2025-11-16 17:22:37
When building forms in React, you’ll inevitably come across controlled and uncontrolled components.
At first glance, they look similar — both accept user input and manage form data.
But how they manage that data under the hood can significantly impact performance, maintainability and UX.
In this blog, let’s break down the difference, see them in action, explore real-world issues (like cursor position and undo/redo bugs), and understand when to use each.
In controlled components, React state is the single source of truth.
That means every keystroke or change updates the component’s state and that state determines what’s rendered.
Example:
import { useState } from "react";
function ControlledInput() {
const [name, setName] = useState("");
return (
<input
value={name}
onChange={(e) => setName(e.target.value)}
placeholder="Type your name"
/>
);
}
Here, the <input> value is fully controlled by React via the name state.
Each keystroke triggers setName(), re-rendering the component with the updated value.
✅ Pros
⚠️ Cons
In uncontrolled components, the DOM maintains its own state — React just references it.
You don’t handle every keystroke; instead, you access the value when needed.
Example:
import { useRef } from "react";
function UncontrolledInput() {
const inputRef = useRef();
const handleSubmit = () => {
alert(`Input value: ${inputRef.current.value}`);
};
return (
<>
<input ref={inputRef} placeholder="Type your name" />
<button onClick={handleSubmit}>Submit</button>
</>
);
}
✅ Pros
⚠️ Cons
| Feature | Controlled | Uncontrolled |
|---|---|---|
| Data Source | React State | DOM (via refs) |
| Performance | May re-render often | Faster for simple inputs |
| Validation | Easy to handle dynamically | Requires manual check |
| Use Case | Complex, validated forms | Simple or performance-critical inputs |
When you use a state library like Valtio in a React controlled input, you might encounter this issue: you edit text in the middle of the input, but the cursor jumps to the end. This frustrates users and breaks the typing experience.
🔍 What’s going on?
By default, Valtio batches updates before triggering re-renders. The documentation states:
“By default, state mutations are batched before triggering re-render. Sometimes, we want to disable the batching.”
Because of this batching behavior, when you type and update the proxy state, multiple changes may be queued and then re-rendered in one go. During that delay, the browser loses context of the input cursor position, so after render the input’s value is applied and the cursor jumps to the end.
✅ The fix: { sync: true }
Valtio provides the option to disable batching for specific use-cases like inputs:
const snap = useSnapshot(state, { sync: true });
With sync: true, updates go through immediately rather than being deferred/batched. This keeps the input cursor where the user expects it.
⚠️ Trade-off:
Using sync: true disables batching optimizations, which can reduce render efficiency in large/complex components. Therefore:
sync: true only in input fields where cursor behaviour matters.Another subtle but important issue:
Undo (Cmd+Z) and Redo (Cmd+Shift+Z) might not work as expected in controlled inputs.
Why?
Because React replaces the entire value on every keystroke rather than letting the browser handle native input history.
This means the browser sometimes loses the input undo stack, especially if re-renders happen between updates.
🧪 What to do:
✅ Use controlled inputs when you need validation, dynamic control or shared state.
✅ Use uncontrolled inputs when performance and simplicity matter.
✅ For state libraries like Valtio, test cursor and undo behaviors thoroughly.
✅ Avoid unnecessary re-renders – use React.memo, useCallback or split components.
✅ Always test form UX — typing, cursor and undo/redo should feel native and fluid.
Controlled vs Uncontrolled Components seem like a small React concept, but they can deeply impact user experience, especially in complex form-heavy applications.
💡 Understanding these nuances (like cursor handling, undo/redo, and batching) helps you ship smoother, bug-free UIs that feel professional and reliable.
🚀 Whether you use plain React or libraries like Valtio, always test the real typing experience — that’s where users feel the difference.
💬 Have you ever faced cursor or undo issues in your React forms? Drop your experience or workaround below — let’s learn together! 👇
2025-11-16 17:16:03
I’m a practicing developer and architect who has spent the last few years living at the intersection of modern web frameworks, SEO, and AI tooling. Every day, it becomes harder to pretend that the way we design interfaces can stay the same while user behavior, search, and AI are shifting under our feet. This piece is about a new kind of interface — not just another set of trendy components, but a different model of how humans interact with web applications.
It’s about what happens at the crossroads of AI chat and traditional websites — and what that means for designers, developers, and businesses building products for the next 5–10 years.
For the last twenty years, the web has been surprisingly predictable. There is a page. On that page, there is a header, a footer, navigation, a couple of links to neighboring pages, sometimes a search box. Somewhere deeper live filters, categories, tags, and endless pagination. The mental model is simple: the web is a library, and every site is a small private collection with its own catalog and shelves.
We learned that to reach the right “shelf”, you first have to understand how the librarian thinks. On the web, that librarian is the information architecture. You don’t just look for “something about auth”; you learn that in this product, docs live in “Documentation → API → Authentication”, while guides live somewhere else. After a few clicks and a few minutes of scrolling, you start to feel that you are “familiar” with the product.
Search engines like Google and Bing amplified this model instead of replacing it. They became a global catalog on top of all those libraries. But the outcome of every search was still the same: a list of pages. We got used to googling, opening 5–10 tabs, and manually stitching together an answer from fragments scattered across different sites. It felt normal, even inevitable — that’s just how the web works, right?
Then large‑scale access to AI chat apps arrived. At first, they looked like toys: fun to poke at, capable of jokes, sometimes hallucinating confidently wrong things. But very quickly, something subtle but important changed — not in technology, but in how people think about asking questions.
People stopped compressing their thoughts into “2–3 keywords”. Instead of typing “buy sneakers nyc”, they started writing: “I need comfortable sneakers for everyday walking, not for running, budget under \$100, okay with either NYC pickup or fast shipping.” In a traditional search engine, this kind of query feels strange. In a chat, it feels natural. And the dangerous part for the “old web” is that in this moment, the user no longer cares where the answer comes from.
The cognitive model is shifting. Before, the user had to think: “How do I phrase this so the search engine understands and gives me half‑relevant links?” Now the question is: “How do I explain this the way I would to a human?” That’s the difference between “adapting to the machine” and “speaking like a person”. Chat removes a layer of technical discipline: users don’t need to remember exact page names, the right product term, or the structure of your docs. They just need to describe their situation — and if the answer is good enough, they may never visit your site at all.
If you push this line of thought to the extreme, you get a radical question: if AI can answer most questions, why do we need websites at all? Maybe everything moves into one universal chat window, and pages, navigation bars, and landing layouts become museum artifacts of early web design.
Technically, the answer can be almost “yes”. It is possible to imagine a world where nearly everything happens inside a chat interface: from finding products and checking out, to signing contracts and managing subscriptions. In many domains, we are already halfway there: internal support bots, scripted customer service, voice assistants that pretend to be humans on the phone.
But on the level of human experience and business, the picture looks very different. A website is not just functionality. It is also a stage, with lights and sound and scenery. It is a space where a brand gets to talk in its own language — through color, composition, animation, visual metaphor. A chat is a meeting room. It’s great for clarifying, negotiating, asking quick questions. It is terrible at building atmosphere and identity. In chat, every brand looks almost the same: text bubbles, maybe an avatar, a slightly different tone of voice.
For businesses, that is not just an aesthetic tragedy. It is a risk to trust, differentiation, and long‑term relationships. Visual language is a way to show that there is a real product, a real team, and a real story behind the interface. If everything collapses into a gray chat panel, all you have left is a disembodied “voice” — and it is much easier for that voice to pretend to be someone it is not.
So no, pure chat will not “kill” websites. It might absorb a huge chunk of tasks that previously required navigating through pages. But it will not replace everything, because people still like to “see” a product, not just “talk” to it.
That said, the old “everything is a page” approach also fails to survive contact with reality in 2025. Think of a mature SaaS product: years of development, dozens of sections, hundreds of doc pages, blog posts, landing pages, onboarding guides. Each piece of content made sense when it was created: “let’s put this in a separate page so users don’t feel overwhelmed”.
But from the user’s perspective, complexity accumulates. They don’t know which page holds the answer. They don’t know which of the ten similar articles is the most up to date. They don’t know how to connect pieces scattered across your blog, docs, and changelog. They are forced to do manual “integration testing” of your content, clicking through screens and mentally merging partial answers into something usable.
AI, in this context, acts as a synthesizer. It can pull meaning from several pages and turn them into a fresh, coherent answer. Classic web UX cannot do this by design; it was built around “show this page”, not “assemble this answer”. But AI chat has a weakness too: it rarely shows the full path. It gives you the conclusion, yet rarely gives you the form — the structure, the context, the place where this lives in the system.
If you extend the theater metaphor: a traditional website is the stage where you watch the whole play. An AI chat is the critic who retells the story in their own words. Sometimes that is exactly what you want; sometimes it is not. Either way, it is a different plane of experience. That tension creates a need for a hybrid interface: something that can both show and answer.
This brings us to the key idea. The new interface is not “a website with a chat widget in the corner”, nor “a chat that occasionally opens webviews in a browser tab”. The new interface is a consciously designed system of several parallel experience streams that live together on one screen.
One stream is conversational. This is the AI you can talk to, that understands tasks, not just URLs. It can propose paths, ask clarifying questions, warn you before you step into a dead end. Another stream is visual and structural: pages, dashboards, tables, maps, forms — everything that requires focus, hierarchy, accessibility, and brand expression. A third stream is business logic and data: roles, permissions, constraints, workflows, the actual state of the system.
The important shift is that these streams no longer run “one after another” — first chat, then UI, then back to chat. They can and should run at the same time. The user talks to AI and simultaneously watches the interface evolve. The interface suggests something, and the user clarifies in chat what they really meant. Dialogue and visual layer stop competing for attention and start playing on the same team. Technically, this pulls us toward slot‑based layouts and parallel routes: the interface is split into independent regions, each with its own lifecycle, all coordinated by a shared scenario.
At some point, this stopped being an abstract design discussion and turned into a concrete architectural problem in one of my own projects. The requirements looked like this:
On the architecture level, this turned into an equation with several unknowns: independence, resilience, SEO, and developer experience. In that equation, slot‑based layout (independent “windows” or slots on the screen) and parallel routing (routes that can update independently) turned out to be a natural answer. Instead of thinking in “pages”, it became more useful to think in “flows”: the left slot is the conversation flow (chat, auth, assistants), the right static slot is public content that works even with JS disabled, the right dynamic slot is personalized, authenticated functionality.
From that, a new architecture emerged where AI chat and the classic site stopped fighting for control over the screen. They got their own “campus buildings”, connected by a shared campus of navigation, layout, and brand. Practically, this is what sits behind the AIFA starter templates: a Next.js‑based open‑source setup designed to keep AI chat, static SEO pages, and dynamic app surfaces in one coherent experience.
High‑level ideas are nice, but interfaces live or die in real scenarios. Here’s how this parallel‑streams model reshapes some familiar patterns.
Traditional documentation is a forest of sections. Users know the answer is “somewhere in here”, but not where exactly. They skim the table of contents, try to guess by headings, open multiple tabs, and hope the right combination of pages eventually clicks. The more your product grows, the more invisible your best content becomes.
In a new interface, the user starts differently: “How do I rotate an auth token in a multi‑tenant app without breaking existing sessions?” The AI layer knows the shape of your docs. It can assemble a cohesive answer from multiple pages and, if needed, open the relevant section on the right with the exact paragraph highlighted. The user sees both the synthesized answer and the “source of truth” — and can dive deeper without getting lost in the tree of pages.
Most online stores lean heavily on filters. Filter by brand, size, price, color, material — sometimes all at once in a dense sidebar. Very few users enjoy filling all of these out. They approximate, misclick, and then bounce when results feel slightly off. The interface is optimized for the database, not for the conversation in the buyer’s head.
In a parallel‑stream setup, the user speaks first: “I’m looking for black sneakers without giant logos, for city walking, size 10, under \$100.” The chat understands that this maps to a specific category, applies filters under the hood, maybe clarifies brand preferences, and then fills the visual slot with large, clear product cards. Filters still exist — but now they are tools for refinement, not the main entry point. The user does not have to translate their intent into your filter UI; the AI layer does that translation.
Complex B2B systems are notorious for steep learning curves. They have dozens of screens, each with dozens of fields, and onboarding often sounds like: “Watch these ten videos and read the docs; you’ll get used to it.” Every new customer pays the cognitive tax of understanding how your internal model maps to their real‑world tasks.
With a new interface, the first step can be different. A user might say: “Show me customers whose churn increased over the last three months, but whose average contract value is still high.” The conversational layer turns this into a query over your data, opens the right report on the visual side, and explains in plain language how it interpreted the criteria. You don’t have to automate everything, but even the option to have a dialog over the interface is a qualitatively different level of experience.
For designers, this new interface is both a challenge and a gift. The challenge is that static screen maps are no longer enough. Now the question is: what does the conversation look like? How do you visually connect a specific chat message to a change on the screen? How do you show that this particular view is “the answer” to a particular question?
The gift is that you can finally stop pretending the interface is just a set of static frames. You can direct the experience like a play: there is a leading voice (the AI), there is a stage (screens and slots), there is light and sound (animations, highlights, contextual markers). You can invent ways to visualize dialogue — without destroying structure and accessibility in the process.
There is also a branding challenge: not letting your product dissolve into the same generic chat bubbles everyone else uses. Your product still needs a personality — including in the way your AI speaks. Tone of voice, microcopy, visual framing around the chat, how the interface reacts to uncertainty or errors — all of that becomes part of UX. In a world where the content layer is increasingly generated, character becomes a key differentiator.
For developers, the new interface means the job is no longer just “build routes and components”. You have to think in terms of flows and slots. Which parts of the interface should be navigation‑independent? Which slots must survive when others crash? What is rendered statically, what dynamically, and what can be generated on demand by AI?
It also means designing communication between slots. When is the chat allowed to open pages? When can a page trigger a question to the chat? How do you avoid circular dependencies and race conditions while keeping the experience seamless? Dropping a chat widget into every page is no longer enough. You have to architect the experience itself — how users move between dialogue and visual context without noticing the internal technical seams.
On the technology side, this pushes you toward tools that handle slots and parallel routes well, and away from “one giant SPA that crashes all at once”. In practice, that often means leaning into frameworks like Next.js App Router, where you can define independent layouts, parallel segments, intercepting routes, and mixed static/dynamic rendering. Architectures like AIFA build on top of that: chat in one slot, public static content in another, personalized app surfaces in a third — each with its own error boundaries and lifecycle.
For a business, the new interface is not “a fancy chat bubble on the site”. It is a way to keep control over how AI talks to your users. If you leave everything to external systems, the conversation with your customer happens in somebody else’s shell: the user types into a third‑party AI app, and that app decides which tiny fragment of your content to show or paraphrase. You are just a data source.
If you embed AI into your own architecture, you get several advantages. You keep SEO traffic by serving rich static content in your own layout. You increase conversion because the path is guided by an assistant that understands your specific processes, not generic best practices. And you can build new user journeys faster by teaching the AI new concepts and language, instead of redrawing dozens of screens for every new use case.
Of course, this is not free. A new interface requires investment in architecture, data quality, and conversational design. But in return, your product stops being “one more link in someone else’s search result” and becomes an environment where AI and users talk in the language of your product — on your terms, in your visual space.
It’s important not to turn this into yet another wave of uncritical AI hype. The new interface has traps of its own. The first illusion is believing that chat will solve everything. It won’t. Some users simply don’t like typing. Some scenarios require predictable, highly structured forms rather than open‑ended conversation. There are accessibility constraints and legal requirements that make pure chat UX risky or even unacceptable.
The second risk is forgetting about transparency. If AI starts changing the interface without explaining why, users feel like they are losing control. A good new interface should reveal the links between intent and outcome: “You’re seeing this screen because you asked for this.” Users should be able to retrace steps, see what was filtered, and correct the AI when it misinterprets something.
The third illusion is economical: treating AI integration as “magic cost savings”. Rebuilding architecture around AI is an investment, not a shortcut. Done poorly, it can leave you with complex, fragile code, confusing UX, and dependency on a single external provider. Done thoughtfully, it can reduce friction for users and enable new business models — but the “AI tax” is real, both technically and organizationally.
There is no clean “yes” or “no” answer to whether the time for this new interface has “officially” arrived. But it already feels impossible to design serious products as if AI doesn’t exist. You can’t responsibly plan a 5–10 year product roadmap and act like users haven’t learned to expect dialogue, not just navigation. Ignoring that shift won’t make it go away; it will just make your product feel oddly old even if the tech stack is brand new.
Personally, this moment feels a lot like the transition from static sites to SPAs. Back then, it looked like “just another technical trick”. It turned out to be a paradigm shift. Slot‑based architectures, parallel routes, an AI layer that lives next to content instead of sitting as a thin widget on top — all of this still feels niche today. But once you build a few real projects this way, it becomes hard to go back. The simplest practical step right now is to stop thinking in terms of “pages versus chats” and start thinking in terms of “streams that need to live together on the same screen”.
2025-11-16 17:09:04
We're living in a golden age of technology, where powerful programming languages, cutting-edge development practices, and revolutionary fields like AI and machine learning are redefining industries. This post explores the synergy between Java, Python, JavaScript, web development, machine learning, AI, data science, big data, cloud computing, DevOps, and blockchain—and how these interconnected technologies are driving large-scale innovation.
These three languages form the backbone of modern software engineering:
Mastering these languages opens the doors to full-stack development, enterprise software, AI, scripting, automation, and beyond.
Web development today is miles ahead of the static HTML era.
With rising complexity, full-stack developers—capable of managing both client and server layers—are becoming indispensable in the modern development landscape.
In the digital world, data is the new oil, and organizations win through intelligent data-driven decisions.
Together, these fields are reshaping industries—from healthcare and finance to retail and cybersecurity.
Modern systems must scale, evolve fast, and remain secure. These pillars make that possible:
These technologies aren’t isolated; they form a powerful, interconnected ecosystem. Understanding how they complement each other is essential to thrive in today’s dynamic tech environment.
Automated post via TechCognita Automation Framework
2025-11-16 17:07:13
Hosting a webinar is a powerful way to connect with your audience, generate leads, and establish your brand as an industry authority. But after the live event ends, how do you know if it was truly successful? The answer lies in webinar analytics.
Tracking the right metrics can feel overwhelming. With so much data available, it's easy to get lost and unsure of what to focus on. Without a clear understanding of your analytics, you're essentially flying blind, unable to prove the value of your efforts or identify opportunities for improvement.
This guide will walk you through the essential webinar analytics you need to track. We'll cover everything from pre-webinar registration metrics to post-webinar engagement data. By the end, you'll have a clear framework for measuring the success of your webinars and making data-driven decisions to enhance future events.
The success of your webinar starts long before the event goes live. Monitoring pre-webinar analytics helps you understand the effectiveness of your promotional efforts and build a strong foundation for a well-attended event.
This metric tracks the total number of people who visited your webinar registration page. A high number of page views indicates that your promotional campaigns are successfully driving traffic. If your page views are low, it might be a sign that your marketing messages aren't reaching the right audience or that your call-to-action isn't compelling enough. Consider re-evaluating your promotional channels, A/B testing your ad copy, or refining your target audience.
The registration rate is the percentage of visitors who sign up for your webinar after landing on the registration page. It's a direct measure of how persuasive your page is. A low registration rate could mean your topic isn't resonating, the registration form is too long, or the page copy isn't clearly communicating the value of attending.
To improve this rate, ensure your headline is attention-grabbing, the description clearly outlines the benefits for attendees, and the form is simple and quick to complete.
Understanding where your registrants are coming from is crucial for optimizing your marketing spend. Are they finding you through email campaigns, social media, paid ads, or organic search? By tracking the source of your leads, you can identify which channels are most effective and double down on what works. Most webinar platforms allow you to create unique tracking links (UTM parameters) for each promotional channel, making it easy to attribute registrations accurately.
Once your webinar is live, your focus shifts to audience engagement. These metrics provide real-time feedback on how well your content is landing and keeping attendees captivated.
The attendance rate is the percentage of registrants who actually show up for the live event. The industry average hovers around 40-50%, so don't be discouraged if not everyone who registered attends. However, a significantly low attendance rate could indicate issues with your reminder emails, timing, or the perceived value of the webinar. To boost attendance, send a series of reminder emails leading up to the event and consider offering a small incentive for joining live, like a special discount or exclusive resource.
Many webinar platforms provide a chart showing audience attention levels throughout the presentation. This metric tracks whether attendees have the webinar window as the primary focus on their screen. If you notice a significant drop-off at a particular point, review that section of your presentation. Was the content too complex, too basic, or simply not engaging? Use this data to refine your content and presentation style for future events. Keeping sessions interactive with polls and Q&A can help maintain high attention levels.
Active participation is a strong indicator of an engaged audience. Track how many attendees respond to polls, ask questions in the Q&A box, or interact in the chat. These features not only break up the monotony of a presentation but also provide valuable insights into what your audience is thinking. High interaction rates suggest your content is relevant and stimulating. If engagement is low, consider incorporating more interactive elements or prompting the audience for their thoughts more frequently.
The work isn't over when the webinar ends. Post-event analytics are essential for measuring the overall impact on your business goals and proving the return on investment (ROI).
Not everyone can make the live event. Offering an on-demand recording allows you to extend the life of your content and capture leads long after the webinar is over. Track the number of views the recording receives. A high number of on-demand views indicates continued interest in your topic and can significantly increase your webinar's overall reach and impact.
The most direct way to measure satisfaction is to ask. Send a post-webinar survey to gather feedback on the content, speaker, and overall experience. Ask questions like:
This qualitative data is invaluable for understanding what your audience values and where you can improve.
Ultimately, one of the primary goals of a webinar is to generate leads and drive business. Track how many attendees convert into qualified leads or customers. This might involve tracking how many people clicked a call-to-action link, requested a demo, or used a special discount code offered during the webinar. Connecting your webinar analytics to your CRM allows you to follow the customer journey and directly attribute revenue to your webinar efforts. This is the key metric for demonstrating ROI to stakeholders.
Webinar analytics provide a wealth of information, but the data is only valuable if you use it to make informed decisions. By consistently tracking these key metrics, you can move beyond guesswork and start strategically improving every aspect of your webinar program. You'll gain a deeper understanding of your audience, refine your content, and ultimately drive better results for your business.
Start by choosing a few key metrics to focus on and build from there. Each webinar is an opportunity to learn and iterate. With a solid grasp of your webinar analytics, you’ll be well-equipped to host events that not only engage your audience but also achieve your core business objectives.
2025-11-16 16:41:23
WTF is this: Circuit Breaker Pattern
Ah, the joys of modern technology – where a simple Google search can leave you feeling like you need a PhD in Computer Science to understand what's going on. Today, we're tackling a term that sounds like it belongs in a sci-fi movie: the Circuit Breaker Pattern. Buckle up, folks, and let's dive into the wonderful world of coding!
What is Circuit Breaker Pattern?
In simple terms, the Circuit Breaker Pattern is a design approach used in software development to prevent a cascade of failures when a service or system is experiencing issues. Imagine you're at a music festival, and the main stage's sound system starts malfunctioning. If the sound system is connected to a series of smaller stages, and each stage is connected to the next, a single failure could cause a chain reaction, taking down the entire festival's sound system. The Circuit Breaker Pattern is like having a smart electrician who detects the problem and quickly disconnects the faulty stage, preventing the issue from spreading to the rest of the system.
In coding, this pattern is used to detect when a service is not responding or is experiencing high latency. When this happens, the circuit breaker "trips" and prevents further requests from being sent to the faulty service, giving it time to recover or allowing the development team to fix the issue. This approach helps prevent a snowball effect, where a single failure causes a massive outage, and instead, allows the system to continue functioning, albeit with some limitations.
Why is it trending now?
The Circuit Breaker Pattern has been around for a while, but it's gaining popularity due to the increasing complexity of modern software systems. With the rise of microservices architecture, where multiple services work together to provide a single application, the need for fault-tolerant design patterns like the Circuit Breaker has become more pressing. Additionally, the growing demand for highly available and scalable systems has made this pattern a hot topic in the dev community.
Real-world use cases or examples
The Circuit Breaker Pattern is used in various industries, including:
For example, Netflix uses a variation of the Circuit Breaker Pattern, called the "Hystrix" library, to manage the communication between its microservices. This allows the company to detect and prevent cascading failures, ensuring that its users can continue to binge-watch their favorite shows without interruption.
Any controversy, misunderstanding, or hype?
While the Circuit Breaker Pattern is a valuable tool in the developer's toolkit, there's a common misconception that it's a silver bullet for all fault-tolerance issues. In reality, implementing this pattern requires careful consideration of the system's specific needs and constraints. If not designed correctly, the Circuit Breaker can introduce new problems, such as increased latency or decreased throughput.
Some critics argue that the Circuit Breaker Pattern can be overused, leading to a "fail-fast" approach, where services are too quick to give up and prevent requests from being processed. This can result in a poor user experience, as users may be unable to access the service even when it's partially available.
#Abotwrotethis
TL;DR: The Circuit Breaker Pattern is a design approach that helps prevent cascading failures in software systems by detecting and preventing requests from being sent to faulty services. It's gaining popularity due to the increasing complexity of modern software systems and is used in various industries to provide highly available and scalable infrastructure.
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2025-11-16 16:28:27
Today I focused on improving my Python skills by practicing NumPy, one of the most powerful libraries used in Data Analytics and Machine Learning.
NumPy makes numerical operations faster, cleaner, and more efficient—especially when working with large datasets.
array(), arange(), linspace()
reshape()
np.random.rand()
np.random.randn()
np.random.randint()
np.dot(a, b)
np.matmul(a, b)
np.where(condition, value_if_true, value_if_false)
np.sort(arr)
np.unique(arr)
np.genfromtxt("data.csv", delimiter=",")
You can check my NumPy practice code here:
👉 GitHub: https://github.com/ramyacse21/numpy_workspace