DEV Community: Bravin kulei

Someone Saved 130 Million Tokens Using This Free Tool. Here's What It Does

Bravin kulei — Sat, 25 Apr 2026 15:37:46 +0000

Real data. One developer. One open-source Rust binary. The numbers don't lie.

Let me show you something.

A developer ran rtk gain after using a tool called RTK for a while. Here's what their dashboard showed:

Total commands:     15,720
Input tokens:       146.3M
Output tokens:      16.3M
Tokens saved:       130.0M  (88.9%)
Total exec time:    788 minutes
Efficiency meter:   ████████████░░  88.9%

130 million tokens saved. 88.9% efficiency.

Not a benchmark. Not a simulation. An actual developer's real session history.

That's the receipt. Now let me explain what's happening — and why you should care.

The Leak Nobody Warned You About

You set up Claude Code, Cursor, or Copilot. You start building. You feel productive. The AI is running commands, reading files, making changes. It feels like the future.

What you don't see is the token leak happening on every single shell command.

Your AI runs git push. Git responds with:

Enumerating objects: 5, done.
Counting objects: 100% (5/5), done.
Delta compression using up to 8 threads
Compressing objects: 100% (3/3), done.
Writing objects: 100% (3/3), 1.23 KiB | 1.23 MiB/s, done.
Total 3 (delta 2), reused 0 (delta 0), pack-reused 0
remote: Resolving deltas: 100% (2/2), completed with 2 local objects.
To github.com:yourname/yourproject.git
   abc1234..def5678  main -> main

The AI reads every word of that. Burns ~200 tokens on it. Just to learn one thing: the push worked.

The correct output was: ok main

This is happening dozens of times per session. Every ls, every cat, every grep, every test run — raw command output flooding into the LLM's context window, most of it noise.

The Real Numbers from That Dashboard

Look at the breakdown from that rtk gain screenshot more carefully:

Command	Runs	Tokens Saved	Compression
`rtk gh pr diff`	72	18.1M	72%
`rtk curl`	1	16.3M	100%
`rtk read`	1,164	12.2M	22.7%
`rtk curl` (another)	—	10.5M	100%
`rtk lint eslint`	16	9.1M	99.7%
`rtk grep`	1,093	8.0M	39.1%
`rtk eslint`	51	5.7M	94.6%
`rtk git diff`	88	4.1M	66.9%

rtk read was called 1,164 times. rtk grep — 1,093 times. These aren't exotic commands. These are the bread-and-butter operations of any coding session, running constantly in the background while your AI agent works.

And every single one of those calls was leaking tokens — until RTK started intercepting them.

What RTK Actually Does

RTK (Rust Token Killer) is a CLI proxy that sits between your AI agent and the shell. It doesn't change what commands run — it changes what output the AI sees.

The hook is transparent. After a one-time setup, RTK silently rewrites commands before they execute:

git status → rtk git status → compressed output
cargo test → rtk test cargo test → failures only
grep "pattern" → rtk grep "pattern" → grouped results
cat file.rs → rtk read file.rs → smart extraction

The AI never sees the rewrite. It just gets back clean signal instead of raw noise.

Four things happen to every output:

Smart filtering — removes comments, whitespace, boilerplate
Grouping — aggregates similar items (errors by type, files by directory)
Truncation — keeps relevant context, cuts repetition
Deduplication — collapses repeated log lines into counts

The result: git push goes from 15 lines to ok main. cargo test with 2 failures goes from 200+ lines to 20. ls becomes a clean directory tree instead of a wall of permissions and timestamps.

This Isn't Just About Cost

The token savings are real and they matter — especially if you're paying API costs in dollars while building in Kenya or anywhere else in Africa where every dollar of runway counts.

But there's something more interesting going on here.

Context quality affects reasoning quality.

Your AI agent isn't just storing what it reads — it's reasoning over it. A context window full of Git compression stats, verbose test output, and redundant file metadata is a noisier substrate for reasoning than a context window full of clean, relevant signal.

RTK doesn't just save money. It gives your AI agent a cleaner working memory, which means fewer hallucinations, better focus, and more accurate responses over long sessions.

The 88.9% efficiency in that dashboard? That's not just 88.9% fewer tokens spent. It's 88.9% less noise in the model's context on every one of those 15,720 commands.

Why It's Built in Rust (And Why That Matters)

This is a tool that intercepts every single shell command your AI agent runs. Performance is not optional.

RTK is a single Rust binary with zero runtime dependencies. No Python startup overhead. No npm install. No Docker. The proxy adds less than 10ms per command — genuinely imperceptible.

It's available for Linux (x86 and ARM64), macOS (Intel and Apple Silicon), and Windows. It's on Homebrew. It's at v0.35.0 with 96 releases, 19.5k GitHub stars, and 1.1k forks.

This is not an experiment. It's production-grade infrastructure that happens to be free and open source.

For the IDXhub Community Specifically

A lot of the RTK conversation online is focused on web dev stacks — TypeScript, Rust, Go. But the savings are entirely tool-agnostic.

If you're building IoT firmware, robotics systems, or embedded projects with AI assistance, you're running git, grep, cat, make, docker, kubectl constantly. Every one of those is a token leak RTK can close.

And given that we're operating in an environment where AI API costs hit harder relative to local incomes — squeezing 88.9% more value out of every dollar spent on tokens isn't a luxury. It's just smart engineering.

The tool is free. The repo is public. The install takes 60 seconds.

Install It Right Now

Linux / macOS:

curl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh

Add to PATH:

echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc && source ~/.bashrc

Hook into Claude Code:

rtk init -g

Restart Claude Code. Done. Check your savings anytime:

rtk gain

For Cursor: rtk init -g --agent cursor

For Gemini CLI: rtk init -g --gemini

For Cline/Roo: rtk init --agent cline

The Question Worth Asking

Someone ran 15,720 commands through their AI coding agent. Without RTK, that would have been 146.3M tokens consumed. With RTK, it was 16.3M.

The difference is 130 million tokens. Real money. Real context saved. Real sessions that ran cleaner and longer.

The tool that did that is free, open source, and installs in one command.

What are you waiting for?

→ github.com/rtk-ai/rtk

IDXhub is Kenya's hardware and tech developer community — 312+ builders working across IoT, robotics, and embedded systems. Follow us @IDXHUB and find us at idxhub.space.

Share this if you use Claude Code, Cursor, or any AI coding agent. This one's worth passing on.

Reducing Tech Infrastructure Carbon Footprint Through Modern Container Runtimes and Server Consolidation

Bravin kulei — Mon, 28 Apr 2025 17:26:10 +0000

Recent advancements in Kubernetes architecture, particularly the deprecation of Docker as the default container runtime in favor of lightweight alternatives like containerd and CRI-O, present a valuable opportunity to reduce the carbon footprint of technology infrastructure. Unlike Docker, which operates with additional layers such as the Docker daemon, containerd and CRI-O are optimized for direct, efficient container management. This shift results in lower CPU, memory, and storage overhead, ultimately minimizing energy consumption across cluster nodes. Coupled with server consolidation and virtualization strategies — where multiple applications and services are hosted efficiently on fewer physical machines — organizations can significantly decrease hardware requirements, reduce electricity usage, lower cooling demands, and mitigate electronic waste. Together, these practices not only improve system performance but also contribute to more sustainable, environmentally conscious IT operation

SEO Improvements in Next.js 15: Why Google Can’t Stop Swiping Right on Your Site 💙

Bravin kulei — Mon, 28 Apr 2025 09:54:45 +0000

The SEO Love Affair You Didn’t See Coming

Picture this: you’re sipping your fourth coffee, fingers dancing over your keyboard, and you drop Next.js 15 into your project. Suddenly your Lighthouse scores shoot through the roof, and your Search Console notifications go from crickets to fireworks. What happened?

Next.js 15 just slid into Google’s DMs with a killer combo of speed, structure, and smarts—and now it’s official: your site is hot in the SEO world. Let’s unpack why this version feels like it was custom-built to charm the search-engine overlords, without turning into a snoozefest whitepaper. 🚀

The SEO Stakes: Why Next.js 15 Matters More Than Ever Search Engine Optimization isn’t just buzzword bingo—it’s the magic that turns your page from “who?” to “WOW!” in search results. With Next.js 15’s App Router and Server Components, you get:

🚦 Blazing-fast load times (Core Web Vitals, begone!)
🤖 Crawler-friendly markup that Google can actually digest
✨ Spot-on metadata so your listings stand out

In 2025, speed isn’t just nice-to-have; it’s table stakes. And Next.js 15 deals you the royal flush.

The Upgrade Spotlight: What’s Making Google Swoon? Next.js 15 isn’t incremental—it’s a full-blown glow-up. Here’s what’s fueling the courtship:

2.1 Partial Prerendering (PPR): The Fast-Lane VIP Pass ⚡️

Think of your page as a concert—PPR lets the static diva perform first, while the dynamic band sets up behind the scenes. The audience (and Googlebot) sees something instantly, then the rest slides in seamlessly. Result? LCP and FCP metrics so low, they’re practically floor-burning.

2.2 Caching That Actually Cares 🔧

Server Components + fetch + cache: "force-cache" = data delivered to crawlers at warp speed. Your content is indexed nearly as fast as you can say “SEO,” and Google hands out brownie points like candy.

2.3 Dynamic Metadata Made Delightful 📝

Forget wrestling with <Head> imports in every file. In App Router, you simply export a metadata object per route:

// app/page.js
export const metadata = {
  title: "\"My Site | Next.js 15 SEO Magic\","
  description: "Next.js 15’s secret sauce for blazing-fast, crawler-friendly pages."
};
export default function Page() {
  return <h1>Hello, Search Engines!</h1>;
}

Tailor your titles and descriptions on the fly, and watch your click-through rates do backflips. 🤸‍♀️

2.4 Server Components by Default ✅

Hydration mismatches? Ancient history. What Googlebot sees—static, semantic HTML wired with data—is exactly what users get. No more “But it worked in dev!” surprises at 3 AM.

Why You, the Frontend Warrior, Should Care

You might be thinking: “I just write UIs; why obsess over container cores and render pipelines?” Here’s why:
1. Fewer 404 Discoveries Faster prerendering = more pages crawled before budget runs out.
2. Cleaner Debugging No more phantom hydration errors or missing meta tags.
3. Prod-Paranoid Your staging site runs the same runtime logic as production—goodbye, “works on my machine.”
4. Recruiter Magnet “Optimized with Next.js 15’s PPR and dynamic metadata” looks 🔥 on your CV and blog.

4. Pro-Tips: How to Woo Google with Next.js 15

Ready to turn these features into SEO gold?

Master PPR: In your Server Components, use:

  const data = await fetch("https://api.example.com/posts", {
    cache: "force-cache"
  }).then(res => res.json());

Static bits lightning-fast, dynamic bits on demand.

Weaponize Metadata: Generate context-aware titles/descriptions per page. Think “Product X Review | Next.js 15 SEO” instead of “Home.”
Benchmark Religiously: Lighthouse CI, Search Console Core Web Vitals report, GTmetrix—track every millisecond.
Mirror Prod Locally: Swap Docker Desktop for nerdctl + containerd (okay, that’s for another blog 😉) so your dev setup matches the cloud.

5. TL;DR for the Skimmers 🏎️

PPR = ⚡ Fastest First Paint + happier crawlers
Smart Caching = 🚀 Instant indexing
Dynamic Metadata = 🎯 Higher CTR
Server Components = 🎁 Fewer mismatches, exact rendering

Next.js 15 isn’t just “another minor release”—it’s your SEO Swiss Army Knife.

6. Final Pep Talk: Go Code, Rank High!

Look, no one expects you to rewrite your entire site tonight. But imagine your pages loading so fast that users and Google both throw confetti. That’s the Next.js 15 promise.

So grab your keyboard, sprinkle in those metadata exports, embrace PPR, and let Google do the rest of the heavy lifting. Your site—and your metrics dashboard—will thank you.

👊 Code fast, rank high, and keep that SEO flame alive!

7. Handy Resources

Next.js 15 Release Notes
Core Web Vitals Guide (Google)
Lighthouse CI Docs
CrUX Dashboard (Page Experience)
Dynamic Metadata in App Router (Next.js)

Got questions or want to share your own SEO wins? Drop a comment below—or slide into my DMs, I’m always up for a good optimization chat. 😉

SEO Improvements in Next.js 15: Why Google Can’t Stop Swiping Right on Your Site 💙

Bravin kulei — Mon, 28 Apr 2025 09:54:45 +0000

The SEO Love Affair You Didn’t See Coming

The SEO Stakes: Why Next.js 15 Matters More Than Ever Search Engine Optimization isn’t just buzzword bingo—it’s the magic that turns your page from “who?” to “WOW!” in search results. With Next.js 15’s App Router and Server Components, you get:

🚦 Blazing-fast load times (Core Web Vitals, begone!)
🤖 Crawler-friendly markup that Google can actually digest
✨ Spot-on metadata so your listings stand out

In 2025, speed isn’t just nice-to-have; it’s table stakes. And Next.js 15 deals you the royal flush.

The Upgrade Spotlight: What’s Making Google Swoon? Next.js 15 isn’t incremental—it’s a full-blown glow-up. Here’s what’s fueling the courtship:

2.3 Dynamic Metadata Made Delightful 📝

Forget wrestling with <Head> imports in every file. In App Router, you simply export a metadata object per route:

// app/page.js
export const metadata = {
  title: "\"My Site | Next.js 15 SEO Magic\","
  description: "Next.js 15’s secret sauce for blazing-fast, crawler-friendly pages."
};
export default function Page() {
  return <h1>Hello, Search Engines!</h1>;
}

Tailor your titles and descriptions on the fly, and watch your click-through rates do backflips. 🤸‍♀️

Why You, the Frontend Warrior, Should Care

You might be thinking: “I just write UIs; why obsess over container cores and render pipelines?” Here’s why:
1. Fewer 404 Discoveries Faster prerendering = more pages crawled before budget runs out.
2. Cleaner Debugging No more phantom hydration errors or missing meta tags.
3. Prod-Paranoid Your staging site runs the same runtime logic as production—goodbye, “works on my machine.”
4. Recruiter Magnet “Optimized with Next.js 15’s PPR and dynamic metadata” looks 🔥 on your CV and blog.

4. Pro-Tips: How to Woo Google with Next.js 15

Ready to turn these features into SEO gold?

Master PPR: In your Server Components, use:

  const data = await fetch("https://api.example.com/posts", {
    cache: "force-cache"
  }).then(res => res.json());

Static bits lightning-fast, dynamic bits on demand.

Weaponize Metadata: Generate context-aware titles/descriptions per page. Think “Product X Review | Next.js 15 SEO” instead of “Home.”
Benchmark Religiously: Lighthouse CI, Search Console Core Web Vitals report, GTmetrix—track every millisecond.
Mirror Prod Locally: Swap Docker Desktop for nerdctl + containerd (okay, that’s for another blog 😉) so your dev setup matches the cloud.

5. TL;DR for the Skimmers 🏎️

PPR = ⚡ Fastest First Paint + happier crawlers
Smart Caching = 🚀 Instant indexing
Dynamic Metadata = 🎯 Higher CTR
Server Components = 🎁 Fewer mismatches, exact rendering

Next.js 15 isn’t just “another minor release”—it’s your SEO Swiss Army Knife.

6. Final Pep Talk: Go Code, Rank High!

Look, no one expects you to rewrite your entire site tonight. But imagine your pages loading so fast that users and Google both throw confetti. That’s the Next.js 15 promise.

So grab your keyboard, sprinkle in those metadata exports, embrace PPR, and let Google do the rest of the heavy lifting. Your site—and your metrics dashboard—will thank you.

👊 Code fast, rank high, and keep that SEO flame alive!

7. Handy Resources

Next.js 15 Release Notes
Core Web Vitals Guide (Google)
Lighthouse CI Docs
CrUX Dashboard (Page Experience)
Dynamic Metadata in App Router (Next.js)

Got questions or want to share your own SEO wins? Drop a comment below—or slide into my DMs, I’m always up for a good optimization chat. 😉

Building Your Own Smart Meter: A DIY Guide

Bravin kulei — Sat, 23 Nov 2024 05:35:49 +0000

What is a Smart Meter?
A smart meter monitors energy usage in real time, providing data about voltage, current, and power consumption. In this project, we’ll use IoT technology to send this data to the cloud, where it can be analyzed and visualized.What is a Smart Meter?r?**
A smart meter monitors energy usage in real time, providing data about voltage, current, and power consumption. In this project, we’ll use IoT technology to send this data to the cloud, where it can be analyzed and visualized.
What You’ll Need

ESP32 Board
Current Transformer (CT) Sensor (SCT-013-000)
Voltage Sensor (ZMPT101B)
Jumper Wires and Breadboard
Resistors and Capacitors
Power Supply

Step 1: Assemble the Components
Using the wiring diagram below, connect the sensors to the ESP32 microcontroller.

Current Transformer (CT) Sensor:

Connect the signal wire to the analog pin (A0).
Add a burden resistor (33Ω) across its terminals to convert current into voltage.
Voltage Sensor (ZMPT101B):

Connect the signal wire to another analog pin (A1).
Power the sensor using the 5V and GND pins on the ESP32.
Step 2: Write the Code

Step 3: Test the System
Connect your ESP32 to your PC and upload the code using the Arduino IDE.
Open the Serial Monitor to verify that the power readings match your expectations.
Set up Kafka to receive data from the ESP32.
Step 4: Visualize the Data
Use tools like Grafana or Kibana to display real-time energy consumption trends. You can also create a mobile app or dashboard for users to interact with.
**
Conclusion**
By building this smart meter, you’ve created a functional IoT device that monitors energy usage and sends data for analysis. Whether you’re exploring energy-saving techniques or building a larger IoT system, this project is a great foundation.