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openhuman logo
OpenHuman OpenHuman is your personal AI super intelligence: a brain that remembers everything, a fantastic orchestrator, a deep researcher. Local-first, simple, powerful. Discussions • Discord • Reddit • X/Twitter • Docs • Follow @senamakel (Creator) 🇺🇸 English | 🇨🇳 简体中文 | 🇯🇵 日本語 | 🇰🇷 한국어 | 🇩🇪 Deutsch | 🇵🇰 اردو Early Beta : Under active development. Expect rough edges. OpenHuman is not AGI. But it is a meaningful architectural step closer, with better memory, better orchestration, and better tooling. 🎉 Within one week of launch, OpenHuman became the number one trending repository on GitHub for nine days in a row. Install Download installers from tinyhumans.ai/openhuman or from the GitHub Releases page. For terminal installs (Homebrew, Debian/Ubuntu .deb , AUR, install scripts, and platform notes), see INSTALL.md . What is OpenHuman? OpenHuman is three things most assistants aren't: a brain that builds a persistent, local memory of your world; a fantastic orchestrator that runs fleets of agents on durable graphs; and a deep researcher that sweeps your data and the web before you finish asking. Every bullet links to the deeper writeup in the docs . 🧠 The brain Memory Tree + Obsidian Wiki : your data compressed into scored Markdown trees in SQLite on your machine, mirrored as an Obsidian vault you can open and edit. No vector-soup black box. 100+ OAuth integrations, 5,000+ MCP servers, 90,000+ Skills : one click into Gmail, Notion, GitHub, Slack and the rest of your stack. Auto-fetch feeds the brain every 20 minutes, so it has tomorrow's context this morning. Goals & Todos : long-term goals, durable per-thread goals, and a shared kanban board per conversation. TokenJuice : tool output compressed before it hits the model: same information, up to 80% fewer tokens. A brain this big would be unaffordable without it. 🕸️ The orchestrator Workflows : the agent proposes the automation; you review it on a canvas and save. Durable, trigger-driven, approval-gated runs on open-source tinyflows . A harness that finishes the job : checkpointed graph runs on open-source tinyagents . Stuck agents get steered, halted ones return a root cause, and every run replays with real per-call costs. A split brain, always on : a fast reflex agent triages inbound traffic while a deep reasoning core delegates to worker fleets, steered by the subconscious. 🔬 The deep researcher & doer Batteries included : managed web search , powered by Exa , is included with your OpenHuman subscription and needs no API key; bring your own Exa key to search directly on your own Exa account and billing. Plus scraper, coder toolset, a real browser , and native voice with in-process Whisper. Model routing picks the right LLM per workload on one subscription. That subscription is a default, not a lock-in: point any workload at your own provider key or a fully local Ollama model , and mix the three however you like. Image & video generation : Seedream/SeedEdit images and Seedance/Veo video, straight into your workspace on the same subscription. 17 messaging channels : Telegram, Discord, Slack, WhatsApp, Signal, iMessage… plus native email (IMAP IDLE + SMTP). Your agent reaches you where you already are. 🧍 Human, private, yours Simple, UI-first & Human : install to working agent in a few clicks, with no config files and no terminal. And it has a face : a mascot that speaks, reacts, and remembers you. Privacy & security : on-device encrypted data, approval gate, OS-keyring secrets, and opt-in sandboxing. There is also Privacy Mode : flip one switch and no inference leaves your machine, enforced in the Rust core. Themes & Theme Studio : five theme families plus a full visual editor, exportable as JSON. Context in minutes, not weeks OpenHuman is the first agent harness that gets to know you in minutes. Inspired by Karpathy's LLM Knowledgebase . Most agents start cold. Hermes learns by watching you work; OpenClaw waits for plugins to ferry context in. Either way, you spend days or weeks before the agent knows enough about your stack to be genuinely useful. OpenHuman summarizes and compresses all your documents, emails & chats; and creates a memory graph that lets your agent remember everything about you. OpenHuman skips the wait. Connect your accounts, let auto-fetch pull data locally on a 20-minute loop, and then have Memory Trees compress everything into Markdown files stored intelligently in a Karpathy-style Obsidian wiki . In just one sync pass, the agent has full (compressed) context of your inbox, your calendar, your repos, your docs, your messages. No training period. No "give it a few weeks.". It becomes you, controlled by you. Already self-host agentmemory across other coding agents? OpenHuman ships an optional Memory backend that proxies to it. Set memory.backend = "agentmemory" in config.toml and the same durable store powers OpenHuman alongside Claude Code, Cursor, Codex, and OpenCode. See the agentmemory backend page for setup. An orchestrator, not a chatbot Most agent harnesses run one agent in one loop. OpenHuman is an orchestrator : Agent-to-agent messaging runs over Signal-protocol end-to-end encryption, so you can connect anything (Claude Code, Codex, OpenClaw, Hermes) and use OpenHuman to orchestrate all of your agents and tools. Graphs, not loops : turns run as checkpointed graphs on tinyagents . They pause for a human, survive a restart, and resume mid-run. Sub-agent fleets : specialists spawn three levels deep; stuck agents become root-cause reports. Agent-to-agent, encrypted : instances orchestrate each other over Signal-protocol E2E sessions with x402 payments. No server ever sees plaintext. Workflows you can see Heavily inspired by n8n and Zapier, workflows bring the same visual, trigger-driven automation to your agent, except the agent builds them for you. Ask for an automation and it proposes one: a tinyflows graph you review on a visual canvas before saving. The agent proposes the workflow; you review it on a canvas and save it. Saved workflows are durable and trigger-driven. They fire on schedules, webhooks, or channel events, survive restarts, and gate side effects behind approvals. OpenHuman vs Other Agent Harnesses High-level comparison (products evolve, so verify against each vendor). OpenHuman is built to minimize vendor sprawl , keep workflow knowledge on-device , and give the agent a persistent memory of your data, not only chat. Claude Cowork OpenClaw Hermes Agent OpenHuman Open-source 🚫 Proprietary ✅ MIT ✅ MIT ✅ GNU Simple to start ✅ Desktop + CLI ⚠️ Terminal-first ⚠️ Terminal-first ✅ Clean UI, minutes Cost ⚠️ Sub + add-ons ⚠️ BYO models ⚠️ BYO models ✅ One sub + TokenJuice Memory ✅ Chat-scoped ⚠️ Plugin-reliant ✅ Self-learning 🚀 Memory Tree + Obsidian vault, optional agentmemory backend Integrations ⚠️ Few connectors ⚠️ BYO ⚠️ BYO 🚀 100+ OAuth · 5k+ MCP · 90k+ Skills Auto-fetch 🚫 None 🚫 None 🚫 None ✅ 20-min sync into memory Orchestration ⚠️ Sub-tasks ⚠️ Single loop ⚠️ Single loop 🚀 Agent graphs + checkpoints + E2E-encrypted A2A Workflows 🚫 None ⚠️ Scripts ⚠️ Scripts 🚀 Visual, durable, agent-proposed, approval-gated Meetings 🚫 None 🚫 None 🚫 None 🚀 Joins Meet/Zoom/Teams/Webex, speaks, live transcript Messaging channels 🚫 None ⚠️ A few ⚠️ A few ✅ 17 incl. native email (IMAP/SMTP) Local-only mode 🚫 Cloud-only ⚠️ BYO local ⚠️ BYO local ✅ One-switch enforced Privacy Mode Observability 🚫 Opaque ⚠️ Logs ⚠️ Logs ✅ Replayable run journals + per-call cost accounting API sprawl 🚫 Extra keys 🚫 BYOK 🚫 Multi-vendor ✅ One account Model routing 🚫 Single model ⚠️ Manual ⚠️ Manual ✅ Built-in Native tools ✅ Code-only ✅ Code-only ✅ Code-only ✅ Code + search + scraper + browser + voice + media gen Contributing from source New contributor? Start with CONTRIBUTING.md for the fork/PR workflow and local validation commands, or use the copy-paste AI-agent prompt in CONTRIBUTING-BEGINNERS.md . The short path is: Install Git, Node.js 24+, pnpm 10.10.0, Rust 1.93.0 ( rustfmt + clippy ), CMake, Ninja, ripgrep, and the platform desktop build prerequisites. Fork and clone the repo, then run git submodule update --init --recursive before pnpm install so the vendored Tauri/CEF sources are present. Use pnpm dev for web-only UI work, pnpm --filter openhuman-app dev:app for the desktop shell, and focused checks such as pnpm typecheck , pnpm format:check , and cargo check -p openhuman --lib before opening a PR. Deeper docs: Architecture · Getting Set Up · Cloud Deploy . Star us on GitHub Building toward AGI and artificial consciousness? Star the repo and help others find the path. Contributors Hall of Fame Show some love and end up in the hall of fame. Contributors get free merch and special access to our Discord .
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twenty logo
twenty is a free, open-source alternative to Salesforce . The #1 Open-Source CRM Website · Documentation · Roadmap · Discord · Figma Why Twenty Twenty gives technical teams the building blocks for a custom CRM that meets complex business needs and quickly adapts as the business evolves. Twenty is the CRM you build, ship, and version like the rest of your stack. Learn more about why we built Twenty Installation Cloud The fastest way to get started. Sign up at twenty.com and spin up a workspace in under a minute, with no infrastructure to manage and always up to date. Build an app Scaffold a new app with the Twenty CLI: npx create-twenty-app my-app Define objects, fields, and views as code: import { defineObject , FieldType } from 'twenty-sdk/define' ; export default defineObject ( { nameSingular : 'deal' , namePlural : 'deals' , labelSingular : 'Deal' , labelPlural : 'Deals' , fields : [ { name : 'name' , label : 'Name' , type : FieldType . TEXT } , { name : 'amount' , label : 'Amount' , type : FieldType . CURRENCY } , { name : 'closeDate' , label : 'Close Date' , type : FieldType . DATE_TIME } , ] , } ) ; Then ship it to your workspace: npx twenty app:publish --private See the app development guide for objects, views, agents, and logic functions. Self-hosting Run Twenty on your own infrastructure with Docker Compose , or contribute locally via the local setup guide . Everything you need Twenty gives you the building blocks of a modern CRM (objects, views, workflows, and agents) and lets you extend them as code. Here's a tour of what's in the box. Want to go deeper? Read the User Guide for product walkthroughs, or the Documentation for developer reference. Learn more about apps in doc Learn more about version control in doc Learn more about primitives in doc Learn more about layouts in doc Learn more about AI in doc Learn more about CRM features in doc Stack TypeScript Nx NestJS , with BullMQ , PostgreSQL , Redis React , with Jotai , Linaria and Lingui Thanks Thanks to these amazing services that we use and recommend for code review (Greptile), catching bugs (Sentry) and translating (Crowdin). Join the Community Star the repo · Discord · Feature requests · Releases · X · LinkedIn · Crowdin · Contribute
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Linkedin Pinpoint Answer logo
Linkedin Pinpoint Answer: Your All-in-One Companion for LinkedIn Brain Games Introduction If you enjoy daily brain challenges on LinkedIn, you may already know games like Pinpoint, Queen, Crossclimb, Tango, Zip, Mini Sudoku, and Patches. These puzzles test logic, pattern recognition, and problem-solving in fun but sometimes difficult ways. That’s where Linkedin Pinpoint Answer comes in. We are an all-in-one companion for solving and mastering LinkedIn puzzle games. Whether you're stuck, trying to keep your streak, or want to understand the logic behind answers, our platform provides instant solutions, clear explanations, and useful strategies across multiple games in one place. Main Features 1. Instant Answers Get fast and accurate solutions for Pinpoint, Queen, Crossclimb, Tango, Zip, Mini Sudoku, and Patches as soon as new puzzles are released. 2. Streak Protection Avoid losing progress. Timely updates help you maintain daily streaks across all LinkedIn puzzle games. 3. Daily Updates Always synced with LinkedIn’s puzzle schedule, offering the latest answers and breakdowns as soon as they go live. 4. Clear Explanations We don’t just give answers—we explain them. Learn solving logic and improve your puzzle skills over time. 5. Multi-Game Hub Play and learn multiple LinkedIn games in one place, from word puzzles to logic and number challenges. 6. Strategy Insights Discover patterns, traps, and solving methods to improve your long-term performance across all games. Conclusion LinkedIn puzzle games are becoming more diverse and challenging, from Pinpoint to Mini Sudoku and beyond. Linkedin Pinpoint Answer supports the full experience—providing answers when needed, explanations when useful, and strategies to help you improve. Whether you're stuck or want to stay ahead, this is your all-in-one hub for LinkedIn puzzle games.
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maigret logo
maigret is a free, open-source alternative to Pipl . Maigret English · 简体中文 Maigret collects a dossier on a person by username only , checking for accounts on a huge number of sites and gathering all the available information from web pages. No API keys required. AI profiling (demo) . Sponsors IPcook provides reliable residential proxies for online research, username discovery, and public data collection workflows. High success rates • 99.99% uptime • Response time under 0.5s • Monthly & Pay-as-you-go • Non-expiring traffic • Up to 10 free sub-accounts for team collaboration • Residential proxies from $0.3–$3.2/GB. Special Offer : FREE 100MB trial available. Use code WELCOME20 for 20% off. RapidProxy provides high-performance residential proxies for Twitter scraping, Selenium automation, and web data extraction. 90M+ IPs • Smart rotation • Anti-block • Non-expiring traffic. Special Offer : Try it free — Plans from $0.65/GB. Use code RAPID10 for 10% off. Contents In one minute Main features Demo Installation Usage Contributing Commercial Use About In one minute Ensure you have Python 3.10 or higher. pip install maigret maigret YOUR_USERNAME No install? Try the community Telegram bot or a Cloud Shell . Want a web UI? See how to launch it . See also: Quick start . Main features Supports 3,000+ sites ( see full list ). A default run checks the 500 highest-ranked sites by traffic; pass -a to scan everything, or --tags to narrow by category/country. Embeddable in Python projects — import maigret and run searches programmatically (see library usage ). Extracts all available information about the account owner from profile pages and site APIs, including links to other accounts. Performs recursive search using discovered usernames and other IDs. Allows filtering by tags (site categories, countries). Detects and partially bypasses blocks, censorship, and CAPTCHA. Fetches an auto-updated site database from GitHub each run (once per 24 hours), and falls back to the built-in database if offline. Works with Tor and I2P websites; able to check domains. Ships with a web interface for browsing results as a graph and downloading reports in every format from a single page. Optional AI analysis mode ( --ai ) that turns raw findings into a short investigation summary using an OpenAI-compatible API. For the complete feature list, see the features documentation . Used by Professional OSINT and social-media analysis tools built on Maigret: Demo Video Reports PDF report , HTML report Full console output Installation Already ran the In one minute steps? You're set. Below are alternative methods. Don't want to install anything? Use the community Telegram bot . Windows Download maigret_standalone.exe from Releases . You can launch it two ways: Double-click it — Maigret will ask for a username, run a default search, and wait at the end so the report links stay visible. Run it from a terminal — open Command Prompt (press Win+R , type cmd , hit Enter) or PowerShell to pass extra options: cd %USERPROFILE% \Downloads maigret_standalone.exe USERNAME maigret_standalone.exe USERNAME --html :: also save an HTML report maigret_standalone.exe --help :: list all options Video guide: https://youtu.be/qIgwTZOmMmM . Cloud Shells Run Maigret in the browser via cloud shells or Jupyter notebooks: Local installation (pip) # install from pypi pip3 install maigret # usage maigret username From source # or clone and install manually git clone https://github.com/soxoj/maigret && cd maigret # build and install pip3 install . # usage maigret username Docker Two image variants are published: soxoj/maigret:latest — CLI mode (default) soxoj/maigret:web — auto-launches the web interface # official image (CLI) docker pull soxoj/maigret # CLI usage docker run -v /mydir:/app/reports soxoj/maigret:latest username --html # Web UI (open http://localhost:5000) docker run -p 5000:5000 soxoj/maigret:web # Web UI on a custom port docker run -e PORT=8080 -p 8080:8080 soxoj/maigret:web # manual build docker build -t maigret . # CLI image (default target) docker build --target web -t maigret-web . # Web UI image Troubleshooting Build errors? See the troubleshooting guide . PDF reports ( --pdf ) are an optional extra — install with pip install 'maigret[pdf]' . They need system-level graphics libraries on Linux/macOS; see the PDF reports section for per-OS install steps. Usage Examples # make HTML, PDF, and XMind reports maigret user --html maigret user --pdf maigret user --xmind # legacy XML with a manifest for XMind 2022+ readers # machine-readable exports maigret user --json ndjson # newline-delimited JSON (also: --json simple) maigret user --csv maigret user --txt maigret user --graph # interactive D3 graph (HTML) maigret user --neo4j # Neo4j Cypher script (graph database) # search on sites marked with tags photo & dating maigret user --tags photo,dating # search on sites marked with tag us maigret user --tags us # highlight sites whose page also mentions specific keywords maigret user --keywords python rust # keyword-matched sites are shown with "[++]" in bright green # search for three usernames on all available sites maigret user1 user2 user3 -a # AI-assisted investigation summary (needs OPENAI_API_KEY) maigret user --ai --neo4j writes a *_neo4j.cypher script of the results graph; import it with cypher-shell -u neo4j -p <password> < report_user_neo4j.cypher or paste it into the Neo4j Browser. Re-imports are idempotent. See the Neo4j export docs . Run maigret --help for all options. Docs: CLI options , more examples . Running into 403s or timeouts? See TROUBLESHOOTING.md . Web interface Maigret has a built-in web UI with a results graph and downloadable reports. Don't want to run it yourself? Deploy the published soxoj/maigret:web Docker image as a hosted app in one click: Runs on Render's free tier (spins down after 15 min idle, spins back up on the next request). No login is set up on the instance, so anyone with the URL can use it. Web Interface Screenshots maigret --web 5000 Open http://127.0.0.1:5000 , enter a username, and view results. Python library Maigret can be embedded in your own Python projects. The CLI is a thin wrapper around an async function you can call directly — build custom pipelines, feed results into your own tooling, or run it inside a larger OSINT workflow. See the full library usage guide for a working example, async patterns, and how to filter sites by tag. Useful CLI flags --parse URL — parse a profile page, extract IDs/usernames, and use them to kick off a recursive search. --permute — generate likely username variants from two or more inputs (e.g. john doe → johndoe , j.doe , …) and search for all of them. --self-check [--auto-disable] — verify usernameClaimed / usernameUnclaimed pairs against live sites for maintainers auditing the database. --ai / --ai-model — run the AI analysis over the search results and stream a short investigation summary to the terminal. AI analysis --ai collects the search results, builds an internal Markdown report, and sends it to an OpenAI-compatible chat completion endpoint to produce a short, neutral investigation summary (likely real name, location, occupation, interests, languages, confidence, follow-up leads). Per-site progress is suppressed and the model's output is streamed to stdout. export OPENAI_API_KEY=sk-... maigret user --ai # pick a different model maigret user --ai --ai-model gpt-4o-mini The key can also be set as openai_api_key in settings.json . The endpoint defaults to https://api.openai.com/v1 , but openai_api_base_url in settings.json can point to any OpenAI-compatible API (Azure OpenAI, OpenRouter, a local server, …). See the settings docs for the full list of options. Tor / I2P / proxies Maigret can route checks through a proxy, Tor, or I2P — useful for .onion / .i2p sites and for bypassing WAFs that block datacenter IPs. # any HTTP/SOCKS proxy maigret user --proxy socks5://127.0.0.1:1080 # Tor (default gateway socks5://127.0.0.1:9050) maigret user --tor-proxy socks5://127.0.0.1:9050 # I2P (default gateway http://127.0.0.1:4444) maigret user --i2p-proxy http://127.0.0.1:4444 Start your Tor / I2P daemon before running the command — Maigret does not manage these gateways. Cloudflare bypass Experimental. The Cloudflare webgate is under active development; the configuration schema, CLI behaviour, and the set of routed sites may change without backwards-compatibility guarantees. A subset of sites in the database require a real browser to solve a JavaScript challenge. Maigret can offload these checks to a local FlareSolverr instance: docker run -d -p 8191:8191 --name flaresolverr ghcr.io/flaresolverr/flaresolverr:latest maigret --cloudflare-bypass < username > The bypass is opt-in ( --cloudflare-bypass or cloudflare_bypass.enabled in settings.json ) and only fires for sites whose protection field matches. See the feature docs for backend options and configuration. Contributing Add or fix new sites surgically in data.json (no json.load / json.dump ), then run ./utils/update_site_data.py to regenerate sites.md and the database metadata, and open a pull request. For more details, see the CONTRIBUTING guide and development docs . Release history: CHANGELOG.md . Commercial Use The open-source Maigret is MIT-licensed and free for commercial use without restriction — but site checks break over time and need active maintenance. For serious commercial use — with a daily-updated site database or a username-check API — reach out: 📧 [email protected] Private site database — 5 000+ sites, updated daily (separate from the public open-source database) Username check API — integrate Maigret into your product About Disclaimer For educational and lawful purposes only. You are responsible for complying with all applicable laws (GDPR, CCPA, etc.) in your jurisdiction. The authors bear no responsibility for misuse. Feedback Open an issue · GitHub Discussions · Telegram SOWEL classification OSINT techniques used: SOTL-2.2. Search For Accounts On Other Platforms SOTL-6.1. Check Logins Reuse To Find Another Account SOTL-6.2. Check Nicknames Reuse To Find Another Account License MIT © Maigret
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