Agents in Production was excellent

Mike's Notes

Some initial reflections. I will add to this post over the coming week.

Resources

• https://www.blog.ajabbi.com/2025/11/agents-in-production-mlops-x-prosus.html

References

• Reference

Repository

• Home > Ajabbi Research > Library >
• Home > Handbook > 

Last Updated

20/11/2025

Agents in Production was excellent

By: Mike Peters

On a Sandy Beach: 20/11/2025

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

I attended the online MLOPs event "Agents in Production held yesterday. It was hosted in the Netherlands. It started at 3am NZ time, so I missed most of it. But all the videos will be available soon, and I'll watch them all.

It was fantastic. The 30 speakers were leaders from engineering teams from every major AI company. NVIDIA, Google, Meta, OpenAI, Microsoft, Redis, Databook, Prosus, etc.

All talking about agents in depth. Lots of architecture here.

The audience was even broader, as great questions came through.

I could follow along and understand what they were talking about. This is in sharp contrast to NZ, where nobody is interested in this stuff. Nobody understands what I'm talking about, even in the NZ AI tech group, which focuses on using AI to build apps. This was about building AI itself and solving its problems.

It was fascinating. 

One thing I learned, which really surprised me, is that none of the AI agents described in the talks can evolve or learn.

The agents in Pipi 9 all evolve and learn.

I also found a solution to a big problem for Ajabbi.

I can't find anyone to help me. In this room, there were plenty of people. Now I know where to look.

It was a lucky moment. I don't recall how I found out about this community and event. Maybe I got an invite. Next event, I will be better prepped and have questions to post in the Q&A. Will also figure out how to contact other participants. I look forward to meeting them.

Future

Maybe I will get to give one of these talks sometime. I find it really useful to bounce ideas in an open discussion. Always come back with more ideas. Listening is more important than talking.

Bolt on

I also had another epiphany last night while sleeping. I could use some of these LLMs as input into Pipi 9, combining the strength of both. This also confirms what I have been learning from testing Krobar.

Tristan, I might have a job for you. :)

I had been thinking about outputting to an LLM, but it never occurred to me to go the other way until I watched these talks and worked it out visually.

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. This replaces 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 will be the first user of this workspace to produce video training content and record online interviews with authors. Most of whom have their writings often reproduced on this blog. Later, recreating historic moments in the discovery of science. Therefore, the modules will be completed to meet Ajabbi's needs. Later, this workspace will be made available to other users and expanded in scope.

Resources

• https://www.blog.ajabbi.com/2025/10/industry-workspace.html
• https://www.blog.ajabbi.com/2024/07/movie-labs-ontology.html
• https://movielabs.com
• https://github.com/MovieLabs/OMC
• https://movielabs.com/prodtech/ML_2030_Vision.pdf
• https://movielabs.com/prodtech/ML_Interoperability.pdf
• https://movielabs.com/prodtech/ML_Urgent_Memo_to_the_C-Suite.pdf
• https://movielabs.com/prodtech/security/ML_Securing_the_Vision.pdf
• https://movielabs.com/prodtech/sdw/ML_Software_Defined_Workflows.pdf
• https://movielabs.com/cwontology/A-Creative-Works-Ontology-for-the-Film-and-Television-Industry-Final-2018-9-24.pdf
• https://mc.movielabs.com/pdf/ML_Visual_Language_Overview_v1.1.pdf
• https://sethero.com/blog/best-call-sheet-software-comparison/
• https://www.evercast.us/blog/film-production-software
• https://www.wrapbook.com/blog/what-is-film-production-management
• https://sethero.com/
• http://www.redworks.co.nz/2010/08/working-on-globe-for-magiciens-de-la.html

References

• Movie Lab

References

• Reference

Repository

• Home > Ajabbi Research > Library >
• Home > Handbook > 

Last Updated

20/11/2025

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.

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

• https://en.wikipedia.org/wiki/Dick_Smith_(make-up_artist)
• https://web.archive.org/web/20160502140404/http://dicksmithmake-up.com/
• https://dicksmithmake-up.com/
• https://www.academymuseum.org/en/programs/series/dick-smith-the-godfather-of-makeup
• https://www.stanwinstonschool.com/

MovieLabs

"MovieLabs is an independent non-profit organization 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 the field of 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.

Existing products

• https://yamdu.com/
• https://www.studiobinder.com/
• http://farmerswife.com/
• Celtx
• Storyboard That
• Gorilla Software
• Movie Magic Scheduling
• https://www.autodesk.com/solutions/film-production-management
• https://vista.co/
• https://www.wrapbook.com/

Features

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
• Customized File Sharing
• Advanced Email Sending Options
• Story Management
• Timesheets
• Activity Logs
• CO₂e calculation with Klimaktiv
• Brand White Labeling
• 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

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.

Variables

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 Finalization	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 Finalization 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)
Color 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
Color/Mastering Cost	Calculation Step	Color 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

• https://krobar.ai/
• https://en.wikipedia.org/wiki/Beta_distribution
• https://en.wikipedia.org/wiki/Log-normal_distribution
• https://www.ibm.com/think/topics/monte-carlo-simulation
• https://www.datacamp.com/tutorial/monte-carlo-simulation-in-excel

Workspace navigation menu

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

Developers can build plugins and add integrations.

• Enterprise Account
• Applications
• Screen (v3)
• Development
• Casting
• Script
• Preproduction
• Budget
• Location
• Previsualisation
• Schedule
• Story Board
• Production
• Craft
• Animals
• Atmosphere
• Costume
• Greens
• Makeup & Hair
• Minature
• Prop
• Set
• Vehicles
• Wardrobe
• Technical
• Audio
• Camera
• Shot
• Grips
• Lighting
• 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

A day in the life of an OpenTelemetry maintainer

Mike's Notes

Note

Resources

• https://opentelemetry.io/
• https://github.com/dmathieu
• https://www.blog.ajabbi.com/2025/03/you-most-likely-dont-need-metrics.html
• https://github.com/open-telemetry/community#special-interest-groups

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.