-G Evaluation │ Match, gaps, comp research, STAR stories, legitimacy │ (reads cv.md) │ └────────┬─────────┘ │ ┌────┼────┐ ▼ ▼ ▼ Report PDF Tracker .md .pdf entry Pre-configured Portals The scanner comes with 100+ companies ready to scan and 45+ search queries across major job boards. Copy templates/portals.example.yml to portals.yml and add your own: AI Labs: Anthropic, OpenAI, Mistral, Cohere, LangChain, Pinecone Voice AI: ElevenLabs, PolyAI, Parloa, Hume AI, Deepgram, Vapi, Bland AI AI Platforms: Retool, Airtable, Vercel, Temporal, Glean, Arize AI Contact Center: Ada, LivePerson, Sierra, Decagon, Talkdesk, Genesys Enterprise: Salesforce, Twilio, Gong, Dialpad LLMOps: Langfuse, Weights & Biases, Lindy, Cognigy, Speechmatics Automation: n8n, Zapier, Make.com European: Factorial, Attio, Tinybird, Clarity AI, Travelperk Job boards searched: 55+ provider modules cover ATS APIs, board-wide feeds, XML/RSS feeds, markdown feeds, and local parsers. See Supported job boards for the full table. By default node scan.mjs (a.k.a. npm run scan ) trusts what each ATS feed returns. Some companies leave stale postings in their public API even after the role is closed, so those expired entries can leak into pipeline.md . Pass --verify to launch Playwright after the API pass and drop expired postings before they hit the pipeline: node scan.mjs --verify # zero-token discovery + Playwright liveness check The verification is sequential and only runs against new offers (after dedup), so the cost stays bounded. Dashboard TUI The built-in terminal dashboard lets you browse your pipeline visually: npm run serve:dashboard # launch the TUI npm run build:dashboard # optional: build the standalone binary Features: 6 filter tabs, 4 sort modes, grouped/flat view, lazy-loaded previews, inline status changes. There is also an experimental web UI (alpha, opt-in — nothing runs unless you start it): see web/README.md . Project Structure career-ops/ ├── AGENTS.md # Canonical agent instructions (all CLIs) ├── CLAUDE.md # Claude Code wrapper (imports AGENTS.md) ├── CODEX.md # Codex wrapper (imports AGENTS.md) ├── OPENCODE.md # OpenCode wrapper (imports AGENTS.md) ├── GEMINI.md # Legacy no-op guard to avoid Antigravity duplicate context ├── cv.md # Your CV (create this) ├── article-digest.md # Your proof points (optional) ├── config/ │ └── profile.example.yml # Template for your profile ├── modes/ # Skill modes │ ├── _shared.md # Shared context (customize this) │ ├── oferta.md # Single evaluation │ ├── pdf.md # PDF generation │ ├── cover.md # Cover letter generation │ ├── email.md # Formal application email drafts │ ├── scan.md # Portal scanner │ ├── batch.md # Batch processing │ └── ... ├── templates/ │ ├── cv-template.html # ATS-optimized CV template │ ├── portals.example.yml # Scanner config template │ └── states.yml # Canonical statuses ├── batch/ │ ├── batch-prompt.md # Self-contained worker prompt │ └── batch-runner.sh # Orchestrator script ├── dashboard/ # Go TUI pipeline viewer ├── data/ # Your tracking data (gitignored) ├── reports/ # Evaluation reports (gitignored) ├── output/ # Generated PDFs (gitignored) ├── fonts/ # Space Grotesk + DM Sans ├── docs/ # Setup, customization, budget guide, architecture └── examples/ # Sample CV, report, proof points Tech Stack Agent : AI coding CLI with shared skills and modes ( AGENTS.md + CLI wrapper) PDF : Playwright + HTML template Cover letters : HTML template + Playwright (A4 PDF, same pipeline as CVs) Scanner : Playwright + Greenhouse API + WebSearch Dashboard : Go + Bubble Tea + Lipgloss (Catppuccin Mocha theme) Data : Markdown tables + YAML config + TSV batch files Also Open Source cv-santiago -- The portfolio website (santifer.io) with AI chatbot, LLMOps dashboard, and case studies. If you need a portfolio to showcase alongside your job search, fork it and make it yours. FAQ What is career-ops? career-ops is an open-source, CLI-agnostic job-search command center. It turns any AI coding CLI into a pipeline that evaluates job offers against your CV, generates ATS-tailored PDFs, finds the right person to contact, and tracks everything in one place — while you keep the final decision. It is the first reference implementation of the CareerOps Manifesto. More at career-ops.org . Can I run career-ops for free, or on a cheaper / local model? Yes. career-ops is CLI-agnostic and runs on free and local models — via OpenRouter free models, Ollama, or any OpenAI-compatible endpoint — so you are not tied to a paid subscription. See docs/RUNNING_ON_A_BUDGET.md for the full setup. I pay for Claude Pro/Max but career-ops is burning API credits. Why? Because an ANTHROPIC_API_KEY in your environment takes precedence over your logged-in subscription: the CLI uses the key and bills per token. Run echo $ANTHROPIC_API_KEY , and if it prints anything, remove it from your shell profile, restart the terminal and run /login . Batch mode is the exception, since claude -p workers do not use the interactive login: run claude setup-token once and export the result as CLAUDE_CODE_OAUTH_TOKEN . Full walkthrough in docs/RUNNING_ON_A_BUDGET.md . Which AI CLIs does career-ops work with? career-ops runs on any major AI coding CLI — Claude Code, Codex, Gemini / Antigravity, OpenCode, Grok, Qwen and more — through the open Agent Skill Standard, so it is never locked to a single vendor. Use the CLI you already have. How do I install career-ops on Windows? career-ops runs on Windows. If skills fail to load with a symlink error during install, the fix is in docs/FAQ.md . Full steps are in docs/SETUP.md . Does career-ops auto-apply to jobs for me? No. career-ops is a filter, not a spray-and-pray auto-applier. The AI evaluates, ranks and drafts; you review and decide. It never submits, sends, or clicks anything — you always have the final call. That human-in-the-loop design is the whole point. Is career-ops free and open source? Yes. career-ops is free and open source, and for the candidate it always will be — it is the first reference implementation of the CareerOps Manifesto . Read it, and if it says what you believe, sign it. About the Author I'm Santiago Fernández de Valderrama Aparicio (santifer) -- Head of Applied AI, former founder (built and sold a business that still runs with my name on it). I built career-ops to manage my own job search. It worked: I used it to land my current role. Curious how this repo is maintained in ~4 hours a week? Read Agentic maintenance: how career-ops is run by a fleet of AI agents . My portfolio and other open source projects → santifer.io Wikidata: Santiago Fernández de Valderrama Aparicio · career-ops . Disclaimer career-ops is a local, open-source tool, NOT a hosted service. By using this software, you acknowledge: You control your data. Your CV, contact info, and personal data stay on your machine and are sent directly to the AI provider you choose (Anthropic, OpenAI, etc.). We do not collect, store, or have access to any of your data. You control the AI. The default prompts instruct the AI not to auto-submit applications, but AI models can behave unpredictably. If you modify the prompts or use different models, you do so at your own risk. Always review AI-generated content for accuracy before submitting. You comply with third-party ToS. You must use this tool in accordance with the Terms of Service of the career portals you interact with (Greenhouse, Lever, Workday, LinkedIn, etc.). Do not use this tool to spam employers or overwhelm ATS systems. No guarantees. Evaluations are recommendations, not truth. AI models may hallucinate skills or experience. The authors are not liable for employment outcomes, rejected applications, account restrictions, or any other consequences. See LEGAL_DISCLAIMER.md for full details. This software is provided under the MIT License "as is", without warranty of any kind. Contributors Every person who has shipped code, docs, translations or tests is listed in CONTRIBUTORS.md — including non-code contributions, which the graph above cannot show. Got hired using career-ops? Share your story! License & Trademark The code is licensed under MIT . The "career-ops" name and brand are governed by the Trademark Policy , permissive for community use, reserved for commercial product naming and endorsement. Let's Connect

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CopilotKit is a free, open-source alternative to CommandBar . CopilotKit Docs · Examples · Enterprise Intelligence Platform · Discord Build agent-native applications — on any framework, on any surface. Generative UI, shared state, and human-in-the-loop workflows for React, Angular, Vue, React Native — and in Slack and Microsoft Teams. What is CopilotKit CopilotKit is a best-in-class SDK for building full-stack agentic applications, Generative UI, and chat applications. What started as a React library is now the horizontal layer between your agents and your users : the same agent can power your web app, your mobile app, and your team's Slack or Microsoft Teams workspace. We are the company behind the AG-UI Protocol - adopted by Google, LangChain, AWS, Microsoft, Mastra, PydanticAI, and more! Quick Start Up and running in under five minutes. All you need is an LLM key (OpenAI, Anthropic, Gemini, etc.). npx copilotkit@latest create Agent Skills CopilotKit ships agent skills that teach your coding agent (Claude Code, Codex, Cursor, Gemini, and others) how to set up, build with, integrate, debug, and upgrade CopilotKit. Install them into any project directory: npx copilotkit@latest skills install Run it again any time to refresh to the latest skills. Bring Your App to Life Whole.Generative.UI.v5.mp4 Add AI to your app in 1 minute Features: Chat UI – A fully customizable chat interface that supports message streaming, tool calls, and agent responses. Backend Tool Rendering – Enables agents to call backend tools that return UI components rendered directly in the client. Generative UI – Allows agents to generate and update UI components dynamically at runtime based on user intent and agent state. Shared State – A synchronized state layer that both agents and UI components can read from and write to in real time. Human-in-the-Loop – Lets agents pause execution to request user input, confirmation, or edits before continuing. Self-Learning (early access) – Agents that continuously improve from user feedback via in-context reinforcement learning (CLHF). 🧩 Works With Your Stack One agent backend. Every frontend. Platform Status Get Started ⚛️ React / Next.js ✅ GA Quickstart 🅰️ Angular ✅ Supported Source Code & Quickstart 💚 Vue ✅ Supported Source Code - Quickstart coming soon 📱 React Native ✅ Supported Quickstart 💬 Slack / Microsoft Teams ✅ Supported Channels · Quickstart 🔜 Discord / WhatsApp / Telegram / Google Chat / iMessage / SMS 🟡 Coming soon Channels Your agent logic stays the same — AG-UI handles the wire protocol, CopilotKit handles the UI layer for each framework and channel. 💬 Channels: One Agent, Every Chat App The Channels SDK takes the agent you already built and drops it into the chat apps your users live in — same tools, same shared state, same human-in-the-loop, no rewrite ( Learn more ). Slack – Agents as first-class Slack apps: threads, tool calls, and human-in-the-loop approvals right in the channel. Microsoft Teams – Bring agentic workflows to the enterprise, where your org already lives. 👉 Explore Channels → 🧠 Self-Learning Agents Improve your product by learning over time. With Continuous Learning from Human Feedback (CLHF) , part of the CopilotKit Intelligence Platform , agents improve with every interaction: In-context reinforcement learning – Agents automatically improve from user interactions, no model fine-tuning required. Automatic prompt augmentation – Agent behavior adapts based on recent interactions and outcomes. Per-user adaptation – Agents learn individual preferences and get better for each user over time. Threads & persistence – Full interaction history — generative UI, human-in-the-loop, shared state — captured across sessions. Available via CopilotKit Cloud or self-hosted. 🔒 Early access: We're onboarding teams now. 👉 Request early access → cpk-cli.mp4 What this gives you: CopilotKit installed – Core packages are fully set up in your app Provider configured – Context, state, and hooks ready to use Agent <> UI connected – Agents can stream actions and render UI immediately Deployment-ready – Your app is ready to deploy Complete getting started guide → How it works: CopilotKit connects your UI, agents, and tools into a single interaction loop. This enables: Agents that ask users for input Tools that render UI Stateful workflows across steps and sessions One agent, deployed across web, mobile, and chat platforms ⭐️ useAgent Hook The useAgent hook sits directly on AG-UI, giving you full programmatic control over the agent connection. // Programmatically access and control your agents const { agent } = useAgent ( { agentId : "my_agent" } ) ; // Render and update your agent's state return < div > < h1 > { agent . state . city } < / h1 > < button onClick = { ( ) => agent . setState ( { city : "NYC" } ) } > Set City < / b u t t o n > < / div > Check out the useAgent docs to learn more. CopilotKit.UseAgent.Graphic.Motion_2.mp4 Generative UI Generative UI is a core CopilotKit pattern that allows agents to dynamically render UI as part of their workflow. demo-generative-ui.mp4 Compare the Three Types Explore: Static (AG-UI Protocol) Declarative (A2UI) Open-Ended (MCP Apps & Open JSON) Generative UI educational repo → 🖥️ AG-UI: The Agent–User Interaction Protocol Connect agent workflows to user-facing apps, with deep partnerships and 1st-party integrations across the agentic stack—including LangChain, CrewAI, Mastra, PydanticAI, and more. npx create-ag-ui-app my-agent-app Learn more in the AG-UI README → 🤝 Community What's New Have questions or need help? Join our Discord → Read the Docs → Try the Enterprise Intelligence Platform → Stay up to date with our latest releases! Follow us on LinkedIn → Follow us on X → 🙋🏽‍♂️ Contributing Thanks for your interest in contributing to CopilotKit! 💜 We value all contributions, whether it's through code, documentation, creating demo apps, or just spreading the word. Here are a few useful resources to help you get started: For code contributions, CONTRIBUTING.md . For documentation-related contributions, check out the documentation contributions guide . Want to contribute but not sure how? Join our Discord and we'll help you out! 📄 License This repository's source code is available under the MIT License .

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