BUKYA NARESH

AI-Native SYSTEMS ARCHITECT “Stability is architected; mastery is earned through consistency.”

INDIA,HYDERABAD Joined on Apr 7, 2026 nareshalluarjun791@gmail.com http://linkedin.com/in/bukya-naresh

BUKYA NARESH

May 14

From SIMD Parsing to AI-Ready Infrastructure: Building Forge-Core v4.3

#c #python #ai #distributedsystems

2 min read

Gemma 4 Challenge: Write about Gemma 4 Submission

BUKYA NARESH

May 10

Gemma 4: A Systems Engineer’s Breakdown of the "Divergent" Edge Architecture

#devchallenge #gemmachallenge #gemma

2 min read

BUKYA NARESH

May 10

How I Scaled a C Ingestion Engine from 4M to 209M Rows/Sec: Engineering for the Silicon

#cpp #c #performance #systems

2 min read

BUKYA NARESH

May 6

What Building a 20GB CSV Validator Taught Me About mmap

#c #programming #linux #performance

2 min read

BUKYA NARESH

Apr 30

Fixing Floating-Point Drift While Speeding Up CSV Ingestion (7.75s 2.7s)

#python #c #datascience #performance

2 min read

BUKYA NARESH

Apr 29

Case Study: Reducing Data Ingestion Latency by 96.4% (24.5x Speedup)

#python #performance #datascience #distributedsystems

2 min read

BUKYA NARESH

Apr 28

🚀 Bypassing the Python GIL: How I Processed 10M Rows in 0.26s with C

#python #cpp #performance #dataengineering

2 min read

BUKYA NARESH

Apr 27

Stop Paying the Abstraction Tax : How I Built a C-Engine 12x Faster than Pandas

#c #dataengineering #python #distributedsystems

1 min read

BUKYA NARESH

Apr 20

Hardening a 1P/3C Broadcast Engine: Achieving 33.92M records/s with C11 Atomics

#showdev #architecture #c #performance

1 min read

BUKYA NARESH

Apr 16

7.22M Logs/Sec on a Laptop: Beating the "Abstraction Tax" with C11 Atomics

#programming #python #architecture #datascience

2 min read

BUKYA NARESH

Apr 15

Why I Bypassed Pandas to Process 10M Records in 0.35s Using Raw C and SIMD

#c #performance #dataengineering #python

2 min read

BUKYA NARESH

Apr 14

Bypassing the "Pandas RAM Tax": Building a Zero-Copy CSV Extractor in C

#c #python #dataengineering #performance

2 min read

BUKYA NARESH

Apr 11

Abolishing the "Python Tax": How I hit $3.06 \text{ GB/s}$ CSV Ingestion in C 🧱🔥

#c #performance #dataengineering #distributedsystems

2 min read

BUKYA NARESH

Apr 10

Stop Paying the "Python Object Tax": 10M Rows in 0.08s with C and Parallel mmap

#python #programming #c #performance

2 min read

BUKYA NARESH

Apr 9

How to Bypass the Pandas "Object Tax": Building an 8x Faster CSV Engine in C

#python #performance #dataengineering #datascience

2 min read

BUKYA NARESH

Apr 8

How I cut Python JSON memory overhead from 1.9GB to ~0MB (11x Speedup)

#python #c #performance #dataengineering

2 min read

BUKYA NARESH

Apr 7

Python was too slow for 10M rows—So I built a C-Bridge (and found the hidden data loss)

#python #cpp #performance #dataengineering

2 min read

DEV Community

BUKYA NARESH

Badges

Writing Debut

From SIMD Parsing to AI-Ready Infrastructure: Building Forge-Core v4.3

Gemma 4: A Systems Engineer’s Breakdown of the "Divergent" Edge Architecture

How I Scaled a C Ingestion Engine from 4M to 209M Rows/Sec: Engineering for the Silicon

What Building a 20GB CSV Validator Taught Me About mmap

Fixing Floating-Point Drift While Speeding Up CSV Ingestion (7.75s 2.7s)

Case Study: Reducing Data Ingestion Latency by 96.4% (24.5x Speedup)

🚀 Bypassing the Python GIL: How I Processed 10M Rows in 0.26s with C

Stop Paying the Abstraction Tax : How I Built a C-Engine 12x Faster than Pandas

Hardening a 1P/3C Broadcast Engine: Achieving 33.92M records/s with C11 Atomics

7.22M Logs/Sec on a Laptop: Beating the "Abstraction Tax" with C11 Atomics

Why I Bypassed Pandas to Process 10M Records in 0.35s Using Raw C and SIMD

Bypassing the "Pandas RAM Tax": Building a Zero-Copy CSV Extractor in C

Abolishing the "Python Tax": How I hit $3.06 \text{ GB/s}$ CSV Ingestion in C 🧱🔥

Stop Paying the "Python Object Tax": 10M Rows in 0.08s with C and Parallel mmap

How to Bypass the Pandas "Object Tax": Building an 8x Faster CSV Engine in C

How I cut Python JSON memory overhead from 1.9GB to ~0MB (11x Speedup)

Python was too slow for 10M rows—So I built a C-Bridge (and found the hidden data loss)