The Future of Edge Computing in 2025
Edge computing is moving from niche to mainstream as latency-sensitive applications demand local processing power.
Written by RTILA X Engineering Team
Navigate user profiles and extract bio data
Lead generation from social platforms
Edge computing is moving from niche to mainstream as latency-sensitive applications demand local processing power.
Distributed teams consistently outperform their office-based peers when supported by the right tools and culture.
New delivery mechanisms are making CRISPR therapies safer and more effective for previously untreatable genetic conditions.
Small daily routines can dramatically improve sleep quality and long-term health outcomes.
Beyond the famous temples and beaches, Southeast Asia offers tranquil villages, dramatic landscapes, and rich local culture.
WebAssembly is quietly becoming the preferred runtime for performance-critical code in the browser and beyond.
Separating storefront from backend gives retailers the flexibility to create unique customer experiences.
Quantum computing continues incremental but meaningful progress, with new qubit architectures and error-correction milestones.
Balanced nutrition and movement patterns are key to steady energy and metabolic health.
The Greek islands offer a remarkable diversity of landscapes, history, and food within easy reach of one another.
First paragraph of the full article content.
Second paragraph with additional details.
Third paragraph wrapping up the article.
{
"name": "Extract_Blog_Archive",
"settings": {
"urls": [
"https://learn.rtila.com/scenarios/social-media/3"
]
},
"datasets": {
"archive_entries": {
"item_selector": "css=.archive-entry",
"properties": [
{
"name": "title",
"type": "text",
"selector": "css=.archive-title a"
},
{
"name": "date",
"type": "text",
"selector": "css=.archive-date"
},
{
"name": "url",
"type": "attribute",
"selector": "css=.archive-title a",
"attribute": "href"
}
]
},
"full_articles": {
"item_selector": "css=.full-article",
"properties": [
{
"name": "full_title",
"type": "text",
"selector": "css=.full-title"
},
{
"name": "author",
"type": "text",
"selector": "css=.full-meta .author"
},
{
"name": "date",
"type": "text",
"selector": "css=.full-meta .date"
}
]
}
},
"commands": [
{
"command": "wait_for_selector",
"params": {
"selector": "css=.archive-entry"
}
},
{
"command": "extract_data",
"params": {
"dataset": "archive_entries"
}
},
{
"command": "for_each",
"params": {
"source_type": "dataset",
"dataset": "archive_entries",
"as": "entry",
"do": [
{
"command": "goto",
"params": {
"url": "${entry.url}"
}
},
{
"command": "wait_for_selector",
"params": {
"selector": "css=.full-article"
}
},
{
"command": "extract_data",
"params": {
"dataset": "full_articles"
}
}
]
}
}
]
}
Archive extraction first collects lightweight index data, then a for_each loop visits each article URL for the full article text.
Copy the project JSON above, open RTILA X, create a new project, and paste it.
This scenario covers 2 RTILA X commands and is referenced in 1 learning article.
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