First, why would you want to use the Playground over ChatGPT?
- Greater system prompt/behavior control
- Save multiple system prompts
- Temperature/creativity control
- Longer outputs for reasoning prompts/working with longer text
- Non-nerfed models :)
- Edit all messages
Etc.
So how can you set it up in a way that makes it as frictionless as using ChatGPT?
We'll do this by creating a 'preset' that enables instant access to an optimized setup.
Asking GPT-4o to judge increasingly terrible business ideas until it finally tells me one is bad…
First up, "Soggy Cereal Café":
Apparently "WhiffBox" has "serious potential".
"Custom alibis as a service" is a "provocative idea". It's not 100% positive on this one, but still doesn't completely shoot it down, instead offering tweaks... wow.
An open-source AI agent that does comprehensive research for you.
Just provide a topic, and the AI will go off, do research, and return a comprehensive report.
How it works:
The approach is really simple.
Given a query, the AI:
- performs searches, views the result pages, and extracts important info
- if it wants to look deeper, it can repeat this process, with new queries
- once it's done, it uses the context to generate a report
That's it!
It's also quite asynchronous, making it very, very fast.
Here's a mega-thread with my most useful o1 / o1 pro prompting tips for coding.
If you get the hang of using these, you'll build much faster and come up with far more elegant solutions!
First — why o1? Compared to other models, it's:
- Capable of solving far more complex problems
- More likely to solve on the first shot, without back-and-forth
- Solutions tend to be more elegant and require fewer code changes
Important — you can't prompt o1 like you'd prompt Sonnet/GPT-4o and expect the same results.
o1 requires goal-driven prompting.
Instead of loosely defining what you want, make your end-state very clear, so the model knows to iterate and think until it has completed your goal.
Introducing OpenReasoningEngine, an open-source test-time-compute engine that can be used with any OpenAI-compatible model.
Image input, function calling, basic continual learning, + more.
This is an early experiment — there are issues that will need to be ironed out.
Thread:
The engine guides the model to think step-by-step, and at each step, allows it to use code interpreters, web search, etc. to iterate solutions, test approaches, and gather info before responding.
So, when it finally responds, the answer is more likely to be accurate.
One of the initial goals is to continually learn from experience, so the system can improve over time. Users can save successful reasoning chains to the memory bank, and the AI can use these memories to plan approaches to solving new problems.