DEV Community: Weejix Team

The Berlin Wall

Weejix Team — Tue, 28 Jul 2026 07:24:47 +0000

The Berlin Wall became one of the most powerful symbols of the Cold War, representing the political and ideological divide between East and West Germany.

Constructed in 1961 by East Germany, the Wall was designed to stop the large number of people escaping to West Berlin in search of greater political freedom and economic opportunities. It eventually stretched for more than 150 kilometres, surrounding West Berlin with walls, fences, watchtowers, and heavily guarded checkpoints.

For nearly 28 years, families, friends, and communities remained separated. Crossing the Wall without permission was extremely dangerous, and many people lost their lives trying to escape.

Everything changed in 1989, when growing public protests and political reforms across Eastern Europe led to the opening of the border. Crowds gathered, celebrated, and began dismantling the Wall, marking one of the defining moments of the twentieth century. Germany was officially reunified in 1990.

The Berlin Wall remains an important topic for students of history and international relations because it explains the tensions between the Eastern and Western blocs during the Cold War.

For a detailed explanation covering its construction, timeline, major events, causes, consequences, and exam-focused notes, visit:
https://www.weejix.com/topic/berlin-wall

Charter Act of 1813

Weejix Team — Tue, 28 Jul 2026 07:21:02 +0000

The Charter Act of 1813 was one of the most significant laws passed by the British Parliament during colonial rule in India. While it renewed the East India Company's charter for another 20 years, it also introduced major political, economic, and educational reforms that shaped modern Indian history.

One of its biggest changes was the end of the Company's monopoly over trade with India, except for tea and trade with China. This allowed private British merchants to enter the Indian market, increasing commercial activity.

The Act also marked the beginning of government support for education in India. It allocated ₹1 lakh annually (one lakh rupees at the time) for promoting literature, science, and education. Although implementation was slow, this provision later influenced the development of modern education in the country.

Another important feature was that Christian missionaries were officially allowed to work in India, leading to the establishment of schools, colleges, and hospitals.

For students preparing for UPSC, SSC, State PCS, and other competitive exams, the Charter Act of 1813 is an important topic because it connects colonial administration, economic policy, and educational reforms.

If you'd like to explore the Act in greater detail, including its provisions, significance, and exam-oriented facts, read the complete guide here: https://www.weejix.com/topic/charter-act-of-1813

Korean Armistice Agreement (1953): Why the Korean War Never Officially Ended

Weejix Team — Fri, 24 Jul 2026 05:21:31 +0000

On July 27, 1953, representatives of the United Nations Command, North Korea, and China signed the Korean Armistice Agreement at Panmunjom. The agreement brought active fighting in the Korean War to an end after three years of devastating conflict.

However, it was not a peace treaty.

The Korean War began on June 25, 1950, when North Korean forces invaded South Korea. The conflict soon became an international war involving the United States and United Nations forces on the side of South Korea, while China supported North Korea. By 1951, the front had largely stabilized near the 38th parallel, creating a military stalemate.

The armistice established an immediate ceasefire and created the Korean Demilitarized Zone (DMZ), a buffer zone approximately four kilometres wide separating the two Koreas. It also arranged for the withdrawal of forces from the area and the exchange of prisoners of war.

One important detail is that South Korea did not sign the agreement. President Syngman Rhee opposed the armistice because he wanted the Korean Peninsula to be reunified under South Korean control.

The agreement was intended to be followed by a political conference that could lead to a permanent peace settlement. That never happened.

As a result, the Korean War has never formally ended with a peace treaty. The armistice stopped the fighting, but North Korea and South Korea technically remain at war.

The Korean Armistice Agreement is still one of the most important documents of the Cold War. It ended large-scale combat but left behind a divided and heavily militarized Korean Peninsula.
Read the full article here: https://www.weejix.com/history-articles/korean-armistice-agreement

Hockey India League Explained: A Professional Platform for the Next Generation of Indian Hockey

Weejix Team — Wed, 22 Jul 2026 05:34:30 +0000

Professional sports leagues can play an important role in developing talent. They provide regular competition, attract experienced players, and create opportunities for younger athletes. In Indian field hockey, the Hockey India League has emerged as an important professional platform.

The league brings together Indian and international hockey players in a franchise-based competition. This creates a unique environment where domestic talent can gain experience while competing at a higher level.

What Is the Hockey India League?

The Hockey India League is a professional field hockey competition organised around franchise teams. It was originally launched in 2013 and later returned after a period of inactivity with a renewed focus on professional hockey.

The league is designed to make domestic hockey more competitive and commercially attractive. It also aims to give players more opportunities to compete in a professional environment.

A Learning Opportunity for Young Players

One of the most important roles of HIL is player development.

Young Indian players can share the field with experienced domestic and international athletes. They are exposed to different tactical systems, match situations, and styles of play.

This type of experience can help players improve their decision-making and prepare for the demands of international hockey.

Why International Participation Matters

The presence of overseas players adds another layer of competition to the league. Players from different hockey nations bring their own experience and tactical knowledge.

More Than Just a Tournament

A professional league can influence an entire sporting ecosystem. It can create opportunities for players, coaches, support staff, broadcasters, sponsors, and local sporting communities.

For hockey fans, franchise-based teams can also create stronger regional connections. Supporters have a team to follow, while cities get an opportunity to become associated with professional hockey.

The Future of HIL

The future of the Hockey India League will depend on maintaining a strong balance between entertainment and sporting development. Consistent scheduling, good facilities, professional team management, and investment in young players will all be important.

Learn more about the Hockey India League, including its history, teams, format, impact, and future:
https://www.weejix.com/topic/hockey-india-league

Edge Computing vs Cloud Computing: What's Actually Different

Weejix Team — Fri, 17 Jul 2026 07:17:28 +0000

Every comparison of edge computing and cloud computing eventually turns into a spec sheet — a column for each, tick marks down the rows, and a conclusion that says "use both." That's not wrong. But it skips the part that actually helps you understand the choice.
Start with one question: when a factory robot is about to make a dangerous error, how long can it wait for a response?
Milliseconds. Not seconds.
That's the entire reason edge computing exists.

The core difference

Cloud computing processes data in centralized data centers. Could be in your country, could be thousands of kilometres away. It scales brilliantly, stores everything cheaply, and runs global analytics without breaking a sweat. But it introduces latency — typically 50–200ms depending on distance, rising to 500ms+ under poor conditions.
Edge computing moves the processing to where the data is generated — local devices, nearby servers, on the factory floor itself. Latency drops to 1–10ms. For autonomous vehicles, surgical robots, or industrial automation, that gap isn't a performance stat. It's the difference between the system working and it failing.

Bandwidth matters too

A connected factory can generate terabytes of sensor data per day. Sending all of it to the cloud is expensive and mostly pointless — most of it is normal readings nobody needs to analyze centrally. Edge computing filters at the source: only the anomaly gets reported upstream.

The honest answer

Neither replaces the other.
Edge handles what needs to happen now, locally. Cloud handles what benefits from scale, history, and centralization. A connected vehicle processes sensor data at the edge for real-time decisions — and uploads driving patterns to the cloud for fleet-level analysis.
The question isn't edge or cloud. It's how to divide the workload between them intelligently.
Read the full article here: https://www.weejix.com/technology-articles/edge-computing-vs-cloud-computing

Fog Computing: Bridging the Gap Between Cloud and Edge

Weejix Team — Tue, 14 Jul 2026 12:22:31 +0000

A self-driving car braking for a pedestrian doesn't have time to wait for a cloud server's reply. That's where fog computing comes in — a processing layer sitting between your devices and the cloud, built for moments when milliseconds actually matter.

What It Is

Cisco coined the term in 2012 — fittingly, since fog is just cloud computing closer to the ground. Instead of sending everything to a distant data center, fog computing processes data at nearby nodes (routers, gateways, local servers), sending only results upward when deeper analysis is needed.

Why It Exists

Cloud-only systems hit a wall with billions of IoT devices streaming constant data — latency breaks real-time apps, bandwidth chokes under the volume, and everything fails if connectivity drops. Fog fixes this by keeping decisions local.

Fog vs. Edge

People use these interchangeably, but they're different. Edge computing means the device itself does the processing. Fog sits one layer back, coordinating multiple devices at once — think a floor manager overseeing several desks before escalating to head office (the cloud).

Where It's Used

Smart traffic lights reacting instantly to local sensors, wearables flagging irregular heartbeats without cloud delay, factory floors catching equipment faults before costly downtime, and remote oil rigs staying operational despite spotty internet.

The Catch

More distributed nodes mean more complexity, more cost, and more points to secure individually. Standardization across vendors is still a work in progress too.
The real story isn't fog replacing the cloud — it's a layered system where cloud handles heavy long-term computation, fog handles near-real-time coordination, and edge handles instant, device-level reactions. Fog is what makes the cloud actually workable for a world that can't afford to wait.
Read full article here: https://www.weejix.com/technology-articles/fog-computing

Edge Intelligence

Weejix Team — Tue, 14 Jul 2026 09:34:14 +0000

If you've built anything with sensors, cameras, or IoT devices, you already know the cloud round-trip problem: capture data → ship it to a server → wait → get a response. Fine for a weather app. Not fine for a robot arm or a self-driving car.
Edge Intelligence (aka Edge AI) fixes this by running the actual AI model on the device — not just the data collection, but the inference itself — right where the data is created.
W

Why it matters technically:

Latency: no network hop means decisions happen in milliseconds, not "milliseconds + RTT."
Bandwidth: instead of streaming raw video/sensor data 24/7, the device only sends what's actually useful (an alert, a summary, an anomaly flag).
Privacy: biometric or sensitive data can be processed and discarded locally instead of leaving the device at all.
Offline resilience: your app keeps working even when connectivity doesn't.

How devs actually pull this off:

Model size is the real constraint. Techniques like quantization, pruning, and federated learning let you squeeze deep learning models onto hardware with a fraction of a GPU's power. Frameworks like TensorFlow Lite, ONNX Runtime, or Core ML exist specifically for this — shrinking a model without gutting its accuracy.
The architecture isn't "edge instead of cloud" — it's edge and cloud. Time-critical inference happens locally; heavier training and long-term analytics still happen upstream.
Real deployments already leaning on this: autonomous vehicle perception stacks, wearable health monitors, industrial defect detection, and on-device voice assistants.
If you're designing an IoT system in 2026 and your architecture assumes constant, cheap connectivity to the cloud — that assumption is probably the first thing worth rethinking.
Full breakdown with benchmarks and challenges: https://www.weejix.com/topic/edge-intelligence

What Is Artificial Intelligence? A Quick Dev's Primer

Weejix Team — Tue, 14 Jul 2026 09:27:35 +0000

If you're building anything with AI today, it helps to know where the field actually came from and what "AI" really covers — because the term gets thrown around loosely.
AI, at its core, is software designed to do things that normally need human intelligence: learning, reasoning, and decision-making. The idea isn't new. Alan Turing's 1950 paper "Can Machines Think?" introduced the Turing Test, and the term "Artificial Intelligence" itself was coined at the 1956 Dartmouth Conference by John McCarthy and colleagues.
From there it went through phases: rule-based expert systems in the 80s and 90s, then a shift toward machine learning post-2000 once compute and data became cheap, and now deep learning, the neural-network-driven wave powering everything from LLMs to image generation.

The Building Blocks

Every AI system leans on some mix of:

Learning — improving from data over time
Reasoning — applying logic to facts
Problem-solving — evaluating options to find the best one
Perception — parsing images, audio, or text
Decision-making — picking an action from analysis

Three Types Worth Knowing

Narrow AI — task-specific, and this is 100% of what's deployed today (chatbots, recommenders, vision models)
General AI — human-level, cross-domain intelligence — still theoretical
Super AI — beyond human intelligence — pure speculation for now

The Pipeline

Practically, AI systems run through: data collection → cleaning/processing → pattern recognition → model training → prediction → feedback loop for improvement. If you've trained any ML model, this loop should look familiar — it's the same cycle, just formalized.
Understanding this history and structure makes it a lot easier to reason about where today's tools (and their limits) actually come from.
Read full article here: https://www.weejix.com/topic/artificial-intelligence-ai-history-components-types

Green Computing

Weejix Team — Sat, 11 Jul 2026 04:59:33 +0000

Every search query burns about 0.3 watt-hours of electricity. So does every ChatGPT query. Multiply that by billions of queries a day, add the servers, add the cooling systems keeping those servers alive — global data centres consumed an estimated 460 TWh in 2025, roughly 1.8% of world electricity demand. The IEA projects that crossing 800 TWh by 2028.

What It Actually Covers

Green computing (green IT) means designing, using, and disposing of computing resources to minimise environmental impact — chip fabrication, data centre cooling, software efficiency, e-waste, renewable sourcing. The term got formal recognition in 1992 when the EPA launched Energy Star, originally targeting monitors and CPUs that ran at full power even when idle.

Where the Energy Actually Goes

Cooling eats 30-40% of total data centre power — the second-biggest draw after computing itself. The efficiency metric here is PUE (Power Usage Effectiveness): industry average improved from 2.5 in 2007 to 1.55 in 2022. Amazon's data centres hit 1.14 in 2025, beating the cloud industry average of 1.25.

The Real Levers

Renewables — tech companies signed ~40% of all corporate renewable power agreements in 2025
Virtualisation — traditional servers ran at just 6% utilisation; running multiple workloads per machine fixed that
Hardware efficiency — modern AI chips use nearly 99% less power per computation than 2008-era hardware
E-waste management — one of the fastest-growing waste streams globally ## Why AI Complicates Everything AI-focused data centre power use is set to triple by 2030. Google's data centres alone consumed 6.1 billion gallons of water in 2024 — up 20% year-over-year. Water, not just electricity, is becoming the real constraint.

Read full article here: https://www.weejix.com/technology-articles/green-computing

Homomorphic Encryption

Weejix Team — Fri, 10 Jul 2026 05:08:56 +0000

A hospital wants a cloud service to analyze patient data for disease patterns — but the cloud must never actually see the data. Not anonymized. Genuinely invisible. The computation happens, a result comes back, and the records stayed encrypted the whole time.
That's homomorphic encryption — and cryptographers spent three decades trying to build it before anyone succeeded.

The Core Idea

Standard encryption requires decryption before you can compute on data — and that decryption moment is the vulnerability. Homomorphic encryption skips it entirely: computation happens directly on encrypted data, and the encrypted result decrypts to the same answer you'd get from the plaintext.

Three Tiers

Partially Homomorphic (PHE) — supports one operation only (RSA: multiplication; Paillier: addition)
Somewhat Homomorphic (SHE) — both operations, but only up to a limited depth before noise corrupts the result
Fully Homomorphic (FHE) — unlimited computation, no ceiling

The 31-Year Gap

Rivest, Adleman, and Dertouzos posed the question in 1978 — the same year RSA was published. Nobody could build it. In 2009, Stanford PhD student Craig Gentry finally did, using a technique called bootstrapping to refresh ciphertexts before noise made them undecryptable.
The catch: Gentry's original scheme took 30 minutes per bit operation. Provably possible, practically unusable.

What Closed the Gap

Modern schemes — BFV, BGV, CKKS — optimized FHE for different workloads. CKKS handles approximate floating-point math, making it the default for machine learning on encrypted data. Microsoft SEAL and IBM HElib, both open-sourced in 2018, brought this from research paper to usable library.

Real Use Cases

Banks run fraud detection on encrypted transactions without a third party ever seeing account details. Hospitals run cross-institution genomic research without exposing any single dataset. Encrypted ballots can be tallied without decrypting a single vote.

Still Not Free

Performance improved from 30 minutes per operation to milliseconds — but FHE remains slower than plaintext computation, and bootstrapping is still the most expensive step in the pipeline.
Read full article here: https://www.weejix.com/topic/homomorphic-encryption

Edge Devices

Weejix Team — Fri, 10 Jul 2026 04:56:36 +0000

Your car's collision system doesn't wait for a cloud reply before braking. A factory sensor flagging an overheating motor doesn't file a ticket and wait. Both decide locally, in milliseconds, on hardware sitting at the very edge of the network. That hardware — edge devices — is the difference between a system that acts in time and one that acts too late.

What They Actually Are

Edge devices process, analyze, or transmit data at or near the source — instead of routing everything to a centralized cloud first. The category is broader than "IoT sensor":

Sensors — temperature, motion, pressure, sound
Gateways — translate local protocols (Bluetooth, Zigbee) into cloud protocols (MQTT, HTTP)
Edge servers — handle compute-heavy tasks like video analytics locally
Intelligent endpoints — smartphones, wearables, autonomous vehicles running AI models on their own data

Two flavors exist: traditional edge devices (transfer data, little local processing) and intelligent edge devices (run inference directly at the source).

Why They Exist

A cloud round-trip takes tens to hundreds of milliseconds — too slow for a car moving 3 metres every 100ms. Streaming raw data from 5.157 billion connected devices (2025 estimate) would saturate networks. And cloud-dependent systems fail the moment connectivity does. Edge devices solve latency, bandwidth, and reliability at once.

Where This Is Going

In 2025, over half of new AI models ran directly on edge devices, cutting latency below 10ms and saving 30–40% on energy. About 70% of new IoT devices now ship with dedicated AI chips. The hardware isn't passive anymore — a camera doesn't just capture, it classifies.

The Catch

Edge environments mix devices from dozens of manufacturers, each with different firmware and protocols — a fragmented attack surface far harder to secure than a data center. It's not a reason to avoid edge devices; it's a constraint that has to be designed for from day one.
Read full article here: https://www.weejix.com/topic/edge-devices

PARC (Palo Alto Research Center)

Weejix Team — Thu, 09 Jul 2026 13:06:54 +0000

Every time you move a mouse, resize a window, connect to a local network, or print a document, you're using something invented at one lab between 1970 and the early 1980s. That lab is PARC — and its story is one of tech history's strangest: a research center that produced more foundational inventions than almost any institution ever, inside a company that failed to profit from most of them.

Why It Existed

By the late 1960s, Xerox's copier business had a ceiling. Chief Scientist Jack Goldman proposed a research lab physically separated from Xerox's Rochester operations, with one mandate: invent technology unrelated to what Xerox already sold. PARC opened on 1 July 1970, led by physicist George Pake, who assembled a team so dense that nearly half the world's top 100 computer scientists were connected to it by the mid-1970s.

What Came Out of It

The list is almost implausible for one decade:

The Alto — first GUI-based personal computer
The modern computer mouse, refined into standard form
Ethernet, invented by Bob Metcalfe and David Boggs
The laser printer — the one invention Xerox actually commercialized
Smalltalk, the first object-oriented language
Ubiquitous computing, pioneered by Mark Weiser

The Steve Jobs Visit

In December 1979, Steve Jobs toured PARC and saw the GUI and mouse. Apple built the Macintosh from what he saw — but Xerox had already tried commercializing it as the Xerox Star, priced at $100,000. The problem wasn't theft; it was that Xerox priced for a market that didn't exist yet.

Legacy

Four PARC researchers won the Turing Award. In 2023, Xerox donated PARC to SRI International. The gap between inventing the future and shipping it never closed — but almost everything you touch on a screen today traces back to this one lab.

Read full article here: https://www.weejix.com/topic/parc-palo-alto-research-center