The Runway Ventures

Get the weekly newsletter to help you learn from startup mistakes and become a better founder.

Every Sunday, we analyse and share:

• 1 failed startup ☠️
• 2 costly mistakes made ⚠️
• 3 lessons learned for founders 🧠

90% of startups fail because founders made mistakes. Don't be one of them.

Subscribe for free here 👉🏻 www.therunway.ventures

Founder

beehiiv

beehiiv empowers you to create, monetize, and grow your newsletter with an all-in-one publishing suite.

(right team, great product, crazy execution)

Angel Investor

Fathom - AI Meeting Assistant

Fathom records, transcribes, highlights, and summarizes your meetings so you can focus on the conversation.

(proud user of Fathom)

X10 Media

X10 Media, a regional influencer agency established over 7 years ago before Influencer Marketing became part of the mainstay, holds a network of over 3,000 Influencers with a combined 30 million network reach in Singapore, Malaysia & Indonesia.

The agency has served over 1,000 brands including Pepsi, Singtel, Capitaland, LVMH, Warner Music, Owndays, Casetify among others.

With its proprietary Leadgram technology and data-driven insights, X10 Media empowers brands to achieve exceptional results through influencer campaigns.

X10 media currently owns 3 business units — Faves, X10 Ventures & TheSpokenTribe, that span regionally across 3 countries in Singapore, Malaysia & Indonesia.

🌏 Faves (micro-influencer marketing arm) runs a combined network of 10 million reach in Singapore, Malaysia & Indonesia.

🌏 X10 Ventures (technology arm) empowers brands with our proprietary technology.

• Leadgram was launched as a first-of-its-kind software in Asia to deepen social media campaign ROI & sales/traffic conversions for brands & companies.
• We retarget consumers that engages on influencers/brand content to create conversations leading to higher call to actions.

🌏 The Spoken Tribe (LinkedIn marketing arm) brings professional voices to endorse companies and brands through the word-of-mouth recommendations.

👉🏻 With a commitment to excellence and innovation, X10 Media continues to drive impactful influencer marketing solutions for brands. Visit www.x10.media to learn more.

CTO (Acquired by TSL Media Group)

Admond Lee

On a mission to make data science accessible to everyone through consulting & corporate training.

Data science consulting and analytics services for startups, SMEs, and marketing agencies.

✅ Previous projects include:

• AI & marketing consulting
• Web analytics & data visualisation
• Web scraping & marketing analytics
• Crypto price prediction model using deep learning with millions of sentiment data points


✅ Corporate & Institutional Training (Data Science / Analytics):

• SPH, DBS, MINDEF, Singtel, NTU, NUS, UTAR, Decoded


▶️ Learn more at www.admondlee.com

Consulting Data Scientist | Corporate Trainer

chatted.ai

Get AI insights from data in seconds and make informed decisions 10x faster.

1. Upload your data
2. Ask questions
3. Get actionable insights

That's it.

❌ No more formulas
❌ No more pivot table
❌ No more learning how to code
❌ No more waiting for your reports

✅ Just ask.

Sign up for free here 👉🏻 chatted.ai

... and start chatting today 🧠

YouTube

Making videos focusing on data science, programming, and tech stuff in general.

👉🏻 Link to channel: youtube.com/@admond1994

Content Creator

Staq

At Staq, we are building the universal API to help fintech companies securely access financial data from SMEs in a quick and easy way across Southeast Asia.

We are reimagining the technical infrastructure to drive financial inclusion for businesses in Southeast Asia.

Our mission is to empower you to build amazing financial products for businesses with more visibility and informed decisions. 

With Staq:
• You can easily and securely access financial data from businesses for SME lending, user identity verification, compliance and KYC etc. 
• Businesses can instantly share their data instead of sifting through tedious UX from multiple banks to extract bank statements. 

Headquartered in Singapore, Staq is backed by Entrepreneur First, the leading startup incubator in the world.

Co-Founder / CTO

Entrepreneur First

Entrepreneur First is the place where the ambitious come together to build globally important technology companies.

Founder in Residence (EFSG10)

Medium

Contributing Writer | Towards Data Science

Co-Founder & CTO @ Staq | Data Scientist

I’m a physicist turned data scientist, now a founder building The Runway Ventures — a weekly newsletter to help people learn from startup failures to become the top 1% founders.

I spent the first 3 years of my career helping companies solve business problems as a data scientist. Meantime, I was also a corporate trainer, speaker, and writer in data science. Then, I spent the next 3 years building startups. 1 failed, 1 acquired.

🧗🏻 This is how I got here:

• Got my degree in Physics from Nanyang Technological University.

• Started as a data scientist at SME (Titansoft), then MNC (Micron) before joining a startup (Hackwagon).

• Quit my job during COVID (wild timing), joined an incubator, found a co-founder, and built my 1st startup (Staq). Spent 1.5 years building Staq but we failed. Despite our mistakes, we learned a lot.

• Was very lost after my 1st startup’s failure. Spent the next 2 months doing self-reflection and exploring my curiosity.

• Started my 2nd startup building Leadgram at an influencer marketing agency (X10 Media). 8 months later, we got acquired by The Smart Local (TSL) Media Group.

***

After 3 years of building startups, I'm convinced that mistakes are the best teachers to become a better founder. For any startup, there are many ways to succeed, but only a few ways to die.

That’s why I started The Runway Ventures. 

Every Sunday, I analyse and share 1 failed startup story, 2 mistakes made, and 3 lessons learned with actionable tips to help you learn from startup failures.

Real life case studies, with key takeaways you can apply in your business.

No theories or BS.

99% of startups failed because founders made mistakes.

You’ll learn why and how startups failed to become the top 1% founder.

🤝🏻 Join us here (it’s free!): www.therunway.ventures

🔗 FB: @admond1994
🔗 Twitter: @admond1994
🔗 Instagram: @admond1994

Admond Lee Kin Lim

💍 She said YES!!!

After being together for more than 5 years, today, I proposed to her.

I still remember when I started my first startup. I was working as a food delivery rider part-time. I was broke af. I had nothing, literally nothing — except fire in my belly to make something people want.

It was the darkest period in my life. Yet, she supported me wholeheartedly throughout the time. She loved me unconditionally.

During that period, I told myself, “I want to marry her. I better don’t fxck this up.”

5 years later, here we are.

I’m sharing this with you because I really want to share the joy and happiness with you. 

You’ve been reading failed startup stories from me, and most startups failed because the founders made the wrong decisions.

Well, guess what?

💟 The most important decision of your life is to decide who you marry — your life partner. 

I’m glad I’ve found mine.

li_at

JSESSIONID

Helping you become the top 1% founder by learning from startup failures | 2x founder | 1 exit | Founder @ The Runway Ventures 🚀

$60M run rate. 5M+ downloads. #2 in Indonesia. Dead overnight.

Perfect metrics couldn't save Sorabel when they only had 6 months of runway.

Picture this: Indonesian husband-wife duo starts selling clothes from their garage in 2014. By 2019, they're sitting on 800 employees, 58% gross margins, and investors lining up for Series C.

But here's what should've terrified everyone:

They NEVER had more than 6 months runway. Even after raising $27M from Gobi Partners and Alpha JWC.

Every month was a race to raise more cash before the music stopped.

They even cracked the trust problem that kills most e-commerce in Southeast Asia. "Try First, Pay Later" — couriers wait 15 minutes while customers try on clothes at home. Only pay for what you keep.

Like a fitting room at your doorstep. Genius, right?

The numbers backed it up — $5M monthly revenue, 2.4x growth after rebrand, and they'd captured 9% of Indonesia's women's fashion e-commerce market.

CEO Jeffrey Yuwono was talking about becoming the Zara of Southeast Asia. Everything looked unstoppable.

March 2020: COVID hits. Investors can't fly to Jakarta. Term sheets pulled overnight. Fashion demand drops 50-70%. Their middle-class customers — the ones buying trendy clothes — all getting laid off.

July 2020: Game over. 800 jobs gone. Dreams dead.

Did COVID kill them, or were they already dying?

Tonight at 8pm, I'm publishing the full Sorabel autopsy. The 2 critical mistakes. The warning signs. Every lesson you need to know.

Subscribe now to get it when it drops → https://lnkd.in/gdDrhgtq

P.S. Innovation without unit economics is just expensive suicide.

$1.4B unicorn faked 4.8x revenue. $40B wiped in 3 days. CEO diverted funds to personal accounts.

Southeast Asia's fraud epidemic just hit critical mass — and 99% of founders are still blind to it.

I've written 90+ failed startup stories. But these 5 fraud cases shocked Southeast Asia:

eFishery (Indonesia's agritech unicorn):
• Claimed $752M revenue → Actually $157M
• Showed $16M profit → Actually $35M loss
• 6 years of dual-book accounting before whistleblower exposed it

LUNA/UST (Terraform Labs):
• $119 peak → $0 in 72 hours
• $40B market cap evaporated
• Millions lost life savings globally

Investree (P2P lending):
• CEO allegedly diverted company funds to personal account
• NPL hit 16% (reg limit: 5%)
• License revoked October 2024

The pattern is brutal: Fake metrics → Fake growth → Dead startup.

Singapore just dropped from #1 to #10 in global fraud resilience. Malaysia fell 52 places. Indonesia ranks dead last in Southeast Asia.

Your dashboards are lying to you. That 30% MoM growth? Could be bots. Those 10,000 signups? Might be fraud farms. Your growth might be fiction.

Nov 19-20, I'm at What the Fraud Summit (hosted by Sumsub) in Singapore. Because knowing these patterns before they hit you is worth more than your next funding round.

2 days learning defense beats 1 fraud scandal destroying everything.

Register here → https://lnkd.in/gN_TXqju
Use code ADMOND15 for 15% off.

P.S. If you think fraud only hits "other" startups, you're already a target.

Sumsub

3 Malaysian founders. 2 weeks. 4 all-nighters. 100,000+ users.

The LinkedIn Unwrapped story nobody's talking about.

Ashvin Praveen and his co-founders (Lizzie Tan, Muhammad Fathy Rashad) at Cleve.ai were behind on growth targets. Desperate. Running out of runway. They had this wild idea — build a "Year in Review" for LinkedIn creators.

Everyone thought they were crazy. Including themselves.

They shipped it anyway. In 14 days flat.

Week 1: 30,000 users (they expected 3,000)
Week 3: 100,000+ users
Countries reached: 100+

Even the CEO of PepsiCo Latin America posted their LinkedIn year-in-review. Alex Hormozi commented on their posts.

But here's what you don't know:

The early Cleve was "absolutely unusable." Users couldn't figure out the UX. 70% were on mobile but the product was built for desktop. Growth was dead.

So they did what most founders won't — burned the whole codebase and rebuilt from scratch.

Their 2 biggest fxckups:
1. Built a product that was "too clever" to use (tried to reinvent UX instead of following patterns people already know)
2. Spent hours in Mixpanel trying to "find the answer" in data instead of talking to users and trusting their gut

From 0 to 20,000 users grinding.
From 20,000 to 100,000+ in weeks.

All because they stopped patching problems and made the hard call.

Tonight 8pm: Why rebuilding from scratch saved them. The UX disasters that nearly killed them. How LinkedIn Unwrapped changed everything.

Subscribe now to get it when it drops → https://lnkd.in/gdDrhgtq

P.S. They rewrote their entire codebase on their one-year anniversary. Most startups die before making that call.

$53 million in customer funds. Vanished. Coinbase was about to drop $40M at a $400M valuation.

Then Zipmex collapsed overnight.

Marcus Lim had already exited OneFlare. Akalarp Yimwilai was Thailand's regulatory heavyweight — Georgetown master's, public admin doctorate, plugged into the Ministry of Finance.

Perfect split. Marcus raised money. Akalarp got licenses.

By August 2021, they'd pulled in $41M from Krungsri Finnovate (Bank of Ayudhya/MUFG), B Capital, Plan B Media. The "Coinbase of Southeast Asia" was crushing it.

Users loved the 10-14% yields on ZipUp+. Move your crypto from Thailand to Singapore entity. Zipmex lends it to Babel Finance and Celsius. You get fat returns.

Except nobody told users their money was actually sitting with leveraged crypto funds.

June 2022: Celsius freezes withdrawals.
Five days later: Babel halts redemptions.
Zipmex exposure: $48M with Babel, $5M with Celsius.

July 20: Zipmex freezes all withdrawals.

The rescue attempt was even worse. V Ventures promised $100M to buy 90% of Zipmex. They missed the first payment. Then the second. Then the third. Then they walked.

Court recovery plan: 5.1% back to creditors.
Straight liquidation: 1.6%.

February 2025: Akalarp gets 5 years in prison for fraud. Thai SEC reported that he moved customer funds offshore before updating terms & conditions.

The entire "regulated, licensed, safe" crypto exchange was just a funnel to risky lenders. When Terra blew up and took down 3AC, the dominoes fell fast.

Tonight 8pm: The full Zipmex autopsy drops. Every red flag. Every warning sign. The exact playbook of how "regulated" platforms blow up customer money.

Subscribe now → https://lnkd.in/gdDrhgtq

P.S. 14% yields in crypto? That money wasn't sitting in a vault. It was funding degens trading 10x leverage. You were the exit liquidity.

Last week, 35+ founders gathered to share their biggest fxckups. No BS. No sugarcoating. Just raw startup failures served with dinner and drinks.

We called it Startup Fxckups: Media Edition.

Willis Wee sold Tech in Asia for 8 figures. Joel Wong built Social Jet to 7 figures with 1,500+ creators. Both stood on stage and admitted: "We fxcked up. A lot."

Willis's confession hit hard:

"I ignored users for too long. Built features nobody wanted. Nearly killed the company."

His survival playbook:
• Ask users obsessively — stop assuming, start listening
• Watch your runway like a hawk — tough times always come
• When looking for best hires, ask HOW questions to know their thought process, review their case studies and do role play.
• Fire fast — 3 months max if they're not the right fit
• Test different ideas, remove what doesn't work, and double down on what works.

Joel doubled down on his biggest lesson.

His hiring revelation:
• The best hires with zero experience outperformed the "expert" every time
• Proactive beats qualified
• They fail fast, learn faster, grow fastest

50 founders. 2 hours. Zero pretense.

This is why we host these events. Not for the LinkedIn posts. Not for the networking cards. But because 90% of startups die from preventable mistakes.

And the only cure? Learning from founders who survived theirs.

Every founder left with at least one mistake they won't make now.

Massive thanks to Wong Wei Jer for co-hosting and making this happen. And to SUNRATE for hosting us and believing in honest founder conversations.

Want to join our next Startup Fxckups event? The only way to know it firsthand is through our newsletter → https://lnkd.in/gdDrhgtq

P.S. Best quote of the night: "If you're not embarrassed by your fxckups, they weren't big enough to learn from."

10 investors. 10 rejections. $10k left. 2 months to live.

I almost became a paper millionaire at 28. Instead, I burned my startup to the ground.

We had everything lined up for Staq. $100k pre-seed in the bank. Singapore's largest digital lenders ready to pilot. Term sheets being drafted for a $1M seed round.

I thought we were unstoppable.

Then reality hit.

The rejections came fast:
• 5 straight "NO"
• 3 investors pulled out: "This space is too tough"
• 2 wanted "more traction" (translation: you're not there yet)

We were burning $5k monthly. Bank balance: $10k.

Do the math. That's 2 months of runway.

Game over.

Here's the brutal truth nobody tells you about raising money:

Investors sense desperation. The moment you NEED their money, you've already lost. We were begging, not negotiating. They knew it. We knew it. Everyone knew it.

The 3 mistakes that killed us:

1. We built a solution looking for a problem. Not the other way around. No amount of VC money can fix that core problem.

2. I wanted to get rich more than I wanted to solve problems. Wrong motivation leads to wrong decisions, always.

3. We tried to raise money from a position of weakness. Never fundraise when you're desperate. Raise from a position of strength.

The shutdown was brutal. Telling customers we're done. Watching 2 years of work disappear.

But here's what failure taught me:

Building a startup isn't about the money. It's about caring so deeply about a problem that you'd solve it for free. Everything else — funding, growth, success — follows.

That's why I built The Runway Ventures. Every week, I dissect a startup failure so you don't have to make the same expensive mistakes.

My first startup cost me $100k to learn these lessons.

You can learn them for free.

Join 20,000+ founders getting the autopsy reports → https://lnkd.in/gdDrhgtq

P.S. The best time to raise money? When you don't need it. The worst time? When you're 2 months from death.

What's the most expensive lesson your startup taught you?

I've written ~100 failed startup stories.

My goal? Run out of them.

Sounds backwards, right? Most creators want endless content. I want to exhaust my supply.

Every Sunday, I dissect another collapse. Style Theory's $25M implosion. eFishery's $1.4B collapse. Bluelearn returning 70% to investors.

Each story means another founder learned what not to do.

When I run out of failures to write about, it means founders stopped making the same preventable mistakes. No more burning through SoftBank millions on broken unit economics. No more chasing vanity metrics while burning cash.

The graveyard is already full. 90% of startups die from self-inflicted wounds.

But if just 100 founders avoid one critical mistake from reading these autopsies, that's 100 companies that survive their first crisis. 100 teams that keep their jobs. Millions in investor capital preserved.

Running out of content means winning.

Until then, I'll keep writing. Every collapse. Every scandal. Every preventable death.

Because the best education costs someone else $25 million, not yours.

Join 20,000+ founders getting these stories before they make the mistakes → https://lnkd.in/gdDrhgtq

P.S. The goal is making this newsletter obsolete. Until then, every Sunday at 8pm.

16 sushi restaurants. $21M in debt. $15.3M in fines.

Australia's biggest worker exploitation scandal just exposed how a "successful" chain was actually a house of cards.

Sushi Bay looked unstoppable. Korean immigrant Rebecca Shin built a conveyor-belt empire across NSW, Canberra, and Darwin. Customers loved the fresh, fast sushi. Investors saw steady expansion.

Behind the scenes? 163 migrant workers getting robbed.

The Federal Court just dropped the hammer:
• $653,129 stolen from staff wages
• Falsified records to hide the theft
• Workers forced to "pay back" their own wages
• Record $15.3M penalty (biggest in Australian history)

Here's the math: They saved maybe $600K by underpaying staff. The penalty? 25x that amount.

Everything collapsed when 2 former workers lodged complaints to the Fair Work Ombudsman about underpayment.

March 2022: Fair Work Ombudsman strikes.
February 2023: Court-ordered liquidations begin.
April 2023: 15 outlets dead.

The collapse was brutal:
• Multiple stores liquidated for unpaid taxes
• $21M total debt left behind
• Only 1 ghost store remains

Tonight at 8pm, I'm revealing the full Sushi Bay autopsy. Why nobody saw it coming. The 2 fatal mistakes that killed a 20-year empire.

The graveyard is full of "successful" chains built on exploitation.

Don't let yours be next.

Subscribe now to get the complete breakdown → https://lnkd.in/gdDrhgtq

P.S. Bad actors get caught. It just sometimes takes 20 years.

$400M valuation. Coinbase circling. Thai bank backing.

Then 95% of customer funds vanished overnight.

Zipmex promised 14% returns. They delivered Southeast Asia's most catastrophic crypto collapse.

Thai users moved funds to "Z Wallet" for higher yields. They thought they were using a regulated platform. Reality? Their money crossed to Singapore, then straight to Babel Finance and Celsius.

$53 million. Gone.

The Thai co-founder just got 5 years in prison. He allegedly moved customer assets offshore before updating the terms & conditions.

I just published the complete Zipmex autopsy. The 2 fatal mistakes. The offshore deception. Why "regulated" meant nothing when $53M disappeared.

Read the full story → https://lnkd.in/gv8AbzRz

P.S. If someone promises you 14% guaranteed returns in crypto, ask yourself: where's that money actually coming from? Zipmex users found out too late.

Profitable cloud kitchen. Major hotel contracts. Big-name clients like Lazada and Maybank.

Still had to sell. Still burned out.

Mojos Kitchen's founders were waking up at 2am to prep breakfast boxes. By 6am, hundreds of meals were out the door. Nasi lemak, chicken curry, Western omelettes — all from one kitchen.

They'd survived COVID. Pivoted from physical kiosks to cloud delivery. Built up to 1,000+ app users. Finally hit profitability in early 2022.

Then sold the whole thing.

Not because the business failed. Because the founders were done.

Shrayas Saranathan told me something that stuck: "We wanted to prove the model first — turn profitable, then pitch. The only thing that happened was that we got burnt out first before we could go for funding."

The F&B grind is brutal. You're competing on razor-thin margins while managing:
• Multiple cuisines simultaneously
• 2am wake-ups for hotel contracts
• Staff turnover that never stops
• Equipment breaking at the worst times
• Compliance costs eating profits

They bootstrapped with over RM 1 million of personal money. Every ringgit reinvested. Zero networking. Zero investor meetings. Just heads down, grinding.

By the time they needed help, they had no one to call.

My parents run a hawker stall. I've watched them work 14-hour days for a decade. F&B isn't just hard — it's relentless.

Mojos Kitchen proved their model worked. They just couldn't survive long enough to scale it.

Full founder interview and autopsy here → https://lnkd.in/gR_Ui2_p

P.S. If you're running F&B and still standing, you have my respect 🫡

I invested in beehiiv in 2024. Best decision I've made.

$32M annual revenue. 3 billion emails monthly. Creators earning $3M monthly on platform. All in 4 years.

Love the investor updates from Tyler Denk 🐝. Real numbers. Real progress. No bullshit.

I'm not just an investor — I'm a power user. The Runway Ventures runs on Beehiiv. Every newsletter. Every subscriber. Every dollar we make flows through their platform.

Tyler says they're building the operating system for the content economy.

Watch what he shipped last month:
• Digital products
• AI website builder
• Native podcasts
• Link-in-bio
• Full analytics suite

10 features. One day.

Most founders would call that their 2026 roadmap.

The playbook is simple:
• Watch creators juggle 7 different platforms
• Build everything native in one place
• Ship faster than anyone thinks possible

Half their team runs newsletters on Beehiiv. Tyler himself grew Big Desk Energy to 125,000 subscribers. They don't build for creators. They ARE creators.

TIME runs on Beehiiv. TechCrunch runs on Beehiiv. Arnold Schwarzenegger runs on Beehiiv.

While competitors debate features in product meetings, Beehiiv ships them before lunch.

The content economy needs its operating system. Someone's going to own it.

My money's on the founder who ships 10 features while others write one roadmap.

I love you beehiiv.

I tried Replit to build an MVP. I'm still processing what just happened.

20 minutes. Plain English. Working app. On my phone.

I've been skeptical of no-code tools for years. They promise the world, deliver toys. So when someone said "just try Replit", I rolled my eyes.

Then I had a problem that pissed me off.

We have 20,000 founders in our TRV community. When someone emails me asking for help, I spend hours trying to find the right founder with relevant experience. Manual search. Memory gymnastics. Usually give up.

Yesterday, I opened Replit.

Typed: "Build an app where founders can ask questions and get matched with the right founder based on their profiles, company stages, and experience."

I expected errors. Setup hell. "Please configure your API keys."

Instead? 20 minutes later, it was live.

The app:
• Analyzes 20,000 founder profiles automatically
• Matches questions to expertise in seconds
• Connects founders instantly

Zero code written. Zero.

I just described what I wanted. In plain English. Like texting a friend.

It connected to our Notion. No OAuth dance. No API documentation. Just logged in and it worked.

Deployment handled. Security built in.

The old way: Find developer. Negotiate price ($5K-10K). Write 20-page specs. Wait 6 weeks. Debug for another month. Still doesn't work right.

The new way: Get pissed. Type what you want. Ship it.

I'm still skeptical of most no-code tools.

But Replit? I don't even know what to call this.

Can it build the next Uber? No. Can it solve 80% of your operational headaches? Absolutely.

Every founder has that one operational nightmare that drives them insane. You know the one. The thing you've been "meaning to fix" for months.

Stop living with it.

Build your first app today → https://replit.com

Willis Wee sold Tech in Asia for 8 figures. Now he's spilling his biggest media startup fxckups.

November 27. Our FINAL Startup Fxckups event in 2025. And we're going out with a bang.

Willis built Tech in Asia from a university project into Southeast Asia's most influential tech media platform.

Joel Wong? Scaled from dropshipping streetwear to running 250+ influencer campaigns. Generated 7-figure revenue. 

The media/agency space is brutal. One wrong hire, one bad client, one misread metric — you're dead.

These founders survived. Most don't.

Forget polished panels. This is founders telling you exactly how they fxcked up so you don't have to.

Free dinner. Free drinks. Unfiltered truth about building in media.

26 founders are joining. Only 4 spots left. When they're gone, they're gone.

This is it. Our last one. Don't miss it.

Register now → https://luma.com/0774rm7j

P.S. If you're building anything content-related or in media space, you need to hear these stories. The graveyard is full of media startups that looked unstoppable.

P.P.S Oh, and don't forget to use "TRV20" for 20% off

Make something people want.

Tech in Asia

Helping Brands Scale With A Full-Funnel Influencer Strategy | Ex E-Commerce Brand Builder

Just read ALL LinkedIn influencer's predictions for 2026, so you don't have to:

OpenAI is dead.

AGI is dead. 

Em-dash? Em-dead

Economy? dead (tomorrow)

Employees? Automated

9 to 5 - somehow also dead?

996? Kills you 

Remote work is dead.

The office is however also dead.

SEO...dead, again. 

AIO start-ups... dead upon arrival

AI Wrappers in general ... oof dead

Seat-based pricing? dead

Pricing in general. Dead - Free is the new MOAT

ARR. Dead

Sales? Replaced by AI SDRs (who are also dead)

The EU? Dead (consent collected) 

Germany? Dead even before the EU 

Stay positive 🫶

They achieved profitability. Landed major hotel contracts. Then sold the company.

Because the founders were done.

Mojos Kitchen just proved that even profitable startups can fail when founders burn out.

Picture this: You're finally profitable after 3 years of grinding. Your Kuala Lumpur cloud kitchen serves thousands of meals daily. Lazada, Maybank, and a major international hotel chain are your clients.

You should be celebrating. Instead, you're waking up at 2am to pack breakfast boxes.

Gaurav Joshi and Shrayas Saranathan put in RM 1 million of their own money. Not investor cash. Their savings.

COVID killed their 8 kiosks across KL overnight. Revenue went to $0. Meat rotting in refrigerators for months.

They pivoted. Built a cloud kitchen. Climbed back to profitability.

But profitability came with a price:
• 2am starts for 6am hotel deliveries
• Producing Malay nasi lemak, Chinese noodles, Indian curry simultaneously
• Managing B2C app orders, corporate boxes, hospital catering all at once
• No time for their kids anymore

The brutal truth about bootstrapping in F&B:

They waited to raise after proving the model. Smart, right?

Wrong. By the time they proved it, they were too exhausted to pitch.

"We wanted to prove the model first — turn profitable, then pitch. The only thing that happened was that we got burnt out first before we could go for funding."

That's what Shrayas told me when I interviewed him.

Two critical mistakes killed them:
1. Zero networking while building (no investor relationships when they needed them)
2. Bootstrapping a capex-heavy business too long (RM 1 million hole they never climbed out of)

They had 4 interested buyers. 3 ghosted. 1 lowball offer they took just to escape.

Tonight at 8pm, I'm publishing my full interview with Shrayas and the Mojos Kitchen story. The hidden cost of bootstrapping RM 1 million in Malaysia's F&B scene that nobody talks about.

The business was winning. The founders were dying.

Get the complete breakdown when it drops → https://lnkd.in/gdDrhgtq

P.S. If you're bootstrapping in F&B, you already have my respect. But please, build your network before you need it.

74% of enterprise deals go to whoever responds first. Not the best product. Not the lowest price. The fastest.

I just ran a B2B enterprise sales masterclass with Ned Phillips for my private founder community. 10 tactical plays that actually close deals:

1. 𝗦𝘁𝗼𝗽 𝘀𝗲𝗹𝗹𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀
Spend 80% of calls listening. Only mention your product in the last 10 minutes — after they ask.

2. 𝗩𝗼𝗹𝘂𝗺𝗲 × 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 = 𝗥𝗲𝘃𝗲𝗻𝘂𝗲
Target: 3 sales meetings/day. That's 30 warm outreaches + 100 cold actions daily. Manual, personal, thoughtful. Not spam.

3. 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗳𝗶𝗻𝗱𝘀 𝗱𝗲𝗮𝗹𝘀, 𝗲𝗺𝗮𝗶𝗹 𝗰𝗹𝗼𝘀𝗲𝘀 𝘁𝗵𝗲𝗺
Post daily about problems you solve (not your product). Comment on prospects' posts. Follow up within 5 minutes of any inbound lead.

4. 𝗨𝘀𝗲 𝗮 𝗖𝗥𝗠 𝗼𝗿 𝗱𝗶𝗲 𝘁𝗿𝘆𝗶𝗻𝗴
HubSpot. Every deal needs a next action assigned. No next action = dead deal.

5. 𝗞𝗶𝗹𝗹 "𝗺𝗮𝘆𝗯𝗲" 𝗳𝗮𝘀𝘁
"No" gives you clarity. "Maybe" wastes months. Walk away if their pain isn't painful enough.

6. 𝗬𝗼𝘂𝗿 𝗰𝗵𝗮𝗺𝗽𝗶𝗼𝗻 𝗰𝗮𝗻'𝘁 𝘀𝗲𝗹𝗹 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹𝗹𝘆
Write their email to their boss. Draft their internal deck. Remove 100% of friction. They just forward and click send.

7. 𝗥𝗲𝗰𝗼𝗿𝗱 𝗲𝘃𝗲𝗿𝘆 𝗽𝗶𝘁𝗰𝗵
Watch yourself for 10 minutes daily. Track: % time talking, questions asked, objections fumbled.

8. 𝗗𝗮𝗶𝗹𝘆 𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲𝘀:
• Find 1 new lead
• Engage 1 warm prospect 
• Follow up 1 existing deal
• Post once on LinkedIn
• 10 total sales actions minimum

9. 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗯𝗲𝗮𝘁𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱
Ned's example: Someone sent him 5 curated running videos → instant meeting. Research manually. Send something personal.

10. 𝗦𝗽𝗲𝗲𝗱 𝘄𝗶𝗻𝘀 74% 𝗼𝗳 𝗱𝗲𝗮𝗹𝘀
Submit RFPs early. Respond same-day to emails. First to follow up usually wins.

The brutal truth? Most founders don't have enough pipeline. They're doing 10 outreaches when they need 100.

Enterprise buyers buy trust, speed, and likability. Not PowerPoints.

My private founder community gets you:
• Private WhatsApp community of vetted founders
• Quarterly private dinners with our members
• Concierge intros + 1-1 founder matching
• Monthly live masterclasses with top founders

Ready to level up? Apply to join:
https://tally.so/r/gDDeyN

P.S. Massive thanks to Ned Phillips for dropping these bombs. You're the GOAT in B2B enterprise sales 🐐

Willis Wee sold Tech in Asia for 8 figures. Joel Wong scaled to 7 figures, then built 250+ influencer campaigns. And they're about to spill their biggest media startup fxckups.

These aren't just any founders. They're the ones who actually survived the media bloodbath.

27 November. SUNRATE Singapore. We're hosting Startup Fxckups: Media Edition — where successful founders share the disasters that almost killed them.

This isn't your polished TED talk. No scripted success stories. Just raw, unfiltered truth about what it really takes to survive in media.

Willis built Tech in Asia from a university project into Southeast Asia's most influential tech media platform. Then sold it to SPH Media for an 8-figure exit.

Joel dropped out to build a streetwear brand. Scaled to 7 figures. Then started Social Jet Singapore - Influencer Marketing Agency — now running campaigns for the region's biggest brands with 1,500+ creators.

The media/agency space is brutal if you get it wrong. One bad pivot, one wrong hire, one missed trend — game over.

And right now, with AI eating content jobs and ad costs through the roof, the margin for error is zero.

That's why we're bringing them together. To share the expensive mistakes they made so you don't have to.

What you'll get:
• Their biggest fxckups + learnings
• The exact pivots that saved their companies
• The hiring disasters that almost ended everything
• How they scaled from $0 to 7-figure revenue (and finally got acquired)

Plus open mic session — share your own fxckups and learn from others in the trenches.

Dinner and drinks included. Capped at 50 people — serious operators only.

Use code TRV20 for 20% off → https://luma.com/0774rm7j

P.S. The last Startup Fxckups sold out in 3 days. 50 seats only. Once it's gone, it's gone.

SUNRATE

Social Jet Singapore - Influencer Marketing Agency

Which T-shirt would you wear? Design A or Design B.

I need your help.

After 2 years of building The Runway Ventures, we're finally making merch for our community. The designs are ready. But I'm stuck.

Both tested well internally. Both cost the same to produce. Both represent what we stand for — learning from mistakes to become better founders.

But I can only pick ONE.

You've been with me through ~100 startup failures. You get to decide what we wear.

Here's the deal:

Comment "A" or "B" below. Most votes wins. Takes 2 seconds. Seriously.

✌🏻

$15.3 million in fines. 163 migrant workers exploited. The biggest worker penalty in Australian history.

Sushi Bay went from a single Sydney outlet to 16 locations across 3 states. National brand. Packed restaurants.

Then the Federal Court found what was really happening.

$653,129 in unpaid wages. Falsified records. Workers forced to pay back part of their wages.

Fair Work Ombudsman Anna Booth:

"If you deliberately underpay migrant workers and try to cover it up with false or misleading records you will be found out and will pay a heavy price."

The price? Record-breaking penalties. $21 million in debt. Every outlet liquidated except one.

The full story reveals how cutting labor costs became a death sentence.

Read the Sushi Bay collapse → https://lnkd.in/g7V-t6F6

Every week I dissect startup failures so you don't repeat them.

HemansAI

Data & AI Consulting, Development, AI Leadership Training.

85% of AI products and projects fail.

The two biggest challenges businesses face with AI:

1. Estimating and demonstrating AI value [VPs, CEOs, product managers, and decision-makers]
2. Lack of talent and skills [AI engineers and data professionals]

Most business leaders fall into the first category.

Even if you’re skilled at your job, your team might lack confidence that the AI solution will deliver the desired outcomes.

It’s frustrating, right? You see others succeeding with AI, but your team your team is stuck planning, fixing, or juggling disconnected tools.

AI is supposed to bring automation, sharper insights, and smarter decisions.

But when you try to implement it, you encounter chaos:

- Lack of clarity on ROI
- Endless troubleshooting instead of progress estimation
- Too many disconnected systems to demonstrate AI value

It’s easy to wonder if AI will ever deliver what it promises for your business and career.

Now, imagine a future where AI doesn’t just work—it works seamlessly in your organization.

Your data isn’t just stored—it powers real-time insights, precise forecasts, and simplified processes.

Picture an AI strategy that works without constant troubleshooting—a setup that scales and brings results.

I’m an AI consultant, here’s how I can help you:

- the AI leadership coaching

It’s about creating an AI path that’s right for your business or your career with real results, not just ideas.

Let’s connect. we’ll talk about AI strategies, products, and career opportunities.
connect@himanshuramchandani.co

Himanshu Ramchandani

Most AI engineers can't explain why their agents fail in production

I reviewed 43 agent systems this quarter

- only 3 had proper error handling
- only 1 had memory that actually worked
- zero had correct guardrails implementation

Here's the production-ready architecture most engineers skip:

PHASE 1 - FOUNDATIONS

- stop calling APIs
- start building cognitive loops

↳ reAct (Thought → Action → Observation)
↳ structured outputs with Pydantic validation
↳ tool execution layers that don't hallucinate
↳ repos - langchain-ai/langgraph

PHASE 2 - COORDINATION

- single agents hit walls fast
- teams scale infinitely

↳ langgraph state machines for memory
↳ multi-agent orchestration patterns
↳ RAG that cites sources correctly
↳ frameworks: /crewAI, microsoft/autogen

PHASE 3 - PRODUCTION REALITY

- this is where 90% fail

↳ guardrails: guardrails-ai/guardrails, NVIDIA/NeMo-Guardrails
↳ human-in-the-loop for critical decisions
↳ state rollback when execution breaks
↳ cost monitoring and model switching

PHASE 4 - THE FRONTIER

- self-improving systems
- autonomous exploration

↳ tree-of-Thought reasoning: princeton-nlp/tree-of-thought-llm
↳ multi-agent debates for validation
↳ systems that learn from failures: OpenBMB/ChatDev

The complete roadmap with 8 production projects → see comments

Engineers shipping Phase 3 systems - $180k-$250k
Engineers stuck in tutorial hell - $80k-$120k

The architecture gap is a $100k salary gap

Which phase are you currently building?

Follow Himanshu Ramchandani for AI engineering that goes beyond tutorials


P.S. - This roadmap took 200+ hours - Now it's free. The implementation is where you prove mastery. Check the roadmap below. 

PPS: Last day for AI Agent Architect Bootcamp Registration, last cohort for this year.

AI Engineering & Leadership • Building an AI product studio and providing Development & Consulting services

AI Agent Architect Roadmap

YOU copying RAG tutorials from YouTube

But 90% fail in production

I spent 4 hours analyzing GitHub's top RAG repositories

Here's what actually works:

1) RAGFlow - 68.7k stars
production-ready engine with deep document parsing

2) RAG_Techniques - 23k stars
30+ advanced techniques with runnable code

3) LangChain/LlamaIndex
Still relevant, but basics only

Bonus one

4) Awesome-RAG
list of Retrieval-Augmented Generation (RAG) applications in GenAI

What separates winners from copypasters:

↳ they chunk semantically, not by character count

→ 150-word fixed chunks = context loss
→ semantic boundaries = 42% better retrieval

↳ they don't just embed and pray

→ add query rewriting
→ implement hybrid search (keyword + vector)
→ use re-ranking (cross-encoders, not just cosine)

↳ they test on YOUR data

→ generic benchmarks lie
→ test retrieval time <200ms
→ measure answer accuracy, not vibes

The brutal truth:

simple RAG = 5-10 likes on your demo
production RAG = actual users who don't complain


Clone the repos, test and break things

Then build what actually scales

Your chatbot shouldn't hallucinate more than your CEO (don't beat me for this 💀 )

Follow Himanshu Ramchandani for daily AI insights that ship.

P.S. Building RAG systems? Start with evaluation metrics first, then code. Join the FREE AI Agents production workshop today at 7:30 PM IST

Those who were asking what to build in AI. Here are 37 AI projects that'll actually get you hired:

1. Build your own sub-4-bit model quantization library
2. Build your own LLM serving layer with dynamic KV cache
3. Build your own intelligent dynamic batching system for inference
4. Build your own high-performance CUDA kernel library for sparse models
5. Build your own distributed checkpoint optimized file system
6. Build your own stateful AI agent framework with reflective memory
7. Build your own next-generation vector database indexer
8. Build your own RAG orchestrator with self-healing retrieval logic
9. Build your own LLM safety and alignment evaluation framework
10. Build your own universal lossless tokenizer converter tool
11. Build your own serverless, real-time feature store
12. Build your own domain-adaptive synthetic data generator
13. Build your own real-time prediction drift detection system
14. Build your own data version control integrated with feature lineage
15. Build your own adversarial attack defense and mitigation toolkit
16. Build your own automated fairness and bias auditing toolkit
17. Build your own GPU-accelerated explainability engine (XAI)
18. Build your own hardware-aware ML performance profiler
19. Build your own modular reinforcement learning environment
20. Build your own universal model format translator with optimization
21. Build your own multimodal vector database supporting text, image, and audio
22. Build your own vision transformer pre-training library optimized for video
23. Build your own 3D object reconstruction pipeline using neural radiance fields
24. Build your own code generation model from natural language commands
25. Build your own differentiable physics engine for robotics simulation
26. Build your own high-fidelity audio generation library using diffusion models
27. Build your own knowledge graph construction engine from LLM output
28. Build your own data leakage detection system for distributed training
29. Build your own secure model inference sandbox with confidential computing
30. Build your own watermarking system for AI-generated content
31. Build your own federated learning orchestrator with differential privacy
32. Build your own zero-trust model access control layer
33. Build your own Model Card automation tool for governance
34. Build your own system for quantifying model uncertainty in predictions
35. Build your own adaptive GPU scheduler based on convergence speed
36. Build your own serverless platform for rapid fine-tuning jobs
37. Build your own model compiler for specialized commodity hardware

PS: Created a repo: Build Your Own AI Everything
Anyone can contribute to it. By building any project.

You are not stuck. You are just comfortable being mediocre AI professional:

quit tutorial hell. stop copying code. read the paper. implement from scratch. understand backprop. actually understand it. maintain a learning log. write what you built today. deploy something broken. fix it in production. read Attention Is All You Need twice. train a model on your own data. your actual data. not MNIST. feel the GPU burn. watch loss curves at 3am. turn off autopilot. code without stackoverflow. think before you import. eat without scrolling. debug without chatgpt first. plan your architecture. draw it on paper. then on excalidraw. then build. organize your experiments. track every hyperparameter. take AI safety seriously. read the original transformer paper. act on ideas fast. find your niche. build in public. write technical blogs. save someone's training run. learn systems design. read research daily. create your own datasets. stay skeptical of benchmarks. refine your prompts. optimize for latency. act with rigor. help junior engineers. be humble. stop using tools you don't understand. follow your curiosity. craft your portfolio. learn MLOps properly. systemize your experiments. write code. write docs. write posts. write papers. iterate on feedback. leave Discord open less. talk to researchers. contribute to open source. visit AI labs. look for unsolved problems. experience production failures. then learn. sit with your confusion. build something real. embody first principles. what do you actually want to build? turn off notifications. experiment. break things. fine-tune models. fall in love with the problem. notice inefficiencies in pipelines. visualize attention maps. talk to domain experts with humility. don't trash other frameworks. be technical. be precise. become an engineer. cherry-pick your tools. discard the hype. failed experiments aren't failures. invite hard problems. accept when simpler works. read Karpathy. be contrarian. choose depth over breadth. do work that compounds. let problems obsess you. lose yourself in implementation. value compute time. experience the full stack.

what did I miss?

Follow me Himanshu Ramchandani for insights on AI that actually compound

Comfort is why most AI engineers stay mediocre

Day 30 of the 75 Hard AI Engineering Challenge is done

Half the cohort is thriving

The other half? Drowning in content they have never seen before

"Himanshu, this is too hard"
"Can we slow down?"
"I don't understand derivatives"

My response?

(this dialogue from KungFuPanda movie)

The engineers building production AI systems aren't comfortable

- they are learning RAG at midnight
- debugging embeddings at 5 AM
- implementing attention from scratch on weekends

discomfort = growth

Here's what I'm seeing from those who push through:

↳ week 1: confused
↳ week 2: frustrated  
↳ week 3: connecting dots
↳ week 4: building confidently

The struggle isn't a bug <> It's the feature

Real AI engineering isn't copying tutorials

It's understanding

- why your training explodes
- why your embeddings cluster weird
- why your inference is slow

45 days to go. The best part?

You're already different than Day 1.

Still accepting latecomers who want the challenge

Follow Himanshu Ramchandani for real AI engineering

Here are some updates:
↳ 75 Hard AI Engineering Challenge ($0) 
↳ AI Agents Bootcamp (Start at $0) - add 7 deployed Agents in your portfolio
↳ 30 Day AI Agents Challenge ($0) starting 2nd December
↳ AI Builder's Bootcamp (Launching in January 2026)
↳ 4 Hands-On Workshop(for professionals)/Webinar(for leaders) in November on AI Agents 
↳ Product Launch - Knowledge Engine at Dextar
↳ AI Leadership Accelerator - The Elite - beta version (DM for pre booking)
↳ Long form AI Engineering deep dives on substack (sharing this week)
↳ Dropping new episode of Humanly Foolish podcast (Ask Anything AI)

- more updates will share later


P.S. - Free AI Agents Workshop this weekend. Link in comments.

You are running local (on-premises) LLMs but don't know if they are actually good

- every AI engineer downloads Llama or Mistral
- runs them locally
- feels excited about - free inference

Then reality hits:

- is this output even correct?
- is GPT-4 actually better?
- am I just burning electricity for worse results?

Here's what nobody tells you:

Running local models without benchmarking is like driving blindfolded

You need three numbers:

↳ speed (tokens/second)
↳ quality (% correctness)
↳ cost (real cost per 1M tokens)

When I started the 75 Hard AI Engineering Challenge,
I thought local = always better

I was wrong

Real example from Day 32:

- mistral 7B locally: 35 tok/s, $1.50 per 1M tokens (estimated)
- gpt-4 API: 18 tok/s, $30,000 per 1M tokens (claimed)

For code generation?

- mistral: 67% correct
- gpt-4: 100% correct

The question is not "which is faster"
The question is "which solves MY problem"

For 100K tokens/month → use APIs
For 10M tokens/month → local wins
For sensitive data → local (on-premises) only

benchmarking:

Before:

- "this model feels slow"
- "the output seems wrong"
- "maybe I should try GPT-4"

After:

- "this model runs at 32 tok/s"
- "it's 89% accurate on my test set"
- "break-even is at 2M tokens/month"


Do this:

↳ pick 100 real examples from your work
↳ run through 3 different models
↳ measure speed, cost, quality
↳ choose based on data, not hype


Follow me Himanshu Ramchandani for daily AI engineering insights that actually matter

PS: Join the FREE 75 Hard AI Engineering Challenge [link in comments]

PPS: Learn AI Agents in the upcoming bootcamp, limited seats, FREE workshop on RAG  Agents today 7:30 PM IST

2026 AI Engineering predictions

1) most AI agents fail in production because we have no way to see why they fail and engineers spend most time manually checking outputs

2) RAG systems work great for 3 months then quietly get worse as embeddings drift and data goes stale

3) traditional monitoring tools show system healthy while the AI gives wrong answers because we can track uptime but not reasoning quality

4) bootcamp graduates with shipped projects are getting hired over CS graduates with perfect GPAs because portfolio beats pedigree when the field moves this fast

5) companies need people who can debug production AI systems not people who can explain transformer architecture

6) job posts now ask for agent orchestration and context architecture but nobody agrees what these terms mean yet

7) generic chatbots are becoming commodities while real money is in narrow deep solutions like AI for dental clinics or solar installers

8) small teams building with AI leverage are shipping faster than large traditional teams because less coordination overhead

9) fine tuning is coming back because RAG alone works for simple cases but companies wanting competitive advantage are training their own models

10) multi agent systems sound great in demos but coordination is brutal in production as every agent you add multiplies the failure points

11) context design matters more than I expected because how you structure knowledge and retrieve information determines reliability more than which model you use

12) EU AI Act compliance deadline is August 2026 for high risk systems and if you serve EU customers in healthcare finance or hiring this is real

some questions: 

13) will voice interfaces actually replace typing for coding because it seems unlikely

14) one person with AI agents really compete with 50 person teams maybe for simple products but skeptical for complex B2B systems

15) are degrees becoming worthless or are we in a temporary bootcamp hype cycle because probably somewhere in between (edtech 2.0)

16) I thought prompt engineering would be the key skill but turns out production reliability and system design matter way more

17) I underestimated how much time goes into data pipeline work because it is not glamorous but it is 60% of the actual job

18) I overestimated how fast companies would adopt agents because most are still figuring out basic RAG implementations

19) learn to build systems that work when you are not watching them because production AI needs different skills than prototype AI

20) understanding failure modes matters more than understanding architectures so learn what breaks and why

21) get comfortable with uncertainty because best practices do not exist yet and you will figure things out as you go

What do you see differently? Where do you think I am wrong?

Follow Himanshu Ramchandani for honest takes on AI engineering, not just hype

If you want to ship a production LLM app by 2025, you have less than 40 days so here's what you are going to do:

Stop calling OpenAI and praying it works

Your app breaks when their API goes down

You have zero control

Here's what production engineers actually do:

Build fallbacks

↳ openAI down? switch to anthropic
↳ both down? use your self-hosted model
↳ all automatic - users never notice

The math that changes everything:

- commercial API only:
$9,500/month at 50M requests

- hybrid approach (API + self-hosted):
$730/month at 50M requests

That's 92% savings

Companies like Notion and Shopify don't use one LLM

They use smart routing between multiple models

This is part of the Day 35 of the 75 Hard AI Engineering Challenge:

↳ multi-provider fallback system
↳ self-hosted model with vLLM
↳ cost optimization logic
↳ production deployment on AWS

This isn't theory

The difference between a demo and a product? 

reliability when things break

Build something that actually ships.


Follow Himanshu Ramchandani for real AI engineering.


PS: Build a fridge AI agent that will order groceries automatically, join the community and bootcamp waitlist.

You're watching everyone else build AI agents while you're still watching tutorials

You told yourself "next year"

But next year never comes

Right now? AI Agents are your unfair advantage

The difference between engineers who build and engineers who watch:

→ builders have 7 production agents in their portfolio
→ watchers have 47 bookmarked tutorials they never finished

Here's what hurts:

↳ that customer support role paying $140K? going to someone with agent experience
↳ that promotion you wanted? given to the engineer who automated incident management
↳ that freelance client paying $15K/project? hired someone who already built RAG systems

233 people are waiting against 60 seats - [4 days left]

In 4 weeks, you'll have:

↳ Customer support agent (cuts costs 30%)
↳ Financial analysis agent (10x faster research)  
↳ RAG Q&A system (the 2025 skill everyone's hiring for)
↳ Code reviewer (48 hours → 30 seconds)
↳ Sales qualification agent (4x team productivity)
↳ Incident management agent ($500K+ ROI)
↳ Content generation agent (5x output)

Not theory. Production-ready systems you can show employers tomorrow

Starts December 2nd, 7:30 PM IST

The engineers crushing it in 2025 are building their portfolios right now

Will you be one of them?

Secure your seat here: https://lnkd.in/gtHqDG4c

P.S. Free access to bootcamp orientation + 30-day AI Agents guide + recordings on Nov 30, 9 AM IST
Production Ready AI Agent workshop Satuday 9 AM IST FREE

I've never shared this before

6 months ago, a senior engineer got rejected for promotion

"You use AI tools. We need someone who can architect AI systems."

That's the gap in 2025

Companies don't want LLM users

They want AI Agent Architects

I'm launching the AI Agents Bootcamp December 2nd.

What you build:

↳ 7 production-ready agents in 4 weeks
↳ ReAct patterns & planning systems
↳ LangGraph state machines
↳ Multi-agent orchestration
↳ Production deployment with guardrails

Real results:

Mamta (Amazon): "made my AI engineering transition"
Saravanan: "went from mechanical to AI engineer"
Meena (Amazon): "hands-on approach changed everything"

FREE workshops before launch:

Nov 19: RAG Agents Production
Nov 26: Deploy FastAPI + Docker

189 waiting. 60 seats only.

Two paths:

1 - Join FREE workshops (link in comments) and get FREE AI Agents Guides
2 - Secure bootcamp seat now

Not for quick hacks.

For engineers ready to architect systems companies fight over.

♻️ Repost if you know someone stuck at "just using ChatGPT"

Follow me Himanshu Ramchandani for AI engineering that matters

STOP using LLM APIs like toys

You're calling OpenAI's API like it's a REST endpoint

It's not

Every junior dev I meet:

- hardcodes API keys in Python files
- no retry logic
- no fallback providers
- no token management

Then their app crashes in production and they blame the API

Here's the truth:

↳ they'll fail on you
↳ llm APIs aren't reliable
↳ anthropic will have outages
↳ openAI will hit you with rate limits

Your $4k bill? That's your retry loop gone wrong

But nobody tells you this because they're too busy showing you toy examples from documentation

Production AI engineering isn't about making API calls

It's about:

↳ circuit breakers
↳ cost optimization
↳ exponential backoff
↳ multi-provider fallbacks

Your GPT-4 call doesn't need GPT-4

Use GPT-3.5 and save 80% on costs

Your system crashes?

You didn't classify your errors properly

Stop copying tutorials

Start building systems that don't break when the API sneezes

Follow me Himanshu Ramchandani for production AI that actually works

PS: This is from the Day 30 guide of 75 Hard AI Engineering Challenge, Join the AI Agents Bootcamp is Live.

[Live Workshop] RAG Agents - Build a production-ready document Q&A system


What we are going to cover:
- Retrieval-Augmented Generation
- Embeddings and Vector Representations
- Vector Databases
- RAG Pipeline
- Hands-On Project

Date: 19th November, 2025 (Today)
Time: 7:30 PM IST

→ why vector DBs aren't enough
→ how persistent memory changes everything
→ what enterprise RAG actually needs


Register by joining the workshop inner circle group [link in comments]

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