Workspaces for Screen

Mike's Notes

This is where I will keep detailed working notes on creating Workspaces for Screen. Eventually, these will become permanent documentation stored elsewhere. I'm looking for a better name than this working title.

This replaces specific coverage in Industry Workspace written on 13/10/2025. 

Testing

The current online mockup is version 3 and will be updated frequently. If you are helping with testing, please remember to delete your browser cache so you see the daily changes. Eventually, a live demo version will be available for field trials.

Learning

Years ago, I was the "hands" of NZ sculptor Neil Dawson, later becoming a commercial sculptor. Then, I was a set builder and set engineer at The Court Theatre set workshop. I was producer and then director of a series of natural history interview videos. I had to build a production management system for logistics.  I also crewed on short films, documentaries, and TV live broadcasts. All lots of fun and learning on the job.

I also did most of the Physical Effects courses at the Stan Winston School of Character Arts, the best school on the planet. My happy place is being in a workshop, making stuff. I will use all these experiences, combined with learning from MovieLabs, to build out Workspaces for Screen for film crews, especially the art department.

Why

Ajabbi Research will be the first user of this workspace to produce video training content and record online long-form interviews with authors to share on YouTube. Most of whom have their writings often reproduced on this blog. Later, recreating historic moments in the discovery of science as a free public education resource.

Therefore, the modules will be completed to meet Ajabbi Research's internal needs. Later, this workspace will be made available to other users and expanded in scope using no-code rapid development.

Resources

References

  • MovieLabs

Repository

  • Home > Ajabbi Research > Library > Industry > Screen >
  • Home > Handbook > Ajabbi Research > Media Unit >

Last Updated

25/03/2026

Workspaces for Screen

By: Mike Peters
On a Sandy Beach: 20/11/2025

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

Open-source

This open-source SaaS cloud system will be shared on GitHub and GitLab.

Dedication

This workspace is dedicated to the life and work of Dick Smith, who pioneered much of makeup special effects and generously coached so many others. A real gentleman.

Dick Smith

Source: https://web.archive.org/web/20160502184445im_/http://dicksmithmake-up.com/wp-content/uploads/2016/03/Dick-Smith.jpg

"Richard Emerson Smith (June 26, 1922 – July 30, 2014) was an American special make-up effects artist and author, (nicknamed "The Godfather of Make-Up") known for his work on such films as Little Big Man (1970), The Godfather (1972), The Exorcist (1973), Taxi Driver (1976), Scanners (1981) and Death Becomes Her (1992). He won a 1985 Academy Award for Best Makeup for his work on Amadeus and received a 2012 Academy Honorary Award for his career's work." - Wikipedia

MovieLabs

"MovieLabs is an independent non-profit organisation founded by Disney, Fox, Paramount, Sony, Universal, and Warner Bros. to advance research and development in motion picture distribution and protection. It maintains project engineering, technology market analysis, and standards development/evangelism among its core areas of focus, and partners with leading universities, corporations, technology startups, service providers, and standards bodies to further explore innovative technologies in digital media.

Key publications and standards available through MovieLabs include:

    • Entertainment ID Registry (EIDR)
    • Common Metadata
    • Content Availability Metadata (Avails)
    • Common Metadata Ratings
    • Next Generation/HDR Video
    • Enhanced Content Protection (ECP)
    • Creative Works Ontology

"- Wikipedia

MovieLabs Digital Distribution Framework

Source: https://movielabs.com/md/images/mddf-workflow-201906.png

Asset Ordering, Delivery and Tracking

Source: https://movielabs.com/md/delivery/OrderingDelivery.png

Narrative Element

Source: https://movielabs.com/wp-content/uploads/2022/09/omc_diagram.png

Change Log

Ver 3 includes development, preproduction, production, post-production, and distribution.

Introduction

This workspace could easily provide management software for physical effects workshops, set construction, YouTube interviews, and small-scale productions. An MVP will be developed where interest is greatest to enable rapid experimentation and learning, and, over time, more features can be added using no-code, based on demand.

Modules from other workspaces can easily be dragged across and included, for example.

  • Nature Conservation
  • Research
Actions

  • An application to attend the Creative Tech Accelerator 2026 April intake has been made to kickstart this rapid development process.

Existing products

This is a basic comparison of features found in screen production software.

Yamdu

Basic

  • Cast and Crew Management
  • Script Import and Breakdown
  • Distribution
  • Shooting Scheduling
  • Call Sheet Builder
  • Production Calendar
  • Budgeting (Beta)
  • Shot List and Storyboard
  • Sustainability
  • Mobile app for iOS and Android
Extra
  • Episodic Feature Set
  • Restrict Access to Sensitive Information
  • Personnel Master Data
  • Units
  • Time cards
  • Credits Generator
  • PDF and Video Watermarks
  • Travel Management
  • Resource Planning
  • Customised File Sharing
  • Advanced Email Sending Options
  • Story Management
  • Timesheets
  • Activity Logs
  • CO₂e calculation with Klimaktiv
  • Brand White Labelling
  • SSO / SAML
  • Dedicated Account Manager

[TABLE]

Data Model

words

Database Entities

  • Facility
  • Party
  • etc

Standards

The workspace needs to comply with all international standards.

  • MovieLabs
  • OpenTimelineIO

Schema

An XML schema needs to be created for scripts. There is none. Different script software can import and export with each other. To import a script into Workspaces for Screen, it would be easier to convert everything to XML and hide it behind the User Interface.

Digital Twin Variables

Note that this simulation did not use realistic min, max, mean and std deviation for these calculations. The model itself was the result and will undergo further work and testing using industry data to refine it. As a digital twin, it will learn over time.

Source: Krombar.ai simulation platform (beta)

Node Name Type Estimates / Formula
Location Scouting Cost per Project Input Variable Normal(μ=12500.0, σ=11119.516638792014)
Cost per Edit Input Variable Normal(μ=1750.0, σ=1853.2527731320024)
Shooting Days Input Variable discrete_normal distribution
Number of Sets Input Variable discrete_normal distribution
Target Markets Input Variable discrete_normal distribution
Campaigns Input Variable discrete_normal distribution
Planning Staff Input Variable discrete_normal distribution
Script Development Cost Input Variable Normal(μ=100000.0, σ=30395.136778115502)
Storyboard Artists per Project Input Variable discrete_normal distribution
Budget & Schedule Finalisation Calculation Step round(Projects in Pre-Production * Available Budget / Average Budget per Project)
Design HODs Cost per Project Input Variable Normal(μ=40000.0, σ=29652.04437011204)
Cost per VFX Shot Input Variable Normal(μ=8500.0, σ=9636.914420286412)
Set Complexity Factor Input Variable Normal(μ=2.0, σ=0.60790273556231)
Creative Assets per Campaign Input Variable discrete_normal distribution
Weeks of Campaign Planning Input Variable discrete_normal distribution
Number of Scripts Input Variable discrete_normal distribution
Rights Acquisition Cost Input Variable Normal(μ=62500.0, σ=22796.352583586628)
Projects in Pre-Production Input Variable discrete_normal distribution
Cast & Crew per Project Input Variable discrete_normal distribution
Market Research & Campaign Planning Calculation Step round(Target Markets * Campaigns per Market * Planning Staff * Weeks of Campaign Planning)
Net Profit Calculation Step Total Revenue - sum(Development Costs,Pre-Production Costs,Production Costs,Post-Production Costs,Marketing & Distribution Costs)
Storyboard/TechScout Cost per Project Input Variable Normal(μ=10000.0, σ=7413.01109252801)
Cost per Sound Edit Input Variable Normal(μ=1250.0, σ=1111.9516638792015)
Unit Set Construction Cost Input Variable Normal(μ=95000.0, σ=15197.568389057751)
Campaigns per Market Input Variable discrete_normal distribution
Press Kits per Campaign Input Variable discrete_normal distribution
Creative Staff Input Variable discrete_normal distribution
Shooting Days per Script Input Variable discrete_normal distribution
Budget/Finance Cost Input Variable Normal(μ=200000.0, σ=60790.273556231004)
Available Budget Input Variable Normal(μ=275000.0, σ=333585.49916376045)
Insurance Policies per Project Input Variable discrete_normal distribution
ROI % Calculation Step 100*Net Profit/sum(Development Costs,Pre-Production Costs,Production Costs,Post-Production Costs,Marketing & Distribution Costs)
Location Scouting & Permits Calculation Step round(max(0, Projects) * Locations per project)
Development & Greenlight Calculation Step round(max(0, Projects) * Development approval rate)
Casting/Crew Contracts Cost per Project Input Variable Normal(μ=80000.0, σ=59304.08874022408)
Cost per Score Input Variable Normal(μ=6000.0, σ=5930.408874022408)
Script Pages per Day Input Variable Normal(μ=6.0, σ=2.43161094224924)
Festival Submissions per Campaign Input Variable discrete_normal distribution
Weeks of Creative Work Input Variable discrete_normal distribution
Packaging/Casting Cost Input Variable Normal(μ=250000.0, σ=91185.41033434651)
Average Budget per Project Input Variable Normal(μ=55000.0, σ=66717.0998327521)
Storyboarding, Shot Listing, Tech Scout Calculation Step round(Projects in Pre-Production * Storyboard Artists per Project)
Set Construction & Prep Calculation Step round(Number of Sets * Set Complexity Factor * Unit Set Construction Cost)
Insurance/Compliance Cost per Project Input Variable Normal(μ=16500.0, σ=12602.118857297617)
Cost per Master Input Variable Normal(μ=1750.0, σ=1853.2527731320024)
Publicity Events per Campaign Input Variable discrete_normal distribution
Press Kits per Release Input Variable discrete_normal distribution
Greenlight Approval Cost Input Variable Normal(μ=35000.0, σ=9118.54103343465)
Designers per Project Input Variable discrete_normal distribution
Casting & Crew Hiring/Contracts Calculation Step round(Projects in Pre-Production * Cast & Crew per Project)
Daily Shooting Calculation Step round(Shooting Days per Script * Number of Scripts + Shooting Days + Script Pages per Day * Number of Scripts)
Cost per Test Screening Input Variable Normal(μ=11000.0, σ=13343.419966550418)
Distribution Contracts per Campaign Input Variable discrete_normal distribution
Number of Releases Input Variable discrete_normal distribution
Insurance & Compliance Calculation Step round(Projects in Pre-Production * Insurance Policies per Project)
Set, Costume, Makeup, Props Design Calculation Step round(Projects in Pre-Production * Designers per Project)
Budget/Schedule Finalisation Cost Calculation Step
Ops per Campaign Input Variable discrete_normal distribution
Number of Festivals Input Variable discrete_normal distribution
Picture Editing & Lock Calculation Step round(max(0, Projects) * Edits per project)
Location Scouting Cost Calculation Step Location Scouting & Permits * Location Scouting Cost per Project
Sales Ops per Campaign Input Variable discrete_normal distribution
Submissions per Festival Input Variable discrete_normal distribution
VFX/Animation Calculation Step round(max(0, Projects) * VFX shots per project)
Design HODs Cost Calculation Step Set, Costume, Makeup, Props Design*Design HODs Cost per Project
Junket Events per Release Input Variable discrete_normal distribution
Trailer, Teaser, Poster Creative Calculation Step round(Campaigns * Creative Assets per Campaign * Creative Staff * Weeks of Creative Work)
Sound Editing, ADR, Foley Calculation Step round(max(0, Projects) * Sound edits per project)
Storyboard/TechScout Cost Calculation Step Storyboarding, Shot Listing, Tech Scout * Storyboard/TechScout Cost per Project
Distribution Deals per Market Input Variable discrete_normal distribution
Press Kit, Screener, Critics Setup Calculation Step round(Campaigns * Press Kits per Campaign * Press Kits per Release * Number of Releases)
Scoring & Recording Calculation Step round(max(0, Projects) * Scores per project)
Casting/Crew Contracts Cost Calculation Step Casting & Crew Hiring/Contracts*Casting/Crew Contracts Cost per Project
Number of Markets Input Variable discrete_normal distribution
Festival & Award Submissions Calculation Step round(Campaigns * Festival Submissions per Campaign * Number of Festivals * Submissions per Festival)
Colour Grade, Titles, Mastering Calculation Step round(max(0, Projects) * Masters per project)
Insurance/Compliance Cost Calculation Step Insurance & Compliance*Insurance/Compliance Cost per Project
DCPs per Release Input Variable discrete_normal distribution
Publicity/Junket & Media Interviews Calculation Step round(Campaigns * Publicity Events per Campaign * Junket Events per Release * Number of Releases)
Test Screening/Studio Review Calculation Step round(max(0, Projects) * Test screenings per project)
Set Construction Cost Calculation Step
Subtitling Ops per Release Input Variable discrete_normal distribution
Daily Shooting Cost Input Variable Normal(μ=875000.0, σ=926626.3865660012)
Distribution Contracts Calculation Step round(Campaigns * Distribution Contracts per Campaign * Distribution Deals per Market * Number of Markets)
Censorship Ops per Release Input Variable discrete_normal distribution
Onset FX/Stunts Cost Input Variable Normal(μ=175000.0, σ=185325.27731320023)
DCP/Subtitling/Censorship Ops Calculation Step round(Campaigns * Ops per Campaign * DCPs per Release * Subtitling Ops per Release * Censorship Ops per Release)
Sales Ops Staff Input Variable discrete_normal distribution
Dailies Review Cost Input Variable Normal(μ=30000.0, σ=29652.04437011204)
Intl & Domestic Sales Ops Calculation Step round(Campaigns * Sales Ops per Campaign * Sales Ops Staff * Weeks of Sales Ops)
Weeks of Sales Ops Input Variable discrete_normal distribution
Prod Office Ops Cost Input Variable Normal(μ=62500.0, σ=55597.583193960076)
Picture Editing Cost Calculation Step Picture Editing & Lock*Cost per Edit
VFX Cost Calculation Step VFX/Animation*Cost per VFX Shot
Sound Editing Cost Calculation Step Sound Editing, ADR, Foley*Cost per Sound Edit
Music Scoring Cost Calculation Step Scoring & Recording*Cost per Score
Colour/Mastering Cost Calculation Step Colour Grade, Titles, Mastering*Cost per Master
Test Screening Cost Calculation Step Test Screening/Studio Review*Cost per Test Screening
Marketing Planning Cost Calculation Step
Creative Asset Cost Calculation Step
Press/Publicity Cost Calculation Step
Marketing & Distribution Costs Calculation Step sum(Marketing Planning Cost, Creative Asset Cost, Press/Publicity Cost, Festivals Cost, PR Junket Cost, Distribution Deal Cost, Delivery/Compliance Cost, Sales Ops Cost)
Festivals Cost Input Variable Normal(μ=62500.0, σ=55597.583193960076)
PR Junket Cost Input Variable Normal(μ=62500.0, σ=55597.583193960076)
Distribution Deal Cost Input Variable Normal(μ=62500.0, σ=55597.583193960076)
Delivery/Compliance Cost Input Variable Normal(μ=62500.0, σ=55597.583193960076)
Sales Ops Cost Input Variable Normal(μ=62500.0, σ=55597.583193960076)
Domestic Box Office Input Variable Normal(μ=17500000.0, σ=18532527.731320024)
International Box Office Input Variable Normal(μ=11500000.0, σ=12602118.857297616)
Digital/VOD Revenue Input Variable Normal(μ=4500000.0, σ=5189107.764769607)
Home Video Revenue Input Variable Normal(μ=2250000.0, σ=2594553.8823848036)
Merchandising Revenue Input Variable Normal(μ=1050000.0, σ=1408472.1075803218)
Licensing Revenue Input Variable Normal(μ=1550000.0, σ=2149773.216833123)
Development Costs Calculation Step sum(Script Development Cost, Rights Acquisition Cost, Budget/Finance Cost, Packaging/Casting Cost, Greenlight Approval Cost)
Pre-Production Costs Calculation Step sum(Budget/Schedule Finalization Cost, Location Scouting Cost, Design HODs Cost, Storyboard/TechScout Cost, Casting/Crew Contracts Cost, Insurance/Compliance Cost)
Production Costs Calculation Step sum(Set Construction Cost,Daily Shooting Cost,Onset FX/Stunts Cost,Dailies Review Cost,Prod Office Ops Cost)
Post-Production Costs Calculation Step sum(Picture Editing Cost,VFX Cost,Sound Editing Cost,Music Scoring Cost,Color/Mastering Cost,Test Screening Cost)
Total Revenue Calculation Step sum(Domestic Box Office, International Box Office, Digital/VOD Revenue, Home Video Revenue, Merchandising Revenue, Licensing Revenue)
Total Footage Hours Input Variable Normal(μ=60.0, σ=24.3161094224924)
Edits per Hour Input Variable Normal(μ=6.0, σ=5.930408874022408)
Total Shots Input Variable discrete_normal distribution
VFX Shot Percentage Input Variable beta distribution
Sound Edits per Hour Input Variable Normal(μ=17.5, σ=18.532527731320023)
Total Minutes of Score Input Variable Normal(μ=75.0, σ=66.71709983275208)
Minutes per Score Input Variable Normal(μ=3.0, σ=2.965204437011204)
Hours per Master Input Variable Normal(μ=5.0, σ=4.447806655516806)
Test Screenings Required Input Variable discrete_normal distribution
Picture Editors Input Variable discrete_normal distribution
Weeks of Editing Input Variable discrete_normal distribution
VFX Shots per Sequence Input Variable discrete_normal distribution
Number of Sequences Input Variable discrete_normal distribution
Sound Editors Input Variable discrete_normal distribution
Weeks of Sound Editing Input Variable discrete_normal distribution
Scores per Film Input Variable discrete_normal distribution
Number of Films Input Variable discrete_normal distribution
Masters per Film Input Variable discrete_normal distribution
Test Screenings per Film Input Variable discrete_normal distribution
Sets per Script Input Variable discrete_normal distribution
Release & Revenue Collection per Project Input Variable log_normal distribution
Development Opportunities Input Variable Normal(μ=30.0, σ=29.65204437011204)
Script Approval Rate Input Variable beta distribution
Scripts Input Variable Normal(μ=17.5, σ=18.532527731320023)
Rights Acquisition Rate Input Variable beta distribution
Projects with Rights Secured Input Variable Normal(μ=11.0, σ=13.343419966550417)
Budget Approval Rate Input Variable beta distribution
Budgeted Projects Input Variable Normal(μ=8.0, σ=10.378215529539213)
Packaging Success Rate Input Variable beta distribution
Packaged Projects Input Variable Normal(μ=5.5, σ=6.671709983275209)
Greenlight Approval Rate Input Variable beta distribution
Available Locations Input Variable discrete_normal distribution
Locations per Project Input Variable discrete_normal distribution
Available Designers Input Variable discrete_normal distribution
Available Storyboard Artists Input Variable discrete_normal distribution
Available Cast & Crew Input Variable discrete_normal distribution
Available Insurance Policies Input Variable discrete_normal distribution
Number of Action Sequences Input Variable discrete_normal distribution
FX Complexity Factor Input Variable Normal(μ=2.0, σ=1.482602218505602)
Review Sessions per Day Input Variable Normal(μ=2.5, σ=2.223903327758403)
Prep Days Input Variable discrete_normal distribution
Wrap Days Input Variable discrete_normal distribution
Project Pitches Input Variable discrete_normal distribution
Greenlight Rate Input Variable beta distribution
Pre-Production Start Rate Input Variable beta distribution
Production Start Rate Input Variable beta distribution
Post-Production Start Rate Input Variable beta distribution
Marketing & Distribution Rate Input Variable beta distribution
SFX Sequences per Script Input Variable discrete_normal distribution
Dailies Reviews per Day Input Variable discrete_normal distribution
Office Staff Input Variable discrete_normal distribution
Production Weeks Input Variable discrete_normal distribution
Projects Input Variable discrete_normal distribution
Scripts per Project Input Variable discrete_normal distribution
Box Office Revenue Input Variable log_normal distribution
Ancillary Revenue Input Variable log_normal distribution
Streaming Revenue Input Variable log_normal distribution
Locations per project Input Variable discrete_normal distribution
Development approval rate Input Variable beta distribution
Edits per project Input Variable discrete_normal distribution
VFX shots per project Input Variable discrete_normal distribution
Sound edits per project Input Variable discrete_normal distribution
Scores per project Input Variable discrete_normal distribution
Masters per project Input Variable discrete_normal distribution
Test screenings per project Input Variable discrete_normal distribution
On-set FX sequences per project Input Variable discrete_normal distribution
Dailies reviews per project Input Variable discrete_normal distribution
Production office days per project Input Variable discrete_normal distribution
Scripts per project Input Variable discrete_normal distribution
Legal reviews per project Input Variable discrete_normal distribution
Budgeting sessions per project Input Variable discrete_normal distribution
Packaging sessions per project Input Variable discrete_normal distribution
Greenlight approval rate Input Variable beta distribution
Distribution fees Input Variable beta distribution
Preproduction rate Input Variable beta distribution
Production rate Input Variable beta distribution
Postproduction rate Input Variable beta distribution
Marketing rate Input Variable beta distribution
On-Set SFX/VFX/Stunts Calculation Step round(max(0, Projects) * On-set FX sequences per project)
Dailies & Production Review Calculation Step round(max(0, Projects) * Dailies reviews per project)
Production Office Operations Calculation Step round(max(0, Projects) * Production office days per project)
Script Development Calculation Step round(max(0, Projects) * Scripts per project)
Rights Acquisition & Legal Calculation Step round(max(0, Projects) * Legal reviews per project)
Budgeting & Finance Calculation Step round(max(0, Projects) * Budgeting sessions per project)
Project Packaging & Casting Calculation Step round(max(0, Projects) * Packaging sessions per project)
Studio Greenlight Calculation Step round(max(0, Projects) * Greenlight approval rate)
Release & Revenue Collection Calculation Step Total Revenue - Distribution fees
Pre-Production Calculation Step round(max(0, Development & Greenlight) * Preproduction rate)
Principal Photography Calculation Step round(max(0, Pre-Production) * Production rate)
Post-Production Calculation Step round(max(0, Principal Photography) * Postproduction rate)
Marketing & Distribution Calculation Step round(max(0, Post-Production) * Marketing rate)

Simulation notes

Formula used
  • Beta Distribution =RAND()*(100-0)+0
  • Log Normal Distribution =RAND()*(100-0)+0
  • Discrete Normal Distribution =RAND()*(100-0)+0

Workspace navigation menu

This default outline needs a lot of work. The outline can be easily customised by future users via drag-and-drop and tick boxes to turn features on and off.

Developers can build plugins and add integrations.

  • Enterprise Account
    • Applications
      • Screen (v4)
        • Development
          • Casting
          • Script
        • Preproduction
          • Budget
          • Location
          • Previsualisation
          • Schedule
          • Story Board
        • Production
          • Craft
            • Animals
            • Armour & Weapons
            • Atmosphere
              • Fog
              • Explosions
              • Rain
              • Snow
            • Costume
            • Greens
            • Makeup & Hair
              • Prosthetics
                • Bake Ovens
                • Moulds
              • Wigs
            • Minature
            • Prop
            • Set
            • Stunts
            • Vehicles
            • Wardrobe
          • Technical
            • Audio
              • Mics
            • Camera
              • Shot
              • Data
              • Filters
              • Lense
            • Grips
              • Cranes
              • Dollys
              • Drone
              • Stands
              • Track
            • Lighting
              • Control
              • Fixtures
              • Power
        • Post Production
          • Editing
          • Music
          • Subtitle
          • Visual Effects
        • Distribution
          • (To come)
    • Customer (v2)
      • Bookmarks
        • (To come)
      • Support
        • Contact
        • Forum
        • Live Chat
        • Office Hours
        • Requests
        • Tickets
      • (To come)
        • Feature Vote
        • Feedback
        • Surveys
      • Learning
        • Explanation
        • How to Guide
        • Reference
        • Tutorial
    • Settings (v3)
      • Account
      • Billing
      • Deployments
        • Workspaces
          • Modules
          • Plugins
          • Templates
            • Client/Agency
            • Episodic TV
            • Feature Film
            • Short Film
          • Users
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A day in the life of an OpenTelemetry maintainer

Mike's Notes

Note

Resources

References

  • Reference

Repository

  • Home > Ajabbi Research > Library > Authors > Damien Mathieu
  • Home > Handbook > 

Last Updated

19/11/2025

A day in the life of an OpenTelemetry maintainer

By: Damien Mathieu
OpenTelemetry: 07/10/2025

I am a software engineer with a focus on backend, resilience and observability, currently working at @elastic. Some of the technologies I work with are Go, Ruby, Kubernetes, OpenTelemetry. I am also a contributor to Open-Source. I am writing about software engineering.

When people think about open source, they often picture lines of code, clever algorithms, or maybe a GitHub repository full of issues and pull requests. What can be harder to see is the human side. The people who quietly keep things moving, who make sure contributions land smoothly and help the community grow in a healthy way. That’s the work of a maintainer.

Maintainers are more than just code reviewers. They are the stewards of the SIG’s (Special Interest Group) health, direction, and community. They balance technical oversight with mentorship, governance with collaboration, and long-term vision with the day-to-day realities of issues and pull requests.

I’m Damien, I’m a maintainer of the OpenTelemetry Go SDK, an approver of the OpenTelemetry Collector and a member of several SIGs. In this post, we’ll take a closer look at what it means to be a maintainer: the responsibilities they carry, the challenges they navigate, and the impact they have on both the project and the broader community.

Open Source mentorship

One of the most rewarding parts of being a maintainer is mentorship. Every open source project depends on new contributors stepping in, learning the ropes, and eventually taking on more responsibility themselves. As maintainers, we’re often the first point of contact for someone who’s never contributed to the project before.

Mentorship can look like many different things. Sometimes it’s as simple as leaving a thoughtful code review that doesn’t just point out what’s wrong, but explains why a change matters. Other times, it’s guiding a contributor through their first issue, helping them understand the project’s structure, or showing them how to run tests locally. And every so often, it means stepping back to give someone room to try, even if they don’t get it right the first time.

The goal isn’t just to fix the immediate bug or land the pull request. It’s to help contributors feel confident enough to come back again. A healthy project grows by sharing knowledge, not hoarding it. Mentorship is how maintainers make sure today’s first-time contributor can become tomorrow’s reviewer, and eventually, the next maintainer.

Setting direction and priorities

Another part of being a maintainer is shaping the project’s roadmap. Open source moves fast: there are always new ideas, bug reports, and feature requests. Left unchecked, a project can easily become a grab bag of loosely connected changes. Part of our job as maintainers is to make sure the work stays aligned with the bigger picture.

That means asking questions like:

  • Does this feature fit with our long-term goals?
  • Is now the right time to tackle it?
  • Do we have the capacity to maintain it once it’s merged?

Sometimes the answer is “not yet” or even “no”, and it’s on us to communicate that clearly while still encouraging contributions.

Shaping a roadmap isn’t about dictating every detail. It’s about setting priorities together with the community—listening to feedback, balancing what users need today with where the project should be tomorrow, and making tradeoffs that keep the project sustainable.

The roadmap gives everyone a shared sense of direction. Contributors know where their work fits in, users can see what’s coming next, and the project as a whole stays focused instead of scattered.

Special Interest Group meetings

One of the maintainer’s roles is also to facilitate the frequent meetings that help their SIG communicate and plan its work.

Facilitating a SIG meeting isn’t about running through an agenda like a checklist. It’s about creating space where everyone feels comfortable speaking up, from long-time contributors to someone joining their very first call. That means keeping discussions focused, making sure quieter voices get heard, and helping the group reach consensus without letting debates drag on forever.

There’s also a practical side: preparing the agenda ahead of time, documenting decisions so they’re visible to the wider community, and following up on action items afterward.

In many ways, SIG meetings are where the “community” part of open source really comes to life. As maintainers, our role is to guide the conversation, not control it, making sure the project keeps moving forward while staying open and inclusive.

Challenges

Of course, maintaining isn’t all smooth sailing. One of the hardest parts is balancing the constant flow of contributions with the need to keep the codebase healthy. Every pull request represents someone’s time and effort, and it’s important to honor that. Yet, at the same time, not every change fits the project’s standards or long-term goals. Saying “no” gracefully is just as important as merging a great contribution.

Maintainers also find themselves balancing priorities that go beyond code. Different contributors, and often the companies backing them, come with their own needs and expectations. One team might want a new feature quickly, another might be focused on stability, while the community as a whole still needs clear direction. Managing those competing priorities, and making decisions that serve the project rather than any single interest, is a constant challenge.

Conflicts are another reality. With so many people involved, it’s inevitable that disagreements will happen. Sometimes it’s about technical design, sometimes about process, and occasionally about interpersonal dynamics. Part of the maintainer role is helping to navigate those moments: keeping discussions respectful, finding common ground, and making sure decisions are made transparently.

And yet, despite the difficulties, the impact of this work is enormous: when maintainers succeed, the entire community thrives.

The importance and impact of Open Source maintainers

When maintainers do their job well, the effects ripple far beyond the codebase. A well-tended project feels reliable and welcoming—contributors know their work will be reviewed thoughtfully, users trust the software to be stable, and the community grows because people want to come back.

Good project maintenance builds momentum. A contributor who feels supported on their first pull request is more likely to return for a second. Clear roadmap and consistent standards give people confidence that their effort matters and will fit into the bigger picture. And when conflicts are handled with respect and transparency, it reinforces the culture of trust that makes open source sustainable.

The impact goes deeper than just keeping a project alive. Effective maintainers create the conditions for others to succeed. That’s the real legacy of this role: not just code, but a thriving ecosystem and community built around it.

Conclusion

Being a maintainer is challenging work, but it’s also some of the most meaningful. It’s about more than merging code. It’s about stewardship, mentorship, and creating a community where people feel empowered to contribute. Every healthy open source project owes its success to the care and commitment of its maintainers.

And while the challenges are real, the rewards are just as tangible: the chance to constantly learn, to collaborate on complex problems, and to connect with people from every corner of the world and every kind of background.

OpenTelemetry’s maintainers embody this balance every day, helping the project grow while keeping its community strong.

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Why Everything in the Universe Turns More Complex

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18/11/2025

Why Everything in the Universe Turns More Complex

By: Philip Ball
Quanta Magazine: 2/04/2025

A new suggestion that complexity increases over time, not just in living organisms but in the nonliving world, promises to rewrite notions of time and evolution.

In 1950 the Italian physicist Enrico Fermi was discussing the possibility of intelligent alien life with his colleagues. If alien civilizations exist, he said, some should surely have had enough time to expand throughout the cosmos. So where are they?

Many answers to Fermi’s “paradox” have been proposed: Maybe alien civilizations burn out or destroy themselves before they can become interstellar wanderers. But perhaps the simplest answer is that such civilizations don’t appear in the first place: Intelligent life is extremely unlikely, and we pose the question only because we are the supremely rare exception.

A new proposal by an interdisciplinary team of researchers challenges that bleak conclusion. They have proposed nothing less than a new law of nature, according to which the complexity of entities in the universe increases over time with an inexorability comparable to the second law of thermodynamics — the law that dictates an inevitable rise in entropy, a measure of disorder. If they’re right, complex and intelligent life should be widespread.

In this new view, biological evolution appears not as a unique process that gave rise to a qualitatively distinct form of matter — living organisms. Instead, evolution is a special (and perhaps inevitable) case of a more general principle that governs the universe. According to this principle, entities are selected because they are richer in a kind of information that enables them to perform some kind of function.

This hypothesis, formulated by the mineralogist Robert Hazen and the astrobiologist Michael Wong of the Carnegie Institution in Washington, D.C., along with a team of others, has provoked intense debate. Some researchers have welcomed the idea as part of a grand narrative about fundamental laws of nature. They argue that the basic laws of physics are not “complete” in the sense of supplying all we need to comprehend natural phenomena; rather, evolution — biological or otherwise — introduces functions and novelties that could not even in principle be predicted from physics alone. “I’m so glad they’ve done what they’ve done,” said Stuart Kauffman, an emeritus complexity theorist at the University of Pennsylvania. “They’ve made these questions legitimate.”

Michael Wong, an astrobiologist at the Carnegie Institution in Washington, D.C.

Katherine Cain/Carnegie Science

Others argue that extending evolutionary ideas about function to non-living systems is an overreach. The quantitative value that measures information in this new approach is not only relative — it changes depending on context — it’s impossible to calculate. For this and other reasons, critics have charged that the new theory cannot be tested, and therefore is of little use.

The work taps into an expanding debate about how biological evolution fits within the normal framework of science. The theory of Darwinian evolution by natural selection helps us to understand how living things have changed in the past. But unlike most scientific theories, it can’t predict much about what is to come. Might embedding it within a meta-law of increasing complexity let us glimpse what the future holds?

Making Meaning

The story begins in 2003, when the biologist Jack Szostak published a short article(opens a new tab) in Nature proposing the concept of functional information. Szostak — who six years later would get a Nobel Prize for unrelated work — wanted to quantify the amount of information or complexity that biological molecules like proteins or DNA strands embody. Classical information theory, developed by the telecommunications researcher Claude Shannon in the 1940s and later elaborated by the Russian mathematician Andrey Kolmogorov, offers one answer. Per Kolmogorov, the complexity of a string of symbols (such as binary 1s and 0s) depends on how concisely one can specify that sequence uniquely.

For example, consider DNA, which is a chain of four different building blocks called nucleotides. Α strand composed only of one nucleotide, repeating again and again, has much less complexity — and, by extension, encodes less information — than one composed of all four nucleotides in which the sequence seems random (as is more typical in the genome).

Jack Szostak proposed a way to quantify information in biological systems.

HHMI

But Szostak pointed out that Kolmogorov’s measure of complexity neglects an issue crucial to biology: how biological molecules function.

In biology, sometimes many different molecules can do the same job. Consider RNA molecules, some of which have biochemical functions that can easily be defined and measured. (Like DNA, RNA is made up of sequences of nucleotides.) In particular, short strands of RNA called aptamers securely bind to other molecules.

Let’s say you want to find an RNA aptamer that binds to a particular target molecule. Can lots of aptamers do it, or just one? If only a single aptamer can do the job, then it’s unique, just as a long, seemingly random sequence of letters is unique. Szostak said that this aptamer would have a lot of what he called “functional information.”

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Video: Robert Hazen and Michael Wong discuss their bold proposal for a new law of nature, centered around the idea that information is as fundamental to the cosmos as mass, energy or charge.

Christopher W. Young/Quanta Magazine

If many different aptamers can perform the same task, the functional information is much smaller. So we can calculate the functional information of a molecule by asking how many other molecules of the same size can do the same task just as well.

Szostak went on to show that in a case like this, functional information can be measured experimentally. He made a bunch of RNA aptamers and used chemical methods to identify and isolate the ones that would bind to a chosen target molecule. He then mutated the winners a little to seek even better binders and repeated the process. The better an aptamer gets at binding, the less likely it is that another RNA molecule chosen at random will do just as well: The functional information of the winners in each round should rise. Szostak found that the functional information of the best-performing aptamers got ever closer to the maximum value predicted theoretically.

Selected for Function

Hazen came across Szostak’s idea while thinking about the origin of life — an issue that drew him in as a mineralogist, because chemical reactions taking place on minerals have long been suspected to have played a key role in getting life started. “I concluded that talking about life versus nonlife is a false dichotomy,” Hazen said. “I felt there had to be some kind of continuum — there has to be something that’s driving this process from simpler to more complex systems.” Functional information, he thought, promised a way to get at the “increasing complexity of all kinds of evolving systems.”

In 2007 Hazen collaborated with Szostak to write a computer simulation(opens a new tab) involving algorithms that evolve via mutations. Their function, in this case, was not to bind to a target molecule, but to carry out computations. Again they found that the functional information increased spontaneously over time as the system evolved.

There the idea languished for years. Hazen could not see how to take it any further until Wong accepted a fellowship at the Carnegie Institution in 2021. Wong had a background in planetary atmospheres, but he and Hazen discovered they were thinking about the same questions. “From the very first moment that we sat down and talked about ideas, it was unbelievable,” Hazen said.

Robert Hazen, a mineralogist at the Carnegie Institution in Washington, D.C.

Courtesy of Robert Hazen

“I had got disillusioned with the state of the art of looking for life on other worlds,” Wong said. “I thought it was too narrowly constrained to life as we know it here on Earth, but life elsewhere may take a completely different evolutionary trajectory. So how do we abstract far enough away from life on Earth that we’d be able to notice life elsewhere even if it had different chemical specifics, but not so far that we’d be including all kinds of self-organizing structures like hurricanes?”

The pair soon realized that they needed expertise from a whole other set of disciplines. “We needed people who came at this problem from very different points of view, so that we all had checks and balances on each other’s prejudices,” Hazen said. “This is not a mineralogical problem; it’s not a physics problem, or a philosophical problem. It’s all of those things.”

They suspected that functional information was the key to understanding how complex systems like living organisms arise through evolutionary processes happening over time. “We all assumed the second law of thermodynamics supplies the arrow of time,” Hazen said. “But it seems like there’s a much more idiosyncratic pathway that the universe takes. We think it’s because of selection for function — a very orderly process that leads to ordered states. That’s not part of the second law, although it’s not inconsistent with it either.”

Looked at this way, the concept of functional information allowed the team to think about the development of complex systems that don’t seem related to life at all.

"Information itself might be a vital parameter of the cosmos, similar to mass, charge and energy." - Michael Wong, Carnegie Institution

At first glance, it doesn’t seem a promising idea. In biology, function makes sense. But what does “function” mean for a rock?

All it really implies, Hazen said, is that some selective process favors one entity over lots of other potential combinations. A huge number of different minerals can form from silicon, oxygen, aluminum, calcium and so on. But only a few are found in any given environment. The most stable minerals turn out to be the most common. But sometimes less stable minerals persist because there isn’t enough energy available to convert them to more stable phases.

This might seem trivial, like saying that some objects exist while other ones don’t, even if they could in theory. But Hazen and Wong have shown(opens a new tab) that, even for minerals, functional information has increased over the course of Earth’s history. Minerals evolve toward greater complexity (though not in the Darwinian sense). Hazen and colleagues speculate that complex forms of carbon such as graphene might form in the hydrocarbon-rich environment of Saturn’s moon Titan — another example of an increase in functional information that doesn’t involve life.

It’s the same with chemical elements. The first moments after the Big Bang were filled with undifferentiated energy. As things cooled, quarks formed and then condensed into protons and neutrons. These gathered into the nuclei of hydrogen, helium and lithium atoms. Only once stars formed and nuclear fusion happened within them did more complex elements like carbon and oxygen form. And only when some stars had exhausted their fusion fuel did their collapse and explosion in supernovas create heavier elements such as heavy metals. Steadily, the elements increased in nuclear complexity.

Wong said their work implies three main conclusions.

  • First, biology is just one example of evolution. “There is a more universal description that drives the evolution of complex systems.”
  • Second, he said, there might be “an arrow in time that describes this increasing complexity,” similar to the way the second law of thermodynamics, which describes the increase in entropy, is thought to create a preferred direction of time.
  • Finally, Wong said, “information itself might be a vital parameter of the cosmos, similar to mass, charge and energy.”

In the work Hazen and Szostak conducted on evolution using artificial-life algorithms, the increase in functional information was not always gradual. Sometimes it would happen in sudden jumps. That echoes what is seen in biological evolution. Biologists have long recognized transitions where the complexity of organisms increases abruptly. One such transition was the appearance of organisms with cellular nuclei (around 1.8 billion to 2.7 billion years ago). Then there was the transition to multicellular organisms (around 2 billion to 1.6 billion years ago), the abrupt diversification of body forms in the Cambrian explosion (540 million years ago), and the appearance of central nervous systems (around 600 million to 520 million years ago). The arrival of humans was arguably another major and rapid evolutionary transition.

Evolutionary biologists have tended to view each of these transitions as a contingent event. But within the functional-information framework, it seems possible that such jumps in evolutionary processes (whether biological or not) are inevitable.

In these jumps, Wong pictures the evolving objects as accessing an entirely new landscape of possibilities and ways to become organized, as if penetrating to the “next floor up.” Crucially, what matters — the criteria for selection, on which continued evolution depends — also changes, plotting a wholly novel course. On the next floor up, possibilities await that could not have been guessed before you reached it.

For example, during the origin of life it might initially have mattered that proto-biological molecules would persist for a long time — that they’d be stable. But once such molecules became organized into groups that could catalyze one another’s formation — what Kauffman has called autocatalytic cycles — the molecules themselves could be short-lived, so long as the cycles persisted. Now it was dynamical, not thermodynamic, stability that mattered. Ricard Solé of the Santa Fe Institute thinks such jumps might be equivalent to phase transitions in physics, such as the freezing of water or the magnetization of iron: They are collective processes with universal features, and they mean that everything changes, everywhere, all at once. In other words, in this view there’s a kind of physics of evolution — and it’s a kind of physics we know about already.

The Biosphere Creates Its Own Possibilities

The tricky thing about functional information is that, unlike a measure such as size or mass, it is contextual: It depends on what we want the object to do, and what environment it is in. For instance, the functional information for an RNA aptamer binding to a particular molecule will generally be quite different from the information for binding to a different molecule.

Yet finding new uses for existing components is precisely what evolution does. Feathers did not evolve for flight, for example. This repurposing reflects how biological evolution is jerry-rigged, making use of what’s available.

Kauffman argues that biological evolution is thus constantly creating not just new types of organisms but new possibilities for organisms, ones that not only did not exist at an earlier stage of evolution but could not possibly have existed. From the soup of single-celled organisms that constituted life on Earth 3 billion years ago, no elephant could have suddenly emerged — this required a whole host of preceding, contingent but specific innovations.

However, there is no theoretical limit to the number of uses an object has. This means that the appearance of new functions in evolution can’t be predicted — and yet some new functions can dictate the very rules of how the system evolves subsequently. “The biosphere is creating its own possibilities,” Kauffman said. “Not only do we not know what will happen, we don’t even know what can happen.” Photosynthesis was such a profound development; so were eukaryotes, nervous systems and language. As the microbiologist Carl Woese and the physicist Nigel Goldenfeld put it in 2011, “We need an additional set of rules describing the evolution of the original rules. But this upper level of rules itself needs to evolve. Thus, we end up with an infinite hierarchy.”

The physicist Paul Davies of Arizona State University agrees that biological evolution “generates its own extended possibility space which cannot be reliably predicted or captured via any deterministic process from prior states. So life evolves partly into the unknown.”

"An increase in complexity provides the future potential to find new strategies unavailable to simpler organisms." - Marcus Heisler, University of Sydney

Mathematically, a “phase space” is a way of describing all possible configurations of a physical system, whether it’s as comparatively simple as an idealized pendulum or as complicated as all the atoms comprising the Earth. Davies and his co-workers have recently suggested(opens a new tab) that evolution in an expanding accessible phase space might be formally equivalent to the “incompleteness theorems” devised by the mathematician Kurt Gödel. Gödel showed that any system of axioms in mathematics permits the formulation of statements that can’t be shown to be true or false. We can only decide such statements by adding new axioms.

Davies and colleagues say that, as with Gödel’s theorem, the key factor that makes biological evolution open-ended and prevents us from being able to express it in a self-contained and all-encompassing phase space is that it is self-referential: The appearance of new actors in the space feeds back on those already there to create new possibilities for action. This isn’t the case for physical systems, which, even if they have, say, millions of stars in a galaxy, are not self-referential.

“An increase in complexity provides the future potential to find new strategies unavailable to simpler organisms,” said Marcus Heisler, a plant developmental biologist at the University of Sydney and co-author of the incompleteness paper. This connection between biological evolution and the issue of noncomputability, Davies said, “goes right to the heart of what makes life so magical.”

Is biology special, then, among evolutionary processes in having an open-endedness generated by self-reference? Hazen thinks that in fact once complex cognition is added to the mix — once the components of the system can reason, choose, and run experiments “in their heads” — the potential for macro-micro feedback and open-ended growth is even greater. “Technological applications take us way beyond Darwinism,” he said. A watch gets made faster if the watchmaker is not blind.

Back to the Bench

If Hazen and colleagues are right that evolution involving any kind of selection inevitably increases functional information — in effect, complexity — does this mean that life itself, and perhaps consciousness and higher intelligence, is inevitable in the universe? That would run counter to what some biologists have thought. The eminent evolutionary biologist Ernst Mayr believed that the search for extraterrestrial intelligence was doomed because the appearance of humanlike intelligence is “utterly improbable.” After all, he said, if intelligence at a level that leads to cultures and civilizations were so adaptively useful in Darwinian evolution, how come it only arose once across the entire tree of life?

Mayr’s evolutionary point possibly vanishes in the jump to humanlike complexity and intelligence, whereupon the whole playing field is utterly transformed. Humans attained planetary dominance so rapidly (for better or worse) that the question of when it will happen again becomes moot.

But what about the chances of such a jump happening in the first place? If the new “law of increasing functional information” is right, it looks as though life, once it exists, is bound to get more complex by leaps and bounds. It doesn’t have to rely on some highly improbable chance event.

What’s more, such an increase in complexity seems to imply the appearance of new causal laws in nature that, while not incompatible with the fundamental laws of physics governing the smallest component parts, effectively take over from them in determining what happens next. Arguably we see this already in biology: Galileo’s (apocryphal) experiment of dropping two masses from the Leaning Tower of Pisa no longer has predictive power when the masses are not cannonballs but living birds.

Together with the chemist Lee Cronin(opens a new tab) of the University of Glasgow, Sara Walker of Arizona State University has devised an alternative set of ideas to describe how complexity arises, called assembly theory. In place of functional information, assembly theory relies on a number called the assembly index, which measures the minimum number of steps required to make an object from its constituent ingredients.

“Laws for living systems must be somewhat different than what we have in physics now,” Walker said, “but that does not mean that there are no laws.” But she doubts that the putative law of functional information can be rigorously tested in the lab. “I am not sure how one could say [the theory] is right or wrong, since there is no way to test it objectively,” she said. “What would the experiment look for? How would it be controlled? I would love to see an example, but I remain skeptical until some metrology is done in this area.”

Hazen acknowledges that, for most physical objects, it is impossible to calculate functional information even in principle. Even for a single living cell, he admits, there’s no way of quantifying it. But he argues that this is not a sticking point, because we can still understand it conceptually and get an approximate quantitative sense of it. Similarly, we can’t calculate the exact dynamics of the asteroid belt because the gravitational problem is too complicated — but we can still describe it approximately enough to navigate spacecraft through it.

Wong sees a potential application of their ideas in astrobiology. One of the curious aspects of living organisms on Earth is that they tend to make a far smaller subset of organic molecules than they could make given the basic ingredients. That’s because natural selection has picked out some favored compounds. There’s much more glucose in living cells, for example, than you’d expect if molecules were simply being made either randomly or according to their thermodynamic stability. So one potential signature of lifelike entities on other worlds might be similar signs of selection outside what chemical thermodynamics or kinetics alone would generate. (Assembly theory similarly predicts complexity-based biosignatures.)

There might be other ways of putting the ideas to the test. Wong said there is more work still to be done on mineral evolution, and they hope to look at nucleosynthesis and computational “artificial life.” Hazen also sees possible applications in oncology, soil science and language evolution. For example, the evolutionary biologist Frédéric Thomas of the University of Montpellier in France and colleagues have argued(opens a new tab) that the selective principles governing the way cancer cells change over time in tumors are not like those of Darwinian evolution, in which the selection criterion is fitness, but more closely resemble the idea of selection for function from Hazen and colleagues.

Hazen’s team has been fielding queries from researchers ranging from economists to neuroscientists, who are keen to see if the approach can help. “People are approaching us because they are desperate to find a model to explain their system,” Hazen said.

But whether or not functional information turns out to be the right tool for thinking about these questions, many researchers seem to be converging on similar questions about complexity, information, evolution (both biological and cosmic), function and purpose, and the directionality of time. It’s hard not to suspect that something big is afoot. There are echoes of the early days of thermodynamics, which began with humble questions about how machines work and ended up speaking to the arrow of time, the peculiarities of living matter, and the fate of the universe.

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