DEV Community: sonu samrat

# I’m Building a Local AI Desktop Agent That Can See, Remember, Act, and Verify

sonu samrat — Wed, 02 Sep 2026 19:59:14 +0000

Most AI assistants today are impressive at one thing:

conversation.

You ask a question → the model generates an answer → you're done.

But I wanted something different.

I wanted an AI assistant that could actually operate my computer.

Not just tell me how to open an application.

Actually open it.

Not just tell me what's on my screen.

Actually inspect the screen.

Not just claim that an action succeeded.

Actually verify that it happened.

That project became Diego.

And after building it, I've realized that the interesting part of an AI desktop agent isn't the LLM.

The interesting part is everything that has to happen around the LLM.

What is Diego?

Diego is a desktop AI agent built around four major capabilities:

Talk → Perceive → Know → Act

The goal is to connect these capabilities into one system rather than treating them as independent features.

At a high level:

                         ┌───────────────┐
                         │    DIEGO      │
                         └───────┬───────┘
                                 │
              ┌──────────────────┼──────────────────┐
              │                  │                  │
             TALK             PERCEIVE             KNOW
              │                  │                  │
        Voice / STT / TTS    Screen / OCR      Local documents
        Wake word            Vision            PC facts
                             Live state         RAG
              │                  │                  │
              └──────────────────┼──────────────────┘
                                 │
                                 ▼
                         Reasoning / Planning
                                 │
                                 ▼
                                ACT
                                 │
                   ┌─────────────┼─────────────┐
                   │             │             │
                Desktop       Browser        Media
                Windows       Search         YouTube
                   │             │             │
                   └─────────────┼─────────────┘
                                 │
                                 ▼
                              VERIFY

The important design principle is that the LLM isn't the source of truth for everything.

1. Voice Isn't Just Speech-to-Text

Diego has a complete local voice interaction pipeline.

The flow is roughly:

Microphone
    ↓
VAD
    ↓
Speech Recognition
    ↓
Transcript Validation
    ↓
Intent / Command Routing
    ↓
Execution
    ↓
Verification
    ↓
Response
    ↓
TTS
    ↓
Listen Again

That distinction matters.

A simple voice assistant might do:

audio → speech-to-text → LLM → response

But real-world audio isn't clean.

There can be:

silence
background noise
incomplete speech
low-confidence transcription
accidental activations
garbage transcripts

So Diego uses confidence/evidence gating before low-quality transcripts are allowed to reach the agent.

This is one of those things that isn't very exciting in a demo.

But it makes a huge difference in an actual continuously running assistant.

2. Wake Word Detection

Diego also has a local wake-word subsystem.

The architecture includes:

local wake-word detection
wake verification
audio backend integration
normal wake-enabled runtime
development mode without wake detection

The wake subsystem is intentionally treated as a relatively stable component while other parts of the system evolve.

That's another lesson I've learned:

Not everything needs to be constantly rewritten.

Sometimes the best optimization is knowing what not to touch.

3. Diego Can Authenticate Who Is Using It

Diego includes a local face-authentication subsystem.

The basic pipeline includes:

Camera
   ↓
Face Detection
   ↓
Known Face Encoding
   ↓
Authentication
   ↓
Agent Access

There's also a development mode that allows authentication to be bypassed when working on the rest of the system.

This becomes especially interesting once an assistant can perform real computer actions.

An assistant that can open applications, interact with your browser, manipulate windows, and access local knowledge needs a different security model from a chatbot.

4. Diego Can See My Screen

One of the biggest differences between a chatbot and a desktop agent is context.

Diego can work with:

screen captures
OCR
visual analysis
screen context
live visual requests

For example:

"What's on my screen?"

should not be answered using something Diego learned about my computer yesterday.

It needs the current state of the screen.

That's why Diego explicitly separates:

LIVE STATE

from:

HISTORICAL / LOCAL KNOWLEDGE

This distinction becomes extremely important as the system grows.

5. Diego Actually Controls the Desktop

This is where things start getting interesting.

Diego can perform real desktop operations, including:

opening applications
focusing applications
switching windows
closing windows
minimizing windows
maximizing windows
listing running applications/windows
interacting with desktop tools

But there is an important difference between execution and successful execution.

A naïve agent might do:

open_app()
↓
"Done!"

Diego's architecture is moving toward:

execute()
    ↓
observe state
    ↓
verify expected result
    ↓
success / failure

Because computers fail.

Applications don't always open.

Windows aren't always where you expect them.

Browser actions can behave differently.

And an AI saying "done" doesn't magically make something true.

6. Diego Knows About Its Own Computer

One of the surprisingly useful capabilities is deterministic system information.

Diego can retrieve information about:

operating system
kernel and architecture
CPU
CPU cores
RAM
GPU
GPU memory
storage
disks
network interfaces
Python environment
project environment

For example:

"How much RAM do I have?"

doesn't need an LLM.

The system snapshot already knows.

That means the request can be answered directly.

This led to one of the architectural principles I'm increasingly using:

Don't use an LLM to answer something the computer already knows.

7. Local Knowledge: 1,618 Documents and 33,051 Chunks

This is one of the parts of Diego I'm most interested in.

The current validated knowledge base contains:

Documents: 1,618
Chunks:    33,051

It can index different types of local content including:

PDF
TXT
Markdown
DOCX
XLSX
CSV
JSON
XML
HTML
PPTX
source code
configuration files
logs
other supported textual formats

The knowledge system supports:

Incremental indexing
Hash-based change detection
Duplicate prevention
Deleted-file cleanup
Background scanning
Cancellation / resume behavior
Local embeddings
Keyword search
Hybrid retrieval
Source citations
Bounded LLM context

This isn't just a folder of documents.

It's becoming a local knowledge layer for the agent.

8. Diego Can Learn About the PC Without Treating Everything as Knowledge

One thing I was particularly careful about was security.

If an AI can scan a computer, you don't want it casually indexing:

.env
.ssh
.gnupg
credentials
API keys
session data
private keys

So the read-only learning pipeline uses:

Approved Roots
      ↓
Read-only Scanner
      ↓
Security Policy
      ↓
Extraction
      ↓
Chunking
      ↓
Embeddings / Keyword Index
      ↓
DuckDB

There are protections around sensitive paths, symlinks, credentials, sessions, keys, .env, .ssh, .gnupg, .git, .venv, node_modules, and other excluded locations.

The goal is simple:

Learn about the machine without turning secrets into searchable knowledge.

9. The LLM Doesn't Need to Be Involved in Everything

This is probably one of the biggest architectural improvements I've made.

Instead of:

User
 ↓
LLM
 ↓
Answer

Diego increasingly works like:

                    User Request
                         ↓
             Can this be answered
                deterministically?
                         │
          ┌──────────────┼──────────────┐
          │              │              │
      System Fact     Live State    Local Knowledge
          │              │              │
     System Snapshot   Live Tools     Retrieval
          │              │              │
          └──────────────┼──────────────┘
                         │
                         ▼
                Reasoning Required?
                         │
                         ▼
                        LLM

For strong local knowledge matches, Diego can answer without an LLM call.

For partial matches:

Local Evidence
      ↓
Small Bounded Context
      ↓
LLM Reasoning

And only when necessary does the request enter the normal LLM/tool pipeline.

This reduces unnecessary inference and keeps the system more deterministic.

10. The Problem With Raw RAG

Another problem appeared once the knowledge system became useful.

Retrieval systems naturally produce things like:

chunk metadata
embedding scores
file paths
document identifiers
duplicate evidence

That's useful internally.

It's terrible as a voice response.

Imagine asking:

"What do you know about Diego?"

and hearing:

/home/user/project/...
score=0.823...
chunk_id=...
/home/user/project/...

That's not an assistant.

That's a database having a nervous breakdown.

So Diego now has a presentation layer that transforms retrieval output into human-friendly responses.

It:

removes raw retrieval dumps
removes embedding scores
removes chunk metadata
deduplicates evidence
limits response length
preserves filenames when explicitly requested
preserves paths when explicitly requested
keeps action instructions intact

The retrieval system and the conversation system therefore have different responsibilities.

11. Web Search Is Another Source of Knowledge

Diego also has browser/search functionality.

It can:

perform web searches
extract search results
open relevant results
read extracted web content
interact with the browser

The idea is not that the web replaces local knowledge.

Instead:

Local Knowledge
      ↓
Enough information?
   ↙       ↘
 YES        NO
  ↓          ↓
Answer     Search Web

This gives Diego multiple knowledge sources rather than forcing everything through a single pipeline.

12. YouTube and Media Control

Diego also has explicit media behavior.

For example:

"Play <song> on YouTube"

is treated differently from:

"Search YouTube for <song>"

That sounds obvious, but distinguishing searching for something from performing an action is important for agent design.

Diego also has media controls such as:

play
pause
resume
media-state verification

Again, the important part is verification.

The assistant shouldn't claim:

"It's playing."

unless it has evidence that playback actually started.

13. Agentic Execution: Plan → Execute → Observe → Verify

This is probably the direction I'm most excited about.

The execution model is:

Goal
 ↓
Plan
 ↓
Execute Step
 ↓
Observe
 ↓
Verify
 ↓
Replan if Necessary
 ↓
Continue
 ↓
Verified Completion

There are safeguards around:

maximum steps
retries
replans
timeouts
loop detection
idempotency
task state

Why?

Because giving an LLM access to tools without boundaries is basically saying:

"Here are some buttons. Good luck."

That's not an agent architecture I'd want running continuously on my machine.

14. Local Models Instead of Permanent Cloud Dependency

This is probably the most controversial part of the project.

I don't want Diego's core existence to depend on:

API key
   ↓
Internet
   ↓
Cloud provider
   ↓
Model

Diego supports local LLM usage through Ollama.

Cloud/provider paths can exist where configured, but local functionality isn't inherently dependent on cloud models.

For me, local inference isn't about saying:

"Cloud AI is bad."

Cloud models are incredibly useful.

It's about having another option:

What if your personal AI could actually belong to your machine?

Running locally also forces you to confront a problem that disappears when you throw unlimited cloud compute at everything:

Optimization.

15. The Real Challenge: Making AI Fit on a PC

A desktop AI agent isn't just an LLM.

You're potentially running:

Voice processing
       +
Wake word
       +
Speech recognition
       +
LLM inference
       +
Embeddings
       +
Vector / keyword retrieval
       +
OCR
       +
Screen capture
       +
Browser automation
       +
Background indexing
       +
Desktop automation
       +
TTS

All on one machine.

That means you start caring about:

RAM
VRAM
CPU utilization
GPU utilization
model loading time
context size
background concurrency
disk I/O
inference latency
caching
startup time

This completely changes how I think about AI engineering.

Instead of:

"Can I run this model?"

the better question becomes:

"Can I run the entire system reliably?"

16. Model Warm-Up Made a Huge Difference

One optimization that produced a measurable result was model warm-up.

The current measurements were approximately:

Cold:  ~2459 ms
Warm:   ~306 ms

That's a dramatic difference.

The idea is simple:

Instead of repeatedly paying the cost of loading a model, Diego can keep the model warm for a configurable period.

This is a small example of a broader principle:

AI performance isn't only about model inference speed.

The surrounding system matters just as much.

17. Background Work Shouldn't Block the Assistant

Another major focus has been making expensive operations happen in the background.

Things like:

knowledge indexing
scanning
embeddings
model warm-up
diagnostics

shouldn't unnecessarily block the interactive voice loop.

Diego therefore uses things like:

Background processing
Bounded concurrency
Timeouts
Cancellation
Caching
Incremental indexing
Graceful fallback

This is where an AI project starts looking less like a prompt-engineering project and more like a distributed systems problem running inside a desktop application.

18. Testing the Agent, Not Just the Model

Another thing I care about is validation.

The latest full validation currently reports:

557 passed
5 skipped

That's important to me because an AI agent isn't useful if its demo works once.

It needs to survive changes.

Especially when you have interconnected components like:

Voice
 ↓
Routing
 ↓
Knowledge
 ↓
Tools
 ↓
Desktop
 ↓
Verification

A change in one component can break something somewhere else.

So automated testing becomes part of the AI architecture—not an afterthought.

19. What Diego Is Today

I don't describe Diego as an AGI.

And I don't call it a fully autonomous self-improving AI.

There are still major things missing.

For example:

❌ True autonomous long-term self-improvement
❌ Reliable self-diagnosis + repair
❌ Deep project relationship graphs
❌ High-quality continuous personal learning
❌ Fully autonomous background task execution
❌ Strong long-term episodic memory
❌ Comprehensive multimodal personal knowledge
❌ Automatic safe remediation

Those are future engineering problems.

And I'd rather be precise about what exists than inflate a project with AI buzzwords.

20. Where I Want to Take Diego

The direction I'm exploring is a much more capable personal AI architecture.

Something that can:

                 ┌───────────────┐
                 │     DIEGO     │
                 └───────┬───────┘
                         │
          ┌──────────────┼──────────────┐
          │              │              │
       PERCEIVE        REMEMBER         ACT
          │              │              │
        Screen        Knowledge        Desktop
        Voice         Memory           Browser
        Vision        Context          Tools
          │              │              │
          └──────────────┼──────────────┘
                         │
                      REASON
                         │
                      VERIFY
                         │
                      IMPROVE

The interesting part isn't adding another chatbot feature.

It's making these systems work together reliably.

The Bigger Question

The more I build Diego, the more I think the interesting AI question isn't:

"How smart is your model?"

It's:

"How much can your AI actually do?"

A model can have an enormous context window.

It can score incredibly well on benchmarks.

It can write beautiful code.

But a personal AI becomes fundamentally different when it can:

listen → see → understand → retrieve → reason → act → observe → verify

And it can do those things on your own machine.

That's what I'm trying to build with Diego.

Not another chatbot.

Not another wrapper around an API.

A local desktop agent that can actually interact with the environment it lives in.

And the most difficult part?

Making all of it work together without needing a datacenter under my desk.

That's the fun part.

Diego is still being built.

And I'm just getting started.

Technical Stack & Concepts I'm Exploring

The project currently involves concepts across:

Local LLM inference
Ollama
RAG
Local embeddings
Hybrid retrieval
DuckDB
Speech recognition
VAD
Wake-word detection
TTS
OCR
Computer vision
Browser automation
Desktop automation
Agentic planning
Action verification
Background processing
Caching
Incremental indexing
Security-aware filesystem scanning
Runtime diagnostics
Automated testing

The goal isn't to use every piece of technology possible.

It's to figure out which pieces actually make a personal AI reliable.

One final thought

We're currently obsessed with making AI models bigger.

I'm increasingly interested in making AI systems better integrated with the environments they operate in.

Because maybe the future of personal AI isn't:

"Open a chat and ask."

Maybe it's:

"It's already there. It knows what's happening, understands the context, and can actually do something about it."

_That's Diego.
_
Let's connect

I'm sharing the development of Diego, the problems I'm solving, and the experiments I'm running along the way.

🔗 GitHub: GitHub

🌐 Portfolio: NASHEDIxCODER

💼 LinkedIn: me

If you're interested in local AI, AI agents, RAG, voice interfaces, desktop automation, or building AI systems from scratch, I'd love to connect and hear what you're working on.

# GhostLine Development Update: From Features to Stability

sonu samrat — Fri, 10 Jul 2026 18:44:39 +0000

Over the past few weeks, I've been spending most of my time building GhostLine—a privacy-first browser communication platform that aims to make secure communication simple.

One thing I decided early on was that I didn't want to build "just another chat application." There are already plenty of those.

Instead, GhostLine focuses on a few core principles:

Privacy first
No account required
Lightweight browser experience
Temporary communication
Fast, reliable interactions

What Has Been Implemented

GhostLine has reached a point where the core communication experience is working end-to-end.

The platform currently supports:

End-to-end encrypted messaging
Real-time room-based communication
Browser-based video calling with WebRTC
Secure file sharing
Shared Secure Notes
Responsive interface for desktop and mobile
User settings that persist locally without requiring an account

The backend is built with Go using Gorilla WebSocket for real-time communication and PostgreSQL where persistence is required. The frontend intentionally stays simple with vanilla HTML, CSS, and JavaScript.

Keeping the stack lightweight has made it much easier to understand every layer of the application and avoid unnecessary complexity.

The Biggest Lesson So Far

One thing I've learned is that adding features is usually the easy part.

Keeping everything stable after multiple iterations is much harder.

Recently, most of my work hasn't been about introducing new functionality. Instead, I've been focusing on improving the startup flow, simplifying the UI, reducing technical debt, removing unnecessary complexity, and making the overall experience more reliable.

In many cases, deleting complexity has been a bigger improvement than adding another feature.

Why Simplicity Matters

As the project evolved, there were several ideas that seemed interesting during development but didn't really improve the user experience.

Rather than continuing to expand the interface, I chose to simplify it and focus on the features that actually support the product's core purpose.

Every screen, button, and interaction should have a reason to exist.

That philosophy has helped keep GhostLine focused instead of turning into a collection of unrelated features.

What's Next?

I'm currently working toward the next major milestone.

The focus is less about adding more and more functionality, and more about making GhostLine feel polished, dependable, and ready for everyday use.

There are some exciting things in development that I'm intentionally not revealing yet. I think they'll make the platform feel even more unique while staying true to the original goal of being fast, lightweight, and privacy-first.

Final Thoughts

Building GhostLine has been one of the most rewarding engineering projects I've worked on so far.

Every bug fixed, every regression solved, and every design decision has reinforced an important lesson: software isn't just about features—it's about delivering an experience that users can trust.

I'm looking forward to sharing the next development update once the next milestone is ready.
ghostline

I Built a Messaging App Where Nothing Stays

sonu samrat — Sat, 16 May 2026 19:49:59 +0000

Most messaging apps say they care about privacy.

Then they ask for:

your phone number
your email
your contacts
your identity
your metadata
your backups

I wanted the opposite.

So I built GhostLine — a real-time messaging platform focused on disappearing communication, minimal data retention, and instant access.

🔗 https://ghostline.sbs

The Idea

I kept thinking:

Why does starting a “private” conversation require creating a permanent digital identity?

Sometimes you just want:

a temporary room
encrypted communication
no account
no installation
no history left behind

That became the core idea behind GhostLine.

Open the site.
Create a room.
Share the code.
Start talking.

No signup flow.
No profile building.
No social graph.

Tech Stack

GhostLine is built with:

Go backend
PostgreSQL
WebSockets for real-time messaging
WebRTC for peer communication
JavaScript + HTML/CSS frontend
Nginx reverse proxy
AWS deployment

The goal was speed and low latency while keeping infrastructure lightweight.

What Makes It Different?

1. No Signup System

You don't create an identity before using the app.

That removes friction and also reduces stored user data.

2. Ephemeral Communication

Rooms are temporary.
Messages are not designed to live forever.

The philosophy is simple:

conversations should disappear unless users intentionally preserve them.

3. Privacy-Focused Architecture

Instead of building a social platform first and privacy later, I tried building privacy into the core workflow.

4. Real-Time Performance

WebSockets handle instant messaging while WebRTC is used for direct communication features.

The result feels much closer to a native app than a traditional website.

Challenges I Faced

WebSocket Stability

One of the hardest parts was dealing with:

reconnect handling
proxy issues
SSL termination
unexpected disconnects
scaling concurrent rooms

Debugging real-time systems is very different from debugging normal REST APIs.

Deployment Issues

Nginx + TLS + WebSockets + reverse proxies can become painful fast.

I spent hours tracking:

1006 WebSocket errors
upgrade header problems
SSL misconfigurations
Cloudflare/Nginx edge cases

Privacy vs Features

Every feature creates new privacy questions.

Even analytics become complicated when you're trying not to collect unnecessary data.

That tradeoff changed how I designed the platform.

What I Learned

Building a real-time privacy-focused app taught me:

simplicity is harder than feature overload
infrastructure matters as much as frontend UX
users care about friction more than most developers realize
“minimal” products often require more engineering discipline

Current Goals

Right now I'm focused on:

improving stability
better mobile experience
stronger room management
scaling concurrent users
improving encryption workflow

Try It

🔗 https://ghostline.sbs

Would genuinely love feedback from developers, security researchers, and people interested in privacy-focused products.

Especially around:

UX
performance
security architecture
real-world use cases

Thanks for reading.

tips

sonu samrat — Fri, 01 May 2026 21:55:27 +0000

When messages are permanent:
people write carefully.

When messages disappear:
people communicate faster.

That difference is bigger than it sounds.

Building a Chat App Where Messages Don’t Exist After You Send Them

sonu samrat — Fri, 24 Apr 2026 21:10:13 +0000

Why I built this

Most messaging systems assume one thing:

Everything should be stored forever.

That works for long-term collaboration, but I kept running into situations where it didn’t make sense:

sharing API keys or temporary credentials
debugging with someone in real-time
sending logs or short-lived data
quick discussions that don’t need history

Yet every tool I used (Slack, Discord, etc.) kept everything.

So I wanted to try something different:
What if conversations didn’t exist after they were done?

The idea: ephemeral chat

I built a small real-time system with a simple concept:

no accounts
instant room creation
messages can auto-delete
rooms are destroyed when inactive

No persistence by default.

Just:
open → share link → chat → disappear

Architecture decisions

I kept the system lightweight:

WebSockets for real-time messaging
in-memory room management (no long-term storage)
lifecycle-based cleanup (rooms destroyed when empty)

The tricky part wasn’t sending messages — it was managing state and cleanup reliably.

What was harder than expected

1. Room lifecycle

Knowing when to delete a room sounds simple, but edge cases show up fast:

user disconnects unexpectedly
browser closes without cleanup
multiple users joining/leaving rapidly

You need a reliable way to detect inactivity and safely destroy state.

2. Real-time consistency

With no persistence, everything depends on live state.

If something breaks:
there’s no fallback.

So handling:

connection drops
reconnections
message ordering

becomes more critical.

3. Random internet traffic

Even with a small deployment, I started seeing:

requests to /phpmyadmin
probes for /admin
random scanning behavior

Which was a reminder:
anything exposed publicly will get hit.

What surprised me

The biggest surprise wasn’t technical.

It was behavioral.

When users know messages won’t be stored:

they type faster
they overthink less
conversations become more direct

It feels closer to a real conversation than a logged system.

The tradeoff

Ephemeral systems aren’t always better.

Sometimes you need history:

debugging issues later
tracking decisions
auditing conversations

So the real challenge is:

When should something disappear, and when should it stay?

Most tools don’t give that choice.

Where this actually works well

So far, the strongest use cases:

sharing temporary credentials
quick debugging sessions
short-lived dev collaboration
anything that shouldn’t persist

What I’m exploring next

Trying to find a middle ground without breaking simplicity:

optional short-lived recovery
lightweight session controls
keeping zero-friction as core

Try it

If you’re curious to see how it works:

👉 https://ghostline.sbs

Final thought

Not every system needs persistence.

Sometimes, removing history changes how people interact — and in some cases, that’s exactly what you want.

it's important

sonu samrat — Thu, 23 Apr 2026 20:54:11 +0000

hey guy's
i have launched https://ghostline.sbs just for you and your privacy use it and comment, i'll bring whatever feature whatever you want.
lets beat everyone make a community.
we are ghost antrackable and untraceable,
we will do whatever we need,

try: https://ghostline.sbs

let's be togather

I built a messaging app where everything disappears (and why)

sonu samrat — Wed, 22 Apr 2026 17:09:08 +0000

The problem

Every messaging app today keeps everything.

Chats, files, links — all stored forever.

That’s fine for conversations, but not for:

sharing passwords
quick dev debugging
temporary collaboration
sensitive information

I found myself constantly thinking:
“Why does this need to exist forever?”

The idea

I wanted something simpler:

No accounts
No history
No friction

Just open → chat → disappear

That’s how Ghostline started.

What I built

Ghostline is a temporary messaging platform with:

Instant rooms (no signup required)
Shareable links
Auto-deleting messages
Rooms that destroy themselves after use

The goal is simple:

Conversations that don’t leave traces

Interesting things I noticed

While testing it, a few patterns stood out:

1. People hate friction

Even entering an email feels like too much for quick chats.

2. Temporary chats feel freeing

When users know messages won’t stay forever, they communicate differently.

3. Developers found unexpected use cases

sharing logs
sending quick tokens
debugging together

Challenges

Building this wasn’t trivial:

Managing real-time messaging (WebSockets)
Handling room lifecycle (creation → destruction)
Ensuring performance without storing long-term data

What’s next

I’m planning to add:

optional encryption indicators
better UI/UX
file sharing with auto-expiry
lightweight dev collaboration tools

Try it

If you’re curious, you can try it here:

👉 https://ghostline.sbs

Would love feedback — especially from developers and privacy-focused users.

Final thought

Not every conversation needs to be permanent.

Sometimes, it’s better if it just… disappears.

ghostline

sonu samrat — Tue, 21 Apr 2026 08:18:13 +0000

****Everyone talks about privacy…

but nobody actually has it.

Your chats are scattered.
Too many groups.
Too many people.
Too much noise.

And honestly… zero control.

Not every conversation should be public.
Not every message should stay forever.

Sometimes you just need a space that’s clean, fast, and yours.

That’s why I built GhostLine.

• Private rooms
• Real-time chat
• No clutter

Just you… and the people you trust.

ghostline.sbs

Move in silence. 🕶️

GhostLine — Real-Time Encrypted Chat (No Signup Required)

sonu samrat — Sat, 18 Apr 2026 19:41:59 +0000

Hey devs 👋

Built a side project I’ve been working on — GhostLine

It’s a real-time, end-to-end encrypted chat app designed to be fast, minimal, and privacy-focused. No accounts, no friction — just share a room and start talking.

🚀 Features
🔐 End-to-End Encrypted Messaging
⚡ Real-Time Communication (WebSockets)
🖼️ Photo Sharing
📹 Video Call Rooms (WebRTC)
🧠 Minimal, hacker-style UI
💡 Why I built this

Most chat apps today require signup, store user data, or feel bloated.
I wanted something instant, anonymous, and clean — open a room and start chatting securely.

🔗 Try it

https://ghostline.sbs

# I Built an Anonymous Chat Platform With Zero Tracking

sonu samrat — Fri, 17 Apr 2026 09:24:45 +0000

Most modern apps start the same way:

“Sign up to continue.”

Email. Phone. Permissions. Tracking.

Before you even use the product… you’ve already given away your identity.

As a developer, that never sat right with me.

So I decided to try something different.

The Idea

What if you could just… open a site and start talking?

No account.
No history.
No identity.

Just pure, real-time interaction.

That’s how GhostLine was born:
👉 https://ghostline.sbs

Core Principles

While building this, I focused on a few things:

1. No Authentication

No login system at all.

This removes:

user tracking
data storage risks
friction

2. No Persistent Identity

No usernames. No profiles.

Every session is temporary.

You are just… present in the moment.

3. Minimal Surface Area

The less you build, the less you expose.

No unnecessary features = smaller attack surface.

4. Real-Time Only

Messages exist in the moment.

No long-term storage.
No history.

Technical Thinking

Instead of adding layers, I kept removing them.

Typical apps:
Frontend → API → Auth → DB → Logging → Analytics

GhostLine approach:
Frontend → Real-time communication → Done.

Less complexity = fewer bugs + fewer security issues.

Security Perspective

Interestingly, removing features actually improves security:

No accounts → no credential attacks
No database → no data leaks
No tracking → no privacy concerns

Sometimes the best defense is… not having anything to attack.

Why This Matters

We’ve normalized surveillance on the internet.

Everything is tracked. Logged. Stored.

But not every interaction needs to be permanent.

Sometimes people just want to talk — freely.

Try It

If you’re curious, try it yourself:
👉 https://ghostline.sbs

No signup. Takes 2 seconds.

Open Question

As developers:

Are we overbuilding systems that don’t need to exist?

Would love to hear your thoughts.