Update SquiggleAI.md

packages/content/content/docs/Ecosystem/SquiggleAI.md

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 title: Squiggle AI
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+![Squiggle AI](https://squigglehub.org/ai)
 
-Squiggle AI is a tool that allows you to interact with Squiggle code using natural language. It is powered by a large language model (LLM) that can understand and generate human-like text.
-
-Squiggle AI is hosted on SquiggleHub, and you can access it [here](https://squigglehub.org/ai). You must be logged in to SquiggleHub to use it.
-
-![Squiggle AI](/img/squiggleAI.png)
+Squiggle AI is a tool that allows you to write and improve Squiggle code using natural language. It's hosted on SquiggleHub, where you must be logged in to use it.
 
 ## Tips for Effective Use
 
-1. Request cost-effectiveness models, which are particularly suited to Squiggle.
-2. Provide comprehensive background information in your prompts, as AutoSquiggle doesn't perform web searches.
-3. Run multiple iterations for diverse results, then combine or average them for optimal outcomes.
+1. Provide comprehensive background information in your prompts, as Squiggle AI doesn't perform web searches.
+2. Be very specific about what kinds of analysis to perform.
+3. Request cost-effectiveness models, which are particularly suited to Squiggle.
+4. For tricky calculations or estimates, you might want to first add [O1] or other expensive language models. Then copy & paste the results into Squiggle AI.
+5. Run 2-4 workflows for the same prompt. Different prompts often lead to very different outputs.
+6. Begin with 0 numeric steps and 0 documentation steps. You can easily add these later.
+7. Be sure to review all key assumptions and to adjust critical parameters. 
 
 ## Limitations
 
-- **3-minute** runtime cap
+- **2-minute** runtime cap
 - **$0.30** cost limit per run
-- Limited access to SquiggleHub libraries
 - **Single file** operations only
-- Typical output: **\~80 lines of code** (soft limit)
+- Typical output: **~100 lines of code**
+- Limited access to SquiggleHub libraries
 
 ## Performance and Costs
 
-LLMs, while powerful, can be slow and expensive. Short runs typically take 10-30s and cost \$0.01 to \$0.05, while longer runs can take 1-3 minutes and cost \$0.06 to \$0.40. Expect this delay as processing occurs in the backend.
+LLMs, while powerful, can be slow and expensive. Short runs typically take 10-30s and cost \$0.01 to \$0.05, while longer runs can take 1-3 minutes and cost \$0.10 to \$0.30. 
 
 The fact that Squiggle is a new language means that more fixes are generally required than would be the case for popular languages.
 
 ## LLM Specifics
 
-AutoSquiggle currently uses Claude Sonnet 3.5 for all operations. It makes use of [prompt caching](https://www.anthropic.com/news/prompt-caching) to cache a lot of information (around 20k tokens) about the Squiggle language. LLM queries typically cost around $0.002 to $0.02 each to run \- more in the case of large models or long execution runs.
+Squiggle AI currently uses Claude Sonnet 3.5 for all operations. It makes use of [prompt caching](https://www.anthropic.com/news/prompt-caching) to cache a lot of information (around 20k tokens) about the Squiggle language. LLM queries typically cost around $0.002 to $0.02 each to run \- more in the case of large models or long execution runs.
 
 You can see most of the prompts used [here](https://github.com/quantified-uncertainty/squiggle/blob/main/packages/llmRunner/src/llmRunner/prompts.ts).
 
 ## Key Run Stages
 
-There are three main stages to writing Squiggle with LLMs. Each has its own LLM prompt and logic.
+There are four main stages to writing Squiggle with LLMs. Each has its own LLM prompt and logic.
 
 1. **Generation**: Makes a first attempt at writing Squiggle code, based on a certain prompt.
-2. **Bug Fixing** (FIX_CODE_UNTIL_IT_RUNS): Attempts to fix errors in Squiggle code. Typically, each LLM call attempts to fix one error at a time. This is used when the Squiggle code can't run at all or if it gives parser errors.
-3. **Improvement** (ADJUST_TO_FEEDBACK): At this point, the code should run. This stage adjusts to the run results and improves the codebase by adding better variable names, annotations, etc.
-
-**Bug Fixing** continues until the Squiggle code runs. During **Improvement**, if no improvement is found, it finishes. If an improvement is found, it tries to make that improvement and goes back to **Bug Fixing**.
+2. **Bug Fixing**: Attempts to fix errors in Squiggle code. Typically, each LLM call attempts to fix one error at a time. This is used when the Squiggle code can't run at all or if it gives parser errors.
+3. **Update Estimates**: Checks the results of the model. If it notices broken tests or numbers that seem suspicious, goes back and suggests changes to the model.
+4. **Document**: Recommends improvements to match the style guide. This typically means improving variable annotation and model documentation, but it sometimes also means adjusting variables or code organization.
 
 ## Future Functionality
-
-We're interested in several enhancements:
-
-**1\. A fine-tuned model:** Using a fine-tuned model instead of Claude with a long prompt could be more effective.
-
-**2\. Web searches:** Allowing AutoSquiggle to do background research on variables using services like Perplexity or specific APIs like those of Metaforecast and Manifold.
-
-**3\. LLM forecasting integration:** Developing a dedicated system for estimating specific parameters to improve accuracy.
-
-**4\. API & Local support:** Expanding support for API requests and possibly providing a local-only solution.
-
-**5\. Support for larger codebases:** Enabling work on multiple files and continuous improvement of larger codebases.
-
-**6\. Use-case optimization:** Improving handling of common use-cases like relative value functions, financial projections, and complex generated data.
-
-**7\. Evaluations:** Developing benchmarks for higher-level, general-purpose mathematical models.
-
-Additional future ideas include:
-
-- Creating multiple runs simultaneously
-- Text summaries of AutoSquiggle runs
-- Better visualizations of runs as they happen
-- User-provided Anthropic keys
-- Saved history of user AutoSquiggle runs
-- Chat interface with AutoSquiggle function calls
-- Code-completion within Squiggle editors
-- Support for other probabilistic estimation languages
-- Visualizations of Squiggle calculations in graphical diagrams
-- Quick up-front estimation of run costs and benefits
-- Squiggle enhancements for AI-written code
-- Code analysis for existing Squiggle code
-- A curated repository of human-overseen Squiggle models
-- Generation of Mermaid diagrams to explain reasoning
+Ideas for future features include:
+1. Web searches: Allowing Squiggle AI to do background research on variables using services like Perplexity or specific APIs like those of Metaforecast and Manifold.
+2. LLM forecasting integration: Developing a dedicated system for estimating specific parameters to improve accuracy.
+3. API & Local support: Expanding support for API requests and possibly providing a local-only solution.
+4. Support for larger codebases: Enabling work on multiple files and continuous improvement of larger codebases.
+5. Use-case optimization: Improving handling of common use-cases like relative value functions, financial projections, and complex generated data.
+6. Evaluations: Developing benchmarks for higher-level, general-purpose mathematical models.
+7. A fine-tuned model: Using a fine-tuned model instead of Claude with a long prompt could be more effective.