Forem: Lohit Kolluri

From chaos to clarity: what Phase 1 taught us about building parametric “income protection”

Lohit Kolluri — Sat, 21 Mar 2026 20:21:41 +0000

Hook (why this matters)

Gig delivery isn’t “unstable income” as a vibe — it’s high-frequency cashflow with low buffer. When disruption hits, the loss isn’t abstract; it’s rent, fuel, and food — this week.

We’re building Oasis for Guidewire DEVTrails 2026: a prototype oriented around parametric automation and weekly pricing, focused on loss of income from external disruptions — not a kitchen-sink insurance app.

Technique 1 — “Define the index before the interface”

Parametric products live or die on one idea: a measurable trigger that correlates with the harm you’re trying to cover — while accepting basis risk (the index can miss individual reality).

Useful takeaway: early teams should write down:

What event counts? (high-level categories, not your secret thresholds)
What proof is acceptable? (public signals vs user claims)
What failure mode is unacceptable? (paying wrong people vs delaying honest people)

That’s how you keep automation honest.

Technique 2 — Weekly pricing isn’t a billing choice; it’s a behavioral match

Gig workers often think in shifts and weeks, not annual policies.

Useful takeaway: if your premium cadence doesn’t match how people earn, you’ll get mis-trust even if the math is “correct.” Weekly framing is a UX + fairness decision as much as finance.

Technique 3 — “Automation-first” needs a human-readable story

If pricing or fraud logic becomes a black box, you lose:

debuggability during demos
credibility with judges
future maintainability for your team

Useful takeaway: prioritize explainability layers in the product narrative:

what inputs exist
what outputs mean
what happens on edge cases

You can be sophisticated underneath while staying legible on the surface.

Technique 4 — Integrations should be layered (mocks are a strategy, not a shortcut)

Useful pattern: separate:

domain logic (rules, lifecycle, eligibility)
provider adapters (weather/news/traffic/payments)
observability (what failed, why, what to retry)

That keeps Phase 1 honest: you can validate the story before you harden the plumbing.

What Phase 1 actually was (high signal, low leakage)

We spent the phase turning ideas into a coherent narrative:

persona + scenarios
weekly model + trigger philosophy
roadmap that matches hackathon phases
a short video + README that explain intent without exposing internals

Closing (forward-looking, still safe)

Phase 2 is where “promising” becomes demonstrable: onboarding, policy lifecycle, dynamic weekly premium, and claims — with multiple automated signals and a UX that feels zero-touch, not “zero-trust.”

If you’re building something similar: the best early artifact is clarity — not a bigger stack diagram.

What Guidewire DEVTrails Taught Me About Building Real Software

Lohit Kolluri — Mon, 09 Mar 2026 11:48:00 +0000

Last year, I had the opportunity to participate in Guidewire DEVTrails, one of the most intense and interesting hackathon-style competitions I have experienced. My team finished as the 1st Runner Up in DEVTrails 2025, and the journey fundamentally changed how I think about building software.

Most hackathons focus on building something quickly and presenting a prototype. DEVTrails is different. It simulates the environment of building a startup over several weeks. You are not just writing code for a demo. You are expected to think like a founder, design systems like an engineer, and make decisions under constraints like a real product team.

What makes the experience unique is the way the competition is structured. Teams start with a fixed amount of virtual capital and operate as startups inside the competition. Every week, operational costs are deducted, deadlines matter, and performance determines whether your project survives or gets eliminated. Instead of simply submitting a project at the end, you continuously iterate, improve, and defend your technical decisions.

Another aspect that makes DEVTrails interesting is the focus on solving real-world insurance problems. The challenges are not hypothetical programming exercises. They are derived from actual industry problems that require participants to think about scalability, reliability, user experience, and domain constraints.

The 2026 challenge, for example, focuses on the gig economy and the financial risks faced by delivery partners across India. Millions of people rely on gig platforms for income, yet their earnings can fluctuate due to factors completely outside their control. Environmental disruptions, sudden restrictions, or external events can reduce their working hours and impact their ability to earn. According to the challenge brief, these disruptions can reduce monthly income by a significant margin for many workers.

DEVTrails pushes participants to approach such problems not just from a coding perspective but from a systems perspective. You have to consider how a real platform would work in production. This includes thinking about automation, fraud detection, pricing models, integrations with external data sources, and the overall reliability of the system.

The competition itself unfolds across multiple phases, each designed to simulate the lifecycle of a startup. Early stages focus on ideation and architecture. Later stages emphasize automation, scalability, and operational reliability. Teams submit demos, pitch their systems, and receive ratings that determine the funding they receive for the next phase of the competition.

What I personally gained from DEVTrails goes far beyond building a project. It changed the way I think about system design and product development. I learned how to approach problems from a broader perspective, where architecture decisions, user impact, and operational constraints matter as much as the code itself.

One of the most valuable parts of the experience was interacting with engineers and experts from the industry. Getting feedback on architecture decisions and understanding how real insurance platforms operate gave me insights that are difficult to obtain through coursework alone.

Finishing as the first runner up was an incredible milestone, but the real value of the experience was the learning process. It pushed me to move beyond building simple prototypes and start thinking about building systems that could realistically operate in the real world.

This year I am participating again, but with a very different mindset. Instead of just trying to build something that works, the goal is to build something that is robust, well-architected, and capable of evolving over time.

DEVTrails has been one of the most challenging and rewarding technical experiences I have had so far. If you ever get the chance to participate in something similar, I would strongly recommend it. Competitions like this teach lessons that go far beyond writing code and they shape how you approach engineering problems for the rest of your career.

If you are also participating in DEVTrails this year, I would love to hear what you are building and what challenges you are tackling.

Happy building.

MegaKernel: Compiling LLMs for Low-Latency Inference

Lohit Kolluri — Sat, 21 Jun 2025 12:26:51 +0000

Introduction

Imagine waiting minutes for an AI model to respond. Frustrating, right? Large Language Models (LLMs) are revolutionizing industries, but their size often leads to significant latency, hindering real-time applications. The need for speed is paramount. This article explores a cutting-edge technique: compiling LLMs into a "MegaKernel" to drastically reduce inference latency.

In this post, you'll learn how MegaKernels work, the benefits they offer, and how they're paving the way for faster, more efficient AI. We will dive into the technical aspects of kernel fusion and optimization strategies, empowering you to understand and potentially implement this technology. Get ready to unlock the potential of low-latency LLMs!

Here’s what we’ll cover:

The challenges of LLM inference and the need for optimization.
What MegaKernels are and how they enable faster execution.
The technical steps involved in compiling LLMs into MegaKernels.
Practical examples and potential applications.
Common pitfalls and pro-tips for successful implementation.

Why This Topic is a Game-Changer

LLMs are powerful but notoriously slow. Think of it like this: traditional LLM inference is like ordering food item by item at a restaurant, waiting for each dish to be prepared separately. A MegaKernel is like a pre-set buffet where everything is ready to go – streamlining the entire process. In essence, MegaKernels fuse multiple operations within the LLM into a single, highly optimized kernel, minimizing overhead and maximizing hardware utilization. This translates directly to lower latency and higher throughput, making real-time applications like chatbots, virtual assistants, and personalized recommendations truly viable.

Prerequisites

Before diving in, you'll benefit from:

A basic understanding of machine learning and neural networks.
Familiarity with Python and common deep learning frameworks like PyTorch or TensorFlow.
Some exposure to GPU programming concepts (CUDA or similar) is helpful but not required.

Step-by-Step Guide to Mastering MegaKernel Compilation

Now, let’s explore how to compile LLMs into MegaKernels. This process is complex and often requires specialized tools and expertise, but understanding the core steps is crucial.

Model Profiling and Analysis:
- The first step is to thoroughly profile your LLM to identify performance bottlenecks. This involves understanding which operations consume the most time and resources. Tools like PyTorch Profiler or TensorFlow Profiler can be invaluable for this.
Kernel Fusion Identification:
- Identify opportunities for kernel fusion. This means finding sequences of operations that can be combined into a single kernel. Common candidates include element-wise operations, matrix multiplications, and activation functions.

Code Generation:

Generate optimized code for the fused kernel. This often involves writing custom CUDA kernels or using specialized libraries like Triton or TVM. The goal is to minimize memory access and maximize parallelism.

# Example of a fused kernel using Triton
import triton
import triton.language as tl

@triton.jit
def fused_matmul_add_kernel(
    A, B, C, M, N, K,  # Pointers to matrices and dimensions
    BLOCK_SIZE_M: tl.constexpr,
    BLOCK_SIZE_N: tl.constexpr,
    BLOCK_SIZE_K: tl.constexpr,
):
    # Compute matrix multiplication C = A @ B + C
    pid = tl.program_id(axis=0)
    num_pid_m = (M + BLOCK_SIZE_M - 1) // BLOCK_SIZE_M
    num_pid_n = (N + BLOCK_SIZE_N - 1) // BLOCK_SIZE_N
    pid_m = pid // num_pid_n
    pid_n = pid % num_pid_n

    offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
    offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
    offs_k = tl.arange(0, BLOCK_SIZE_K)

    a_ptrs = A + offs_am[:, None] * K + offs_k[None, :]
    b_ptrs = B + offs_k[:, None] * N + offs_bn[None, :]

    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_SIZE_K):
        a = tl.load(a_ptrs)
        b = tl.load(b_ptrs)
        accumulator += tl.dot(a, b)
        a_ptrs += BLOCK_SIZE_K * K
        b_ptrs += BLOCK_SIZE_K * N

    c = tl.load(C + offs_am[:, None] * N + offs_bn[None, :])
    c += accumulator
    tl.store(C + offs_am[:, None] * N + offs_bn[None, :], c)

# Example Usage (requires proper tensor initialization)
# triton.runtime.driver.compile(fused_matmul_add_kernel, ...)

Integration and Testing:
- Integrate the generated MegaKernel back into your LLM inference pipeline. Thoroughly test the performance and accuracy of the compiled model to ensure it meets your requirements.
Optimization and Iteration:
- Optimization is an iterative process. Continuously profile, analyze, and refine your MegaKernel to achieve the best possible performance. Experiment with different fusion strategies and code generation techniques.

🚀 Pro-Tip: Leverage Auto-Tuning

Many kernel compilation tools offer auto-tuning capabilities. This allows you to automatically explore different kernel configurations and find the optimal settings for your specific hardware and workload. Tools like TVM's AutoTVM can be incredibly helpful here.

🛑 Common Pitfall: Ignoring Memory Access Patterns

One of the biggest mistakes is neglecting memory access patterns. Optimizing for memory locality is crucial for achieving high performance. Ensure that your kernels access memory in a contiguous and predictable manner to minimize cache misses.

Conclusion: Your Next Adventure in Tech

Compiling LLMs into MegaKernels represents a significant step towards low-latency inference. By understanding the core principles and following the steps outlined in this article, you can unlock the potential of faster, more efficient AI. As hardware and software continue to evolve, MegaKernels and similar optimization techniques will become even more critical for deploying LLMs in real-world applications.

What are your biggest challenges when deploying LLMs? Share your thoughts and experiences in the comments below!

Cloud Cost Optimization: Mastering Rationalization for Efficiency

Lohit Kolluri — Fri, 20 Jun 2025 11:55:35 +0000

Introduction

Ever feel like your cloud bill is a runaway train, hurtling towards financial doom with no brakes in sight? You're not alone. Many companies, especially those new to the cloud, find themselves overspending due to inefficient resource allocation and a lack of understanding of their actual needs. This isn't just about saving money; it's about smart cloud management that directly impacts your bottom line and allows you to invest in innovation rather than infrastructure bloat.

In this article, we'll dive deep into the world of cloud cost rationalization. We'll explore how to identify and eliminate wasteful spending, optimize your resource utilization, and gain control over your cloud expenses. By the end, you'll have a practical, step-by-step guide to becoming a cloud cost optimization master. Get ready to put those cloud bills on a diet!

We'll cover:

Understanding Cloud Cost Drivers
Identifying and Eliminating Waste
Implementing Right-Sizing Strategies
Leveraging Automation for Continuous Optimization
Monitoring and Reporting on Cost Savings

Why This Topic is a Game-Changer

In today's rapidly evolving tech landscape, cloud computing is no longer a luxury; it's a necessity. But with great power comes great responsibility... and potentially great expense! Cloud cost optimization isn't just a nice-to-have; it's a critical component of any successful cloud strategy. Ignoring it is like driving a race car with a leaky gas tank – you'll burn out (and burn through your budget) before you reach the finish line.

Think of cloud resources like ingredients in a recipe. You wouldn't buy a whole sack of potatoes if you only needed one for your soup, right? Similarly, you shouldn't be paying for cloud resources you aren't actively using. Cost rationalization is about ensuring you're only buying the exact amount of each ingredient you need to create a delicious (and cost-effective) cloud experience.

Prerequisites

To get the most out of this article, a basic understanding of cloud computing concepts is helpful. Specific knowledge of a particular cloud provider (AWS, Azure, GCP) isn't required, but familiarity with their core services (compute, storage, networking) will be beneficial. Also, having access to cloud billing dashboards will allow you to follow along and apply the techniques discussed. It's also helpful to have basic understanding of scripting (Bash or Python) as that might be useful for some automation tasks.

Step-by-Step Guide to Mastering Cloud Cost Rationalization

Gain Visibility: Understand Your Cloud Spend.

The first step is understanding where your money is going. Most cloud providers offer detailed billing dashboards and cost analysis tools. Dive into these reports to identify your top spending services, regions, and resources. Look for trends and anomalies that might indicate areas of waste. This involves tagging resources correctly and enabling detailed billing reports.

# Example Python script to parse AWS Cost Explorer data
import boto3
import json

# Replace with your AWS region
REGION = 'us-west-2'

# Create a Cost Explorer client
ce = boto3.client('ce', region_name=REGION)

# Get cost and usage data for the last month
response = ce.get_cost_and_usage(
    TimePeriod={
        'Start': '2024-01-01',
        'End': '2024-01-31'
    },
    Granularity='MONTHLY',
    Metrics=['UnblendedCost']
)

# Print the results in JSON format
print(json.dumps(response, indent=4))
#This script uses boto3 to connect to Cost Explorer and retrieves monthly cost data.
#Make sure you have AWS credentials configured to run this script

Identify Idle Resources: The Ghosts in Your Cloud.

One of the biggest sources of cloud waste is idle resources – virtual machines, databases, or storage volumes that are running but not actively being used. Use your cloud provider's monitoring tools to identify these resources and determine if they can be stopped, terminated, or downsized. Regular audits are key here.
Right-Size Your Instances: Goldilocks Computing.

Are your virtual machines oversized for their actual workload? Many companies overestimate their initial resource requirements, leading to unnecessary expense. Monitor CPU utilization, memory usage, and network traffic to identify instances that can be downsized to a smaller, more cost-effective size. This is about finding the "just right" instance size.
Leverage Reserved Instances or Savings Plans: Commit and Save.

If you have predictable workloads, consider purchasing reserved instances or savings plans. These offer significant discounts (up to 70% in some cases) in exchange for a commitment to use a certain amount of resources over a fixed period (typically 1 or 3 years). This is like buying in bulk – you pay less per unit.
Automate Start/Stop Schedules: Control the On/Off Switch.

For non-production environments (development, testing, staging), automate start/stop schedules to ensure resources are only running when they're needed. This can dramatically reduce costs, especially for resources that are only used during business hours. Services like AWS Instance Scheduler can automate this process.
Optimize Storage: Tiering and Lifecycle Policies.

Cloud storage costs can quickly add up. Implement storage tiering to move infrequently accessed data to lower-cost storage classes (e.g., AWS S3 Glacier). Define lifecycle policies to automatically archive or delete data that is no longer needed. This is about managing your data like a librarian – keeping the valuable stuff accessible and archiving the rest.

🚀 Pro-Tip: Implement Cost Allocation Tags religiously!

Tagging resources is crucial for accurate cost tracking and allocation. Use a consistent tagging strategy to categorize resources by department, project, environment, or any other relevant criteria. This allows you to generate detailed cost reports and identify which teams or projects are driving the most cloud spend. Without proper tagging, you're flying blind.

Conclusion: Your Next Adventure in Tech

Cloud cost rationalization is an ongoing process, not a one-time fix. By understanding your cloud spend, identifying waste, implementing right-sizing strategies, and leveraging automation, you can gain control over your cloud costs and free up resources for innovation. The future of cloud computing is about efficiency and optimization, and by mastering these techniques, you'll be well-positioned for success.

What are your favorite cloud cost optimization techniques? Share your thoughts and experiences in the comments below!

Beautiful Soup: Web Scraping's Delightful Deception

Lohit Kolluri — Fri, 20 Jun 2025 06:15:09 +0000

🥣 Beautiful Soup: The Unsung Hero of Personal Projects

Ever stumbled upon a library that feels just right for a personal project, only to realize it’s rarely spotted in professional environments?

For me, that library is Beautiful Soup. It’s Python’s friendly‑neighborhood web‑scraping helper—perfect for side projects, but often overshadowed by heavyweight frameworks in enterprise stacks.

⚡ Quick vibe‑check meme

When you discover how easy BS4 makes HTML parsing.

In this post we’ll explore why hobbyists adore Beautiful Soup, where it falls short for huge teams, and how to wield it like a pro.

📌 Why This Matters

Web scraping powers dashboards, research pipelines, and hobby hacks alike. Choosing the right tool can save you hours (and gray hairs).

Tool	Strengths	Weaknesses
Beautiful Soup	Simple API, excellent docs, tiny footprint	No async crawling, can’t run JavaScript
Scrapy	Ultra‑fast, asynchronous, built‑in pipeline system	Steeper learning curve
Selenium / Playwright	Renders JavaScript, simulates browsers	Heavy, slower, resource‑intensive

For 80 % of one‑off scripts, Beautiful Soup is more than enough. 🌟

🧠 Prerequisites

Basic Python knowledge
Familiarity with HTML
Python 3.6 + installed

🚀 How‑To: Scraping Dev.to with Beautiful Soup

1️⃣ Install dependencies

pip install beautifulsoup4 requests

2️⃣ Fetch HTML

import requests

url = "https://dev.to"
try:
    resp = requests.get(url, timeout=15)
    resp.raise_for_status()          # 4xx / 5xx? -> kaboom
    html = resp.content
    print(f"Fetched {len(html):,} bytes from {url}")
except requests.exceptions.RequestException as exc:
    print(f"Network error: {exc}")

3️⃣ Parse with Beautiful Soup

from bs4 import BeautifulSoup

soup = BeautifulSoup(html, "html.parser")
print("HTML parsed ✅")

4️⃣ Extract article titles

for h2 in soup.find_all("h2", class_="crayons-story__title"):
    print(h2.text.strip())

This prints every Dev.to headline, neatly stripped of whitespace.

🎨 Visual break: “What actually happens?”

5️⃣ Run the full script

Save it as scraper.py and launch:

python scraper.py

A list of Dev.to headlines should greet you in your terminal.

✅ Pro Tips for Bulletproof Scraping

import time, random, logging

headers = {"User-Agent": "Mozilla/5.0 (DIY‑Scraper 🤖)"}
resp = requests.get(url, headers=headers, timeout=15)
# …
time.sleep(random.uniform(1.0, 2.5))   # be kind to servers

Rotate user‑agents
Respect robots.txt
Randomize delays to avoid rate limits
Catch requests.exceptions.RequestException to handle network hiccups gracefully

🧾 Conclusion

Beautiful Soup shines for quick‑and‑clean scraping jobs. It’s intuitive, well‑documented, and perfect for learning or prototyping. When your project evolves into a distributed crawler or needs to execute JavaScript, consider hopping over to Scrapy, Playwright, or Selenium.

Ready to ladle some data out of the web? 🍲
Tell me in the comments what you’ll scrape first!

📺 Bonus: Watch It in Action

Click the thumbnail to open the YouTube tutorial in a new tab.

Unveiling the Secrets of a 10x Engineer

Lohit Kolluri — Fri, 20 Jun 2025 06:12:59 +0000

Introduction

Ever wondered what sets a truly exceptional engineer apart? It's not just about lines of code; it's a mindset, a methodology, and a relentless pursuit of efficiency. In this interview, we'll dive deep into the strategies and habits of a "0.1x engineer" – someone who achieves extraordinary results with focused effort. You'll learn how to optimize your workflow, prioritize effectively, and become a more impactful contributor, no matter your experience level.

Why This Matters

In today's fast-paced tech landscape, efficiency is paramount. Understanding how top performers operate can help you streamline your own processes, reduce burnout, and deliver higher-quality work, faster. This isn't about working harder; it's about working smarter. By adopting some of these techniques, you can significantly boost your productivity and impact.

Prerequisites

A desire to improve your engineering workflow
Basic understanding of software development principles
An open mind to new ideas and approaches

The How-To: An Interview with an Efficiency Expert

I recently had the opportunity to interview Sarah, a lead engineer at a prominent tech company renowned for her efficiency and impact. Here's what I learned:

Ruthless Prioritization: Sarah emphasizes the importance of saying "no" to tasks that don't align with her core objectives. She uses the Eisenhower Matrix (Urgent/Important) to categorize tasks and delegate or eliminate those that aren't critical. "It's about focusing on the 20% of tasks that yield 80% of the results," she says.
Deep Work Blocks: Sarah schedules dedicated blocks of uninterrupted time for focused work. During these periods, she eliminates distractions – turning off notifications, closing unnecessary tabs, and informing colleagues that she's unavailable. "I find that I can accomplish more in a 2-hour deep work session than in an entire day of fragmented work," she explains.

Automation is Key: Sarah is a strong advocate for automation. She constantly looks for opportunities to automate repetitive tasks, whether it's writing scripts to automate deployments or using tools to streamline code reviews. "If I find myself doing something more than twice, I try to automate it," she says.

# Example Python script to automate a common task: deploying a website
import subprocess

def deploy_website(environment="production"):
    """Deploys the website to the specified environment."""
    try:
        # Step 1: Build the website
        print(f"Building website for {environment}...")
        subprocess.run(["npm", "run", "build"], check=True)

        # Step 2: Upload the build to the server
        print(f"Uploading build to {environment} server...")
        subprocess.run(["scp", "-r", "./dist", f"user@{environment}.example.com:/var/www/html"], check=True)

        # Step 3: Restart the web server
        print(f"Restarting web server on {environment}...")
        subprocess.run(["ssh", f"user@{environment}.example.com", "sudo systemctl restart nginx"], check=True)

        print(f"Website deployed to {environment} successfully!")

    except subprocess.CalledProcessError as e:
        print(f"Deployment failed: {e}")

# Example usage:
deploy_website()

Continuous Learning: Sarah is committed to continuous learning and improvement. She regularly reads books, articles, and blog posts on software development, attends conferences, and participates in online communities. "The tech landscape is constantly evolving, so it's essential to stay up-to-date on the latest trends and technologies," she advises.
Effective Communication: Sarah emphasizes the importance of clear and concise communication. She uses tools like Slack and email to communicate effectively with her team, and she always strives to provide context and rationale for her decisions. "Good communication can prevent misunderstandings and save a lot of time and effort in the long run," she notes.

✅ Pro-Tip: Embrace the Pomodoro Technique!

Sarah swears by the Pomodoro Technique – working in focused 25-minute intervals with short breaks in between. This helps her maintain focus and prevent burnout. Try it out and see if it works for you!

Conclusion

Becoming a more efficient engineer is a journey, not a destination. By adopting some of the strategies and habits outlined in this interview, you can significantly boost your productivity, reduce stress, and become a more impactful contributor. What specific technique will you incorporate into your workflow this week? Let me know in the comments!

VAC Attack: Unmasking Valve's Anti-Cheat System

Lohit Kolluri — Thu, 19 Jun 2025 09:04:06 +0000

Introduction

Ever wondered how Valve keeps Counter-Strike and other games relatively cheat-free? The answer lies in their Anti-Cheat system, known as VAC. But how does it really work? Is it a magical black box, or something more understandable? This article will peel back the layers of Valve Anti-Cheat (VAC), exploring its mechanisms, limitations, and impact on the gaming world. We'll delve into its history, how it detects cheats, and what you can do to avoid accidentally triggering it. Get ready to level up your understanding of game security!

Why This Matters

Understanding anti-cheat systems like VAC is crucial for both gamers and developers. For gamers, it provides insight into fair play and how to avoid false positives. For developers, it offers valuable lessons in security and cheat prevention. In an era where online gaming is more popular than ever, maintaining a level playing field is paramount. VAC is a cornerstone of that effort, and knowing its strengths and weaknesses is increasingly important.

Prerequisites

While this article is designed to be beginner-friendly, some basic knowledge will be helpful:

Basic understanding of computer systems: Familiarity with processes, memory, and file systems.
General gaming knowledge: Experience playing online multiplayer games.
Curiosity: A willingness to learn about the technical side of gaming!

The How-To: A Step-by-Step Guide

Let's break down how VAC works, from its core principles to its methods of cheat detection.

What is VAC? VAC stands for Valve Anti-Cheat. It's a system designed to detect and ban players who are using cheats or hacks in multiplayer games on the Steam platform. When VAC detects a cheat, it permanently bans the user's account from playing VAC-secured games.
How VAC Detects Cheats: VAC primarily uses signature-based detection. This means it looks for known patterns or "signatures" of cheat programs in a user's system memory. Think of it like a virus scanner for games. It doesn't detect new cheats immediately, but once a cheat is identified, its signature is added to VAC's database.
VAC's Scanning Process: When you play a VAC-secured game, VAC scans the memory of your computer for these known cheat signatures. It doesn't scan your hard drive or personal files; it's specifically looking for running processes that match known cheat patterns. This scanning happens continuously while you're playing the game.
Delayed Bans: A key aspect of VAC is that bans are often delayed. This is a deliberate strategy to make it harder for cheat developers to understand how their cheats were detected. By delaying the ban, Valve can gather more data about the cheat and its usage, making it easier to detect in the future. This can lead to some frustration among players who suspect others are cheating, but haven't been banned yet.
False Positives: Although rare, false positives can occur. If VAC incorrectly identifies a program as a cheat, it can lead to an unfair ban. Valve has a process for investigating and overturning false bans, but it can be a lengthy process. This is why it's important to avoid running any suspicious programs while playing VAC-secured games.
What Triggers a VAC Ban? Only running cheat programs or modifying game files directly will trigger a VAC ban. Simply using third-party programs or overlays is unlikely to trigger a ban, unless those programs are specifically designed to cheat. However, it's always best to err on the side of caution and avoid running any unnecessary programs while playing VAC-secured games.

Example: A Simple Cheat Detection Simulation (Python):
Let's imagine a simplified version of cheat detection using Python. This is just a conceptual example and doesn't represent the complexity of VAC, but it illustrates the basic principle of signature-based detection.

# A simplified example of cheat detection

known_cheats = {
    "aimbot": "A04B23",
    "wallhack": "C91D57"
}

def scan_memory(process_memory):
    for cheat_name, cheat_signature in known_cheats.items():
        if cheat_signature in process_memory:
            return f"Cheat detected: {cheat_name}"
    return "No cheats detected"

# Simulate process memory
memory_snapshot = "Some game data and A04B23"

result = scan_memory(memory_snapshot)
print(result) # Output: Cheat detected: aimbot

✅ Pro-Tip: Avoid Third-Party Software

While not all third-party software will trigger a VAC ban, it's best to avoid running any unnecessary programs while playing VAC-secured games. This includes custom skins, scripts, or any other modifications that aren't officially supported by the game. The risk of a false positive is always present, so it's better to be safe than sorry.

Conclusion

Valve Anti-Cheat (VAC) is a complex system that plays a vital role in maintaining fair play in online games on Steam. Understanding how it works can help you avoid accidental bans and appreciate the efforts to combat cheating in the gaming world. Now you can confidently dive back into your favorite VAC-secured games, knowing a little more about the invisible guardian protecting the integrity of the experience. What other game security topics are you curious about? Let me know in the comments!

Decoding the Math Mystery: Why LLMs Struggle with Numbers

Lohit Kolluri — Wed, 18 Jun 2025 20:25:11 +0000

Introduction

Have you ever asked a Large Language Model (LLM) a simple math problem, only to receive a nonsensical answer? It's a common frustration, and it highlights a key limitation of these powerful tools. This article dives into why LLMs often falter with mathematical calculations, exploring the underlying reasons and offering practical strategies to mitigate this weakness. You'll learn about the core challenges and discover how to use LLMs effectively despite their mathematical limitations.

Why This Matters

Understanding the limitations of LLMs in mathematical contexts is crucial for responsible development and deployment. As LLMs become increasingly integrated into various applications, from data analysis to financial modeling, recognizing their mathematical shortcomings helps prevent errors and ensures reliable results. Ignoring this limitation could lead to inaccurate predictions, flawed decision-making, and even financial losses.

Prerequisites

Basic understanding of Large Language Models (LLMs)
Familiarity with programming concepts (optional, for code examples)

The How-To: A Step-by-Step Guide

Understanding the Nature of LLMs: LLMs are trained on massive datasets of text and code, learning to predict the next word in a sequence. They excel at pattern recognition and generating human-like text, but they don't inherently understand mathematical concepts in the same way humans do. They don't perform calculations; they predict the most probable sequence of characters that looks like a correct answer.
The Symbol-Grounding Problem: A significant challenge is the symbol-grounding problem. LLMs associate symbols (like "2 + 2") with patterns in the data, but they lack a direct connection to the underlying mathematical meaning. They can't directly manipulate numerical representations in the way a calculator does.
Strategies for Mitigating Math Limitations:
- Symbolic Calculation Libraries: Instead of directly asking an LLM to solve a complex equation, use it to generate code that a symbolic calculation library (like SymPy in Python) can then execute. This leverages the LLM's strength in code generation while relying on dedicated libraries for accurate mathematical computation.
- Structured Input: Provide mathematical problems in a highly structured format. Clearly define variables, operators, and the desired output. Ambiguity is the enemy of accurate LLM responses.
- Step-by-Step Problem Solving: Break down complex problems into smaller, more manageable steps. This helps LLMs focus on individual parts, reducing the chance of cumulative errors.
- Verification and Validation: Always verify the LLM's output using independent methods. Never rely solely on an LLM for critical mathematical calculations.
Example using Python and SymPy:

from sympy import *

x = Symbol('x')

# LLM generated equation (example)
eqn = 'solve(x**2 - 4*x + 3 == 0, x)'

# Execute the equation using SymPy
solution = eval(eqn)

print(solution)

This code demonstrates how to use an LLM to generate a SymPy equation and then use SymPy to solve it reliably. Remember to sanitize LLM-generated code before execution to prevent security vulnerabilities.

✅ Pro-Tip: Don't Forget This!

Always treat LLM-generated mathematical results with skepticism. Independent verification is essential, especially in situations where accuracy is paramount.

Conclusion

LLMs are powerful tools, but their mathematical abilities are limited by their fundamental architecture. By understanding these limitations and employing the strategies described above, you can harness the power of LLMs while mitigating the risk of inaccurate mathematical results. Now, go forth and build! What creative applications can you develop by combining LLMs with robust mathematical libraries?

Unlock LLMs' Reasoning: A Developer's Deep Dive

Lohit Kolluri — Wed, 18 Jun 2025 19:55:45 +0000

Unlock LLMs' Reasoning: A Developer's Deep Dive

Introduction

Ever felt like you're just scratching the surface with Large Language Models (LLMs)? You're not alone! While generating text and translating languages is cool, LLMs' real power lies in their reasoning abilities. But how do you actually use that power? In this article, we'll demystify advanced reasoning techniques with LLMs, giving you practical strategies to level up your projects.

We'll explore techniques like chain-of-thought prompting, knowledge graphs, and even some tricks to coax better reasoning from your models. Get ready to transform your LLM interactions from simple Q&A to complex problem-solving.

Why This Matters

In today's AI landscape, reasoning is the key differentiator. It allows LLMs to tackle complex tasks like debugging code, planning strategies, and even making informed decisions. Understanding these techniques gives you a massive edge in building smarter, more capable AI applications. Plus, with the rise of open-source models, optimizing reasoning can unlock significant cost savings compared to relying solely on massive, proprietary models.

Prerequisites

Basic understanding of Large Language Models (LLMs).
Familiarity with Python.
An OpenAI API key (or access to another LLM provider).

The How-To: A Step-by-Step Guide

Set up your environment: First, you'll need to install the OpenAI Python library. This will allow you to easily interact with the OpenAI API.
```
pip install openai
```

Import necessary libraries: Import the openai library and set your API key. Remember to keep your API key secure!

import openai
import os

openai.api_key = os.getenv("OPENAI_API_KEY") # Or set it directly, but securely!

Implement Chain-of-Thought (CoT) Prompting: CoT encourages the LLM to break down a problem into smaller, more manageable steps. This significantly improves reasoning accuracy. Instead of directly asking for the answer, prompt the model to "think step by step."

def generate_response(prompt):
    response = openai.Completion.create(
        engine="text-davinci-003",  # Or your preferred model
        prompt=prompt,
        max_tokens=200,  # Adjust as needed
        n=1,
        stop=None,
        temperature=0.7, # Adjust for creativity
    )
    return response.choices[0].text.strip()

# Example problem
problem = "Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have?"

# Chain-of-Thought prompt
cot_prompt = f"{problem}\nLet's think step by step:"

# Get the response
solution = generate_response(cot_prompt)

# Print the solution
print(solution)

Analyze the output: The LLM should now provide a step-by-step solution, rather than just the final answer. Examine the reasoning process. Does it make sense? If not, refine your prompt.
Incorporate Knowledge Graphs (Advanced): For more complex reasoning, consider integrating knowledge graphs. These structured databases provide LLMs with external knowledge and relationships. Tools like Neo4j can be used to build and query knowledge graphs. The complexity of this is beyond this short guide, but keep it in mind for future scaling!

✅ Pro-Tip: Prompt Engineering is Key!

The quality of your prompt directly impacts the LLM's reasoning. Experiment with different phrasings, examples, and instructions. Be explicit about the desired format and level of detail.

Conclusion

Congratulations! You've taken your first steps towards unlocking the advanced reasoning capabilities of LLMs. By mastering techniques like chain-of-thought prompting, you can build more intelligent and powerful AI applications. Now, experiment with different prompts and problems. What complex reasoning tasks can you solve with these newfound skills? Share your discoveries in the comments below!

AI Code Reviews: Spotting Bugs Like Apple

Lohit Kolluri — Wed, 18 Jun 2025 19:49:08 +0000

AI Code Reviews: Spotting Bugs Like Apple

Ever wondered why some bugs slip through even the most rigorous code reviews? Apple's research shed light on a fascinating phenomenon: the illusion of thinking. Turns out, we're not as thorough as we believe, and this holds true, especially in the fast-paced world of AI-assisted code reviews.

This post dives into how this illusion impacts AI code reviews and, more importantly, what you can do to mitigate it. Let's get started!

The Illusion in Code

What exactly is this "illusion of thinking"? It's the cognitive bias where we overestimate our ability to catch errors, especially when we think we're being diligent. Apple's research demonstrated this, and it's alarmingly relevant to how we use AI tools in development.

Think about it: you run an AI code review tool, it flags a few potential issues, you approve the changes. You might feel like you've thoroughly vetted the code. But have you really?

That's where the illusion kicks in. We tend to trust the AI's judgment (or our initial assessment), potentially overlooking subtle but critical flaws. This is especially true with complex code or unfamiliar libraries.

"The illusion of thinking can lead to complacency, even when using advanced tools like AI code reviewers." – Apple Research

So, how do we combat this?

Takeaway: Understanding the illusion of thinking is the first step towards more effective code reviews. Consider: How can we design better AI tools that actively combat this bias?

Augment, Don't Automate

AI code review tools are powerful, but they are not a replacement for human oversight. The key is to use them to augment your abilities, not automate the entire process.

Here’s a breakdown of how to integrate AI code reviews effectively:

Initial Scan: Use AI tools like DeepSource or SonarQube to perform an initial scan of the codebase. These tools can quickly identify common errors, security vulnerabilities, and style violations.
Focused Review: Don't blindly accept all AI suggestions. Instead, use the AI's findings to focus your attention on specific areas of the code.
Human Insight: Apply your knowledge of the application's business logic and context to evaluate the AI's suggestions. Ask yourself: Does this change make sense in the bigger picture?
Pair Review: Even with AI assistance, a second pair of human eyes can catch errors that you and the AI might have missed.

✅ Pro Tip: Customize your AI code review tool's rules and configurations to match your project's specific needs and coding standards. This reduces false positives and ensures the AI focuses on the most relevant issues.

Consider this Python example:

python

Function to calculate discount price

def calculate_discount(price, discount_percentage):
# Check if the inputs are valid
if not isinstance(price, (int, float)) or price <= 0:
raise ValueError("Price must be a positive number")
if not isinstance(discount_percentage, (int, float)) or not 0 <= discount_percentage <= 100:
raise ValueError("Discount percentage must be between 0 and 100")

# Calculate the discount amount

discount_amount = price * (discount_percentage / 100)

  
  
  Calculate the discounted price


discounted_price = price - discount_amount

return discounted_price

Example usage

price = 100
discount_percentage = 20

Call the function and print the result

discounted_price = calculate_discount(price, discount_percentage)
print(f"The discounted price is: {discounted_price}")

An AI tool might flag the lack of docstrings or suggest more descriptive variable names. However, it won't understand the specific business rules around discount calculations. Your human insight is crucial to ensure the logic is sound.

Takeaway: AI tools are great assistants, but human reviewers are still essential for understanding context and ensuring code quality. What strategies do you use to balance AI assistance with human oversight?

Test, Test, Test!

No code review process is complete without thorough testing. Unit tests, integration tests, and end-to-end tests are all critical for catching errors that might slip through the code review process.

Here’s why testing is so important:

Automated Validation: Tests provide an automated way to validate that your code behaves as expected.
Regression Prevention: Tests help prevent regressions by ensuring that new changes don't break existing functionality.
Confidence: Comprehensive testing gives you confidence that your code is robust and reliable.

✅ Pitfall Alert: Don't rely solely on positive test cases. Be sure to include negative test cases to verify that your code handles invalid inputs and edge cases gracefully.

For example, test the calculate_discount function with negative prices, discount percentages outside the valid range, and different data types to ensure it raises the appropriate exceptions.

Takeaway: Comprehensive testing is a crucial safety net for catching errors that code reviews might miss. How can you improve your testing strategy to catch more edge cases?

Embrace the Feedback Loop

Treat every bug as a learning opportunity. When a bug slips through the code review process, analyze why it happened and take steps to prevent similar issues in the future.

Consider these steps:

Root Cause Analysis: Identify the underlying cause of the bug. Was it a lack of understanding of the code? A flawed assumption? A missing test case?
Process Improvement: Update your code review process to address the root cause. This might involve adding new checks, improving training, or refining your testing strategy.
Tool Enhancement: If the bug was related to an AI code review tool, consider adjusting its configuration or reporting the issue to the tool vendor.

Takeaway: Continuous improvement is key to building a robust and reliable software development process. What metrics do you track to measure the effectiveness of your code review process?

Level Up Your Code Reviews

By understanding the illusion of thinking and adopting a balanced approach to AI-assisted code reviews, you can significantly improve the quality and reliability of your code. Remember to augment, not automate, test thoroughly, and continuously improve your processes.

Ready to take your code reviews to the next level? Start by exploring the AI code review tools mentioned earlier and experimenting with different testing strategies. Happy coding!

FastAPI: Your First Production-Ready API

Lohit Kolluri — Wed, 18 Jun 2025 18:45:25 +0000

Level Up Your Python APIs with FastAPI

Ever felt like building APIs was more complex than it needed to be? You're not alone! Many developers find themselves wrestling with boilerplate code and confusing configurations. FastAPI swoops in as a modern, high-performance web framework for building APIs with Python 3.7+ that's actually fun to use. This guide will walk you through creating your first production-ready FastAPI application, even if you're a complete beginner.

What Makes FastAPI So Special?

Why choose FastAPI over other frameworks like Flask or Django REST Framework? Well, FastAPI offers several key advantages:

Speed: Built on top of Starlette and Pydantic, FastAPI delivers blazing-fast performance, comparable to NodeJS and Go.
Automatic Data Validation: Pydantic handles data validation and serialization, reducing errors and simplifying your code.
Automatic API Documentation: FastAPI generates interactive API documentation (using Swagger UI and ReDoc) automatically, making it easy to test and explore your API.
Type Hints: Leverages Python type hints for improved code readability and maintainability.
Dependency Injection: A powerful design pattern built right in that simplifies testing and code organization.

Let's dive into creating a simple "To-Do" API to illustrate these features.

Setting Up Your Environment

Before we write any code, let's set up our development environment. I recommend using a virtual environment to isolate your project's dependencies.

1. Create a Virtual Environment

python3 -m venv venv

2. Activate the Virtual Environment

On macOS/Linux:

source venv/bin/activate

On Windows:

venv\Scripts\activate

3. Install FastAPI and Uvicorn

Uvicorn is an ASGI (Asynchronous Server Gateway Interface) server that we'll use to run our FastAPI application.

pip install fastapi uvicorn

✅ Takeaway: A virtual environment keeps your project dependencies separate, avoiding conflicts. Always activate it before working on your project.

Building Your First API Endpoint

Now for the fun part! Let’s create a simple API endpoint that returns a list of to-do items. Create a file named main.py and add the following code:

from fastapi import FastAPI
from typing import List
from pydantic import BaseModel

# Define a data model for a to-do item
class Todo(BaseModel):
    id: int
    task: str
    completed: bool = False

# Create a FastAPI app instance
app = FastAPI()

# Sample to-do data
todos = [
    Todo(id=1, task="Learn FastAPI", completed=True),
    Todo(id=2, task="Build a to-do API", completed=True),
    Todo(id=3, task="Deploy the API", completed=False),
]

# Define a GET endpoint to retrieve all to-do items
@app.get("/todos", response_model=List[Todo])
async def get_todos():
    """Retrieves all to-do items."""
    return todos

# Define a POST endpoint to create a new to-do item
@app.post("/todos", response_model=Todo)
async def create_todo(todo: Todo):
    """Creates a new to-do item."""
    todos.append(todo)
    return todo

Breakdown:

Import Statements: We import FastAPI, List, and BaseModel from their respective modules.
Todo Model: Todo is a Pydantic model. It validates request data and enforces types.
App Instance: We instantiate the FastAPI app.
Sample Data: Our demo uses a basic in-memory list of to-dos.
GET Endpoint: Returns the full list of to-dos.
POST Endpoint: Validates and adds a new to-do item to the list.

✅ Pro Tip: FastAPI uses type hints extensively. This not only improves code readability but also enables automatic data validation and API documentation.

Running Your API

To start your FastAPI app, run:

uvicorn main:app --reload

main: the filename (without .py)
app: the FastAPI instance
--reload: restarts the server automatically when code changes (great for development!)

Visit:
➡️ http://127.0.0.1:8000/docs — Swagger UI
➡️ http://127.0.0.1:8000/redoc — ReDoc

✅ Takeaway: uvicorn runs your FastAPI app. The --reload flag is great for development but avoid it in production.

Going Further: Automatic API Documentation

One of FastAPI’s standout features is its built-in automatic documentation. Based on your models and type hints, FastAPI auto-generates:

Swagger UI at /docs
ReDoc at /redoc

✅ Pro Tip: Write clear docstrings for each endpoint — they show up directly in the documentation UI!

Conclusion

In this tutorial, you've learned how to create a simple yet powerful API using FastAPI. You've seen how FastAPI leverages type hints, Pydantic, and automatic documentation to streamline the development process.

Next steps? Explore dependency injection, middleware, security, and deployment options to build even more sophisticated APIs. Dive into the official FastAPI documentation (linked below) for comprehensive guidance.

💡 Resources:

Ready to build something amazing? Start coding!

Level Up Your Dev Journey: My Keploy Fellowship Journey

Lohit Kolluri — Tue, 17 Jun 2025 16:39:32 +0000

I've always believed that the best way to learn is by doing. So, when I got the opportunity to join the Keploy API Fellowship, I jumped right in! I'm thrilled to share that my journey has officially begun, and it’s already been an incredible learning experience.

For those who might not know, Keploy is an open-source tool that creates E2E tests and stubs from API calls, essentially making testing faster and more efficient for developers. The Keploy API Fellowship is a program designed to mentor and guide developers into the world of APIs and open-source contributions.

Our first session, "Open Source 101," was the perfect kickoff. It was a masterclass in the fundamentals of open source, Git, and GitHub. It demystified the process and showed us how accessible contributing to open source can be.

Now, with that knowledge under my belt, it's time to get my hands dirty with our first official assignment!

🚀 The First Mission: From Theory to Practice

The first assignment is cleverly designed to build our foundation and confidence. It’s broken down into three key tasks:

Task 1: Building My Digital Storefront - The GitHub Profile README

The first challenge is to create a standout GitHub Profile README. This isn't just about listing skills; it's about crafting a narrative. A good README is your personal billboard in the developer world. It’s a chance to showcase your projects, highlight your achievements, and let your personality shine through. I’m genuinely excited to polish mine and create a profile that truly represents my journey and aspirations as a developer.

You can check my GitHub profile here

Task 2: Making My Mark - My First Open-Source Contribution

This is the task I’m most excited about: making a real contribution to an open-source project by submitting a Pull Request (PR). The beauty of how Keploy has structured this is the sheer variety of options available. They have a whole list of active projects, from their core Go repository to collections of public APIs, documentation, and code samples in various languages like Python, Java, and TypeScript.

This inclusive approach means everyone can contribute, regardless of their comfort level with coding. You can fix a typo, add to a list of engineering prompts, or tackle a labeled issue. The core message is clear: every contribution matters. I’ve decided to start by adding some valuable public APIs to the Keploy Public APIs Collection to get my feet wet in the contribution process.

Task 3: Sharing the Journey & Building in Public!

This final, optional task is all about community and transparency. We're encouraged to share what we've learned through a social media post or a blog (like this one!). It’s a fantastic way to practice "building in public," a concept I'm a huge fan of. It helps solidify your learning, connects you with the wider tech community, and documents your progress.

Plus, Keploy is offering some awesome goodies for the best posts! Who doesn't love a bit of swag? 😉

Why This Fellowship is a Game-Changer

What's already clear to me is that the Keploy API Fellowship isn't just another online course. It’s a hands-on, community-driven experience. The emphasis on practical application and the supportive environment creates the perfect incubator for growth.

I’m so grateful to the Keploy team for putting together such a thoughtful and impactful program. This is just the beginning, and I’m incredibly excited to see where this journey takes me, the skills I’ll gain, and the contributions I’ll make.

Stay tuned for more updates!

Forem: Lohit Kolluri

From chaos to clarity: what Phase 1 taught us about building parametric “income protection”

Hook (why this matters)

Technique 1 — “Define the index before the interface”

Technique 2 — Weekly pricing isn’t a billing choice; it’s a behavioral match

Technique 3 — “Automation-first” needs a human-readable story

Technique 4 — Integrations should be layered (mocks are a strategy, not a shortcut)

What Phase 1 actually was (high signal, low leakage)

Closing (forward-looking, still safe)

What Guidewire DEVTrails Taught Me About Building Real Software

MegaKernel: Compiling LLMs for Low-Latency Inference

Introduction

Why This Topic is a Game-Changer

Prerequisites

Step-by-Step Guide to Mastering MegaKernel Compilation

🚀 Pro-Tip: Leverage Auto-Tuning

🛑 Common Pitfall: Ignoring Memory Access Patterns

Conclusion: Your Next Adventure in Tech

Cloud Cost Optimization: Mastering Rationalization for Efficiency

Introduction

Why This Topic is a Game-Changer

Prerequisites

Step-by-Step Guide to Mastering Cloud Cost Rationalization

🚀 Pro-Tip: Implement Cost Allocation Tags religiously!

Conclusion: Your Next Adventure in Tech

Beautiful Soup: Web Scraping's Delightful Deception

🥣 Beautiful Soup: The Unsung Hero of Personal Projects

⚡ Quick vibe‑check meme

📌 Why This Matters

🧠 Prerequisites

🚀 How‑To: Scraping Dev.to with Beautiful Soup

1️⃣ Install dependencies

2️⃣ Fetch HTML

3️⃣ Parse with Beautiful Soup

4️⃣ Extract article titles

🎨 Visual break: “What actually happens?”

5️⃣ Run the full script

✅ Pro Tips for Bulletproof Scraping

🧾 Conclusion

📺 Bonus: Watch It in Action

Unveiling the Secrets of a 10x Engineer

Introduction

Why This Matters

Prerequisites

The How-To: An Interview with an Efficiency Expert

✅ Pro-Tip: Embrace the Pomodoro Technique!

Conclusion

VAC Attack: Unmasking Valve's Anti-Cheat System

Introduction

Why This Matters

Prerequisites

The How-To: A Step-by-Step Guide

✅ Pro-Tip: Avoid Third-Party Software

Conclusion

Decoding the Math Mystery: Why LLMs Struggle with Numbers

Introduction

Why This Matters

Prerequisites

The How-To: A Step-by-Step Guide

✅ Pro-Tip: Don't Forget This!

Conclusion

Unlock LLMs' Reasoning: A Developer's Deep Dive

Unlock LLMs' Reasoning: A Developer's Deep Dive

Introduction

Why This Matters

Prerequisites

The How-To: A Step-by-Step Guide

✅ Pro-Tip: Prompt Engineering is Key!

Conclusion

AI Code Reviews: Spotting Bugs Like Apple

AI Code Reviews: Spotting Bugs Like Apple

The Illusion in Code

Augment, Don't Automate

Function to calculate discount price

Calculate the discounted price

Example usage

Call the function and print the result

Test, Test, Test!

Embrace the Feedback Loop

Level Up Your Code Reviews

🥣 Beautiful Soup: The Unsung Hero of Personal Projects

🚀 How‑To: Scraping Dev.to with Beautiful Soup

3️⃣ Parse with Beautiful Soup