o1 takes over, MS powers up, and I’m drowning in UI design!

We’re at stage 2 of 5 on the AI-to-AGI path, folks. o1’s the big boss of models, so here’s the prompting advice you need to make it perform best.

Oh, and Microsoft just signed the biggest-ever nuclear power deal to juice their AI even more. Casual.

Meanwhile, it’s day 2 of me building product 1, and things are nuts. What do you think about the design of it? (I am so bad at UI design! 🥲)

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Sam Altman: o1 is now Level 2 of 5 on the AGI path

(See video from 51:00 to 1:06:00)

On the T-Mobile Capital Markets Day, 2024, Sam Altman elaborated on the o1 and its reasoning capabilities. Noteworthy:

• o1 (not preview) crushes it in math and code → elaboration on it in earlier post.

• o1 is like GPT-2 in Chatbots (Level 1)

• Level 2 enables Level 3 (mature AI agents)

I wrote about the leaked 5 levels of AI from OpenAI.

How does the level-2-reasoning actually work? And some prompting advice

(Source)

o1 models now use "reasoning tokens" to break down prompts and explore different response methods. After “thinking” with these tokens, they generate visible completion tokens as the final answer while ditching the reasoning tokens.

In a multi-step convo, input/output tokens are carried over, but the reasoning tokens get tossed. Here's how it plays out.

OpenAI’s advice on prompting o1

Reasoning models, i.e. o1, shine with clear, direct prompts. Over-complicating things, like using few-shot prompting or telling the model to "think step by step," often backfires.

OpenAI’s 4 best practices:

1. Be direct: The simpler the prompt, the better.

2. Skip chain-of-thought: No need to ask for reasoning or explanations; the model already handles that behind the scenes.

3. Use delimiters: Structure your inputs with tools like triple quotes or tags to make sections clear.

4. Keep RAG context tight: Only give the model the most important info. Too much context can lead to a messier response.

In a nutshell, keep it simple.

Two examples by OpenAI:

1. Using reasoning for data validation

2. Using reasoning for routine generation

The Computing Power Race Just Cranked to Eleven

(Source)

Last week, I wrote about Oracle, xAI, and Groq ramping up computing power to meet the demands of future AI models. → Post.

Last Friday, Microsoft leapfrogged Oracle’s plans to integrate three small nuclear plants by signing the largest-ever power purchase agreement with Constellation Energy, the US’ largest producer of “clean, carbon-free energy. “

The agreement will span 20 years, and the plant is expected to reopen in 2028.

[Day 2, Product 1] Building Products with AI - Project decision & first execution - Huge progress 🤯

(A mini-series very transparently documenting how I build products with AI, sharing the wins and screw-ups.)

Last time, I wrote about preparing product development, the setup of mind, and the product manifesto you should have before starting.

I finished the second day working on the product, and the progress was crazy. Let me share.

First half of the day: Choosing the right product to start with

Developing products correctly with AI, as we have taught in our course Everyone can Code!✨, means not getting stuck with paralysis-analysis.

Choose fast but informed. A wrong decision isn’t a catastrophe—you can always pivot and create a new solution in record time.

I have many ideas, of which 21 have been shortlisted. Next, I decided with the help of this matrix (full transparency 😅):

I decided which project to start with based on the following questions that I tried to answer fast:

• How technical feasible is the idea?

• What is the market demand based on forums, Google trends, and some social media?

• Can I earn money with that idea? (And, indicatively, how much?)

• Lastly, is there already competition? How do they address the problem?

I chose the Buy-It-For-Life (BIFL) Product Page.

What is BIFL, more specifically the BIFL subreddit? 
The BIFL (Buy It For Life) subreddit shares recommendations for durable, high-quality products meant to last a lifetime, focusing on long-term value and sustainability.
There are 2.4 million members in the group.

To be more precise, I am building an AI-driven e-commerce store. The AI scans the BIFL subreddit and other pages, extracts valuable products mentioned, maps it against Amazon affiliate links, and lists them in a filterable overview page.

Second half of the day: Building the Product, part one

In literally 4 hours, I did this:

• Bought biflproduct.com (but didn’t push the code yet)

• Built the first version of the AI scrapers: works but is buggy. Need to spend one more session.

• Built all backend code.

• Designed the webpage.

Do you like it like that? 🫠 

That’s a wrap! I hope you enjoyed it.

Martin

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