An open AI
community
for all
We are a community of practitioners building an open-source,
composable AI ecosystem. Our goal is to reduce the complexity of
the AI industry. Join our thriving community and share, collaborate
and innovate with us.
An open AI
community
for all
We are a community of practitioners building an open-source, composable AI ecosystem. Our goal is to reduce the complexity of the AI industry. Join our thriving community and share, collaborate and innovate with us.
Find
AiFoundry.org
Projects
# llamagator
# project-prompt
# open-llms
Out more
Welcome Susan!
Glad to join this community!
Mara joined the party
Everyone welcome Nina
Glad you're here, Tuana!
Find
out
more
AiFoundry.org
Projects
# llamagator
# project-prompt
# open-llms
Welcome Susan!
Glad to join this community!
Mara joined the party
Everyone welcome Nina
Glad you're here, Tuana!
View and join our
open-source projects
AI Foundry open source projects are Apache V2 licensed. View the code on
our GitHub, and collaborate with project contributors in our Discord Server.
Llamagator
Llamagator (your LLM aggregator) is a multi-LLM prompt testing tool.
- Test prompts against multiple LLMs or LLM versions
- Observe the relative performance of generated responses
- Run prompts multiple times to observe the reliability
- Supports local and API access to LLMs
- Licensed under Apache License, Version 2.0
View repo
$blame.ai
$blame.ai is a demo app for testing AI infrastructure from LLMs to inference engines.
We're building an open data set, application code, and reference implementation that any developer can use to showcase their AI infrastructure.
View repo
Quantization-Aware Training
A community research project to investigate data-free quantization-aware training.
View repo
View and join our
open-source
projects
AI Foundry open source projects are Apache
V2 licensed. View the code on our GitHub, and
collaborate with project contributors in our
Discord Server.
Llamagator
Llamagator (your LLM aggregator) is a multi-LLM prompt testing tool.
- Test prompts against multiple LLMs or LLM versions
- Observe the relative performance of generated responses
- Run prompts multiple times to observe the reliability
- Supports local and API access to LLMs
- Licensed under Apache License, Version 2.0
$blame.ai
$blame.ai is a demo app for testing AI infrastructure from LLMs to inference engines.
We're building an open data set, application code, and reference implementation that any developer can use to showcase their AI infrastructure.
Quantization-Aware Training
A community research project to investigate data-free quantization-aware training.
What's happening
in the community
Upcoming podcasts, virtual and physical community events for AI Foundry.
february
march
april
FOSDEM fringe:
AI plumbers conference
By Tanya Dadasheva & Roman Shaposhnik
Feb 3, 2025 Monday
9:00 AM - 5:00 PM GMT+1
Gent, Belgium
What's happening
in the community
Upcoming podcasts, virtual and physical
community events for AI Foundry.
february
march
april
FOSDEM fringe:
AI plumbers conference
By Tanya Dadasheva & Roman Shaposhnik
Feb 3, 2025 Monday
9:00 AM - 5:00 PM GMT+1
Gent, Belgium
Explore Our Latest Insights
Stay updated with our community-driven content. Read about past events and access to summaries and video replays of prior AI Hack Labs and Podcasts.
Explore Our Latest
Insights
Stay updated with our community-driven content.
Read about past events and access to summaries
and video replays of prior AI Hack Labs and
Podcasts.
Vision
& Mission
Our long-term vision is to help the industry evolve to a structure where model training and deployment for any company are open, transparent, and concise.
We desire to see the industry aligned on the principles of how those systems are structured at a high level from which every company can deploy its own engine or system, configured the way each specific company needs it. This approach will lower risks and improve AI results for all companies and practitioners. Therefore, we foster, build, and support projects that can become the engineering foundation of such training and deployment systems. We support practitioners willing to benefit from AI in their work and life, specifically models and their fine-tuning and deployment.
We are a community of practitioners building an open-source composable AI ecosystem. By collaborating on standards for everyone, we aim to reduce the complexity of the AI industry.
Our core values
Openness
We don't just mean you can use our tools, we mean you are free to use it however you want.
Integrity
Honesty, ethics, and follow-through foster trust within the community.
Empathy
Our community members are mindful and curious about other people’s motivations and ideas and help each other with compassion and kindness.
Cooperation
Our community seeks win-win solutions with mutually beneficial outcomes.
Vision
& Mission
Our long-term vision is to help the industry evolve to a structure where model training and deployment for any company are open, transparent, and concise.
We desire to see the industry aligned on the principles of how those systems are structured at
a high level from which every company can
deploy its own engine or system, configured the
way each specific company needs it. This approach will lower risks and improve AI results for all companies and practitioners. Therefore, we foster, build, and support projects that can
become the engineering foundation of such training and deployment systems. We support practitioners willing to benefit from AI in their work and life, specifically models and their fine-tuning and deployment.
We are a community of practitioners building an open-source composable AI ecosystem. By collaborating on standards for everyone, we aim to reduce the complexity of the AI industry.
Our core values
Openness
We don't just mean you can use our tools, we mean you are free to use it however you want.
Integrity
Honesty, ethics, and follow-through foster trust within the community.
Empathy
Our community members are mindful and curious about other people’s motivations and ideas and help each other with compassion and kindness.
Cooperation
Our community seeks win-win solutions with mutually beneficial outcomes.
AI Foundry has been a game-changer for my development journey. The collaborative spirit and innovative projects inspire me every day.
Jordan Smith
Developer, TechCorp
What kinf of sourcery is this? I couldn't make heads or tails of it!
Bob Pensioneer
Really important dude!
AI Foundry has been a game-changer for my development journey. The collaborative spirit and innovative projects inspire me every day.
Jordan Smith
Developer, TechCorp
What kinf of sourcery is this? I couldn't make heads or tails of it!
Bob Pensioneer
Really important dude!
Join the ai
revolution
today
revolution
today
Join the ai
revolution
today
revolution
today
Faqs
Find answers to your questions about AI Foundry
and our community-driven projects.
We believe that economically viable, decentralized, self-governed, developer-centric "Open Source AI" alternatives are essential and downright critical for the future of business and society.
Machine learning and application development are entirely different disciplines with disparate communities, practices, and tools. The current state of the practice for application development includes continuous integration and deployment, treating configuration as code, and cross-functional integrated teams that own all aspects of delivering business value to production. The current state of the practice for machine learning engineers includes dealing with large datasets, model evaluation, and exploratory analysis tools like Jupyter notebooks. These divergent practices often end up causing friction in application development.
We do, but just like with DevOps (when the wall between Developers and Operations was finally broken), practices and tooling make a difference. As AI reshapes the industry, we believe bringing the two disciplines of machine learning and application development together is critical. The two communities have much to learn from one another. Further, we believe cross-functional teams with representation from both specialties will be more productive and yield more significant innovation. Therefore, we believe that increasing the collaboration between the two communities will create the conditions to improve both the practices and the tooling across the board.
AI Hack Labs are similar to unconferences since the agenda is fluid and determined by the participants. However, an AI Hack Lab involves hands-on work like a Hack Room at FOSDEM. Participants are expected to bring their laptops.
Consider just one tooling example: although it is theoretically possible to version Jupyter notebooks in Git, in practice, the default configuration doesn’t play nicely with diff. Noisy diffs break the usual Git pull request workflow.
Faqs
Find answers to your questions about AI Foundry
and our community-driven projects.
We believe that economically viable, decentralized, self-governed, developer-centric "Open Source AI" alternatives are essential and downright critical for the future of business and society.
Machine learning and application development are entirely different disciplines with disparate communities, practices, and tools. The current state of the practice for application development includes continuous integration and deployment, treating configuration as code, and cross-functional integrated teams that own all aspects of delivering business value to production. The current state of the practice for machine learning engineers includes dealing with large datasets, model evaluation, and exploratory analysis tools like Jupyter notebooks. These divergent practices often end up causing friction in application development.
We do, but just like with DevOps (when the wall between Developers and Operations was finally broken), practices and tooling make a difference. As AI reshapes the industry, we believe bringing the two disciplines of machine learning and application development together is critical. The two communities have much to learn from one another. Further, we believe cross-functional teams with representation from both specialties will be more productive and yield more significant innovation. Therefore, we believe that increasing the collaboration between the two communities will create the conditions to improve both the practices and the tooling across the board.
AI Hack Labs are similar to unconferences since the agenda is fluid and determined by the participants. However, an AI Hack Lab involves hands-on work like a Hack Room at FOSDEM. Participants are expected to bring their laptops.
Consider just one tooling example: although it is theoretically possible to version Jupyter notebooks in Git, in practice, the default configuration doesn’t play nicely with diff. Noisy diffs break the usual Git pull request workflow.