DEV Community: Akshay Kumar

How I Ranked #2 Out of 25,000+ GSSoC 2025 Contributors: My Open Source Journey

Akshay Kumar — Sun, 12 Apr 2026 14:46:10 +0000

The global developer population exploded to over 180 million in 2025, with India projected to become the world's largest developer hub by 2028. In this landscape, standing out requires more than just knowing how to code — it requires a proven track record of collaboration.

GirlScript Summer of Code (GSSoC) has become a premier proving ground for students and early-career developers. With GSSoC 2026 just around the corner, here's exactly how I ranked #2 out of 25,000+ contributors in the 2025 edition — and what you can learn from it before you apply.

2,361 contributions — the green streak from July to October tells the whole story.

🔑 Key Takeaways

Consistency is King: My rank was built on 120+ days of daily contributions, not occasional bursts
Quality Over Quantity: High-quality PRs and clear communication were the real drivers of my success
Go Deep, Not Wide: Contributing seriously to two projects beat spreading thin across many

What is GSSoC?

GirlScript Summer of Code (GSSoC) is a four-month open-source program that connects student contributors with real-world projects under active mentorship. The 2025 edition had 25,000+ global contributors competing on a points-based leaderboard.

The program is beginner-friendly — but the leaderboard is not. To rank, you need strategy, not just effort.

How Did I Reach 2,014 Points?

I focused on two main projects rather than jumping across many repositories:

1. TalkHeal
An empathetic mental health support assistant built with Python and Streamlit. I contributed to backend logic, feature improvements, and documentation.

2. BuildOnCoffee
An open-source platform for discovering developer tools and CS learning resources, built with React. I focused on frontend component optimization and UI fixes.

Going deep into two codebases let me build genuine rapport with maintainers and understand the architecture well enough to propose my own feature enhancements — something that earned far more trust (and points) than surface-level fixes.

My Git and PR Strategy

Every PR I submitted followed this rule:

Explain why the change was made, not just what changed.

This single habit made my PRs faster to review and merge. Maintainers are busy — if you reduce their mental load, they will prioritize your work.

My PR checklist for every submission:

Clear title describing the change
A "Why" section in the description
Screenshots for any UI changes
No merge conflicts (rebase before pushing)
Linked to the relevant issue

This level of discipline across both Python and React repositories helped me build a reputation as a reliable contributor within the first few weeks.

The Consistency Challenge

Staying active for 120 consecutive days is what separates top rankers from everyone else. Most contributors burn bright for the first two weeks and then fade.

My approach was simple: treat GSSoC like a daily appointment.

Mornings: Research open issues, plan the day's work
Evenings: Implement, commit, and push

On low-energy days, I didn't force complex code. I wrote documentation, reviewed other contributors' PRs, or explored the codebase. The goal was to never let a day go by without adding some value.

The lesson I learned: Some of the most impactful contributions aren't hundreds of lines of code. Fixing a critical bug or improving onboarding docs for new contributors can earn just as much respect — sometimes more.

What I Actually Gained

The #2 rank is a nice headline, but the real outcome was:

Stronger Git fundamentals — rebasing, conflict resolution, branch management
Cross-stack experience — Python/Streamlit backend + React frontend
Code review skills — learning to give and receive feedback professionally
Community trust — going from reading code to having my reviews sought out by mentors

FAQ

How many points do I need for GSSoC Top 10?
The #2 spot required 2,014 points in 2025. Focus on quality contributions consistently — the rank will follow.

Is GSSoC good for absolute beginners?
Yes. Many tasks involve documentation, UI fixes, and accessibility improvements — perfect for learning Git workflows before touching complex logic.

Which projects are beginner-friendly?
Look for repos with active maintainers who respond within 24–48 hours. BuildOnCoffee and TalkHeal both had excellent mentorship during 2025.

Final Thoughts

Don't open the leaderboard on day one. Open an issue instead.

The ranking is just a byproduct of four months of showing up daily, writing clean code, and treating every PR like it matters — because it does.

Let's connect — I'd love to hear about your open-source journey too.

💼 LinkedIn
🐙 GitHub

Build Beautiful App UIs Without Being a Designer — Google Stitch + Antigravity

Akshay Kumar — Fri, 20 Mar 2026 03:56:30 +0000

If you've ever built an app and thought "the logic works great, but it looks terrible" — this one's for you.

Most developers, especially students, are strong on the backend and weak on UI. Getting the functionality right is satisfying. Getting it to look good? That's where hours disappear into tweaking CSS, picking colors, second-guessing layouts, and ultimately settling for something mediocre.

Google just changed that completely with two tools — Google Stitch and Google Antigravity — and when you combine them, the results are genuinely impressive.

What Is Google Stitch?

Google Stitch is a design tool that turns plain text descriptions into complete UI designs for web and mobile apps. Think of it as "vibe designing" — the design equivalent of vibe coding.

You describe your app idea in plain English, and Stitch generates:

Full wireframes for every screen
Complete user journeys and navigation flows
Editable designs you can annotate and modify
Exportable code for the final designs

The key thing that sets Stitch apart from just generating a single mockup image is that it thinks in flows — it understands that real apps have multiple screens and designs all of them together, consistently.

Google Stitch generating a full set of screens from a single prompt

What Is Google Antigravity?

Google Antigravity is an agentic development platform — similar in spirit to Claude Code or Cursor, but built by Google. You describe what you want to build, and Antigravity writes real, working code for it.

Where it stands out is in its MCP (Model Context Protocol) support — it can connect to external tools and services to extend what it can do. And one of those tools is now Google Stitch.

Why Combine Them?

Here's the problem with building UIs using just a coding agent: the agent knows your code, but it has to guess at good design. It doesn't know your color preferences, your target users, or what visual hierarchy makes sense for your specific app.

Stitch solves the design problem. Antigravity solves the engineering problem. Together, they solve both — without you having to be a designer or manually bridge the two.

The workflow looks like this:

Step	Tool	What Happens
1	Antigravity	Builds initial working app
2	Stitch (via MCP)	Designs beautiful UI for the app
3	Antigravity	Implements the Stitch design into the app
4	You	Review, refine, ship

Setting It Up: 3 Steps

Step 1: Add Google Stitch as an MCP Server in Antigravity

Open Google Antigravity and look for the agent window on the right side. Click the three dots (ellipses) and select MCP Servers. Search for "stitch" and click install.

Installing the Stitch MCP server inside Google Antigravity

This is the same MCP pattern you may have seen with other AI tools — it's becoming the standard way AI agents connect to external capabilities.

Step 2: Get a Stitch API Key

Head to stitch.withgoogle.com and sign in. Click your profile picture at the top right → Stitch Settings → Create API Key.

Copy the key and paste it into the MCP server configuration screen back in Antigravity.

That's it — the two tools are now connected.

Step 3: Prompt Away

Now whenever you want Stitch to design something for your app, just mention it in your Antigravity prompt:

Use the Stitch MCP server to design a visual interface for this app

Antigravity will automatically pass your app's context — what it does, what screens exist, what the data looks like — to Stitch. This is much better than prompting Stitch directly, because Antigravity already knows your entire codebase and can write a far more detailed design brief than you'd write manually.

Antigravity analyzing the existing Signup.tsx and calling the Stitch MCP server to generate a redesigned UI

Antigravity automatically reads your existing code, passes the full context to Stitch, and starts generating. You can watch the entire process in real time on the right panel — every MCP tool call, every decision, completely transparent.

A Real Example: From Ugly to Impressive

Here's what this looks like in practice. Say you've built a simple app that parses changelogs and shows you what's new with a library — functional, but visually plain.

Before Stitch — the default output from Antigravity looks something like this: white background, plain text, basic layout. It works, but it's not something you'd show in a portfolio or share with users confidently.

A functional but visually plain app before applying Stitch designs

After Stitch — Antigravity calls the Stitch MCP server, generates a proper design proposal, then implements it. Antigravity even switches to a better tech stack (React + Tailwind) if it determines that's more suited to rendering the design correctly.

The same app after Antigravity implemented the Stitch-generated design

The difference is significant — and the key thing is you didn't design any of it manually.

What Makes This Workflow So Efficient

A few things make this combination genuinely powerful rather than just a gimmick:

Stitch exports HTML/CSS files directly. When Antigravity pulls in a Stitch design, it gets actual code files — not just images to interpret. This means it doesn't have to guess how to translate the design into code. The design and the implementation stay in sync.

Antigravity writes the design brief automatically. When you ask Antigravity to use the Stitch MCP server, it generates the prompt to Stitch based on your app's actual functionality. This produces much more accurate designs than if you described your app to Stitch yourself from scratch.

You stay in one place. The entire workflow — coding, designing, implementing — happens inside Antigravity. You're not switching between four different tools and manually copying things across.

Should You Use This?

Yes, if:

You're a developer who struggles with UI design
You're building a portfolio project and want it to look polished
You want to prototype and demo an app quickly without hiring a designer
You already use Google Antigravity for coding

Maybe not if:

You have a dedicated designer on your team
You need highly custom, brand-specific design that requires precise control
You're working on a project that doesn't use web or mobile UI at all

Honest Take

This isn't magic. The designs Stitch generates are good — genuinely good — but they still benefit from your review and feedback. Think of it as having a junior designer who produces solid first drafts that you then refine, rather than a senior designer who nails it perfectly on the first try.

But for students and solo developers? That junior designer would have cost you hours of struggling with CSS or money hiring someone on Fiverr. Getting a solid, functional, good-looking UI in a few prompts is a real productivity win.

The MCP integration is what makes this feel like a glimpse into how AI-powered development will work going forward — specialized tools connected together, each doing what it does best, coordinated by an agent that understands your full context.

Final Thoughts

First impressions matter in software just as much as anywhere else. A well-designed UI signals effort, professionalism, and care — even if the underlying logic is identical to a plain-looking app.

Google Stitch + Antigravity removes the biggest excuse developers have for shipping ugly apps: "I'm not a designer." Now you don't need to be.

Have you tried Google Stitch or Antigravity? Drop a comment below — would love to hear what you built with it.

Claude Code vs Cursor vs Antigravity: Which AI Coding Tool Should You Use in 2026?

Akshay Kumar — Fri, 20 Mar 2026 03:50:32 +0000

Claude Code vs Cursor vs Antigravity: Which AI Coding Tool Should You Use in 2026?

Every CS student and developer I know is using at least one AI coding tool right now. The problem isn't finding one — it's figuring out which one is actually worth your time, and whether the paid ones are worth the money.

I've personally used all three — Claude Code, Cursor, and Google Antigravity — across real projects, not just hello world demos. Here's what I actually think.

Quick Overview

Before diving deep, here's the one-line summary of each:

Tool	Best Described As	Cost
Claude Code	AI agent that lives in your terminal	$20–$200/month
Cursor	VS Code with AI baked into everything	Free–$200/month
Antigravity	Mission control for autonomous AI agents	Free (for now)

Claude Code

Claude Code is Anthropic's agentic coding tool — and it's fundamentally different from what most people picture when they think "AI coding assistant."

There's no GUI. No sidebar chat. No autocomplete popup. Claude Code runs entirely in your terminal and treats your whole codebase as its working memory. You give it a goal — "add JWT authentication to the API" — and it reads your existing code, makes a plan, implements the changes across multiple files, runs your tests, fixes failures, and reports back when done.

The key thing that sets it apart is how it handles complex, multi-step work. It doesn't just write code — it thinks through the problem first. Plan Mode shows you the full strategy before anything is touched, which builds a lot of trust.

What works really well:

200,000 token context window — it actually understands large codebases
Production-quality output — thorough error handling, proper architecture
GitHub integration — tag it on a PR and it responds to reviewer comments
MCP support — connects to external tools including databases, APIs, and more

What's not ideal:

Takes longer than the others — it's thorough, not fast
Terminal-only experience isn't for everyone
Costs add up quickly during heavy agentic sessions

Pricing (USD & INR):

Plan	USD/month	INR/month
Pro	$20	₹1,670
Max 5x	$100	₹8,350
Max 20x	$200	₹16,700

Student tip: You can run Claude Code completely free using Ollama with open-source models. We tested this on a MacBook Air — check the full guide here: Run Claude Code for Free Using Ollama

Cursor

Cursor is what happens when you take VS Code — already the world's most popular editor — and rebuild it from scratch with AI as a first-class citizen rather than a plugin.

Everything you already know works. Your extensions, your themes, your keyboard shortcuts, your muscle memory. The only difference is that AI is now woven into every layer of the editor. The autocomplete doesn't just complete the current line — it predicts entire functions based on what you're building. The Agent Mode doesn't just answer questions — it takes over your project, makes changes across files, runs tests, and iterates.

What makes Cursor stand out is speed. Most tasks complete in under 30 seconds. For rapid prototyping, hackathons, or quick feature work, nothing comes close.

What works really well:

Zero learning curve — productive from minute one
Fastest output of the three by a significant margin
Background agents that work while you're doing other things
Free tier is genuinely useful, not crippled
Team features are the most mature of the three

What's not ideal:

Occasionally takes shortcuts on code quality for the sake of speed
Context window (128K standard) can struggle on very large codebases
Background agents send your code to external servers — worth knowing

Pricing (USD & INR):

Plan	USD/month	INR/month
Free	$0	₹0
Pro	$20	₹1,670
Max 5x	$100	₹8,350
Max 20x	$200	₹16,700

Google Antigravity

Antigravity is the most ambitious tool of the three — and the hardest to explain, because it's genuinely unlike anything most developers have used before.

The core shift: in Claude Code and Cursor, you are the developer and AI assists you. In Antigravity, AI agents are the developers and you are the orchestrator. You set the goal, the agents figure out how to get there — writing code in the Editor, running commands in the Terminal, and verifying results in a real Browser, all simultaneously.

The Browser piece is what surprised me most. Antigravity doesn't just write code and hope it works — it actually opens Chrome, navigates your app, takes screenshots, and confirms the feature works before marking the task done. That level of autonomous verification is genuinely impressive.

The other standout feature is Artifacts — instead of raw code diffs or log files, Antigravity gives you readable task summaries, screenshots, and browser recordings of what the agents did. It's far easier to trust than staring at hundreds of changed lines.

What works really well:

Completely free right now — genuinely powerful at zero cost
Browser-based verification means fewer "it works on my machine" moments
MCP support is excellent — works seamlessly with Google Stitch for UI design
Multi-agent orchestration lets different agents tackle different parts simultaneously
Artifacts make it easy to review and understand what happened

What's not ideal:

Steepest learning curve of the three — the agent-first model takes adjustment
Still early — some features marked "coming soon"
Free pricing won't last forever once Team/Enterprise tiers launch

Pricing (USD & INR):

Plan	USD/month	INR/month
Public Preview	Free	₹0
Team (coming soon)	TBD	TBD

Seen what Antigravity can do with UI? We used it with Google Stitch MCP to completely redesign an app's signup page — no design skills needed: Build Beautiful App UIs Without Being a Designer

Direct Comparison

Speed

Task	Cursor	Antigravity	Claude Code
Simple feature	~2 min	~5 min	~10 min
Complex feature	~5 min	~8 min	~25 min
Code review	Instant	Fast	Thorough

Cursor wins on speed. Claude Code wins on quality. Antigravity sits in the middle — slower than Cursor but it verifies its work automatically.

Claude Code v2.1.44 running in terminal — the methodical approach that produces production-quality output

Code Quality

Cursor produces clean, working code fast — but occasionally cuts corners. Great for prototypes, needs review before production.

Claude Code writes like a senior developer who has been through enough production incidents to never skip error handling — thorough, well-architected, and heavily commented. Takes longer but saves debugging time later.

Antigravity produces solid, well-structured code. Occasionally overengineers simple solutions, but the browser verification means what you get actually works end-to-end.

Codebase Understanding

Claude Code's 200,000 token context window is the largest of the three — it can hold your entire codebase in memory at once. Ask it "why are users seeing slow load times?" and it'll analyze your database queries, API calls, and rendering logic together. Context stays consistent throughout long sessions.

Cursor uses semantic search — it doesn't need your exact words to find relevant code. Ask "where do we handle payment failures?" and it finds the right files even if they're named something different. Excellent for navigating unfamiliar codebases quickly.

Antigravity builds a knowledge base over time. After a few sessions it starts remembering your preferred patterns and conventions without being told. The most promising approach long-term, but the least mature right now.

For Students and Budget Developers

This is where it gets practical.

Running Claude Code free with Ollama — the best option for students on a budget

Situation	Best Choice
Zero budget	Antigravity (free) + Cursor free tier
Hackathon	Cursor Pro (₹1,670/month)
Final year project	Claude Code or Antigravity
Learning to code	Cursor (familiar IDE)
Internship / job prep	Claude Code (production quality)
Running Claude Code free	Claude Code + Ollama

My Honest Take

After using all three across real projects, here's where I actually land:

Antigravity is the best starting point for students right now — it's free, it's powerful, and using it teaches you the agent-first model that's going to define how software gets built going forward. The fact that it's free won't last forever, so take advantage of this window.

Cursor is the best all-rounder — the free tier is genuinely useful, the Pro plan at ₹1,670/month is the most accessible paid option, and the speed makes it ideal for rapid work. If you can afford one paid tool, this is the one.

Claude Code is the best for serious work — when code quality actually matters, when you're building something production-grade, when you need an AI that thinks deeply rather than acts fast. Pair it with Ollama if budget is a concern.

The good news? You don't have to pick just one. Many developers use Cursor for day-to-day speed, Claude Code for complex tasks, and Antigravity for experimental projects. They solve slightly different problems and work well together.

Three Questions Before You Commit

Privacy: All three send your code to external servers. For sensitive or proprietary projects, are you comfortable with that? This matters more than people admit.

Dependency: These are cloud services. What happens when they go down during a critical deadline? Having a fallback plan is worth thinking about.

Your fundamentals: The developers who get the most out of these tools are the ones who understand what's happening under the hood. Use AI to go faster — not as a substitute for understanding what you're building.

Final Thoughts

The question in 2026 isn't whether to use AI coding tools. It's which ones match how you actually work.

Speed and familiarity — Cursor. Depth and code quality — Claude Code. The future of agentic development at zero cost — Antigravity.

Pick one, go deep, and figure out where it actually fits. That's worth more than jumping between all three without mastering any of them.

Which one are you using? Drop a comment below — would love to know what's working for developers and students in the EduLinkUp community.

Run Claude Code for Free Using Ollama (No API Bill, No Compromise)

Akshay Kumar — Mon, 02 Mar 2026 04:15:55 +0000

If you've been using Claude Code — Anthropic's powerful agentic coding tool — you already know how good it is. The problem? The API bills can add up fast, especially if you're a student, indie developer, or just someone experimenting. A small team can easily burn through hundreds of dollars a month.

Here's the good news: you don't have to pay a single rupee to use Claude Code anymore.

Thanks to a recent update in Ollama (v0.14.0+), Claude Code now supports local and cloud open-source models out of the box. No hacks, no fragile adapters — just a clean, simple setup. This guide will walk you through everything, from installation to running your first AI-assisted coding session, completely free.

Quick note: If you've been exploring self-hosted AI tools, you might already be familiar with Moltbot (formerly OpenClaw) — a free, self-hosted AI assistant. This guide takes a similar philosophy — powerful AI, zero cloud cost — but focused entirely on your coding workflow.

What Changed? Why This Works Now

Older guides for connecting Claude Code to non-Anthropic models were messy — requiring custom wrappers, brittle API shims, and constant maintenance. Every update risked breaking your setup.

That's changed. Ollama now speaks the Anthropic Messages API format natively. This means Claude Code can talk to any Ollama model the same way it talks to Anthropic's servers — no middle layer, no workarounds.

What you gain by going local or using free cloud tiers:

Cost: Local models are completely free. Some cloud alternatives cost up to 98% less than Claude Opus.
Privacy: Your code never leaves your machine.
Speed: No network round trips when running locally.
Flexibility: Switch between models without changing your workflow.

Before You Begin: Does Your Machine Qualify?

This is the most important section to read before diving in. Local LLMs are hungry for RAM.

RAM Available	What You Can Run	Experience
8 GB	Very small models only	Not recommended for coding
16 GB	Small models (7B–14B)	Rough — expect slow edits and retries
32 GB	Mid-size models (24B–30B)	Good — comfortable for daily use
64 GB+	Large models (30B–70B)	Excellent — near-Claude quality

The honest truth: If you have 16 GB RAM, local models will work but can feel sluggish for real coding tasks — more wrong edits, more retries. At 32 GB (Apple Silicon unified memory or PC RAM), it becomes genuinely productive. Below that, the free cloud model options covered later in this guide are a better bet.

Step 1: Install Claude Code

First, you need Claude Code installed on your system.

macOS / Linux / WSL:

curl -fsSL https://claude.ai/install.sh | bash

Windows (PowerShell):

irm https://claude.ai/install.ps1 | iex

Windows (CMD):

curl -fsSL https://claude.ai/install.cmd -o install.cmd && install.cmd && del install.cmd

Step 2: Install Ollama

Ollama is what makes this whole setup possible. It runs open-source models locally and now speaks Claude Code's language natively.

curl -fsSL https://ollama.com/install.sh | sh

Once installed, Ollama runs as a background service on http://localhost:11434.

Ollama — the easiest way to run open-source models locally while keeping your data safe

Step 3: Pick and Pull a Model

This is where your RAM decides what you can run. Pull the model that matches your machine:

# Great starting point for 32 GB machines
ollama pull devstral-small-2

# Stronger coding ability, still works on 32 GB
ollama pull qwen3-coder:30b

# Good speed-to-quality tradeoff (quantized, lighter)
ollama pull glm4.7-flash:q8_0

Model cheat sheet by RAM:

RAM	Recommended Model	Why
16 GB	`glm4.7-flash:q8_0`	Quantized, lighter footprint
32 GB	`devstral-small-2`	Best balance of speed + quality
32 GB (prefer quality)	`qwen3-coder:30b`	Better output, slightly slower
64 GB+	`qwen3-coder:30b` or larger	Comfortable at full precision

Tip: For coding tasks, always use a model that supports at least 64k tokens context length. Shorter context means Claude Code loses track of your codebase mid-session.

Step 4: Connect Ollama to Claude Code

Now the magic part. You just need to set two environment variables to point Claude Code at Ollama instead of Anthropic's servers.

Add these to your ~/.zshrc or ~/.bashrc:

export ANTHROPIC_AUTH_TOKEN="ollama"
export ANTHROPIC_BASE_URL="http://localhost:11434"

Then reload your shell:

source ~/.zshrc

Run Claude Code with your local model:

claude --model devstral-small-2

That's it. You're now running Claude Code completely free, on your own machine.

Claude Code v2.1.44 running successfully via glm-4.7:cloud on a MacBook Air — no local GPU needed

Alternatively, Ollama has a shortcut command that skips the manual env setup:

ollama launch claude --model devstral-small-2

This auto-wires everything. Great for a quick start.

Don't Have 32 GB RAM? Use Free Cloud Models

If your machine doesn't meet the RAM bar for local models, Ollama has a clever trick: :cloud variants. These run on Ollama's cloud infrastructure but use the exact same CLI commands as local models. No separate API keys to manage.

Pull a cloud model:

ollama pull glm-4.7:cloud
ollama pull minimax-m2.1:cloud

Run it:

claude --model glm-4.7:cloud

Same workflow, no local GPU required. Ollama's free tier has usage limits, but for students and casual coders it's more than enough for regular sessions.

Using Ollama with the Anthropic SDK (For Developers)

If you're building apps with the Anthropic SDK, switching to Ollama is just a one-line change — swap the base_url.

Python:

import anthropic

client = anthropic.Anthropic(
    base_url='http://localhost:11434',
    api_key='ollama',  # required field, but ignored by Ollama
)

message = client.messages.create(
    model='qwen3-coder',
    messages=[{'role': 'user', 'content': 'Write a function to check if a number is prime'}]
)

print(message.content[0].text)

JavaScript:

import Anthropic from '@anthropic-ai/sdk'

const anthropic = new Anthropic({
  baseURL: 'http://localhost:11434',
  apiKey: 'ollama',
})

const message = await anthropic.messages.create({
  model: 'qwen3-coder',
  messages: [{ role: 'user', content: 'Write a function to check if a number is prime' }],
})

console.log(message.content[0].text)

Your existing Anthropic-based apps will work with Ollama models with zero other changes.

What Features Does Claude Code Still Support?

You might wonder if going local means losing features. Here's what works with Claude Code + Ollama:

Feature	Supported?
Multi-turn conversations	✅ Yes
Streaming responses	✅ Yes
System prompts	✅ Yes
Tool / function calling	✅ Yes
Extended thinking	✅ Yes
Vision (image input)	✅ Yes

The full feature set is there. The only real trade-off is model quality versus the flagship Claude Opus — which we address below.

Honest Performance Expectations

Let's be real about what you can expect, because this guide won't sugar-coat it.

On a MacBook Pro M1 with 32 GB RAM:
devstral-small-2 (24B) runs at an acceptable speed for daily tasks. qwen3-coder:30b works but is noticeably slower — not ideal for quick iterations.

On a machine with 16 GB RAM:
Expect slower responses and occasional wrong edits that require re-prompting. The :cloud variants from Ollama will serve you better.

Quality vs Claude Opus:
Open-source models at the 24B–30B range are genuinely impressive for routine coding — boilerplate, refactoring, debugging, writing tests. For complex architectural decisions or tricky algorithms, Claude Opus still has an edge. The free setup is not a perfect replacement, but for everyday coding tasks, it gets the job done.

Choosing the Right Setup for You

Your Situation	Best Option
32 GB+ RAM, privacy-conscious	Local model via Ollama
Under 16 GB RAM	Ollama cloud model (free tier)
Want free + no setup	`ollama launch claude --model glm-4.7:cloud`
Building apps with Anthropic SDK	Swap `base_url` to Ollama (Python/JS example above)
Need the absolute best quality	Claude Opus via official API (paid)

A Note on What This Setup Is (and Isn't)

This guide solves the cost problem for Claude Code. It's ideal for:

Students learning to code
Indie developers prototyping ideas
Anyone who wants to experiment without worrying about credits

It's not for you if:

You need production-grade AI assistance at scale
Your tasks regularly require Opus-level reasoning
You want zero setup and don't mind paying

For those use cases, the official Anthropic API is still worth it. But for the rest of us? This free setup is a game-changer.

Final Thoughts

Claude Code is one of the best agentic coding tools out there. The fact that you can now run it completely free — either locally on your own hardware or through Ollama's free cloud tier — is a massive win for students and developers on a budget.

The setup literally takes five minutes. Install Claude Code, install Ollama, pull a model, set two environment variables. That's the whole thing.

If you enjoyed this and want to explore more free self-hosted AI tools, check out our guide on Moltbot — a free, self-hosted AI assistant that runs on a $5/month VPS and connects to WhatsApp, Telegram, and more.

Have a model recommendation or a setup that works great on your machine? Drop a comment below — the community would love to hear it.