2026-01-25 05:20:51
Computing has advanced over time and is now essential across industries to help organizations make better decisions and boost profits through data analysis. A simple tool like Microsoft Excel serves as an excellent starting point for analyzing various datasets, including medical records, sales data, student performance, and social media metrics.
Data analysis involves examining, cleaning, organizing, and interpreting data to uncover useful insights, identify patterns, and support decision-making.
Workbook - The entire Excel file containing multiple sheets.
Cell - The smallest "box" where data is entered (e.g., A1).
Column - Vertical groups of cells, labeled A, B, C... at the top.
Row - Horizontal groups of cells, labeled 1, 2, 3... on the left.
Formulas must start with =. They can include numbers, cell references (A1, B2), operators (+, -, *, /), and functions.
Logical functions test conditions and return results based on TRUE/FALSE.
=IF(logical_test, value_if_true, value_if_false)
=IFS(condition1, result1, condition2, result2, ...)
Examples:
=IF(G2>30,"OLD","YOUNG")
=IFS(G2>50,"OLD", G2>40,"ABOVE-MEDIUM", G2>30,"MEDIUM","YOUNG")
Examples:
=IF(AND(G2>40,E2>70000),"H--R","OTHERS")
=IF(OR(G2>40,E2>70000),"H--R","OTHERS")
Negates a condition: =NOT(logical_test) or use <>.
Other operators: <, >, =, <>, <=, >=.
Filter Function Example:
=FILTER(A2:F10, D2:D10="HR")
Pivot tables summarize, sort, count, and reorganize data. They convert rows to columns and perform calculations (Max, Min, Average, etc.).
Data visualization uses charts (bar, line, pie, scatter plots) to reveal patterns and relationships.
Benefits:
An Excel Dashboard consolidates key visuals (PivotTables, charts, slicers) into one interactive screen for at-a-glance insights.
Key Components:
2026-01-25 05:11:27
नीचे Machine Learning ke lagbhag sabhi major algorithms ko category-wise list kiya gaya hai. (Practical duniya me “sabhi” ka matlab hota hai commonly used + academically important algorithms.)
(Labelled data hota hai)
(Label nahi hota)
(Deep Learning ML ka hi part hai)
Agar aap chaho to main:
bhi bana sakta hoon 🙂
kaunsa algorithm kab use kare
ye bhi batatao
ठीक है 🙂
नीचे “kaunsa Machine Learning algorithm kab use kare” ko simple decision guide + examples ke saath samjha raha hoon.
👉 Example: House price prediction
👉 Example: Spam vs Not Spam
⚠️ Deep Learning avoid karo (overfitting hota hai)
| Situation | Best Algorithm |
|---|---|
| Linear relation | Linear Regression |
| Outliers present | Ridge / Lasso |
| Non-linear | Random Forest |
| High accuracy chahiye | XGBoost |
| Condition | Algorithm |
|---|---|
| Binary classification | Logistic Regression |
| Text classification | Naive Bayes |
| High dimension | SVM |
| Interpretability | Decision Tree |
| Best accuracy | XGBoost / RF |
| Data | Algorithm |
|---|---|
| Well-separated clusters | K-Means |
| Noise present | DBSCAN |
| Unknown clusters | Hierarchical |
| Probabilistic | GMM |
👉 Example: Customer segmentation
👉 Example: Image / gene data
👉 Example: Stock price, weather
👉 Example: Sentiment analysis
👉 Example: Face recognition
👉 Example: Recommendation system
👉 Example: Game AI, Robotics
1️⃣ Simple model se start karo
2️⃣ Overfitting check karo
3️⃣ Ensemble try karo
4️⃣ Deep Learning tab hi jab data zyada ho
Agar chaho to main:
bas bolo 👍
2026-01-25 05:07:44
Introduction: Building a robust offline-first application is more than just caching data; it's about managing consistency. After struggling with manual sync logic in complex ERP-style apps, I decided to build a standard solution.
Meet SynapseLink v1.0.3: An Enterprise-Grade Offline Sync Engine that bridges the gap between Hive and Dio.
Key Technical Insights:
Intelligent Batching: Groups multiple offline actions into a single API request upon reconnection to save battery and server resources.
Auth-Aware Engine: Automatically pauses the sync queue when a 401 error occurs and resumes once the token is refreshed.
Deep Merge Resolution: A logic-heavy conflict resolver that ensures the most recent nested data is preserved.
Get Involved: The project is fully Open Source and I’d love to hear your feedback on the architecture.
📦 Pub.dev: https://pub.dev/packages/synapse_link
🚀 GitHub: https://github.com/wisamidis/synapse-links
💬 Discord: Connect with me at Vortex Shadow.
2026-01-25 05:07:20
I was filling out a survey on my phone.
It wasn’t short. Five minutes in, maybe more, I was only about a quarter of the way through.
Then it happened. A small, accidental scroll. The page refreshed.
Everything was gone.
No error. No warning. No recovery. Just an empty form.
I closed the page. I didn’t complain. I didn’t try again. I simply gave up.
This kind of failure is easy to dismiss. It doesn’t look like a bug. Nothing crashes. Nothing breaks loudly.
And yet, from the user’s perspective, everything just failed.
Data loss in web forms is not a new problem. But in modern applications, it has become strangely invisible.
The issue did not disappear. It shifted.
Today’s web apps are used more on mobile, across unstable networks, with aggressive browser lifecycle management. Forms are longer, more complex, often multi-step, and users are interrupted constantly.
The result is not necessarily more bugs, but more opportunities for normal human behavior to cause silent data loss.
Most teams measure what breaks loudly: server errors, crashes, failed payments, validation errors. Dashboards are full of red bars and alerts. When something goes wrong at scale, we usually know.
What we almost never measure is what disappears quietly.
Absence of evidence is not evidence of absence.
— Carl Sagan
There are very few modern, maintained, and simple solutions dedicated to protecting user-entered data in web forms. Existing approaches tend to fall into three categories:
Yet form data loss is still happening. Frequently. Silently.
In customer experience research, this pattern is well documented.
Multiple studies consistently show that only a small minority of dissatisfied users ever complain directly to a company. The vast majority do not escalate the issue, do not provide feedback, and do not explain what went wrong.
Instead, they leave.
Visible complaints therefore represent only a thin surface layer of actual frustration. Beneath each one sits a much larger volume of silent abandonments that never appear in dashboards, metrics, or support tickets.
Product teams are not careless. They are usually very good at measuring what is visible.
Errors are tracked. Crashes are logged. Performance regressions show up quickly. When something breaks in a noisy way, it leaves a trace.
Data loss rarely does.
When a user refreshes a page, closes a tab, or loses a form because the browser crashed, nothing necessarily looks wrong from the system’s point of view. No error is thrown. No alert fires. No red line appears on a dashboard.
The user simply leaves.
From the product’s perspective, nothing failed. From the user’s perspective, everything did.
That gap is the blind spot Savior was built for.
I did not build Savior to optimize performance charts or polish UX details. I built it because people are human.
They get interrupted. They mis-scroll. They reload a tab without thinking. Browsers crash. Phones do what phones do.
None of this is exceptional. It is everyday behavior.
Savior exists as a local safety net for those moments. It quietly keeps form data around so that a brief interruption does not wipe out several minutes of effort.
For the user, it removes a small but very real source of frustration. For the product, it prevents a failure that would otherwise leave no trace at all.
No jargon. No ceremony. Just protection where things usually go wrong.
Savior exists because data loss is common, frustrating, and almost always silent.
If you have ever lost form data and quietly gave up, this is exactly why Savior exists.
Savior Core is an open-source library for basic form persistence.
Savior SafeState Recovery adds deterministic recovery for crashes and edge cases.
More at https://www.zippers.dev
2026-01-25 05:04:37
We all got drunk on 1-prompt apps in 2025. Now, the technical debt is calling, and it’s time to sober up.
Let’s be real: 2025 was one long, glorious party for developers. When Andrej Karpathy coined “Vibe Coding,” we all felt the magic. For a moment, it felt like the “end of syntax” had actually arrived . We were shipping full-stack apps with a single prompt, “vibing” with our LLMs, and pretending the code didn’t exist.
But it’s January 2026, and the hangover is brutal.
Now engineers spend more time helping teams rescue “Vibe-coded” projects that hit the complexity wall. It starts with a demo that looks like magic, but within three months, it turns into a “Black Box” that no one — not even the person who prompted it — can explain . If you can’t explain your code, you don’t own it; you’re just a passenger in a car with no brakes.
The Rise of “Slopsquatting” and Refactoring Hell
The biggest shock of 2026 isn’t that AI makes mistakes — it’s that those mistakes are now being weaponized. Have you heard of Slopsquatting? Attackers are now registering malicious packages on NPM and PyPI that have names LLMs frequently “hallucinate”.
If you’re blindly clicking “Accept All” in Cursor or Windsurf, you might be importing malware directly into your production environment without even knowing the package exists.
Beyond security, we’re seeing a “Technical Debt Tsunami”.
Vibe-coded software often ignores modularity and optimized queries. What looks clean in a chat window is costing companies tens of thousands of dollars in unnecessary cloud compute because the AI wrote a “brute force” solution that doesn’t scale.
Moving to the “Head Chef” Model
In 2026, the best engineers I know have stopped being “prompt monkeys” and started being Head Chefs.
The AI is your kitchen staff. It can chop the onions and prep the sauce (the boilerplate), but you must design the menu (the architecture) and taste every dish before it leaves the kitchen (the review). Even Linus Torvalds, who recently admitted to vibe-coding a visualizer for his audio projects, kept the reins tight on the actual logic.
The 2026 Rulebook for Agentic AI
To build systems that actually survive their first 1,000 users, you need a framework. This is how we’re doing it now:
Architecture by Contract (YAML/JSON): Never ask an AI to "build a system." Give it a YAML file that defines your domain model, security boundaries, and API schemas first.
Model Context Protocol (MCP) is the new USB-C: Stop writing "glue code." Use MCP to connect your agents to your databases and tools in a standardized, secure way.
Sequential Prompting: Don't dump 50 requirements at once. Break it down: Domain -> Auth -> Logic -> Integrations. Validate at every step.
Engineering isn't dead. It just got a lot more interesting. We’re moving from writing lines to designing systems. Less "vibes," more rigor.
Resources:
(https://modelcontextprotocol.io/specification/) – The open standard for connecting AI agents to real-world data.
(https://www.veracode.com/resources/analyst-reports/2025-genai-code-security-report) – Why 45% of AI-generated code is a security risk.
(https://thenewstack.io/the-head-chef-model-for-ai-assisted-development/) – Redefining the role of the engineer in the agentic era.
(https://www.langchain.com/langgraph) – How to build agents that actually follow a plan.
(https://medium.com/elementor-engineers/cursor-rules-best-practices-for-developers-16a438a4935c) – Training your agent to behave like a teammate, not a "yes-man".
2026-01-25 05:01:04
A while back I needed a data grid for a project. You know the drill – looked at AG Grid (too heavy, enterprise pricing), TanStack Table (great but headless, didn't want to build all the UI), various React/Vue-specific options (locked into one framework).
I was also working across multiple projects – some Angular, some React, some just vanilla JS. The idea of maintaining different grid implementations for each was... not appealing.
So I did what any reasonable developer does: I spent way more time building my own thing than I would have spent just using an existing solution. 😅
Web components have been "almost ready" for years, but they're actually pretty solid now. Custom elements, CSS nesting, adoptedStyleSheets – the browser APIs are there.
So I built @toolbox-web/grid:
<tbw-grid></tbw-grid>
<script type="module">
import '@toolbox-web/grid';
const grid = document.querySelector('tbw-grid');
grid.columns = [
{ field: 'name', header: 'Name' },
{ field: 'email', header: 'Email' },
];
grid.rows = data;
</script>
No framework. Just a custom element. Works in React, Angular, Vue, or plain HTML.
The basics you'd expect:
And some stuff I needed for specific projects:
Everything is a plugin, so you only ship what you use. The core is pretty small (~40KB gzipped with common plugins).
I started with Shadow DOM for style encapsulation. Seemed like the "right" way. But:
Switched to light DOM with CSS nesting. You get scoping without the isolation headaches.
Early versions had render calls scattered everywhere. Change a property? Render. Resize a column? Render. Sort? Render. This caused layout thrashing and flickering.
Now everything goes through a single scheduler:
state change → queue → batch → one RAF → render
Doesn't matter how many things change in a frame, there's one render pass.
I went back and forth on the plugin architecture. Ended up with simple classes:
class MyPlugin extends BaseGridPlugin<MyConfig> {
readonly name = 'myPlugin';
processRows(rows) {
// transform data before render
return rows;
}
afterRender() {
// do stuff with the DOM
}
}
Constructor injection for config, lifecycle hooks for different phases. Nothing fancy, but it works well.
The raw custom element works everywhere, but framework-specific wrappers make life nicer.
React:
import { DataGrid } from '@toolbox-web/grid-react';
<DataGrid
rows={employees}
gridConfig={{
columns: [
{ field: 'name' },
{ field: 'status', renderer: (ctx) => <Badge value={ctx.value} /> },
],
}}
/>
Angular:
<tbw-grid [rows]="data" [gridConfig]="config">
<tbw-grid-column field="status">
<my-badge *tbwRenderer="let value" [status]="value" />
</tbw-grid-column>
</tbw-grid>
You get proper types, native component renderers, and framework-idiomatic APIs.
Just for context – not trying to say mine is "better", they're different tools:
| @toolbox-web/grid | AG Grid | TanStack Table | |
|---|---|---|---|
| Bundle | ~40KB | ~500KB | ~15KB (headless) |
| UI included | Yes | Yes | No |
| Framework | Any | Wrappers | React/Vue/Solid |
| License | MIT | MIT + paid | MIT |
AG Grid is way more mature and feature-complete. TanStack is brilliant if you want full control over rendering. This sits somewhere in between – batteries included but not as heavy.
It's MIT licensed, free, no tracking, no enterprise upsell. Just a thing I built that might be useful to others.
npm install @toolbox-web/grid
Would love feedback, bug reports, or feature ideas. I'm actively working on it and trying to make it as useful as possible.
What data grid do you use? Curious what features people actually care about.