DEV Community: Chidari Sandeep

XeL Studio: The Power of an Autonomous Agentic Workflow

Chidari Sandeep — Wed, 13 May 2026 20:38:40 +0000

If you've been following my updates, you might already be familiar with XeL Studio. If you haven't seen it yet, a quick search on Google, Bing, Yahoo, or DuckDuckGo will bring it right up. Just look for the first Vercel-hosted result focused on "AI research and cybersecurity."

Today, let's dive into what makes XeL Studio truly unique: its fully autonomous, agentic workflow.

A Self-Driving Newsroom
The standout feature of the platform is the "AI Tech News" section, which operates entirely on autopilot. Every 30 minutes, the system wakes up, scours the internet for the latest tech news, writes an article, generates a relevant accompanying image, and publishes the complete post.

More importantly, it is a self-managing system. The AI automatically cleans up its database every single day, deleting outdated news to keep the feed fresh. I haven't manually updated the code in two months, yet the platform continues to run flawlessly day in and day out with absolutely zero human intervention.

Interactive AI Labs
Beyond the automated newsfeed, I’ve also added our Rising Chat AI into the platform's AI Labs section. This makes it incredibly easy for anyone to jump in and start chatting with the bot directly on Telegram.

Under the Hood: A Robust Architecture
Managing this level of automation requires a solid technical foundation. But why use three different databases? Since the project relies entirely on free tiers, storage space is strictly limited. By weaving together three distinct systems, I engineered a unified, high-capacity storage network that bypasses individual limits while keeping operations fast and cost-free.

Supabase (PostgreSQL): Acts as the primary brain for the news section, autonomously running queries and deleting old entries.
Firebase (NoSQL): Powers the frontend, ensuring blazing-fast load times and handling local storage.
JSON-Configured Database: A dedicated setup specifically tuned to manage the automated news pipeline without bloating the main databases.

The Tech Stack & Infrastructure
The workload is split across two main environments. Why not just run everything on Vercel? While Vercel is fantastic for hosting frontends, serverless functions have strict timeout limits that kill long-running background tasks. To bypass this, GitHub Actions acts as a dedicated, secondary backend server to handle the heavy lifting (like news scraping and AI generation), while Vercel handles serving the dynamic frontend.

Built from scratch with a unique concept, the platform leverages a modern stack. Why use multiple languages? Because each is perfectly suited for its specific job:

Python: The absolute powerhouse behind the scenes, handling data scraping, automation logic, and AI processing.
Next.js, React, & TypeScript: These dominate the frontend, working together to deliver a blazing-fast, highly interactive, and structurally secure user interface.

It’s a substantial codebase with over 50+ files. For an added touch, I’ve also integrated Microsoft’s text-to-speech voice to enhance accessibility.

100% Free, Forever
Despite the advanced AI integrations and heavy backend automation, the best thing about XeL Studio remains the same: it is completely free. Even as I plan to roll out more features and expand its capabilities, it will always remain at zero cost to the users.

Search for it today and experience a truly agentic website in action!

GitHub Link: https://github.com/SandeepAi369/xel-studio

XeL Studio: The Power of an Autonomous Agentic Workflow

Chidari Sandeep — Tue, 12 May 2026 20:44:55 +0000

Content:
XeL Studio: The Power of an Autonomous Agentic Workflow

Today, let's dive into what makes XeL Studio truly unique: its fully autonomous, agentic workflow.

Supabase (PostgreSQL): Acts as the primary brain for the news section, autonomously running queries and deleting old entries.
Firebase (NoSQL): Powers the frontend, ensuring blazing-fast load times and handling local storage.
JSON-Configured Database: A dedicated setup specifically tuned to manage the automated news pipeline without bloating the main databases.

Built from scratch with a unique concept, the platform leverages a modern stack. Why use multiple languages? Because each is perfectly suited for its specific job:

Python: The absolute powerhouse behind the scenes, handling data scraping, automation logic, and AI processing.
Next.js, React, & TypeScript: These dominate the frontend, working together to deliver a blazing-fast, highly interactive, and structurally secure user interface.

It’s a substantial codebase with over 50+ files. For an added touch, I’ve also integrated Microsoft’s text-to-speech voice to enhance accessibility.

Search for it today and experience a truly agentic website in action!

GitHub Link: https://github.com/SandeepAi369/xel-studio

XeL Studio: The Power of an Autonomous Agentic Workflow

Chidari Sandeep — Tue, 12 May 2026 20:39:20 +0000

Content:
XeL Studio: The Power of an Autonomous Agentic Workflow

Today, let's dive into what makes XeL Studio truly unique: its fully autonomous, agentic workflow.

Supabase (PostgreSQL): Acts as the primary brain for the news section, autonomously running queries and deleting old entries.
Firebase (NoSQL): Powers the frontend, ensuring blazing-fast load times and handling local storage.
JSON-Configured Database: A dedicated setup specifically tuned to manage the automated news pipeline without bloating the main databases.

Built from scratch with a unique concept, the platform leverages a modern stack. Why use multiple languages? Because each is perfectly suited for its specific job:

Python: The absolute powerhouse behind the scenes, handling data scraping, automation logic, and AI processing.
Next.js, React, & TypeScript: These dominate the frontend, working together to deliver a blazing-fast, highly interactive, and structurally secure user interface.

It’s a substantial codebase with over 50+ files. For an added touch, I’ve also integrated Microsoft’s text-to-speech voice to enhance accessibility.

Search for it today and experience a truly agentic website in action!

GitHub Link: https://github.com/SandeepAi369/xel-studio

XeL Studio: The Power of an Autonomous Agentic Workflow

Chidari Sandeep — Tue, 12 May 2026 20:32:50 +0000

Content:
XeL Studio: The Power of an Autonomous Agentic Workflow

Today, let's dive into what makes XeL Studio truly unique: its fully autonomous, agentic workflow.

Supabase (PostgreSQL): Acts as the primary brain for the news section, autonomously running queries and deleting old entries.
Firebase (NoSQL): Powers the frontend, ensuring blazing-fast load times and handling local storage.
JSON-Configured Database: A dedicated setup specifically tuned to manage the automated news pipeline without bloating the main databases.

Built from scratch with a unique concept, the platform leverages a modern stack. Why use multiple languages? Because each is perfectly suited for its specific job:

Python: The absolute powerhouse behind the scenes, handling data scraping, automation logic, and AI processing.
Next.js, React, & TypeScript: These dominate the frontend, working together to deliver a blazing-fast, highly interactive, and structurally secure user interface.

It’s a substantial codebase with over 50+ files. For an added touch, I’ve also integrated Microsoft’s text-to-speech voice to enhance accessibility.

Search for it today and experience a truly agentic website in action!

GitHub Link: https://github.com/SandeepAi369/xel-studio

XeL Studio: The Power of an Autonomous Agentic Workflow

Chidari Sandeep — Tue, 12 May 2026 19:56:27 +0000

Why I rewrote my 90+ Engine Meta-Search in Rust 🦀

Chidari Sandeep — Wed, 06 May 2026 10:13:15 +0000

Just testing out my automated dev-log pipeline for SearchWala. Moving from Python to Rust dropped my RAM from 512 MB → 38 MB and made cold starts nearly instant. Here's the short version of why and how.

The Problem

SearchWala aggregates results from 90+ search engines — Google, Bing, DuckDuckGo, Brave, Mojeek, and dozens of niche/academic sources. The original Python stack (FastAPI + asyncio + BeautifulSoup) worked, but:

RAM hungry: Each worker held parsed DOM trees in memory. Under load, a single instance ate ~512 MB.
Cold start pain: On a fresh container, Python import chains + dependency init took 4-6 seconds.
GIL bottleneck: True parallelism across 90 engines was faked with async I/O, but CPU-bound parsing still serialized.

The Rust Rewrite

I rewrote the core in Rust using tokio for async, reqwest for HTTP, and scraper for HTML parsing. The results:

Metric	Python	Rust
RAM (idle)	512 MB	38 MB
Cold start	4.2s	0.3s
P95 latency (90 engines)	2.8s	0.9s
Binary size	~180 MB (venv)	12 MB

The dual-path LLM synthesis pipeline (lite mode for speed, research mode for depth) stayed as a sidecar microservice, but all search orchestration, ranking (BM25 + Reciprocal Rank Fusion), and content extraction now run natively in Rust.

Key Takeaway

If your I/O-heavy Python service is eating memory and you need predictable latency — Rust with tokio is the move. Not everything needs a rewrite, but the hot path absolutely does.

Check out the full source code and drop a star on GitHub: SearchWala on GitHub

Building a 90+ engine meta-search in Rust [TEST-0x8A7B]

Chidari Sandeep — Wed, 06 May 2026 08:57:53 +0000

Just testing out my automated dev-log pipeline for SearchWala. Moving from Python to Rust dropped my RAM usage significantly.

Building a 90+ engine meta-search in Rust

Chidari Sandeep — Wed, 06 May 2026 08:50:29 +0000

I just set up an automated dev

SearchWala: I Built a Blazing-Fast Meta-Search Engine in Rust That Queries 90+ Engines Simultaneously

Chidari Sandeep — Tue, 05 May 2026 12:10:48 +0000

Hey devs! I want to share a project I have been working on called SearchWala (Swift-Search-RS).

What is it?

SearchWala is an open-source meta-search engine written in Rust that queries 90+ search engines simultaneously including Google, Bing, DuckDuckGo, Brave, Yandex, Baidu, and many more. It aggregates the results, scrapes the actual content, and can synthesize an AI-powered answer using any LLM provider you bring (Groq, OpenAI, Cerebras, etc).

Key Features

90+ Search Engines: Google (15 regional variants), Bing, DuckDuckGo, Brave, Yahoo, Qwant, Mojeek, Startpage, Wikipedia, and 60+ more
Dual LLM Pipeline: Lite mode (fast BM25-ranked answer) and Research mode (iterative deep analysis across 200+ sources)
5-Tier Content Extraction: Intelligent article extraction that goes beyond simple scraping
Built in Rust: Blazing fast with async Tokio runtime, 24 concurrent scrapers
BYOK (Bring Your Own Key): Works with any OpenAI-compatible LLM provider
Privacy First: No tracking, no ads, self-hostable
Docker Ready: Multi-stage build producing a tiny 15MB image

Performance

Scrapes and ranks 420 URLs in under 10 seconds
BM25 ranking with phrase match bonuses
Browser fingerprint rotation across 12 profiles to avoid blocks

Try It

GitHub: https://github.com/SandeepAi369/Swift-Search-Rs

Would love to hear your feedback! Drop a comment if you have questions.