2026-01-26 14:10:00
Most developers focus on writing clean code, improving performance, and shipping features faster—and that’s exactly how it should be. But one important aspect often gets ignored: SEO.
SEO isn’t just a marketing thing. Even a technically perfect website can struggle if search engines don’t understand it properly. Learning a few SEO fundamentals can make your work more impactful and your projects more discoverable.
SEO Is Not Just Keywords
When people hear SEO, they usually think of keywords and blog posts. In reality, a big part of SEO is technical—and that’s where developers already have an advantage.
Things like:
Page speed
Mobile responsiveness
Clean URLs
Proper HTML structure
Accessibility
All of these directly affect search visibility.
How Developers Influence SEO (Without Extra Work)
Here are a few simple areas where developers already help SEO without realizing it:
Using proper heading hierarchy (h1, h2, h3) and semantic tags (article, section, nav) helps search engines understand content structure.
Faster websites rank better and convert better. Optimizing images, reducing JS bloat, and improving Core Web Vitals are SEO wins.
Google indexes the mobile version first. Responsive layouts and touch-friendly UX directly affect rankings.
Readable URLs and logical site structure make crawling easier and improve user experience.
SEO Helps You Communicate Better With Non-Developers
Understanding basic SEO also improves collaboration with:
Content writers
Marketers
Business owners
Instead of seeing SEO requests as “random changes,” you understand why they matter and can implement them more cleanly.
You Don’t Need to Become an SEO Expert
Developers don’t need to master keyword research or content strategy. Just understanding the basics is enough to:
Build more discoverable products
Avoid SEO-breaking mistakes
Add extra value to your projects and clients
Small awareness → big long-term impact.
Final Thoughts
SEO and development aren’t separate worlds. When both work together, websites perform better—not just in rankings, but in usability and reach.
Even a basic understanding of SEO can help developers build websites that don’t just work well, but also get found.
➤I share beginner-friendly SEO, WordPress, and digital growth tips here:
╰┈➤ https://kevin.digitalxwebctr.co.in/
2026-01-26 14:09:34
Most point-of-sale (POS) systems in restaurants are still designed around a single terminal mindset. Others swing too far in the opposite direction—cloud-only, fragile, and dependent on third-party integrations for even basic workflows.
While working on CounterFlowPOS, we set out to design a POS platform that treats reliability, data consistency, and extensibility as first-class concerns—while still enabling modern capabilities like online ordering and AI-driven promotions.
This post breaks down the technical architecture, design trade-offs, and lessons learned.
From an engineering perspective, restaurant POS systems face a few non-negotiable constraints:
Most existing solutions fail at least one of these.
CounterFlowPOS is built around a single-backend, multi-client architecture.
Clients
├── Windows WPF POS / Kiosk / Back Office
├── Next.js Web Store (Browser)
Backend
├── Node.js + Express (Single API Layer)
├── REST / JSON Contracts
Data
├── PostgreSQL (Single Shared Database)
Platform Services
├── Payments (Stripe)
├── AI Marketing Agents (Azure Foundry)
The guiding principle:
one source of truth, one API surface, multiple user experiences.
Despite cloud adoption, restaurants still experience:
A POS that fails during service is unacceptable.
CounterFlowPOS Lite is a standalone Windows application that:
When connectivity is restored, the system can optionally sync data upstream.
This model intentionally avoids:
Lite is optimized for predictability over feature sprawl.
CounterFlowPOS Pro extends the same domain model into a connected platform.
Windows WPF applications for:
Next.js web app for:
A single Node.js / Express API exposes domain-driven routes:
All clients communicate via HTTP/REST + JSON.
No client-specific logic leaks into the backend.
The platform uses a single PostgreSQL database to store:
This eliminates:
Every client—POS, kiosk, or web—sees the same state.
Instead of embedding AI directly into transactional flows, we treat marketing as an autonomous system layered on top of core data.
Different marketing tasks have different constraints:
Each agent is responsible for a narrow function:
Agents operate asynchronously and never block core order processing.
AI services are deployed using Azure Foundry, allowing:
This keeps AI powerful but non-invasive.
Payments are intentionally decoupled from the POS core.
This avoids the vendor lock-in common in POS ecosystems.
We’re continuing to:
2026-01-26 14:07:34

LDAP is still a core part of authentication in many environments, especially for internal systems and infrastructure. In theory it’s reliable and standards-based. In practice, it often becomes difficult to manage over time.
We’ve seen the same patterns repeatedly: limited guidance for production-ready setups, too much manual work, confusion around schemas and directory structure, and very little visibility into what changes are being made.
Over the past few months, we’ve been putting together a set of practical guides that focus on day-to-day LDAP operations. This includes single-node and multi-node OpenLDAP setups, directory structure design, schema handling, validation, monitoring, running LDAP in containers, and integrating it with other systems.
Alongside this, we released LDAP Manager V1 as a simple web interface to make LDAP operations more structured and easier to follow.https://vibhuvioio.com/ldap-manager/
2026-01-26 14:03:18
We hear the phrase “think like a hacker” so often in this industry that it’s basically become background noise. It’s on every job description and at the start of every slide deck, but we rarely stop to talk about what it actually means. Most of the time, we’re too busy chasing the latest compliance checkbox or tweaking firewall rules to notice that the game has changed. While we’re focused on the "how" of security, the tools and the patches, the people on the other side are obsessing over the "why". They don’t see our defences as a wall; they see them as a puzzle that’s just waiting to be taken apart. If we’re going to actually protect our systems, we have to move past the stereotypes and get real about how an attacker’s brain actually works.
For too many organisations, security is treated like a predefined chore: install the firewall, patch the known CVEs, and check the compliance boxes. We view security as a series of physical locks to be bolted, forgetting that an attacker doesn’t see our security posture as a wall. They see it as a puzzle.
This "technification" of cybersecurity has created a dangerous intelligence gap. We have become experts at managing our tools, but we remain amateurs at understanding our opponents.
We think defensively and act defensively, but the former must change. We should think offensively; only then can we properly act defensively
While we obsess over our internal infrastructure, the enemy is obsessed with breaking it. They aren’t just looking at how a system works; they are investigating exactly how it can be forced to fail. To close this gap, we have to stop treating security as a technical hurdle and start treating it as a contest of human creativity. Defensive instincts are reactive, while an attacker’s mindset is investigative. It is the difference between hoping the lock holds and understanding exactly why someone wants to pick it in the first place.
Adversarial thinking is often glamorised as a Hollywood trope, specifically the lone genius in a hoodie. In reality, it is the sophisticated application of the triarchic theory of intelligence. In InfoSec, being "tech-smart" is just the baseline. To actually thrive, you need a mix of three distinct types of intelligence:
Many highly skilled developers fail at security because they are "left-brained", meaning they are heavy on the analytical side but light on the creative side. They build according to the rules, while the attacker thrives in the spaces where those rules bend.
Surprisingly, the best place to foster an attacking mindset isn't a coding bunker; it is a language classroom. Learning a second language (L2) mirrors the hacker's journey because language itself is a protocol-rich system governed by phonetic, syntactic, and pragmatic rules.
Adversarial thinking is the ingrained habit of investigating how things fail. It is nourished by situational awareness, which is the ability to notice the thing that doesn't fit. In a high-stakes environment, this follows a rigorous four-step loop:
The most impactful attackers don't just hunt for random bugs; they look for the specific "glitch" that suggests a logical oversight.
There is a common misconception that you are either born a "hacker" or you aren't. In reality, strategic thinking isn't like weightlifting, where you are constrained by physical limits. It is like learning to windsurf or fly a plane. It feels unnatural at first, but it is a masterable skill.
Look at the Akira Ransomware group. Since 2023, they have extracted over $244 million from 470 organisations. Their success isn't just due to "bugs". It is due to this "Triple Threat" of intelligence:
Adopting this mindset transforms security from a "department" into a strategic business capability. When you move from a "checkbox" culture to a threat-informed culture, the focus shifts.
Instead of a vague worry that "ransomware exists", a strategic defender knows that specific groups are targeting their sector via vulnerabilities like CVE-2023-20269 in Cisco VPN appliances. This allows for:
Building a threat-informed defence isn't a project with a deadline; it is a continuous evolution of perception. It requires us to look into the mirror and see our systems through the eyes of those who wish to disrupt them. If we cannot imagine how we would break ourselves, we certainly cannot defend against someone who can.
As you evaluate your own posture today, ask yourself: if you were tasked with breaking into your own most important system, where would you start? Does your current defence even have a sensor in that room?
2026-01-26 13:48:48
If you are building an AI Agent (using OpenAI, LangChain, or AutoGen), you likely face the biggest pain point: The Knowledge Cutoff.
To fix this, we need to give the LLM access to Google or Bing.
Typically, developers turn to SerpApi or Google Custom Search JSON API. They are great, but they have a massive problem: Cost.
I recently found a new alternative on RapidAPI called SearchCans. It provides both Search (SERP) and URL-to-Markdown Scraping (like Firecrawl) but at a fraction of the cost (~90% cheaper).
Here is how to integrate it into your Python project in under 5 minutes.
First, go to the RapidAPI page and subscribe to the Basic (Free) plan to get your key. It gives you 50 free requests to test (Hard Limit, so no surprise bills).
👉 Get your Free SearchCans API Key Here
You don't need to install any heavy SDKs. Just use requests.
Here is a clean SearchClient class I wrote that handles both searching Google/Bing and scraping web pages into clean text for your LLM.
import requests
import json
class SearchCansClient:
def __init__(self, rapid_api_key):
self.base_url = "[https://searchcans-google-bing-search-web-scraper.p.rapidapi.com](https://searchcans-google-bing-search-web-scraper.p.rapidapi.com)"
self.headers = {
"X-RapidAPI-Key": rapid_api_key,
"X-RapidAPI-Host": "searchcans-google-bing-search-web-scraper.p.rapidapi.com",
"Content-Type": "application/json"
}
def search(self, query, engine="google"):
"""
Search Google or Bing and get JSON results
"""
payload = {
"s": query,
"t": engine, # 'google' or 'bing'
"d": 10000, # timeout
"p": 1 # page number
}
response = requests.post(f"{self.base_url}/search", json=payload, headers=self.headers)
return response.json()
def scrape(self, url):
"""
Scrape a URL and convert it to clean text/markdown for LLMs
"""
payload = {
"s": url,
"t": "url",
"b": True, # return body text
"w": 3000, # wait time
"d": 30000, # Max timeout
"proxy": 0
}
response = requests.post(f"{self.base_url}/url", json=payload, headers=self.headers)
return response.json()
# --- Usage Example ---
# 1. Replace with your Key from RapidAPI
MY_API_KEY = "YOUR_RAPIDAPI_KEY_HERE"
client = SearchCansClient(MY_API_KEY)
# Test 1: Search for something real-time
print("🔍 Searching...")
results = client.search("latest spacex launch news")
# Print the first result title
if 'data' in results and len(results['data']) > 0:
print(f"Top Result: {results['data'][0]['title']}")
print(f"Link: {results['data'][0]['url']}")
# Test 2: Scrape the content for RAG
print("\n🕷️ Scraping content...")
# Let's scrape the first link we found
target_url = results['data'][0]['url']
page_data = client.scrape(target_url)
# Show snippet
print(f"Content Scraped! Length: {len(str(page_data))} chars")
else:
print("No results found.")
For my side projects, I couldn't justify the monthly subscription of the big players.
| Feature | SerpApi | SearchCans |
|---|---|---|
| Price per 1k req | ~$10.00 | ~$0.60 |
| Search Engine | Google/Bing | Google/Bing |
| Web Scraper | No (Separate tool) | Included |
| Setup | Easy | Easy |
If you are building an MVP or a personal AI assistant, this saves a ton of money.
You can try the Free Tier here:
SearchCans on RapidAPI
Happy coding! 🚀
2026-01-26 13:46:17
In modern technical workflows, we have no shortage of tools.
Converters, calculators, scripts, plugins, and AI assistants are everywhere.
Yet, in daily work, many engineers still keep simple reference documents nearby.
At first glance, this might seem unnecessary.
Why keep a static document when everything can be calculated instantly?
The answer is not about speed — it’s about context.
In real-world projects, information rarely comes from a single source.
You might see:
Before doing any calculation, the first step is usually alignment, not computation.
That’s where simple reference material helps.
It reduces cognitive load and avoids mistakes during quick checks.
Automation is great when:
But many daily checks are:
In these cases, opening a heavy tool or writing a script can be overkill.
A small reference document can be faster and safer.
Simple documents offer:
They don’t replace tools — they complement them.
That’s why PDFs, notes, and cheat sheets are still passed around inside teams.
In practice, I sometimes keep a simple unit reference open in another tab when checking specs or cleaning up data.
It’s not something I rely on heavily, but for quick alignment between metric and imperial values, having a lightweight reference like https://mmtocm.net can be convenient during context switching.
The persistence of reference documents isn’t resistance to modern tools.
It’s a reflection of how humans actually work.
We don’t always need automation.
Sometimes, we just need clarity.
As systems become more complex, simple artifacts gain new value.
Not everything needs to be dynamic.
Some things just need to be reliable.