The federal government just told schools that AI literacy is a funding priority. Effective May 13, schools that demonstrate AI integration will score higher on federal grant applications.
But here is what I keep thinking about: 300+ public comments on this policy included explicit requests for vendor transparency requirements and parental consent mandates. The Department declined both, saying those decisions belong at the state and local level.
Meanwhile, 12 state bills have been introduced in 2026 alone pushing back against digital learning tools, and NYC just pulled the plug on its first AI-focused public high school after parent opposition.
So here is the question that matters for anyone on the front lines of this work: In your experience, does AI adoption work better when it starts from the top (policy, mandates, grant incentives) or from the bottom (individual educators building skills and sharing with colleagues)?
Not a trick question. I have seen compelling evidence for both. Curious where you land and why.
Full breakdown: https://www.harvestkernel.com/federal-ai-literacy-funding-priority-department-education/
Three new activity workshops just dropped in the SeedStacking Resources space. Each one has four hands-on activities mapped to the SeedStacking phases: Seed, Sprout, Grow, and Harvest.
The Hidden Language of AI: Learn Markdown, JSON, and build your first AI skill file. Four activities from formatting text to writing a deployable skill.
How to Buy Back Time with AI: From a 15-minute time audit to building and sharing a complete AI workflow. Four activities from awareness to automation.
Anyone Can Vibe Code: From describing an app in plain English to shipping a working tool. Four activities from product spec to deployment.
Every activity set is designed so you can start wherever you are. Seed level takes 5-15 minutes. Harvest level produces something you can share with your team or your students. Pick a topic, pick your level, and post your results.
Today's topic: The Hidden Language of AI
Most people think the files behind AI tools are code. They're not. They're closer to a grocery list than a programming language.
Markdown is just text formatting written by hand. A hashtag becomes a heading. Asterisks become bold. Dashes become bullets. If you've ever read a README on GitHub, you've been reading Markdown your whole internet life.
JSON is just labels and values. Think of a recipe card: "Dish": "Lasagna", "Prep Time": "30 minutes", "Ingredients": ["noodles", "sauce", "cheese"]. Curly braces hold it together. That's it.
YAML is an indented outline. CSV is a spreadsheet saved as text. Plain text is what you type into a prompt box every day.
None of these require coding. All of them were designed so humans could read them. And understanding them is the fastest bridge between "I use AI" and "I build with AI."
Here's your challenge: Open any AI tool's documentation page today and look for a .md or .json file. See if you can read it without any help. You'll probably surprise yourself.
Drop a comment below: What's the one "technical" AI thing that turned out to be way simpler than you expected?
Something I have been thinking about: we have spent years building AI policies for students, and almost no time building them for ourselves.
EdTPA portfolios, National Board Certification, state licensing evaluations. Teachers are already using AI for all of this. The detection tools being used have virtually no research behind them for professional writing.
Here is what I want to know from you: if a teacher uses AI to surface patterns in their student data and then writes their own reflective commentary based on what they found, is that cheating? Or is that exactly what professional competence looks like in 2026?
Where do you draw the line between AI-assisted reflection and AI-generated filler in your own professional work?
Most people are using AI like a novelty. Open it, ask one question, close it, forget about it for a week.
That is not a time strategy. That is a demo.
Today I am breaking down something I call Time Stacks. These are repeatable AI workflows that replace the exact moments in your day that eat the most minutes. Not the big dramatic time wasters. The small drips. The 8 minutes reformatting. The 12 minutes searching. The 15 minutes rewriting the same kind of email for the hundredth time.
Three stacks cover about 80% of the problem:
The Drafting Stack kills the blank page. You give AI context and structure, it gives you a first draft in 30 seconds. You still edit, but you skip the hardest part.
The Research Stack replaces 15 browser tabs with a focused 5-minute synthesis. Ask the right question, get 80% of the picture, then spend your energy on the 20% that requires your judgment.
The Format Stack is the one nobody thinks about. Notes to slides. Meeting summaries to action items. Raw data to presentations. AI handles format translation instantly.
The real return is not just time saved. It is cognitive energy preserved. Psychologists call it decision fatigue. By 3 PM, your creative thinking drops by nearly 40% from all those small decisions. When you stack AI into those moments, you show up sharper for the work that actually matters.
Try this week: Keep a log of every task that feels repetitive. At the end of the week, pick your top three. Those are your Time Stacks. Start there.
What is the biggest time-waster in your daily workflow right now? Let me know below and I will tell you which Time Stack would fix it.
Cognitive surrender is when you accept an AI output without evaluating it. How often do you do it?
Researchers at the University of Pennsylvania coined the term this week to describe the moment you stop being an active thinker and start being a passive consumer of AI output. Pasting an email without reading it. Trusting a summary without checking the source. Following a recommendation without asking whether the framing is right.
But here is what makes it interesting: the research also shows that the people who use AI the MOST effectively are not the ones who use it least. They are the ones who treat it as a partner rather than an oracle. More delegation, but also more critical evaluation. Both at the same time.
So here is the question for today: think about the last time you used AI for something important. Did you evaluate the output before acting on it, or did you trust it and move on? What does your honest answer tell you about your relationship with AI?
Full breakdown: Your Brain Is Outsourcing Itself
A university just used AI to chop up professor lectures into 60 second clips, wrap them in generated text, and sell them for $5/month. Most of the professors found out through social media, not their employer.
This raises a question worth sitting with: who should decide how your teaching gets repackaged by AI?
The professors say they were blindsided. The university says their IP policy gives them the right. The content produced was, by multiple accounts, low quality and riddled with errors.
Here is what I want to hear from you: If your institution started using AI to repackage your work without telling you, how would you respond? Is there a version of this that could actually work if educators were brought in as partners instead of treated as raw material?
Have you heard of vibe coding?
It's one of the most accessible entry points into AI fluency that exists right now, and it requires zero programming experience.
Vibe coding is exactly what it sounds like: you describe what you want to build in plain English, and an AI writes the code. A budget tracker. A student progress dashboard. A custom CRM. Tools like Cursor, Bolt, and Claude Code make this possible today.
The skill that separates good vibe coders from frustrated ones is not technical knowledge. It is clarity of communication. Describing outcomes instead of implementation details. Keeping each request small and testable. Building one feature at a time.
Sound familiar? That is the same skill that makes someone effective at prompting any AI tool.
Here is a question for everyone: if you could vibe code one tool for your work or personal life right now, what would it be? What problem would you solve first?
Here's something I want this community to wrestle with today.
70% of college students now see AI as a direct threat to their job prospects. Nearly half of Gen Z workers say AI's risks outweigh its benefits. And students are responding by switching majors, hunting for something "AI-proof."
But when they ask professors and advisors for guidance, nobody has answers. Even university presidents are admitting publicly that they don't know which skills will matter in 10 years.
I think the answer is simpler than people are making it. The divide isn't between majors. It's between people who learn to work with AI and people who try to hide from it.
For those of you building AI fluency right now, what convinced you to lean in rather than pull away? Was there a specific moment where avoiding AI stopped feeling like a viable option?
Today's teaching topic: The Basics of Skill MD Files
If you've ever been frustrated by AI giving you generic responses, this one's for you.
A Skill MD file is a simple text document that acts as a permanent instruction manual for your AI assistant. Instead of re-explaining who you are and what you need every single conversation, you write it once and the AI reads it automatically.
The structure is straightforward. Four sections: Purpose (what the skill does), Inputs (what you provide), Process (steps to follow), and Outputs (what the finished product looks like).
If you can write an email with bullet points, you can write a skill file. No coding required.
The real benefit is not time savings, though you'll get plenty of that. The real benefit is that building a skill file forces you to articulate what you actually want from AI. That clarity changes everything about your output quality.
Today's Daily Seed breaks down exactly how to build your first one in 10 minutes. What task would you build a skill file for first?
