#medium | Anand S - Things I Learned

Thu, Oct 2, 2025. Create a bookmarket that converts the latest ChatGPT Markdown response into Marp slides. #chatgpt #markdown #medium

Thu, Oct 2, 2025. Create an npm package that exposes functions for LLMs/agents to use as tools to run JavaScript (browser), Python (WASM) and SQL (DuckDB WASM) code. #code-agents #future #llm-ops #markdown #medium

Tue, Aug 19, 2025. Implement apply_patch in JS. GPT models are trained on a specific diff format useful for code diff-patching. PseudoPatch is a Python package that implements their apply_patch() function. Implement this in JavaScript #github #markdown #prompt-engineering #medium

Sat, Aug 16, 2025. [x] DataGen. Create a synthetic data generator tool. #code-agents #dev #markdown #prompt-engineering #medium
1. User enters their scenario in textbox (e.g. "Synthetic data to demonstrate credit card fraud using transaction data. Mid-sized, multiple tables, complex. Excel file")
2. Click "Submit". This should send the scenario to an LLM asking it to
  - Think carefully and identify a schema, stating assumptions (e.g. number of rows to generate, table/column inter-relationships, output file format, etc.)
  - List typical real-world behaviors that could be incorporated into the data and will lead to positive results when testing these hypotheses (e.g. transactions over $10,000 are more likely to be declined, overseas transactions in low-income countries have high failure rates, etc.)
  - Write Python code that generates a random dataset in line with the above, writes the output to a bytestream and returns the filename and bytestream
3. Stream the response. Render it as markdown. Use highlightjs to syntax-highlight the code block using a dark theme.
4. Run the code in the browser using Pyodide, generate the output, and allow the user to download it
5. Allow the user to enter changes in their scenario. Have the LLM make changes to the code by sending the change and the original response. Repeat from step 3.

Sat, Aug 16, 2025. Table OCR Evals. Evaluate how well LLMs parse tables using Promptfoo. #gpu #medium
- Generate a single image with a table containing
  - 20 rows x 20 columns of psuedo-random (seed-generated) numbers between -1 to +1 million, to 2 decimals.
  - Column & row headers with psuedo-random words
- Send this to various vision models, e.g. gpt-5-mini, claude-4-sonnet, gemini-2.5-flash, etc. with the same prompt to convert the result into a CSV, re-running this 5 times
- Compare the result against the original. For each value (text or number) in the original:
  - 0 if the value is in the correct cell
  - -1 if the value is present in an incorrect cell
  - -2 if the value is not present anywhere in the CSV
- Show the results as a sortable table with columns: Model, Average score, Score in run 1, Score in run 2, ... and cell values are the score, colored on a white-red gradient (white=0, red=most negative score)
- Next, measure the impact of resolution. Resize the same image to resolutions of 50%, 75%, 150%, and 200%.
- Repeat the analysis with 3 runs each and compute the average score.
- Show the results as a sortable table with columns: Model, Average score at 50% resolution, 75%, 100%, 150%, 200% ... with the same cell values and colors.

Fri, Aug 15, 2025. Impact analyzer. Analyze & prioritize service request categories based on impact and effort #medium
- You will get an dataset with 2 tables
  - Types: Category, Subcategory, Effort per issue, Impact per day. This table has one row for each category + subcategory of service request issues raised in an organization, along with the typical effort (USD) to fix it and typical cost impact (USD) to the organization if left unfixed.
  - Summary: Date, Category, Subcategory, Count, Days to fix. This table has the number (count) of service requests raised daily in each category + subcategory, along with how long it took to fix it. This can be used to compute the overall effort and impact on the organization.
- Visualize this as a zoomable scatterplot.
  - X-axis has the effort. Y-axis has the impact. (Top-left = quick wins: low-effort, high-impact)
  - Show one circle for each category. Size = sqrt(count). Color = distinct, based on category
  - Clicking on any category "explodes" the category into subcategories, i.e.
    - Hide the category bubble
    - Show the subcategory bubbles in the same color as the category
    - Animate them so that they appear from the center of the category and move to their positions
  - Clicking on any subcategory "collapses" the subcategories into the category, i.e. the animated reverse of the explosion.
  - Hovering on any category or subcategory neatly shows the category name, subcategory name (if applicable), count of issues, effort per issue, total effort (count x effort), impact per day, total impact (count x impact x days)
- Add a range slider that allows users to move two markers representing the start and end dates. This acts as filters. As the user moves these, the animation smoothly resizes the bubbles to capture the count in this period.
- Add a "play" button. Clicking on this steadily moves the range slider forward, 10 days per second.
  - When moving, the range slider preserves the duration, i.e. the start and end dates move at the same pace
  - When the end slider reaches the last date, stop the play button
  - When playing, the play button becomes a pause button that pauses playing when clicked