DEV Community: Prakash Pawar

Anthropic: From Pandemic-Era Safety Concerns to a $350B AI Company

Prakash Pawar — Sun, 04 Jan 2026 05:02:08 +0000

In early 2021, at the height of the COVID-19 pandemic, a small group of former OpenAI researchers made a decision that would quietly reshape the AI industry.

What began as masked, socially distanced backyard meetings would become Anthropic, an AI company valued at over $350 billion by November 2025 — built around a single core belief:

Powerful AI systems must be safe, interpretable, and aligned by design.

This is the story of how that belief evolved from internal disagreement to one of the most influential AI companies in the world.

The Genesis: A Safety-First Vision

Anthropic was founded by seven former OpenAI researchers led by siblings Dario and Daniela Amodei.

The group left OpenAI due to directional disagreements, particularly around how aggressively AI capabilities should be scaled relative to safety research. While OpenAI pushed forward with increasingly powerful models, this group believed safety needed to be more foundational — not an afterthought.

Those early discussions, held during the second wave of COVID-19, laid the groundwork for a new company with a fundamentally different philosophy.

The Founders: A Complementary Partnership

Dario Amodei — Technical Leadership

Dario Amodei brought deep technical credibility to the venture. As OpenAI’s Vice President of Research, he led the development of GPT-2 and GPT-3 and co-invented reinforcement learning from human feedback (RLHF).

His background spans:

Physics at Stanford
Computational neuroscience at Princeton
Research roles at Baidu and Google Brain
OpenAI, where he joined in 2016

This mix of theoretical depth and practical AI scaling experience shaped Anthropic’s research direction.

Daniela Amodei — Operations and Policy

Daniela Amodei complemented her brother’s technical focus with operational leadership.

Her background includes:

English Literature, Politics, and Music at UC Berkeley
Five years at Stripe in operational roles
Vice President of Safety and Policy at OpenAI

At Anthropic, she focused on ethical deployment, governance, and institutional design, ensuring safety principles extended beyond model training into company structure.

Constitutional AI: Anthropic’s Core Innovation

Anthropic’s defining breakthrough was Constitutional AI (CAI) — a training approach designed to scale safety without relying exclusively on human moderators.

Unlike traditional RLHF, Constitutional AI introduces explicit, inspectable values into the training process.

Phase 1: Supervised Self-Critique

The model generates responses
It critiques its own outputs against a written “constitution”
It revises and learns from those critiques

The constitution consisted of 75 principles, drawing from sources like the UN Universal Declaration of Human Rights.

Phase 2: Reinforcement Learning from AI Feedback (RLAIF)

AI systems, not humans, evaluate responses
A preference model is trained on constitutional compliance
Harmful behavior is trained out without exposing humans to disturbing content

This made safety scalable — a critical requirement for frontier models.

Importantly, Anthropic made these principles explicit and editable, rather than embedding values opaquely inside model weights.

Claude: From Internal Experiment to Public Model

Anthropic completed training the first version of Claude in summer 2022 but deliberately chose not to release it immediately.

The concern was triggering an AI arms race focused purely on capability.

Three months later, OpenAI released ChatGPT.

Claude finally launched publicly in March 2023, named after Claude Shannon, the founder of information theory. The name was intentionally masculine, contrasting with assistants like Alexa and Siri.

From the beginning, Claude stood out for:

A 200,000-token context window
Emphasis on being helpful, harmless, and honest

Two versions launched initially:

Claude
Claude Instant (faster, lighter)

Early Foundations: From Concept to Code

Founding Discussions (2020–2021)

Before infrastructure existed, the team aligned on a key insight: scaling laws.

They recognized that:

More compute + more data + simple algorithms
Leads to broad cognitive improvements

These insights drove their belief that safety had to scale alongside capability.

After brainstorming names like Aligned AI, Sparrow, and Sloth, they settled on Anthropic — human-centered and available as a domain.

Early Research Focus (2021)

Rather than launching products quickly, Anthropic focused on fundamental research.

The founding team (15–20 people) met weekly, often outdoors, to refine ideas that would later culminate in Constitutional AI, published in December 2022.

Infrastructure Evolution: From Cloud Rentals to Megascale Compute

Phase 1: Google Cloud (2021–2023)

Anthropic initially relied on standard GPU clusters via Google Cloud, allowing fast iteration without massive capital expenditure.

This phase validated the Constitutional AI approach on smaller models.

Phase 2: Multi-Cloud Strategy (2023–2024)

Amazon invested $4 billion, becoming Anthropic’s primary training partner.

AWS committed to developing custom Trainium chips optimized for Anthropic’s workloads.

Phase 3: Massive Scale-Up (2024–2026)

Anthropic’s compute footprint exploded:

AWS — Project Rainier

Hundreds of thousands of Trainium2 chips
1,200-acre campus in Indiana
Over 1.3 GW of dedicated IT capacity
Custom kernel-level optimizations

Google Cloud

Commitment to deploy up to one million TPUs
Seventh-generation Ironwood TPUs
Tens of billions of dollars in infrastructure
Distribution via Vertex AI

Nvidia GPUs

Used selectively for specialized workloads
Ensured flexibility and avoided vendor lock-in

Software Stack and Training Pipeline

Core Technologies

Python (primary language)
PyTorch and JAX
Distributed training on cloud infrastructure

Training Data

Web scrapes
Licensed content
Contractor-provided examples
User interactions (opt-in)

A major initiative involved digitizing millions of books, which later resulted in a $1.5B copyright settlement.

Claude Model Evolution

Claude 1 & 2 (2023)

Initial public release
Expanded context windows

Claude 3 Family (March 2024)

Haiku (fast, small)
Sonnet (balanced)
Opus (largest, most capable)
Multimodal input and massive context

Claude 3.5 Sonnet (June 2024)

Introduced Artifacts (live code previews)
Launched Computer Use, enabling Claude to operate software directly

Claude 4 Family (May 2025)

Hybrid reasoning modes
Classified as ASL-3, triggering enhanced safeguards

Claude 4.5 (Late 2025)

State-of-the-art coding performance
Aggressive pricing reductions
Strong agentic and tool-use capabilities

Beyond the Chatbot: Product Ecosystem

Core Products

Claude API
Claude.ai (consumer interface)
Claude Code (developer-focused coding assistant)
Claude Enterprise

Platform Innovations

Model Context Protocol (MCP)
Secure code execution
Persistent file context
Agent Skills
Tool discovery
Web search integration

Business Model and Growth

Revenue Streams

API usage (70–75%)
Subscriptions (10–15%)
Enterprise partnerships

Anthropic reached:

$5B ARR by mid-2025
$500M annualized revenue from Claude Code within 8 weeks

Governance and Safety

Anthropic operates as a Public Benefit Corporation (PBC) with a Long-Term Benefit Trust, embedding safety into corporate governance.

The Anthropic Safety Levels (ASL) framework defines deployment thresholds and safeguards, inspired by biosafety protocols.

Challenges and Controversies

Copyright litigation
Security incidents involving misuse
Political scrutiny around funding sources

Despite this, Anthropic continued to emphasize transparency and controlled scaling.

Looking Ahead: 2026 and Beyond

Anthropic is now focused on:

Multi-agent orchestration
Digital executive-style agents
Deeper OS-level integrations

The question is no longer whether AI can act — but how much autonomy we can safely trust it with.

Conclusion: Safety Without Sacrifice

From masked backyard meetings to global infrastructure spanning millions of AI chips, Anthropic’s journey shows that safety and capability are not mutually exclusive.

Their work on Constitutional AI, ASL, Computer Use, and governance structures has influenced the entire industry.

Whether Anthropic ultimately succeeds in fully aligning powerful AI systems with human values remains uncertain.

But their story proves one thing:

Principled AI development can scale — technically, commercially, and ethically.

Feel free to drop a comment or reach out on
X (Twitter)

Thanks for reading.

I Found an Interesting Library with 4,000+ n8n Workflows

Prakash Pawar — Mon, 22 Dec 2025 01:18:10 +0000

I Found an Interesting Library with 4,000+ n8n Workflows — and It Completely Changes How You Start Automation

If you’ve worked with n8n long enough, you’ve probably felt this pain:

You know automation is possible.
You know n8n is powerful.
But starting from a blank canvas every single time is slow.

Recently, I came across a community-maintained library that made me stop and rethink how we should be building automations with n8n.

A curated collection of 4,000+ production-ready n8n workflows.

Not templates.
Not demos.
Actual workflows you can browse, search, download, and import.

Why This Caught My Attention

Most automation tools advertise “thousands of integrations”, but what they really give you is:

Empty nodes
Generic examples
Marketing demos

This project is different.

It focuses on real workflows, already wired together, covering practical use cases across multiple domains.

Once you see the scale of it, it becomes clear why this matters.

By the Numbers (This Is Not Small)

Here’s what the collection currently offers:

4,300+ ready-to-use workflows
365+ unique integrations
29,000+ total n8n nodes
15+ organized categories
100% import success rate

This isn’t a random dump.
It’s structured, searchable, and surprisingly fast.

What This Library Actually Solves

1. The “Blank Canvas” Problem

Instead of starting from scratch, you can:

search for an existing workflow
import it into n8n
modify it to fit your needs

This alone can save hours.

2. Learning by Real Examples

If you’re learning n8n, this is gold.

You can see:

how people structure workflows
how error handling is done
how multiple services are chained together

This is far more valuable than documentation snippets.

3. Faster Prototyping for Production

For freelancers, startups, and internal tools:

MVP automation becomes trivial
Proof-of-concepts take minutes, not days
You can test ideas quickly before hardening them

How You Access It (No Setup Required)

One thing I appreciated is that you don’t even need to clone the repo to explore it.

There’s a live, searchable web interface:

👉 https://zie619.github.io/n8n-workflows

You get:

full-text search
category filters
direct workflow downloads
mobile-friendly UI

It feels more like a workflow marketplace than a GitHub repo.

Under the Hood (Why It’s Fast)

Technically, the project is well thought out.

At a high level:

User
→ Web UI
→ FastAPI backend
→ SQLite (FTS5 full-text search)
→ Workflow JSON files

Because it uses SQLite FTS5, search is extremely fast — even with thousands of workflows.

This matters more than it sounds.
If search is slow, a collection like this becomes unusable.

Running It Locally (Optional)

If you want to self-host it or explore locally:

git clone https://github.com/Zie619/n8n-workflows.git
cd n8n-workflows
pip install -r requirements.txt
python run.py

Or via Docker:

docker run -p 8000:8000 zie619/n8n-workflows:latest

Within minutes, you have your own local workflow library.

Why This Matters for the n8n Ecosystem

n8n’s real strength isn’t just integrations — it’s composability.

This library turns n8n into something closer to:

a workflow marketplace
a learning platform
a rapid automation toolkit

It lowers the barrier for:

beginners
solo developers
non-technical teams
fast-moving startups

Final Thoughts

We talk a lot about automation, AI agents, and workflows — but most productivity gains come from reuse, not reinvention.

A large, searchable, real-world workflow library like this:

saves time
spreads best practices
accelerates adoption

If you’re using n8n seriously, this is worth bookmarking.

👉 Explore it here: https://zie619.github.io/n8n-workflows

If you’ve used large workflow libraries before — or if you build reusable automations yourself — I’d love to hear how you approach it.

Feel free to drop a comment or reach out on
X (Twitter).

Thanks for reading.

Not Everything You See Is an AI

Prakash Pawar — Fri, 19 Dec 2025 05:14:10 +0000

How many “AI-powered” websites are just well-engineered scrapers

Over the last few years, “AI-powered” has quietly become the most overused label in tech.

If a product:

works on complex websites,
handles JavaScript-heavy pages,
or produces clean, structured output,

it’s very often described as AI.

But here’s a reality check that many engineers already know:

Most of these products are not powered by AI at all.

They are powered by code.
Good, old-fashioned, well-engineered code.

This article explains why so many products look like AI, how they actually work, and how the same behavior can be replicated without using any machine learning.

The Illusion of Intelligence

Let’s start with a simple experiment.

If you run:

curl https://music.youtube.com

You’ll get a mostly empty HTML shell.

No playlists.
No songs.
No meaningful content.

So when a website claims it can “read YouTube Music”, “understand Instagram pages”, or “extract content from any site”, the natural assumption is:

“There must be AI involved.”

In most cases, there isn’t.

Why Traditional Scraping Appears to Fail

Modern websites are fundamentally different from older server-rendered pages.

Most of them are:

Single Page Applications (React / Vue / Angular)
Hydrated entirely on the client
Loaded via background API calls
Rendered progressively as the user scrolls

Tools like curl or requests fail because they:

fetch only source HTML
do not execute JavaScript
do not trigger lazy loading

A real browser, however, does all of that automatically.

What’s Actually Happening Behind the Scenes

Many products branded as “AI website readers” follow a pipeline like this:

Incoming URL
→ Headless browser (Chromium)
→ Execute JavaScript
→ Wait for network to settle
→ Scroll the page
→ Capture rendered DOM
→ Remove UI noise (menus, scripts, ads)
→ Convert HTML into Markdown / text
→ Return response

Every step here is deterministic.

There is:

no model training
no prediction
no reasoning
no inference

Just a browser executing code exactly the way it was designed to.

A Common Pattern You’ll See in “AI” Products

You may have noticed products with names like:

“AI Web Reader”
“AI Content Extractor”
“AI Website Analyzer”

Let’s take a hypothetical example — “SmartReader AI”.

From the outside, it:

accepts a URL
works on complex websites
returns clean Markdown or JSON

Under the hood, it:

launches a headless browser
scrolls the page
extracts the DOM
applies deterministic cleanup rules

The AI part, if present at all, might only be used later—for summarization or formatting.

The core functionality works perfectly without AI.

Why This Feels Like AI to Users

This illusion comes from three factors:

1. JavaScript execution

Once JavaScript runs, all backend APIs have already returned data.
The browser simply assembles it into the DOM.

2. Content normalization

Navigation bars, ads, and UI chrome are removed, leaving only the “useful” content.

3. Clean output formats

Markdown and structured text feel intentional and intelligent.

But none of these require machine learning.

Rebuilding the Same System Using Only Scrapers

You can replicate the same behavior using standard tools.

Step 1: Render the page

Use a headless browser like Playwright or Puppeteer to load the site exactly like a real user.

This unlocks:

dynamic data
lazy-loaded sections
client-side API responses

Step 2: Scroll programmatically

Many pages load content only on scroll.

A simple scroll-and-wait loop is enough.

Step 3: Capture the DOM

Once rendering stabilizes, extract the final HTML.

At this point, everything visible to the user already exists in the DOM.

Step 4: Extract main content

Use deterministic tools such as:

Mozilla Readability
DOM heuristics
Tag-based filtering

This removes:

headers
sidebars
menus
scripts

Step 5: Convert formats

Transform the cleaned HTML into:

Markdown
JSON
plain text

The output looks “smart” because it’s curated—not because it’s intelligent.

Why AI Is Often Unnecessary at This Stage

Scraping and rendering are deterministic problems.

AI systems are probabilistic.

If the data:

already exists in the DOM
has a consistent structure
is visually rendered

then introducing AI usually adds:

cost
latency
operational complexity
uncertainty

For extraction tasks, engineering is usually the better tool.

Where AI Actually Makes Sense

AI becomes valuable after the data is extracted, not before.

Good use cases include:

summarizing long articles
clustering related content
semantic search
question answering across documents

In short:

AI helps you understand content — not fetch it.

The Engineering Reality

Many so-called “AI-powered” products are better described as:

Browser automation platforms with a clean UX.

That’s not a criticism.

It’s a reminder that:

not everything impressive is AI
fundamentals still matter
browsers are incredibly powerful execution engines

Final Thoughts

The next time you see a product that:

works on JavaScript-heavy websites
extracts clean content
feels magically intelligent

ask a simple question:

Is this AI — or just a browser running code really well?

Often, the answer is the latter.

And sometimes, the smartest systems are the ones that don’t pretend to be intelligent at all.

If you have any questions or want to discuss this further, feel free to leave a comment or
Tweet me.
Thanks for reading.

Building an Open-Source Market Signal Bot Using Airtel’s Free Perplexity Pro API Access

Prakash Pawar — Wed, 10 Dec 2025 07:12:19 +0000

Airtel’s 1-year Perplexity Pro offer includes full API access to the sonar-pro model. Instead of using it only for search, I used the free API credits to build an open-source real-time market signal bot that monitors price action, filters noise using LLM reasoning, and sends actionable alerts to Telegram.

The entire system runs on simple components but produces high-signal outputs suited for active traders.

🧱 System Architecture

1. Price Feed (Finnhub)

The bot polls Finnhub’s /quote endpoint for:

US stocks
Crypto pairs (e.g., BINANCE:BTCUSDT)

Only symbols with >|1.5%| intraday movement move forward.

2. LLM-Based Market Reasoning (Perplexity)

Filtered snapshots are sent to Perplexity’s sonar-pro via:

https://api.perplexity.ai/chat/completions

The prompt instructs the model to return structured JSON:

should_alert: true/false
concise reason
sentiment classification (bullish, bearish, unclear)

This replaces custom heuristics with dynamic context-aware filtering.

3. Alert Formatting

Signals are composed into a short Telegram-friendly message with sentiment emojis.

4. Telegram Push Delivery

Alerts are delivered instantly using Telegram Bot API.

🔄 Core Processing Loop

FETCH prices
→ FILTER >1.5% movers
→ ANALYZE using Perplexity (JSON output)
→ FORMAT alerts
→ SEND to Telegram

This creates a low-overhead 24/7 signal layer driven entirely by free API credits.

🧪 Code Overview (Repository Ready)

Your bot contains four main functions:

get_latest_price() → real-time quotes
analyze_trends_with_perplexity() → LLM signal classification
format_alert_message() → compact alert formatting
send_telegram_message() → asynchronous delivery

The main loop continuously aggregates movement, analyzes it, and pushes signals.

⚡ Why This Made Productive Use of Airtel’s Free Perplexity Pro

Zero-cost LLM inference for market reasoning
No need for ML models or rule-based systems
LLM filters out noise, preventing alert spam
Open-source and easy to deploy (just env vars + Python)
Works for both crypto and equities

Instead of just using Perplexity Pro for normal search, the API became the intelligence layer of a real-time signal engine.

Github: link

📦 What should we add next?

Volume spike detection
Multi-timeframe trend scoring
News + price combined signals
Discord & Slack integrations
Web dashboard for recent alerts

If you have any Query regarding this post let me know in comment or Tweet me. thank you for reading this.

How to Add Your Inquiry API Responses into Google Sheets (Automatic, No Duplicates)

Prakash Pawar — Tue, 09 Dec 2025 05:30:21 +0000

Have an API that collects customer inquiries, leads, or form submissions—and want those responses to show up automatically inside a Google Sheet?

This tutorial shows you exactly how to:

Fetch API responses into a sheet
Format data correctly (columns → values)
Prevent duplicate entries by id
Auto-refresh with a time trigger
Keep pagination metadata updated

Perfect for dashboards, lead management, or syncing external APIs with Google Workspace.

✅ Step 1 — Create a New Google Sheet

Open a new sheet and name it something like:

Inquiries

✅ Step 2 — Open Apps Script

Go to:

Extensions → Apps Script

Delete any existing code.

✅ Step 3 — Paste This Script (Fully Working)

This script fetches your API, inserts formatted rows, avoids duplicates, and updates pagination fields.

function fetchInquiries() {
  const url = "YOUR_API_URL_HERE";  // Example: https://www.example.com/api/v1/inquiry
  const response = UrlFetchApp.fetch(url);
  const json = JSON.parse(response.getContentText());
  const items = json.items;

  const ss = SpreadsheetApp.getActive();
  const sheet = ss.getSheetByName("Inquiries") || ss.insertSheet("Inquiries");

  // ---------- 1. Ensure headers exist ----------
  const headers = ["id", "name", "email", "phone", "whatsapp_number", "created_at"];

  if (sheet.getLastRow() === 0) {
    sheet.appendRow(headers);
  }

  // ---------- 2. Collect all existing IDs safely ----------
  let existingIds = new Set();

  const lastRow = sheet.getLastRow();
  if (lastRow > 1) {
    const idColumnValues = sheet.getRange(2, 1, lastRow - 1, 1).getValues();
    idColumnValues.forEach(row => {
      if (row[0]) existingIds.add(row[0].toString());
    });
  }

  // ---------- 3. Create rows only for NEW inquiries ----------
  const newRows = [];

  items.forEach(item => {
    if (!existingIds.has(item.id)) {
      newRows.push([
        item.id,
        item.name,
        item.email,
        item.phone,
        item.whatsapp_number,
        item.created_at
      ]);
    }
  });

  // ---------- 4. Append only new rows ----------
  if (newRows.length > 0) {
    sheet.getRange(sheet.getLastRow() + 1, 1, newRows.length, headers.length)
         .setValues(newRows);
  }

  // ---------- 5. Update pagination fields (DO NOT duplicate) ----------
  const summaryStart = sheet.getLastRow() + 2;

  sheet.getRange(summaryStart, 1).setValue("total");
  sheet.getRange(summaryStart, 2).setValue(json.total);

  sheet.getRange(summaryStart + 1, 1).setValue("page");
  sheet.getRange(summaryStart + 1, 2).setValue(json.page);

  sheet.getRange(summaryStart + 2, 1).setValue("page_size");
  sheet.getRange(summaryStart + 2, 2).setValue(json.page_size);

  sheet.getRange(summaryStart + 3, 1).setValue("total_pages");
  sheet.getRange(summaryStart + 3, 2).setValue(json.total_pages);
}

Replace:

YOUR_API_URL_HERE

with your actual API endpoint.

✅ Step 4 — Run the Function Manually Once

Click the Run ▶ button.

Google will ask for permissions → click Allow.

Your sheet will now populate with:

One row per inquiry
Proper column formatting
Pagination metadata below the table

✅ Step 5 — Enable Auto-Refresh (Optional but Recommended)

To auto-run every minute, hour, or day:

Open Apps Script
Go to: Triggers
Click Add Trigger
Select:

Function: fetchInquiries
Event Source: Time-driven
Interval: every 1 minute, 5 minutes, or 1 hour

Now your sheet updates automatically—even when it’s closed.

🎯 What This Solves

✔ Eliminates manual exporting and copying

✔ Works as a real-time API → Sheet integration

✔ Prevents duplicate entries (checks by `id`)

✔ Allows multiple team members to view data instantly

✔ Perfect for dashboards, sales teams, CRMs, automation systems

🔌 Bonus: Extend This Further

Once your data lands in Sheets, you can:

Send new inquiries to Slack, Telegram, or WhatsApp
Create charts & dashboards
Auto-update Notion, Airtable, AppScript APIs
Trigger email notifications when a new inquiry arrives

✨ Final Thoughts

Integrating an external API directly into Google Sheets is powerful — especially for startups collecting leads or form submissions. With just a few lines of Apps Script, you can build a lightweight CRM, a reporting dashboard, or even automate your entire inquiry pipeline.

If you have any Query regarding this post let me know in comment or Tweet me. thank you for reading this.

Building a Blockchain in 2026: From-Scratch Engineering vs. Modern SDKs

Prakash Pawar — Tue, 02 Dec 2025 11:35:02 +0000

The blockchain ecosystem has evolved dramatically since Bitcoin’s launch in 2009. In the early years, designing a blockchain meant implementing every component yourself: networking, consensus, block validation, mining, difficulty adjustment, peer discovery, and wallet logic. Today, developers have access to mature frameworks such as Cosmos SDK, Substrate, Polygon CDK, and the OP Stack. These frameworks abstract many of the lower-level components, allowing teams to focus on business logic instead of reinventing infrastructure.

This raises an important question for engineers: Is it still worth building a blockchain entirely from scratch, like Bitcoin, or is relying on an SDK the practical approach in 2026?
This blog breaks down the engineering reality.

1. What “from scratch” really means in blockchain engineering

Many people think building a blockchain from scratch means creating new block types and hashing them. In reality, a “true from-scratch blockchain” requires implementing several subsystems:

1.1 Network Layer

Responsible for:

Peer discovery
Message propagation
Gossip protocol
Maintaining network topology
Flood protection
Peer scoring
Bandwidth control

This layer alone can require months of engineering effort.

1.2 Consensus Algorithm

From scratch means you implement:

Proof-of-Work or Proof-of-Stake logic
Block proposal, voting, and finality
Fork choice rules
Difficulty or validator rotation
Security assumptions
Recovery logic

Bitcoin uses PoW with a simple longest-chain rule, while modern chains often use BFT-style consensus, which is significantly more complex.

1.3 Mempool & Transaction Lifecycle

You need to define:

Transaction format
Validation logic
Prioritization rules
Fee market design
Relay policies

This becomes even more challenging if you support smart contracts.

1.4 State Machine

Either a:

UTXO model (like Bitcoin)
Account model (like Ethereum)
Hybrid or custom model

State transition functions must be deterministic, secure, and efficient.

1.5 Cryptography

A custom chain requires decisions about:

Signature schemes
Hashing algorithms
Merkle tree construction
Key management
Wallet compatibility

Bitcoin uses ECDSA secp256k1 and SHA-256 double hashing. Many chains today use Ed25519, BLS signatures, or STARK/STARK-friendly hash functions.

1.6 Storage & Database Layers

You must implement or integrate:

Block storage
Indexing
State database
Pruning
Archival modes

Ethereum, for example, uses LevelDB/RocksDB with complex trie structures optimized over years.

1.7 Node Instrumentation

A production-grade blockchain node needs:

RPC server
WebSocket endpoints
Debug API
Logging and metrics
Prometheus integration
Monitoring and telemetry

Without these, external developers cannot build on your chain.

1.8 Wallet Infrastructure

You need to provide:

Keypair generation
Signing utilities
Address formats
Hardware wallet integrations
Client libraries

Building a blockchain from scratch means building all of this.

It is fundamentally a distributed systems engineering project, not a Crypto Twitter idea.

2. Why SDKs exist (and why most chains use them)

Modern blockchain SDKs exist to save developers from rebuilding components that have already been solved millions of times.

A framework like Cosmos SDK or Substrate already includes:

Peer-to-peer networking
Production-tested consensus
RPC and gRPC servers
Modular governance
IBC or cross-chain communication
Staking and slashing
WASM or EVM execution environments
State management and storage layers

Using an SDK provides:

Faster development times
Safer consensus implementation
Easier upgrades
Existing toolchains
Wallet compatibility
Easier validator bootstrapping

Today, more than 90% of new blockchains are built on one of these frameworks. Entire L1 ecosystems such as Osmosis, Sei, Evmos, Injective, and dYdX are built using the Cosmos SDK.

The reason is simple: developers want to build chains, not reinvent distributed networking from scratch.

3. The dependency misconception

A common concern is:
“If I use Cosmos SDK or Substrate, am I dependent on their codebase?”

The truth is more nuanced. SDKs are open-source frameworks, not proprietary platforms.
You can:

Fork the code
Modify modules
Replace consensus
Build custom state machines
Write new modules in Go or Rust

This is no different from building a web server on top of Linux instead of writing your own operating system.
You are not dependent; you are leveraging battle-tested engineering.

4. When it makes sense to build from scratch

Despite the complexity, there are legitimate reasons to build your own blockchain from zero.

4.1 You are designing new consensus

For example:

A novel PoW algorithm
ASIC-resistant mining
New BFT variants
DAG-based systems
Order-fairness consensus like Pulp Systems or Wimble

4.2 You need a fully custom architecture

Examples:

Bitcoin’s UTXO model
Nano’s block-lattice
Kadena’s braided chains
Solana’s proof-of-history timestamping
Chia’s proof-of-space

4.3 You want full control of networking

Especially relevant for:

IoT blockchains
On-premises or air-gapped systems
Ultra-low-latency trading chains

4.4 You are building a research chain or academic project

Building from scratch is the best way to understand blockchain internals at a low level.

4.5 You want to avoid existing technical debt

Some teams believe building a new stack is better than inheriting old architectural compromises.

But these are specific cases, not general needs.

5. When using an SDK or framework is the right decision

For 99% of modern blockchain projects, the goal is:

Launching a chain that supports smart contracts
Providing cheap blockspace
Enabling developers to build apps
Creating custom tokenomics or modules
Interoperability with other ecosystems
Setting up validators and RPC infrastructure

If this describes your use case, writing your own P2P networking library or consensus engine provides no additional value.

The Priority in 2026 is:

Fewer teams care about reinventing block propagation.
They care about:

throughput
tooling
developer experience
stability
interoperability
predictable upgrades

And these are areas where SDKs excel.

6. The engineering cost of “from scratch”

If your goal is a production-grade chain, here are realistic numbers:

Team Requirement

3–6 core protocol engineers
2 distributed systems engineers
1–2 cryptographers
3 devops + SRE for network ops
2 QA engineers
1 project manager
1 documentation engineer

Timeframe

Minimum 18–36 months for mainnet
Additional 12 months for tooling, wallets, SDKs

Cost Breakdown

Engineering salaries (core protocol): $2–5M
Infrastructure (testnet & mainnet): $200k+
Security audits: $300k–$1M
Maintenance: ongoing costs

This is why frameworks dominate.
Not because developers are “lazy”, but because rebuilding the entire Bitcoin stack is prohibitively expensive unless you are innovating at the protocol level.

7. The hybrid approach: what most serious chains do today

Many successful blockchains take an in-between approach:

Use an SDK for networking and consensus
Replace or extend the execution environment
Write custom modules
Write a custom mempool or fee mechanism
Modify state machine logic
Add their own governance logic

Examples:

dYdX replaced CosmWasm with a custom Rust engine
Celestia uses Cosmos SDK but created a new DA layer
Aptos built a new VM but reused classical networking
Arbitrum Orbit uses OP Stack + custom proving logic
Sei modified mempool design for parallel execution

You avoid rewriting everything while still achieving deep differentiation.

8. Conclusion

Building a blockchain from scratch is technically possible and still relevant for certain categories of projects, particularly when designing novel consensus or architectures. However, in 2026, most blockchains do not need to reimplement Bitcoin’s network or Ethereum’s fundamental components. The ecosystem has matured to the point where leveraging frameworks is not a shortcut but an industry standard.

If your goal is to launch a secure, scalable chain with modern tooling, smart contract support, and interoperability, using a mature SDK is the effective path.
If your goal is fundamental protocol innovation or academic exploration, building from scratch remains a valuable pursuit.

The key question is not whether you can build a blockchain from scratch, but whether it is the best use of your engineering time in an ecosystem where mature, open-source frameworks already exist.

n8n: A Great Starting Point, But Not Where Real Engineering Lives

Prakash Pawar — Fri, 21 Nov 2025 08:52:15 +0000

Low-code platforms like n8n have gained popularity among beginners, freelancers, and non-technical users exploring automation and AI workflows. They promise fast development, visual orchestration, and “code-optional” integration.

For many, n8n is their first exposure to automation and their first glimpse into how software engineers think. And that is its true strength:
n8n is an excellent learning and experimentation tool, not an engineering platform.

Just like WordPress introduces people to websites, n8n introduces people to automation. It is ideal for exploring concepts, but it is not where scalable, maintainable, production-grade systems are built.

n8n Is a Great Entry Point—Especially for Beginners

If you're new to software automation—or even if you're 10 years old and curious about how software workflows behave—n8n is a great place to start. It visually introduces concepts like:

Triggers and event-based workflows
Connecting APIs
Basic data transformation
Conditional logic
Webhooks and automation flows

It helps beginners imagine how software engineers think: how inputs move, how decision-making works, and how systems are connected.

But that’s where it ends.

Visual automation helps you understand automation, not engineer automation.

Why n8n Is Not Designed for Professional Engineering

1. Limited Logic and No Real Architecture

Software engineering requires much more than connecting nodes. It includes:

Engineering Requirement	n8n	Software Engineering (Code)
Version control (Git)	Very limited	Fully supported
Modular architecture	Limited	Flexible and scalable
Testing (unit/integration)	Not supported	Standard practice
Dependency control	Basic	Fully customizable
Code reuse and maintainability	Minimal	Core principle
CI/CD, deployments	Workarounds	Full tooling

n8n simplifies automation, but it also removes the practices that make it scalable, testable, and maintainable.

2. Visual Workflows Don’t Scale

Simple workflows remain clear in n8n. But when your logic gets complex—like AI agent behaviors, decision trees, recursion, or multi-layered branching—it becomes:

Hard to debug
Hard to trace failures
Hard to maintain over time
Near impossible to version, test, or document properly

As soon as real engineering maturity is required, visual drag-and-drop becomes a limitation—not a convenience.

3. Data Handling and Performance Constraints

As multiple users on Reddit observed, n8n struggles with:

File processing (PDFs, images, binary data)
Bulk or batch operations
Long-running workflows
Reliable retries and task queueing
Parallel execution and concurrency

These issues arise because n8n is not built to handle compute-heavy or data-intensive work—it is built to orchestrate.

Meanwhile, Python (or JavaScript, Go, Rust) easily handles advanced tasks using:

Pandas, Polars, or NumPy for data processing
Celery, RQ, or RabbitMQ for distributed tasks
FastAPI for scalable webhooks and API orchestration
Docker-based deployment for consistency and scalability

Engineering requires control. n8n gives you convenience, not control.

n8n Is Not Software Engineering — It Is Software Exploration

Best Use Cases for n8n	When You Move to Python or Code
Prototypes, demos	Production-grade automation
Simple integrations	Scalable orchestration systems
Learning automation concepts	Handling logic-heavy workflows
Non-technical team workflows	AI agents, RAG, LLM pipelines
Local experimentation	Enterprise architecture
Connecting simple APIs	Microservices, APIs, event systems

n8n teaches what automation looks like
Python (or code) teaches how automation is built.

The Hybrid Approach: Where n8n Still Makes Sense

Many professionals use n8n only as an orchestrator:

Use n8n to capture webhooks, trigger emails, or call APIs
Offload critical tasks (AI logic, file processing, workflows) to Python, FastAPI, or Lambda
Expose your own microservices using HTTP endpoints
Keep integration logic visual, but keep core logic in code

n8n is best positioned as a front-facing interface, not a back-end automation system.

Conclusion

n8n is an excellent sandbox for beginners, visual learners, and non-engineers. It can help people understand data flow, automation logic, and basic integration. It can even help businesses prototype internal workflows quickly.

But once automations become critical, scaled, customizable, or logic-heavy, real engineering begins—and visual automation tools are no longer sufficient.

n8n helps you imagine what automation can do.
Engineering helps you build what automation should do.

If you have any Query regarding this post let me know in comment or Tweet me. thank you for reading this.

How Cryptocurrency Prices Work Across Different Blockchains

Prakash Pawar — Sun, 16 Nov 2025 05:47:43 +0000

Crypto often feels confusing when you see Bitcoin (BTC) listed at slightly different prices on Binance, Coinbase, OKX, KuCoin, and even other blockchains like Ethereum or BNB Chain.
If Bitcoin lives on its own blockchain, how can it exist and trade on so many different platforms at once?

This article breaks down exactly how crypto pricing works, why BTC always stays nearly the same price everywhere, and the big difference between:

Real BTC on the Bitcoin blockchain, and
Wrapped or pegged BTC on other chains.

🟠 1. Bitcoin Lives Only on the Bitcoin Blockchain

Bitcoin is a native asset of the Bitcoin blockchain.
It does not live on Ethereum, Solana, BNB Chain, Polygon, or anywhere else.

When you see BTC trading on:

Binance
Coinbase
Bybit
OKX
KuCoin
Gate
MEXC

…they are all trading real native Bitcoin.

Even though the trades happen off-chain inside each exchange, the underlying asset is still real BTC, withdrawn and deposited using:

Bitcoin blockchain → 10-minute blocks → real UTXO transactions

🟠 2. So Why Are There Different Prices on Exchanges?

If you look at a price aggregator (like CoinMarketCap, CoinGecko or CoinPaprika), you’ll see something like this:

Binance BTC/USDT → $95,946
Coinbase BTC/USD → $95,927
Bybit BTC/USDT → $95,942
KuCoin BTC/USDT → $95,924
Upbit BTC/KRW → $99,503

These differences happen because each exchange has:

its own users
its own orderbook
its own liquidity
its own buyer/seller demand

The Bitcoin blockchain does not set the price.

Exchanges do.
The price comes from market trading, not from miners or nodes.

🟠 3. Then Why Are Prices Almost the Same Everywhere? (Arbitrage)

Because of arbitrage traders.

If BTC is cheaper on Coinbase than on Binance:

traders buy BTC on Coinbase
sell the same BTC on Binance
pocket the difference

They do this instantly using bots.

As a result:

✔ price differences shrink
✔ all exchanges converge
✔ BTC stays globally uniform

Arbitrage is the main reason why Bitcoin doesn’t have 10 different prices around the world.

🟠 4. What About BTC on Other Blockchains? (Wrapped BTC)

Bitcoin cannot move directly to chains like:

Ethereum
BNB Chain
Solana
Polygon
Avalanche

So these chains use wrapped or pegged versions.

Examples:

Blockchain	Bitcoin Token	Type
Ethereum	WBTC	Custodial wrapped BTC
BNB Chain	BTCB	Binance-backed wrapped BTC
Solana	SolBTC	Wrapped on Solana
Polygon	WBTC	Bridged version

These tokens are not real native BTC.

They are tokenized representations.

For example:

1 BTC locked by BitGo → 1 WBTC minted on Ethereum

These always try to stay 1:1 pegged to real Bitcoin.

🟠 5. Why Does WBTC Have the Same Price as Real BTC?

Again, because of arbitrage.

If:

Real BTC = $96,000
But WBTC = $95,500

Traders will:

Buy WBTC cheap
Redeem it for 1 real BTC
Sell real BTC at a higher price

This pushes WBTC → back up to real BTC price.

Same happens in reverse.

Because of this mechanism:

wrapped BTC always follows the real BTC price.

🟠 6. Key Difference: CEX BTC vs Wrapped BTC

Platform	BTC Type	Explanation
Binance Spot	Real BTC	Stored in BTC-chain wallets
Coinbase Spot	Real BTC	Withdraws on Bitcoin blockchain
Bybit / KuCoin / OKX	Real BTC	All native BTC
Uniswap (Ethereum DEX)	WBTC only	Cannot trade native BTC
PancakeSwap (BNB Chain)	BTCB only	Wrapped version

CEX = real BTC
EVM/DeFi = wrapped BTC

🟠 7. Why Centralized Exchanges Don’t Use Wrapped BTC

Because they don’t need to.

A centralized exchange controls:

its own BTC wallets
an internal ledger
user account balances

They simply record buying and selling in their database.
Only withdrawals go to the real blockchain.

Wrapped BTC is only needed for blockchains that cannot hold native BTC.

🟠 8. Putting It All Together

✔ Bitcoin’s price does not come from the blockchain

It comes from global trading activity.

✔ Price differences across exchanges happen

But arbitrage trading makes them almost equal.

✔ On exchanges → BTC is real

When you withdraw, it goes to the real Bitcoin network.

✔ On other blockchains → BTC is wrapped

These are synthetic versions that follow Bitcoin’s price using a 1:1 peg.

✔ Everything converges

Because traders keep the markets aligned.

🟠 Conclusion

The crypto market is a network of:

exchanges
blockchain tokens
wrapped assets
arbitrage bots
liquidity pools

All of these combined create a global price for Bitcoin that remains almost identical everywhere, even though:

different exchanges trade independently
different blockchains use wrapped versions
Bitcoin itself never leaves its own chain

This system is one of the biggest reasons crypto functions efficiently across hundreds of platforms while remaining decentralized.

If you have any Query regarding this post let me know in comment or Tweet me. thank you for reading this.

How I Built a Free AnyDesk Alternative Using Sunshine, Moonlight & Tailscale

Prakash Pawar — Wed, 22 Oct 2025 05:27:45 +0000

As a Engineer (and gamer), I’ve always wanted a fast, free, and secure way to access my Windows PC remotely — without paying for tools like AnyDesk or TeamViewer.

After trying a bunch of solutions, I finally found the perfect stack:
👉 Sunshine + Moonlight + Tailscale

This trio gives me a hardware-accelerated, self-hosted, and end-to-end encrypted setup that’s better than most commercial options.
And yes — it’s completely free.

💡 Why This Setup?

Here’s why I switched:

⚡ Full GPU acceleration — buttery-smooth 60 FPS streaming.
🔐 Zero trust networking — no cloud dependency or logins.
💰 100% free & open source.
🌍 Remote anywhere access (thanks to Tailscale).
🧠 Control over my data and latency.

🖥️ Step 1: Install Sunshine on Your Windows PC

Sunshine is the open-source host app that handles all the encoding and streaming from your Windows machine.

Setup:

Download from Sunshine’s GitHub releases.
Install it normally (run as Administrator).
Once it’s running, open your browser and go to:

   https://localhost:47990

Set a username and password.
Under Applications, add “Desktop” (or any app you want to access).
Allow Sunshine through Windows Firewall when prompted.
Make sure GPU acceleration is enabled (NVENC / AMD VCE / Intel QuickSync).

That’s it — Sunshine is now your personal “streaming server”.

🍎 Step 2: Install Moonlight on Your MacBook Air

Moonlight is the open-source client that connects to Sunshine.
Think of it as your portable window into your PC.

Setup:

Download from Moonlight’s official site (or from the Mac App Store).
Launch Moonlight — it should auto-detect your PC.
If it doesn’t, manually add your PC’s IP address.
Enter the pairing PIN shown on your Mac into Sunshine’s web dashboard.

After pairing, you’ll see your PC and the list of apps (or the full desktop).
Click “Desktop”, and boom — you’re controlling your PC from your Mac with near-zero latency.

🌐 Step 3: Go Truly Remote with Tailscale

This is where it gets powerful.
Tailscale gives you a private, encrypted mesh VPN — so you can connect to your PC from anywhere in the world.

Setup:

Install Tailscale on both devices:

Windows
macOS
1. Log in using the same account (Google, GitHub, etc.).
2. Both devices will now appear in your Tailscale network.
3. Copy your Windows PC’s Tailscale IP (100.x.x.x).
4. Add that IP to Moonlight — and you’re done.

Now you can connect to your PC even when it’s on a different Wi-Fi or behind NAT. No port forwarding. No headache.

⚙️ Step 4: Optimize for Best Performance

Inside Moonlight settings:

Resolution: 1080p / 60 FPS for most networks
Bitrate: 20–40 Mbps (depending on Wi-Fi)
Audio: Enable “Stereo” for minimal lag
Input: Enable “Low Latency Mode”

If you’re gaming or coding remotely, it feels almost native.

🔧 Optional: Auto-start Sunshine on Boot

If you want your PC to be accessible anytime:

Open Services in Windows.
Find “Sunshine Service”.
Set Startup Type → Automatic.

Now it’ll always be ready to stream whenever your PC is on.

✅ Final Stack Overview

Tool	Purpose	Notes
Sunshine	Streams your Windows desktop	Uses GPU encoding
Moonlight	Client on macOS	Smooth, low-latency viewer
Tailscale	Secure remote tunnel	Access from anywhere

🧠 My Experience

I’ve been using this setup daily — for development, remote debugging, and even gaming sessions.
It’s been stable, fast, and secure, with no external servers or fees.

Once you try this combo, you’ll never look back at AnyDesk.

🚀 TL;DR

If you want a free, open-source, and self-hosted AnyDesk alternative:

Install Sunshine on Windows.
Install Moonlight on your Mac (or any device).
Add Tailscale to access it from anywhere.

That’s it — your private remote desktop system is ready.

If you have any Query regarding this post let me know in comment or Tweet me. thank you for reading this.

How to Start with OpenAI APIs in 2024⚡

Prakash Pawar — Sun, 17 Mar 2024 03:40:00 +0000

In today's rapidly evolving technological landscape, leveraging artificial intelligence (AI) capabilities has become essential for businesses and developers alike. OpenAI, a leading organization in AI research and development, offers a suite of powerful APIs that enable developers to integrate cutting-edge AI models into their applications with ease. In this article, we will explore how to get started with OpenAI APIs using a simple Python code example.

Setting Up OpenAI API Key

The first step in using OpenAI APIs is to obtain an API key, which grants access to their services. This key serves as a secure authentication mechanism, allowing developers to interact with OpenAI's models securely. To obtain an API key, you need to sign up for an account on the OpenAI platform and generate your unique key.

Once you have your API key, you can set it in your Python environment using the os.environ module, as demonstrated in the code snippet below:

import os

os.environ["OPENAI_API_KEY"] = "sk-xxx"  # Replace "sk-xxx" with your actual API key

Using OpenAI API with Python
Now that we have set up our API key, let's dive into using OpenAI's language model to generate text based on a prompt.

In our project, we've installed two essential packages: "langchain-openai" and "ipykernel."

"langchain-openai" facilitates seamless interaction with OpenAI APIs, enabling us to leverage powerful language models effortlessly for tasks like text generation and language understanding.

"Ipykernel" enhances our Python shell experience, offering features like improved debugging and syntax highlighting to streamline our coding process.

These packages are instrumental in boosting our productivity and enabling us to leverage advanced AI capabilities effectively in our projects.

The OpenAI API includes a crucial hyperparameter called "temperature," which plays a pivotal role in shaping how the model generates output text. This parameter influences the calculation of token probabilities within the large language model. Specifically, the temperature value spans from 0 to 2, where lower values signify a higher degree of determinism, and higher values signify an increase in randomness within the generated text.

from langchain_openai import OpenAI

llm = OpenAI(temperature=0.6)  # Instantiate the OpenAI object with desired parameters

Generating Text with OpenAI API
With our OpenAI object initialized, we can now generate text by providing a prompt to the model. The invoke method takes a string as input, representing the prompt for the AI model. It then returns the AI-generated text based on the provided prompt.

text = "what is the human?"  # Prompt for the AI model
generated_text = llm.invoke(text)  # Generate text based on the prompt
print(generated_text)

In this example, we provided the prompt "what is the human?" to the OpenAI language model. The model then generated a response, which we printed to the console. The generated text showcases the language model's ability to understand and respond to natural language prompts.

Conclusion
OpenAI APIs offer a powerful suite of tools for integrating state-of-the-art AI capabilities into applications. By following the simple steps outlined in this article, developers can quickly get started with OpenAI APIs and harness the power of AI to enhance their projects. Whether you're building chatbots, content generators, or language understanding systems, OpenAI's APIs provide the foundation for creating intelligent and innovative solutions. Start exploring the possibilities today!

The ultimate crypto terminology list

Prakash Pawar — Fri, 11 Aug 2023 05:54:07 +0000

🚀 Calling all members of the Dev.to Crypto family! Let's create the ultimate crypto terminology list for everyone to grasp the world of crypto better. I'll kick it off with a few, If you spot any missing terms, drop them in the replies! Let's spread the KNWL! 🧵#CryptoTerminology

🌐 Cryptocurrency: Digital coins secured by cryptography for transactions and new issuance. Examples: Bitcoin, Ethereum, Ripple. #Cryptocurrency #DigitalCurrency

⛓️ Blockchain: Decentralized digital ledger recording transactions in linked blocks. Immutable and transparent. #Blockchain #Decentralization

📈 Bitcoin: Pioneering cryptocurrency, digital cash, and store of value. Works on proof-of-work mechanism. #Bitcoin #DigitalGold

💱 Altcoin: Any cryptocurrency other than Bitcoin, like Ethereum, Litecoin, and Ripple. #Altcoin #Diversify

🛍️ Wallet: Software/hardware to store, send, and receive crypto. Hot (online) or cold (offline) for security. #CryptoWallet #Security

🔑 Private Key: Secret cryptographic key to access a wallet. Must be kept secure. #PrivateKey #Cryptosecurity

🗝️ Public Key: Derived from private key, used for receiving transactions. Shareable publicly. #PublicKey #Cryptography

🏢 Address: Alphanumeric string from public key to receive crypto in a wallet. #Address #CryptoTransaction

⛏️ Mining: Process validating transactions and adding to blockchain. Miners use computers to solve complex puzzles. #Mining #Blockchain

🔗 Proof of Work (PoW): Consensus mechanism (e.g., Bitcoin) where miners solve puzzles to add blocks. #PoW #Mining

💼 Proof of Stake (PoS): Consensus mechanism based on validators' stake. Energy-efficient alternative to PoW. #PoS #Blockchain

🍴 Fork: Blockchain split leading to potential new cryptocurrency. Forks can be soft or hard. #Fork #Blockchain

💰 Token: Unit of value on a blockchain representing assets or access to services. #Token #Blockchain

🤝 Smart Contract: Self-executing contracts with code-based terms. Auto-executes when conditions met. #SmartContract #Automation

💡 DeFi: Decentralized Finance - blockchain-based financial services aiming to decentralize traditional systems. #DeFi #Blockchain

💸 ICO: Initial Coin Offering - fundraising by selling new crypto in exchange for established ones. #ICO #Fundraising

💼 Market Cap: Total value of a cryptocurrency calculated by its price multiplied by circulating supply. #MarketCap #Valuation

🔄 Exchange: Platform to buy, sell, and trade cryptocurrencies. Vital for the crypto market. #CryptoExchange #Trading

💪 Pool: In crypto mining, a pool combines computational power to improve block mining odds. Rewards are shared based on contributions. #MiningPool #Collaboration

📜 Protocol: In crypto, a protocol sets rules for blockchain interactions. It guides consensus, transactions, and communication. #BlockchainProtocol #Rules

🏭 Farm: A crypto "farm" is a setup of mining rigs in energy-efficient spaces. These rigs mine cryptocurrencies effectively. #CryptoFarm #MiningEfficiency

✈️ Airdrop: A crypto airdrop is a token distribution to holders or specific criteria meeters. It's a way to promote, reward, or create awareness. #CryptoAirdrop #TokenDistribution

💱 Pair: In crypto trading, a "pair" combines two cryptocurrencies for exchange. BTC/ETH represents trading Bitcoin for Ethereum. #CryptoPair #TradingCombo

🏦 Token: A "token" is a digital asset representing ownership, utility, or access rights on a blockchain. It can range from assets to services. #CryptoToken #DigitalOwnership

🔄 DEX: A "DEX" is a decentralized exchange where users trade directly using smart contracts, enhancing control and security. #DecentralizedExchange #DirectTrading

⏳ Locked-in Period: The "locked-in period" restricts actions like fund transfers for a specified time. Promotes stability and engagement. #LockedInPeriod #Cryptostability

💱 Stablecoin: A "stablecoin" maintains stable value, often pegged to fiat like USD. Useful for trading and as a value store during crypto volatility. #Stablecoin #Stability

💼 ICO: An "ICO" is a crypto startup's fundraising method. New tokens sold to investors for established cryptos or fiat. Fuels project development. #ICO #Fundraising

🏛️ DAO: A "DAO" is a blockchain-based organization operating via code-defined rules. Token holders participate in governance and decisions. #DAO #DecentralizedOrg

📈 Market Order: Instantly buy/sell a cryptocurrency at current market price. Quick execution, suitable for immediate transactions. #MarketOrder #InstantExecution

💱 Limit Order: Set a specific price to buy/sell crypto. Executes when market reaches set price. Control over entry/exit points. #LimitOrder #PriceControl

🕯️ Candlestick Chart: Graphic representation of price movement. Shows open, close, high, low prices in chosen time frame. Visual trend analysis. #CandlestickChart #PricePatterns

🐂 Bull Market: Rising prices, positive sentiment. Opportunity for gains. Positive investor outlook. #BullMarket #PriceRise

🐻 Bear Market: Falling prices, negative sentiment. Caution, potential losses. Pessimistic investor outlook. #BearMarket #PriceDecline

💎 HODL: Hold onto crypto despite market fluctuations. Meme-turned-strategy for long-term gains. #HODL #LongTermHold

💲 Fiat Currency: Traditional government-issued currency. USD, EUR, JPY, not backed by commodities like gold. #FiatCurrency #TraditionalMoney

📜 Whitepaper: Document detailing crypto project's tech, goals. Informs potential investors. Insights into project's vision. #Whitepaper #ProjectDetails

🔒 Private Sale: Early investors buy tokens directly from project team before public sale. Exclusive opportunity. #PrivateSale #EarlyInvestment

🛍️ Public Sale: General public can buy tokens. Fixed price, limited allocation. Project funding from broader community. #PublicSale #CommunityParticipation

🤖 Tokenomics: Economic model of crypto project. Supply, distribution, incentives for tokens. Influences value and utility. #Tokenomics #EconomicModel

🔍 Proof of Concept (PoC): Demonstration to showcase tech's feasibility. Potential for project's success. #ProofOfConcept #TechDemo

⛽ Gas Fee: Transaction fee for processing on blockchain. Incentivizes miners/validators. Faster transactions with higher fees. #GasFee #TransactionCost

📑 Smart Contract Platform: Blockchain network supporting smart contracts. DApps built on top. Ethereum, Binance Smart Chain. #SmartContracts #BlockchainApps

🌐 Node: Computer maintaining blockchain network. Validates transactions, enforces rules. Essential for network's integrity. #Node #BlockchainParticipant

🍴 Fork: Blockchain split resulting in two chains. Planned (upgrades) or contentious (disagreements). Affects ecosystem. #Fork #BlockchainSplit

⛏️ Mining Reward: Miners rewarded for adding new block to blockchain. Combines newly minted coins, transaction fees. Incentive for network security. #MiningReward #BlockValidation

📣 Airdrop: Distribution of tokens to holders. Promotes project, rewards community. Encourages engagement. #Airdrop #TokenDistribution

📊 Market Cap: Cryptocurrency value calculated by price x total circulating coins/tokens. Reflects project's size in market. #MarketCap #CryptoValue

🌐 Decentralization: Power, control distributed across network. No single authority. Enhances security, autonomy. #Decentralization #DistributedControl

🔄 Token Swap: Exchange tokens from one blockchain for another during migration or upgrade. Ensures compatibility and continuity. #TokenSwap #BlockchainUpgrade

💼 Wallet Address: Unique string from public key. Receives crypto transactions. Key to secure transactions. #WalletAddress #CryptoReception

❄️ Cold Wallet: Offline crypto storage. Shielded from web threats. Extra layer of security. #ColdWallet #OfflineStorage

🔥 Hot Wallet: Online crypto wallet. Frequent transactions, convenience. More vulnerable to hacking. #HotWallet #OnlineStorage

🐋 Whale: Holds significant crypto amount. Influences market due to large holdings. Impact on prices. #CryptoWhale #MarketInfluence

📈 Altcoin Season: Period of altcoin price surge vs. Bitcoin. Diverse investment opportunities. Watch for trends. #AltcoinSeason #PriceSurge

📈🚀 All-Time High (ATH): Cryptocurrency's peak price since inception. Milestone for investors. Historical reference. #AllTimeHigh #PeakPrice

🔥 Token Burn: Intentional destruction of tokens to decrease supply. Potential value increase. Controlled scarcity. #TokenBurn #SupplyReduction

⚒️ Mining Reward: Incentive for miners adding blocks. New coins + transaction fees. Sustains network security. #MiningReward #IncentiveSystem

📊 Mempool: Queue of unconfirmed transactions. Awaiting block inclusion. Transaction processing delay. #Mempool #TransactionQueue

😰 FOMO: Fear of Missing Out. Anxiety about missing crypto opportunities. Impact on trading decisions. #FOMO #OpportunityFear

😟 FUD: Fear, Uncertainty, Doubt. Negative rumors, info to manipulate markets. Psychological impact on investors. #FUD #MarketManipulation

🏗️ BUIDL: Focus on building, not just speculation. Encourages product development. Long-term value creation. #BUIDL #BuildNotSpeculate

💰 Reserve Currency: Widely accepted currency held in reserves. US Dollar, Euro examples. Global trade influence. #ReserveCurrency #GlobalTrade

⛽ Gas Limit: Max computational work in Ethereum transaction. Resource control and efficiency. Prevents overuse. #GasLimit #ResourceControl

💹 Gas Price: Payment per computational unit. Transaction execution control. Faster processing at higher price. #GasPrice #TransactionFee

↔️ Cross-Chain: Interactions between different blockchains. Asset and data transfer. Enhanced functionality. #CrossChain #BlockchainInteraction

⚪ White Hat Hacker: Ethical hacker. Fixes software vulnerabilities. Cybersecurity improvement. #WhiteHatHacker #EthicalHacking

⚫ Black Hat Hacker: Malicious hacker. Unauthorized access, harm, data theft. Security threat. #BlackHatHacker #MaliciousHacking

❄️ Cold Storage: Offline crypto protection. Prevents online vulnerabilities. Safety for holdings. #ColdStorage #OfflineSecurity

🌐 Web3: The internet's evolution with DApps and blockchain. Empowers P2P interactions and data ownership. A new digital era. #Web3 #DecentralizedInternet

📲 DApp: Runs on decentralized networks, often using smart contracts. Facilitates interactions, transparency, and security. #DApp #DecentralizedApps

🔗 Distributed Ledger: Database across multiple nodes, ensures transparency and security. Crucial for blockchain systems. #DistributedLedger #DataSecurity

🔒 Immutable Ledger: Blockchain's core feature. Data once in, can't be altered or deleted. Guarantees data integrity. #ImmutableLedger #DataIntegrity

🗝️ Multi-Signature Wallet: Requires multiple keys for transactions. Adds layers of security, preventing single-point control. #MultiSigWallet #EnhancedSecurity

⚙️ Cryptojacking: Unauthorized use of computing power for mining. Hidden threat, drains resources. Stay vigilant against it. #Cryptojacking #ResourceTheft

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💧 Liquidity Pool: Users' contributed funds in DEX. Enables trading without traditional intermediaries. A self-sustaining ecosystem. #LiquidityPool #DEXTrading

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Quick start with Postgres ⚡

Prakash Pawar — Mon, 31 Jan 2022 03:16:17 +0000

0. Introduction

RDMS are with us for the long time & provides as an organized way to store and access information. In relational databases realm, PostgreSQL and MySQL has many similarities.
I am not going to bore you with the specific differences between these two, but if you are curious to read about this, I am mentioning 2 articles below here, that clarifies both pro and con about postgres.
1. Dzone's article
2. Uber's article

1. Installation

I will discuss moslty about installation in linux, but I will also mention links for windows and mac installation.

Installation steps: 1. update the current system:
sudo apt update
2. Install the postgresql & contrib package for additions tools.
sudo apt install postgresql postgres-contrib

great !! installtation is done, now you have postgres in your system.

2. Create

PostgresSQL comes with by default user and db named as postgres. so let's create a user of your choice first.
sudo -u postgres createuser --login --pwprompt test_user
It will show a prompt for password like below:
Enter password for new role: Enter it again:

Lets create a database now:
sudo -u postgres createdb --owner=test_user test_db

now this new "test_db" belongs to the "test_user" and it will be only accessed by test_user.
Now let's restart the postgres service:
sudo service postgresql restart

Now if you're not currently using user profile in which you want to use postgres, then either switch to the that user profile example: "postgres" or "test_user" or use psql as that user without switching.

option 1: switching to different user profile: sudo -i -u postgres
then
psql

option 2: or just use that user to open psql prompt:
sudo -u postgres psql

Great going !! now lets create our first DB.
sudo create -u postgres createdb test_db
or if you want to create db for another user, you can also do it.
sudo -u postgres createdb --owner=test_user test_db2
So far we have created a "user" & a "database" in postgreSQL.

Now understand how to create a table.
CREATE TABLE table_name ( column_name1 type (length of field) constraints, column_name2 type (length of field), column_name3 type (length of field) );

how to read above code ? here :
CREATE TABLE table_name (); is the standard way to initialize a new table. inside that we have columns.

1. column_name : name of the column (ex: name, date, username, password, etc.)
2. type: type of column (ex: Interger, varchar,boolean, etc.) with field length (ex: varchar(250) ).
3. constraints: conditions for the column (ex: NOT NULL, check (col_name in ("4 wheeler", "3 wheeler", "16 wheeler")), UNIQUE, etc.)

Let's create a table now:
CREATE TABLE flights ( flight_id serial PRIMARY KEY, type varchar (50) NOT NULL, size varchar (25) NOT NULL, location varchar(25) check (location in ('delhi', 'jaipur', 'kolkata', 'gujarat', 'chennai', 'bengalore', 'chandhigarh', 'meghalaya')), flight_date date, flight_time timestamp );
Above command will create a table which you can check by using "\d".

2. Insert

Now we have created user, database & table in postgreSQL, let's insert a entry.
INSERT INTO flights(type, size, location, flight_date, flight_time) VALUES('one-way', 'charter', 'delhi', '01-02-2022', '2015-08-07 05:00:01');
to check if the entered data is saved or not write :
SELECT * FROM flights;
And it will show the "flights" table with data in your terminal.

Awesome 🎊!! we have just learnt basics of postgres and also created & Inserted data in our postgres database. to learn further about more postgres topics in deep go to https://www.postgresqltutorial.com/ .

Thanks for reading this, Let me know if find any improvisation in this article, I am Prakash Pawar and you can follow me on twitter & Instagram . Thank you.