# Show HN: MatrAIx – simulate users before reality (Survey/Chat/Web/App) > Source: > Published: 2026-08-20 03:34:52+00:00 **Simulate before reality.** Population-scale, persona-driven infrastructure for evaluating AI systems and interactive products with heterogeneous simulated users. **MatrAIx** is a population-scale, persona-driven infrastructure for evaluating AI systems and interactive products with heterogeneous simulated users. Instead of testing against a generic or interchangeable user, MatrAIx instantiates sampled persona records as LLM agents and runs them through reproducible tasks across four environments — **Survey**, **AI Chatbot**, **Web**, and **App** (native desktop and mobile, including macOS and iOS). At its foundation is a shared schema of **1,290 categorical dimensions** covering background, psychology, capability, and behavior. Personas combine dependency-aware synthetic generation with evidence-aware human grounding; a deterministic, quality-filtered coreset of **one million personas** is released for research on [Hugging Face](https://huggingface.co/datasets/MatrAIx2026/MatrAIx_Persona_1M_Public_Release). Shared telemetry, task-owned verification, and reporting connect individual responses and trajectories to subgroup- and population-level findings. The name nods to *The Matrix*: a simulated world useful for exploration, stress testing, and hypothesis generation, **not a replacement for evidence from real people**. **[2026-08-11]** Academic commentary:by Mayank Kejriwal (*Can we simulate the world?*).*AI Scientist***[2026-08-10]** Featured as an[X Trending Story](https://x.com/i/trending/2086626337561911419):*Harvard and MIT Unveil MatrAIx with 8.3 Billion Virtual Personas*. Also covered across tech media, including[36Kr](https://eu.36kr.com/en/p/3932853833759876),[Numerama](https://www.numerama.com/tech/2308727-ces-chercheurs-ont-cree-83-milliards-dhumains-virtuels-pour-tester-des-produits-a-notre-place.html),[Infobae](https://www.infobae.com/tecno/2026/08/10/asi-prueba-la-ia-un-mundo-con-8300-millones-de-personas-digitales-matraix-es-el-metaverso/),[AI타임스](https://www.aitimes.com/news/articleView.html?idxno=213824),[CryptoBriefing](https://cryptobriefing.com/matraix-simulation-harvard-mit-ai-personas/), and[Startup Fortune](https://startupfortune.com/harvard-and-mit-built-an-ai-model-of-83-billion-people-to-test-products-on/), among others. Also discussed by Cisco VP & CTO[Gianpaolo Barozzi](https://lnkd.in/p/gE9cV2nw).**[2026-08-04]** Technical report on arXiv:[MatrAIx: Simulating the World with 8.3 Billion Persona Agents](https://arxiv.org/abs/2608.04205)(`2608.04205` ). Also featured on[Hugging Face Daily Papers](https://huggingface.co/papers/date/2026-08-10)([paper page](https://huggingface.co/papers/2608.04205)).**[2026-08-01]** Released[Persona 1M](https://huggingface.co/datasets/MatrAIx2026/MatrAIx_Persona_1M_Public_Release)on Hugging Face (~1M quality-filtered personas).**[2026-07-31]** Open-sourced the Playground and task library:[MatrAIx-Persona-8B](https://github.com/MatrAIx-ai/MatrAIx-Persona-8B).**[2026-07-29]** Position note:[From Personas to Simulated Users](https://matraix.ai/research/survey-from-personas-to-simulated-users.html). [Docker](https://docs.docker.com/get-docker/)— needed for Web and OS-app tasks[uv](https://docs.astral.sh/uv/)and Python 3.12- Node.js 20+ (Playground / viewer frontends only) - Model API keys for real persona runs — see [agents.md](/MatrAIx-ai/MatrAIx-Persona-8B/blob/main/docs/environment/agents.md)(the install checks below do not need a key) Windows users: run everything inside[WSL2]— open PowerShell, run`wsl --install` (installs Ubuntu), then clone this repoinside the WSL filesystem(e.g.`~/MatrAIx` , not`/mnt/c/…` , which is much slower) and enableWSL integrationin Docker Desktop → Settings → Resources. Every command in this README then works exactly as written. Native PowerShell/cmd is not supported (the task verifiers require`bash` ). ``` git clone && cd MatrAIx uv venv --python 3.12 uv pip install -e . uv pip install pytest pytest-asyncio httpx uv pip install -e packages/playground uv pip install -e packages/harbor-langsmith uv pip install -e packages/rewardkit ``` Run jobs with ** uv run matraix run …**. After install, use the [smoke tests](#smoke-tests)below to confirm Survey, Chat, Web, and OS-app are ready (no API key). Summarize a finished job with **. Advanced runtime tools stay under** `uv run matraix results ` `uv run harbor …` .Set a model API key before real GUI or CLI runs (smoke checks do not need one): ``` export ANTHROPIC_API_KEY="sk-ant-..." # anthropic/claude-* models # export OPENAI_API_KEY="sk-..." # openai/gpt-* models ``` See [agents.md](/MatrAIx-ai/MatrAIx-Persona-8B/blob/main/docs/environment/agents.md) for the full key matrix. Playground can also load keys from `application/playground/.env.local` . The in-repo `matraix-persona-dev-sample` (~200) is for smoke only. For real cohorts and Playground sampling, import the public 1M coreset: ``` huggingface-cli download MatrAIx2026/MatrAIx_Persona_1M_Public_Release \ --repo-type dataset \ --local-dir persona/datasets/matraix-persona-1m/release ``` Playground: Dataset → ** matraix-persona-1m**. CLI: `--dataset persona/datasets/matraix-persona-1m` . Details: [Handbook § Persona 1M](/MatrAIx-ai/MatrAIx-Persona-8B/blob/main/docs/README.md#3-persona-1m-recommended). Two quick checks after install — no API key. Together they cover the default path for all four task types (Survey, Chat, Web, OS-app): | Check | Confirms you can run | Command | |---|---|---| Without Docker | Survey and Chat | `uv run matraix smoke application/tasks/example-survey_product-feedback` | With Docker | Web and OS-app | `uv run matraix run -c configs/jobs/example-job-recipe/harbor-smoke-local.yaml` | The first finishes in seconds and should print `Smoke: ok` . The second builds a small local image on first run (a few minutes), then writes under `jobs/harbor-smoke-local/` . Step-by-step: [quickstart §3](/MatrAIx-ai/MatrAIx-Persona-8B/blob/main/docs/quickstart.md#3-smoke-tests-two-lanes). Playground picks tasks, samples personas, and launches the same Matraix Playground jobs as CLI auto mode. Start API + frontend (two terminals): ``` # Terminal A — API VENV=.venv bash application/playground/backend/run_dev.sh # Terminal B — frontend cd application/playground/frontend && npm ci && npm run dev ``` Open ** http://localhost:5173** → Playground → pick a persona cohort → pick Survey / Chat / Web / OS app tasks → **Lock pipeline**→ **Run eval**. Details: [Playground §10](/MatrAIx-ai/MatrAIx-Persona-8B/blob/main/docs/quickstart.md#10-playground--play-tasks-visually). **Develop** — copy a reference task under `application/tasks/` , edit `task.toml` / `instruction.md` / `input/` / verifier, then register it for Playground ([task-guide.md](/MatrAIx-ai/MatrAIx-Persona-8B/blob/main/docs/application/task-guide.md)): ``` cp -R application/tasks/example-survey_product-feedback \ application/tasks/ ``` | Type | Reference task | |---|---| | Survey | `application/tasks/example-survey_product-feedback` | | Chat | `application/tasks/example-chat-api_support_chatbot` | | Web | `application/tasks/example-web-playwright_quote-choice` | | OS-app | `application/tasks/example-computer-use-linux_note-to-csv` | **Run** — generate a Matraix Playground job (pins agent + model), then execute it: ``` uv run python application/scripts/generate_application_job.py \ --task application/tasks/example-survey_product-feedback \ --execution-mode auto \ --persona-ids 0042 \ --model-name anthropic/claude-sonnet-4-6 # Use the export lines + recipe path the script prints, e.g.: uv run matraix run -c configs/jobs/application-task-job-recipe/example-survey-product-feedback-auto-n1.yaml ``` Batch (`--sample-size N` ), filters, and chat / web / os-app examples: [docs/quickstart.md](/MatrAIx-ai/MatrAIx-Persona-8B/blob/main/docs/quickstart.md). ** MatrAIx Handbook** — guides, persona / application / environment docs. ``` MatrAIx/ ├── persona/ Schema, datasets, synthesis/curation/validation pipelines │ ├── schema/ 1,290-dimension persona schema │ ├── datasets/ Dev sample pool and persona YAMLs │ ├── validation/ Grounding / quality validation suites │ └── scripts/ Persona job & pipeline helpers ├── application/ │ ├── tasks/ Survey · chat · web · os-app task specs │ ├── task-spec/ Shared task contracts │ ├── playground/ Visual runner (backend API + frontend) │ └── scripts/ generate_application_job.py and task tooling ├── environment/ │ ├── runtime/ Matraix Playground runtime │ ├── agents/ Persona-conditioned agents │ ├── task-environments/ Docker images / sidecars │ └── adapters/ External adapters (e.g. SimpleQA) ├── packages/ playground · rewardkit · harbor-langsmith ├── apps/viewer/ Frontend paired with `harbor view` ├── configs/jobs/ Curated & generated Matraix Playground job recipes ├── docs/ Handbook — persona/ · application/ · environment/ ├── examples/ Minimal example tasks ├── src/matraix/ Python package entrypoints ├── scripts/ Repo-level helpers ├── tests/ Unit / environment tests └── jobs/ Local Matraix Playground run outputs (gitignored) ``` Large generated datasets stay outside git (see the Hugging Face release above). - Join Discord — nickname . Fill the Google Form (background, interests, paper authorship / acknowledgements).`Full Name - Affiliation` - Say hi to us! We like to connect you for the shared interest or experience! - Participating MatrAIx research community for collaboration or contribution! If you use MatrAIx, the Persona 1M dataset, or results from this repository, please cite: ``` @article{li2026matraix, title = {MatrAIx: Simulating the World with 8.3 Billion Persona Agents}, author = {Li, Xiaomin and Hao, Yuexing and Hou, Jianheng and Huang, Jintao and Wen, Qianfeng and Huang, Shirley and Liu, Yifan and Liu, Xiaoyi and Fan, Yilan and Wang, Yijun and others}, year = {2026}, eprint = {2608.04205}, archivePrefix = {arXiv}, primaryClass = {cs.AI}, url = {https://arxiv.org/abs/2608.04205} } ``` Paper: [arXiv:2608.04205](https://arxiv.org/abs/2608.04205) · Full authors: GitHub **Cite this repository** (`CITATION.cff` ) · Dataset: [Persona 1M on Hugging Face](https://huggingface.co/datasets/MatrAIx2026/MatrAIx_Persona_1M_Public_Release). MIT — see [LICENSE](/MatrAIx-ai/MatrAIx-Persona-8B/blob/main/LICENSE).