This year we’re breaking it down into three different tracks:
BLAM 101 focuses on AI for productivity, leveraging Gemini across Google Workspace and more.
BLAM 201 is specifically created for scientists where time will be spent focusing on deep research, science productivity tools, running domain-specific AI models on LRC and more.
BLAM Coder is AI for programming, dipping our tech toes into Claude, RooCode Extension and discussing cost management for AI
Each week, in addition to the three tracks, we’ll feature some interesting reading/listening/watching for all Blammers about where AI is going, how AI is changing and may change our lives (and not necessarily in good ways). We’ll also be featuring a cutting-edge AI science application at the Laboratory each week as well.
Before we start we recommend the following for all levels:
Add yourself to the the Google Chat group, and review the Chat group details. This is where people will post their work from the weekly challenges and ask questions
Add the BLAM calendar to the Google calendar, so you won’t miss any of the webinars
Sign up for email reminders about BLAM! during the week.
BLAM! AI Readings: Shifts, Signals, and Side Effects
Each week brings more critical discussion regarding AI and its impact on the workplace. Cade Metz of the New York Times wrote a fascinating article about AI’s “jagged intelligence,” exploring how AI can excel at tasks like math and programming while struggling with critical thinking. This duality is forcing a reframing of how we view AI in the workforce.
AI Reporting:
Stanford released its 9th annual AI Index Report, which is considered one of the most comprehensive, data-driven views of artificial intelligence. Recognized as a trusted resource by global media, governments, and leading companies, the AI Index equips policymakers, business leaders, and the public with rigorous, objective insights into AI’s technical progress, economic influence, and societal impact. Coverage on the report is varied from overall positive with concerns for jobs (KQED) to concerns that AI is running out of data (Fortune). However, there are some particularly interesting chapters to the Berkeley Lab, such as Science, Policy and Government, and Responsible AI.
AI at the Lab:
We’re all familiar with the DOE’s Genesis Mission, but there’s also a dedicated website showcasing how Berkeley Lab is advancing AI across its divisions. The AI for Science at Berkeley Lab site highlights available AI resources and features the innovative ways colleagues are applying AI in their research. Its goal is to support the Lab’s collaborative culture by encouraging staff to connect, share insights, and learn from others at different stages of their AI journey.
BLAM 101
Theme: Studio and Flows
Learning Goal: This week, we’re focusing on Studio and Flows!
Google Workspace Studio is a powerful, no-code command center designed to turn your everyday ideas into intelligent AI agents. Think of it as the bridge between "having a to-do list" and "having a team that handles it for you." Instead of manually moving data between apps or writing complex scripts, Studio allows you to build custom helpers using simple, natural language. It’s deeply integrated into the tools you already use—like Gmail, Drive, and Sheets—meaning your AI agents aren't just generic chatbots; they understand the specific context of your documents and conversations.
Studio is powered by Flows, which are automated, multi-step sequences that put your agents to work to automate the "busy work" of coordination and reporting. Studio and Flows allow you to reclaim your time for the creative and strategic work that actually moves the needle.
Resources:
Google Workspace Studio training and help: This page is an excellent starting point and resource, linking to a quick start guide, cheat sheet, productivity guides, and troubleshooting.
Create Your First Flow Tutorial: A hands-on guide that walks you through using pre-built templates, like getting a daily summary of your unread emails in Google Chat.
Get Started with Google Workspace Studio (3 minutes)
Understanding Flow Starters and Steps (3 minutes)
Please Go to the BLAM home page to add these meetings to your calendar or better yet subscribe to the BLAM calendar!
This week’s webinar:
Wednesday, April 22 at noon. Studio and Flows with Luis Corrales, where he’ll walk you through examples of how to use Studio and Flows for productivity and project management. You can add it to your calendar from the BLAM home page.
Office Hours:
Thursday, April 23 at 9:30AM You can add it to your calendar from the BLAM home page.
Use Studio to create a Flow that helps you and your work. Here are a couple of examples of different uses could be:
Create a Priority Filter for your Gmail: Create a Workspace Studio that analyzes incoming messages in real time. Here’s the Flow:
Trigger: A new email arrives in Gmail.
Logic: The AI (Gemini) scans the body for "urgent" sentiment or specific client keywords.
Action: If it’s high priority, the flow automatically labels it "Action Required," sends a notification to your mobile device, and drafts a brief summary of the request so you don’t have to read the whole thread.
Create a Post-Meeting Action Tracker: This workflow sorts out who does what easily with the following flow:
Trigger: A Google Meet recording or transcript is saved to Drive.
Logic: The flow processes the transcript to extract action items, owners, and deadlines.
Action: It automatically creates tasks in Google Tasks (or Jira/Asana) and sends a follow-up email to all attendees with the summarized notes and their specific assignments.
You can also check out studio.workspace.google.com and look at the "Discover" page. Google has built-in templates there that act as "live tutorials"—they show you exactly how a Flow is structured so you can reverse-engineer it!
BLAM 201
Theme: Running Domain-specific AI Models on LRC / Supercomputers
Learning Goal: Learning how to run domain-specific AI models on systems like the Lawrencium Research Cluster (LRC) and other supercomputers empowers scientists to tackle complex, data-intensive problems at scale. By leveraging high-performance computing, researchers can train and deploy models tailored to their fields—whether in biology, materials science, or climate research—leading to faster insights and more accurate results.
Developing these skills enables scientists to fully utilize advanced infrastructure, accelerate discovery, and stay at the forefront of modern, AI-driven research.
Resources - spend some time this week working with the following AI models:
AlphaFold: AlphaFold is an AI-powered, deep-learning system developed by Google DeepMind that predicts a protein’s 3D structure from its amino acid sequence with high accuracy, solving a 50-year-old "protein folding problem". It has revolutionized biology by making structural predictions for nearly all known proteins, accelerating research in drug discovery, disease understanding, and biotechnology.
Boltz AI: Boltz (specifically Boltz-1 and Boltz-2) is an open-source AI platform developed by MIT researchers and supported by partners like Recursion, designed to revolutionize drug discovery by predicting how proteins and small molecules interact. It accelerates drug development by being over 1,000 times faster than traditional physics-based methods while offering similar accuracy
Foldy: LBL Foldy is a democratized protein folding platform developed by the Keasling Lab. We use state-of-the-art AI models to predict protein structures and provide advanced analysis tools for structural biology research.
Materials Project: The Materials Project (MP) is an open-access, AI-ready database and computational tool designed to accelerate material discovery by predicting properties of known and unknown inorganic materials.
Weekly Challenge - Select two of the AI models discussed in the webinar from the list of resources and try them out. Share your opinions about the pros and cons of each result with the Google Chat Group for 201.
Go to the BLAM home page to add these meetings to your calendar or better yet subscribe to the BLAM calendar!
This Week’s Webinar - Wednesday, April 22 at 1:30 with Andrew Schmeder and Saroj Adhikari, Computer Systems Engineer 3, ScienceIT. You can add it to your calendar from the BLAM home page.
This Week’s Office Hours - Wednesday, April 22 at 11:00 AM. You can add it to your calendar from the BLAM home page.
Select two of the productivity tools discussed in the webinar and give them the same prompt, on a scientific topic or question you are familiar with. Share your opinions about the pros and cons of each result with the Google Chat Group for 201.
Theme: Building an interactive web app
Learning Goal: Learning to build an interactive web app using AI coding tools is an accessible and rewarding way to bring ideas to life. With tools that can generate code, suggest features, and troubleshoot issues in real time, even beginners can quickly move from concept to working prototype. By combining basic web fundamentals with AI assistance, you can focus more on creativity and user experience while accelerating development and learning as you go.
Go to the BLAM home page to add these meetings to your calendar or better yet subscribe to the BLAM calendar!
This week’s Webinar - Wednesday, April 22 at 3:00 with Tim Fong, who will give a live demonstration and walk through of this week’s work. This webinar is 1 ½ hours with the last hour for overall questions. You can add it to your calendar from the BLAM home page.
Weekly Challenge - Try to build an interactive web app!