DEV Community: Charan Gutti

Used a cheap old android mobile(1gb ram) to include a load balancer, cached web server as a fun way to learn system design

Charan Gutti — Sat, 19 Sep 2026 18:41:39 +0000

I am a CS student and have been preparing for the system design interviews. I liked the concepts very much and I wanted to test the concepts without any burden of subscriptions. I got the question of "Well what if I just use the resources that I have at hand", so I did that.

I used an old Samsung Galaxy J1 mobile with 1GB RAM and 16GB storage (didn't use the storage here). I was immediately faced with an issue: the phone bootloader was locked as it is a Verizon variant (my plan of using a Linux-based operating system failed here). So I did the next best thing: removed the bloatware to free up RAM using the adb program.

Note for people who don't know what ADB is: Android Debug Bridge is a small program that runs in Android-based mobiles and can be accessed by enabling USB debugging in the mobile.

So once we are done with this, I started to question how can I run the server. I was suggested the idea of creating a cross-compiled executable that can run in the specific mobile that I use. In Go, I did this by:

GOOS=linux GOARCH=arm GOARM=7 go build -o server .

Part 1: Hello from a Phone
Part 2: A Load Balancer, in 60 Lines
Part 3: A Cache, with Numbers
Part 4: Fixing What the Numbers Showed
The CPU That Was Never Ours
Making It Public, from the Phone
What I Actually Learned

So let's start to write the code.

Part 1: Hello from a Phone

A server is just a program that listens on the network and answers. The machine does not matter. The J1 has a Linux kernel and Wi-Fi, which is enough.

The trick is that you do not install anything on the phone. Go cross-compiles a single static binary on my Mac that runs on the phone's chip. Then adb, the Android Debug Bridge, copies it over and runs it.

func hello(w http.ResponseWriter, r *http.Request) {
    host, _ := os.Hostname()
    fmt.Fprintf(w, "hello from %s, you asked for %s\n", host, r.URL.Path)
}

func main() {
    http.HandleFunc("/", hello)
    log.Fatal(http.ListenAndServe(":8080", nil))
}

GOOS=linux GOARCH=arm GOARM=7 go build -o server .
adb push server /data/local/tmp/
adb shell "chmod 755 /data/local/tmp/server"
adb shell "trap '' HUP; /data/local/tmp/server > /data/local/tmp/server.log 2>&1 &"

Port 8080 because a non-root user cannot open ports below 1024. /data/local/tmp is the one folder on an unrooted phone where you may execute files. trap '' HUP makes the server ignore the hang-up signal the phone sends when adb disconnects, so I can unplug the cable.

Things that went wrong, in order:

adb push failed with device unauthorized. The phone was waiting for me to tap "Allow USB debugging" on its screen.
ip addr prints 192.168.0.142/24. I pasted the /24 into curl. It is the subnet mask, not part of the address.
nohup server & inside adb shell was a race. adb closes the session milliseconds after the &, and if the hang-up signal arrives before nohup is ready, the server dies with an empty log. Half my deploys silently failed until I understood that.
Android 5.1 has no pkill, pgrep, awk, cut, tail or tr. My "kill the old server" line was silently doing nothing. I rewrote it as ps | grep tmp/ | while read user pid rest; do kill $pid; done.
ps shows a process by the exact path it was started with. I started servers as ./server, then grepped for tmp/server. Nothing matched, and I accumulated orphaned servers holding every port.

$ curl http://192.168.0.142:8080/test
hello from localhost, you asked for /test

A phone from 2015 answering HTTP on my desk, unplugged, on Wi-Fi and a charger.

Part 2: A Load Balancer, in 60 Lines

One server can only do so much, and if it dies everything is down. So I ran two copies on ports 8081 and 8082 and wrote a balancer for port 8080. Go's standard library does the forwarding (httputil.ReverseProxy), so the balancer is only two ideas:

Round robin. Request 1 goes to A, request 2 to B, request 3 to A.
Health checks. A goroutine asks each backend /stats every two seconds. If it fails, stop routing there. When it recovers, resume.

The first thing the balancer did was mark backend 8082 dead. The backend was fine. I had typed http//127.0.0.1:8082 in the config, no colon. The system stayed up on one backend and the only way I found out was reading the log line the health checker printed when the state changed. Lesson: log state changes, not steady state.

Then I killed a backend on purpose. The next one or two requests failed, the health check noticed, and everything flowed to the survivor. That gap is a real design parameter. A shorter check interval catches failures faster and burns more CPU on checking. That trade-off is the whole job.

Part 3: A Cache, with Numbers

A cache only makes sense when something is slow, and you only believe it when you measure. So I added /user/<id>, which "looks up" a user in a fake database that takes 50 ms, and an in-memory cache in front of it: a Go map behind a read-write lock. I load tested from the Mac with hey.

Test through the balancer	Requests/sec	99% latency
`/` trivial, 50 concurrent	280	481 ms
`/user/42` cache off, 50 concurrent	251	403 ms
`/user/42` cache off, 5 concurrent	67	190 ms
`/user/42` cache on, 5 concurrent	223	96 ms
`/user/42` cache on, 50 concurrent	290	510 ms
`/` direct to one backend, 50 concurrent	1930	64 ms

Four things I did not expect:

Concurrency hides latency. With 50 requests in flight, the 50 ms database barely mattered. The cache only showed its 3x gain at 5 concurrent. Throughput and latency are different problems.
The balancer was the bottleneck, by 7x. One backend alone did 1930 requests/sec. Through the balancer, 280.
Cache stampede. Each backend showed 2 or 3 misses for one user, not 1. Several concurrent first requests all missed before the first had filled the cache. Scale that to a hot key expiring under real traffic and it is the burst that takes down a database.
Two caches, not one. Each backend missed independently. Ten backends would mean ten misses per user. That is the argument for a shared cache node.

Part 4: Fixing What the Numbers Showed

Bounded cache with LRU eviction. The map grew forever. I replaced it with a map plus a doubly linked list (container/list): every get or set moves the entry to the front, and when the count exceeds the limit the back entry, the least recently used, is removed. I set the limit to 200 and requested 1000 distinct users: each backend ended at 200/200 with exactly 300 evictions. One consequence I had not appreciated: an LRU turns reads into writes, because every hit mutates the list, so the read-write lock had to become a plain mutex.
TTL. Nothing in my fake database changes, but the cache does not know that. The day I add a write path, every server would serve the old value until eviction. Every entry now expires after a configurable time.
Single-flight. The first request to miss on a key does the lookup. Everyone else arriving for the same key meanwhile waits for that result. Forty simultaneous first requests for a new user: 1 database call, 39 waited.
Connection pooling in the balancer. Go's default transport keeps 2 idle connections per host, so under 50 concurrent requests the balancer was opening and closing TCP connections constantly. One shared transport with 100 idle connections per backend, plus removing a per-request log write to flash:

Through the balancer, 50 concurrent	Before	After
`/`	280 rps	396 rps
`/user/42` cached	290 rps	404 rps

A 40% gain, not the 7x I hoped for. top on the phone explained it: 99% CPU, the balancer alone taking 39% and the kernel 34% moving packets. Every request through the balancer is handled twice on the same four slow cores. The rest of the gap is not a code problem. It is the balancer living on the same box as the things it balances. The fix is a second machine, which is the next post.

The CPU That Was Never Ours

While building the dashboard I added a phone CPU tile, and it read 96% with nothing running. top on the phone showed Google Play Services at 29%, the Play Store at 27% and system_server at 26%. The logcat was a loop of authentication errors: a wiped phone with no Google account, and Play Services trying anyway, forever.

pm disable-user is refused for the adb shell user on Android 5.1. am force-stop is not. One force-stop each and the phone went from 98% CPU to 3%. Play Services restarted itself a minute later, calm this time, so I put the force-stop in the deploy script.

Then I re-ran the same load tests:

50 concurrent	Before	After
`/` through balancer	396 rps	1073 rps
`/user/42` cached, through balancer	404 rps	1182 rps
`/` direct to one backend	1930 rps	3920 rps

Everything nearly tripled and I had not changed a line of my code. That is the most important lesson of the day: when you measure, measure the whole machine. I had spent an hour tuning connection pools while two thirds of the CPU was being eaten by something I had never looked at. Real servers have their equivalent, a log shipper, a backup agent, a cron job someone forgot. The dashboard tile is there so nobody has to rediscover it.

Making It Public, from the Phone

The phone serves a live dashboard at /dashboard: request flow, per-backend routing counts, cache hits, misses, shared lookups, evictions and expiries, refreshed every second, with a button that fires 100 requests so you can watch the counters move.

First attempt at a public URL was a Cloudflare quick tunnel from my Mac pointed at the phone. One command, worked in a minute. Then I asked myself the question this whole lab exists for: what if the Mac is down? The link dies. The Mac was a single point of failure.

So the tunnel had to run on the phone. Cloudflare ships a 32-bit ARM build, it ran, and it failed on the first DNS lookup. Go programs on Linux read /etc/resolv.conf to find a DNS server. Android has no such file, so Go falls back to asking localhost:53, where nothing listens. Go's own source has a comment: "DNS requests don't work on Android". Our server never noticed because it never resolves a name. I could not run a forwarder on port 53 either, that needs root.

Cloudflare's anonymous quick tunnel has no way around that. ngrok's agent does: dns_resolver_ips tells it which DNS server to use by IP, and crl_noverify skips one side request that still used the broken resolver. With those two lines in its config and a free account, the phone registered its own tunnel and got a fixed hostname:

THE SERVER IS LIVE HERE:
https://wispy-uplifted-recycler.ngrok-free.dev/dashboard

Nothing but the phone, a charger and Wi-Fi. Tailscale, my first thought, needs Android 8 and builds a private network rather than a public link.

What I Actually Learned

Everything above is in every system design book. The difference is that I now have a number attached to each concept, a failure I caused with my own hands, and a phone on my desk that still answers. When someone asks me about health check intervals, I remember the two failed requests.

Next up: The CPU load is only throttling around an average of 5% as of now, we can push this even further! Will be testing more concepts as well. If anyone has any good suggestions or ideas, do share it.

Code repository: github.com/g-charan/go-j1

Can Agents Replace Developers? How to Use Them Efficiently via Structured SDLC Loops

Charan Gutti — Tue, 07 Jul 2026 13:00:01 +0000

There is a growing narrative around AI that autonomous agents are on the brink of completely replacing human software engineers. From my perspective, agents are simply not there yet.

When left to their own devices, autonomous agents consistently miss critical organizational context, architectural nuances, and domain-specific constraints unless we feed them exact, highly tailored requirements.

However, lacking full autonomy does not mean we cannot use them with incredible efficiency. To unlock their true potential, we need to return to the foundational roots of software engineering: The Software Development Life Cycle (SDLC).

The Lessons of Traditional SDLC Models

Throughout software engineering history, whether practicing Waterfall, Agile, or Rapid Application Development (RAD), every successful methodology relies on a structured, phased progression. At each stage, specific, deterministic tasks must occur:

Requirements Gathering: Understanding market gaps and defining business logic.
Architectural Design: Planning system components and tool integration.
Implementation: Writing code and connecting APIs.
Verification: Running test suites and validating edge cases.
Deployment & Maintenance: Releasing to production and gathering feedback.

When we treat an AI agent like a magic oracle—dumping a vague prompt and expecting a production-ready codebase—we violate these fundamental rules of software engineering. The loop breaks because we are skipping the life cycle.

Mapping the Developer's Inner Loop to Agents

What happens when we view AI agents through the lens of a developer's daily routine? When I sit down to implement a complex feature, I don't write 500 lines of code blindly in one pass. I move through a precise, iterative mental loop:

Step 1: Conceptualize & Specify: What exact feature am I building, and what are the strict boundary constraints?
Step 2: Plan & Select Tools: How can I implement this cleanly? Which libraries, external tools, or system packages are required?
Step 3: Integrate & Execute: How do I connect these tools into the existing architecture without breaking downstream dependencies?
Step 4: Verify & Test: Does the code pass unit and integration tests? Where does it fail under edge conditions?
Step 5: Review & Refine: Even if the tests pass, is the user experience (UI/UX) intuitive? Is the latency and memory performance acceptable?

I repeat this loop continuously until the artifact meets engineering standards.

Automating the Agentic SDLC Loop

Instead of expecting an agent to do all of this in a single, unstructured turn, we must automate and enforce this exact developer lifecycle in our agent runtimes.

Figure 1: The Automated Agentic SDLC Loop

By structuring agent workflows into discrete SDLC phases, we transform erratic generative outputs into deterministic engineering pipelines:

Context Injection: Feed the agent targeted domain specifications rather than global project summaries.
Architectural Deconstruction: Force the agent to generate an explicit execution plan and tool-selection matrix before writing code.
Sandbox Execution: Allow the agent to write code inside isolated environments equipped with required tooling.
Autonomous Testing: Couple the agent directly to automated test runners. If a test fails, the agent must inspect the stack trace and patch the logic autonomously.
Human-in-the-Loop Review: Reserve human developer focus for high-level architectural evaluation, UI refinement, and performance gating.

Conclusion: The Future of Agent-Assisted Development

Agents will not replace developers who understand systems architecture and lifecycle design. Instead, developers who orchestrate agents across structured SDLC loops will replace those who rely on brute-force manual coding.

I am currently testing and benchmarking these automated lifecycle runtimes across real-world repositories, and I will be publishing the qualitative and quantitative results of my ongoing research soon.

What lifecycles or verification loops are you building into your agent workflows? Let me know in the comments below!

Code in the Context: How Agent Skills Evolved from Simple Prompts to Package Managers

Charan Gutti — Mon, 06 Jul 2026 15:23:53 +0000

Cramming instructions, code snippets, and behavioral guidelines into a single, massive system prompt is a relic of early LLM engineering. It wastes context window, dilutes model focus, and introduces structural fragility.

Instead, the ecosystem is moving toward package-managed, context-aware "agent skills." These are modular runtimes that contain their own instructions, scripts, assets, and verification loops—loaded dynamically only when needed.

Why Prompt Bloating Breaks Down

Early AI applications relied on permanent, monolithic system prompts. You wrote a 100-line text file listing every possible tool, response format, and behavioral constraint, then passed it to the LLM on every single API turn.

This approach fails at scale for three critical reasons:

Context Drift: Models pay progressively less attention to instructions buried in the middle of a massive context window ("lost in the middle" phenomenon).
Cost & Latency: Sending a 2,000-token system prompt on every single turn burns through token budgets and increases time-to-first-token latency.
Fragility: Modifying one instruction for a specific edge case can trigger unintended regressions across entirely unrelated features.

Modular agent skills solve this by loading instructions on-demand. If a user isn't asking the agent to write a technical blog, the blog-writing guidelines are never injected into the active context window.

Anatomy of a Modern Agent Skill

A skill is no longer just a flat text file. It is a structured, executable directory package that can be versioned, tested, and distributed across teams.

Figure 1: Anatomy of an Agent Skill Package Directory

At its core, a modern skill consists of:

SKILL.md: The entry point. It contains structured YAML frontmatter defining exactly when the skill triggers (matching keywords, categories, or regex patterns) and active markdown instructions for the agent once initialized.
scripts/: Executable Python or Bash tools that the agent can execute directly in its sandbox to automate deterministic workflows.
resources/ & references/: Local JSON templates, sample payloads, or documentation PDFs that the agent can read on-demand to restore deep architectural context.

This evolution turns prompt engineering into building executable software packages.

Package-Managing the AI Context

Just as JavaScript engineers use npm to manage code dependencies, AI systems engineers now use agent package managers like the Skills CLI (npx skills) and decentralized registries like skills.sh.

Figure 2: Agent Skill Discovery, Security Audit, & Dynamic Loading Pipeline

When an agent needs a specialized capability, it discovers the skill on the registry, audits the codebase for security vulnerabilities (integrating automated Gen, Socket, or Snyk scans), and pulls it into the workspace directory (~/.agents/skills/). The runtime then manages lifecycle injection dynamically based on task triggers.

For example, when an engineer asks the agent to draft a Sentry-specific technical post, the runtime:

Identifies the trigger syntax and activates the installed blog-writing-guide skill.
Dynamically injects the SKILL.md instructions from ~/.agents/skills/blog-writing-guide into the active context window.
Completely unloads the guidelines from memory the moment the writing task completes, keeping subsequent turns pristine and lightweight.

Building on Real-World Skills

This modular approach is already driving next-generation open-source tooling. Some of the most widely adopted packages on the skills.sh registry demonstrate this shift:

getsentry/skills@blog-writing-guide: Packages Sentry's engineering writing standards, banned marketing terminology, and skimmability rules into a reusable, trigger-based format.
anthropics/skills@mcp-builder: Enforces strict architectural patterns and code templates for dynamically compiling Model Context Protocol (MCP) servers on the fly.
vercel-labs/agent-skills@vercel-react-best-practices: Provides direct performance standards and layout guidelines to prevent suboptimal Next.js and React server component layouts.

Prompts Are Now Code

Writing prompts is no longer a creative exercise in styling prose or pleading with a model. It is a systems engineering challenge.

If you find yourself copy-pasting repetitive instructions across multiple AI runtimes, stop. Initialize a new modular package with npx skills init, structure your execution scripts and reference dependencies, and let your agent runtime manage the package lifecycle.

Stop Measuring Parameter Count: Why Fable 5’s Real Power Lies in the Orchestration Loop

Charan Gutti — Sun, 05 Jul 2026 08:26:58 +0000

Everyone in the AI industry is fixated on Fable 5’s parameter count. They are measuring the exact wrong thing.

When engineers call Fable 5 "powerful," they don't mean it simply memorized more trivia or has a marginally lower perplexity score. Fable 5’s true breakthrough comes from system-level orchestration that shifts the fundamental unit of AI work from a single-turn answer to owning a long-horizon, multi-step project.

Why the One-Shot Answer Breaks Down

Traditional Large Language Models (LLMs) operate on a stateless, request-response pipeline: you prompt the model, it generates text, and you evaluate the output. This loop is fine for writing a quick regex script or debugging a localized syntax error.

It completely falls apart when you need to migrate an entire production codebase.

A single turn cannot hold deep architectural context, execute file changes across directories, run the test suite, analyze stack traces, and iteratively patch regressions. In traditional workflows, the model stops generating after one shot, leaving the human engineer to manually act as the glue—copy-pasting errors, managing state, and driving the project loop.

How Fable 5 Paces Work Across Long Horizons

Fable 5 isn't just generating text; it is engineered for sustained, denser test-time computation. It holds and operates across massive context windows with persistent, file-like memory.

Because it maintains state, Fable 5 preserves coherence across complex engineering milestones. Instead of rushing to produce a one-shot guess, the model paces its execution. It deconstructs goals, iterates on implementations, re-evaluates assumptions, and refines artifacts across dozens of steps over a long time horizon.

The Paradigm Shift in AI Workflows

💡 DEV.to Author Note: For the highest quality rendering, drag and drop diagram-1-workflow.png (from your workspace) directly below this line! Alternatively, the live rendered diagram URL is embedded below:

Self-Verification and Looped Workflows

An agentic model that iterates is only useful if it can reliably catch its own mistakes. Fable 5 incorporates autonomous self-verification: it checks its own outputs against defined constraints, executes linters and test suites, and autonomously corrects errors before surfacing the final result.

This self-correcting loop dramatically increases the effective reliability of complex engineering tasks. You don't need to babysit the model to spot a downstream regression; it finds the failure, patches the underlying logic, re-verifies the build, and continues progressing toward the goal.

The Runtime Harness: Why Raw Size is Irrelevant

Fable 5 does not exist in a vacuum as a naked weights file. It is deployed inside a sophisticated runtime harness that surrounds the core model with specialized skills, risk classifiers, persistent memory, and automated evaluators.

This system architecture orchestrates safe tool execution and allows the model to interact with external artifacts, code repositories, and APIs during execution. By coupling the core model with an active feedback loop, the system harness multiplies practical real-world capability far beyond what raw parameter size could ever achieve.

System Architecture & Safety Harness

💡 DEV.to Author Note: Drag and drop diagram-2-runtime.png here, or use the live embedded diagram URL below:

Safety Gating as Practical Power

High capability requires high responsibility. Anthropic pairs Fable 5 with strict real-time classifiers and dynamic model fallbacks. If a query or intermediate action triggers a risk threshold, the execution path is automatically downgraded to safer, more restricted fallback models.

This means the available compute is immensely powerful, yet rigidly bound within safety rails. Rather than hindering productivity, this dynamic gating provides engineering teams with the confidence to deploy autonomous agentic loops into real-world production environments without sacrificing security or compliance.

Owning the Project Loop

Consider that codebase migration again. A traditional LLM might correctly answer a question about a deprecated API syntax, leaving you to apply the change across 300 files by hand.

Fable 5 can read the entire repository, map dependencies, propose architectural edits across hundreds of files, run the integration test suite, detect a regression in an unexpected downstream microservice, and iteratively rework the fix until the entire test suite passes green. It owns the project loop.

When engineers say Fable 5 represents a paradigm shift, stop looking at the parameter count on a benchmark chart. Start looking at the orchestration loop.

Demystifying "Agent 2.0": Why Agent Loops Are the Future of AI

Charan Gutti — Sat, 04 Jul 2026 03:52:53 +0000

The term "Agent 2.0" is being thrown around everywhere lately. Whether you're scrolling through AI Twitter, watching quick "slop" videos, or reading dense PDFs on the latest generative models, people are praising it as the next massive leap in how AI agents work.

But what does it actually mean? Are these agents running 24/7? Do they only work while you're actively monitoring them? Are they actually efficient, or are they just going to burn through your month’s token budget in a matter of minutes?

Based on a deep dive into the recent developments around autonomous systems, I’m going to answer these questions and break down what makes Agent 2.0—and specifically the concept of "Agent Loops"—so powerful.

The Core Mechanic: Reason -> Act -> Observe

To understand Agent 2.0, you first need to understand the fundamental rhythm of an AI agent. It always comes down to this core loop:

Reason -> Act -> Observe

An "Agent Loop" is essentially a system or a recursive goal where the AI iterates through this cycle again and again to complete a task. Unlike traditional chatbots that require a human to read every response and prompt the next step, a looped agent evaluates its own output.

However, an agent cannot do this endlessly without guidance. You need to set up explicit measures for what is considered "complete" and "incomplete." It operates as an internal feedback and iteration loop.

Solving the Human Burden

Think about your own workflow. Let’s say you are using an AI to code a feature or write a comprehensive report. How many times does it take you to reach an endpoint where you are actually satisfied with the output?

Maybe it takes 10 iterations. Maybe 14. For complex tasks, it might take 30. It varies a lot.

What an Agent Loop does is take that manual burden off your shoulders. Instead of you prompting, reading the mistake, and reprompting, you place a bounded loop around the agent's process. The agent reruns its own cycle, checking its work against the completion metrics you defined, until it reaches the end goal. This solves the burden of the human operator and makes complex AI workflows significantly simpler.

Addressing the Big Questions

So, with this automated iteration, how does it play out in practice?

Do they run 24/7 or only when you are working?

Agent loops do not run blindly 24/7 unless you specifically engineer an ongoing, background system (like a scheduled nightly log sweep). For standard tasks, they run until the stopping condition is met. Once the goal is achieved or a blocker is hit, the loop terminates. They work while you step away, but they don't run indefinitely without purpose.

Are they actually efficient?

Yes, but efficiency depends entirely on your setup. A well-designed loop (like those found in structured catalogs like the Loop Library) operates on bounded constraints. Because the agent checks its work against rigid acceptance criteria, it avoids going down useless rabbit holes, making it much more efficient than unstructured prompting.

Will they burn my tokens in minutes?

This is the most common fear. Without proper stopping conditions, a recursive AI could iterate infinitely and burn through your token budget. However, modern Agent Loops are built with strict limits—such as maximum iterations, defined budgets, and explicit failure states (e.g., stopping if no progress is made after 3 attempts). By setting these boundaries, you get the benefit of autonomous iteration without the risk of runaway costs.

Conclusion

Agent 2.0 isn't magic; it’s just better systems engineering. By wrapping the standard Reason -> Act -> Observe cycle into bounded, goal-oriented Agent Loops, we are moving from being AI micromanagers to AI managers. You define what success looks like, set the boundaries, and let the loop do the heavy lifting.

🚀 The RAM Disk Revival & In-Memory Architectures

Charan Gutti — Fri, 03 Jul 2026 06:20:25 +0000

If you ask any senior backend engineer or database administrator how to instantly make a slow, disk-bound application faster, their first answer is almost always: "Put it in memory."

But why is memory so preferred, and how do modern architectures utilize RAM to achieve sub-millisecond latencies? We're seeing a massive revival of RAM disks and in-memory architectures. Let's explore why computer experts are increasingly treating RAM like a hard drive.

1. The Physics of Storage: Why RAM Wins

To understand the shift towards in-memory architectures, we have to look at the numbers.

Hard Disk Drives (HDDs): Mechanical spinning disks. Seek times are around 2-5 milliseconds.
Solid State Drives (SSDs): Flash memory. Seek times are around 0.1 milliseconds (100 microseconds).
RAM (Random Access Memory): Volatile silicon. Access times are around 100 nanoseconds.

RAM is roughly 1,000 times faster than an SSD and 10,000 to 50,000 times faster than an HDD. When you have a high-throughput system serving millions of requests per second, waiting for a disk to seek is an eternity.

2. In-Memory Databases: Redis and Memcached

The most common implementation of this principle in modern backends is the In-Memory Database.

How They Work

Instead of writing every transaction to an SSD, systems like Redis and Memcached store the entire dataset directly in RAM. This bypasses the OS file system cache, disk I/O bottlenecks, and complex B-tree traversals required by traditional relational databases like PostgreSQL or MySQL.

The Trade-off: Durability

RAM is volatile. If the server loses power, all data is gone. So how do in-memory databases survive crashes?

Snapshots (RDB in Redis): Periodically dumping the entire memory state to disk.
Append-Only Files (AOF in Redis): Logging every write operation to a disk sequentially. Sequential writes to disk are significantly faster than random writes.

This hybrid approach gives you the read/write speed of RAM with a "good enough" durability guarantee for most use cases (like caching, session stores, and leaderboards).

3. The Return of the RAM Disk

Beyond databases, engineers are explicitly creating RAM Disks (or tmpfs in Linux) for extreme performance. A RAM disk is a portion of your system's memory that the OS formats and mounts as if it were a physical hard drive.

Real-World Use Cases

CI/CD Pipelines: Compiling massive codebases (like Chromium or Android) requires millions of small disk reads and writes. Mounting the build directory to a RAM disk can cut compilation times by up to 50%.
High-Frequency Trading: In algorithmic trading, milliseconds mean millions of dollars. Trading engines often run entirely off RAM disks to eliminate any unpredictable disk latency.
Ephemeral Video Transcoding: Processing 4K video requires intense I/O. Storing the temporary frames in a RAM disk prevents SSD burnout (since SSDs have limited write cycles) and speeds up the rendering pipeline.

4. The Future: NVMe vs. RAM

You might be thinking, "But NVMe drives are getting incredibly fast!" It's true. PCIe Gen 5 NVMe drives can push 10+ GB/s of sequential read throughput.

However, RAM bandwidth is still on another level (DDR5 can exceed 50 GB/s), and the latency gap remains. More importantly, modern architectures are leaning towards Memory-Tiering.

We are moving towards a world where your "database" isn't a single monolith, but a tiered structure:

L1 (Hot Data): Stored entirely in RAM.
L2 (Warm Data): Stored on ultra-fast NVMe SSDs.
L3 (Cold Data): Archived on cheaper HDD arrays or cloud object storage.

Conclusion

The shift towards in-memory computing isn't just a trend; it's a fundamental architectural shift driven by the need for speed. Whether you are using Redis to cache database queries or mounting a RAM disk to speed up your Docker builds, understanding how to exploit RAM is a superpower for any backend engineer.

⚙️ Under the Hood: How Databases Handle Multiple Users Concurrently

Charan Gutti — Tue, 30 Jun 2026 11:06:09 +0000

In a previous guide, we discussed the high-level concepts of how databases manage multiple users—touching on transactions, locks, and connection pools. But if you're a backend engineer or preparing for a system design interview, you need to know what’s actually happening under the hood.

When 10,000 users hit a database simultaneously, how does the engine ensure data integrity without grinding to a halt? Let's dive deep into the architecture of database concurrency.

1. Concurrency Control Mechanisms

At the core of any relational database (like PostgreSQL or MySQL) is its Concurrency Control system. There are three primary strategies databases use to manage simultaneous access.

Pessimistic Concurrency Control (Locking)

Pessimistic concurrency assumes that conflicts will happen frequently. Therefore, it locks resources before modifying them.

2PL (Two-Phase Locking): A strict protocol where a transaction acquires all the locks it needs (Expanding Phase) and then releases them (Shrinking Phase).
The Problem: Deadlocks. If Transaction A locks Row 1 and needs Row 2, while Transaction B locks Row 2 and needs Row 1, both freeze forever. The database handles this using a background Deadlock Detector that periodically scans for circular waits and forcefully kills one of the transactions (rolling it back).

Optimistic Concurrency Control (OCC)

Optimistic concurrency assumes conflicts are rare. It doesn't use locks when reading or updating. Instead, it reads data, modifies it locally, and right before committing, it checks a version number or timestamp.

If the version hasn't changed since it was read, the commit succeeds.
If the version has changed (someone else modified it), the transaction is aborted and must be retried.
Use case: Great for read-heavy workloads (like Elasticsearch), but terrible for high-contention systems (like a ticket booking app) because the CPU wastes time rolling back and retrying.

MVCC (Multi-Version Concurrency Control)

This is the holy grail used by modern databases like PostgreSQL and MySQL (InnoDB).
The core philosophy of MVCC is: "Readers do not block writers, and writers do not block readers."

Instead of updating a row in place, MVCC creates a new version of the row.

Every transaction is assigned a unique, monotonically increasing Transaction ID (XID).
When a row is updated, the database writes the new data as a completely new tuple (row version) and marks the old tuple with an expiration XID.
When User A runs a SELECT query, the database looks at User A's XID and only shows them row versions that existed before their transaction started. They see a completely consistent "snapshot" of the database, even if User B is currently modifying those exact rows!

(Note: Because MVCC keeps old versions around, databases need a background process—like PostgreSQL's **Vacuum—to eventually clean up dead rows to prevent storage bloat.)

2. The Buffer Pool and Latches (Not Locks!)

Databases do not read and write directly to the hard drive for every query—that would be catastrophically slow. Instead, they use a massive block of RAM called the Buffer Pool.

When a user requests data, the database loads an entire "Page" (usually 8KB of data) from the disk into the Buffer Pool. But what happens if two threads try to modify the exact same 8KB page in RAM at the exact same microsecond?

They use Latches.

Locks protect logical database concepts (Rows, Tables) for the duration of a transaction.
Latches protect physical memory structures (Pages, B-Trees) for the duration of a CPU instruction.

Latches are incredibly lightweight and fast. To handle thousands of users, the database relies on read/write latches (shared vs. exclusive) to ensure that in-memory data structures don't get corrupted by multi-threading.

3. Write-Ahead Logging (WAL)

If thousands of users are modifying data purely in the in-memory Buffer Pool, what happens if the server loses power? All that data is gone, right?

Enter the Write-Ahead Log (WAL).

Before the database modifies any page in the Buffer Pool, it must write a record of the intended change to a sequential log file on the disk (the WAL).

Writing sequentially to a disk is blazing fast compared to random I/O.
If the power fails, upon reboot, the database simply replays the WAL to reconstruct the exact state of the Buffer Pool before the crash.

This is how databases achieve extreme concurrent throughput while still guaranteeing Durability (the 'D' in ACID).

4. Transaction Isolation Levels Explained

When users run concurrently, they can experience "Phenomena" (anomalies). The SQL standard defines Isolation Levels to combat these:

Read Uncommitted: You can see data that another transaction hasn't saved yet (Dirty Reads). Extremely fast, but dangerous. Rarely used.
Read Committed: You only see saved data. However, if you query a row, wait, and query it again, the data might have changed (Non-Repeatable Reads). This is the default in PostgreSQL.
Repeatable Read: If you read a row, it is guaranteed to look the exact same for the duration of your transaction. However, a concurrent user might insert new rows that suddenly appear in your COUNT(*) query (Phantom Reads). This is the default in MySQL InnoDB.
Serializable: The strictest level. The database uses complex locking (or Serializable Snapshot Isolation) to mathematically guarantee that concurrent transactions yield the exact same result as if they were executed one-by-one in a single-file line. It prevents all anomalies but severely limits concurrent throughput.

5. Threading and Connection Architectures

Finally, how does the database server OS handle the network connections of thousands of users?

Process-per-connection (PostgreSQL): Every new connection forks a completely new OS process. This provides great memory isolation but uses a lot of RAM. This is why PostgreSQL highly recommends using a connection pooler like PgBouncer to multiplex 10,000 client connections onto a small pool of 100 actual database processes.
Thread-per-connection (MySQL): Uses a single process but spawns a lightweight thread for every connection. It consumes less memory per connection than Postgres but still suffers from context-switching overhead at high scale.

🏁 Summary

Behind the scenes, a modern relational database is a marvel of concurrent engineering.

It uses MVCC to ensure readers and writers don't block each other.
It protects memory structures with microsecond Latches while protecting logical rows with Locks.
It ensures durability without sacrificing speed via Write-Ahead Logging.
It balances speed vs. correctness using configurable Isolation Levels.

Understanding these concepts is the key to tuning databases, debugging deadlocks, and architecting systems that scale gracefully to millions of users.

🗺️ How Google Maps Tracks Everyone Efficiently: Algorithms & Hidden Gems

Charan Gutti — Tue, 30 Jun 2026 11:02:41 +0000

Have you ever wondered how Google Maps instantly knows that the highway you’re about to take is backed up by 15 minutes? Or how it calculates the fastest route from New York to Los Angeles in just a few milliseconds?

Google Maps is a masterclass in system design, spatial data processing, and graph theory. Handling over a billion monthly active users—all moving, querying, and updating simultaneously—requires some incredibly clever engineering.

In this guide, we’ll dive under the hood of Google Maps to understand the algorithms, data structures, and engineering "gems" that make it so magical.

📡 The Secret to Live Traffic: Crowdsourcing

The first thing to understand is that Google isn't relying on a magical network of satellites watching cars move. You are the sensor.

If you have Google Maps open (or even just location services enabled in the background on an Android or iOS device), your phone is constantly sending anonymous bits of data back to Google:

Your current coordinates
Your speed
Your direction of travel

When Google sees 50 Android phones suddenly slow down from 65 mph to 10 mph on Interstate 95, its systems instantly recognize a traffic jam and update the map from green to red.

But handling millions of these pings every second requires extreme efficiency.

🧠 The Core Algorithms

To make sense of the physical world, Google Maps relies on a few fundamental computer science concepts.

1. Graph Theory & Routing (A* Search and Dijkstra's)

To Google Maps, the world isn't a picture of roads—it's a massive Graph.

Nodes (Vertices): Intersections where you can turn or change roads.
Edges: The roads connecting the intersections. Each edge has a "weight" (the time it takes to travel it).

To find the fastest route, Google uses optimized versions of classic routing algorithms:

Dijkstra’s Algorithm: Explores all possible paths to find the shortest one. (Too slow on a global scale).
A* (A-Star) Search: A smarter version of Dijkstra's that uses "heuristics" (educated guesses) to prioritize exploring paths that lead toward your destination, rather than blindly searching in all directions.

2. Contraction Hierarchies (The Shortcut Algorithm)

Even A* is too slow if you're driving across the country.

To solve this, Google Maps uses Contraction Hierarchies. This algorithm pre-calculates "shortcuts" between major nodes. When you ask for a route from Seattle to Miami, the algorithm doesn't check every local street along the way. It immediately snaps your route to the major highways (the pre-calculated shortcuts), drastically reducing the calculation time from minutes to milliseconds.

3. Kalman Filters (Fixing GPS Jumps)

GPS is actually quite messy. Tall buildings, trees, and clouds can bounce the signal around, making it look like your car is jumping across the street.

Google uses a mathematical algorithm called a Kalman Filter. It takes your messy, noisy GPS data and combines it with the physics of how a car actually moves (momentum, direction, speed). If your GPS suddenly says you moved 50 feet sideways through a brick wall, the Kalman Filter recognizes that this is physically impossible and "smooths" your blue dot back onto the road.

💎 The Engineering Gems

Beyond the algorithms, Google Maps uses some brilliant architectural "gems" to keep the app blazing fast and battery efficient.

Gem 1: Geohashing and S2 Geometry (Spatial Indexing)

How does Google instantly find all the gas stations near you without searching the entire Earth's database?

They use spatial indexing systems like Geohashing or Google's own S2 Geometry. Instead of storing locations purely as Latitude/Longitude numbers, they divide the Earth into a grid of tiny squares. Each square is assigned a short string of characters (like 9q8yy).

If you are in square 9q8yy, the database only looks for restaurants and gas stations that also share that exact string. This turns a complex geographical math problem into a lightning-fast text search!

Gem 2: The Fused Location Provider (Saving Your Battery)

Connecting to GPS satellites drains your phone's battery incredibly fast.

To save battery while still tracking you, Google uses the Fused Location Provider. Instead of always using GPS, your phone looks at:

Wi-Fi Networks: Your phone recognizes the names of nearby Wi-Fi routers (even if you don't connect to them) and checks Google's massive database of where those routers are located.
Cell Towers: It triangulates your distance from nearby 4G/5G cell towers.
Sensors: Your phone's accelerometer and gyroscope detect when you are walking vs driving.

It only fires up the heavy-duty GPS chip when absolutely necessary (like when you actually start turn-by-turn navigation).

Gem 3: Vector Tiles vs. Raster Tiles

In the early days, digital maps were made of Raster Tiles (static PNG images stitched together). If you zoomed in, the app had to download a brand new set of higher-resolution images. It was slow and consumed a lot of data.

Today, Google Maps uses Vector Tiles. Instead of sending an image of a road, the server sends a tiny packet of code that says: "Draw a grey line from Point A to Point B, thickness 4px." Your phone's graphics chip renders the map locally in real-time. This allows for smooth, infinite zooming, 3D buildings, and drastically reduces data usage.

🏁 Conclusion

Google Maps feels like magic, but it’s actually a beautiful symphony of data. By turning millions of smartphones into real-time sensors, utilizing graph theory for routing, smoothing data with Kalman Filters, and indexing the globe with spatial grids, Google created a system that can track and guide humanity with unprecedented efficiency.

Next time you hear that reassuring "You are on the fastest route" chime, you'll know exactly the kind of heavy lifting happening behind the screen!

🗄️ How Does a Database Work for Multiple Users? A Beginner's Guide

Charan Gutti — Tue, 30 Jun 2026 10:59:24 +0000

If you’ve ever built a small project, you might have used a simple file—like a CSV or a JSON file—to store your data. It works great when you’re the only person using the app.

But what happens when 1,000 users try to like a post, buy a ticket, or update their profile at the exact same millisecond?

If you were just using a regular file, it would result in chaos. Data would get overwritten, corrupted, or simply lost.

This is exactly why we use Databases. In this guide, we’ll explore how modern databases handle thousands of concurrent users without breaking a sweat, explained in plain English.

🛑 The "Shared Spreadsheet" Problem

Imagine you and your coworker are both trying to edit the exact same cell in a shared Excel spreadsheet at the exact same time.

You type: "100"
Your coworker types: "200"

Who wins? Usually, whoever hit "Save" last. The first person's data is completely erased. In computer science, this is known as a Race Condition.

Now imagine this happening on an airline booking site. If two people try to book the last remaining seat on a flight simultaneously, a bad system might sell the same seat twice. A database is designed specifically to prevent this from ever happening.

🛡️ How Databases Save the Day

Databases use a few incredibly smart concepts to manage multiple users at once. Let’s break down the most important ones.

1. Transactions (All or Nothing)

A Transaction is a sequence of operations performed as a single, logical unit of work.

Think of transferring $50 from Alice to Bob. It requires two steps:

Deduct $50 from Alice's account.
Add $50 to Bob's account.

If the power goes out right after step 1, Alice lost her money, but Bob never got it!

Databases solve this using transactions. They guarantee that either both steps happen, or neither step happens. If step 2 fails, the database performs a "rollback" and undoes step 1.

💡 The ACID Rule: You might hear engineers talk about ACID compliance. It stands for Atomicity, Consistency, Isolation, and Durability. It's just a fancy way of saying: "The database promises to keep your data safe and accurate, even if the server crashes."

2. Locks (Taking Turns)

How does a database stop two people from booking that last airline seat? Locks.

When User A starts booking the seat, the database places a temporary "lock" on that specific row of data.
When User B tries to book it a millisecond later, the database says: "Hold on, someone else is looking at this right now. Wait in line."

There are different types of locks:

Row-level locks: Only locks the specific row being edited (e.g., Seat 12B). This is great because other users can still book Seat 14C at the same time.
Table-level locks: Locks the entire table. (Rarely used for simple updates because it slows everything down).

3. Isolation Levels (Hiding the Mess)

What happens if User A is in the middle of updating a massive amount of data, and User B tries to read that data?

Should User B see the half-finished update?
No! That would be confusing.

Databases use Isolation Levels to decide what users can see. Most databases are configured so that User B will only see the old data until User A completely finishes their transaction and hits "commit". This ensures users only ever see consistent, fully updated data.

4. Connection Pooling (Managing the Crowd)

Opening a connection to a database takes time and memory. If 10,000 users visit your website, opening 10,000 individual database connections would crash your server instantly.

Instead, backend applications use Connection Pooling.

Think of it like tellers at a bank. You don't need 10,000 tellers for 10,000 customers. You only need a "pool" of maybe 50 tellers. Customers wait in a quick line, use a teller for a fraction of a second, and then the teller is immediately available for the next person.

By keeping a small pool of open connections and reusing them rapidly, databases can serve thousands of users smoothly without running out of resources.

🏁 Summary

So, how does a database handle multiple users?
It acts as the ultimate traffic controller.

Transactions ensure that multi-step actions succeed entirely or fail gracefully.
Locks prevent people from stepping on each other's toes when updating the same data.
Isolation ensures nobody sees half-finished, messy data.
Connection Pools ensure the server doesn't get overwhelmed by too many people at once.

The next time you successfully snag the last concert ticket or send money on an app, you can thank the database working tirelessly behind the scenes!

Did you find this guide helpful? What other backend concepts would you like to see broken down for beginners? Let me know!

🚀 CI/CD for FastAPI: From Your First GitHub Action to Production-Ready DevOps

Charan Gutti — Sun, 28 Jun 2026 13:00:18 +0000

"Great developers write code. Great engineers automate everything around it."

One of the biggest transitions you'll make as a software engineer isn't learning another programming language—it's learning how software gets delivered.

Many beginners think writing code is the hard part.

In reality, getting that code from your laptop to production reliably, safely, and automatically is what separates hobby projects from professional software.

That's where CI/CD comes in.

In this guide, you'll learn:

✅ What CI/CD actually means
✅ Build a simple FastAPI CI/CD pipeline with GitHub Actions
✅ Add GitHub Secrets securely
✅ Deploy automatically
✅ Learn production engineering practices
✅ Understand how senior DevOps and Platform engineers think

Whether you're building your first API or preparing for DevOps interviews, this guide will give you a strong foundation.

🤔 What is CI/CD?

CI/CD stands for:

CI → Continuous Integration
CD → Continuous Delivery (or Continuous Deployment)

Think of it as an automated factory for your software.

Instead of manually doing this:

Write Code → Commit → Push → Run Tests → Build → SSH into Server → Deploy → Restart Server

CI/CD does it automatically.

🏗 Understanding the Pipeline

Every software project follows roughly the same lifecycle:

Developer → Git Push → GitHub → GitHub Actions → Tests → Build → Deploy → Production Server

The goal is simple: Remove repetitive manual work.

💡 Why Every Developer Should Learn CI/CD

Imagine you're working in a team of 30 developers. Without CI/CD:

Someone forgets to run tests.
Someone deploys the wrong version.
Someone pushes broken code.
Someone forgets an environment variable.

Eventually... Production breaks.

CI/CD exists to reduce these kinds of mistakes by automating repeatable checks and deployment steps.

🐍 Our Example Project

Let's say our FastAPI project looks like this:

fastapi-backend/
├── app/
│     ├── main.py
│     ├── routes.py
│     └── services.py
├── tests/
│     └── test_api.py
├── requirements.txt
├── Dockerfile
└── .github/
      └── workflows/

Everything related to GitHub Actions lives inside .github/workflows/.

🚀 Step 1 — Create Your First GitHub Action

Create: .github/workflows/backend.yml

Basic workflow:

name: FastAPI CI

on:
  push:
    branches:
      - main

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Setup Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.12"

      - name: Install dependencies
        run: |
          pip install -r requirements.txt

      - name: Run Tests
        run: |
          pytest

Every time you push to main, GitHub will:

Create a fresh Ubuntu VM
Clone your repository
Install Python
Install dependencies
Run your tests

No manual work required.

🧪 Why Tests Come First

Never deploy code before testing it.

Think of tests as airport security. Passengers (your code) don't board the plane (production) until they've been checked.

A simple FastAPI test (tests/test_api.py):

from fastapi.testclient import TestClient
from app.main import app

client = TestClient(app)

def test_home():
    response = client.get("/")
    assert response.status_code == 200

If this test fails... Deployment stops. Exactly what we want.

🔐 Step 2 — Adding GitHub Secrets

Never write this:

DATABASE_URL = "postgres://user:password@server"

Or:

OPENAI_API_KEY="abc123"

Those values should never be committed to Git. Instead, GitHub provides Repository Secrets.

Go to: Repository → Settings → Secrets and Variables → Actions → New Repository Secret

Example Secrets:

DATABASE_URL
OPENAI_API_KEY
JWT_SECRET
SUPABASE_KEY

These values are encrypted and only available to your workflows.

Using Secrets in GitHub Actions

Example:

env:
  DATABASE_URL: ${{ secrets.DATABASE_URL }}

Now your application can access it:

import os

database = os.getenv("DATABASE_URL")

This keeps credentials out of your repository while allowing your workflow to use them securely.

🌍 Step 3 — Deploy Automatically

Imagine your server is running Docker. After tests pass:

Developer → Push → GitHub Actions → SSH → Docker Pull → Docker Restart → Production Updated

Example deployment step:

- name: Deploy
  uses: appleboy/ssh-action@v1
  with:
    host: ${{ secrets.SERVER_HOST }}
    username: ubuntu
    key: ${{ secrets.SERVER_KEY }}
    script: |
      cd backend
      git pull
      docker compose up -d --build

Now every push automatically updates your production server.

🐳 Better: Deploy Docker Images

Instead of pulling Git code on the server... Build a Docker image.

Pipeline:
Push → Build Docker → Push Image → Deploy Image

Example:

docker build -t my-backend .
docker push ghcr.io/company/backend:latest

Production simply downloads the latest image. Much cleaner. Much more reliable.

📦 Understanding Pipeline Stages

Professional CI/CD pipelines usually look like this:

Checkout → Install → Lint → Unit Tests → Integration Tests → Build → Security Scan → Docker Build → Publish → Deploy → Smoke Tests → Notify Slack

Notice how deployment is only one step. Most of the work happens before deployment.

🚦 Branch Strategies

Many companies don't deploy every push. Instead:

feature/login → Pull Request → CI → Review → Merge → Deploy

This prevents unfinished work from reaching production.

A common setup:

Feature branches → Tests only
develop → Deploy to staging
main → Deploy to production

🌍 Multiple Environments

Professional applications rarely have just one environment. Instead:

Developer → Development → Staging → Production

Each environment has:

different databases
different API keys
different secrets
different URLs

GitHub Environments let you manage secrets separately for each stage.

🔍 Lint Before Testing

Before running tests, check code quality.

Example:

- name: Run Ruff
  run: ruff check .

Or:

- name: Black
  run: black --check .

A healthy pipeline often follows this order:
Lint → Format → Type Check → Tests

This catches many issues before your test suite even starts.

🧪 Add Type Checking

Python benefits greatly from static analysis.

Example:

- name: MyPy
  run: mypy app

This helps detect type-related bugs before runtime.

🐳 Build Once, Deploy Everywhere

One important production principle: Build once. Deploy many times.

Don't rebuild your application separately for staging and production.

Instead:
Build Docker Image → Push Registry → Deploy Same Image → Staging → Production

This ensures you're deploying exactly what was tested.

🔐 Security Scanning

Modern pipelines also scan dependencies. Examples:

Safety
Bandit
Trivy
Snyk
Dependabot

These tools help detect vulnerable packages, exposed secrets, insecure code, and outdated dependencies. Security is becoming a standard part of CI/CD.

📈 Monitoring After Deployment

Deployment isn't the finish line. A mature workflow continues with:

Deploy → Health Check → Logs → Metrics → Alerts

If something goes wrong, you'll know quickly. Popular tools include:

Prometheus
Grafana
Sentry
OpenTelemetry

🚀 Moving Toward Advanced CI/CD Engineering

Once you're comfortable with the basics, start exploring:

✅ Matrix Builds

Test multiple Python versions:

strategy:
  matrix:
    python-version: ["3.10", "3.11", "3.12"]

✅ Reusable Workflows

Instead of copying YAML across repositories:
Company Workflow → Repository A, Repository B, Repository C
One workflow. Many projects.

✅ Self-Hosted Runners

Large companies often use their own machines instead of GitHub-hosted runners. Benefits include custom hardware, private networking, GPU support, and faster builds.

✅ Artifact Storage

Store build outputs for later deployment:
Tests → Build → Artifact → Deploy
Useful for traceability and rollbacks.

✅ Progressive Deployments

Instead of deploying to everyone at once:
10% → 25% → 50% → 100%
This reduces deployment risk. Common strategies include Blue-Green Deployments, Canary Releases, and Rolling Updates.

💡 Tips Every CI/CD Engineer Should Know

Fail Fast: Run inexpensive checks (linting, formatting) before expensive ones (integration tests).
Keep Pipelines Fast: Developers shouldn't wait 30 minutes for feedback. Optimize caching and parallel jobs where possible.
Never Store Secrets in Git: Use GitHub Secrets, Cloud Secret Managers, Vault, AWS Secrets Manager, or Azure Key Vault.
Automate Everything: If you perform the same deployment step repeatedly, ask: "Can the pipeline do this instead?" Often, the answer is yes.
Pipelines Are Code: Treat workflow files like production code. Review them. Test them. Version them. Improve them.

🛠 Tools Every DevOps Engineer Should Know

Category	Popular Tools
CI/CD	GitHub Actions, GitLab CI, Jenkins, CircleCI
Containers	Docker, Podman
Orchestration	Kubernetes, Docker Swarm
Infrastructure	Terraform, Pulumi
Configuration	Ansible
Monitoring	Prometheus, Grafana
Logging	Loki, ELK Stack
Security	Trivy, Snyk, Bandit
Secrets	Vault, AWS Secrets Manager, GitHub Secrets

🎯 Learning Roadmap

If you're serious about DevOps or Platform Engineering:

Git → Linux → Docker → GitHub Actions → FastAPI → Nginx → Reverse Proxy → Terraform → Cloud (AWS/GCP/Azure) → Kubernetes → Observability → GitOps → Platform Engineering

Each skill builds on the previous one. Don't rush. Master the fundamentals first.

🏁 Final Thoughts

CI/CD isn't just about deploying code. It's about building confidence.

When every commit is automatically tested, validated, packaged, and deployed using a repeatable process, your team spends less time worrying about releases and more time delivering features.

Start with a simple GitHub Actions workflow. Add tests. Introduce secrets. Automate deployments. Then gradually expand into security scanning, reusable workflows, infrastructure as code, and advanced deployment strategies.

Over time, you'll stop thinking of CI/CD as a tool—and start seeing it as an essential part of how modern software is built.

"The best deployment is the one nobody has to think about—because it's already automated, tested, and reliable."

📱 Styling React Native Has Changed Forever — Understanding NativeWind, Tamagui, and the New Era of UI Development

Charan Gutti — Sat, 27 Jun 2026 05:47:03 +0000

"For years, React Native developers looked at Tailwind CSS with envy. Today, they don't have to."

If you've built web applications with Tailwind CSS, you probably remember how much faster it made development.

Instead of writing:

.container {
    display: flex;
    justify-content: center;
    align-items: center;
    padding: 16px;
}

You simply wrote:

<div class="flex items-center justify-center p-4">

Clean.

Fast.

Readable.

But React Native never had anything quite like it.

Developers were stuck with:

StyleSheet.create()
large style objects
inline styles
custom design systems
CSS-in-JS libraries

Things worked...

…but they weren't nearly as enjoyable.

Fortunately, that has changed dramatically.

Today, React Native has an entire ecosystem of modern styling solutions that make building beautiful apps faster than ever.

Let's explore what changed—and which solution might be right for you.

🤔 Why Styling React Native Was Different

React Native doesn't use HTML.

It doesn't use CSS.

Instead, every component is a native component.

Instead of writing:

<div></div>

You write:

<View />

Instead of CSS:

color: red;

React Native expects JavaScript objects:

const styles = StyleSheet.create({
    title: "{"
        color: "red"
    }
});

Then:

<Text style={styles.title}>
    Hello
</Text>

This works perfectly...

Until your application becomes large.

😵 The Traditional Problem

Imagine a screen with 40 components.

Soon your file starts looking like this:

const styles = StyleSheet.create({

container:{...},

header:{...},

title:{...},

subtitle:{...},

button:{...},

card:{...},

image:{...},

footer:{...}

});

Eventually...

Finding one style becomes harder than writing the component itself.

This is exactly the problem Tailwind solved on the web.

🌿 Enter NativeWind

NativeWind brings the Tailwind developer experience to React Native.

Instead of this:

<View
style={{
padding:16,
backgroundColor:"#2563eb",
borderRadius:12
}}
>

You simply write:

<View className="p-4 bg-blue-600 rounded-xl">

That's it.

If you've used Tailwind before...

You already know NativeWind.

🧠 How NativeWind Works

Many beginners assume NativeWind somehow adds CSS to React Native.

It doesn't.

Instead, NativeWind compiles Tailwind utility classes into optimized React Native style objects during the build process.

Conceptually:

className

↓

NativeWind

↓

StyleSheet Objects

↓

Native Components

Your app still uses native styling.

You're simply writing it in a much nicer syntax.

🚀 Why Developers Love It

Imagine building this card.

Traditional React Native:

const styles = StyleSheet.create({

card:{
backgroundColor:"#fff",
padding:20,
borderRadius:16,
shadowColor:"#000"
}

});

With NativeWind:

<View className="bg-white p-5 rounded-2xl shadow-lg">

Much easier to scan.

Much easier to edit.

Much easier to remember.

💻 A Complete Example

Traditional:

import { StyleSheet, Text, View } from "react-native";

const styles = StyleSheet.create({
  container: {
    flex: 1,
    justifyContent: "center",
    alignItems: "center",
    backgroundColor: "#3b82f6",
  },
  title: {
    color: "white",
    fontSize: 28,
    fontWeight: "bold",
  },
});

export default function App() {
  return (
    <View style={styles.container}>
      <Text style={styles.title}>
        Hello NativeWind
      </Text>
    </View>
  );
}

NativeWind:

export default function App() {
  return (
    <View className="flex-1 justify-center items-center bg-blue-500">
      <Text className="text-white text-3xl font-bold">
        Hello NativeWind
      </Text>
    </View>
  );
}

Same UI.

Far less code.

🎨 Why Tailwind Utilities Work So Well

Tailwind isn't popular because it's shorter.

It's popular because developers begin thinking in layout patterns.

Instead of remembering:

justifyContent

alignItems

paddingHorizontal

paddingVertical

marginTop

You naturally think:

flex

items-center

justify-between

p-4

mt-8

Your brain starts recognizing reusable design patterns instead of individual style properties.

🧩 NativeWind Isn't the Only Option

React Native's ecosystem has evolved significantly.

Let's look at some of the major libraries.

⚡ Tamagui

One of the most exciting modern UI frameworks.

Unlike NativeWind, Tamagui is much more than styling.

It includes:

UI components
theming
design tokens
animations
responsive layouts
compiler optimizations

Example:

<Button
theme="blue"
size="$5"
borderRadius="$6"
>
Save
</Button>

Notice something?

You're working with design tokens, not raw values.

That means changing your design system later becomes much easier.

Why Companies Love Tamagui

Large applications often need:

Dark mode
Multiple themes
Shared design systems
Web + Mobile support

Tamagui solves all of these.

Even better...

It works on:

React
React Native

using almost the same components.

🎨 React Native Paper

If you want Google's Material Design:

<Button mode="contained">
Login
</Button>

You instantly get:

accessibility
ripple effects
elevation
typography
Material Design

without building everything yourself.

💎 Gluestack UI

Another modern library that's becoming increasingly popular.

It offers:

component library
design system
accessibility
NativeWind compatibility
Expo support

Think of it as:

Shadcn/UI

↓

React Native

🎯 Restyle

Created by Shopify.

Instead of utility classes, Restyle focuses on:

type safety
design tokens
scalable systems

Perfect for enterprise applications.

🧠 Which One Should You Choose?

NativeWind

Best for:

beginners
Tailwind developers
startups
rapid development

Tamagui

Best for:

cross-platform apps
design systems
larger applications
shared web/mobile codebases

React Native Paper

Best for:

Material Design apps
admin panels
business applications

Gluestack UI

Best for:

modern UI kits
Expo projects
component-driven development

Restyle

Best for:

enterprise teams
strict design systems
scalable applications

🧩 Can You Combine Them?

Absolutely.

A common stack looks like:

Expo

↓

NativeWind

↓

React Navigation

↓

React Query

↓

Zustand

↓

FlashList

↓

Reanimated

↓

React Native Gesture Handler

Or:

Expo

↓

Tamagui

↓

React Query

↓

Supabase

↓

React Navigation

Modern React Native is becoming incredibly modular.

🚀 Performance Considerations

One common misconception is:

"Utility classes must be slower."

Actually...

NativeWind compiles utilities into optimized React Native styles.

You're not parsing CSS at runtime like a browser.

That means you get the productivity benefits without sacrificing native rendering.

Performance still depends on factors such as rendering strategy, component structure, image optimization, and state management—not just the styling library.

💡 Tips for Writing Better React Native UI

1. Build reusable components

Instead of:

<View className="bg-white rounded-xl p-4">

everywhere...

Create:

<Card>

Now your design system stays consistent.

2. Learn Flexbox deeply

Whether you use:

NativeWind
Tamagui
StyleSheet

Everything ultimately relies on Flexbox.

Understanding:

flex
flexGrow
justifyContent
alignItems
gap

will make every styling library easier to use.

3. Use Design Tokens

Instead of:

text-[17px]

Prefer semantic values like:

text-primary

text-lg

bg-card

rounded-xl

Design tokens make your application easier to maintain and theme.

4. Keep Components Small

A good rule:

If a screen exceeds 200–300 lines, consider extracting reusable UI pieces.

For example:

ProfileCard

↓

Avatar

↓

Stats

↓

ActionButtons

Small components are easier to test and reuse.

5. Don't Chase Every New Library

The React Native ecosystem moves quickly.

Instead of switching libraries every few months:

Master one.

Understand its philosophy.

Build real projects.

The concepts transfer much more easily than you might think.

📚 Useful Libraries Worth Exploring

Library	Best For
NativeWind	Tailwind-style utilities for React Native
Tamagui	Cross-platform UI framework and design system
Gluestack UI	Modern accessible component library
React Native Paper	Material Design components
Shopify Restyle	Type-safe design systems
React Native Reanimated	High-performance animations
React Native Gesture Handler	Native gestures
FlashList	High-performance lists
React Navigation	Navigation between screens

🎯 Final Thoughts

React Native styling has evolved dramatically.

A few years ago, developers mainly relied on StyleSheet.create().

Today, you can choose from utility-first styling, design systems, component libraries, and cross-platform frameworks that dramatically improve the developer experience.

There's no single "best" choice.

The right tool depends on your project:

NativeWind if you love Tailwind and rapid development.
Tamagui if you're building a scalable design system across web and mobile.
React Native Paper if you want Material Design out of the box.
Gluestack UI if you prefer a modern, accessible component ecosystem.

What's most important isn't the library itself—it's understanding the principles behind good UI architecture: reusable components, consistent design tokens, accessible interfaces, and maintainable code.

"Great React Native apps aren't built by choosing the trendiest styling library—they're built by creating a UI system that your entire team can understand and evolve."

🚀 Supercharge Your AI Coding Workflow with OfficialSkills.sh

Charan Gutti — Thu, 25 Jun 2026 13:14:44 +0000

"The best developers don't memorize everything—they build systems that remember for them."

As AI coding assistants become more capable, there's one problem many developers eventually run into:

The AI is powerful... but it doesn't always know how you want things done.

Maybe you always deploy with Docker.

Maybe your company has its own coding standards.

Maybe you frequently work with React Native, Next.js, Kubernetes, Stripe, or Supabase.

Every new chat starts from scratch.

Wouldn't it be nice if your AI assistant could instantly become an expert in those topics?

That's exactly what OfficialSkills.sh solves.

🤔 What is OfficialSkills.sh?

OfficialSkills.sh is a community-driven directory of reusable AI agent skills.

Instead of writing the same prompts repeatedly, you install a skill that teaches your AI agent how to solve a specific class of problems.

Think of it like installing extensions in VS Code.

VS Code becomes more powerful because of extensions.

AI agents become more powerful because of skills.

OfficialSkills.sh hosts skills from organizations including OpenAI, Anthropic, Stripe, Cloudflare, Microsoft, and many community contributors.

🧠 What Exactly is a Skill?

A skill is structured knowledge that teaches an AI agent:

when to use it
how to solve a problem
best practices
workflows
examples
documentation
edge cases

Instead of repeatedly explaining:

"Generate a Next.js component using Tailwind, TypeScript, and Shadcn."

You install a skill once.

The agent already knows.

💡 Think of Skills Like VS Code Extensions

Without skills:

You

↓

AI

↓

Repeated prompts

↓

Repeated explanations

With skills:

You

↓

AI

↓

Installed Skills

↓

Consistent high-quality answers

Instead of prompting better...

You're teaching your AI better.

🌍 Why Skills Matter

Imagine you work on five different technologies.

Next.js

Docker

Git

Kubernetes

Stripe

Normally, every conversation starts like:

Use TypeScript.

Follow Airbnb lint rules.

Use Server Components.

Don't use client components unless needed.

Write production-ready Dockerfiles.

Again...

and again...

and again.

Skills eliminate this repetition.

📦 Installing a Skill

OfficialSkills provides installation instructions for each skill. Depending on the agent or tool you're using, this usually involves placing the skill in the directory your AI assistant watches for installed skills.

A typical command may look like:

git clone https://github.com/example/nextjs-skill

skill install nextjs

The exact installation method depends on the AI agent you use.

Always follow the installation instructions provided with each skill.

🧩 What Does a Skill Contain?

Most skills include information such as:

Skill

├── Description
├── Instructions
├── Examples
├── Best Practices
├── References
├── Documentation
└── Tests (sometimes)

Think of it as a miniature knowledge base focused on one topic.

🚀 Real Productivity Examples

Next.js Skill

Instead of asking:

Build a Next.js dashboard using Server Components.

The skill already knows:

App Router
Server Components
Route Handlers
Metadata
Image Optimization
Loading UI
Error Boundaries
Caching
Best folder structures

The responses become much more consistent.

Docker Skill

Instead of explaining:

Use multi-stage builds.

Keep images small.

Use Alpine.

Don't run as root.

The Docker skill already understands those practices.

Git Skill

Instead of asking:

Help me recover commits.

The Git skill understands workflows involving:

rebase
cherry-pick
stash
reflog
merge conflicts
interactive rebase

without re-explaining your expectations.

Kubernetes Skill

Need deployment YAMLs?

The skill already understands:

Deployments
Services
Ingress
ConfigMaps
Secrets
Autoscaling

🧠 The Pattern Behind Skills

Every skill follows roughly the same pattern.

Problem

↓

Knowledge

↓

Instructions

↓

Examples

↓

Execution

Notice something?

This is exactly how humans learn.

We don't memorize every answer.

We learn patterns.

Skills allow AI agents to do the same.

🎯 Skills vs Prompts

Many developers confuse the two.

Prompt

Do this once.

Example:

Build a login page.

Done.

Conversation ends.

Skill

Teach the AI how to solve
this category of problems forever.

Instead of writing:

Build login page

Use NextAuth

Use Prisma

Use PostgreSQL

Use Tailwind

Use TypeScript

Follow accessibility

every day...

The skill already knows.

⚙️ Skills vs MCP Servers

These are different things.

Skills	MCP Servers
Teach knowledge	Provide capabilities
Static guidance	Dynamic actions
Explain workflows	Execute workflows
Improve reasoning	Improve functionality

A good mental model:

Skills

↓

Brain

↓

Agent

↓

MCP

↓

Hands

Skills help the AI think.

MCP lets the AI act.

Together they create powerful AI assistants.

💻 Real Workflow

Imagine you're building a SaaS.

Installed skills:

Next.js

Tailwind

Docker

Git

PostgreSQL

Stripe

Testing

Now ask:

Build a subscription dashboard.

The AI already understands:

architecture
coding style
deployment
folder structure
testing strategy
API design

without repeated prompting.

🏢 Team Productivity

One of the biggest benefits is consistency.

Imagine a team of 20 developers.

Without skills:

Everyone prompts differently.

Everyone gets different outputs.

With shared skills:

Everyone gets:

similar architecture
similar naming
similar testing
similar best practices

AI becomes much more predictable across the team.

🧩 Can You Build Your Own Skills?

Absolutely.

In fact, many experienced teams do.

Examples:

Company Coding Standards

↓

Internal Skill

Deployment Workflow

↓

Internal Skill

API Guidelines

↓

Internal Skill

Security Checklist

↓

Internal Skill

Instead of creating long onboarding documents...

You create skills.

New developers—and AI assistants—immediately benefit.

🚀 Best Skills Every Developer Should Install

If you're building modern applications, these are excellent starting points:

⚛️ React
⚡ Next.js
🎨 Tailwind CSS
🐳 Docker
🌐 Kubernetes
🗄 PostgreSQL
🔐 Authentication
🧪 Testing
🧙 Git
📦 Package Management
🚀 CI/CD
🔍 Debugging

Over time you'll naturally build a personal library tailored to your work.

💡 Productivity Tips

1. Install skills for technologies you use every day

If you work with Next.js daily, a dedicated skill provides more value than one you'll use once a year.

2. Keep skills updated

Technologies evolve.

Refreshing your skills ensures your AI assistant keeps pace with current best practices.

3. Don't install everything

Too many overlapping skills can introduce unnecessary complexity.

Focus on the technologies that matter most to your workflow.

4. Create company-specific skills

If your organization has coding standards, naming conventions, deployment steps, or architecture guidelines, encode them as reusable skills instead of repeating them in every conversation.

5. Combine skills thoughtfully

For example:

Next.js

+

Tailwind

+

Docker

+

Git

+

Testing

Now your AI can help across the entire development lifecycle, not just one part of it.

🌍 Real-World Examples

Building a SaaS

Next.js Skill

↓

Authentication Skill

↓

Stripe Skill

↓

Docker Skill

Open Source Project

Git Skill

↓

Testing Skill

↓

Documentation Skill

AI Agent Development

Hermes

↓

MCP

↓

Skills

↓

Production Workflow

📚 Resources

OfficialSkills.sh — Browse official and community-contributed AI agent skills.
Documentation for your AI coding assistant to learn how it discovers and loads installed skills.
GitHub repositories linked from OfficialSkills for installation and contribution guides.

🎯 Final Thoughts

OfficialSkills.sh isn't just another package repository.

It's a way of teaching your AI assistant instead of repeatedly instructing it.

The biggest productivity gain doesn't come from writing better prompts.

It comes from giving your AI reusable knowledge that matches how you and your team build software.

The more your skills reflect your real workflows, the less time you spend repeating instructions—and the more time you spend building.

"The future of AI-assisted development isn't asking better questions every day. It's giving your AI the right knowledge once, then letting it grow with you."

DEV Community: Charan Gutti

Used a cheap old android mobile(1gb ram) to include a load balancer, cached web server as a fun way to learn system design

Table of Contents

Part 1: Hello from a Phone

Part 2: A Load Balancer, in 60 Lines

Part 3: A Cache, with Numbers

Part 4: Fixing What the Numbers Showed

The CPU That Was Never Ours

Making It Public, from the Phone

What I Actually Learned

Can Agents Replace Developers? How to Use Them Efficiently via Structured SDLC Loops

The Lessons of Traditional SDLC Models

Mapping the Developer's Inner Loop to Agents

Automating the Agentic SDLC Loop

Figure 1: The Automated Agentic SDLC Loop

Conclusion: The Future of Agent-Assisted Development

Code in the Context: How Agent Skills Evolved from Simple Prompts to Package Managers

Why Prompt Bloating Breaks Down

Anatomy of a Modern Agent Skill

Figure 1: Anatomy of an Agent Skill Package Directory

Package-Managing the AI Context

Figure 2: Agent Skill Discovery, Security Audit, & Dynamic Loading Pipeline

Building on Real-World Skills

Prompts Are Now Code

Stop Measuring Parameter Count: Why Fable 5’s Real Power Lies in the Orchestration Loop

Why the One-Shot Answer Breaks Down

How Fable 5 Paces Work Across Long Horizons

The Paradigm Shift in AI Workflows

Self-Verification and Looped Workflows

The Runtime Harness: Why Raw Size is Irrelevant

System Architecture & Safety Harness

Safety Gating as Practical Power

Owning the Project Loop

Demystifying "Agent 2.0": Why Agent Loops Are the Future of AI

The Core Mechanic: Reason -> Act -> Observe

Solving the Human Burden

Addressing the Big Questions

Do they run 24/7 or only when you are working?

Are they actually efficient?

Will they burn my tokens in minutes?

Conclusion

🚀 The RAM Disk Revival & In-Memory Architectures

1. The Physics of Storage: Why RAM Wins

2. In-Memory Databases: Redis and Memcached

How They Work

The Trade-off: Durability

3. The Return of the RAM Disk

Real-World Use Cases

4. The Future: NVMe vs. RAM

Conclusion

⚙️ Under the Hood: How Databases Handle Multiple Users Concurrently

1. Concurrency Control Mechanisms

Pessimistic Concurrency Control (Locking)

Optimistic Concurrency Control (OCC)

MVCC (Multi-Version Concurrency Control)

2. The Buffer Pool and Latches (Not Locks!)

3. Write-Ahead Logging (WAL)

4. Transaction Isolation Levels Explained

5. Threading and Connection Architectures

🏁 Summary

🗺️ How Google Maps Tracks Everyone Efficiently: Algorithms & Hidden Gems

📡 The Secret to Live Traffic: Crowdsourcing

🧠 The Core Algorithms

1. Graph Theory & Routing (A* Search and Dijkstra's)

2. Contraction Hierarchies (The Shortcut Algorithm)

3. Kalman Filters (Fixing GPS Jumps)

💎 The Engineering Gems

Gem 1: Geohashing and S2 Geometry (Spatial Indexing)

Gem 2: The Fused Location Provider (Saving Your Battery)

Gem 3: Vector Tiles vs. Raster Tiles

🏁 Conclusion

🗄️ How Does a Database Work for Multiple Users? A Beginner's Guide

🛑 The "Shared Spreadsheet" Problem

🛡️ How Databases Save the Day

1. Transactions (All or Nothing)

2. Locks (Taking Turns)

3. Isolation Levels (Hiding the Mess)

4. Connection Pooling (Managing the Crowd)

🏁 Summary

🚀 CI/CD for FastAPI: From Your First GitHub Action to Production-Ready DevOps