OpenLoop is a Pseudo Lab community project that explores how content storytelling and AI-powered workflow automation can bring open-source projects to global audiences.
Throughout the season, we will design, test, and document a repeatable workflow that transforms existing Pseudo Lab project content into English-language content for global discovery — while creating clearer pathways for potential contributors to discover and engage with projects through GitHub.
Project Page: https://pseudo-lab.com/projects/f19e207c-1c3d-426b-b485-d1744655c7a0?tab=home
OpenRel = Open Source Relations
Based in South Korea, Pseudo Lab is home to a growing community of builders, researchers, learners, and open-source contributors exploring AI and emerging technologies.
Across the community, projects continuously generate research, technical insights, experiments, and stories worth sharing. However, bringing these stories to global audiences requires more than translation.
It requires a repeatable process for understanding project context, adapting content for global audiences, maintaining technical accuracy, and connecting content discovery with opportunities to contribute.
OpenLoop explores how this process can be standardized, automated, tested, and ultimately reused by other Pseudo Lab projects.
Build a reusable AI-powered content workflow that helps Pseudo Lab projects reach global audiences and connect with potential open-source contributors.
Rather than simply creating English content, OpenLoop aims to turn the content-production process itself into a reusable workflow.
Our long-term goal is to enable any Pseudo Lab project to use the OpenLoop workflow when it is ready to reach global audiences — making it easier to publish English content, share project stories, and create new entry points for participation through GitHub.
Build manually. Standardize what works. Automate what repeats.
Throughout the season, we will first establish what good global content looks like, translate those learnings into a standardized process, and then experiment with repository-based AI workflows using Codex or Claude Code.
The goal is not to automate every decision. Instead, we aim to identify repetitive parts of the process that AI can handle effectively while preserving human review for storytelling, technical accuracy, and final publication decisions.
- Review existing Pseudo Lab content and global LinkedIn references
- Explore effective post and visual formats for global audiences
- Establish content criteria and global storytelling approaches
- Develop a standardized OpenLoop content format
- Define common steps, guidelines, inputs, outputs, and QA criteria
- Establish the workflow from blog translation to LinkedIn content
- Identify repetitive tasks and opportunities for AI automation
- Build repository-based AI content workflows using Codex or Claude Code
- Define standardized inputs, workflow steps, outputs, and QA requirements
- Test individual AI workflows using the same selected Pseudo Lab project assets
- Compare output quality, consistency, processing time, and required human intervention
- Consolidate them into one shared OpenLoop AI workflow
- Document the consolidated OpenLoop workflow
- Develop reusable guidelines, templates, and supporting materials
- Apply and finalize the workflow using real Pseudo Lab projects
By the end of the season, OpenLoop aims to establish a reusable OpenLoop Toolkit consisting of:
- Global Content Guidelines — Principles for adapting Pseudo Lab project stories for global audiences
- Standardized Content Workflow — A repeatable process connecting Blog Translation → LinkedIn Visual → LinkedIn Post
- AI-Powered Workflow — A repository-based workflow built and tested using Codex and/or Claude Code
- Workflow Documentation — Instructions and templates that allow other Pseudo Lab members to reproduce the process
- Real Project Outputs — Content produced by applying the OpenLoop workflow to selected Pseudo Lab projects
Ultimately, success means that the OpenLoop workflow can be understood, reproduced, and adapted by Pseudo Lab projects beyond the original team.
Every Monday | 18:50–20:00 KST | ONLINE / OFFLINE
| Week | Stage | Date | Format | Key Activities | Expected Outcome |
|---|---|---|---|---|---|
| W01 | OT | 2026.10.05 | OFFLINE | Project introduction & orientation; introduce the OpenLoop vision, milestones, and expected outcomes | Team alignment |
| W02 | BUILD | 2026.10.12 | ONLINE | Review existing Pseudo Lab assets; each participant researches and shares LinkedIn content best practices across post and visual formats | Best-practice reference pool |
| W03 | BUILD | 2026.10.19 | ONLINE | Develop a proposed LinkedIn content format; adapt one Pseudo Lab project asset; share and compare formats | Individual content prototypes |
| W04 | BREAK | 2026.10.26 | — | Vote asynchronously on one standardized content format for the first OpenLoop collection | Selected content format |
| W05 | CURATE | 2026.11.02 | OFFLINE | Translate the selected content format into a standardized workflow; define steps, guidelines, inputs, outputs, and QA criteria | Standardized OpenLoop content workflow |
| W06 | LAUNCH | 2026.11.09 | ONLINE | Identify repetitive tasks and automation opportunities; each participant builds one repository-based AI content workflow using Codex or Claude Code | Individual AI workflow prototypes |
| W07 | AMPLIFY | 2026.11.16 | ONLINE | Test each AI-powered workflow using the same selected Pseudo Lab project(s); record quality, consistency, processing time, and human intervention | Workflow test results |
| W08 | AMPLIFY | 2026.11.23 | ONLINE | Share test results, challenges, and learnings; compare approaches and identify the strongest elements | Workflow comparison |
| W09 | AMPLIFY | 2026.11.30 | ONLINE | Incorporate successful elements into one shared OpenLoop AI workflow; test and refine the consolidated workflow | Consolidated OpenLoop AI workflow |
| W10 | SCALE | 2026.12.07 | OFFLINE | Document the consolidated workflow; define inputs, steps, outputs, human review points, and QA process | Workflow documentation |
| W11 | SCALE | 2026.12.14 | OFFLINE | Complete documentation, templates, and supporting materials; test whether the workflow can be reproduced using documentation alone | Reusable OpenLoop toolkit |
| W12 | SCALE | 2026.12.21 | OFFLINE | Share the workflow with Pseudo Lab members; collect feedback and identify usability issues | Community feedback |
| W13 | SCALE | 2026.12.28 | OFFLINE | Apply the finalized workflow to selected projects; record interventions and improvement opportunities; finalize workflow and documentation | Final OpenLoop workflow |
| W14 | BREAK | 2027.01.04 | — | Project retrospective and preparation for final sharing/presentation | Project retrospective |
| W15 | BREAK | 2027.01.09 | — | Share project with whole community | Grand Gathering Event |
Build manually. Standardize what works. Automate what repeats.
Blog Translation
↓
LinkedIn Visual
↓
LinkedIn Post
↓
QA & Human Review
↓
Publication
↓
Global Discovery
↓
GitHub Contribution
↻
OpenLoop begins with human-led content experimentation before gradually introducing AI automation.
This allows us to first understand what good content looks like, then determine which parts of the process are repetitive enough to automate, rather than automating the process before it has been validated.
A core part of OpenLoop is exploring how the standardized content process can be translated into a repository-based AI workflow.
Each participant will build and test one workflow using either Codex or Claude Code.
A typical workflow may follow a structure such as:
openloop-workflow/
│
├── AGENTS.md / CLAUDE.md
│
├── guidelines/
│ ├── translation-guide.md
│ ├── linkedin-guide.md
│ ├── visual-guide.md
│ └── qa-guide.md
│
├── workflows/
│ ├── translate.md
│ ├── visual.md
│ ├── linkedin.md
│ └── review.md
│
├── input/
│ └── project/
│
└── output/
└── project/
The exact implementation may differ between participants. However, workflows will follow common OpenLoop requirements for inputs, outputs, content guidelines, and QA, allowing different approaches to be meaningfully compared.
AI workflow experiments will be evaluated across:
| Criterion | What We Evaluate |
|---|---|
| Accuracy | Does the output preserve source facts and technical meaning? |
| Content Quality | Is the content clear and appropriate for global audiences? |
| Consistency | Does the output follow the standardized OpenLoop format? |
| Efficiency | How much time does the workflow require? |
| Human Intervention | How much manual editing or correction is needed? |
| Reusability | Can the same workflow be applied to another project without rebuilding it? |
The strongest elements from individual experiments will be incorporated into the shared OpenLoop workflow.
OpenLoop encourages all members to contribute across Content Storytelling and AI Automation.
| Role | Name | Focus |
|---|---|---|
| Builder | @Alice |
Project direction & coordination |
| Runner | @name |
Content Storytelling / AI Automation |
| Runner | @name |
Content Storytelling / AI Automation |
| Runner | @name |
Content Storytelling / AI Automation |
| Runner | @name |
Content Storytelling / AI Automation |
| Runner | @name |
Content Storytelling / AI Automation |
- Create Together — Share ownership across storytelling, translation, design, workflow development, testing, and documentation.
- Experiment & Learn — Test content formats and AI-powered workflows, compare results, document challenges, and improve each iteration.
- Automate What Repeats — Use AI to streamline repetitive tasks while keeping human judgment where it matters.
- Build for Repeatability — Turn successful experiments into simple, documented workflows that can be reused beyond a single season.
OpenLoop welcomes anyone interested in bringing open-source projects to global audiences through content storytelling and AI-powered workflow automation.
We're looking for people with:
- Experience or interest in content curation and storytelling
- Interest in AI-powered workflow automation for content and marketing
- Interest in open-source communities and global engagement
- Working proficiency in English
- Experience or interest in visual content design using Canva, Figma, or similar tools
- Familiarity with LinkedIn, Instagram, X, or other social content formats
- Willingness to collaborate, experiment, document learnings, and contribute consistently
No single participant is expected to be an expert across every area. OpenLoop is designed for members to learn by building, testing, and sharing workflows together.
Let's OPEN the LOOP together.
There are many ways to participate in OpenLoop:
- 🧭 Builder — Help shape and coordinate the project
- 🏃 Runner — Build, test, and document content and AI workflows
- 👀 Open Participant — Join open sessions, follow experiments, and share feedback
- 💻 Contributor — Improve the OpenLoop workflow or contribute directly to featured Pseudo Lab projects through GitHub
❗️Join the community: Pseudo Lab Discord
❗️Communication channel: Discord #{{channel-name}}
Anyone interested in OpenLoop is welcome to join our open sessions.
You can participate by:
- Joining our regular open sessions through the Pseudo Lab Discord
- Participating during Magical Week
- Meeting the OpenLoop team at Pseudo Lab community events
- Sharing feedback on the OpenLoop workflow and documentation
- Exploring featured projects and contributing directly through GitHub
This section documents the workflows, experiments, outputs, and learnings created throughout OpenLoop.
- 🔄 OpenLoop Workflow:
URL - 📖 Content Guidelines:
URL - 🤖 AI Workflow:
URL - 📋 Templates:
URL - 🧪 Workflow Experiments:
URL
| Collection | Featured Projects | Platform | Link |
|---|---|---|---|
OpenLoop Collection #1 |
TBD | URL |
|
OpenLoop Collection #2 |
TBD | URL |
| Date | Update | Link |
|---|---|---|
2026.10.26 |
Standardized Content Format Selected | URL |
2026.11.30 |
Shared OpenLoop Workflow Consolidated | URL |
2026.12.28 |
Final Workflow Test & Documentation | URL |
OpenLoop is developed as part of Pseudo Lab's Open Academy.
This project is made possible by the builders, runners, contributors, and project teams who openly share their work and ideas across the Pseudo Lab community.
Special thanks to everyone helping make Pseudo Lab projects more accessible to the global open-source community. Every contribution opens another path between project discovery and participation — and keeps the loop moving.
Pseudo Lab is a non-profit community focused on advancing machine learning and AI through open collaboration.
Built around the values of Sharing, Motivation, and Collaborative Joy, Pseudo Lab brings together builders, researchers, learners, and contributors to experiment, share knowledge, and create open-source projects together.
This project is licensed under the MIT License.