Lifestyle Updates: Don Don Donki and some online grocery stores to display unit prices under expanded pilot

Source:

https://www.straitstimes.com/singapore/don-don-donki-redmart-and-some-online-grocery-stores-to-display-unit-prices-under-expanded-pilot

ChatGPT:

The Straits Times article is about Singapore expanding its supermarket unit-pricing pilot from 1 October 2026, so shoppers can more easily compare the real cost of products rather than just comparing the headline price of different-sized packages. (The Straits Times)

🛒 What is changing?

The first pilot ran from September–December 2025 at FairPrice, Sheng Siong, Prime, Cold Storage and Giant. CCS and CASE said consumer feedback was encouraging, particularly for comparing different brands and pack sizes. (CCCS)

The second phase is substantially broader:

Change	Previous pilot	Expanded pilot
Supermarket operators	5	7
Don Don Donki	❌	✅
RedMart	❌	✅
Online shopping	❌	✅
Product range	More limited	40 grocery categories
Duration	Trial	8 weeks per operator

From 1 October, FairPrice, Sheng Siong, Prime, Don Don Donki and RedMart start. Cold Storage and Giant follow from 1 November. (CCCS)

What exactly is "unit pricing"?

Instead of just:

Rice — $8.90

you might see:

Rice — $8.90
$4.45/kg

Or:

Cooking oil — $6.90
$0.69/100ml

The idea is that you don't have to calculate whether a 1.5kg $7.50 pack is actually cheaper than a 2kg $9.20 pack.

CCS specifies standard units depending on the product — for example, eggs can be priced per 100g, cooking oil per 100ml and leafy vegetables per item. (CCCS)

🥛 The expansion is quite significant

The pilot now covers more everyday products including:

• Rice, flour, bread, noodles and pasta

• Meat, poultry and seafood

• Milk, cheese and eggs

• Cooking oil and butter

• Fruits and vegetables

• Sugar, spreads and preserves

• Coffee and tea

• Soft drinks and other beverages

• Detergents, tissue, shower products and diapers

• Snacks and biscuits

• Soups, sauces, spices and salt

So this isn't merely a rice/oil/egg experiment anymore. It covers a much larger proportion of a normal family's grocery basket. (CCCS)

📱 The interesting part: RedMart

This is probably the most useful change for online shoppers.

RedMart and Sheng Siong will display unit prices online from 1 October. Other supermarket operators are expected to progressively introduce online unit pricing. (CCCS)

That means online shoppers should eventually be able to compare something like:

Brand A: $5.90 / 500g → $1.18/100g
Brand B: $8.90 / 1kg → $0.89/100g

without doing the maths themselves.

For people who frequently shop groceries online, this could actually be more useful than the physical-store labels.

---

💬 What are Singaporeans saying?

There's an important caveat here: the September 2026 expansion is extremely new, so I found much more substantive discussion around the original 2025 pilot than around the new ST article itself. Searches across Reddit, HardwareZone and indexed social results haven't produced a large body of meaningful discussion specifically about the 28 September announcement.

The existing discussion, however, is remarkably consistent.

Reddit: generally positive

The 2025 r/singapore discussion received strong engagement, with many commenters welcoming the idea.

One recurring sentiment was essentially:

finally, shoppers don't need to calculate price per gram/100g themselves.

Others specifically highlighted shrinkflation and imported products with awkward package sizes as areas where unit pricing is useful. (Reddit)

There was also a particularly practical observation: unit pricing can expose situations where a "30% off" smaller pack is still more expensive per gram than a larger pack that isn't on promotion. (Reddit)

HardwareZone: more sceptical about supermarket pricing practices

HardwareZone discussions aren't specifically about this new announcement, but they show why unit pricing could resonate with some shoppers.

There are longstanding complaints about:

• confusing shelf labels

• prices not matching the product

• electronic price tags not displaying information

• consumers needing to check prices through supermarket apps

• significant price differences between supermarkets.

One HardwareZone thread, for example, discusses an instance where an electronic price tag allegedly wasn't displaying a price after the product price had increased. Other users chimed in with their own experiences of supermarket price-tag problems. (HardwareZone Forums)

Another HWZ discussion illustrates the broader Singaporean habit of actively comparing supermarket prices across chains, down to individual products. (HardwareZone Forums)

So the underlying consumer behaviour already exists; unit pricing potentially makes that comparison much easier.

---

🔎 What people seem to like

Across the discussions surrounding the pilot, the strongest practical benefits are:

1. Makes promotions easier to judge

"20% off" doesn't necessarily mean better value.

Unit pricing lets you ignore the marketing headline and compare the actual cost.

2. Helps detect shrinkflation

If a product goes from:

500g → 450g

while remaining $5, the change in unit price becomes obvious.

This was specifically mentioned in the earlier discussions. (Reddit)

3. Makes house-brand comparisons easier

Instead of:

FairPrice brand $4.50
Brand X $5.80

you can compare the actual $/100g.

4. Particularly useful for families

Large families buying rice, milk, cereal, snacks, detergent, diapers, sauces etc. have many situations where pack sizes differ.

5. RedMart is potentially a big deal

Online grocery shopping normally makes this comparison surprisingly annoying because different products are presented in different sizes.

The new online unit pricing should make RedMart's search results considerably more useful for price comparison.

---

⚠️ But unit pricing doesn't automatically mean "cheapest"

This is the important distinction.

A unit price tells you:

How much you're paying per unit.

It doesn't tell you:

Whether you actually need the product.

For example:

1kg cereal — $10/kg

may be better value than:

500g cereal — $6/kg

but you're still spending $6 versus $10.

Likewise, a 2-for-$10 promotion might look attractive, but if you only need one, the unit price doesn't necessarily make the promotion economically better for your household.

And unit price doesn't account for:

• taste

• quality

• ingredients

• country of origin

• brand preference

• freshness

• wastage

• loyalty points

• cashback

• vouchers

• supermarket-specific promotions.

That's particularly relevant in Singapore because FairPrice/RedMart/Donki/etc. promotions, vouchers and cashback can materially change the effective price.

---

🧠 My read of the significance

I think the RedMart component is actually more interesting than the Don Don Donki component.

Physical supermarkets already allow you to pick up two products and calculate the difference. Online grocery shopping makes this much harder.

The progression is effectively:

2022 → Price Kaki
↓
2025 → physical supermarket unit-pricing pilot
↓
2026 → more supermarkets + more products
↓
2026 → online unit pricing begins

CASE says its Price Kaki app now covers more than 13,900 daily essentials, while this pilot is testing how unit pricing works directly at retailers. (CCCS)

So this is gradually moving Singapore toward a grocery-shopping environment where "How much does this actually cost per 100g/100ml/kg?" becomes visible at the point of purchase rather than something shoppers have to calculate themselves.

One important limitation

This remains a pilot, not a permanent mandatory nationwide requirement. Each participating operator is involved for eight weeks, and CCS says consumer feedback from the expanded phase will inform future decisions. (CCCS)

Bottom line: The reaction to the underlying idea is mostly positive, especially among price-conscious shoppers. The most useful consumer benefit is not that groceries become cheaper—it is that different pack sizes, "discounts" and potential shrinkflation become much easier to compare objectively. The big new test will be whether this works equally well online, particularly on RedMart.

Investing Updates: What to Expect in the Week Ahead (MU Earnings & NFP)

Source:

https://www.moomoo.com/community/feed/what-to-expect-in-the-week-ahead-mu-intc-meta-117342330748934?futusource=news_headline_topnews&report_id=1000248949&report_type=market&src=3&client_hour_clock=24&clientlang=2&clienttype=121&clientver=16.33.17808&is_visitor=0&main_broker=WwoJMTAwOApdCg%3D%3D&my_cid=103096561&sg_cid=103096561&skintype=3&us_cid=103096561&user_id=103096561&user_id_type=3&global_content=%7B%22promote_content%22%3A%22mm%3Afeed%3A117342330748934%22%2C%22invite%22%3A%22103096561%22%7D

ChatGPT:

Here’s the article + current investor/forum reaction as of 28 September 2026. The Moomoo page itself is currently returning a 403 when opened directly, but its indexed article text confirms the main thesis and I cross-checked the key events against company and market sources. (Moomoo)

1. What the Moomoo article is saying

The article’s central message is that this is an unusually important week for both AI stocks and the broader US market.

The three stocks it highlights are:

• Micron (MU) — earnings Wednesday

• Intel (INTC) — AI/chip-sector momentum and recent rally

• Meta (META) — AI/Muse and upcoming product developments

But the article isn't only about those three. The other major event is the US September jobs report (NFP), which is a key input into expectations for interest rates.

The week's major catalysts

Date	Event	Why investors care
Sep 29	JOLTS + consumer confidence	Early read on employment/consumer strength
Sep 30	Micron earnings	Major test of AI memory demand
Sep 30	US PCE inflation	Fed's preferred inflation gauge
Oct 1	ISM manufacturing	Industrial/economic health
Oct 2	Nonfarm payrolls	Key labour-market/rates signal

Micron has officially confirmed that its fiscal Q4 results will be released 30 September 2026 after the market close. (Micron Technology Investors)

So essentially:

MU tells us whether AI memory demand remains exceptionally strong; PCE/NFP tell us about rates; META/INTC provide additional AI-demand signals.

---

2. Micron is the biggest event

This is the part I'd pay the most attention to if you're following the AI/chip trade.

Micron has become one of the clearest beneficiaries of the AI infrastructure buildout because AI accelerators require enormous amounts of memory, particularly HBM (High Bandwidth Memory).

S&P Global describes HBM as increasingly important to AI accelerators because it provides high bandwidth while reducing power consumption. (S&P Global)

The market therefore isn't just interested in:

"Did Micron beat earnings?"

It wants to know:

① How strong is HBM demand?

② How long can memory pricing remain elevated?

③ Can Micron maintain extremely high margins?

④ What does management say about 2027 demand?

That last point may actually be more important than the quarter that just ended.

---

3. The online Micron discussion is extremely bullish — but also nervous

This is where the social-media reaction gets interesting.

On r/MU_Stock, investors are debating whether Micron can deliver another huge beat.

One current thread has users discussing whether revenue could reach $60B, while others warn that expectations have already been raised dramatically. (Reddit)

Another thread argues that the important issue isn't simply another increase in DRAM pricing, but whether Micron can maintain very high gross margins and generate enormous free cash flow. (Reddit)

And there is a very noticeable:

"Great earnings may already be priced in."

argument.

That's important.

The bull case is:

AI demand → HBM shortage → higher pricing → huge Micron earnings → higher guidance.

The bear/caution case is:

Everyone already knows this → expectations become unrealistic → even excellent earnings could trigger profit-taking.

The current options discussion illustrates just how much anticipation there is around the event, including a reported $16.7 million call-option bet expiring shortly after earnings. (Reddit)

---

4. The Michael Burry factor

This has become another major talking point.

Michael Burry has reportedly increased his bearish position on Micron, arguing that additional memory supply could eventually undermine the current shortage.

That has created an interesting online battle:

Burry thesis:

Memory is cyclical → supply eventually increases → current margins aren't sustainable.

versus

MU bulls:

AI has fundamentally changed memory demand → HBM demand is structural → this isn't the old Micron cycle.

Reddit is particularly entertaining here because the discussion has effectively become "Burry vs the MU bulls." (Reddit)

The important analytical point is that both sides can be right at different times: Micron can have extraordinary earnings today while the memory cycle eventually normalises.

---

5. Intel: completely different story

Intel isn't primarily an earnings-week story in this article.

The Intel story is about a dramatic turnaround in investor perception.

Intel has benefited from:

• AI infrastructure demand

• stronger CPU demand

• progress on its 18A manufacturing process

• foundry ambitions

• Nvidia's strategic investment

• broader enthusiasm for US semiconductor manufacturing

Intel's shares recently pulled back after a major rally, with reports attributing the decline partly to profit-taking and concerns that valuation had moved ahead of near-term fundamentals. (tradingkey.com)

This is the crucial distinction:

MU

"How enormous can the earnings become?"

INTC

"Can the turnaround actually justify the new valuation?"

---

6. Intel has become a social-media momentum stock

This is quite striking.

Intel was traditionally viewed as a struggling legacy semiconductor company.

But 2026 investor discussion has increasingly shifted toward:

Intel = US semiconductor manufacturing + AI infrastructure + foundry turnaround.

Intel was among the stocks generating significant WallStreetBets attention during recent rallies, alongside MU and META. (Yahoo Finance)

There is still a substantial skeptical camp, however.

The argument is basically:

"The stock has run much faster than the fundamental turnaround."

That's why Intel is potentially more sensitive to valuation/profit-taking than Micron.

---

7. Meta is the third piece

Meta's story is different again.

The catalyst currently attracting investors is Muse, its new AI personal-agent initiative.

Recent reports say investor enthusiasm around Muse helped push META shares higher, with the stock gaining strongly during the week. (ABS-CBN)

The bullish argument is that Meta could eventually turn AI from a gigantic cost centre into another source of revenue.

Potential monetisation includes:

• AI assistants

• advertising

• subscriptions

• smart glasses

• AI-powered commerce

• AI services

There is also the possibility of Meta monetising its enormous AI infrastructure investment more directly.

---

8. META's investor debate is different from MU

For Meta, the question isn't:

"Does AI demand exist?"

Everyone already knows Meta is spending heavily on AI.

The question is:

"Will the enormous AI spending eventually produce enough additional revenue/profit to justify the investment?"

That makes Meta's upcoming product announcements and Muse adoption particularly relevant.

Recent market commentary notes that enthusiasm around Muse has helped revive the META story, while investors remain focused on how the company's enormous AI spending translates into monetisation. (Barchart)

---

9. What Reddit is saying overall

The Reddit discussion is surprisingly concentrated around MU, rather than treating all three stocks equally.

The current r/WallStreetBets weekly earnings thread explicitly singles out MU as the stock people are watching most closely for the coming earnings period. (Reddit)

And broader Reddit activity supports this.

For the week of 18–25 September:

• META: 549 mentions

• MU: 315

• INTC: less prominent but still heavily discussed

according to a tracker of WallStreetBets discussions. (WSBTracker)

Another cross-platform tracker for 14–20 September had:

• META: 119 mentions

• MU: 92

• INTC: 67

with bullish commentary outweighing bearish commentary for all three during that period. (Quantral)

So MU/META/INTC are all part of the current AI-stock conversation, but MU is the immediate earnings obsession.

---

10. HardwareZone / Singapore angle

I searched specifically for current HardwareZone discussion around MU/INTC/META and this particular week.

There isn't a substantial indexed HardwareZone thread directly discussing this specific Moomoo article.

That's quite different from Reddit, where there are dedicated MU communities and active earnings threads.

For a Singapore investor, however, the implications are fairly straightforward:

MU

Singapore investors who already own US AI/semiconductor stocks are watching whether Micron's results confirm that the memory shortage is still accelerating.

INTC

The question is whether Intel's huge 2026 rerating can continue to be supported by actual operational improvements.

META

The question is whether AI monetisation can justify Meta's massive infrastructure spending.

---

11. X / Facebook / Instagram / TikTok / Threads

I also searched these platforms for publicly indexed discussion.

There is a limitation here: X, Facebook, Instagram, Threads and TikTok expose considerably less searchable public conversation than Reddit, and TikTok's public search is particularly difficult to index reliably.

So I would not describe the absence of searchable posts as absence of discussion.

The publicly visible conversation is broadly consistent with the Reddit/market discussion:

MU → earnings + memory shortage

INTC → turnaround + AI/foundry + momentum

META → Muse + AI monetisation

But Reddit provides by far the clearest detailed investor discussion at the moment.

---

12. The bigger story: AI is moving down the supply chain

This is actually the most interesting thing about the three stocks.

Think of the AI ecosystem as:

Nvidia/AMD GPUs

↓

HBM / memory → Micron

↓

CPUs / infrastructure → Intel

↓

AI applications / monetisation → Meta

So you're looking at three different layers of the same AI investment cycle.

Company	AI role	Main question
Micron	Memory/HBM	Is AI memory demand sustainable?
Intel	CPUs/foundry	Can Intel execute its turnaround?
Meta	AI applications/platform	Can AI generate enough revenue?

That makes the week unusually interesting because the three companies test different parts of the AI thesis.

---

13. The biggest risk the article doesn't make obvious

There's a potentially nasty setup here:

Expectations are extremely high.

Micron has already experienced an extraordinary rerating, and Intel and Meta have also attracted strong AI enthusiasm.

That creates a classic market situation:

Good news ≠ stock goes up.

The market needs:

Good news + better-than-expected news + higher future expectations.

That's why the MU Reddit discussions repeatedly return to the idea that even spectacular earnings might not be enough. (Reddit)

And this is particularly important for Micron because the stock has already risen dramatically in 2026; recent market commentary has highlighted the extraordinary run-up ahead of the September 30 earnings report. (24/7 Wall St.)

---

14. My condensed takeaway

If I boil the whole article and online discussion down to one dashboard:

🧠 AI demand

Still very strong

💾 Micron

The week's biggest fundamental test

Watch:
HBM demand + DRAM pricing + gross margin + forward guidance

🏭 Intel

Turnaround/momentum story

Watch:
18A execution + foundry customers + AI/CPU demand + valuation

🤖 Meta

AI monetisation story

Watch:
Muse adoption + AI advertising + capex + new revenue streams

📊 Macro

PCE + NFP

Watch:
inflation + employment → interest-rate expectations → tech-stock valuations

💬 Social sentiment

MU: extremely bullish but increasingly nervous about expectations

INTC: bullish momentum mixed with "has it run too far?"

META: bullish on Muse/AI monetisation, but questions remain over massive AI spending

---

One thing I'd watch especially closely

For MU, don't just look at whether earnings beat estimates.

I'd pay much more attention to management's forward guidance and commentary on HBM/memory supply and margins.

That is also where the more sophisticated Reddit discussion is moving: the debate is no longer simply "Will Micron beat?" but "How sustainable is this extraordinary earnings/margin environment?" (Reddit)

And that's the real investment question behind the Moomoo article: is the AI memory boom becoming a structural change in the semiconductor industry, or are investors extrapolating an unusually strong part of the memory cycle too far? S&P Global's analysis confirms that AI/HBM demand is currently a major driver of the memory market, but that doesn't by itself establish how long today's economics will last. (S&P Global)

Technology Updates: Low on code, high on miles: S’porean uses AI to create tool to maximise credit card rewards

Source:

https://www.straitstimes.com/tech/low-on-code-high-on-miles-sporean-uses-ai-to-create-tool-to-maximise-credit-card-rewards

ChatGPT:

This article is particularly relevant to Singapore’s miles/card community because it describes KiasuMiles, an AI-powered tool built specifically around the problem of remembering which card to use at which merchant.

1. What the Straits Times article says

The Straits Times article was published on 26 September 2026 and profiles Hosan Swee, a Singaporean engineer and AI educator who built KiasuMiles despite not having traditional software-development skills. (The Straits Times)

His problem was familiar to serious miles chasers: he and his wife had around a dozen cards, each with different:

• bonus categories

• minimum-spend requirements

• monthly caps

• exclusions

• payment-method rules

• merchant/MCC treatment

Remembering all of this at the point of payment became cumbersome.

His solution was to build an AI-powered recommendation system that answers questions such as:

Which card should I use at Cold Storage with Apple Pay?

or

Which card should I use for GrabFood?

The important difference from simply asking ChatGPT

KiasuMiles isn't intended to be a generic chatbot that guesses an answer.

It has a structured database of Singapore credit-card rules, currently covering roughly:

• 50 credit cards

• 3,300+ merchants

The system matches the user's cards against the merchant, spending category and payment method, while taking into account caps and conditions. (The Straits Times)

For example, the article uses Cold Storage and says KiasuMiles may identify the DBS yuu card, while also warning about its conditions such as the monthly spending requirement and participating yuu merchants. (The Straits Times)

---

2. The really interesting part: AI did the coding

This is arguably the bigger story than the miles tool itself.

Swee had the idea but wasn't a conventional programmer. He used:

• ChatGPT

• Codex

• ChatGPT Work

to turn natural-language instructions into working software.

One of his prompts essentially told the AI to build a structured knowledge base containing:

card → earn rate → minimum spend → categories → caps → exclusions.

The AI then gathered information from bank and merchant websites and structured it.

He says something that is quite important for the current AI era: the AI is not replacing the need to think about the product; it is dramatically reducing the cost of implementation. (The Straits Times)

He also built it incrementally rather than asking AI to create everything in one giant prompt, testing the recommendation logic with realistic scenarios before expanding it.

---

3. Why KiasuMiles is potentially more useful than asking ChatGPT directly

This is the strongest technical point in the article.

A normal ChatGPT question might be:

"What's the best card for Cold Storage?"

The problem is that a general-purpose AI may have:

• outdated card rules

• incomplete information

• incorrect MCC assumptions

• forgotten spending caps

• incorrect exclusions

• incomplete payment-method rules

KiasuMiles instead maintains a specialised knowledge base and uses that to produce the recommendation. (The Straits Times)

The current KiasuMiles site also says it returns the card from your own wallet, rather than simply recommending the theoretically best card available in Singapore. (KiasuMiles)

That's an important distinction.

---

4. What KiasuMiles actually looks like now

The product has evolved beyond the description in the newspaper article.

Its current site describes a workflow where you connect it to a compatible AI agent and tell it which cards you own. It then answers:

Merchant + payment method → your eligible cards → best card + conditions + fallback. (KiasuMiles)

Its open-source GitHub project describes the same concept as a Singapore credit-card rewards optimisation MCP. (GitHub)

The project's published architecture is interesting because the hosted service says it doesn't require card numbers, expiry dates, CVVs or banking credentials. It works with card names and reward rules rather than payment credentials. (GitHub)

The current project reports 48 cards and about 3,308 merchants, which is broadly consistent with the figures reported by the article. (GitHub)

---

5. What the online miles community is saying

I searched specifically for discussion around KiasuMiles + the article, rather than treating general Singapore credit-card discussions as reactions to this particular story.

Important caveat

The article is only a couple of days old, so there isn't yet a huge amount of independently indexed discussion.

I could find much more substantive material around KiasuMiles itself than around the article on Facebook/Instagram/X/Threads/TikTok.

That means I wouldn't claim there is already a broad social-media consensus.

The reaction so far is better understood through several themes.

---

🟢 Theme 1: “This is exactly the problem miles hackers have”

This is the most obvious appeal.

Singapore's credit-card ecosystem has become sufficiently complicated that even experienced miles collectors can make mistakes.

The creator himself explains that the losses aren't necessarily one giant mistake; they're lots of small incorrect taps over time. His own earlier write-up describes discovering that he was sometimes using the wrong card despite following the miles community. (Hosan's Substack)

The current KiasuMiles product page makes the same argument visually:

many cards → one cashier → a few seconds to decide. (TinkerHaus)

That is a very Singapore-miles-community problem.

---

🟢 Theme 2: The interesting innovation isn't “AI recommends a credit card”

This is where I think the article has more significance than it initially appears.

People have been asking ChatGPT questions like:

"Which card gives the most miles for dining?"

for a while.

The more interesting idea is:

AI + structured data + personal wallet + merchant database

rather than simply:

AI chatbot + question

The current KiasuMiles architecture even breaks the recommendation into steps such as:

identify merchant → determine MCC → filter user's wallet → rank eligible cards → return recommendation. (TinkerHaus)

That is much closer to an actual decision-support system than a chatbot.

---

🟡 Theme 3: Accuracy is going to be the biggest concern

This is the biggest weakness that experienced miles users are likely to focus on.

Credit-card rewards change constantly.

Banks can change:

• earn rates

• minimum spend

• caps

• exclusions

• MCC eligibility

• wallet/payment-method treatment

• merchant partnerships

So the question isn't merely:

"Can AI build the database?"

It's:

"Can the database stay correct?"

KiasuMiles says it regularly updates its rules, and the creator explicitly warns that simply asking a general AI chatbot can produce incomplete or outdated information. (The Straits Times)

That makes data maintenance and verification arguably more important than the AI itself.

---

🟡 Theme 4: MCC/payment-method edge cases remain difficult

This is an especially important issue for Singapore miles collectors.

For example:

Merchant → payment method → payment processor → MCC → card rules

can all affect the actual earn rate.

Apple Pay isn't necessarily equivalent to every other payment method.

Online transactions aren't necessarily equivalent to physical transactions.

A merchant's actual MCC may differ from what consumers expect.

The KiasuMiles approach attempts to explicitly incorporate merchant category and payment method rather than simply saying "use Card X for groceries." (The Straits Times)

That is probably one of the strongest aspects of the idea.

---

🟢 Theme 5: Open-source makes it more interesting

The project is publicly available on GitHub under an MIT licence. (GitHub)

That potentially allows developers/miles enthusiasts to:

• inspect the implementation

• identify bugs

• contribute improvements

• see how the recommendation engine works

• build their own integrations

The creator's Tinkerhaus page describes it as an open-source AI card-recommendation skill. (TinkerHaus)

That gives it more credibility than a mysterious black-box "AI miles optimiser."

---

6. Facebook / Instagram / X / Threads / TikTok

I specifically searched these platforms for the article and KiasuMiles.

The indexed public discussion is currently quite thin, especially compared with Reddit and specialist Singapore miles communities.

That's important because social-media search engines often don't expose all posts, especially:

• private Facebook groups

• Instagram comments

• Threads posts

• TikTok comments

• X posts behind indexing/login restrictions

So I wouldn't interpret the lack of indexed posts as "nobody is interested."

What is visible points to the story being picked up primarily as an AI/product-building story, rather than becoming a major controversy around credit-card rewards.

---

7. The bigger discussion this article has triggered

I think there are actually two stories hidden inside this article.

Story A — Miles

Singapore's credit-card optimisation game is becoming so complicated that there is now a need for a dedicated AI decision engine.

That's a sign of how sophisticated the local miles ecosystem has become.

Story B — AI

A non-traditional programmer can now go from:

"I wish there was a tool that did this"

to

a functioning specialised financial application

using AI coding tools.

That second story is arguably more significant.

The creator himself says that learning basic software-development terminology improved his ability to communicate with AI. In other words, you don't necessarily need to become a professional programmer; you need enough technical understanding to direct, test and challenge the AI. (The Straits Times)

---

8. My overall read of the article

I'd summarise it this way:

The article isn't really about getting more miles. It's about the transition from "AI answers questions" to "AI operates specialised decision systems."

KiasuMiles takes a particularly Singaporean problem — "Which of my 8–12 credit cards should I tap right now?" — and turns it into a structured AI workflow.

The strongest parts are:

• Personalised: only considers cards you actually own.

• Merchant-specific: rather than generic category recommendations.

• Condition-aware: considers caps/minimum spends.

• Payment-aware: attempts to account for payment method.

• Structured: relies on a maintained database rather than pure chatbot memory.

• Open-source: implementation can be inspected. (KiasuMiles)

The biggest thing I'd watch is data accuracy. A miles tool can be 95% right and still give you a wrong recommendation at exactly the merchant where the remaining 5% matters. Credit-card T&Cs also change frequently.

---

And there's a funny Singapore angle

The name KiasuMiles is actually quite appropriate.

The traditional miles enthusiast workflow is:

carry 8 cards → remember 20+ rules → check Telegram/Reddit/blogs → remember monthly caps → remember MCC → stand at cashier → panic → tap card → later check whether you got the right miles.

KiasuMiles tries to turn that into:

"I'm at Sheng Siong. Which of my cards?" → one answer.

Its own current demo illustrates this with merchants such as Sheng Siong and Shell, showing the recommended card, earn rate, cap and fallback. (TinkerHaus)

Given that you already use a fairly sophisticated Singapore miles stack involving Citi Rewards, UOB cards, Amaze/HeyMax etc., this is actually much closer to your real-world use case than a generic credit-card comparison site.

News Updates: Singapore now a super-aged society, with 21.4% of citizen population aged 65 and older

Source:

https://www.straitstimes.com/singapore/singapore-now-a-super-aged-society-with-21-4-of-citizen-population-aged-65-and-older

ChatGPT:

Here’s a combined article + social/forum reaction summary, with the important distinction between the official demographic facts and what commenters are speculating about.

1. The article in a nutshell

The Straits Times article reports that Singapore has crossed the “super-aged society” threshold, with 21.4% of its citizen population aged 65 and above in 2026, compared with 20.7% in 2025 and only 13.7% in 2016. The UN benchmark is 21%. (The Straits Times)

The bigger story is that ageing is accelerating while births remain extremely low.

Key numbers

Indicator	Latest figure	What it means
Citizens aged 65+	21.4%	Crossed “super-aged” threshold
Citizens aged 65+ in 2016	13.7%	Huge increase over 10 years
Citizens aged 80+	152,000	Up 60% from 95,000 in 2016
Expected 65+ by 2035	>25%	More than 1 in 4 citizens
Citizens aged 20–64	59.3%	Down from 64.3% in 2016
Working-age : 65+ citizens	2.8 : 1	Down from 4.7 : 1 in 2016
Projected ratio in 2035	2.2 : 1	Further pressure on working-age population
Median Singaporean age	44.1	Up from 43.7 in 2025
Resident TFR	0.87	Extremely low fertility
Citizen births in 2025	26,071	Down 10.8% from 2024

The official SingStat figures similarly show the resident population's 65+ share rising from 12.4% in 2016 to 19.5% in 2026. (Singapore Department of Statistics)

One important technical point

The 21.4% figure refers specifically to citizens, not everyone physically living in Singapore.

That's significant because Singapore's definition of “super-aged” for this announcement uses the citizen population, whereas the UN definition normally considers the broader population. Including PRs, Singapore's resident 65+ proportion is 19.5%, so the terminology needs some care. (Singapore Department of Statistics)

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2. The second major issue: very low births

The ageing problem isn't occurring in isolation.

In 2025:

• Citizen marriages fell 5.6%

• Citizen births fell 10.8%

• Resident TFR fell from 0.97 to 0.87

• First-time mothers' median age increased from 30.3 in 2015 to 31.9

• Singapore's population of singles increased across most age groups. (HardwareZone Forums)

So Singapore is simultaneously experiencing:

more elderly people + fewer babies + people marrying later + fewer working-age people relative to elderly people.

That combination is what makes the demographic issue more consequential than simply saying “Singaporeans are living longer.”

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3. Immigration is the other big part of the story

This is where the online discussion becomes much more heated.

Singapore's total population reached 6.21 million in June 2026, up 1.6%.

But the components moved very differently:

• Citizens: 3.68m, +0.7%

• PRs: 0.55m, roughly stable

• Non-residents: 1.98m, +3.7%

The increase in non-residents was mainly driven by work-permit holders, particularly for construction and infrastructure projects. (The Straits Times)

Singapore also granted 25,094 citizenships and 35,352 PRs in 2025. The government's report explicitly says immigration helps moderate the effects of ageing and low birth rates and prevents the citizen population from shrinking over the long term. (HardwareZone Forums)

This is the part that generated the most debate online.

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4. HardwareZone reaction

The HardwareZone Forums thread is particularly active, and the tone is considerably more cynical than the official report.

HardwareZone discussion thread

I would group the discussion into roughly five themes:

A. “This is going to get much worse”

Some commenters focus on the trajectory rather than today's 21.4%.

They point out that the proportion is expected to exceed 25% by 2035, while the working-age/elderly ratio continues falling. There are comments predicting that today's 30s and 40s will have difficulty achieving a conventional retirement.

That's an opinion rather than something established by the demographic data, but the underlying concern is based on a real trend. (HardwareZone Forums)

B. Retirement and CPF anxiety

A surprisingly large portion of the thread turns into discussion about:

• CPF

• retirement age

• CPF withdrawal age

• adequacy of retirement savings

• healthcare costs

• GST

• whether younger Singaporeans will be able to retire comfortably

Some comments sarcastically suggest that CPF withdrawal ages will keep increasing.

Those are commenters' fears/jokes, not announced policy changes. The demographic report itself does not say that CPF withdrawal rules will be changed. (HardwareZone Forums)

C. Immigration becomes the central argument

This is probably the biggest HWZ theme.

One camp essentially argues:

Without immigration, Singapore would face an even sharper demographic decline.

Another camp argues that immigration merely delays the underlying problem because immigrants eventually age too.

The interesting thing is that both sides are responding to a genuine feature of the government's demographic strategy: the official report explicitly says immigration is helping to moderate ageing and low birth rates. (HardwareZone Forums)

So the disagreement isn't really about whether immigration exists as a demographic tool; it is about whether it is a sustainable long-term solution.

D. “Why are we still talking about population growth?”

Another recurring reaction is essentially:

If Singapore is becoming old, why does the population keep increasing?

This comes from the fact that Singapore simultaneously has:

6.21m total population + 21.4% of citizens aged 65+ + extremely low fertility.

That seems contradictory at first, but it isn't. Population growth is currently being driven substantially by the non-resident population, while the citizen population itself is ageing. (The Straits Times)

E. Cost of living / fertility

Some HWZ commenters connect low fertility with:

• housing costs

• raising children

• childcare

• general cost of living

• career pressures

That is a popular interpretation, but it's important not to treat it as proven causation. The demographic report establishes that fertility is falling; it doesn't establish that any single factor explains the decline.

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5. Reddit reaction

The Singapore Reddit discussion is somewhat more analytical than the HWZ thread, although there is still considerable cynicism.

(Reddit)

The strongest recurring themes:

1. Immigration vs natural population ageing

Several commenters noticed that immigration is helping keep the working-age population from falling as quickly.

Others argue that this simply postpones the problem because immigrants eventually become older too. (Reddit)

2. Caregiving is a major concern

People raise the possibility of Singapore eventually requiring substantially more:

• nurses

• healthcare workers

• caregivers

• foreign domestic workers

This is particularly relevant because the 80+ population has increased 60% in a decade. (The Straits Times)

3. Japan comparisons

A number of commenters compare Singapore's trajectory with Japan, particularly around:

• elderly-heavy communities

• shortages of caregivers

• reliance on foreign workers

• shrinking younger generations

That's a comparison rather than a prediction that Singapore will follow Japan's exact path.

4. “Who will pay for everything?”

Another recurring concern is the economic burden created by a shrinking ratio of working-age people to elderly people.

That concern has a legitimate demographic basis: the ratio has fallen from 4.7 working-age citizens per elderly citizen in 2016 to 2.8 today, and is projected to reach 2.2 in 2035. (CNA)

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6. What I found across other social media

I searched for public/indexed discussion on Facebook, Instagram, X and Threads as well as Reddit and HardwareZone.

The publicly indexed results for Facebook/Instagram/X/Threads are much less comprehensive than Reddit/HWZ, so I wouldn't pretend that I can measure sentiment on those platforms accurately.

The strongest searchable discussion is currently on:

1. HardwareZone — highly cynical, immigration/CPF/cost-of-living focused.

2. r/singapore — more demographic/economic discussion, particularly immigration and caregiving.

3. r/worldnews — broader international reactions, often focusing on ageing Asian societies, housing and labour. (Reddit)

The Reddit world-news thread is particularly interesting because the discussion shifts from Singapore specifically to a broader question: what happens when many wealthy Asian countries simultaneously experience ultra-low fertility and rapid ageing? (Reddit)

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7. What I think is the most important takeaway from the article

The headline “21.4% are 65+” actually understates the bigger structural change.

The more important numbers are:

13.7% → 21.4% elderly share in 10 years

and

4.7 → 2.8 working-age citizens per elderly citizen

and

0.87 fertility rate

and

152,000 people aged 80+

Those four figures together describe a much more significant demographic transformation than simply crossing an arbitrary 21% threshold. (Singapore Department of Statistics)

At the same time, there is an important counterpoint: Singapore isn't simply heading toward an uncontrolled population collapse. The government expects the 20–64 population share to remain broadly stable through 2035, partly because younger cohorts are entering the working-age population and through managed immigration. (CNA)

So the picture is more accurately:

Singapore is ageing rapidly → births aren't replacing the population → immigration is being used to moderate the demographic/worker shortage → but the underlying citizen-ageing trend continues.

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8. What this could mean for ordinary Singaporeans

The demographic shift potentially affects five areas particularly strongly:

• 🏥 Healthcare: greater demand for chronic disease management, hospitals, nursing and eldercare.

• 👷 Labour: greater reliance on productivity improvements, automation and foreign workers.

• 💰 Retirement: individuals may need to plan for longer retirements and potentially higher healthcare expenses.

• 🏠 Housing: more households will eventually involve elderly singles/couples rather than families with children.

• 👨‍👩‍👧‍👦 Family structure: fewer children will increasingly have to support ageing parents.

There is also an economic opportunity. Health, senior living, insurance, retirement products, wellness, tourism and technology aimed at older consumers could become increasingly important. Singapore's health minister has previously described this as part of a potential “longevity economy.” (Reddit)

Bottom line

The online reaction is substantially more pessimistic than the official demographic presentation. The data itself is unambiguously showing rapid ageing, but some of the more dramatic claims on HWZ/Reddit — such as “there will be no retirement,” inevitable CPF changes, or Singapore eventually becoming mostly foreign — are commentary/speculation, not conclusions established by the report.

The genuinely important issue is the combination of very low fertility + longer life expectancy + rapidly growing elderly population + fewer working-age citizens per elderly person. Immigration can moderate the pace, but it does not reverse the ageing of the existing citizen population. (The Straits Times)