FOSSASIA Summit 2026

 Attending the FOSSASIA Summit: A Refreshing Dive into Open Source and AI

I recently had the opportunity to attend the FOSSASIA Summit for two days, on March 9th and 10th, and it turned out to be an incredibly enriching experience. From insightful talks to reconnecting with familiar faces and meeting new people, the event truly captured the spirit of the open-source community.

Day 1: Learning, Conversations, and a Social Evening
The first day kicked off with a strong focus on the intersection of humanity and technology. One of the most memorable sessions I attended was “Stay Human In the AI Age” by Mario Behling. It set the tone for the day by reminding us that while AI is rapidly evolving, human values and ethics must remain at the center of innovation.

Another engaging talk was “Hardware Freedom in an AI-Dominated World: How to use AI as a Tool (Instead of Becoming a Tool)” by Mitch Altman. It provided a refreshing perspective on maintaining control over technology and emphasized the importance of open hardware in an increasingly AI-driven ecosystem.

The keynote on “Protecting Children Online: What Role Can Open Source Play?” was both thought-provoking and timely. It highlighted the responsibility of the open-source community in building safer digital spaces for younger users.

I also attended “Open Source Tools to Build Next Generation Chip Designers” by Harish Pillay, which gave a glimpse into how open-source tools are shaping the future of hardware design. It was fascinating to see the depth and possibilities in this space.

Beyond the sessions, the highlight of the day was reconnecting with Hong Phuc. It was wonderful meeting her again after Fossasia 2017 in Singapore—it brought back a lot of great memories and reminded me how strong and connected the community is.

In the evening, I joined the social event, which was a perfect setting to unwind and network. I had the chance to meet a few Red Hatters from Pune, and we spent some quality time discussing work, open source, and shared experiences. It was great to connect with them in such an informal and friendly environment.

Day 2: Deep Dives into Tools, Testing, and Performance
The second day continued with equally engaging sessions. “ZTA and PQC Support in FreeIPA” by Sumedh  Sidhaye offered insights into security advancements and how open-source identity management is evolving to meet modern challenges.

“Standardizing Developer Experience: Eclipse Che and the Power of Cloud Development Environments” by Rohan shed light on how cloud-based development environments are transforming workflows and improving consistency across teams.

One of the most relatable talks was “The Pizza Problem: What AI Doesn’t Get About Testing” by Deepak Koul. It cleverly illustrated the limitations of AI in understanding real-world testing nuances, using simple analogies that resonated with everyday engineering challenges.

I also attended “Autonomous Playwright Test Remediation via MCP and GitHub Actions,” which explored automation in testing and how AI-driven remediation can streamline development pipelines.

Finally, “Biscuit: Solving PostgreSQL’s Wildcard Search Performance Problem” by Sivaprasad Murali provided a deep technical dive into database performance optimization—an excellent session for anyone interested in backend efficiency.

Reflections
Overall, attending the FOSSASIA Summit was a rewarding experience. It offered a balanced mix of technical depth, forward-looking discussions, and meaningful human connections. From AI and hardware to testing and developer experience, the sessions covered a wide spectrum of topics that are shaping the future of technology.

More than anything, the event reinforced the importance of community in open source. Whether it was reconnecting with familiar faces or meeting new ones, every interaction added value to the experience.

I’m glad I could be part of it, and I look forward to attending again in the future.

Teach Your AI New Tricks with InstructLab

 InstructLab is an open-source toolkit and workflow designed to fine-tune foundation models using a community-driven, taxonomy-based process.

 It enables experts—even those unfamiliar with ML—to contribute small “skill recipes” that enhance a model’s knowledge base.

How does it work?

1. Install the CLI tool(ilab)
1. pip install instructlab
2. Initialize a project
1. ilab config init  > It sets up a taxonomy repo and configuration.
3. Download a base model
1. ilab model download > such as IBM granite or Merlinite
2. It downloads pre-trained models from HuggingFace
4. List the downloaded model
1. ilab model list
5. Serve the model
1. ilab model serve
6. Chat with the model
1. ilab model chat

Contribute knowledge

1. Create skills via recipes 
1. Add examples(question and answer: YAML format), 
2. Add and validate your new data.
1. ilab taxonomy diff
3. Run synthetic data generation.
1. ilab data generate
4. Train or fine tune the model 
1. ilab model train --pipeline=simple
5. Serve the trained model
1. ilab lodel serve --model-path <path>
6. Chat with the model
1. ilab model chat --model <model>
7. Submit recipes as pull requests.

Understanding the Basics of Artificial Intelligence

 What exactly AI is and why everyone is talking about it.

Artificial intelligence, or AI, has now become a part of our daily lives, and many of us are not aware of that. It's working behind the scenes, quietly and efficiently. Few examples are: phone unlocking with facial recognition, a chatbot helping you book a flight, or Netflix suggesting your next favorite show.

What is Artificial Intelligence?

In simple terms, Artificial Intelligence refers to machines or software that mimic human intelligence. A machine can learn patterns and make predictions. AI does not replace human decision-making; instead, it adds value to human judgment.

Types of AI:

• Narrow AI(Weak AI)
•  AI is designed for a specific task.  eg, Siri, Google Maps, facial recognition
• General AI (Strong AI)
• AI that can understand, learn, and apply knowledge across a wide range of tasks, like humans. eg, Meta AI, GPT4
• Super AI(AGI and ASI)
• Hypothetical AI that surpasses human intelligence in all aspects. A future concept, not yet developed.

How does AI work?

AI systems are built using techniques like:

• Machine Learning:
• ML is a subset of AI that provides machines the ability to automatically learn and improve from experience without being explicitly programmed. Types of ML:
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning
• A type of ML that mimics the human brain's network(neural network)
• Natural Language Processing
• Helps the computer understand human language.

Why AI Matters?

AI has the power to transform industries, improve efficiency, and solve global problems- from climate modeling to personalized education.

As AI continues to evolve, it will become even more integrated into our lives. The key is to use it responsibly. ensuring that it benefits society as a whole.