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Taqui — Mon, 05 Oct 2026 14:43:36 +0000

Hacktoberfest Weekend Challenge: Build for a Friend Submission 🤝

Taqui

Oct 4

TiffinTally - my contribution to my friend's family business

#devchallenge #weekendchallenge #hf26challenge #webdev

6 min read

TiffinTally - my contribution to my friend's family business

Taqui — Sun, 04 Oct 2026 20:41:53 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built 🤟

Hey there, my name is Taqui. I am a fullstack web developer, and this time I wanted to build something useful outside my usual developer projects.

So I built TiffinTally, a small contribution to my friend’s family kitchen :)

The family story below is fictionalized. Customer counts and messages are illustrative, and real seller feedback is still pending.

The setup is a friend's family business in Noida, in the Gautam Buddh Nagar district of Uttar Pradesh, India. His mother and older sister prepare daily lunches for people who moved there for work.

Some customers live away from their families. They don't want restaurant food every afternoon. They want a simple home-cooked lunch that reaches them at the office
.

The business has around 18 to 24 monthly regulars. Small enough to know everyone, but enough for daily changes to become difficult.

You wonder,
What's the Problem i solving with this app ?

My Answer -

The cooking isn't the confusing part. The WhatsApp messages are.

“Aunty, no lunch tomorrow.”

“Please send two extra on Friday.”

“I am going home for three days.”

One person is cooking. Another is packing. A message arrives between both jobs.

Now someone has to remember the change, update the lunch count, tell the delivery helper, and account for it at the end of the month.

A missed cancellation means an extra lunch gets packed. A missed request means someone doesn't get theirs. And later, “I skipped those three days” becomes another chat to search.

TiffinTally keeps the request, the approved change, the packing list, and the monthly statement connected.

Customers can keep using WhatsApp. The kitchen gets a clearer way to manage what happens after the message arrives.

Demo

Live app: TiffinTally

Here is the workflow, one screen at a time.

1. Start with today's kitchen desk

The dashboard shows the selected service date, approved meal total, and requests waiting for review.

It has a personal greeting and a kitchen-notebook layout. I wanted it to feel like a working lunch register, not a dashboard full of unrelated charts.

2. Connect the kitchen's WhatsApp

The seller connects the kitchen phone through a QR code and links contacts to customer records.

Incoming messages from linked customers can be captured for review. Automatic analysis needs the AI providers configured.

There is also a manual “Add message” option.

3. Read voice notes too

Not every customer types their changes.

The WhatsApp audio path uses ElevenLabs Scribe v2 to turn a voice note into text. That text then enters the same review workflow.

This integration is implemented with mocked API tests. A live customer voice-note walkthrough is still pending.

4. Review before changing anything

The seller can read the original message beside the proposed change, check the dates and quantities, and edit or approve it.

Unclear requests stay marked for attention.

AI creates a draft. It does not approve the order.

5. Keep regular customers organized

Customer records hold names, packing notes, and schedules. Manual proposals handle changes that arrive through a call or conversation instead of WhatsApp.

Settings hold the kitchen timezone, planning time, cutoff, and customer limits.

Prices and delivery setup add each customer's agreed meal rate, billing number, building, and delivery note.

6. Finalize the packing sheet

Approved changes feed the daily packing quantities.

The seller finalizes a version of the sheet before handing it over. If current packing facts change, a fresh sheet is needed.

A pending draft cannot quietly change what gets packed.

7. Explain the monthly bill

The Bill book shows dated quantities and charges from finalized sheets.

Skips and extras reconcile into the total without deducting the same skipped meal twice. An agreed half-portion charge can be entered as a dated adjustment with a reason.

Issued statements are saved as immutable versions. Revisions reference the previous statement and warn customers not to pay both.

Sharing opens a WhatsApp composer for the seller to review and send. It does not mark a bill paid or prove a delivery happened.

8. Give the helper Ramesh Mode

Ramesh Mode groups positive meal quantities by the buildings or routes the seller entered.

Cancelled stops appear separately under “Do not stop today.” Unassigned deliveries remain visible instead of disappearing.

The helper gets a practical handover summary, not the whole billing workspace.

This is building-based grouping, not GPS route optimization.

9. Keep forecasts separate from orders

The forecasting section is advisory. It cannot change confirmed meals.

The planned TabPFN path uses historical meal changes and structured message signals. Hosted prediction integration and real-history validation are still unfinished, so I am not claiming a working demand forecast.

Code

Source repository: TiffinTally

How I Built It

I used Next.js, React, TypeScript, Tailwind CSS, and shadcn/ui for the app.

TanStack Query handles reads and refreshing data after actions. Better Auth provides Google sign-in. Zod validates inputs and model outputs.

Gemma reads the request

The configured model is Gemma 4 31B IT, accessed through Backboard using its OpenRouter provider.

It extracts proposed meal changes with supporting quotes from the original message.

The app checks those quotes against the source. A confident-looking answer with invalid evidence does not become an approved order.

JEV asks a different set of questions

TypeSafe's JEV, pinned to jev-1.13.0, independently classifies the request.

Is it a pause, a quantity change, or something unclear? Does it explicitly replace the regular schedule? Are necessary details missing?

Disagreement or uncertainty becomes a review warning. JEV is another signal, not a guarantee that Gemma is correct.

Backboard connects both stages

Both models use the Backboard SDK, with separate threads and memory disabled.

An approved fictional live test produced a validated review candidate through the Gemma/JEV pair. That checks the integration, not accuracy across real customer conversations.

MongoDB keeps the approved facts

The persistence layer targets MongoDB Atlas, using seller-scoped records, revision checks, transactions, and request receipts.

Approval and money calculations are ordinary application code, not model-generated arithmetic.

Amounts use integer paise. Repeated requests use idempotency keys. If a save result is uncertain, the UI checks the original receipt rather than blindly submitting it again.

The checks have limits too

I checked the layouts with 279 isolated browser-rendering cases, covering mobile, tablet, desktop, and short landscape sizes using fictional data.

Those checks do not replace watching someone use the app on their own phone. The authenticated seller walkthrough and real family handover remain next steps.

Why Does Open Innovation Matter?

Gemma is an open-weight model, so the extraction stage is not tied permanently to one closed model API.

I can inspect the prompts, change the validation, and evaluate another serving option without rebuilding the packing and billing rules.

A closed API could also extract messages. The benefit here is having more control over the model choice and the surrounding workflow.

Open-weight does not mean offline. This build uses hosted inference, and ElevenLabs transcription is hosted too.

The important boundary is visible in the code: models suggest changes, while the seller approves them and deterministic code calculates the result.

My Agent Session

I built TiffinTally with AI assistance in omnirush.ai.

The work included the review pipeline, billing reconciliation, delivery handover, and responsive layouts.

One useful failure was a Gemma quote with an incorrect evidence offset. The validator rejected it. I corrected the prompt and kept the strict check instead of weakening it to accept the answer.

Prize Categories

Best Use of Gemma: Open-weight message extraction with source-linked review drafts.
Best Use of Backboard: The shared SDK integration for the Gemma and JEV stages, tested with fictional live requests.
Best Use of MongoDB Atlas: The application's persistence design for seller-scoped approvals, packing sheets, and statements. Live authenticated commerce verification remains pending.
Best Use of ElevenLabs: Scribe v2 integration for WhatsApp voice-note transcription. Live end-to-end transcription remains pending. ElevenLabs

I am not claiming TabPFN or Sentry categories from configuration or unfinished integration alone.

The goal is pretty simple: let the family spend more time cooking, and less time figuring out which lunch changed.

Thankyou for reading, i hope you liked it :)

Guys need your shitty feedback here

Taqui — Tue, 29 Sep 2026 17:00:53 +0000

Taqui

Sep 28

My Craziest Side Project Yet: Drawva

#mlhacks #devchallenge #hackathon #ai

4 min read

My Craziest Side Project Yet: Drawva

Taqui — Mon, 28 Sep 2026 16:52:46 +0000

This is a submission for the MLH x DEV Writing Challenge

What I Built

Hey there, my name is Taqui. I am a fullstack web developer. When I started coding back in 2020, things were simple and skills had the highest priority. Lately, with the rapid rise of AI and the tough job market, I have felt pretty cookeddd. But instead of sitting back and questioning my career choice, I decided to dive headfirst into Agentic AI and GenAI to build a serious, real-world system from scratch.

That project is Drawva.

Drawva is an infinite canvas whiteboard engine powered by a multimodal AI perception agent. Unlike typical whiteboards where your strokes are just static pixels, Drawva actually looks at your canvas, understands your hand-drawn gestures, and turns them into live diagrams, interactive code widgets, and math equations.

DeepSeek Harness (DSH) Agent Runtime

Under the hood, Drawva runs a modular DeepSeek Harness system built on a Cordis microkernel runtime:

Agent Loop & Tool Runtime: Handles multi-turn tool calling through typed contracts (canvas_apply, canvas_snapshot, canvas_read, canvas_patch_widget).
Token Meter & Compaction: Uses a tool-result pruner and basic compaction to keep agent context tight and cost-effective during long sessions.
Session Projection: Preserves agent state and conversation history across streaming SSE steps.

Specialized Prompt Techniques

Getting an LLM to accurately interact with a freehand 2D canvas required custom prompt engineering contracts:

Coordinate Contract: Mathematical pixel-to-global transforms so the AI calculates exact millimeter coordinates from visual snapshots.
Zero-Heading Redundancy: The AI treats the user's handwritten words as the title. It never outputs duplicate title banners, keeping the canvas looking naturally co-authored.
Hybrid Wireframe Utilization: If you sketch a 3-box wireframe, the AI does not erase it. It leaves your pen lines intact and renders live data directly inside your drawn boxes with 100% transparent backdrops.
Two-Command In-Place Replacement: When turning a rough sketch into an interactive applet, the agent emits an atomic erase command first to clear the ink, followed immediately by the mounted widget.
Native Sketchnoting: A dedicated mode that uses pure vector strokes to sketch 3D loss surfaces, neural net graphs, and complex planes.

Extensible On-Demand Plugins

Drawva features 11 extensible runtime plugins loaded dynamically via load_plugin:

Live Data: Weather, Stocks, Tech News (Hacker News), Earthquakes (USGS), Space Weather, and Currency Exchange Rates.
Developer & Media: GitHub Pulse (live repo stars, forks, velocity), Image Search (server-side photo resolution for sandboxed widgets), and Flowcharts.

7 Diagram Engines & P2P Sync

Drawva supports 7 sandboxed diagram runtimes (Mermaid, Graphviz DOT, Vega-Lite charts, SMILES molecular structures, BPMN, Cytoscape, GeoJSON) alongside MathJax LaTeX typesetting. Everything autosaves to IndexedDB, and users can collaborate peer-to-peer over WebRTC with zero central server databases.

Demo

Here is a full walkthrough video showing Drawva in action, from freehand sketching to live diagram rendering:

Direct video link: Drawva Demo on YouTube

GitHub Repository: https://github.com/taqui-786/drawva
Live App: https://drawva.vercel.app/
Community cooks: https://drawva.vercel.app/community

Partner Technologies

Building Drawva pushed me to integrate modern fullstack and AI technologies:

DeepSeek Harness (DSH): Powered the core agent runtime, tool execution lifecycle, token metering, and compaction pruning.
Next.js 16 (App Router) & React 19: Provided the streaming foundation, handling Server-Sent Events (SSE) and fast client hydration.
Vercel AI SDK & Multimodal LLMs: Connected the canvas to vision models like Google Gemini 2.5, OpenAI GPT-4o, and local Ollama models for high-resolution visual inspection.
Tailwind CSS v4 & Base UI: Used to build our dark-mode UI with custom neon lime tokens, floating tool docks, and accessible dialogs.
GitHub: The entire codebase is open-source on GitHub, where I tracked features, plugin contracts, and releases.

Hackathon Experience

Tbh this was my first hackathon 😅. And Over the past few months, I had been losing my confidence as a developer, wondering if AI was making my hard-earned skills obsolete.

Building Drawva was not the only goad for this hackathon. The main purpose is that new Deepseek Harness in the market and it is opensources, So i decided to learn how the harness is build and end up integrating the mini-kernal in this project.

This experience proved to me that as developers, our value is not gone. We just have to evolve, pick up Agentic AI, and build ambitious tools that solve real problems.

That's it man

Look, the tech market is wild right now and we all feel cooked some days. But if AI is taking over the world, I am not going down without building the craziest open-source tools I possibly can.

Go open Drawva, draw the ugliest, most crooked scribble you can think of, and watch the AI turn it into art. Aslo also also don't forget to share you crazyy dravs to the comminuty page (we have one our community page)

And if it crashes, roast me in the GitHub issues or open a PR. And if this gave you even 1% of your developer spark back, smash a star on the repo. Let us reclaim our builder aura :)

Microsoft Up 15%. Me? 100% Down.

Taqui — Sun, 02 Aug 2026 15:33:51 +0000

hey there, so i wanted to share something with you. this is a bit personal but i have been watching the news this week and bruh... things are not going good. i am jobless right now, no other source of income, and day by day things are getting worse. and the worst part? AI is literally fuking every job in the software field. so when i saw this week's stock market drama, it hit me different.

Microsoft popped. Meta tanked. Same AI boom. Two completely opposite reactions. and all i could think was... yeah, this is exactly my life right now.

The Numbers, Bruh

let me break it down real quick because this is wild:

Microsoft jumped like 15% after beating expectations. Azure grew 43%. full year Azure revenue crossed $100 billion. insane.
Meta dropped 8–9% after missing on guidance. their free cash flow collapsed 91% year-over-year to just $784 million. like... 91%?? gone.

same AI boom. same crazy spending. and the market said "you're amazing" to one and "you're done" to the other.

Why Microsoft Won

Microsoft actually showed receipts. they didn't just talk about AI, they showed the money coming in. Azure is growing, Copilot is making real revenue, investors can literally see the line between billions spent and billions earned.

lesson? Wall Street doesn't hate AI spending. it hates AI spending without proof.

Why Meta Lost

Meta's problem is that nobody can see where the money comes back. Zuckerberg talked about the "AI capacity dilemma" — how much compute to keep for yourself vs sell to others. but guidance missed, free cash flow went to hell, and the market was like... nah bro, i need answers.

and honestly? i relate to that feeling more than i want to admit. putting everything into something and people still saying "not enough."

The Bigger Picture

this week is a preview of everything coming. companies are dumping hundreds of billions into data centers, chips, and models, all betting AI demand keeps exploding. the ones who can prove it pays off? they get rewarded. the ones who can't? they get crushed.

and us devs? we're the ones caught in the middle. chips are booming — Lam Research jumped 20%, Micron went crazy. but the jobs? they're disappearing.

bruh, i remember the days before Claude and Cursor. things were so good. i had my aura, man. i was a fuking awesome engineer. i built cool projects, wrote articles that people genuinely loved, got stars and forks on my repos, my articles had views, likes, comments... and i loved replying to every single one of them. i had so much self confidence back then.

i miss those days so badly. i wish i could get back to them.

What This Means for Us

AI isn't a single trade anymore. it's a scoreboard. companies that turn AI into a revenue engine win. companies that treat it like a science project lose. Microsoft showed the winning formula. Meta showed what happens when you can't explain the money.

and for people like me? i guess it means we have to adapt. find the new aura. rebuild. i don't know how yet, but i have to.

ahh... i wish those days would come back again. i wish i get my aura back again.

anyway, thanks for reading. this one was personal. i hope things get better for all of us out here.

thankyou.

Great Read

Taqui — Mon, 25 May 2026 15:34:46 +0000

Sylwia Laskowska

Dec 1 '25

Nobody Writes Clean Code. We All Just Pretend

#discuss #webdev #programming #productivity

211

128

4 min read

The Company Is Strong. So Why Are 4,000 People Gone?

Taqui — Sat, 28 Feb 2026 06:10:58 +0000

Going to be a rant.

We just watched a CEO tweet that they’re making their company “smaller” and not because revenue collapsed, not because the product failed, not because customers left, but because they can. Nearly half the company, over 4,000 people, gone in one move while the business is still growing, improving profitability, and serving more customers.

Read that again. This wasn’t survival mode, this was optimization mode.

The tone of the tweet was calm, transparent, even generous with severance and benefits, which somehow makes it more unsettling.

The logic was simple: intelligence tools plus flatter teams equal a new way of running a company. Faster decisions, fewer layers, more output per person. So leadership chose to act now instead of cutting slowly over years. And this is what people are missing in the AI debate. It’s not about whether AI writes better code. It’s about compression.

If a strong, profitable company can say “we need fewer people because AI changes how work gets done,” then the baseline just shifted. Layoffs used to be reactive. Now they’re proactive. “Profitable” used to mean safe.

Now it doesn’t. This isn’t framed as survival, it’s framed as restructuring for an intelligence-driven future. And once companies realize 10 people can do the work of 15, they don’t keep 15 out of kindness. They optimize for margins. That’s the uncomfortable truth.

AI isn’t just a tool inside products anymore, it’s becoming the operating system of companies. And operating systems are designed for efficiency. If this is happening while models are still early, what happens in three years? Maybe the real question isn’t whether AI replaces developers. Maybe it’s how many developers a modern company actually needs. Because once leaders decide that number is lower, they don’t drift toward it. They jump. And that jump is what we just saw.

Rant over.

Built Something Crazy This Week – Inkash

Taqui — Sat, 14 Feb 2026 13:45:51 +0000

Hey dev's 👋 it's me Taqui

This week I built something that actually pushed me hard.

It’s called Inkash.

Live: https://inkash.vercel.app
GitHub: https://github.com/taqui-786/inkash (Drop a Star ⭐)

Inkash is a smart link generator that compresses real data into a short shareable URL.

No database.
No backend storage.
The data lives inside the URL itself.

That was the fun part.

What Inkash Actually Does

The idea is simple:

Take user input
Compress it using the deflate algorithm
Convert it into a URL-safe string
Generate a shareable link
On load, decompress and restore the original content

So large content becomes a compact link.

When someone opens it, everything rebuilds instantly on the client.

It feels kind of crazy seeing big data turn into a short URL and still restore perfectly.

it also has markdown editor you can us

Sharing Feature:

The cool thing is,

You can generate the QR-code of the link and share it

Core Compression Logic (Simplified)

Here’s a simplified version of how compression and decompression works:

import { deflate, inflate } from "pako";

export function compressData(input: string) {
  const compressed = deflate(input);
  return btoa(
    String.fromCharCode(...compressed)
  );
}

export function decompressData(encoded: string) {
  const binary = atob(encoded);
  const bytes = Uint8Array.from(binary, (c) => c.charCodeAt(0));
  return inflate(bytes, { to: "string" });
}

Under the hood, it:

Uses the deflate algorithm to reduce payload size
Converts binary compressed data into Base64
Makes the output URL safe
Restores everything safely on load

Technical Challenges

The real challenge was not just compression.

It was making sure everything works safely inside a browser URL.

Here are some things I had to handle:

Making compressed output URL safe
Handling corrupted or invalid hashes
Respecting browser URL length limits
Preventing UI crashes during decompression
Managing edge cases with large payloads
Keeping everything fully client-side

If even one character in the URL changes, decompression can fail. So proper validation and error handling was very important.

Tech Stack

Next.js
TypeScript
Client-side compression using pako
Modern UI components
Clean and scalable architecture

There is no database involved.

The link itself is the storage.

That was the main experiment and honestly the most fun part.

What I Learned

Building Inkash taught me:

How the deflate algorithm works in real usage
Binary to Base64 transformations
URL encoding and safety concerns
Browser URL length constraints
How easy it is to break decoding
How to build something useful without a backend

This project broke many times before it became stable. But once it worked, it felt clean and kind of magical.

If you check it out, I would really love honest feedback.

What would you improve or add to something like this?

This portfolio isn’t perfect, but it’s 100% mine

Taqui — Sat, 24 Jan 2026 06:26:18 +0000

Hey devs 👋 its me Taqui

Just wanted to share the story behind my last portfolio.

I already had a portfolio before this. It looked cool, it worked fine, but for some reason it never felt like it was really mine. I couldn’t fully connect with it.

The truth is, my older portfolio was mostly a rebuild of chandai’s portfolio. I learned a lot from building it, especially about layout, animations, and structure. But at the end of the day, it still felt like I was wearing someone else’s design.

So I decided to start again. From zero.

This time, I made one clear rule for myself:
Build something from my own ideas, my own layout decisions, and my own taste.

I did take inspiration from other great portfolios (because that’s how we all learn), but every section, spacing choice, and interaction was designed and built by me. No copying. No rebuilding someone else’s work.

Tech stack I used:

Next.js
Tailwind CSS (mostly custom styles)
Very little shadcn/ui, only where it made sense, like the contact form
Small UI details and interactions done manually instead of relying on libraries

When you open the site, you’ll notice a subtle fade-in blur animation. That was intentional. I wanted the site to feel calm and smooth, not flashy or loud. Something that feels pleasant to scroll through.

This isn’t my first portfolio.

But it’s the first one that truly feels like *me*.

And because of that, I’m calling it my last portfolio.

I’ve attached a demo banner image above so you can get a quick idea of the look and feel.

Live URL (new portfolio):

Checkout its live 🚀

Source code:

Drop a ⭐ on Github

If you’re curious, feel free to check it out.

And if the code helps you or inspires you in any way, dropping a GitHub star would honestly mean a lot ⭐

Feedback is welcome.

Happy Show Off Saturday 🚀

I Built Everything Fast… Then One Page Destroyed My Confidence

Taqui — Tue, 09 Dec 2025 07:04:12 +0000

Hey buddy,

So… I built almost the whole app. Shipped every hard thing. Solved every annoying bug.

And then one single page came out of nowhere and said:

“Bro, sit down.”

Welcome to my life with Snapgroove - a tiny tool I’m building to make your screenshots look less like a cry for help. Give them shadows, gradients, device frames… all in one click. Basically: make your work look expensive without knowing design.

And guess what?

The core app is done.

Like actually done.

Editor works
Image export is smooth
All features tested
Logo and branding ready

Live here → https://snapgroove.vercel.app

But then…

The landing page happened.

And suddenly I felt like I forgot how to think.

The Fun Part: Building the Actual App

As a full-stack dev, building features is my comfort zone.

Give me logic, give me APIs, give me bugs that ruin my sleep - I will survive.

Snapgroove came together pretty fast:

A clean editor
Nice export flow
Simple UI
It’s all open-source too: taqui-786/Snapgroove

Github Repo

I even vibe-coded some layouts for the landing page.

But honestly? Everything felt too generic or too messy.

I’m just not great at design stuff. And this is the part that always slows me down on every project I make.

And yes, I know the app itself is still not perfect. It’s in beta. I’m improving it as fast as I can. But landing pages… bro… they hit different.

The Not-So-Fun Part: The Landing Page Breakdown

It’s wild how I can build a full editor in a single sitting, but a landing page will take my brain hostage for days.

I tried multiple layouts.

I tried being creative.

I even told myself, “Just ship it, nobody cares.”

But I care.

And nothing feels right.

Landing pages are weird.

You need:

Good design
Clean layout
The right words
The right vibe
Something that doesn’t scream “bro, we know you’re a developer”

And I’m still figuring that out.

What I Need Help With (Yes, You 😭)

If you’re reading this, I’d honestly love your input.

Take a quick look at the app
Tell me what confused you
Suggest what the landing page should highlight
Share any design inspo sites you use

Anything helps.

I’m a developer who’s way better at building things than selling them.

So any tips, design ideas, or copy advice… I’ll take it all.

Why I’m Even Sharing This

Because building stuff is fun.

But being stuck alone makes it harder.

And I know I’m not the only dev who can ship complex features in 2 hours but can’t pick a font for 2 weeks.

If you feel the same, you’re not alone.

We’re in the same boat, staring at Figma like it’s a final exam.

Thanks for Reading 🙏

If you have even 2 minutes, your design brain cells could save my entire project.

Drop any tips.

Share any inspo.

Tell me what looks weird.

Or just say “yo this looks clean” so I don’t cry.

App: https://snapgroove.vercel.app

Codebase: taqui-786/Snapgroove

Bun is joining Anthropic 🔥

Taqui — Wed, 03 Dec 2025 06:48:42 +0000

Big news - Bun is now joining Anthropic.

According to the official announcement, Anthropic acquired Bun so it can power Claude Code, the Claude Agent SDK, and future AI-dev tools.

👉 Official News

Bun - the super fast JavaScript/TypeScript runtime, bundler, package manager, and test runner — will now serve as core infrastructure for Anthropic’s developer-focused AI tools.

💠 Key points that i extracted

Bun becomes part of Anthropic’s core engineering stack
Bun will directly power Claude Code and future dev tools
Faster performance, better DX, and more stability expected
Bun stays open source
Anthropic will invest full-time engineering support into Bun
Goal: build a stronger, AI-supercharged dev workflow

What the community on Reddit is saying

Reddit thread: https://www.reddit.com/r/webdev/comments/1pch41f/bun_is_joining_anthropic/

A quick vibe check from the top comments:

“Is this the rug pull people were worried about?”
“They bought it because it’s critical infrastructure for them.”
“People are overreacting. It’s not a rug pull.”
Some users feel this is good for Bun’s long-term health
Some are worried it might become paid later (“Bun Pro”)
Others think Bun still isn’t mature enough for real-world teams

A few devs also pointed out they love Bun for:

Faster installs
Less tooling overhead
A cleaner experience than Node
Good standard library
Solid for new projects

But yep - the skepticism is still alive.

✅ Pros of the Bun x Anthropic deal

Stronger engineering support
Anthropic has the resources to make Bun more stable and production-ready.

Better dev tools powered by AI
Since Bun will power Claude Code and other AI tools, we might see faster, cleaner workflows.

Still open source
This is a big relief for many devs.

Better performance gains
Bun already has speed benefits - now with more backing, it could get even better.

⚠️ Cons & concerns from developers

Fear of monetization
A common worry: Bun might slowly drift into “Bun Pro” territory.

Corporate ownership shift
Some devs loved Bun’s indie vibe - now they’re unsure if direction will stay community-focused.

Optimized for Anthropic first
There’s a worry Bun will become tailored mainly for AI tooling, not general web development.

Maturity concerns
A few devs said Bun didn’t feel stable yet in real-world jobs.

🔥 My quick take

Honestly, this is a big move - and mostly a good one.

If Anthropic keeps Bun open-source and improves stability, we might see a new era of:

Faster dev workflows
AI-first tooling
Modern runtimes built for real projects

Still… the concerns are real.

Nobody wants to wake up one day and find half the runtime behind a paywall.

For now, I’m excited.

Let’s see how Bun grows with Anthropic behind it. 👀

That's it

What do you think is this a good deal or another free tool to be converted into paid stuff,

Let me know your honest thoughts on this in comments📩

See you in next blog, till then Happy Coding

Follow me in Github⭐

TaquiFollow

I'm a self-taught Web developer who is always learning and creating cool Project stuffs.

Is Self-Taught Coding Still Worth It?

Taqui — Mon, 07 Jul 2025 08:02:59 +0000

So you’re broke, confused, 22, hate your job, and think learning to code will fix it all.

Hello friends it's me Md taqui imam and WELCOME to the most popular side quest of the 2020s.

You probably googled:

"How to learn to code in 3 months"
"Is self-taught coding dead?"
"Can I get a dev job with no degree and bad WiFi?"

And now you’re here, trying to figure out if teaching yourself programming is still a thing or just another internet scam like dropshipping crypto monkey NFTs.

Let’s talk 😅

✅ Yes. Self-taught coding still works.

If anyone tells you it doesn’t, they probably just failed at it and now run a bootcamp charging $10k to copy-paste ChatGPT responses into React components.

The truth is: companies still care more about what you can build than where you learned. A solid GitHub > random diploma from “Any Tech Institute.”

But no, it’s not “easy now because of AI”

Yeah yeah, you’ve seen the TikToks:

“Learn Python in 10 minutes!”

“Make $120k with just HTML and ChatGPT!”

Sure, and I can become a surgeon by watching Grey’s Anatomy.

AI is a tool, not a cheat code. It helps you learn faster, but it won’t do the learning for you. You still need to suffer through broken layouts, error messages, and existential dread like the rest of us.

What should you actually learn?

You don’t need a PhD in rocket science to start. Here’s a basic path:

HTML/CSS: Make things look not horrible.
JavaScript: Make things do stuff.
React or Next.js: Make things do stuff better.
GitHub: So you can tell employers “Look, I actually built something.”
Optional: Python + Flask/Django: If you prefer backend and chill.

That’s it. Don’t get lost in “Should I learn Rust or Go or Zig or Assembly for fun?” You’re not building a compiler, you’re building your way out of retail.

A request to senior dev's who are reading this, Can you please comment 📩 down a proper Web development roadmap for the new beginners of 2025.

Build real things. Not to-do apps.

Everyone makes a to-do app. Stop it. Build dumb but fun projects:

“Rate My Chai” — users rank local tea stalls
“Is It A Scam?” — submit WhatsApp messages for instant sussy score
“BrokenCSS” — a portfolio of your worst designs (trust me, it’s relatable)

Build stuff that you enjoy. Passion beats perfection.

Will you get a job?
Eventually, yes. But not on Day 30 of your Udemy course.

Be ready to:

Apply to 100+ jobs
Hear nothing
Doubt everything
Land one job

Suddenly feel like a genius again
This is normal. This is the grind. And yes, it’s still worth it.

Final thoughts

Self-taught devs built half the internet. The other half was built by unpaid interns and stackoverflow.com.

You don’t need permission to start. Just curiosity, caffeine, and stubbornness. So stop overthinking, pick a tutorial, and start building broken crap until it’s less broken.

And remember: The only thing worse than bad code is never writing any.

Now go. Learn. Build. Break. Repeat.
The future still belongs to those who Google error messages best.

Seeee you in next blog 👋

Happy Learning & Coding