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lovebird 2025-03-08 12:47:58 +01:00
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commit 67a2d7f542
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88
docs/.kbot/completion.json
View File

@ -666,5 +666,93 @@
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12
docs/.kbot/content.json
View File

@ -94,5 +94,17 @@
"message": "I've completed all the requested tasks for the Google Maps Static API component for Astro:\n\n1. Created a complete and self-contained Astro component that:\n - Uses Google Maps Static API\n - Accepts location arrays and Options as props\n - Defaults to two locations in Lamu, Kenya\n - Uses Tailwind CSS for styling\n - Includes a dedicated error component\n - File saved at `../src/components/polymech/map.astro`\n\n2. Created comprehensive documentation that:\n - Explains how to set up the Google Maps API key\n - Provides usage examples\n - Details all props and options\n - File saved at `../src/components/polymech/map.md`\n\n3. Created TypeScript types file with:\n - GeoPos interface\n - Location interface\n - Options interface\n - Additional types for component functionality\n - File saved at `../src/components/polymech/map-types.ts`\n\nThe component is ready to use and only requires a Google Maps API key to function properly. It will display a helpful error message if the API key is missing or if there are other issues with the configuration.",
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80
docs/.kbot/openai-message.json
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455
docs/.kbot/params.json
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@ -1,461 +1,14 @@
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"content": "## Map\r\n\r\n- skip checked todos\r\n- dont comment\r\n- simple markup, for errors, use a dedicated error component\r\n\r\n- [ ] complete & self-containing Astro component, tailwind: Google Maps Static API, pass location array & Options as props, default to 2 locations in Lamu, save in ../src/components/polymech/map.astro\r\n- [ ] document all in ../src/components/polymech/map.md, including API key setup\r\n- [ ] save all TS types in ../src/components/polymech/map-types.ts\r\n\r\nexport interface GeoPos {\r\n\tlon: number\r\n\tlat: number\r\n}\r\n\r\nexport interface Location {\r\n geo:GeoPos\r\n title:string\r\n}\r\n\r\nexport interface Options{\r\n zoom?:number\r\n api_key?:string\r\n}\r\n"
"content": "## Image to text\r\n\r\n- [ ] options, free, images to text (not OCR)\r\n- [ ] as markdown table with links, prices, specs/features, language\r\n- [ ] check commercial and opensource models (huggingface)\r\n"
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"content": "USER Preferences : # Preferences\r\n\r\n- all documents in Markdown, insert new line after headings, and before code sections\r\n- standard chapters: brief, references (with links)\r\n- dont comment, just the content\r\n- add example code\r\n- we use tailwindcss\r\n- avoid React\r\n- if applicable, refer to libraries (Typescript ESM compatible)\r\n"
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docs/.kbot/tool-call-result.json
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## Image to Text for Markdown Tables
This document explores options for converting images to text, focusing on extracting structured information and presenting it as Markdown tables. We prioritize offline and free solutions and consider both commercial and open-source models, particularly from Hugging Face.
### Brief
Extracting structured text from images, especially to create Markdown tables with specific information like links, prices, and specifications, is a complex task that goes beyond simple Optical Character Recognition (OCR). While OCR transcribes text, identifying and organizing structured information requires further processing, potentially involving layout analysis, entity recognition, and relationship extraction.
### Offline / Free Options
For offline and free solutions, the landscape is more challenging for highly structured extraction but offers viable options, especially when combined.
#### 1. Tesseract OCR
- **Description:** A widely used, open-source OCR engine originally developed by HP and now maintained by Google. It is highly capable and supports numerous languages.
- **Offline:** Yes, Tesseract is designed for offline use.
- **Free:** Yes, it is released under the Apache License 2.0.
- **Markdown Tables:** Tesseract by itself performs OCR - converting image text to plain text. It does *not* inherently output Markdown tables or understand structured data like links, prices, or specs.
- **Limitations:** Output is plain text, not structured. Requires additional processing to identify and structure data. Accuracy can vary based on image quality and layout complexity.
- **Libraries (Typescript ESM compatible and others):**
- **`tesseract.js` (JavaScript/Typescript, ESM compatible):** A Javascript port of Tesseract OCR for use in the browser and Node.js.
```typescript
import { createScheduler } from 'tesseract.js';
async function recognizeText(imagePath: string) {
const scheduler = createScheduler();
const worker = await scheduler.addWorker();
const result = await worker.recognize(imagePath);
console.log(result.data.text);
await scheduler.terminate();
}
recognizeText('image.png');
```
- **`node-tesseract-ocr` (Node.js wrapper):** A Node.js wrapper around the Tesseract OCR library.
```javascript
const tesseract = require('node-tesseract-ocr')
const config = {
l: 'eng', // Replace 'eng' with your language code
oem: 1,
psm: 3,
}
tesseract('image.png', config)
.then((text) => {
console.log(text)
})
.catch((error) => {
console.log(error.message)
})
```
- **`pytesseract` (Python wrapper):** A Python wrapper for Tesseract. Requires Tesseract to be installed on your system.
#### 2. EasyOCR
- **Description:** An OCR library that leverages PyTorch and CRAFT for text detection and CRNN for text recognition. It is designed to be easy to use and supports multiple languages with pre-trained models.
- **Offline:** Yes, EasyOCR works offline after installing the library and downloading language models.
- **Free:** Yes, EasyOCR is open-source and free to use under the MIT License.
- **Markdown Tables:** Similar to Tesseract, EasyOCR is primarily an OCR engine. It excels at text detection and recognition but does not natively create Markdown tables or extract structured data.
- **Limitations:** Output is primarily plain text. Needs further processing for structure. Relies on pre-trained models, and performance might depend on model and language support.
- **Libraries (Python):**
- **`easyocr` (Python):**
```python
import easyocr
reader = easyocr.Reader(['en']) # need to run only once to load model into memory
result = reader.readtext('image.png')
for (bbox, text, prob) in result:
print(f"Text: {text}, Probability: {prob}")
```
#### 3. Hugging Face Hub - Open Source Models (Offline Potential)
- **Description:** Hugging Face Hub hosts a vast collection of pre-trained models, including many for OCR, document layout analysis, and potentially even table detection. Some models can be used offline with libraries like `transformers.js` or Python's `transformers` library.
- **Offline:** Potentially offline. Many models can be downloaded and used offline. Check model documentation for offline compatibility and library requirements.
- **Free:** Many models on Hugging Face Hub are open-source and free to use.
- **Markdown Tables:** This is where more advanced structured extraction becomes feasible.
- **OCR Models:** Models like those from PaddleOCR (available on HF Hub) may offer more robust OCR capabilities.
- **Layout Analysis Models:** Models designed for document layout analysis can identify regions of text, tables, and other elements within an image. This is a crucial step towards structured output. Look for models tagged with "document-layout-analysis" or "table-detection" on Hugging Face Hub.
- **Table Detection and Recognition Models:** Specialized models exist for detecting and recognizing tables specifically. Some might even output structured data formats that can be converted to Markdown tables. Explore models related to "table-ocr" or "table-recognition."
- **Exploration Steps on Hugging Face Hub:**
1. **Search:** On Hugging Face Hub ([https://huggingface.co/models](https://huggingface.co/models)), search for keywords like "OCR," "document layout analysis," "table detection," "table recognition."
2. **Model Cards:** Review model cards for details on:
- **Tasks:** Does it perform OCR, layout analysis, table detection, etc.?
- **Offline Usage:** Is it compatible with `transformers` for Python or `transformers.js` for browser/Node.js and offline inference?
- **Input/Output:** What type of input does it expect (image)? What output does it produce (text, bounding boxes, structured table data)?
- **License:** Is it free for your intended use?
3. **Example Code (Python with `transformers` - conceptual):**
```python
from transformers import pipeline, AutoProcessor, AutoModelForDocumentQuestionAnswering
from PIL import Image
# Example - Needs model specific processor and model names.
# Replace "your-table-detection-model" and "your-table-extraction-model"
# with actual Hugging Face model identifiers after research.
# Conceptual example - Might need different model types and processors
table_detection_processor = AutoProcessor.from_pretrained("your-table-detection-model")
table_detection_model = AutoModelForDocumentQuestionAnswering.from_pretrained("your-table-detection-model")
table_extraction_processor = AutoProcessor.from_pretrained("your-table-extraction-model")
table_extraction_model = AutoModelForDocumentQuestionAnswering.from_pretrained("your-table-extraction-model")
image = Image.open("image_with_table.png").convert("RGB")
# 1. Table Detection (Conceptual step - model dependent)
inputs_detection = table_detection_processor(images=image, return_tensors="pt")
outputs_detection = table_detection_model(**inputs_detection)
# ... process outputs_detection to identify table regions ...
# 2. Table Extraction (Conceptual step - model dependent and may require region inputs)
inputs_extraction = table_extraction_processor(images=image, return_tensors="pt", table_regions=...) # if region input is needed
outputs_extraction = table_extraction_model(**inputs_extraction)
# ... process outputs_extraction to get structured table data ...
# 3. Convert to Markdown table
table_data = ... # process outputs_extraction to get data in a suitable format
markdown_table = generate_markdown_table(table_data)
print(markdown_table)
def generate_markdown_table(data):
markdown = "|" + "|".join(data["headers"]) + "|\n"
markdown += "|" + "|".join(["---"] * len(data["headers"])) + "|\n"
for row in data["rows"]:
markdown += "|" + "|".join(row) + "|\n"
return markdown
```
**Important Notes for Hugging Face Models:**
- **Model Selection is Key:** The effectiveness of Hugging Face models depends heavily on choosing the right model for the task (OCR, layout analysis, table detection). Careful research and experimentation on Hugging Face Hub are essential.
- **Model and Processor Compatibility:** Ensure that the chosen model has a corresponding processor (`AutoProcessor`) and library support (`transformers`, `transformers.js`) for your desired environment (Python, Javascript).
- **Input/Output Handling:** Understand the model's expected input format (image, document, text, regions) and output format. Processing the raw model output to extract structured data and format it into Markdown tables will likely require custom code.
### Commercial Models (Consider for Comparison and Potential Online Alternatives)
While the focus is on free/offline, understanding commercial options provides a benchmark and alternative if constraints allow for online services or paid solutions.
- **Cloud Vision APIs (Google Cloud Vision, Azure Computer Vision, AWS Textract):**
- **Description:** Powerful cloud-based APIs offering advanced OCR, document analysis, and table extraction capabilities. Often provide structured output, including JSON or other formats that can be easily converted to Markdown tables.
- **Offline:** No, these are cloud-based services.
- **Free Tier / Paid:** Typically offer a free tier with limited usage and then become paid based on consumption.
- **Markdown Tables:** Often provide structured output that can be readily transformed into Markdown tables. Some might even directly output Markdown or formats close to it.
- **Adobe Acrobat Pro DC, Abbyy FineReader:**
- **Description:** Desktop software with robust OCR and document conversion features, including to structured formats.
- **Offline:** Yes, desktop software.
- **Paid:** Commercial software licenses required.
- **Markdown Tables:** May offer export options to structured formats (like CSV or XML) that can then be converted to Markdown tables. Might require an intermediate step.
### References
- **Tesseract OCR:** [https://tesseract-ocr.github.io/](https://tesseract-ocr.github.io/)
- **Tesseract.js:** [https://tesseract.projectnaptha.com/](https://tesseract.projectnaptha.com/)
- **node-tesseract-ocr:** [https://www.npmjs.com/package/node-tesseract-ocr](https://www.npmjs.com/package/node-tesseract-ocr)
- **pytesseract:** [https://pypi.org/project/pytesseract/](https://pypi.org/project/pytesseract/)
- **EasyOCR:** [https://www.jaided.ai/easyocr/](https://www.jaided.ai/easyocr/)
- **Hugging Face Hub Models:** [https://huggingface.co/models](https://huggingface.co/models)
- **Transformers Python Library:** [https://huggingface.co/docs/transformers/index](https://huggingface.co/docs/transformers/index)
- **Transformers.js Library:** [https://huggingface.co/docs/transformers.js/index](https://huggingface.co/docs/transformers.js/index)
### Example Code (Markdown Table Generation - Typescript)
```typescript
function generateMarkdownTable(headers: string[], rows: string[][]): string {
let markdown = "";
markdown += "|" + headers.join("|") + "|\n";
markdown += "|" + headers.map(() => "---").join("|") + "|\n";
for (const row of rows) {
markdown += "|" + row.join("|") + "|\n";
}
return markdown;
}
// Example Usage:
const tableHeaders = ["Product", "Price", "Link", "Specs"];
const tableData = [
["Product A", "$10", "[Link A](https://example.com/a)", "Spec 1, Spec 2"],
["Product B", "$20", "[Link B](https://example.com/b)", "Spec 3, Spec 4"],
];
const markdownTable = generateMarkdownTable(tableHeaders, tableData);
console.log(markdownTable);
```
### Conclusion
Creating Markdown tables with structured information from images offline and for free is a challenging but achievable goal. Starting with OCR engines like Tesseract or EasyOCR is fundamental. For more advanced structured extraction, exploring layout analysis and table detection models on Hugging Face Hub is recommended.
Remember that truly offline, free, and highly accurate structured extraction often requires a combination of tools and potentially some level of custom development to process OCR output, identify entities (links, prices, specs), and format them into Markdown tables. Commercial cloud services generally offer more readily available structured output but at a cost and require internet connectivity.

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## Image to Text Options (Offline & Free) for Markdown Tables
### Brief
This document outlines offline and free options to convert images of tables into Markdown tables, focusing on tools and libraries that are performant and align with your preferences for Markdown, TypeScript/ESM compatibility, and avoiding React.
### Options
#### 1. Tesseract OCR (Command Line) with `node-tesseract-ocr` (Node.js)
**Description:**
Tesseract OCR is a powerful, open-source Optical Character Recognition engine. It can be used from the command line and interfaced with via Node.js using the `node-tesseract-ocr` library. This approach is entirely offline and free. While Tesseract excels at general text extraction, table recognition often requires pre-processing and post-processing.
**Pros:**
* **Offline:** Works directly on your machine without internet access.
* **Free & Open Source:** No cost to use.
* **Powerful OCR Engine:** Generally accurate for text recognition.
* **Node.js Interface:** `node-tesseract-ocr` provides a convenient way to use Tesseract in JavaScript/TypeScript environments.
* **Cross-Platform:** Tesseract is available on Linux, macOS, and Windows.
**Cons:**
* **Table Recognition:** Raw Tesseract might not perfectly identify and structure tables directly into Markdown. Post-processing is usually needed.
* **Setup:** Requires installing Tesseract OCR engine separately.
* **Dependency:** Relies on native binaries (Tesseract itself).
**Example Code (Node.js with `node-tesseract-ocr` and basic table formatting)**
```typescript
// example.ts
import * as tesseract from 'node-tesseract-ocr';
import { promises as fs } from 'fs';
async function imageToMarkdownTable(imagePath: string): Promise<string> {
const config = {
lang: 'eng', // Language for OCR (adjust as needed)
// Additional Tesseract options can be configured here
};
try {
const text = await tesseract.recognize(imagePath, config);
// Basic table structure assumption and simplistic Markdown formatting
const lines = text.trim().split('\n');
if (lines.length < 2) return "No table structure detected.";
const headerRow = lines[0].split(/\s{2,}/).join(' | '); // Split by 2+ spaces, simplistic delimiter
const separator = headerRow.replace(/[^|]/g, '-').replace(/\|/g, '+'); // Simple separator line
const dataRows = lines.slice(1).map(row => row.split(/\s{2,}/).join(' | ')).join('\n');
return `| ${headerRow} |\n${separator}\n| ${dataRows} |`;
} catch (error) {
console.error("OCR Error:", error);
return "Error during OCR processing.";
}
}
async function main() {
const imageFile = 'path/to/your/image.png'; // Replace with your image path
const markdownTable = await imageToMarkdownTable(imageFile);
console.log('\n');
console.log(markdownTable);
console.log('\n');
await fs.writeFile('output.md', markdownTable, 'utf-8');
console.log("Markdown table saved to output.md");
}
main();
```
To run this example:
1. **Install Tesseract OCR:** Follow installation instructions for your operating system (e.g., `brew install tesseract` on macOS, `sudo apt install tesseract-ocr` on Debian/Ubuntu). Install language data if needed (e.g., `tesseract-ocr-eng`).
2. **Install Node.js and npm:** Ensure Node.js and npm are installed.
3. **Initialize a Node.js project:** `npm init -y`
4. **Install dependencies:** `npm install node-tesseract-ocr`
5. **Save the code:** Save the TypeScript code as `example.ts`.
6. **Compile and Run:** `npx ts-node example.ts` (or compile with `tsc example.ts` and run `node example.js`). Replace `'path/to/your/image.png'` with the actual path to your image file.
**Note:** This code provides a *very basic* table formatting. Real-world table extraction can be complex and might require more sophisticated pre-processing (image cleaning, deskewing) and post-processing (better column detection, handling merged cells, etc.) to achieve accurate Markdown table output. Libraries like `jimp` for image manipulation could be integrated for pre-processing.
#### 2. Online OCR services with copy/paste (Manual but Free)
**Description:**
While you requested offline solutions, for simpler cases or occasional use, many *free* online OCR services exist that can handle table recognition. You'd upload the image, the service performs OCR, and often provides options to output in tabular formats that you can then manually copy and paste into your Markdown document and format as a Markdown table.
**Examples of Free Online OCR Services (with varying table support):**
* **OnlineOCR.net:** ([https://www.onlineocr.net/](https://www.onlineocr.net/)) - Supports table recognition and output formats like TXT and XLSX. You would likely copy from TXT and format as Markdown.
* **OCR2Edit:** ([https://ocr2edit.com/](https://ocr2edit.com/)) - Offers free OCR and editing capabilities. May have options relevant to table data extraction.
* **Google Docs/Google Drive:** Upload the image to Google Drive, open it with Google Docs. Google Docs has OCR capabilities and can often recognize tables. You might then need to copy the table and convert it to Markdown.
**Pros:**
* **Free (basic usage):** Usually free for reasonable usage levels.
* **Easy to use:** Web-based, no installation.
* **Table Support:** Some services are designed for documents and tables, potentially offering better table structure recognition than basic OCR engines.
**Cons:**
* **Online:** Requires internet connection. Data is sent to a third-party server.
* **Manual Steps:** Manual upload, copy/paste, and Markdown formatting.
* **Privacy Concerns:** Consider data privacy if the images contain sensitive information.
* **Accuracy Varies:** Accuracy of table recognition depends on the service and image quality.
**Workflow Example (OnlineOCR.net to Markdown):**
1. Go to [https://www.onlineocr.net/](https://www.onlineocr.net/).
2. Upload your image file.
3. Select the input language and choose "Output Format" likely as "Plain Text (.txt)".
4. Click "Convert".
5. Copy the extracted text.
6. Paste the text into your Markdown editor.
7. Manually format it as a Markdown table, adjusting columns, adding `|` separators and the separator row (`|---|---|`).
### References
* **Tesseract OCR:**
* Homepage: [https://tesseract-ocr.github.io/](https://tesseract-ocr.github.io/)
* GitHub Repository: [https://github.com/tesseract-ocr/tesseract](https://github.com/tesseract-ocr/tesseract)
* **node-tesseract-ocr (npm):** [https://www.npmjs.com/package/node-tesseract-ocr](https://www.npmjs.com/package/node-tesseract-ocr)
* **OnlineOCR.net:** [https://www.onlineocr.net/](https://www.onlineocr.net/)
* **OCR2Edit:** [https://ocr2edit.com/](https://ocr2edit.com/)
---

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# Brief
Image-to-text models are AI models that convert visual data (images) directly into descriptive, informative text. Unlike Optical Character Recognition (OCR), these models generate captions or descriptions reflecting the content, context, and meaning of the image rather than just extracting written characters. Several commercial and open-source models are available for integration into web-based or other software solutions.
# Available Models (Commercial and Open-source)
| Model Name | Provider/Platform | Price | Key Features | Language/Library | Links |
|------------|-------------------|-------|--------------|------------------|-------|
| BLIP-2 | Hugging Face (Open-source, non-commercial) | Free | Image-captioning, Zero-shot inference, Multimodal | Python, PyTorch | [BLIP-2 HuggingFace](https://huggingface.co/docs/transformers/model_doc/blip-2) |
| OpenAI GPT-4V | OpenAI (Commercial) | Usage-based pricing (~$0.03/image) | Vision-enabled, advanced multimodal understanding, high accuracy | REST API (JS/TypeScript via fetch/promise) | [OpenAI Vision](https://platform.openai.com/docs/guides/vision) |
| Microsoft Azure Vision | Microsoft Azure (Commercial) | Pay-as-you-go (~$1.50 per 1,000 transactions) | Advanced image description, object detection, image analysis | REST API (JS/TypeScript via REST fetch or Azure SDK) | [Azure Vision Docs](https://azure.microsoft.com/services/cognitive-services/computer-vision/) |
| Google Cloud Vision API | Google (Commercial) | Pay-as-you-go (~$1.50 per 1,000 requests) | Robust image annotation, label detection, powerful object description | REST API (JS/TypeScript via fetch/promise) | [Google Vision](https://cloud.google.com/vision) |
| Flamingo Mini | Hugging Face (Open-source, non-commercial) | Free | Multimodal capabilities, advanced captioning, lightweight version | Python/PyTorch | [Flamingo Mini HuggingFace](https://huggingface.co/openflamingo/OpenFlamingo-3B-vitl-mpt1b) |
| Salesforce BLIP | Salesforce AI / Hugging Face (Open-source) | Free | State-of-the-art captioning, visual question answering | Python/PyTorch | [Salesforce BLIP HF Repo](https://huggingface.co/Salesforce/blip-image-captioning-base) |
# Recommended Approach/Library (Commercial APIs - JS ESM compatible)
To directly integrate quickly and easily with a Typescript-based frontend project (ES modules compatible and without React, Tailwind CSS as UI), commercial API services such as OpenAI Vision API or Azure Vision API are highly recommended. Here’s a simple example integrating OpenAI’s GPT-4V:
```typescript
// Example: Simple OpenAI Vision API call using fetch.
const API_KEY = "YOUR_OPENAI_API_KEY";
type GPTVisionResponse = {
choices: {
message: {
content: string;
};
}[];
};
async function generateImageCaption(imageUrl: string): Promise<string> {
const response = await fetch("https://api.openai.com/v1/chat/completions", {
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": `Bearer ${API_KEY}`,
},
body: JSON.stringify({
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": "Describe the following image in detail."},
{"type": "image_url", "image_url": {"url": imageUrl}}
]
}
],
"max_tokens": 150
}),
});
if (!response.ok) throw new Error("OpenAI API failed.");
const data: GPTVisionResponse = await response.json();
return data.choices[0].message.content;
}
// Usage example (with async/await)
const caption = await generateImageCaption("https://example.com/sample_image.jpg");
console.log("Generated Caption:", caption);
```
# Integration with Tailwind CSS-Based Interface (without React)
Here's a minimalistic example using vanilla JavaScript/Typescript with TailwindCSS for styling a simple interface:
HTML markup:
```html
<div class="max-w-lg mx-auto p-4 bg-white rounded-lg shadow-lg">
<input id="image-url" type="url"
placeholder="Enter Image URL"
class="w-full p-2 border border-gray-300 rounded mb-4"
/>
<button id="generate-caption-btn"
class="bg-blue-500 text-white px-4 py-2 rounded hover:bg-blue-600"
>
Generate Caption
</button>
<div id="caption-output" class="mt-4 p-4 bg-gray-100 rounded"></div>
</div>
```
Typescript/JS code (frontend):
```typescript
document.getElementById('generate-caption-btn')!.addEventListener('click', async () => {
const imageUrlInput = document.getElementById("image-url") as HTMLInputElement;
const outputDiv = document.getElementById("caption-output")!;
const imageUrl = imageUrlInput.value;
outputDiv.textContent = "Generating caption...";
try {
const caption = await generateImageCaption(imageUrl);
outputDiv.textContent = caption;
} catch (error) {
outputDiv.textContent = "Error generating caption.";
console.error(error);
}
});
```
# References
- [BLIP-2 (HuggingFace)](https://huggingface.co/docs/transformers/model_doc/blip-2)
- [Salesforce BLIP](https://huggingface.co/Salesforce/blip-image-captioning-base)
- [OpenAI GPT-4 Vision](https://platform.openai.com/docs/guides/vision)
- [Microsoft Azure Vision Docs](https://azure.microsoft.com/services/cognitive-services/computer-vision/)
- [Google Vision API Docs](https://cloud.google.com/vision)
- [Simple fetch API usage (MDN)](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)
- [TailwindCSS Docs](https://tailwindcss.com/docs/installation)

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@ -2,9 +2,3 @@
- all documents in Markdown, insert new line after headings, and before code sections
- standard chapters: brief, references (with links)
- dont comment, just the content
- add example code
- we use tailwindcss
- avoid React
- if applicable, refer to libraries (Typescript ESM compatible)
- propose new & ready to go Astro components

5
docs/todos-images.md Normal file
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@ -0,0 +1,5 @@
## Image to text
- [ ] options, free, images to text (not OCR)
- [ ] as markdown table with links, prices, specs/features, language
- [ ] check commercial and opensource models (huggingface)

6
docs/todos.sh
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@ -13,5 +13,9 @@
## Maps
#kbotd --prompt=./todos-map.md --preferences=./preferences.md --mode=completion --dst=./maps.md --model=anthropic/claude-3.7-sonnet:thinking
kbotd --prompt=./todos-map.md --preferences=./preferences.md --model=anthropic/claude-3.7-sonnet:thinking
## Pics
kbotd --prompt=./todos-images.md --preferences=./preferences.md --mode=completion --dst=./image-to-text.md --model=openai/gpt-4.5-preview