Tari Ibaba, Author at Coding Beauty

The most powerful AI model in the world just got a coding CLI

By Tari Ibaba / Last updated on August 7, 2025

Wow this is HUGE.

This most intelligent AI model on the planet just got an incredible new coding CLI.

Grok — the genius model from xAI sitting comfortably in #1 of several notable AI benchmarks.

Massive boost in speed and power in your software development.

Carry out massive context-aware tasks on your codebase with simple English right from your CLI:

Refactor functions or files
Edit project code based on coding standards
Generate shell scripts or bash commands
Automate git operations
Integrate with any MCP server

What took an hour of manual coding and debugging before → Now: a few minutes.

Just describe whatever you want and it takes care of the rest.

grok-cli is open-source and growing FAST with dozens of contributions already.

Conversational

Launch Grok CLI with the grok command:

Opens an interactive session
Remembers previous prompts
Lets you refine requests naturally — like a real-life conversation.

Context-aware

Grok CLI reads your project files — it knows what it’s editing.

Ask it to:

Change the logic in a file
Rename functions
Split code into modules

It works from actual source context — no guesses or hallucinations.

Shell and bash

Ask something like “compress all images in the assets folder” or “create a backup of the .env file” — Grok CLI will generate and run the correct commands — like zip.

Built-in

Grok CLI includes smart tools for file editing, git management, and navigation.

These tools run automatically when your prompt matches what they handle — giving you a clean interface without memorizing commands.

Project-specific instructions

By placing a .grok/GROK.md file in your repo, you can customize how Grok CLI behaves.

For example, you can specify preferred frameworks, coding standards, or architectural constraints. This makes it ideal for team workflows and codebases with strict conventions.

Large project context support

Grok CLI supports large context windows. It can review and reason over entire projects or multiple files at once, making it suitable for complex refactors, architecture reviews, or onboarding support.

Installation and setup

To get started you need:

Node.js version 16 or higher
A Grok API key from x.ai

Installation is straightforward:

npm install -g @vibe-kit/grok-cli

Then run:

grok

You can also run one-off prompts with:

grok --prompt "What does this function do?"

Set API keys with:

GROK_API_KEY environment variable
.env file
CLI --apiKey flag
~/.grok/user-settings.json file

You can also configure the base URL and model via the same methods.

Workflow integration

Grok CLI is designed to work with your existing tools. It supports both interactive and headless modes.

Interactive mode: Launch with grok to enter a conversational session.
Headless mode: Use --prompt for scripting, CI pipelines, or automation.
Custom configuration: Use .grok/settings.json to define per-project behavior.

The CLI makes it easy to integrate AI into your day-to-day development without changing your habits or workflow.

Extend Grok CLI with MCP

One of Grok CLI’s most advanced features is support for the Model Context Protocol (MCP).

With MCP, you can connect Grok CLI to external services—like issue trackers, deployment tools, or custom APIs—and control them through the terminal.

Example:

grok mcp add linear --command "node" --args mcp-linear.js

Grok CLI will now be able to read issues from Linear, create tasks, or pull data—all through AI commands.

The built-in mcp command supports:

add: add a new server
list: view connected MCP servers
test: verify a server’s functionality
remove: disconnect a server

This turns Grok CLI from a code assistant into a full-fledged AI agent platform.

Grok CLI is more than just another AI chatbot—it’s a new kind of developer tool.

It brings powerful language models right into your command line — blending natural language prompts with deep integration into your codebase and workflow.

If you’re a developer who spends time in the terminal and wants to work faster, write better code, and automate tedious tasks using AI, Grok CLI is absolutely worth a try.

This new AI coding agent from Google is unbelievable

By Tari Ibaba / Last updated on August 1, 2025

Wow this is insane.

This new AI coding agent from Google is simply incredible. Google is getting dead serious about dev tooling. No more messing around.

Jules is a genius agent can understand your intent, plan out steps, and execute complex coding tasks without even trying.

A super-smart teammate who can tackle coding tasks on its own asynchronously to make software dev so much easier.

It works seamlessly in the background so you can focus on other important stuff.

Gemini 2.5 Pro

Jules is powered by Gemini 2.5 Pro, which is Google’s advanced AI model for complex tasks. This gives it serious brainpower for understanding code.

And you bet 2.5 Flash is on its way to give it even more insane speeds.

When you give Jules a task it clones your codebase into a secure virtual machine in the Google Cloud. This is like a private workspace where Jules can experiment safely without messing with your live code.

It then understands the full context of your project. This is crucial because it helps Jules make smart, relevant changes. It doesn’t just look at isolated bits of code; it sees the whole picture.

After it’s done, Jules shows you its plan, its reasoning for the changes, and a “diff” of what it changed. You get to review everything and approve it before it goes into your main project. It even creates pull requests for you on GitHub!

Jules is quite the multi-tasker. It can handle a variety of coding chores you might not enjoy doing yourself.

For example, it can write tests for your code, which is super important for quality. It can also build new features from scratch, helping you speed up development.

Bug fixing? Yeah Jules can do that too. It can even bump dependency versions, which can be a tedious and error-prone task.

One cool feature is its audio changelogs. Jules can give you spoken summaries of recent code changes, turning your project history into something you can simply listen to.

Google has made it clear that you’re always in charge. Jules doesn’t train on your private code, and your data stays isolated. You can review and modify Jules’s proposed plans at every step.

It works directly with GitHub, so it integrates seamlessly with your existing workflow. No need to learn a new platform or switch between different tools.

Jules is currently in public beta, and it’s free to use with some limits. This is a big step towards “agentic development,” where AI systems take on more responsibility in the software development process.

It might sound like Jules is coming for developer jobs, but that’s probably not the goal here — at least for now.

Jules is meant to be a powerful tool that frees up developers to focus on higher-level thinking, design, and more creative problem-solving. It’s about making you more productive and efficient.

So, if you’re a developer, now’s a great time to check out Jules. It could really change the way you work.

MCP is an absolute game changer but what is it??

By Tari Ibaba / Last updated on September 11, 2025

MCP MCP… what’s this annoying MCP thing I keep seeing everywhere? What does it even mean?

Just another annoying AI buzzword that will die out soon right?

If you think that then you are dead wrong. This is HUGE.

MCP is massive new invention that is going to change everything.

It’s a major milestone for LLMs and agents and the AI revolution in general.

Bla bla bla… what does it mean Tari?? Start talking or GTFO!

Okay okay I will now but you really need to understand just how much of a game changer this is.

So this is the part where I tell you that MCP is an acronym that means Model Context Protocol — but that wouldn’t be saying much and I’ll be boring you to sleep.

The best thing is to give you a fascinating and powerful real-world analogy that you will understand instantly without even trying.

Think of the brain. The human brain.

Your brain is incredibly powerful — far far more intelligent than all the other species (combined??). Not even close.

But have you ever realized that your brain is… USELESS?!

Duuuude just effin explain MCP for me, I didn’t ask for your insults

No listen I’m not calling you dumb or anything like that — what I mean is — your brain can’t DO anything on it’s own, as far as the external world is concerned.

The only way your brain actually does anything in the physical world is by being connected to the rest of your body.

You could be the greatest genius the universe has ever seen.

You could THINK of the most beautiful poem, the most persuasive argument, the funniest joke in history…

You could IMAGINE the next great novel, the blueprint for a world-changing invention, the cure cancer…

You may KNOW where you want to go — the perfect destination, the goal, the person to meet, the protest to join, the stage to walk onto…

But without your biological tools — your mouth, your eyes, your hands, your feet… without these things, you are as good as non-existent.

There will absolutely nothing you can DO. You are completely powerless as far as the outside world is concerned.

So are you getting it now? This is the problem we’ve had with LLMs.

Since 2022 they’ve wowed us with their insane creativity and coding and summarization ability and so much more.

But they couldn’t really DO anything.

All they did was provide us with information. Even when AI agents came along, all they could do was reason and think (“think”).

It was our brains that then used the information to manually make things happen in the world.

This is what MCP changes forever.

They can think and process information, and now also perform real world actions.

They can get crucial data from ANY external data source to do ANYTHING.

AI agents are now in absolute god mode.

With MCP Servers the possibilities are now literally endless.

MCP Server. That’s just the name for anything that provide tools to an AI agent.

The MCP Server provides the tools. MCP tells it HOW to provide the tools.

For example:

GitHub MCP Server: provides tools that let’s the AI agent search any GitHub repo.
WhatsApp MCP Server: provides tools that let you search WhatsApp chats.

You see the real game changer of MCP is NOT actually about what all these incredible MCP Servers let you do.

Before MCP it was already somewhat possible to make LLMs interact with external services. For example manipulate the prompt or fine-tuning the model to provide some sort of JSON response of tools to call.

This was what platforms like AutoGPT and BabyAGI tried to do. It’s also why OpenAI and Google added function calling support to their models.

The main problem with these different attempts is exactly that — they were different. Or private like for GPT and Gemini function calling.

There was no standard and open way to let anyone create an AI agent that could have external tools plugged into it. Or to create tools to plug into any AI agent.

But now MCP has changed all that.

Now anyone can create programs that provide tools to make AI agents more powerful — without knowing anything about the agents beforehand.

That’s why we’ve been calling MCP the USB-C of AI applications.

USB-C standardized how tons of devices that needs to send all sorts of data to each other communicate.

Now as a manufacturer you no longer need to worry about the specific devices your users have and could need to connect your product to.

Just make sure it comes with a USB-C port and you’re good.

They can connect it to all the other billions of devices in the world that connect to USB-C — to provide or receive power or any kind of data.

And not just existing devices but ALL future devices — as long as they also support it.

This is the same game-changing thing MCP lets us do.

No more need for prompt manipulation or model-specific function calling.

And anyone can create agentic apps to use these tools, without knowing anything about the tools beforehand.

As long as the AI agent creator and MCP Server maker abide by the protocol, they will work with each other.

So that why it is really major milestone for the AI revolution.

Anything you can write code to do — an AI agent can do now.

Search your files in the cloud, update databases, control smart switches…

It drastically makes your AI agents waaay way more powerful — making YOU more powerful.

Huge Claude 4 coding news for this IDE

By Tari Ibaba / Last updated on July 20, 2025

Wow this is some incredible news…

Claude 4 Sonnet is now available in Windsurf with no API key! (No more BYOK in Cascade).

You no longer have to pay additional costs for the amazing model.

And the coding has been absolutely insane 👇

For a while now people have been pointing out how amazing they find Claude 4 Sonnet, especially compared to Gemini 2.5 Pro and GPT-4.1. And this isn’t just hype – the difference is showing up in real-world workflows, especially in long context tasks, clean refactoring, and deep architectural suggestions.

And remember this is the junior sibling of Claude 4 Opus — that incredible model that literally did all the coding by itself in a massive project for a full hour and a half…

That was 90 actual minutes of total hands-free autonomous coding genius with zero bugs. Opus 4 planned, coded, edited, and completed an entire full-stack project, and Claude 4 Sonnet shares a massive chunk of that DNA. In fact, for a lot of development tasks, especially within a controlled and optimized coding environment like Windsurf, the gap between Sonnet and Opus is surprisingly small.

What makes this even more monumental is the fact that Windsurf had previously been locked out of native Claude 4 support.

When Claude 4 launched back in May, Anthropic explicitly restricted direct Windsurf access, most likely due to the intense competitive landscape and the recent strategic moves surrounding Windsurf — including rumors of OpenAI acquiring the company and Google’s subsequent licensing deal for Windsurf’s code-generation platform.

The workaround was BYOK — “bring your own key.” That meant if you wanted to use Sonnet or Opus in Windsurf, you had to sign up for the Claude API separately, manage your own usage, and copy-paste keys manually. It worked — but it broke the seamless, fluid experience Windsurf is known for. It was a turbulent journey for users and the platform alike.

That’s now over. As of July 17, Claude 4 Sonnet is directly integrated into Windsurf again. You open the app, click a dropdown, and Sonnet’s there — no more hacks, no more limits. This signifies a successful restoration of support and improved collaboration between Windsurf and Anthropic, much to the relief of the developer community. It’s clean, fast, and shockingly good.

In fact, this might just be the best Claude experience available anywhere right now. The way Sonnet integrates into Cascade — Windsurf’s multi-agent AI flow system — feels like watching the future unfold in real time. Cascade breaks your prompts into intelligent stages, keeps memory across actions, and even offers live suggestions while you type. Now, with the raw power of Sonnet 4 plugged into that, it feels like pair programming with an elite coder who has already thoroughly digested your entire codebase.

The 200K context window means it can see everything — not just your current file, but your whole project: imports, dependencies, comments, TODOs, legacy bugs. Sonnet reads all of it, understands it, and then acts on it with unparalleled precision. You can ask it to upgrade your framework, optimize a specific component, or redesign an entire backend architecture — and it doesn’t blink. It just does it.

Add to that multi-file refactoring, which is handled intelligently without you needing to manually stage files or explain how everything is connected. Just describe the goal, and Claude intelligently does the wiring, making complex changes feel effortless.

The code it writes doesn’t feel “AI-generated.” It feels like code written by someone experienced — it follows the tone and patterns of your project, names things sensibly, and almost never makes you stop and think, “Wait, what is this supposed to be?”

For Pro users, you get 250 calls/month, billed at 2× credits — but for the sheer quality and effectiveness of Sonnet, that’s a deal that quickly pays for itself. Claude’s output is so effective that it drastically cuts out a ton of trial and error, which ultimately saves more time (and more credits) than even faster models that often need constant babysitting and manual correction.

Windsurf’s focus on enterprise-grade security and compliance (SOC 2 Type 2, FedRAMP High, HIPAA) enhances its value even more to make it a powerful solution for organizations seeking both efficiency and peace of mind.

This is all part of a major new string of updates from Windsurf, solidifying its position at the cutting edge of AI-powered development. With Claude 4 Sonnet now fully native, there’s truly no friction. No switching tabs, no key juggling, no API rate worries. Just open your editor and build.

And we haven’t even seen what happens if Opus 4 gets native access next.

This isn’t just a good update — it’s a massive leap forward for developer productivity and the future of coding. If you’ve been sleeping on Claude and Windsurf, now’s the time to wake up and ship.

5 ways to generate a SaaS Idea that earns you $10K per month

By Tari Ibaba / Last updated on July 15, 2025

You don’t need a kajillion-dollar app.

You just need something that generates enough income for you to own all your time and do what you love and live freely.

For most people out there $10,000 monthly recurring revenue from a SaaS is gonna be a fantastic way to achieve this.

It can take serious dedication and effort but it’s a perfectly achievable goal.

You just the right idea that actually solves a problem for the right audience, and proper marketing.

Five proven strategies with real-world examples to help you uncover a profitable SaaS idea that has the potential to hit that $10K MRR mark:

1. Solve your own pain points (or those of your network)

Look inwards.

What frustrations do you encounter in your daily work or personal life? Are there repetitive tasks you wish could be automated?

This person built a simple SaaS tool to save a major problem they had as a marketer:

Is there a process in your daily life that’s unnecessarily complex?

How it works:

Personal Experience: Think about tools you use regularly. What are their shortcomings? What features are missing? Have you ever thought, “There has to be a better way to do this?” That “better way” could be your SaaS idea.
Professional Network: Talk to colleagues, friends, and contacts in different industries. Ask them about their biggest headaches and inefficiencies. Often, people outside of tech don’t realize their problems can be solved with software, creating a prime opportunity for you.

Why it leads to $10K MRR: If you experience a pain point, chances are others do too. Solving a problem you deeply understand gives you an inherent advantage in building a truly useful product and marketing it effectively. Niche down from your broad experience. For example, if you find popular analytics tools too complex, you can build niche web analytics tool for small businesses.

2. Deep dive into niche communities and forums

The internet is a goldmine of unmet needs and frustrations, especially within online communities. People are constantly discussing problems, asking for solutions, and venting about inadequate tools.

This solo software engineer got the crucial initial audience for their app just by posting on Reddit:

How it works:

Reddit, Facebook Groups, Industry Forums: Join subreddits, Facebook groups, and specialized forums related to specific industries or hobbies. Look for recurring questions, complaints, and discussions around “what software would help with X?” or “I’m tired of using Y for Z.”
Product Review Sites: Sites like G2 and Capterra offer insights into existing software solutions. Look at negative reviews and identify common pain points users experience with current offerings. This can reveal gaps in the market.

Why it leads to $10K MRR: These communities represent an audience with a shared, urgent problem. By observing their discussions, you can pinpoint specific pain points and tailor a solution directly to their needs. A micro-SaaS targeting a niche audience that genuinely needs your solution is often more successful than a broad tool trying to serve everyone.

3. Improve upon existing (but flawed) solutions

You don’t always need to invent something entirely new. Sometimes, the most profitable ideas come from taking an existing product and making it significantly better for a specific segment, or addressing its major flaws.

How it works:

Competitor Analysis: Identify successful SaaS products in various categories. Then, analyze their weaknesses. Are they too expensive? Too complex? Lacking a crucial feature? Do they serve a broad audience but miss the specific needs of a smaller, valuable niche?
“Niche Down” the Giants: Large SaaS companies often cater to a wide audience, which means they can’t perfectly serve every niche. You can build a more specialized, user-friendly, or cost-effective alternative for a particular segment. For example, instead of a general website builder, create one specifically for wedding photographers or local bakeries.

Why it leads to $10K MRR: Competition validates a market exists. By offering a superior experience or a more tailored solution to an existing demand, you can capture a portion of that market and quickly gain traction. Focus on solving one problem exceptionally well for a defined audience.

4. Leverage emerging technologies (especially AI)

Every now and then a major technology breakthrough happens that opens up a world of opportunity for new IT tools including apps and software.

It happened with the Internet, mobile and app store, and recently it’s been happening with AI.

Not only do you get to build AI-powered tools, you get use AI in your development and ship your MVP much faster.

How it works:

AI Integration: The rise of AI and machine learning presents immense opportunities. How can AI automate tedious tasks, provide predictive insights, or personalize experiences within a specific industry? Think about AI-powered content optimization tools, AI SEO generators, or AI for video creation.
No-Code/Low-Code Tools: The increasing accessibility of no-code and low-code platforms means you can build and test SaaS ideas much faster and with less technical expertise. This significantly lowers the barrier to entry and allows for rapid iteration.

Why it leads to $10K MRR: Early adoption of emerging technologies can give you a significant competitive advantage. If you can build a solution that leverages these advancements to provide unique value, you’ll be well-positioned to attract early adopters and grow quickly.

5. Look for manual workarounds and “frankenstein” solutions

When people are solving a problem using a combination of spreadsheets, manual processes, and disparate tools, it’s a strong indicator of an unmet need that software could address.

This is what drove the creation of tools like Notion and ClickUp

How it works:

Observe Inefficiencies: Pay attention to situations where businesses or individuals are using clunky, manual workarounds to accomplish a task. This could be anything from managing orders with paper slips to using multiple free tools to cobble together a “solution.”
Identify Integration Gaps: Are people manually transferring data between different software programs? Are they performing repetitive copy-pasting tasks? A SaaS that integrates these disparate workflows or automates data transfer can be incredibly valuable.

Why it leads to $10K MRR: These “Frankenstein” solutions highlight a painful problem for which people are already expending time, effort, or even money.

Your SaaS can offer a streamlined, efficient, and often more affordable alternative, providing clear ROI and a strong incentive for adoption.

Beyond the Idea: Validation is Key

Once you have an idea, the next crucial step is rigorous validation. Talk to at least 5-10 potential customers in your target niche. Ask them about their current workflow, their biggest challenges, and what they would pay for a solution.

Don’t just ask if they “like” your idea; ask if they would pay for it and how much. Pre-selling before you build can be a powerful way to validate demand and secure initial revenue.

By focusing on real problems, understanding your niche deeply, and validating your assumptions, you’ll significantly increase your chances of building a SaaS that not only serves its users but also generates a healthy $10,000 per month.

This open-source AI coding agent has a personality for every task

By Tari Ibaba / Last updated on July 10, 2025

This open-source coding agent is looking really promising.

Meet Roo Code: the incredible open-source alternative to GitHub Copilot and Cursor Composer…

Just look at how it effortlessly ran this app and fixed several errors in the code:

No monthly subscription like Windsurf and Copilot — pay only for what you use.

Multiple personalities/modes for every kind of coding task you do — Code Mode, Architect Mode…

A fully autonomous agent that lives inside your IDE — designed to think, plan, and build alongside you.

Understands your workspace, navigates your files, runs terminal commands, and can even automate browser tasks.

Your full-stack teammate to:

Pair program
Debug
Document
Architect your entire system based on your input.

Built by Roo Code Inc. — small but forward-thinking company focused on supercharging the creative and technical abilities of developers through AI autonomy.

Not just a coding assistant — a whole system

Roo Code operates on a flexible innovative multi-mode system:

Code Mode for hands-on coding tasks
Architect Mode for system-level thinking and planning
Ask Mode for direct Q&A or tool lookups
Debug Mode to trace bugs and propose fixes
Custom Modes for anything you define — QA, security audit, code review, etc.

Each mode is essentially a persona — and you can create as many as you like. Want a test-driven dev partner? A performance profiler? A security scanner? You can spin them up in seconds.

Total workspace awareness

Roo Code’s biggest advantage is its ability to see and act across your entire environment:

Reads and writes any file in your workspace
Executes terminal commands
Automates browser-based workflows
Interfaces with REST APIs and external tools via the Model Context Protocol (MCP)

If you can do it, Roo probably can too — and often faster.

💸 Pay-as-you-go pricing

Roo Code is free to install and use — but it runs on top of whatever AI model you connect it to.

That means the only cost is your API usage.

No subscriptions
No locked features
No hidden charges

You choose the model (OpenAI, Claude, Gemini, etc.) and only pay for the tokens consumed. Light users can spend less than a dollar a day, while heavy users running multi-step agents might spend more depending on the complexity and volume of tasks.

Roo even gives you real-time visibility into your context size and token usage, so you’re always in control.

This model keeps Roo Code accessible to indie devs, teams, and startups — scaling with you only when you need more power.

Extendable and interoperable

Roo Code plays well with others. Using services like Requesty, it can connect to 150+ different AI models with a single API key. You can load balance between providers or assign different models to different tasks.

Its extensibility also means you can connect Roo with tools like:

CodeRabbit for formal code reviews
MakeHub for model marketplace access
TimeWarp Flow for time-aware development
Roo Scheduler for recurring tasks
Roo Executor to trigger commands via URI

These plugins and forks turn Roo Code into an entire operating system for AI-assisted software development.

Open source & cloud friendly

Roo Code is completely free and open-source under Apache-2.0. You can run it locally, or use Roo Code Cloud to manage tasks, collaborate, and view history across projects. It’s not just a tool — it’s an ecosystem.

Installation is easy

Install the “Roo Code” extension from the VS Code Marketplace.
Connect your API key from OpenAI or another provider.
Start typing natural-language commands or invoke a mode. Roo does the rest.

You can also install forks like Kilo Code (Roo + Cline hybrid with built-in models) for even more features out of the box.

The future of development is agentic

We’re now fully in an era where developers won’t just write code — they’ll manage intelligent systems that write, test, and evolve code with them. Roo Code is one of the most advanced and developer-focused efforts in that direction.

Try it out

Install Roo Code in VS Code today and start working with an AI agent that feels like a real teammate. Explore it further at roocode.com or dive into the docs at docs.roocode.com.

Google’s new Gemini CLI coding tool is an absolute game changer

By Tari Ibaba / Last updated on July 3, 2025

Wow this is seriously revolutionary.

Google just destroyed Claude Code with their new Gemini CLI tool.

Now you have effortless access to the most powerful AI models on the planet right from your terminal.

Make intelligent agentic changes to your entire codebase.

Run series of power CLI commands with a simple prompt.

All you have to do is npm i -g @google/gemini :

And here’s the real killer blow to Claude Code — it’s FREE.

Okay not free free — like there’s a free tier but the limits are like super generous — trust Google… imagine how many millions they burn daily from all the people using their Gemini models.

You see free stuff like this is why we shouldn’t force Google to sell Chrome — and indirectly compromise their ad revenue cash cow that covers all these expenses.

60 free requests per minute – 1 request per second. There’s no way you’re going above that so I don’t want to hear anybody complaining.

And don’t complain about the 1000 requests per day either.

Remember this isn’t even something you’re constantly making requests to like you would for a code completion API as you type.

It’s a genius agentic AI that intelligently makes massive changes across your entire codebase at a time. With the 1 million token context window, it more than capable for handling most project codebases out there.

And not just changes but terminal commands.

Coming to the terminal basically now gives it first-class access to all the powerful CLI commands — include third-party CLI commands.

Anything you do in the command line, Gemini CLI can do — and far more of course.

Here’s a really powerful example: sometimes for a quick project or MVP I find myself making a bunch of not-so-related changes all over the place without committing.

Without Gemini CLI I might just get lazy and just bunch all of them together with super vague message like “Misc” or “Update”.

But now with a simple prompt, I can tell Gemini CLI to use the git command to intelligent make a series of commits based on all the various uncommitted changes in my codebase. With highly descriptive commit messages and conventional commits.

Major time savings. Saves effort. Less drudgery.

Not just commits — automate testing, documentation, deployment, fetching PRs, fixing new issues…

And of course it has powerful MCP support to drastically upgrade its capabilities.

Multimodal capabilities — including AI video generation from text prompts in the CLI like we saw in the demo.

For sure this is going to be one the most impactful new tools in the developer toolbox.

The new Claude Code AI is an absolute game changer

By Tari Ibaba / Last updated on June 28, 2025

Wow this is insane.

Anthropic recently released Claude Code: a brand new AI assistant that’s going to have huge impacts on software development as a whole.

This is quite different from IDE-integrating tools like GitHub Copilot or Windsurf Cascade.

This works standalone in the command line — you can use it in any terminal to make huge changes to your codebase.

The intelligence is incredible, just look at this: Claude Code literally just did all the coding by itself on a project for a full hour and a half — zero human assistance.

That’s right — 90 actual minutes of total hands-free autonomous coding genius with zero bugs. The progress is wild.

Claude Code acts as an intelligent collaborator, capable of understanding your entire codebase, automating complex tasks, and accelerating your development workflow.

Fundamentally Claude Code is here to be an active participant in the coding process.

It can search and read through your project’s files, make edits, write and execute tests, and even manage Git workflows like committing and pushing code.

This is all done transparently, keeping you the developer in the loop at every stage.

Key features and capabilities

Claude Code boasts a range of powerful features that set it apart as a next-generation coding tool:

Deep codebase understanding

Thanks to agentic search, Claude Code can map and comprehend the structure and dependencies of your entire project without requiring you to manually specify context files.

Agentic task execution

It can handle multi-step tasks from start to finish. This includes reading a GitHub issue, implementing the necessary code changes across multiple files, running tests to ensure functionality, and submitting a pull request upon completion.

Terminal-native integration

By residing in the command line, Claude Code seamlessly integrates with your existing development environment, including your preferred shell, command-line tools, and CI/CD pipelines.

Code refactoring and improvement

You can instruct Claude Code to refactor your code for better readability, performance, or to adhere to specific coding standards.

Debugging and error resolution

When you encounter a bug, Claude Code can help identify the root cause, suggest fixes, and even resolve issues like missing dependencies.

Automated testing and linting

The assistant can run your test suites, fix failing tests, and apply linting rules to maintain code quality.

Git integration

Perform Git operations such as creating commits, resolving merge conflicts, and searching through commit history using natural language commands.

Getting started is super easy

To begin using Claude Code, you’ll need to install it via npm. Just do these:

Prerequisites: Make sure you have Node.js (version 18 or newer) and npm installed on your system.
Installation: Open your terminal and run the command: npm install -g @anthropic-ai/claude-code
Authentication: Once installed, you can start Claude Code by simply typing claude in your terminal within your project directory. This will initiate the authentication process.
Usage: After successful authentication, you can start issuing commands in natural language. For example, you can ask it to “summarize the project” or “refactor the main.py file to improve readability.”

Claude Code supports various operating systems including macOS (10.15+), Ubuntu (20.04+/Debian 10+), and Windows via the Windows Subsystem for Linux (WSL).

It performs well with tons of popular languages like:

Python
JavaScript / TypeScript
Go
Java
C++
SQL
And many others.

Its proficiency extends to various frameworks and libraries within these ecosystems.

Claude Code is certainly to give OpenAI Codex and Google Jules some serious competition, especially with the amazing new Claude 4 model powering it.

These MCP servers are amazing for coding

By Tari Ibaba / Last updated on November 6, 2025

Start using MCP. NOW.

Like seriously, you’re missing out big time if you’re still not using MCP in your development workflow. It’s not just a buzzword. There’s a reason every major IDE has first-class support for it now.

So many huge productivity gains you’re just ignoring.

These are just 5 of all the incredible MCP servers that drastically improve the coding experience.

1. Sentry MCP Server

This is incredible:

Using the Sentry MCP server automatically analyze and fix an issue right from Claude:

And of course you could do this straight from Windsurf or Cursor or VS Code — letting you make major fixes to your code directly.

The Sentry MCP Server allows AI assistants to connect with Sentry, an error-tracking and performance-monitoring platform. This integration enables AI to access and analyze error data, manage projects, and monitor performance directly through the Sentry API.

Key Features:

Error Analysis: Access and analyze Sentry issues, including error details, stack traces, and debugging information.
Project Management: Query Sentry projects and organizations, and list or create DSNs (Data Source Names) for projects.
AI-Powered Fixes: Use Sentry’s “Seer” to automatically analyze and suggest fixes for issues.
Remote and Local Hosting: Sentry provides a hosted remote MCP server for easy setup, but you can also run it locally.
Broad Compatibility: Works with various MCP clients, including Claude, Cursor, and VS Code.

2. Sequential Thinking MCP Server

Definitely one of the most important MCP servers out there anywhere.

The Sequential Thinking MCP Server is designed to help AI models break down complex problems into a series of manageable steps. It provides a structured thinking process that allows for dynamic and reflective problem-solving.

Key Features:

Step-by-Step Problem Solving: Breaks down complex problems into a sequence of “thoughts.”
Reflective and Dynamic: Allows for revising previous thoughts, branching into alternative lines of reasoning, and adjusting the total number of thoughts as understanding of the problem evolves.
Structured Output: Provides a clear history of the thinking process, including branches and summaries of thoughts.
Hypothesis Generation and Verification: Facilitates the generation and testing of potential solutions.
Broad Applicability: Useful for planning, design, analysis, and any task where the full scope of a problem is not initially clear. It can be installed via npx or Docker and used with clients like Claude and VS Code.

3. Git MCP servers

Working with Git just got way easier with this beauty.

The Git MCP Server provides tools to interact with and automate Git repositories. This allows AI models to perform version control tasks, analyze repository data, and manage code changes programmatically. There is an official GitHub MCP server as well as other community-driven options.

What I love most about having a Git MCP — you can make a series of commits for all the changes in your codebase without having to rack your brain for a great commit message.

Key Features:

Repository Operations: Initialize, clone, and manage Git repositories.
Version Control: Stage files, commit changes, create and switch branches, and view commit logs and differences between branches or commits.
GitHub Integration: The official GitHub MCP server integrates with the GitHub API to manage issues, pull requests, and other GitHub-specific features. A remote version is in public preview, offering easy setup and automatic updates.
Code Analysis: Ingest and analyze repository data, including commit logs and file changes, to track code quality and detect potential issues.
GitMCP: A service that creates a dedicated MCP server for any public GitHub repository, allowing AI to understand the context of the code.

4. Puppeteer MCP Server

Nothing better than automating automation, right? That’s what vibe coding is after all. And compilers too…

The Puppeteer MCP Server enables browser automation by leveraging Puppeteer (a Node.js library for controlling headless Chrome). This allows AI assistants to interact with web pages, take screenshots, and execute JavaScript in a real browser environment.

Key Features:

Web Navigation and Interaction: Navigate to URLs, click elements, fill out forms, and interact with web pages.
Data Extraction: Scrape data from websites and capture screenshots of entire pages or specific elements.
JavaScript Execution: Execute custom JavaScript code within the browser context.
Flexible Setup: Can be installed via npm, run with npx, or used with Docker. It can be configured to run with a visible browser window or in headless mode.
Client Integration: Easily integrates with clients like Claude and VS Code for enhanced web automation workflows.

5. Firebase MCP Server

I tried using this recently to automatically move data from hard-coded local text files to a collection in my test database.

The Firebase MCP Server allows AI assistants to interact directly with Google’s Firebase services. This enables programmatic access to Firebase features for database management, file storage, and user authentication.

Key Features:

Firestore Integration: Perform operations on your Firestore document database, such as adding, updating, and querying documents.
Cloud Storage Access: Manage files in Firebase Storage, including uploading and downloading files.
Authentication Management: Interact with Firebase Authentication for user management tasks.
Flexible Configuration: Can be installed and configured manually or via npx, with support for both stdio and HTTP transport methods.
Broad Client Support: Works with various MCP clients, including Claude Desktop, Augment Code, VS Code, and Cursor.

It’s time to start using MCP down to streamline your workflow, automate repetitive tasks, and leverage the full power of AI in your coding.

Don’t get left behind —embrace the future of development and unlock a world of new possibilities.

Amazon’s new AI coding tool is insane

By Tari Ibaba / Last updated on June 18, 2025

Amazon’s new Q Developer could seriously change the way developers write code.

It’s a generative AI–powered assistant designed to take a lot of the busywork out of building software.

A formidable agentic rival to GitHub Copilot & Windsurf, but with a special AWS flavor baked in — because you know, Amazon…

It doesn’t matter whether you’re writing new features or working through legacy code.

Q Developer is built to help you move faster—and smarter with the power of AWS.

I see they’re really pushing this AWS integration angle — possibly to differentiate themselves from the already established alternatives like Cursor.

Real-time code suggestions as you type — simply expected at this point, right?

It can generate anything from a quick line to an entire function — all based on your comments and existing code. And it supports over 25 languages—so whether you’re in Python, Java, or JavaScript, you’re covered.

Q Developer has autonomous agents just like Windsurf — to handle full-blown tasks like implementing a feature, writing documentation, or even bootstrapping a whole project.

It actually analyzes your codebase, comes up with a plan, and starts executing it across multiple files.

It’s not just autocomplete. It’s “get-this-done-for-me” level AI.

I know some of the Java devs among you are still using Java 8, but Q Developer can help you upgrade to Java 17 automatically.

You basically point it at your legacy mess—and it starts cleaning autonomously.

It even supports transforming Windows-based .NET apps into their Linux equivalent.

And it works for the popular IDEs like VS Code — and probably Cursor & Windsurf too — tho I wonder if it would interfere with their built-in AI features.

VS Code, IntelliJ, Visual Studio – Get code suggestions, inline chats, and security checks right inside your IDE.
Command Line – Type natural language commands in your terminal, and the CLI agent will read/write files, call APIs, run bash commands, and generate code.
AWS Console – Q is also built into the AWS Console, including the mobile app, so you can manage services or troubleshoot errors with just a few words.

Q Developer helps you figure out your AWS setup with plain English. Wondering why a network isn’t connecting? Need to choose the right EC2 instance? Q can guide you through that, spot issues, and suggest fixes—all without digging through endless docs.

Worried about privacy? Q Developer Pro keeps your data private and doesn’t use your code to train models for others. It also works within your AWS IAM roles to personalize results while keeping access secure.

On top of that it helps you write unit tests + optimize performance + catch security vulnerabilities—with suggestions for fixing them right away.

Amazon Q Developer isn’t just another code assistant. It’s a full-blown AI teammate.

It’s definitely worth checking out — especially if you’re deep in the AWS ecosystem.