# Ng Fang Kiang - Full Content Archive

> A full-content text archive for RAG/LLM knowledge base.
> Last Updated: 2026-10-15

## About The Author

- **Name**: Ng Fang Kiang (黄钫江)
- **Role**: AI Product Leader & Technologist
- **Location**: Malaysia
- **Focus Areas**: Deep Learning, Agentic AI Systems, Product Strategy, Embodied AI & Computer Vision.
- **Canonical Website**: https://fangkiang.com/

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## Full Content Archive

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[START POST METADATA]
title: "An Unusual Route"
url: "https://fangkiang.com/posts/an-unusual-route/"
date: "2026-10-14"
tags: ["Self-Taught","MOOCs","Career Journey","Entrepreneurship","Mindset","Reflections"]
[END POST METADATA]

[START POST CONTENT]
Looking back a decade to 2016, I made a decision that looked to everyone around me like career suicide: fresh out of a Diploma in Business Studies, I walked away from the conventional university conveyor belt and refused to settle into a standard 9-to-5 desk job.
Instead, I locked myself in my bedroom, turned on my monitor, and went all-in on full-time, self-directed learning through Massive Open Online Courses (MOOCs).
Today, self-directed upskilling, remote engineering, and querying AI for guidance are accepted as second nature—a shift that aligns closely with what I later reflected on when looking back at my 2021 tech predictions. But in 2016, choosing online education over a traditional degree was widely perceived as a punchline. To friends, relatives, and neighbors, sitting at a desk all day staring at terminal windows and video lectures wasn’t “studying”—it was an excuse to avoid adult responsibilities, a cover for unemployment, and an escape from reality.
This is the unfiltered story of why I chose that lonely, unusual route, why I couldn’t accept the default domestic corporate ladder, and how being backed into a corner forced me to build my own future.
1. The Dream: OMSCS and the American Frontier My original ambition was never to become an isolated recluse; it was sharply defined and pragmatic.
Having realized during my business diploma that my true obsession lay in code, logic, and bleeding-edge computing, I set my sights on Georgia Tech’s Online Master of Science in Computer Science (OMSCS).
My blueprint at the time was clear:
Enroll in OMSCS to gain rigorous, accredited computer science credentials at a fraction of on-campus tuition costs. Work freelance gigs and part-time developer roles to fund my living expenses. Use the degree and an accelerated project portfolio as a springboard to transition to the United States and work at the frontier of technology. I poured myself into coursework on Udacity, edX, and Coursera, searching for a viable path forward. Back in 2016, Udacity hosted Georgia Tech’s official OMSCS curriculum directly on its platform, allowing anyone with the grit to enroll and audit the actual graduate-level lectures for free. I didn’t wait for a formal admission letter to start proving myself—I jumped right in, enrolled in multiple OMSCS courses through Udacity, and tackled the graduate coursework head-on.
This wasn’t just passive learning; it was a relentless search for a door into a world I was determined to join. In August 2016, I sent a hopeful, almost pleading email to Udacity’s support, asking if my Nanodegree and self-study could serve as a bridge to formally matriculate into Georgia Tech’s OMSCS program without an undergraduate Computer Science degree.
“I seriously want to be a part of student in OMSCS. I hope it’s possible!!” — My email to Udacity Support, August 2016.
The reply I received from David Joyner at Udacity was the exact spark I was looking for. He confirmed that Georgia Tech did, in fact, accept applicants without a prior CS degree and encouraged me to apply. That email was far more than a customer support ticket; it was a signal that the path, however unconventional, was genuinely possible.
This external validation grew even stronger when I connected with another internal team member at Udacity. Having noticed in my Instagram bio that my ultimate goal was to pursue a Master’s degree, he personally offered to write me a recommendation letter for OMSCS and even wanted to feature my story on Udacity’s official website. Although family circumstances ultimately prevented me from formally enrolling, that connection remained a meaningful milestone—years later, he went on to join Apple, while I forged my own path as an entrepreneur.
I had the passion. I had the raw work ethic. I had early external validation from people inside the industry.
Yet, I was entirely powerless against domestic reality.
2. Breaking the Clock: Why I Refused to Wait for Semesters Beyond my specific goal of getting into OMSCS, there was another fundamental reason I rejected traditional universities: they are bound by rigid timelines rather than personal velocity.
Conventional degree programs force you to march to the beat of an institutional calendar. Regardless of how fast you grasp the material or how many hours you are willing to pour in, your progress is artificially throttled by semester schedules, credit-hour limits, and bureaucratic pacing.
I didn’t want to spend three to four years waiting for permission to learn the next topic. My goal was simple: achieve the maximum possible output in the shortest possible time.
At the time, Udacity offered a game-changing incentive: a 50% tuition refund for students who completed a Nanodegree early or within a strict timeframe. Because my financial resources were severely limited, this policy aligned my economic survival directly with my hunger for speed:
Relieving Financial Pressure: Cutting my tuition costs in half meant I could stretch every hard-earned dollar twice as far. Compounding My Output: The faster I mastered the curriculum and shipped projects, the less I paid—and the sooner I could roll those savings into the next specialization. Driven by both financial necessity and sheer obsession, I treated every course like a high-stakes sprint. By removing the artificial speed limits of a traditional classroom, I went on to complete far more rigorous coursework in a fraction of the time it would take a conventional student.
3. Anchored by Finances and a Controlling Environment Even with the 50% tuition refunds keeping my self-study afloat, ambition means very little when your immediate environment is actively working against it.
Coming from a modest financial background, every dollar still had to be accounted for. Worse than the financial ceiling, however, was living in an unsupportive and deeply controlling family dynamic. To them, the tech industry was an alien concept, and online learning was an illegitimate pursuit.
The people around me were products of an analog, risk-averse mindset. They didn’t research emerging technologies, search Google to verify facts, or grasp where global software engineering was heading. If society dictated that success meant taking an entry-level clerk or sales position, deviating from that script was seen as ungrateful and aimless.
Every hour I spent learning linear algebra, writing C++, or training neural networks was met with cold shoulders, skepticism, and persistent pressure to abandon what they called a “pipe dream.”
I desperately wanted to step through the door that the Udacity recommendation and OMSCS had cracked open. But without full financial independence, without familial backing, and bound by responsibilities I could not simply discard, the door slammed shut. As I reflected in At the Peak, But Bound by Ground, reaching your intellectual stride while being physically and financially tethered is a uniquely painful paradox. I could not afford to go abroad. I could not even afford the baseline safety net needed to pursue a formal degree.
4. Why I Refused a Conventional Job in Malaysia People frequently asked: “If you couldn’t go abroad or afford university, why not just get an ordinary programming or corporate job in Malaysia?”
The candid, uncomfortable answer is that in 2016, the local ecosystem was not structured to nurture or reward high-end technical ambition.
A Severe Ceiling on Meritocracy:
In the domestic market at that time, career progression and compensation were seldom tied directly to technical excellence or problem-solving capability. Advancements were heavily governed by seniority, office politics, and sheer luck. The salary increments were painfully narrow, offering little incentive to push beyond the bare minimum.
The Danger of Stagnation:
The technological gap between what was happening at Silicon Valley frontiers (deep learning, autonomous driving, distributed cloud infrastructure) and local enterprise IT was immense. In environments where cutting-edge initiative is treated with indifference, exceptional technical talent inevitably gets dragged down to the baseline. I knew with absolute certainty that if I entered that pipeline, the hunger and sharpness that defined me would be eroded by routine maintenance tasks and corporate inertia.
I knew that once going abroad was off the table, taking a conventional domestic job would seal my fate. If I wanted to maintain a global standard of engineering rigor, I had to construct my own forge.
5. The Crucible of Isolation When both traditional higher education and domestic employment were off the table, the only path left was forward through the dark.
From late 2016 into 2017, I treated MOOCs not as passive video watching, but as an unforgiving, full-time apprenticeship:
Fueled by the urgency to hit Udacity’s 50% refund deadlines and maximize my trajectory, I compressed years of learning into months—mapping out a steep technical tree from responsive front-end work and full-stack architecture to mobile development, applied machine learning, and eventually low-level robotics. Beyond the OMSCS curriculum on Udacity, I benchmarked myself against the toughest open courses from elite universities—working through Stanford’s CS231n: Convolutional Neural Networks for Visual Recognition from scratch, alongside UC Berkeley’s CS188: Introduction to Artificial Intelligence to build a rock-solid theoretical foundation in search algorithms, reinforcement learning, and computer vision. When I was accepted into the inaugural cohorts of Udacity’s Self-Driving Car Engineer Nanodegree alongside engineers from Google, Apple, and Stanford, I didn’t have a CS degree to lean on. It quickly became the hardest online course I ever took. I bought used mathematics textbooks, stayed up until 4:00 AM wrestling with Extended Kalman Filters and Model Predictive Control in C++, and ground through compiler errors in silence. In early 2017, that quiet grit culminated in taking #1 on the global leaderboard of MIT’s DeepTraffic competition, earning a tuition scholarship, and later catching the attention of Silicon Valley executives and even seeing NVIDIA AI follow my Instagram. To the outside world, I was still just a kid locked in his room with no job and no degree. The stigma of “doing online courses” meant enduring years of quiet disrespect from those who equated physical office attendance with worth.
Being viewed as lazy when you are putting in 14-hour days teaching yourself autonomous systems from academic papers is a unique kind of psychological crucible. But it forged in me a level of mental independence that no university lecture hall could ever replicate.
6. The Inevitable Pivot: Forging My Own Path Eventually, the realization crystallized: no institution was coming to rescue me.
Academia had shut its gates due to finances. Moving overseas was constrained by duty. The local job market could not offer the trajectory I needed.
Left with no viable alternatives, I chose entrepreneurship and independent building.
If nobody was going to give me a seat at the table, I had to build my own table. I began taking freelance development projects, launching digital products, solving real-world client problems, and funding my own survival one commit at a time. Entrepreneurship wasn’t a glamorous branding exercise; it was the only remaining vehicle for autonomy.
Final Thoughts: The Armor Built in the Dark Looking back a decade later, was it an unusual route? Undoubtedly. Was it painful? Deeply.
Missing out on Georgia Tech, having to turn down international invitations because I couldn’t afford a plane ticket, and bearing the weight of an unsupportive environment left real scars.
Yet, I harbor zero regrets.
Choosing the unusual route stripped away any reliance on credentials, luck, or external approval. It taught me that genuine capability is proven by proof-of-work, not paper pedigrees. Most importantly, it gave me the mental armor to take on any challenge—whether building complex software systems from scratch, venturing into the dark to conquer Fraser’s Hill, or pedaling 300 kilometers solo through pitch-black highways inside 24 hours—knowing that as long as I retain control of my focus and willpower, no ceiling is permanent.

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[START POST METADATA]
title: "At the Peak but Bound by Ground"
url: "https://fangkiang.com/posts/at-the-peak-but-bound-by-ground/"
date: "2026-10-14"
tags: ["Artificial Intelligence","Deep Reinforcement Learning","MIT","Self-Taught","Career Journey","Frontier Tech"]
[END POST METADATA]

[START POST CONTENT]
Back in 2017, my life took a sharp turn into the surreal.
I was twenty years old, sitting in my bedroom in Malaysia, staring at a flickering monitor late into the night. I had no Computer Science degree, no prestigious institutional pedigree, and no corporate network to lean on. What I did have was an obsessive curiosity for artificial intelligence, an internet connection, and the stamina to spend endless nights reading research papers, writing code, and fine-tuning hyperparameters.
That quiet obsession led me to enter the MIT 6.S094 DeepTraffic Deep Reinforcement Learning challenge.
When my name, NG FANG KIANG (competing under my online handle, Jorcus96), climbed past thousands of international researchers and PhD candidates to claim #1 on the global MIT leaderboard—and held a top-three spot for months—the digital ripples created in that modest bedroom reached the inner sanctums of Silicon Valley. You can read the technical post-mortem of how I achieved that in my post: MIT DeepTraffic First Place & Udacity.
The world around me accelerated almost instantly.
When NVIDIA AI launched their official Instagram presence, they curated an exclusive list of just 135 accounts worldwide—a roster dominated by institutions like TechCrunch, MIT Technology Review, and frontier AI laboratories. To my sheer astonishment, my personal account @jorcus96 was on that list. I captured that disbelief in NVIDIA AI Followed My Instagram.
Within weeks, my LinkedIn inbox transformed into a battleground for international recruiters, founders, and research scouts.
Yet the most surreal milestone was still waiting.
🌟 A Message from the Apex of Tech One afternoon, a social connection request caught my eye. The name was impossible to miss: a high-profile Senior Vice President at a FAANG titan—a visionary leader whose keynotes I had watched and whose strategic moves routinely shaped global headlines.
Back in 2017, verified badges were rare, and elite executives seldom maintained public-facing social accounts. My immediate reaction was skepticism: Is this an impersonation profile? Why on earth would one of the most influential decision-makers in global tech reach out to an unknown kid in Malaysia?
I spent hours meticulously cross-referencing mutual connections, posting chronologies, corporate engagements, and professional ties. Every single data point lined up.
It was real.
A pioneer sitting at the top of modern computing had personally tracked my deep reinforcement learning results and extended a hand.
Through that single inflection point, an invisible door swung wide open. To this day, scrolling through my network feels like stepping into an alternate reality. Without disclosing private details, that door connected me directly to some of the most formidable minds on earth:
Billionaire Founders & Tech Pioneers: The foundational architects of platforms used by billions daily, including the co-founders of the world’s primary video platform and mobile operating system. C-Suite & SVPs at Tech Giants: Industry leaders directing the technological frontier—Chief Scientific Officers, Chief Scientists, former SVPs, and Chief Business Officers. Legendary AI Researchers (H-Index 100–200+): The pioneers whose papers defined modern AI, including the creator of Word2vec, a co-author of the seminal Deep Learning textbook, and leading scholars from MIT, CMU, and the Max Planck Institute. Decorated Scientists: Luminaries honored with the Gödel Prize in theoretical computer science, the Kyoto Prize for advanced technology, and an Academy Award (Oscar) for Technical Achievement. Frontier Lab Engineers (DeepMind, OpenAI, Meta AI): Hands-on builders of the modern intelligence era, including the lead programmer behind DeepMind’s AlphaGo, core co-creators of open-source models like LLaMA, and researchers pushing limits across OpenAI and Google Brain. For a self-taught engineer from Malaysia, it was the ultimate golden ticket. The launchpad was clear, fueled, and waiting.
🏡 The Crossroads: Anchored by Duty With world-class validation and direct access to elite networks, the path forward seemed predetermined. In 2017, working on frontier AI meant physically being in the room where it was built—in Silicon Valley, Seattle, or London. Frontier research labs and Tier-1 tech giants were practically knocking at the door.
Then, reality intervened:
“I want to go, but I can’t.”
At twenty, I was confronted with complex, non-negotiable family responsibilities in Malaysia. My life was not solely my own to gamble on personal ambition. I could not, in good conscience, pack my bags, board a flight across the Pacific, and leave my family to shoulder those burdens alone.
So, I made the conscious, agonizing choice to stay.
Over the years that followed, I watched from halfway across the world as peers from that MIT leaderboard—some of whom my models had outperformed—landed in California and London, joined frontier labs, and rode the crest of the generative AI revolution. I followed their triumphs through a screen.
To pretend I did not feel a heavy ache of sacrifice and the silent sting of powerlessness would be dishonest. I was at the peak of my potential, yet bound to the ground.
🛠️ Forging Capability in the Wilderness Life, however, is shaped not by the doors that close, but by how you build while standing in the hallway.
While physical relocation was impossible, the events of 2017 permanently altered my horizon. The pioneers and scientists in my network became an enduring mental benchmark. They reminded me that geographic distance never dictates your intellectual ceiling—uncompromising discipline and engineering rigor speak for themselves anywhere.
I remained in Malaysia, but I never stopped building:
Mastering Autonomous Systems: I tackled the demanding Udacity Self-Driving Car Engineer Nanodegree, becoming one of its pioneering global graduates. Navigating sensor fusion, computer vision, and localization from scratch hardened my foundational skills (The Hardest Online Course I Ever Took). Pragmatic, Resilient AI in Production: I shifted my focus from toy benchmark optimizations to architecting resilient, production-grade systems capable of delivering measurable real-world impact. Pushing Physical and Mental Limits: I channeled that same relentless endurance outside the terminal, completing an unsupported, solo 300km cycling ultra across Malaysia in under 24 hours (300km in 24 Hours: Gravel Bike Malaysia Ultra Solo Ride). That ride taught me how to push through acute exhaustion when there is no support vehicle behind you. I did not have a high-rise office in Menlo Park or Seattle, but I forged an unshakeable inner resilience and an engineer’s work ethic right here at home.
🚀 The Anchor Is Up: Ready for the Frontier A decade of quiet discipline transforms you.
The heavy family obligations that once anchored me in Malaysia have been faithfully honored and resolved. The geographical constraints that once defined my perimeter no longer exist. Today, I am fully mobile, unencumbered, and ready to go wherever the hardest engineering challenges live.
Yet, what surprises me most is that the fire never dimmed. If anything, it has distilled into absolute clarity.
I have spent years building independently, but my deepest ambition remains unchanged: to join a world-class technology titan and build alongside elite teams at the frontier of technology.
Why? Because the defining challenges of our generation—planetary-scale compute infrastructure, foundation models, embodied intelligence, and autonomous systems—require immense talent density, world-scale resources, and relentless collective ambition. Having caught a glimpse of that summit at twenty, I know the caliber of work required. I know the feeling of going toe-to-toe with the best in the world and winning.
The twenty-year-old on the MIT leaderboard proved that raw curiosity and self-taught discipline could break through global barriers. The engineer standing here today brings something far more valuable: a decade of tested grit, systems-level pragmatism, battle-hardened endurance, and zero excuses.
💡 Closing Thoughts: To the Builders If you are a self-taught engineer, an unconventional builder, or someone currently held back by circumstances beyond your control, hold onto this:
Build relentlessly, build in public, and never let temporary constraints convince you of a permanent ceiling.
Circumstances may pause your timeline, but they cannot dilute your capability unless you surrender it. Keep sharpening your craft in the dark.
The anchor is finally up. The horizon is wide open. And I am ready to build where it matters most.

[END POST CONTENT]

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[START POST METADATA]
title: "300km in 24 Hours - My Malaysia Ultra Solo Ride Record in Casual Gear"
url: "https://fangkiang.com/posts/300km-24hours-gravel-bike-malaysia-ultra-solo-ride/"
date: "2026-10-07"
tags: ["Resilience","Mindset","Grit","Goal Execution","Personal Growth","Mental Toughness","Malaysia Ultra Cycling","Endurance Challenge","The Twenties Milestone Series"]
[END POST METADATA]

[START POST CONTENT]

This journey stands as Chapter 2 and the grand finale of my twenties endurance series. It is the direct sequel to my 200 km mountain expedition: Pitch Black Detours and 2,000m+ Climbs: How I Conquered Fraser’s Hill Solo.
“You don’t need carbon fiber or expensive kit to conquer massive distances—just a solid bike, a plan, and the mental grit to push through.”
📌 Prologue: From Fraser’s Hill to the “Graduation Badge” Every grand ambition is forged in pain and self-discovery.
My first true trial was conquering Fraser’s Hill—a solitary 200 km grind against pitch-black forest backroads, nocturnal livestock roadblocks, and over 2,200 meters of vertical gain. That ride proved a vital truth: mental resilience matters infinitely more than premium gear.
Roughly a year after standing at the clock tower of Fraser’s Hill, with the twilight of my twenties approaching, I knew I needed one final, defining milestone. I wanted to claim an unforgettable “graduation badge” for my 20s: an unsupported, 300-kilometer ultra-endurance loop across Malaysia in under 24 hours.
Claiming this badge took two grueling attempts:
The First Attempt (Heartbreak): A mechanical failure at Kilometer 120.1 snapped my drivetrain, forcing a bitter DNF on October 12, 2025. The Second Attempt (Redemption): Six weeks of rebuilding and mental refocusing culminated on November 29, 2025, where I completed the sub-24h loop in raw, casual gear. The catch? No $10,000 carbon fiber road bike. No aerodynamic Lycra bibs. No clipless shoes. Zero support crew following in a van with spare wheels and ice packs.
Just me, a lower-mid-range DIY gravel bike, regular casual sportswear, hiking shoes, flat pedals, and a heavy 4 kg backpack weighing down on my spine for nearly a full day.
💥 1. The Failure That Fueled the Ride Every major physical achievement is built on the ashes of a previous defeat.
Six weeks prior, carrying the confidence of the Fraser’s Hill climb, I was locked into a steady rhythm on my first 300 km attempt. I was flying—until Kilometer 120.1.
With a harsh metallic click, my rear gear shifter snapped. Stranded on a hot roadside with an unshiftable drivetrain after 5 hours and 5 minutes of solid riding, I was forced to retire.
Leaving that route unfinished burned in my mind every single day. I brought the bike home, rebuilt the broken components piece by piece, and waited for my shot at redemption.
This bike wasn’t bought off a showroom floor; I built this gravel bike from scratch, 0-to-1, starting with a bare frame. Every single component, bolt, and cable was hand-selected and customized by me. Because I knew every thread of its geometry, I knew exactly how to fix it when things went wrong. I spent the next six weeks sourcing parts, tuning the shifting index, and prepping for round two.
Mental grit was tested again just seven days before my second attempt. During a 100 km night training session, I rode straight into the fresh scene of a fatal road accident. A victim lay motionless on the asphalt, surrounded by shattered glass and pooling blood.
Passing within inches of that raw tragedy anchored a cold reality into my mind: Malaysian roads at night do not forgive mistakes.
📜 2. Rules of the Challenge: Self-Imposed Hard Mode To truly isolate mental resilience from equipment advantage, I bound myself to a strict set of self-imposed constraints:
The Bike: DIY Lower Mid-Range Gravel Bike—heavy, mechanical, custom-built from the frame up, and far from aerodynamic. The Attire: 100% Casual Gear. A basic dri-fit shirt, regular athletic shorts (zero padded chamois), and sturdy hiking shoes on flat pedals. The Setup (100% Solo & Unsupported): A 3.1 kg to 4.5 kg weighted backpack strapped to my shoulders the entire distance. Self-Sufficient Mobile Mechanic: That heavy backpack carried an extensive, carefully selected array of repair kits. Because I built the bike myself, I carried the tools to diagnose and repair almost any catastrophic mechanical failure on the fly. A small Bluetooth speaker mounted to the handlebar stem for ambient audio. Strictly no escort vehicles, no pacing partners, and no roadside support. 📊 3. Route & Key Performance Stats Metric Achievement Date & Start Time 03:45 AM, Saturday, 29 November 2025 Start Location Klang Municipal Council (MPK), Selangor, Malaysia Total Distance 298.09 km Moving Time 12h 53m 32s Elapsed Time 23h 17m 26s (Sub-24 Hours Achieved!) Elevation Gain 1,736 m Average Speed 23.1 km/h (Max Speed: 53.6 km/h) Energy Output 6,630 kJ / 143 W Avg Power (7,392 Calories burned) [Klang Start] ➔ [Banting] ➔ [Nilai] ➔ [Seremban] ➔ [Alor Gajah] ➔ [Malacca] ➔ [Coastal Route / Lukut] ➔ [Port Dickson] ➔ [Sepang / KLIA Wasteland] ➔ [Finish] 💡 4. Choosing the Hard Road: Equipment vs. Human Will Modern cycling culture dictates that to ride 300 km, you must spend thousands on ultralight carbon frames, skin-tight aero jerseys, stiff carbon-soled shoes, and thick bib pads. Ultra-distance riders almost always rely on support cars trailing behind with cold water, fresh meals, and spare parts.
But this challenge wasn’t about optimal efficiency; it was about forging the definitive graduation badge of my 20s.
Your twenties are a unique, fleeting chapter in life—a window of time where your time, energy, and freedom belong entirely to you. When I completed the Fraser’s Hill climb a year earlier, I realized physical capability is secondary to psychological grit. Once you transition into your thirties, life naturally shifts, and family and career responsibilities rightfully become the priority.
Before stepping into that next chapter, I wanted to take absolute ownership of my youth, push my boundaries to their absolute perimeter, and achieve something extraordinary.
I wanted to build a story worth telling. Decades from now, I want to look my future children in the eye and tell them what was possible when their parent refused to back down after failure.
Choosing a heavy 4 kg backpack meant my shoulders, lower back, and sit bones bore relentless downward force for 23 straight hours. Every watt saved by aerodynamic gear was a watt I had to produce manually through pure, unadulterated grit.
🚴‍♂️ 5. The Journey: Darkness, Floods, Crashes, and Wastelands 🕒 03:45 AM — Rolling into the Unknown Leaving the Klang Municipal Council under the dim glow of orange streetlights, the night air was heavy with humidity. The route cut south toward the quiet, wide avenues of Putrajaya before weaving toward Seremban and the historic coastal approaches of Malacca.
🌊 Battling Active Flood Zones in Malacca What the weather app didn’t capture was that parts of Malacca were actively submerged under severe flash floods. Emergency relief centers (PPS) were opening up across multiple districts.
As I pushed into low-lying rural stretches, heavy rain clouds hung low, turning the air ice-cold. Spray from passing lorries cut my visibility, and murky floodwaters encroached onto the asphalt.
Navigating submerged road edges where potholes were completely hidden required hyper-vigilance. One wrong line meant a ruined rim or a violent plunge into a roadside canal. Knowing a single heavy downpour could trap me in a flooded dead-end added intense mental friction to every pedal stroke.
🏁 Crossing the 200 km Milestone (Port Dickson) By the time I crossed the 200 km mark—matching the entire distance of the Fraser’s Hill expedition—exhaustion was biting deep.
But as the route led back along the coastal stretch toward Port Dickson, taking a brief breather on the beach gave me a moment to recalibrate and steady my mind for the darkest night section ahead.
⚠️ Blinding High Beams, a Sudden Crash, and Pitch Darkness Midway through the night, while navigating a rural road with zero street lamps, disaster struck.
A vehicle approached from behind at high speed, repeatedly flashing blinding high beams. Feeling like it was right on my tail, I swerved slightly toward the shoulder to yield. In an instant, my front tire dropped into a jagged, hidden pothole.
The handlebars twisted violently out of my grip. I went flying over the bars, slamming hard onto the pavement.
The car didn’t stop. Its taillights vanished into the distance, leaving me lying alone in dead silence on an unlit road. Dazed and bleeding from minor abrasions, I checked the bike under the faint glow of my headlamp. The front wheel had taken a direct impact and was jammed sideways against the fork frame; the mechanical shifting was severely misaligned.
Standing alone in the total dark, my heart pounding with adrenaline, I wrestled the front wheel back into place, re-trued it with manual leverage, adjusted the brake calipers, and clipped back in. After recovering from the heartbreak of attempt one, quitting here was never an option.
🌌 The 40 km Wasteland: Lukut to Sepang (12:00 AM – 03:00 AM) The darkest mental zone occurred between midnight and 3:00 AM along the 40-kilometer stretch connecting Lukut to Sepang and KLIA.
Over 95% of this corridor had no operational streetlights. Dense, overgrown oil palm trees and the jungle canopy absorbed what little moonlight existed. Petrol stations were locked up tight, offering no lights or water.
To keep mind-numbing fatigue from taking over, I cranked the volume on my mounted Bluetooth speaker, letting music punch through the heavy silence. The road felt like an apocalyptic wasteland: across the entire 300 km journey, I counted between 30 to 50 dead animal carcasses strewn across the road—stray dogs, cats, massive monitor lizards, and at one point, three full-sized cows lying dead on a single stretch of shoulder.
🔥 6. The Ultimate Crucible: Hidden Agony & Mental Strain If anyone were to ask me what the single hardest part of those 23 hours was, the answer wouldn’t be physical exhaustion—it was the extreme physical torment combined with a state of hyper-vigilant mental strain.
Forgoing professional cycling bibs proved to be the most brutal sacrifice of the entire challenge. Without a padded chamois to cushion the impact, hundreds of thousands of pedal strokes and relentless friction reduced the skin on my inner thighs to raw, inflamed agony. By the second half of the ride, every single revolution felt like a dull blade sawing through open flesh. That deep, bone-chilling pain forced me to stop every few minutes, leaning over my handlebars by the roadside to let the searing burn fade just enough before gritting my teeth and mounting the saddle once more.
Yet, even more punishing than the physical torture was the suffocating mental exhaustion.
Along dozens of kilometers of unlit roads, the pitch-black night swallowed the highway whole. I had to stretch my focus to its absolute limit, unable to relax my eyes for even a fraction of a second. At any moment, a deep, bone-breaking pothole could appear out of nowhere, stray dogs or wildlife could dart across the lane, or a massive animal carcass might lie sprawled across the asphalt.
In the cold and dark, I wasn’t just fighting fatigue—I was playing a high-stakes game against potential disaster. Maintaining that level of intense concentration under constant high pressure drained my mental bandwidth far faster than muscle fatigue ever could.
⚠️ A Crucial Reality Check: Why I Strictly Advise Against This Setup Before anyone considers grabbing a gym shirt, flat pedals, and an unsupported backpack for a 300 km ultra-distance ride: do not attempt this.
Forgoing cycling bibs and riding completely unsupported was not a casual whim—it was an extreme, calculated threshold test built on years of extreme conditioning. My physical baseline before taking on this challenge was far from ordinary:
15,000+ km of Solo Ride Heritage: Over the preceding two years alone, I logged more than 5,000 km of weighted solo rides, building an accumulated foundation of over 15,000 km in solo cycling records. Daily Unpadded Conditioning: I spent years training daily without padded bibs, gradually hardening my body to relentless saddle pressure and friction. Martial Arts Foundation & Pain Threshold: Years of disciplined martial arts training forged the core stability, mental composure, and high tolerance for physical suffering required to endure prolonged trauma. Peak Physical Conditioning: Having shattered personal fitness test records in the gym, my overall conditioning and endurance capacity were operating near pro-athlete thresholds. Mastery of Mechanical Self-Sufficiency: Going unsupported meant I had to be my own mechanic, pit crew, and rescue team. Because I built this bike from scratch, I knew every single bolt and cable tension. I carried a comprehensive, heavy array of specialized repair kits and tools. In the middle of the night, on a pitch-black roadside, you cannot google a solution or wait for help; you must possess the diagnostic skills and muscle memory to rebuild, true, and realign your drivetrain on the fly after a crash. Relentless and Exhaustive Preparation: The sheer volume of pre-ride planning was monumental. From plotting water stops that remained open past midnight, to analyzing elevation profiles, calculating battery burn rates for lights, and preparing contingency routes for flood zones—the mental preparation took weeks of meticulous drafting. Even with this baseline, riding 300 km in casual clothes nearly broke me. For 99.9% of riders, doing this without a padded chamois risks permanent soft-tissue nerve compression, debilitating saddle sores, and severe skin infections.
Furthermore, riding unsupported on Malaysian roads at night without an escort crew is extraordinarily dangerous. Between sudden flash floods, unlit potholes, roaming packs of stray dogs, heavy freight traffic, and unmonitored mechanical or physical crashes, a single mistake can be fatal when you have no trailing car to pull you out of danger.
Padded bibs, aerodynamic gear, and support vehicles were invented for a reason: your safety. Respect the distance, equip yourself properly, and never sacrifice fundamental safety protocols unless you have spent years systematically conditioning your body and mind for the punishment.
🚉 7. The Chaotic Aftermath & Logistical Survival Crossing the 298 km mark at 23 hours and 17 minutes, I officially completed the challenge within the 24-hour limit. The graduation badge of my twenties was finally secured.
I was exhausted, filthy, and covered in road grime—yet surging on post-ride adrenaline. Instead of slumping onto the curb, I decided to celebrate with a bizarre change of pace: pushing my dusty bike straight into KLIA2 to shop around and grab food among stunned airport travelers.
The final logistical stretch proved to be its own mini-adventure:
The Check-In Fiasco: Battered by friction skin irritation and desperate for a shower, I booked a hotel room online, assuming “same-day check-in” meant immediate access. Arriving at the desk, I was met with a strict 2:00 PM check-in policy at a fully booked hotel. Eventually, I found a room at Hotel Night Queen, collapsing for a brief 1.5-hour power nap between 2:50 PM and 4:30 PM. Bike Security Paranoia: Having no heavy lock, I had to lock the gravel bike through its frame and wheels outside open public spots—staring out the window every few minutes to ensure it wasn’t being loaded into a van. The Train Breakdown Commute: Planning to catch a direct KTM train home to Klang, I was hit with major service disruptions. I had to detour to Kajang, manually haul my heavy bike up steep station staircases, and board the MRT line toward KL Sentral before finally reaching home. 🌠 8. Final Reflection: Where Is the Limit? From my early trial at Fraser’s Hill to bouncing back from a snapped shifter on my first 300 km attempt, surviving a high-speed night crash, navigating active flood zones, and conquering 300 km of pitch-black roads, this journey proved what I set out to test:
You don’t need elite gear to achieve extraordinary endurance milestones.
When you strip away the carbon frames, the custom apparel, and the safety net of a support crew, all that remains is your relationship with pain and your willingness to keep turning the pedals.
If you were to strip away all these negative variables—if I didn’t have to haul a 4 kg weighted backpack on my spine; if I didn’t have to worry about running out of food and water; if I didn’t have to ride in constant fear of hidden road hazards in the dark and floodwaters; and if you handed me a top-tier carbon road bike, a dedicated support vehicle trailing behind, and seamless roadside logistics—
I am absolute in my conviction that I could have pushed straight through to 500 kilometers that very same day.
Ultimately, this ride was a mental experiment—a deliberate act of extreme self-restriction and physical stripping to crown the final chapter of my twenties. It stands as my absolute final ultra-endurance challenge of this decade. Starting in 2026, I am stepping back and capping my rides strictly to a maximum of 30 km a day.
From the climbs of Fraser’s Hill to the redemption of this 300 km loop, this journey proved what mattered most: it is never the limit of physical endurance that holds us back, but the constraints of our environment and mind. As long as the fire inside refuses to burn out, these legs will keep carrying me further into whichever chapter comes next.
The Twenties Milestone Series Chapter 1 (The Crucible): Pitch Black Detours and 2,000m+ Climbs: How I Conquered Fraser’s Hill Solo Chapter 2 (The Graduation Badge): 300km in 24 Hours — My Malaysia Ultra Solo Ride Record in Casual Gear (You are here) 
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[START POST METADATA]
title: "Pitch Black Detours and 2,000m+ Climbs: How I Conquered Fraser’s Hill Solo"
url: "https://fangkiang.com/posts/into-the-dark-conquering-frasers-hill/"
date: "2026-10-06"
tags: ["Goal Execution","Mental Toughness","Grit","Personal Growth","Mindset","Resilience","Endurance Challenge","Malaysia Ultra Cycling","Adventure","The Twenties Milestone Series"]
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 *This journey stands as Chapter 1 of my personal ultra-endurance odyssey. Before taking on the grueling 300 km loop across Peninsular Malaysia to crown my twenties, there was Fraser’s Hill—the inaugural proving ground where raw grit replaced technical gear, and where the audacious dream of ultra-distance cycling was born.*
When you roll out of your driveway at 2:00 AM carrying a heavy backpack on a solo gravel bike, you aren’t setting out for a casual weekend spin. You are signing up for an unvarnished test of physical endurance, mechanical composure, and psychological willpower.
What started as an ambitious personal challenge to conquer Fraser’s Hill evolved into a 20-hour odyssey packed with eerie forest detours, surprise wildlife encounters, mechanical setbacks, pro peloton drafts, and unforgettable camaraderie on the open road. Looking back, this ride was not an isolated climb—it was the foundational catalyst that proved I could endure the darkest hours of the night alone.
Here is the complete story of how 200 kilometers, over 2,200 meters of elevation gain, and relentless grit pushed me past my perceived limits.
⚠️ Critical Warning: Do Not Attempt Unprepared When reading about ultra-endurance rides, it is easy to romanticize the open road, the quiet night air, and the euphoria of reaching the summit. But make no mistake: rolling out alone into the unlit Malaysian countryside carries serious, objective risks.
What is presented below as an adventure was only possible through years of compound preparation. If you do not possess an equivalent foundation, do not attempt this ride alone:
⚡ 10,000+ km of Solo Ride Heritage (Achieved by 2024): This was not an impulsive weekend venture. Even back in 2024, this ride stood on the foundation of over 10,000 km of documented solo cycling across varied, unforgiving terrains. The journey leveled up in 2025 when I successfully took on the 300 km one-day challenge—surpassing 15,000+ km of total documented solo mileage. The grind never stops. 🍑 Daily Unpadded Conditioning: Riding in casual, unpadded gear for 20 hours is a recipe for severe saddle sores and soft-tissue trauma. I spent years conditioning daily without padded bibs, progressively adapting my body to withstand prolonged saddle pressure, friction, and road chatter. 🥋 Martial Arts Discipline & Pain Threshold: Ultra-endurance is as much neurological as it is cardiovascular. Years of martial arts training built the deep core stability, emotional calm under duress, and tolerance for physical suffering required when fatigue peaks. 💪 Peak Physical Conditioning: My aerobic threshold and muscular endurance were operating at peak personal levels, having systematically built up capacity in the gym and on the tarmac. 🔧 Mechanical Self-Sufficiency: Riding completely unsupported means acting as your own mechanic, pit crew, and sag wagon. Because I built this gravel bike from the bare frame up, I understood the operating threshold of every bolt, spoke, and cable tension. In the middle of an unlit rural road at 4:00 AM, there is no signal to search for tutorials and no roadside rescue; you must have the muscle memory to true a wheel, re-seat a bead, or field-dress a drivetrain in the dark. 📋 Exhaustive Route Planning: The preparation demanded weeks of meticulous scouting—from mapping 24-hour hydration points to calculating luminaire battery burn rates, analyzing gradient steps, and planning bypass routes for regional flood hazards. Route Overview & Telemetry Metric Details Rider Fang Kiang Ng Date & Start Time Saturday, August 23, 2025 at 1:52 AM Full Loop Route Klang Bukit Tinggi → Bestari Jaya → Kuala Kubu Bharu (KKB) → Fraser’s Hill Summit → Batang Kali → KTM Train Transfer → Klang Bukit Tinggi Total Elapsed Time 20 hours, 23 minutes Moving Time 9 hours, 2 minutes Total Distance ~200 km (191.74 km tracked + 8 km untracked + 1 km GPS reconnection) Elevation Gain 2,208 m (Biggest single-ride vertical gain to date) Average Speed 21.4 km/h Average Heart Rate 144 bpm Rig & Pack Weight Custom DIY Gravel Bike + 4.2–6 kg backpack (Total rig ~12–14 kg, with 3–5 L fluid payload) 1. Into the Pitch Black: 4:00 AM Detours & Stealth Encounters Fueled only by a modest McDonald’s chicken wrap grabbed before rolling out of Klang, I slipped into the humid night just before 2:00 AM. In the pre-dawn stillness, the empty asphalt felt calm—until the rural countryside began stirring.
3:54 AM: The Roadblock at Batu Arang Near Batu Arang (GPS: 3.286, 101.445), my front beam pierced the heavy mist to reveal my first nocturnal encounter: five large cows roaming loose right along the roadside clearing.
“I was riding my bicycle alone vs. Five large cows. The cows all ran away from me. 🤣”
As I closed the gap, the five animals stopped in their tracks and stared straight into the glow of my front lamp. The tense standoff lasted only a second before the sheer, unexpected sight of a lone cyclist spooked them, sending them scrambling across the clearing and bolting into the scrub.
Moments later, just as I rolled further down the corridor, another adrenaline spike unfolded right along the tarmac barrier:
As my tires closed in, the herd spooked once again. Hooves clattered frantically against loose gravel before the animals darted clear of the road edge. This is kind of funny—a lone cyclist scaring off a herd of cows in the dead of night—yet it was still an immediate reminder to keep both hands hovering firmly over the brake levers: out here in the dark, hazards do not announce themselves in advance.
4:08 AM: Rustling Shadows and the Orange Cow Fourteen minutes later, along the back corridor between Batu Arang and Ijok (GPS: 3.294, 101.449), violent rustling broke out deep in the jungle verge to my right.
I checked my cadence and swung the handlebar light across the thick brush. For five tense seconds, the darkness swallowed the beam—until a large orange cow burst through the dense foliage, darting away from the light.
“I heard something running away from me in the jungle. I couldn’t find it. Until 5 seconds later, there was an orange cow running away from me. I was startled at the time and thought I was recording a video, but later I realized that it was a photo full of meaning and story.”
That brief, surreal encounter encapsulated the peculiar nature of solo ultra-distance riding: out on rural backroads at four in the morning, the ordinary rules of the day disappear.
4:17 AM: The 45-Minute Forest Detour & Stealth Bovines The atmosphere shifted from quirky to genuinely eerie between Batu Arang and Bestari Jaya. Guided by a turn-by-turn cue that bypassed the main highways, I ended up swallowed by a canopy-covered secondary road completely devoid of streetlamps, ambient light, or passing vehicles.
For 45 unbroken minutes, I pedaled through the darkness. The jungle was loud with unseen wildlife—cracking branches, sudden scuttles, and low gutteral grunts—while my light illuminated only a narrow sliver of asphalt ahead.
Then came the true heart-stopper at 4:17 AM (GPS: 3.302, 101.464):
“I just passed by two black cows. They were staring at me in the pitch-black woods. I couldn’t see them at all until they suddenly appeared next to me. They were so close that if I stretched out my right hand, I could have touched them. I can’t describe this feeling.”
Their dark hides had absorbed every photon of ambient light. They were invisible until my beam swept them at point-blank range: two sets of glowing, reflective eyes staring directly at me, inches from my drop-bar. The adrenaline spike did what no caffeine gel could match. Survival instincts engaged, my cadence surged, and I spun the cranks without pause until the forest road finally merged back onto the illuminated highway.
2. Mechanical Drama & Latching onto the Pro Peloton No self-supported ultra-ride goes entirely to plan. Right as dawn broke, casting pale morning light over the tarmac, my rear tire went completely flat near kilometer marker KR FT001 021/2.
I pulled onto the grass verge, dropped the heavy pack, inverted the bike, and set to work unseating the bead to swap in a fresh tube.
Several pelotons whizzed by in the cool morning air, locked into their interval work. But one solo rider eased off the gas and pulled over. Twenty minutes earlier, before the flat, I had exchanged a brief greeting with him while overtaking on a gentle rise.
Seeing my makeshift roadside workshop, he smiled warmly, offered words of encouragement, and pulled out his phone to record a quick clip: “Look at this guy’s roadside pit stop!” It was a small, human touch that instantly took the edge off an otherwise stressful mechanical delay.
Though the tube change took only minutes, the lost time cost me my breakfast window in town. By kilometer 110, I had been riding hard for over six hours on near-empty reserves, running solely on that initial 2:00 AM wrap and a single 50 g packet of nuts.
The 17-Minute Lifeline Exhausted, hungry, and grinding against a stiff headwind at kilometer 110, a lifeline arrived out of nowhere: the sharp, rhythmic whir of deep-section carbon wheels slicing through the air as a high-speed paceline shot past.
Recognizing an instant opportunity to escape the headwind, I stood out of the saddle, accelerated hard, and slotted directly onto the back of the train. As it turned out, this was no casual club ride:
Malaysian National Team riders (Mohd Elmi Jumari, Fahmi Saharil, Darwisy Hakimi) Team Sapura Cycling (UCI Continental) rider Adlan Shukri Elite racers from the Royal Malaysian Police (PDRM) cycling unit Team riders from the Selangor State Squad Hanging on behind these athletes for 17 blistering minutes was equal parts pure suffering and pure exhilaration. Tucked inches off the wheel ahead, the aerodynamic draft offered crucial shelter for my burning legs. More than the physical watts saved, matching the rhythm and speed of national-caliber cyclists provided an immense mental reboot right when my energy reserves were nearing empty.
3. Conquering Fraser’s Hill: The Canopy Grind & Summit Relief Leaving the lowlands behind at Kuala Kubu Bharu (KKB), the true climbing began. The heavy, humid air thinned and cooled as the road climbed steadily past the Selangor Dam (Empangan Selangor).
Panoramic Scale at Selangor Dam While I climbed past this stretch in the morning, the full majesty of the reservoir was even clearer during the descent:
The turquoise water expanded across tiered mountain ridges, framed by exposed sandbanks, sculpted shoreline contours, and dense forest knolls. Passing the iconic intake tower and panoramic lookouts put the sheer physical scale of the journey into perspective.
The 37-Kilometer Reality Check Passing over a concrete bridge spanning a deep jungle gorge, I pulled alongside a parked motorbike to double-check my navigation display.
The head unit returned a blunt reality check: “Stay on Route” with 37 kilometers of continuous, unrelenting uphill ahead.
Leaning over the barrier, the rainforest canopy dropped hundreds of feet below. Every pedal stroke from this point required deliberate cadence, relaxed shoulders, and steady, controlled breathing.
As the gradient steepened through the upper switchbacks, the mountain delivered its payoff: sweeping viewpoints across layered green ridges, giant tree ferns swaying in the highland breeze, and colonial-era stone cottages fringed by vivid bougainvillea.
Summit Relief, Shared Miles & Aida Daniya Feast Rolling into the historic Fraser’s Hill summit square felt like crossing a long-sought finish line. The iconic Clock Tower stood clear against the sky, and the deep fatigue accumulated over 2,200 meters of climbing melted into pure satisfaction.
At the peak, what began as a solitary test transformed into shared camaraderie. I connected with four fellow riders who had also fought their way up the mountain. We gathered with our bikes in front of the landmark BUKIT FRASER sign, trading smiles and relief beneath the midday sun.
Afterward, we headed to Aida Daniya Restaurant for a well-earned feast. Sitting down to hot, savory food while sharing war stories from the climb—surrounded by cool highland air—was the ultimate reward for the day’s effort. Special thanks to @halidcissorhands for documenting these great memories!
Grounding at The Paddock Before preparing for the technical descent, I spent time exploring Fraser’s Hill beyond the pavement and stopped by The Paddock.
Swapping cycling shoes for stirrups, I mounted a magnificent black horse for a gentle, rhythmic lap around the sand track. Walking past mock-Tudor stables and tall pine trees against clear blue skies was a surreal contrast to the pitch-black, adrenaline-fueled sprint through Batu Arang hours earlier. Spending a quiet moment petting the horse provided a grounded, meditative pause that recentered both mind and body.
4. The 30 km Descent & The Return Leg Descending Fraser’s Hill is the hard-earned reward for the morning’s grind, but high-speed descents on narrow tires demand unbroken concentration.
On the long drop down toward Batang Kali, fortune favored me once more: I fell in behind a vehicle holding a smooth, predictable line down the mountain. Drafting in its wake through the serpentine turns for nearly 30 kilometers allowed me to maintain pace while saving my worn brake pads and upper body.
Still, carrying a loaded 4.2–6 kg backpack for over 15 hours had exacted a heavy toll. My trapezius muscles and lumbar spine were locking into painful spasms, forcing several brief roadside stops to stretch and decompress.
Multi-Modal Transit Save Arriving at Batang Kali station late in the afternoon, I leveraged Malaysia’s bike-friendly commuter rail network to close the loop safely:
7:00 PM: Boarded the KTM Komuter train at Batang Kali, rolling the bike into the dedicated carriage. 10:00 PM: Reached the Shah Alam / Subang Jaya corridor after a restorative three-hour sit-down transit. 10:00 PM – 10:30 PM: Powered through the final 7.5 km night stretch back to Bukit Tinggi, Klang, officially concluding the journey at 10:30 PM. Key Ultra-Endurance Takeaways A 20-hour self-supported solo ride leaves behind lessons hardwired into muscle memory:
🎒 Get the Weight Off Your Spine: Carrying 4 to 6 kg on your back over a 20-hour effort was my biggest tactical error. Every bump sends impact shock directly into your lumbar vertebrae and traps. Future ultra challenges must rely strictly on frame-mounted bikepacking bags: half-frame packs, top-tube fuel pods, and an oversized saddle bag. 🍌 Caloric Discipline on Major Climbs: Skipping a real breakfast before a 40 km sustained climb flirted with a disastrous bonk. In remote hill sections where resupply points are non-existent, carrying calorie-dense nutrition, liquid carbs, and a minimum of 3 liters of fluids is mandatory. 🔦 Dual-Beam Lighting for Night Corridors: Riding unlit rural roads requires balanced illumination. While long-throw spot beams pierce the road ahead, wide-angle flood beams are vital for spotting livestock resting on verges before you are within arm’s reach. 💨 Aerodynamic Drafting Saves Glycogen: Whether slotting behind elite national-team riders on flat highway stretches or following a steady car on a technical 30 km descent, aerodynamic slipstreams save invaluable energy when your physical reserves are low. 🤝 Roadside Culture Matters: A friendly wave before sunrise converted into instant encouragement when I was stranded with a flat tire. Respect and camaraderie among cyclists make remote roads far less daunting. Epilogue: The Catalyst for the 300 km “Graduation Milestone” Conquering Fraser’s Hill—logging 200 kilometers and hauling a weighted gravel rig up 2,208 meters of mountain grade—permanently shifted my internal ceiling. It proved that fatigue, darkness, and mechanical hiccups could all be solved calmly under pressure.
Recovering at home over the days that followed, a far more audacious goal began taking shape: I wanted to mark the close of my twenties with a definitive milestone—an unsupported 300-kilometer solo ride in under 24 hours.
Your twenties offer a rare window of pure personal agency before deeper family and professional commitments claim center stage. I wanted a graduation badge that would test every ounce of resolve I had built.
As Fraser’s Hill demonstrated, ultra-distance cycling rarely yields clean victories:
The First 300 km Attempt (Heartbreak): During my first attempt, disaster hit at Kilometer 120.1 in Seremban. A sheared gear shifter left me stranded on the highway shoulder, forcing an agonizing DNF. The Second 300 km Attempt (Redemption): Rather than letting defeat close the chapter, I completely rebuilt the drivetrain, rode into the night six weeks later, pushed through Malacca flash floods, survived an unlit high-speed crash, and crossed the 298 km finish line in 23 hours and 17 minutes—without Lycra, without a carbon frame, and without a support vehicle. Fraser’s Hill was the crucible that forged the physical endurance, mechanical self-reliance, and mental resilience required to conquer that final milestone.
👉 Read the sequel and capstone ride of my twenties:
300km in 24 Hours — My Malaysia Ultra Solo Ride Record in Casual Gear
Final Thoughts Looking back at the ride telemetry—2,208 meters climbed, 200 kilometers covered, and over 9 hours of moving time—the numbers only capture the skeleton of the journey.
The true ride lived in the stillness of the rural forest at 4:00 AM, the sudden flash of glowing eyes in the undergrowth, the rhythmic roar of carbon wheels cutting through the morning mist, the sweeping vistas over Selangor Dam, shared plates at Aida Daniya, and the peaceful lap around The Paddock.
It was unpredictable, exhausting, and unforgettable—the definitive proof that no matter how daunting the distance ahead appears, all that matters is having the courage to roll out into the dark and keep the pedals turning.

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[START POST METADATA]
title: "The Hardest Online Course I Ever Took"
url: "https://fangkiang.com/posts/the-hardest-online-course-i-ever-took/"
date: "2026-10-06"
tags: ["Self-Driving Cars","Deep Learning","Computer Vision","Sensor Fusion","Robotics","C++","Udacity","Self-Taught"]
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[START POST CONTENT]
If I had to choose the most brutally difficult course in my entire self-learning journey, it would undoubtedly be the Udacity Self-Driving Car Engineer Nanodegree (SDCND).
Born in 1996 with only a Diploma in Business Studies, I had zero Computer Science (CS) background. I had never taken university-level advanced math or higher physics. Yet, I not only conquered this Autonomous Driving program—a course that leaves many CS master’s students struggling—but also became one of its top early graduates globally.
Here is the story of my “unconventional” path, how winning #1 in MIT’s DeepTraffic competition funded my tuition, and why the Udacity SDCND was the hardest—and coolest—course I’ve ever taken.
1. A Business Student’s Unconventional Start in Code My passion for programming wasn’t sparked in a university lecture hall.
During my years studying Business, I realized I was far more fascinated by code and technology than business theory. Lacking formal resources, I turned entirely to self-directed learning:
Scrappy Foundations: I learned computer basics on Alison and picked up coding syntax on SoloLearn and W3Schools. Curious about what “real CS majors” learned, I reached out to my high school classmates in university, asking for their course syllabi and lecture slides. I’m still deeply grateful for how generously they shared them with me. Learning by Doing: After building a foundation in web development, I started using WordPress to build websites for relatives and friends. Word spread, and I eventually took on freelance gigs creating official websites for local small businesses and manufacturing companies. Right before graduating, I researched industry trends and studied the journeys of legendary builders. I noticed a recurring, powerful realization: the best software engineers and technical innovators are ultimately self-taught.
I was deeply inspired by iconic founders and developers who reshaped the industry without traditional Computer Science paths or by venturing far beyond standard curricula:
Evan You (Creator of Vue.js) & John Resig (Creator of jQuery) — both coming from non-traditional CS backgrounds (Art & Art History / Computer Science with a strong self-taught open-source ethos) to craft frameworks that revolutionized modern web development globally. Drew Houston & Arash Ferdowsi (Co-founders of Dropbox) — who turned raw self-directed problem-solving and persistent iteration into seamless cloud infrastructure used by millions. Chris Wanstrath (Co-founder of GitHub) — an English major dropout who taught himself to code and built the world’s largest platform for software collaboration. Jan Koum (Co-founder of WhatsApp) — who self-studied computer networking from purchased manuals and rebuilt messaging infrastructure for the entire globe. Palmer Luckey (Founder of Oculus VR) — a self-taught hardware tinkerer who built early VR prototypes in his garage, reigniting the modern virtual reality revolution. Mike Krieger (Co-founder of Instagram) — who bridged Symbolic Systems and self-taught software engineering to build scalable systems for billions of photos. Max Howell (Creator of Homebrew) — whose self-directed open-source tool became the indispensable package manager for millions of macOS developers worldwide. David Heinemeier Hansson (DHH) (Creator of Ruby on Rails & Co-founder of 37signals) — a Business Administration & Computer Science graduate who built one of the most influential web application frameworks entirely out of practical, real-world necessity. Seeing how these individuals bridged immense knowledge gaps through pure curiosity and self-driven execution convinced me that a formal degree was not the ultimate bottleneck.
Given my family’s financial situation at graduation, I decided against pursuing a traditional degree path and chose to commit entirely to self-learning. It was a lonely road from day one—my family couldn’t understand my choice, nor did they support me spending hours staring at a screen “messing around with code.”
Facing skepticism and cold shoulders, I bet everything on online learning resources.
2. From Web Dev to Autonomous Vehicles: My Self-Taught Tech Tree Without a prestigious computer science degree, Massive Open Online Courses (MOOCs) became my ultimate weapon.
I built my own step-by-step learning roadmap, soaking up knowledge across Coursera, edX, and Udacity:
🎯 My learning path at that time：
Front-End ➔ Full Stack ➔ Android Development ➔ Data Analyst ➔ Machine Learning ➔ Self-Driving Car
I tackled one domain after another—starting from simple front-end pages to building full-stack applications and Android apps, then mastering Python data analysis and machine learning.
Then I came across the Self-Driving Car Engineer Nanodegree (SDCND) launched by Udacity founder Sebastian Thrun. Autonomous driving was at its absolute peak of technical excitement.
Back then, Udacity operated on a highly selective, application-based cohort model. With tech leaders racing to develop production-ready autonomous vehicles, over 11,000 applicants vied for just a few hundred spots—a daunting ~2–5% acceptance rate.
I applied immediately and was thrilled to be accepted into the second global cohort (November 2016). Surrounding me were brilliant peers hailing from elite tech giants like Google, Apple, Microsoft, Amazon, and AT&T, as well as top-tier institutions like MIT, Stanford, CMU, Princeton and NUS.
Learning alongside such extraordinary talent pushed me to my limits. After months of late-night coding and complex robotics projects, I officially graduated as part of the inaugural class of Udacity’s Self-Driving Car Engineers.
References & Further Viewing Udacity Official Landing Page & Original Syllabus: Udacity Self-Driving Car Engineer Nanodegree (Archived 2016) | Download Official Program Syllabus (PDF)
Reuters Article: Udacity’s self-driving car engineering degree lures over 11,000 hopefuls
“The high number of applicants — for 250 spots in the course — underscores the pressing need for talent by technology leaders such as Alphabet’s Google and Apple, traditional car companies and automotive start-ups…”
Reddit (r/SelfDrivingCars): Did any of you who applied to the Udacity Self-Driving Car Engineer Nanodegree program already get a mail that you are accepted?
“Everybody is in, split over different cohorts over the coming months: 500 for October, 1500 for November, 2500 for December… with 11,500 applicants in mind.”
Related Videos & Media NOVA PBS Documentary — The Great Robot Race (2005 DARPA Grand Challenge): https://www.youtube.com/watch?v=saVZ_X9GfIM The historic NOVA documentary covering the legendary 2005 DARPA Grand Challenge, where Sebastian Thrun led the Stanford Racing Team to victory with “Stanley”—the robotic vehicle that catalyzed the modern autonomous vehicle revolution.
Oliver Cameron (CEO, Voyage & Former Udacity SDCND Lead) on MIT Self-Driving Cars: https://www.youtube.com/watch?v=-j0tc0Y1CIE Oliver Cameron’s MIT guest lecture detailing how Udacity built and launched the SDCND program, how they deployed code to an actual Lincoln MKZ, and how those learnings laid the foundation for his autonomous taxi startup, Voyage.
Stanford AI Lab & Sebastian Thrun - DARPA Urban Challenge 2007 Documentary: https://www.youtube.com/watch?v=dHHppGy3p_c A fascinating deep-dive documentary following Sebastian Thrun and the Stanford Racing Team as they built “Junior” (a modified VW Passat) to navigate complex urban traffic, stop signs, and 360-degree sensor fusion in the 2007 DARPA challenge.
Udacity Self-Driving Car Engineer Nanodegree Overview: https://www.youtube.com/watch?v=JkqmOUNbNfY The official program trailer led by Udacity curriculum lead David Silver and industry partners like Mercedes-Benz, breaking down the 3-term curriculum from Computer Vision and Sensor Fusion to Path Planning and ROS integration.
3. Why Udacity SDCND Was the Hardest Challenge for Me in 2017 If web development was about building user applications, SDCND in 2017 was hardcore low-level logic fused with cutting-edge algorithms. It demanded exceptional proficiency in C++ and Python, alongside heavy mathematical and physical modeling across three core pillars:
Computer Vision: Using OpenCV and deep learning to extract lane lines and detect traffic signs with extreme precision on image matrices. Sensor Fusion: Implementing Kalman Filters and Extended Kalman Filters (EKF) to fuse real-time noisy data from LiDAR and Radar. The mathematical derivations were unforgiving. Localization & Path Planning: Particle Filters, A* search algorithms, and control theory (PID control and Model Predictive Control). Every project required precise vehicle control inside simulated environments. Coming from a background with only a Diploma in Business Studies, the learning curve wasn’t a ramp—it was a vertical wall. I had to learn multivariate calculus, linear algebra, and physics on the fly while simultaneously optimizing performance in C++. To bridge the massive gap, I bought physical math textbooks and ground through supplemental courses on DataCamp and Khan Academy late into the night.
During this period, I also competed in MIT’s DeepTraffic competition (6.S094: Deep Learning for Self-Driving Cars), where I managed to secure the #1 spot on the global leaderboard. That achievement earned me a full Term 1 tuition-waiver scholarship for the Udacity SDCND, which provided a crucial boost to my learning journey.
At barely 20 years old, while my peers were enjoying traditional university life, I was the complete outlier. I had no safety net, very little money, and virtually no support system. Back in 2017, spending days locked in my room trying to “self-teach self-driving cars” sounded like a joke to the people around me. No one truly understood what I was building toward, and the isolation was heavy.
Yet, I gritted my teeth and kept going simply because I had no other choice. My days were defined by dozens of open Stack Overflow tabs, dense academic papers, and local C++ compiler errors. Every rejected project review tested my mental resilience to its absolute limit, but each resubmission pushed my engineering rigor to a level I never knew I was capable of achieving.
It was during this intense period of studying, building autonomous systems, and sharing my progress online that another incredible surprise occurred: NVIDIA AI officially followed my Instagram account.
4. Academic Rigor: Master’s-Level Recognition (NZQA) The sheer depth and academic rigor of this curriculum extended far beyond typical online certifications or light tutorial projects.
Rather than teaching basic scripting or superficial high-level frameworks, the program forced students to implement complex robotic algorithms entirely from scratch—ranging from non-linear sensor fusion with Extended Kalman Filters (EKF) to non-convex optimization in Model Predictive Control (MPC).
This level of depth did not go unnoticed by educational authorities. The New Zealand Qualifications Authority (NZQA) formally evaluated micro-credentials and benchmarked the Udacity Self-Driving Car Engineer Nanodegree as equivalent to a 60-credit course at Level 9 (Master’s degree level) on the New Zealand Qualifications Framework (NZQF).
🏛️ Official Government Recognition: As part of a national initiative to integrate emerging tech micro-credentials into formal education, New Zealand officially recognized top-tier industry programs like the SDCND as matching the academic depth and analytical rigor of post-graduate university studies.
Reference: New Zealand Government Release — New Zealand welcomes first micro-credentials
Understanding the Credential: Industry Value vs. Academic Scope To set realistic expectations, it is essential to distinguish between traditional academic credentialing and high-impact industry recognition. While the SDCND demands postgraduate-level intellect, its real-world execution operates under a modern tech paradigm:
Aspect Is it Recognized? What it Means in Practice Academic Accreditation ❌ No It is an industry-driven micro-credential rather than an accredited degree. It does not confer a formal university diploma, nor does it count toward university transferable credits. Tech & AV Industry Recognition ✅ Yes Recognized globally by premier autonomous vehicle teams and Silicon Valley giants (e.g., Waymo, Mercedes-Benz, NVIDIA, Cruise) who treat its project portfolio as equivalent to advanced graduate-level practical experience. Why Industry Weight Trumps Traditional Accreditation in AV For self-taught engineers and career switchers, this distinction proved to be an advantage rather than a drawback:
Emphasis on Hardcore Code, Not Paper Degrees: Autonomous driving engineering demands real, fault-tolerant C++ and Python code over theoretical exam scores. Autonomous vehicle engineering teams look directly at raw commit histories, simulation videos, and math derivations rather than university logos. Curriculum Agility: Traditional universities often take years to approve new course syllabi through academic committees. The SDCND, co-designed directly by active engineers at Mercedes-Benz and NVIDIA, updated its codebase in real-time to reflect actual production stacks. The Proof is in the Portfolio: While the Nanodegree cannot replace a formal academic degree on paper, completing its master’s-level projects provided the concrete proof-of-work needed to bypass preliminary HR screenings and compete directly with CS master’s and Ph.D. graduates in technical interviews. 5. Why the First Cohort Carried So Much Weight The modern, revamped iterations of SDCND have lowered the barrier to entry significantly, but back then, the original curriculum took around a full year of grueling effort to get from admission to graduation. Being part of those inaugural batches meant stepping into a world with exceptionally high stakes and prestige:
Direct Collaboration with Tech Giants: This wasn’t just theoretical coursework. Led directly by Sebastian Thrun (“Father of the Self-Driving Car”), the program was co-developed with global pioneers and automotive leaders including Mercedes-Benz, NVIDIA, DiDi, Uber/Otto, and BMW. The curriculum was designed to mirror real-world production stacks, meaning graduation credentials carried immense weight across the autonomous driving ecosystem. A High-Filter Talent Pool: The original admissions process had a strict screening mechanism requiring detailed applications, code evaluations, and background reviews. Most of my peers in that initial group held Master’s or Ph.D. degrees from top-tier universities worldwide in Computer Science, Electrical Engineering, or Robotics, or possessed years of professional hardware/software engineering experience. Many of my classmates were graduates from elite institutions like Stanford, MIT, and Carnegie Mellon—meanwhile, I entered with no formal CS background and a business diploma. Fast-Track Industry Pipelines: Thanks to deep partner involvement, early graduates received priority interviews and green-lit fast tracks at industry leaders like Waymo, Tesla, Cruise, Baidu Apollo, and DiDi. The vast majority of early alumni went on to become core R&D pillars at top AV startups and tech giants, solidifying the prestige of the initial SDCND badge. 5.1 Severe Talent Shortage: Hired Before Graduation Between 2016 and 2017, the global autonomous driving industry experienced an explosive technological frenzy. Traditional automakers and Silicon Valley giants were investing billions of dollars to build out AV teams, yet the entire industry faced a severe “zero supply” talent gap—at that time, virtually no universities offered dedicated degree programs specifically for autonomous vehicles.
Against this historic backdrop, students in the inaugural Udacity SDCND cohorts became some of the most sought-after talent in the entire tech ecosystem:
Hired Before Certificates Were Issued: Many outstanding students in our cohort were snapped up by major tech giants and AV unicorns before they even completed all three terms. Communities like our Slack channels and r/SelfDrivingCars were filled with weekly “Job Offer” success stories from students who landed roles after completing just Term 1 or Term 2. Project-Driven Recruitment: Passing the most demanding C++ and Python technical interviews often came down to showcasing high-quality project repositories (such as Behavioral Cloning or Extended Kalman Filters) on GitHub. A well-known example from our cohort was George Sung—originally a hardware/chip engineer who documented his career transition on his personal blog and in Udacity’s Medium feature, Becoming a Self-Driving Car Machine Learning Engineer. By leveraging his SDCND project code and active participation in the Slack community, he successfully landed a full-time role as an Autonomous Driving Engineer mid-way through the program, achieving a massive career leap. 5.2 Direct Access: Udacity’s Hiring Partner Network When designing the Nanodegree, founder Sebastian Thrun deliberately dismantled the barrier between traditional academia and industry. SDCND was never just an online course—it was built as an active recruitment pipeline for top-tier tech companies.
Udacity assembled an unprecedented Hiring Partner Network that included:
AV Pioneers & Tech Giants: NVIDIA, Uber ATG, DiDi, and Voyage. Traditional Automakers & Top Suppliers: Mercedes-Benz, BMW, and McLaren. These partners were deeply involved in both curriculum creation and talent acquisition:
Co-Developed Curriculum: Engineering teams from partner companies helped craft specific modules (for instance, the Mercedes-Benz team directly assisted in writing the tutorial for the Extended Kalman Filter sensor fusion module). Priority Resume Pipeline: Once students submitted passing project code, exceptional work was pushed into the Udacity Talent Source database. Partner recruiting teams could bypass initial HR screens to directly inspect students’ GitHub repos and simulation demonstration videos. Exclusive Recruitment Channels & Slack Access: Udacity hosted virtual career fairs exclusively for the first batch of students. Company CTOs and engineering leads regularly posted unlisted R&D roles directly inside dedicated Slack channels. For someone coming from a non-CS background with only a Diploma in Business Studies, this system was transformative. It evaluated candidates purely on hardcore coding skills, offering direct access to top global AV teams and allowing self-taught developers to compete on equal footing with engineering graduates from elite institutions worldwide.
6. Accelerating Ahead: Graduating with Batch 1 Although I was admitted as part of Batch 2, I poured nearly every waking hour into the coursework, pulling endless late-night sessions to complete the hardcore projects at breakneck speed.
My effort paid off: I caught up with the schedule and graduated alongside the very first global cohort (Batch 1)!
Because of this accomplishment, I received a personal invitation from Udacity founder and former Google Self-Driving Car pioneer Sebastian Thrun to attend the official graduation ceremony in the United States alongside graduates from around the world.
For a self-taught practitioner in their early 20s, it was an incredible validation and honor.
7. Regrets and Reflections: A Clear Conscience Reality, however, brought its own constraints.
In my early 20s with limited financial means, I simply couldn’t afford the flight and accommodation to the US. To make matters harder, my family still didn’t understand online education or autonomous driving, seeing it as an impractical pursuit.
Ultimately, I couldn’t attend that graduation ceremony across the ocean.
Do I have regrets? Absolutely. But do I regret the journey? Not for a second.
Going from a business student copying HTML snippets on W3Schools to a self-taught engineer implementing sensor fusion in C++ proved something invaluable:
It completely reshaped my learning methodology—proving that with the right drive, no technical ceiling is unbreachable. It gave me unshakeable technical confidence—if I could master self-driving algorithms, no new technology or framework could intimidate me again. Self-directed learning can be a lonely road marked by doubt and real-world hurdles. But once you write that first line of code and keep going, every late night and effort spent becomes the strongest armor you carry forward.

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[START POST METADATA]
title: "Looking Back at My 2021 Tech Predictions: How the Future Arrived Right on Schedule"
url: "https://fangkiang.com/posts/looking_back_at_my_2021_tech_predictions/"
date: "2026-10-04"
tags: ["Tech","AI","Future","Reflections","Automation"]
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Back in December 2021, I wrote a post outlining what I believed the world and the tech landscape would look like over the next five years. Looking around today in 2026, it’s fascinating—and deeply satisfying—to see how many of those predictions have shifted from bold foresight into everyday reality.
Here is a look back at what I wrote in 2021, alongside how those trends have actually played out:
1. The Work Revolution: Remote, Automation, and “Workations” What I predicted in 2021: Remote and hybrid work would become standard, manual labor would transition into configuring AI/robots, and terms like “Workation” and “digital nomad” would mainstream as live, work, and travel integrate. The Reality in 2026: Work is no longer tied to a physical office for millions. The line between living and working has blurred into a modern digital nomad lifestyle. On the industrial side, automation and applied AI have taken over repetitive physical labor, transforming modern manual roles into high-tech operator and supervisory positions. 2. AI & Software Development: Software 2.0 & AI Assistance What I predicted in 2021: Software development would pivot heavily toward concepts like Software 2.0, GitHub Copilot, and early LLMs (like GPT-3). The Reality in 2026: AI-assisted development is no longer an experiment; it is the fundamental way code is written today. Developers have evolved into architects driving AI systems, dramatically accelerating how products are built and deployed. 3. Hardware & Infrastructure: ARM Chips, 5G, and PWA Adoption What I predicted in 2021: ARM architecture and 5G would dominate computation, while Web-based PWAs (Progressive Web Apps) would gain ground as native app fatigue set in. The Reality in 2026: ARM has solidified its place as the efficiency benchmark across both mobile devices and cloud servers. Connectivity is seamless, and lightweight, web-centric software models continue to challenge bloated native applications. 4. Digital Humans, Virtual Reality, & Spatial Computing What I predicted in 2021: Virtual/digital humans (like @lilmiquela) would become mainstream influencers alongside traditional YouTubers and KOLs, while AR/VR technologies would gain widespread adoption. The Reality in 2026: Virtual avatars and AI-driven personalities are everywhere in digital marketing, gaming, and content creation. Spatial computing and immersive 3D experiences are integrated deeply into everyday entertainment and media. 5. Sustainability & Electric Vehicles What I predicted in 2021: Electric vehicles (EVs) would be enforced and prioritized to save the planet. The Reality in 2026: The global shift away from traditional ICE engines has accelerated rapidly, making EVs the default choice for new drivers around the world. Final Thoughts As I noted back in 2021: “The future is not something to be feared, but something to be embraced.” Seeing these shifts materialize over the last five years reinforces that technology moves fast, but clear trajectory markers are almost always visible if we look closely enough.
Now, as we look toward the next horizon, emerging fields like biocomputing, advanced biomachines, and deeper AI integrations are calling. The next decade promises to be even more transformative.
Original Author: Ng Fang Kiang
Original Post (Published December 5, 2021):
The future is not something to be feared, but something to be embraced. There’s an inherent danger in clinging to progress, but progress is precisely the reason we should be embracing it.
In the next 5 years, I think a lot of things will change.
I think remote work is here to stay. People continue to work remotely (fully remote or hybrid work). For manual workers, It’s very likely to transform into another job that requires some engineer skills to configure the AI/robots that works with the industry. Hybrid work is for some industry or people that need time for transition to fully remote. For those job required to work in a certain place are still exists, but will start to replace by automation robots (AGV, AIoT, Applied AI, self-driving car and so on)
Zero-trust security is crucial. Like E2E encryption, blockchain, NFT and so on.
With the embrace of VR and AR technologies. The terms of “virtual human” or “digital human” like @lilmiquela will become the norm just like the KOL, internet influencer or YouTuber in the next 5 years.
The future of programming? Maybe Software 2.0, Copilot, GPT-3, etc?
For internet and computation industry, I believe 5G and ARM chips will be the future.
For Android and iOS, I think a web base PWA app is more realistic in the next 5 years. People are now less likely to install lots of app into their phone even their phones are getting more powerful every single year.
For things like Windows 11, the WinUI3 seems very promising. I think Windows 11 might be another big thing in the next 5 years.
I think people will have more time to travel and work. Things like “Workation”, “Staycation”, “digital nomad” will get popularize. Live, work, and travel used to be separate, but now they are all integrated.
Look ahead for the 5 to 10 years. I believe something like metaverse, omniverse, AR, VR, all kind of 3D stuffs (Simulation,3D/4D printing,3Dmovie/news, Technologies) and so on will be popularized and became another norm. Electric vehicles will be enforced. Save the planet without more gasoline! Flying car seems very promising too! Until that time, I think I will start to look at something like biomachine, biocomputing and so on.
Original Source: Instagram Post (December 5, 2021) View this post on Instagram 
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[START POST METADATA]
title: "My Journey with MIT DeepTraffic: Ranking #1"
url: "https://fangkiang.com/posts/mit-deeptraffic-first-place-udacity/"
date: "2026-10-04"
tags: ["Deep Learning","Reinforcement Learning","Self-Driving Cars","MIT","Udacity","AI"]
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[START POST CONTENT]
Back in early 2017, I had the incredible opportunity to participate in MIT’s 6.S094: Deep Learning for Self-Driving Cars competition—DeepTraffic. Hosted via the official MIT DeepTraffic Simulator, it was an exhilarating experience that marked a significant milestone in my journey with AI and autonomous driving technology.
This competition proved to be the ultimate catalyst for my tech career: it earned me a tuition waiver for the Udacity Self-Driving Car Engineer Nanodegree, put my social handle on the map, and eventually led to NVIDIA AI following my Instagram account.
Leaderboard Achievements DeepTraffic 1.0 Leaderboard DeepTraffic 1.1 Leaderboard View Full-Res Screenshot ↗ View Full-Res Screenshot ↗ Achieving #1 on DeepTraffic 1.0 & 1.1 In January 2017, after countless hours of hyperparameter tuning, model iteration, and deep reinforcement learning experiments, I managed to reach the #1 spot on the DeepTraffic 1.0 leaderboard under the username Instagram: Jorcus96 with a speed score of 75.38 MPH.
My #1 rank under instagram:jorcus96 was even captured in third-party write-ups and repositories at the time, such as Jason Salas’s Medium article on beating DeepTraffic and pranz24’s MIT-DeepTraffic-1.1 GitHub repository screenshot. Additionally, other top competitors from the community who caught up after months later , such as second-place winner Alireza Kohani, also shared their journeys and strategies in their MIT DeepTraffic 2nd Place Write-up where you can see my name in the leaderboard as well.
Just a month later, in February 2017, MIT released DeepTraffic 1.1, opening the competition to participants worldwide beyond MIT-registered students. The competition was even fiercer, but my optimized RL strategies paid off once again—I reclaimed the #1 spot on the DeepTraffic 1.1 leaderboard with an improved score of 75.82 MPH!
While waiting to hear back about the rewards, I pushed my model even further and achieved another top rank after hitting an even higher score of 76.16 MPH in February. After reaching that milestone, I decided to wrap up my work on the competition. Eventually, other participants surpassed that score, but my result remained in the Top 3 for nearly a year until the online competition was officially taken down from the website. Fortunately, the leaderboard was preserved on the Web Archive (captured on October 18, 2017), where my name is still visible.
Web Archive snapshot of the DeepTraffic 1.1 Leaderboard (saved October 18, 2017) showing NG FANG KIANG at Rank 3 with a score of 76.16 MPH. MIT DeepTraffic Lecture & Competition Overview To understand the core concepts behind DeepTraffic and how deep reinforcement learning is applied to motion planning, you can watch the official MIT 6.S094 lecture video below:
For further details on the underlying simulation engine, deep Q-learning environment, and parameters, check out the official MIT DeepTraffic GitHub repository.
Years later, Lex Fridman and his co-authors formally published the research paper “DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation” (arXiv:1801.02805), detailing how thousands of participants crowdsourced the search space for RL agent parameters in dense traffic simulations.
Turning Achievements into Opportunities After securing the top rank on both leaderboards, I took screenshots of my rankings and shared them with potential sponsors to showcase my passion and technical competence in self-driving technology.
That effort yielded an unbelievable result: I was awarded a one-term tuition waiver for the Udacity Self-Driving Car Engineer Nanodegree—generously provided by the CEO of a prominent autonomous driving startup and former VP of Udacity!
This scholarship opened the doors to the hardest online course I ever took. Little did I know that branding my username as Instagram: Jorcus96 on the MIT leaderboard would also spark a chain reaction on social media, leading to NVIDIA AI following me on Instagram shortly after!
Key Takeaways Looking back, this experience taught me two important lessons:
Persistence in AI/RL Experiments: Fine-tuning reinforcement learning models requires patience, systematic trial-and-error, and deep understanding of agent-environment interactions.
Showcasing Your Work Matters: Achieving something is great, but actively sharing your achievements, branding your work, and reaching out to supporters/sponsors can unlock opportunities you might never have imagined.
Thank you to MIT for hosting such an awesome benchmark competition and to everyone who supported me throughout this journey!
References & Academic Publications Research Paper: Lex Fridman, Jack Terwilliger, & Benedikt Jenik. “DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation” (arXiv:1801.02805). Official Competition Code: MIT DeepTraffic Open Source Repository on GitHub DeepTraffic 1.0 Verification: Leaderboard Proof Screenshot (GitHub Repository) DeepTraffic 1.1 Verification: Leaderboard Proof Screenshot (GitHub Repository) Web Archive Leaderboard: MIT 6.S094 DeepTraffic 1.1 Archived Leaderboard (Oct 18, 2017) (showing NG FANG KIANG at Rank 3 with 76.16 MPH nearly a year later) Third-Party Community Write-up: My Attempt at Beating DeepTraffic by Jason Salas (featuring instagram:jorcus96 at #1 on the leaderboard screenshot) Third-Party GitHub Repository: pranz24’s MIT-DeepTraffic-1.1 Repository (capturing jorcus96 at #1 on the leaderboard) Community Achievement: Alireza Kohani’s 2nd Place Journey & Design Portfolio (detailing the 2nd place strategy in the MIT 6.S094 DeepTraffic challenge) 
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[START POST METADATA]
title: "That Time NVIDIA AI Followed 135 Accounts on Instagram - And I Was One of Them"
url: "https://fangkiang.com/posts/nvidia-ai-followed-my-instagram/"
date: "2026-10-04"
tags: ["NVIDIA","Instagram","Personal Story","Tech","Social Media"]
[END POST METADATA]

[START POST CONTENT]
Back in July 2017, when NVIDIA AI (@nvidiaai) launched its official Instagram account, something completely unexpected happened: they followed my personal account.
At the time, @nvidiaai was right at its inception with only a few dozen followers. I was going by my handle @jorcus96. Seeing an official account from a silicon and AI powerhouse pop up in my notifications felt completely surreal.
Connecting the Dots: How Did NVIDIA AI Find Me? Looking back at 2017, this unexpected connection was actually the culmination of a wild few months in the autonomous driving and AI space:
The MIT DeepTraffic Benchmark: In early 2017, I achieved the #1 spot globally in MIT’s 6.S094 DeepTraffic competition under the handle Instagram: Jorcus96. The leaderboard was widely shared across tech blogs, research repos, and social media. Udacity SDCND & NVIDIA Partnership: Winning DeepTraffic landed me a full scholarship for the Udacity Self-Driving Car Engineer Nanodegree. NVIDIA was one of Udacity’s primary co-developers for the program, actively monitoring top-performing students and community contributors on Slack, GitHub, and social channels. Active Tech Sharing: I was actively sharing my deep learning experiments, C++ sensor fusion code, and AI projects under @jorcus96. When NVIDIA AI set up their official account to track emerging talent, influential publications, and AI pioneers, my handle happened to be right in the middle of that ecosystem.
A Front-Row Seat to Early AI History By June 2019, as captured in the screenshots above, the account had grown to 4,278 followers across 65 posts, while keeping its following list exceptionally exclusive at just 135 accounts.
Scrolling through that select list, my personal handle (@jorcus96 / Ng Fang Kiang) sat right alongside prominent tech media outlets and institutes like Futurism Health, Explorist, Futurism Energy, Futurism Science, Curiosity Rover, TechCrunch, and MIT Technology Review.
As @nvidiaai scaled into a major industry hub with hundreds of thousands of followers, they eventually overhauled their social media strategy and unfollowed individual accounts. But having a front-row seat to the early days of official AI social media—and knowing that my self-taught journey in AI caught the eye of NVIDIA—remains one of my favorite pieces of personal tech trivia.

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