Happy Holidays Everyone!

Thank you for showing up week after week. I know your attention is the most valuable currency you have, and I genuinely appreciate you spending a few minutes here with me.

🎄In Founder’s Corner, we lean into the fun side of AI with holiday creativity. Simple experiments that help you build real fluency while making something memorable with the people you care about.

✍️In AI Education, we close out the LLM series by getting honest about where these models break down and how to use them with better judgment, especially when the stakes are real.

🎁In 10-Minute Win, we keep it seasonal with a Holiday Menu Copilot that turns a pile of recipes into one grocery list, a cook schedule, and a simple command sheet you can actually follow.

1) New updates to the Gemini app, December 2025

Summary: Google’s December “Gemini Drop” makes Gemini 3 Flash the headline upgrade, adds more precise image editing with Nano Banana, and improves grounding by letting you pull NotebookLM notebooks directly into Gemini as sources.

Why it matters: Google is pushing AI from “chat” into default workflows: faster research + better grounding + easier creation is how mainstream adoption compounds.

2) The new ChatGPT Images is here

Summary: OpenAI released an updated ChatGPT Images powered by a new flagship image model, focused on more precise edits, better detail consistency, and up to 4× faster image generation.

Why it matters: Image generation is shifting from “cool outputs” to useful editing. That’s what makes it sticky for normal people (content, reselling, small biz marketing, family photo edits).

3) AI’s economic growth effects

Summary: Vanguard argues AI investment could drive stronger-than-expected economic growth and stabilize labor markets, potentially changing the path of interest rates and productivity over time.

Why it matters: This is the “AI = macro” angle. If AI boosts growth, it impacts everything from rates to earnings expectations—especially for tech-heavy markets. 

4) US Senator Bernie Sanders calls for AI data center construction moratorium

Summary: Sanders called for a moratorium on new AI data center construction to “give democracy a chance to catch up,” raising concerns about speed, governance, and who benefits from the buildout.

Why it matters: This is an early signal that AI infrastructure could become a political flashpoint (power use, water, land, jobs). Regulation risk isn’t just about models—it’s about physical buildout.

5) George Osborne joins OpenAI: ex-chancellor adds tech post to his CV

Summary: Former UK chancellor George Osborne is joining OpenAI to lead an initiative focused on government relationships and national-level AI partnerships.

Why it matters: AI is being treated like national infrastructure now. OpenAI (and rivals) are racing to shape policy and secure country-level deals—this will influence adoption, regulation, and geopolitics.

A lot of the AI conversation is centered around the themes I cover throughout Neural Gains Weekly: productivity and automation at work, the job market, politics, markets, and the infrastructure buildout behind it all. It matters because these changes will shape our everyday lives, and we all need to be ready for a future that looks different than today. But there is another side to this technology that often gets overlooked: creativity.

I’ve found it challenging to tap into my creative side for a variety of reasons. My work forces me to think critically and solve complex problems in the healthcare space, but that doesn’t always translate into creative energy at the end of the day. I stay busy with hobbies like weightlifting, yoga, cooking, and time with friends and family, but it can still be hard to break out of routine and create something new. This is where my AI journey has helped reshape how I think and build new skills for the future. 

AI is a creativity tool that helps you create things you normally would not make, and we all have access to dozens of free tools that make experimentation easy. You can generate and edit images with natural language. You can bring a concept to life in a short video with a bit of prompt practice. The “what’s possible” in this space seems to evolve weekly as the tools get more powerful and more accessible. More importantly, this kind of use builds AI fluency. Every small experiment teaches you how to give better instructions, how to iterate, and how to get closer to the output you actually want.

That is why the holidays are such a great setting to try something new and tap into your creative side. The goal is not perfection or productivity. The goal is connection and fun. Holiday traditions are also a low-stakes way to show friends and family the fun side of AI, especially for people who have not experimented with it much yet.

This Thanksgiving, I started a new tradition with Suno AI and turned inside jokes and shared memories into a custom song for the whole family. I have zero musical talent, but in about 10 minutes I created something we will laugh about for years. It also sparked real curiosity about AI, because the output made it feel relatable instead of intimidating. If you want to try the same kind of experiment, start here. Below is a short list of holiday ideas you can test in minutes to bring more creativity and joy into your celebrations.

Ideas for your holiday celebrations

1. AI Image Pictionary: One person uses an AI image generator to generate a "hyper-realistic" or "abstract" version of a holiday movie or song. The rest of the family has to guess the title based on the AI’s interpretation.
2. The "Ugly Sweater" Makeover: Take a photo of family members in normal clothes and use an AI editor (like Google Photos’ "Magic Editor" or Adobe Firefly) to "AI-generate" the wildest, most ridiculous holiday sweaters onto them.
3. Collaborative "Exquisite Corpse" Story: Start a story in a chatbot. Each family member adds one sentence or a specific detail (e.g., "The turkey grows wings and flies away"). After everyone contributes, ask the AI to turn it into a cohesive, professional-sounding holiday fable to read aloud.
4. Mystery Guest Trivia: Ask an AI to "act" as a famous historical figure or holiday character (like Ebenezer Scrooge or a 1920s jazz singer). Family members take turns asking it questions to guess who the AI is pretending to be.
5. The "Year in Review" Poem: Paste a few bullet points of your family’s 2025 highlights into an AI and ask it to write an epic poem or a "Twas the Night Before Christmas" style summary of your year.
6. Signature Holiday Mocktails: Tell the AI what flavors your family likes (e.g., "cinnamon, apple, but not too sweet") and ask it to invent a "signature family drink" for 2025 with a fun name.
7. AI Table Talk Prompts: Ask an AI to generate "10 deep, meaningful, and slightly weird conversation starters for a multi-generational family dinner" to keep things lively.
8. Custom Place Cards: Use an AI to generate a "unique spirit animal wearing a Santa hat" for each guest based on their personality, then print them as place cards for the table.
9. Family Portrait "Time Travel": Use an AI image-to-image tool to "reimagine" a 2025 family photo as if it were taken in the 1800s, the 1970s, or even 100 years in the future.
10. Holiday "News Broadcast": Use a video AI tool or a simple voice-cloner to create a "North Pole News Report" that mentions family members by name and their "status" on the Naughty or Nice list.

Quick recap

You now have:

• A mental model of what an LLM is
• A training story for how it learns patterns
• A use story for what happens from your prompt to its answer

The last piece is judgment:

Where do these models break down, and how should you work with them?

Hallucinations: confident, wrong answers

A large language model is trained to continue text in a way that looks plausible. It is not trained to say “I do not know” by default.

Hallucinations show up when:

• The question is vague or poorly defined
• The model has seen many conflicting patterns in training
• There is no clear answer, but the model generates one anyway

Result: Text that sounds right but is factually wrong.

For example, asking it to “summarize a law” without giving the actual text can lead to a clean, but incorrect summary. It is continuing patterns about what legal text usually looks like, not reading the specific law unless you provide it.

Shallow and generic answers

If you ask:

• “Analyze my finances”
• “Tell me how to get better with money”

with no data or structure, you will get vague, generic advice.

Why:

• Your prompt is vague
• There is no specific context to latch onto
• The model falls back to common patterns it has seen many times

You get better answers when you:

• Narrow the task: “Review these three budget categories and suggest one change.”
• Provide structured context: short summaries and labeled sections
• Specify format and length

Context window and forgetting

The model cannot see beyond its context window. That means:

• Very long chats can push earlier details out of view
• Huge pasted documents may be cut off
• If a key detail is not inside the current window, the model will act as if it never saw it

This is not “bad memory” in a human sense. It is a fixed input size.

Practical moves:

• Restate key constraints near your final ask
• Keep long tasks in separate, focused chats
• Summarize earlier parts into short reminders instead of pasting everything again

Bias and gaps

Because training data reflects human writing and behavior, it can contain:

• Bias
• Gaps in representation
• Outdated views

Those patterns can surface in answers. Extra training and safety steps reduce this but do not remove it.

This is one more reason to:

• Be cautious with topics involving identity, health, or sensitive decisions
• Treat outputs as drafts and starting points, not final truth

What large language models do not do

Important reminders:

• They do not understand the world the way humans do
• They do not have intentions, goals, or feelings
• They do not see your bank account or live financial statements unless you or an app send that data
• They do not have real-time awareness of markets or laws unless connected to live tools

They are powerful text pattern machines. Nothing more, nothing less.

The bottom line

Large language models are:

• Trained on huge amounts of text
• Built to predict the next token based on patterns
• Limited by their context window, training data, and lack of real understanding

When you know that, you stop treating them like magic oracles. Instead, you:

• Use them to clarify, summarize, and explore options
• Give them structured, focused input
• Keep final judgment and high-stakes decisions in human hands

That is how you turn an LLM from a mystery into a useful tool in your day-to-day life.

🍗 Holiday Menu Copilot

Holiday meals don’t fail because you can’t cook. They fail because you’re juggling 4–6 recipes, overlapping ingredients, and everything needing the oven at the same time. In 10 minutes, you’ll use ChatGPT to turn your recipes into one consolidated grocery list and a cook schedule that gets food to the table on time, without kitchen chaos.

Model tip (optional): If you see GPT-5.2 Thinking in your ChatGPT model/tools menu, use it for Step 3 (scheduling). It’s especially good at sequencing and constraint juggling. Free users can access GPT-5.2 with message limits.

Step 1 — Give ChatGPT your recipes (2 min)

You have three easy options. Use whichever is fastest:

• Option A: Paste links to recipe pages (best if they’re publicly accessible).
• Option B: Upload documents (PDFs, screenshots, images of a recipe card, etc.). Free users can upload files but with daily limits.
• Option C: Paste the text (ingredients + instructions).

Then paste this prompt (and attach/upload your files if using Option B):

You are my Holiday Menu Copilot.I’m giving you recipes via links and/or uploaded documents and/or pasted text.

Task: Convert everything into a clean “MENU PACK” using ONLY the recipe content you can access.

MENU PACK format (required):

• Serve date + serve time: ___
• Guests: ___
• Diet/allergies: ___
• Kitchen limits: (one oven? how many burners? slow cooker?) ___
• Menu items: (dish names)

For each recipe, create a “RECIPE BLOCK”:

• Dish name
• Servings it makes
• Ingredients (exact list)
• Key timings: prep time, cook time, rest/chill time, oven temp (if any)
• Critical notes: make-ahead? must serve hot? reheats well?

Rules:

1. Do not invent ingredients or times.
2. If a recipe page is blocked or missing timing info, ask me for the minimum missing details in a short bullet list and STOP.
3. Keep it tight and structured so we can reuse it in the next steps.

Here are my recipes:

• Links: [paste 3–6 links]
• Uploads: [attached]
• Pasted text: [optional]

Once ChatGPT outputs your Menu Pack, you’re ready.

Step 2 — Generate a consolidated grocery list (3 minutes)

Paste this prompt below your Menu Pack:

Goal: Turn my Menu Pack into a single consolidated grocery list for one shopping trip.

Rules (must follow):

1. Use ONLY ingredients in the Menu Pack. Do not invent items.
2. Combine duplicates and total quantities.
3. Normalize units when possible (tsp/tbsp/cups/oz/lb). If uncertain, keep both and flag it.
4. Group by aisle: Produce, Meat/Seafood, Dairy, Bakery, Pantry, Spices, Frozen, Drinks/Other.
5. Output as a table: Aisle | Item | Total Quantity | Notes (brand/size/subs)
6. Add a short “Confirm you already have it” checklist (foil, parchment, oil, salt, pepper, etc.) but do NOT add those items to the list.
7. Then output the same grocery list as CSV only (no commentary) so I can paste into Google Sheets.

Optional: paste the CSV into Google Sheets (cell A1) for a clean, checkable list on your phone.

Step 3 — Generate a cook schedule (3 minutes)

Paste this prompt below the Menu Pack:

Goal: Create a realistic cook schedule that gets everything ready on time with minimal stress.

Constraints:

• Serve time: [enter time]
• Kitchen limits: [one oven / burners / slow cooker / etc.]
• Priority: avoid oven conflicts and last-minute chaos.

Rules (must follow):

1. Use ONLY timings/temps in the Menu Pack. If anything is missing, ask me for the minimum missing details and STOP.
2. Output two sections:
   • Prep Ahead (Day Before / Morning Of)
   • Day-Of Timeline (back-planned from serve time)
3. Call out oven conflicts explicitly and propose a workaround (cook earlier + reheat, stagger temps, etc.).
4. Output Day-Of as a table: Time | Task | Duration | Uses (Oven/Stove/None) | Notes

Step 4 — Create a one-page “Kitchen Command Sheet” (2 minutes)

Paste this prompt:

Create a one-page Kitchen Command Sheet from the grocery list + cook schedule you created.

 Include:

• Final menu in serving order
• Top 8 critical tasks (the ones that affect timing)
• Oven plan (temps needed and when)
• Reheat/hold plan (what can rest, what can reheat, what must be served immediately)
• Format it so I can screenshot or print it. Keep it tight and skimmable.

The Payoff

You just turned “holiday cooking chaos” into a simple system: one grocery list, one cook schedule, and one command sheet. Less duplicate buying, fewer forgotten ingredients, fewer “everything needs the oven at 5:00pm” disasters, and way more calm.

Transparency & Notes for Readers

• Free tools: ChatGPT + optional Google Sheets.
• Uploads: ChatGPT supports file uploads on Free tier, but limits apply (so use links or paste text if you run out).
• Accuracy rule: If recipe timings/temps are missing, the assistant should ask you instead of guessing.
• Food safety: Use common sense for holding/reheating; AI is not a food safety authority.
• Educational workflow — not financial advice.