I went into this research convinced I already knew the answer.

After more than a decade in People Analytics, the last few years at Meta, I had a working theory about why tech employees leave their jobs within the first year. Two things, I believed, were doing most of the damage: whether someone was getting promoted, and how often they were socialising with their immediate team outside of work. The first felt obvious. The second felt like the kind of human factor the industry consistently underweights.

I was half right.

When I surveyed 205 tech professionals globally and trained a machine learning model to predict early attrition, promotions came out as the single strongest signal in the dataset. But socialisation? It barely registered. And the factors that did matter alongside promotions pointed somewhere I hadn’t fully anticipated. Early attrition in tech isn’t primarily a culture problem. It’s a career momentum problem.

That finding changed how I think about retention. I suspect it might do the same for you.

Tech has always had an attrition problem

The technology industry has one of the highest attrition rates of any sector. Median tenure at many tech companies sits at around one year, regardless of company size. This isn’t a post-pandemic hangover or a hot job market anomaly. It’s been the structural baseline for as long as the industry has existed, and the industry has never really solved it.

The costs are well documented. Replacing an employee can run up to 2.5 times their salary once you factor in recruiting, onboarding, lost productivity and the institutional knowledge that walks out the door with them. Research suggests that a single standard deviation increase in attrition rate correlates with an 8.9% drop in profits. In an era where tech companies are simultaneously pouring billions into AI infrastructure and scrutinising every other line of their cost base, haemorrhaging money on preventable attrition is a harder position to defend than it used to be.

What’s less well understood is why the problem persists despite enormous investment in trying to fix it. Tech companies spend heavily on perks, engagement programmes, culture initiatives and manager training. Some of it works at the margins. None of it has bent the curve in any meaningful way.

Part of the reason, I’d argue, is that most retention efforts are reactive. Someone signals they’re unhappy, or worse, hands in their notice, and the response kicks in. By then it’s usually too late. The question that has always interested me professionally isn’t how to respond to attrition once it’s happening. It’s whether you can see it coming early enough to do something about it.

There was no dataset for this, so I made one

The first problem I ran into was data. There’s no shortage of public datasets on employee attrition, but almost none of them specify industry. The most widely used one is a fictional HR dataset created by IBM data scientists, which has been recycled across dozens of academic studies. It’s clean, it’s accessible, and it tells you nothing specific about the technology sector.

So I built my own. I designed a 24-question survey and distributed it globally to professionals in the tech industry, with one hard requirement: both their current and previous employers had to be technology companies. After removing duplicates and incomplete responses, I had 205 usable records. Not a massive dataset by industry standards, but clean, specific, and purpose-built for the question I was trying to answer.

I defined “early attrition” as leaving a job within the first year. Every respondent was then classified as either an early attrition or not, and that classification became the target the model was trained to predict.

From there, I trained five machine learning algorithms on the data and tested each one across multiple configurations. I utilized an F1 score rather than simple accuracy to measure performance, and the reason matters. A model predicting whether someone left within a year could technically achieve high accuracy just by labelling everyone “stayed longer” since that’s the more common outcome. An F1 score accounts for that imbalance and gives a more honest picture of how well the model is actually working. The best-performing setup combined an algorithm called Extra Trees Classifier with a technique called SMOTE, which addressed the imbalance in the dataset by generating synthetic examples of the minority class. That combination achieved an F1 score of 0.97 out of a possible 1.

The model worked. The more interesting question was what it had learned.

Promotion was the loudest signal in the room

Of all the variables in the dataset, the number of times someone had been promoted in their previous job was the single strongest predictor of whether they left within the first year. The correlation was -0.54, which in plain terms means this: the fewer promotions someone had received, the more likely they were to be an early attrition. Not marginally more likely. Significantly more likely.

This confirmed half of my original hypothesis, and it shouldn’t surprise anyone who has worked in tech. Promotion isn’t just a title change or a pay increase. For most people, especially earlier in their careers, it’s the primary signal that the company sees them and is investing in their future. When that signal doesn’t come, people start looking for it elsewhere.

Nearly half of the respondents in my survey, 49%, had never been promoted in their previous job. That number sat with me. In an industry that prides itself on meritocracy and moving fast, nearly half the sample had never received a single formal recognition of progression. The model was picking up on something that was hiding in plain sight.

Alongside promotions, three other factors emerged as meaningful predictors. Each one is worth unpacking individually because the directionality isn’t always what you’d expect.

Age. Younger workers were significantly more likely to be early attritions. The correlation between age and early departure was -0.49, meaning the older the respondent, the less likely they were to have left within the first year. This makes intuitive sense when you think about it from a career psychology perspective. Earlier career employees carry less sunk cost, face more aggressive recruiting from competitors, and tend to have higher expectations of rapid progression. When those expectations aren’t met quickly, they move. For HR leaders, this means early-career and new graduate hires deserve disproportionate attention in the first twelve months. Visible career pathing and early promotion signals aren’t a nice-to-have for this cohort. They’re retention infrastructure.

Internal role changes. This one cuts against a common assumption. Employees who had experienced more role changes within their previous company were actually less likely to have been early attritions, with a correlation of -0.49. The instinct is often to treat internal mobility as a sign of restlessness. The data suggests the opposite. Movement inside a company appears to be a marker of engagement and investment, not instability. People who get moved around, who change teams or functions, are people who have been given reasons to stay invested. Rotational programmes and internal transfers aren’t just good for skill development. They’re retention tools.

Manager changes. The most counterintuitive finding in the dataset. Employees who had experienced more manager changes in their previous company were less likely to have left within the first year, with a correlation of -0.44. The assumption most people make is that manager instability drives attrition, and there is plenty of research supporting that at a general level. But within this dataset, the relationship ran the other way.

One thing worth being transparent about across both of these findings: someone who left within the first year simply had less time to accumulate role changes or manager changes than someone who stayed longer. That tenure effect is real and worth acknowledging. But the directional signal still holds. Employees who had weathered multiple manager changes or moved across teams had, by definition, found reasons to stay through organisational disruption. They had built enough roots that a change in reporting line or a shift in scope wasn’t enough to push them out. The dependency on any single manager or single role appears to be highest in the early months, before an employee has built broader organisational depth.

Taken together, these four factors point toward a consistent underlying pattern. Early attrition in tech tends to cluster around employees who are younger, less promoted, less mobile internally, and less embedded in the organisation. They haven’t been stagnant necessarily, but they haven’t been invested in either. The model wasn’t identifying people who were inherently likely to leave. It was identifying people who hadn’t yet been given enough reasons to stay.

The socialisation hypothesis didn’t survive contact with the data

I want to be honest about the part of my hypothesis that was wrong, because I think it’s actually the more instructive finding.

My original assumption was that how frequently employees socialised with their immediate teammates outside of work would be a meaningful predictor of early attrition. The logic felt sound. A sense of belonging, of actually liking the people you work with enough to spend time with them voluntarily, seemed like exactly the kind of human glue that keeps people in their seats during the first year when everything else is still uncertain.

The data didn’t support it. Socialisation frequency came out as one of the weakest signals in the entire model, with a near-zero correlation to early attrition after balancing the dataset.

I’ve thought about why that might be. One possibility is that socialisation outside work is a symptom of a good job rather than a cause of staying in one. People socialise with their teams when things are going well, when they feel settled, when they’re not spending their evenings on LinkedIn. It may be more of a trailing indicator than a leading one. Another possibility is that within the specific context of the first year, career momentum simply carries more weight than social connection. You can like your team and still leave if you’re not being promoted, not being moved, not being invested in.

What this tells me, practically, is that companies leaning heavily on culture and social programming as a retention strategy may be solving for the wrong thing, at least in the early tenure window. Those investments aren’t wasted. But if promotion cadence and internal mobility are broken underneath, no amount of team offsites is going to close the gap.

The signal is already in your data

Here is what I find most striking about these findings. None of the factors the model identified as predictive of early attrition are hidden. They’re not buried in sentiment data or detectable only through expensive listening programmes. Promotion history, age, internal mobility, manager changes. Every one of those data points exists in your HRIS right now. Most companies are sitting on the signal and not reading it.

The pattern the model learned to recognise looks something like this. An employee who is earlier in their career, has been in role for several months without a promotion conversation on the horizon, has never moved teams or changed scope internally, and whose entire organisational identity is still tied to a single manager they may or may not have a strong relationship with. That person is not necessarily unhappy yet. They may not have even consciously decided to leave. But the conditions for early attrition are already in place.

The traditional response to that situation, if it gets noticed at all, is reactive. A skip-level conversation after someone flags dissatisfaction. A retention offer after a competing offer has already landed. A manager coaching conversation after the engagement survey comes back low. By that point the decision is usually already made, or close to it.

What the data suggests is that the intervention window is much earlier than most organisations treat it. The first six months of someone’s tenure is when the pattern is being set. Are they getting feedback that signals a future at this company? Are they being considered for stretch opportunities or cross-functional projects? Is someone actively managing their career trajectory, or are they simply being left to onboard and get on with it?

This doesn’t require a machine learning model to act on. It requires People Analytics teams and HR business partners to start treating early tenure as a risk period that deserves structured attention, not just a probationary formality. Simple cohort analysis on your existing workforce data can surface who is sitting in the high-risk pattern right now. Who is under 30, has been in their role for more than eight months, has never changed teams, and has not had a promotion discussion documented? That list exists in your data today.

The AI investment angle matters here too. At a moment when technology companies are making significant bets on artificial intelligence and scrutinising headcount and operational costs more carefully than they have in years, the economics of preventable attrition look different than they did in a looser environment. Losing an early-career employee within the first year and absorbing the cost of replacing them, which can reach up to 2.5 times their salary, is not just a talent problem. It is a financial inefficiency that sits alongside every other cost a leadership team is being asked to justify.

Retention, viewed through that lens, stops being a soft HR metric and starts looking like an operational priority.

The harder question isn’t who’s leaving. It’s who you’ve given a reason to stay

Predicting attrition is the easier half of the problem. I want to be clear about that. A model can learn to recognise the pattern of someone who hasn’t been invested in. What it can’t do is tell you why your organisation keeps producing that pattern, or what it would actually take to change it.

That’s the question I’d leave with every HR and People Analytics leader reading this. Not “how do we build a model like this” but “what would we find if we ran this kind of analysis on our own workforce today?” Because the data is there. The pattern is legible. The gap is almost always in whether anyone is looking for it with enough time to act.

Tech companies are currently navigating one of the more complicated cost environments the industry has seen in a while. AI infrastructure spending is accelerating at a pace that is putting real pressure on every other budget line. Headcount decisions are being made with more scrutiny. The tolerance for inefficiency, financial or operational, is lower than it has been in years. In that context, the cost of losing an employee within the first year and absorbing the full weight of replacing them sits in uncomfortable tension with the AI investment conversation happening in the same leadership meeting.

You cannot cut your way to efficiency while quietly haemorrhaging talent at the bottom of the tenure curve. The two conversations need to be in the same room.

The research I did was a starting point, not a solution. A survey of 205 professionals, a machine learning model, a set of findings that confirmed some assumptions and challenged others. What it pointed toward, more than anything, is that early attrition in tech is not mysterious. It follows a pattern. That pattern is detectable. And in most organisations, the data needed to detect it already exists.

The question is whether anyone is looking.

Researchers at MIT reached back to 1985 and pulled an old design out of storage. What they built with 3D printers now turns three floppy plastic strips into a solid beam in seconds. The device carries a simple name: the Y-zipper. Its triangular profile locks parts together so tightly that soft tentacles become load-bearing supports. Engineers can print the whole assembly in ordinary plastic and watch it switch states on command.

Bill Freeman came up with his initial fastener design while working as an electrical engineer at Polaroid. His goal was to develop a fastening mechanism that would allow chairs, tents, or bags to effortlessly transition between loose and taut forms without the need for additional hardware. Back then, however, companies lacked the ability to produce the three matching strips or the slider that looped around all three corners. Freeman submitted the patent but kept the drawings in a drawer. It wasn’t until 40 years later that the CSAIL team decided it was time to put it into production.

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At its core, the system is made up of three separate bands, each with a row of interlocking teeth along two of its long edges. And on top of those bands is a single slider that pulls them all firmly into a triangle shape as it moves. Once the slider is tight, the shape will not bend or twist because triangles distribute stresses evenly. Slide the slider back, and the bands simply pull apart, leaving behind three wonderfully flexible (and independent) ribbons that can bend in almost any way you desire. No tools are required, and the whole operation reverses in no time.

To make it all work, you simply need some software. You give the computer the size of each band, the direction it should curve, and the overall shape you desire (straight line, mild curve, tight spiral, or smooth twist). The software then generates a printable file, and the printer prints the bands made of either stiff (polylactic acid for heavy loads) or flexible (thermoplastic polyurethane for a bit of give). The layers glue together so well that those teeth fit together properly on the first try, with no handfitting required.

Tests have demonstrated how sturdy this assembly is. One system they tested went through 18,000 full open-and-close cycles without displaying even the least indication of wear on a single tooth, and the material flexes just enough to spread out any pressure and prevent snapping. They’ve also conducted load simulations and discovered that the zipped up triangle shape can support far more weight than a flat strip ever could.

Robots are already showing how advantageous this fastening system can be. They are employing a four-legged prototype with a zipper inside each limb. The motors then simply slide the fasteners up and down as needed. When the zippers are tightened, the legs become higher and stiffer, allowing the robot to stroll over rough terrain with ease. When the zippers loosen, the identical limbs drop down low and become much softer, allowing the robot to glide through tiny gaps, and the entire process happens in less than a blink.

Tents are pitched with the same simple hardware that has always been used. Three printed arms emerge and hook onto the cloth panels, and a short burst of power closes the zippers and the entire thing leaps into place in less than a minute and twenty seconds. No more wrestling poles for hours on end; setup is now a breeze. The tent still packs up neatly because when the zippers are closed, the printed arms fold flat.

Medical casts have also become much more comfortable, with one version that wraps over your wrist like a luxury wristband and can be left open during the day to allow your patient to walk about freely. However, as night falls, the slider clicks shut and the support begins to firm up to protect the healing bones. The patient gets to choose when to make the change by flipping a small switch. Artists have even come up with inventive uses for this technology, such as a mechanical flower sitting on a table.
[Source]

The new roles will be in areas such as quality, regulatory, supply chain, finance and support functions, according to Evumed.

Cork Airport Business Park has announced plans to host the European headquarters of Evumed, a biopharmaceutical company that has committed to a multimillion-euro investment. 

The move will see the creation of the 30 new jobs at Evumed throughout 2026, in areas such as quality, regulatory, supply chain, finance and support functions. Evumed aims to advance healthcare by ensuring continuity of supply and enabling patients across Europe to access high-quality, affordable medications.

At Cork Airport Business Park, Evumed’s headquarters are located alongside globally recognised names such as Amazon, IBM, McKesson, Emerson, Aviva, GSK, Statkraft and Red Hat and letting agents are currently in talks with an additional three global organisations regarding potential tenancies at the site. 

Colm Moynihan, the president of Evumed, said: “Cork Airport Business Park’s exceptional global connectivity and modern infrastructure, coupled with Cork’s position as a global hub for pharmaceutical manufacturing and innovation offers Evumed the ideal location to grow as we aim to develop and deliver generic, branded and biologic medicines and bring innovation and accessibility together to improve lives.”

There are a range of career opportunities open to Cork-based professionals currently. In April, US software development company MongoDB, which has locations in Dublin and Cork, announced plans to invest €74m into its Irish operations – a move that will generate 200 new jobs. 

In March, data management and cloud data platform provider Qumulo officially launched its new European software R&D hub in Cork, amid a plan for expansion that will create 50 new jobs in the area over the next three years. New roles will include opportunities in engineering, R&D and customer service.

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

The 2026 American Innovation $1 Coin project will honor former Apple CEO Steve Jobs with a one-dollar coin bearing his image that costs, at best, $1.55 each.

In 2018, the United States Mint (Mint) announced that it would honor American innovation and pioneering efforts of individuals or groups with $1 coins. Coins will be issued for each of the 50 states, the District of Columbia, and the five U.S. territories.

This year, the Mint will be issuing California’s coin, which will feature “a young Steve Jobs sitting in front of a quintessentially northern California landscape of oak-covered rolling hills.” The Mint works directly with the office of the Governor or other Chief Executive for each state, territory, or city to determine both the subject and design.

“The innovative and entrepreneurial spirit of Steve Jobs embodied the best of California, creating the future we all know today,” Governor Newsom said in a press release on Monday. “His tenacity and fearless pursuit of the California Dream made so many American dreams possible. May we all attempt to fill his shoes as we seek our own California Dreams.”

According to the Mint, the Secretary of the Treasury selects the final design for each coin.

“I’m pleased to celebrate the release of the California American Innovation $1 Coin,” said Mint Director Paul Hollis.

“Steve Jobs was a remarkable innovator and transformed how the world connects and communicates,” Hollis said. “Through this coin, the Mint honors his incredible work and recognizes his importance to both California and the United States.”

The design includes the inscriptions “Steve Jobs” and “Make Something Wonderful.” The latter is a nod to a larger phrase that Jobs said from an internal Apple employee-communications meeting held on October 23, 2007, as noted by The Steve Jobs Archive.

The whole quote, for those interested, is:

There’s lots of ways to be as a person, and some people express their deep appreciation in different ways, but one of the ways that I believe people express their appreciation to the rest of humanity is to make something wonderful and put it out there.

Those interested in purchasing the coins can do so via the United States Mint website. Prospective buyers can select between a roll of 25 coins or a bag of 100 coins, and can pick whether the coins are minted in Denver or Philadelphia.

A roll of coins, regardless of mint location, will cost a buyer $61, or $2.44 each. A bag of coins will cost $154.50, or roughly $1.55 each.

Orders for the 100-coin bags are limited to 10 per household. Orders for the rolls are also limited to 10 per household.

Coins go on sale via the United States Mint website starting on May 12, 2026. There will be a total 25,950 Steve Jobs dollar coins manufactured.

Previous states and their innovations include Pennsylvania and the Polio vaccine, New Jersey and the lightbulb, Ohio and the Underground Railroad, and Texas and the International Space Station. To date, 33 states have had coin designs released or revealed.

[This is a sponsored article with MRANTI.]

In a milestone achievement, Malaysia’s National Technology & Innovation Sandbox (NTIS) programme won the Gold Stevie Award for Innovation in Technology Management, Planning & Implementation (Government Category) in Macau, China. 

This win underscores the global standard of Malaysia’s technology sandboxing framework, where we competed with over 1,000 nominations from 29 Asia-Pacific markets to secure gold.

For context, the Stevie Awards is a global premier business award competition that evaluates thousands of the world’s most respected executives, entrepreneurs, innovators, and business educators.  

Securing its spot on the list distinguishes the NTIS programme as a prominent regional industrial and technological player. The panel of judges even praised the programme as one that sets a new benchmark for strategic technology planning.

The Minister of Science, Technology and Innovation (MOSTI), YB Datuk Chang Lih Kang said, “This recognition reflects the progress we have made in strengthening Malaysia’s innovation ecosystem through NTIS.”

“This award belongs to the entire Malaysian innovation ecosystem. By working alongside regulators, industry partners, and government agencies, NTIS has proven that a collaborative ‘living lab’ approach can yield extraordinary economic results,” he added.

A cornerstone of Malaysia’s innovation ecosystem

A national government initiative spearheaded by MOSTI with Malaysian Research Accelerator for Technology and Innovation (MRANTI) as its lead secretariat, NTIS provides a structured platform for researchers and startups to efficiently validate high-growth technologies like autonomous systems and advanced robotics.

It’s a dynamic and open innovation ecosystem that allows researchers and entrepreneurs to test their products and services in a live environment. Participants are then able to qualify for grants to bring their products and services to market, thereby bridging the gap between innovative concepts and being commercially viable.

Since its inception in 2020, the programme has matured from a strategic pilot into a cornerstone of Malaysia’s innovation ecosystem.

It has delivered substantial results under the 12th Malaysian Plan (RMKe-12), including but not limited to:

• facilitating over RM17.8 million in total commercialisation value,
• distributing RM142.2 million in funding to support 229 high-impact projects,
• catalysed the creation of 333 high-income jobs, strengthening the nation’s technical workforce,
• generating RM122.8 million in export value through 14 focused international projects, and
• managing a robust portfolio of 66 Intellectual Properties (IPs), with 36 new IPs filed to safeguard local innovation.

Part of the NTIS programme’s success can be attributed to its strategic partners, such as Malaysian Technology Development Corporation (MTDC) in funding, as well as MIMOS and Futurise as regulatory and evaluation secretariats. 

Each of these government agencies play crucial roles in coordinating the nation’s innovation ecosystem and advancing the growth of local innovators.

En route to becoming a high-tech nation

Bagging this award signals a major shift in Malaysia’s approach towards economic development, where we’re moving beyond just focusing on technological progress. Instead, we’re now focusing on getting our homegrown innovations adopted, commercialised, and ready to deliver real impact to the nation.

Ashran Dato’ Ghazi, the CEO of MRANTI and the lead secretariat of NTIS programme expressed, “Winning the Gold Stevie Award is a testament to Malaysia’s unwavering commitment to becoming a high-tech, high-income nation.”

In alignment with this, YB Datuk Chang Lih Kang shared that moving forward, the NTIS programme will be scaling its efforts in accelerating market adoption and expanding the global presence of Malaysian technologies under the 13th Malaysian Plan (RMKe-13).

As NTIS enters its next chapter, it’ll be shifting the focus towards “Deepening Impact” through its Global Testbed & Sandbox Alliance (GTSA), facilitating cross-border collaboration and regulatory harmonisation.

• Learn more about NTIS Programme here. 
• Read articles we’ve written about Malaysian business here. 

HIDDEN is an outdoor escape room game that allows players to explore Singapore’s neighbourhoods & history 

Singapore is not short of escape rooms, ones that are usually built indoors and feature puzzles for participants to solve for them to beat the game and “escape” the room.

However, there is one business that is taking the concept of escape room outdoors, and that is HIDDEN.

We spoke with HIDDEN co-founders, Lim Yee Hung, 40 and Loh Jun Wei, 40, to find out how they captivate “thousands of people” weekly with their game that blends the physical world with the virtual.

Returning home inspired HIDDEN

Having been in the United Kingdom for 14 years, Yee Hung had spent his time there as a terrain analyst in London for Deloitte and a property developer, particularly specialising in theme park projects.

One of his projects was Castle Black from Game of Thrones, for which he signed a license with HBO to turn it into a theme park. 

When COVID-19 hit, Yee Hung sold off his projects in Britain and returned to Singapore, where he taught himself to code and built HIDDEN with his co-founder and ex-JC classmate, Jun Wei. The duo launched in May 2022, a time Yee Hung called “perfect” during the circuit breaker period. 

“People loved that it got them out of the house, having fun learning about their own backyards, while still being compliant with social distancing rules,” he said.

Despite spending close to two decades away from Singapore, Yee Hung said he never really lost touch with home. Instead, returning made him realise just how much the country had changed while he was away.

“When I left Singapore in 2007, Marina Bay Sands, Gardens by the Bay, and Jewel didn’t exist. The population was under five million. Coming back, it was a very different Singapore: over six million people, a new skyline, and a new energy to the city,” Yee Hung recalled.

At the same time, many of the places from his childhood had disappeared too, from the neighbourhood shops he used to frequent, to the flat he grew up in and the fields where he played football.

That sense of loss eventually became the inspiration behind HIDDEN Singapore. To Yee Hung, the game was a way of preserving both the Singapore that no longer exists and the present-day Singapore that could one day disappear too.

It was my way of preserving the Singapore that’s gone and the Singapore now that we will no doubt miss 10 years from today, because Singapore changes so, so fast.

Digging through Singapore’s archives to build HIDDEN

Drawing from his experience working with global brands like HBO, Yee Hung wanted HIDDEN to go beyond surface-level storytelling and create experiences that genuinely immerse people in Singapore’s history and culture.

“When you’re working at that level, it’s not enough to just capture the vibe. You’re expected to do the research, really understand the source material, the characters, the story, and the world. And more importantly, your work has to add to it, not just reference it,” he shared.

While working on the Castle Black theme park project, Yee Hung became deeply familiar with the Game of Thrones universe, even down to the exact spots where key scenes involving Jon Snow took place. That experience taught him the importance of treating source material with depth, care, and respect.

It was a philosophy he carried over when building HIDDEN.

Instead of creating generic sightseeing trails, Yee Hung and Jun Wei spent around six months developing and coding the game. This included researching Singapore’s archives, piecing together historical materials and identifying the core narrative behind each neighbourhood.

Rather than developing a standalone app, they intentionally chose to have a chatbot integrated into WhatsApp as the platform to make the experience more accessible, especially for older users, while also making interactions feel more immersive by allowing players to “chat” directly with in-game characters.

How the game works

All this being said, you might be curious how the game actually works. HIDDEN turns Singapore’s architecture and neighbourhoods into a real-life immersive game.

Led by Void Deck Cat, the game’s in-house mascot and WhatsApp chatbot, players receive clues that guide them on quests across Singapore. By analysing architecture and their environment, they solve puzzles while unlocking historical photographs and stories.

As players explore different locations, they can text their answers to the chatbot or ask for hints if they get stuck.

Players start at a designated location, where they scan a QR code to begin. Each adventure has its own theme and starting point. For example, the MONOPOLY: Katong-Joo Chiat experience begins at the Singapore Visitor Centre.

All public adventures are self-guided and take around 60 minutes to two hours to complete. Each experience is priced from S$29.90 and comes with merchandise as well as freebies from local merchants exclusive to that adventure.

While the game can be played solo, Yee Hung recommends playing in groups of two to five so everyone can actively take part in solving clues. There are currently seven public adventures and seven corporate adventures available across Singapore.

As players move through neighbourhoods, they interact with Void Deck Cat via WhatsApp, receiving clues that prompt them to observe their surroundings.

Along the way, players can also unlock perks that direct them to local independent businesses such as pineapple bakery Kele for breaks and surprise rewards. In MONOPOLY-themed games, players even receive MONOPOLY money to spend at participating merchants.

At the end of the game, players submit their final answers and receive a summary page showing their completion time, missed clues, and the photos taken during their journey.

Strong reception locally and abroad

Building on its strong early traction since launch, HIDDEN has gone on to win the TripAdvisor Travellers’ Choice Award in its first year and has continued to receive it annually.

Yee Hung also shared that the experience has attracted more than 100,000 players to date, including both locals and tourists, with many returning to try different adventures.

In addition, HIDDEN has secured major partnerships with brands such as Hasbro and the National Gallery Singapore, for its MONOPOLY and FRAMED tours respectively.

However, Yee Hung noted that the early days came with significant business challenges. These included convincing people to try a completely new concept, designing experiences that would genuinely delight players, and persuading local businesses and venues to participate in an untested format with no established track record.

With limited marketing budgets at the start, the team relied heavily on organic growth. “Word of mouth is free and so much more powerful,” he said.

On the operational side, Yee Hung also highlighted the technical demands of running HIDDEN at scale. The team had to ensure their systems could handle thousands of simultaneous chatbot interactions without crashing, which led them to build proprietary technology capable of managing the “heavy lifting” seamlessly.

They also had to carefully pace game sessions to avoid overwhelming their independent F&B partners with sudden surges of players.

In November 2024, HIDDEN expanded beyond Singapore into its first overseas market.

Yee Hung shared that the company became the first Singaporean business to sign a partnership agreement with a Māori tribe, Ngāti Kahungunu ki Tāmaki-nui-a-Rua in New Zealand, to develop outdoor adventure games rooted in Māori cultural stories. One of these projects is Hidden Aotearoa.

He noted that while the technology framework remains the same, the approach to storytelling changes completely depending on context. “The backend tech stays the same, but the cultural lens shifts entirely,” he said.

Reflecting on building Hidden Aotearoa for a New Zealand context, he added: “We learnt from our Māori partners that we mustn’t try to tell other people’s stories for them.”

In the future, Yee Hung sees continued global expansion on the cards. “I want HIDDEN to join the likes of Lego and IKEA, to become a global brand, bringing joy to people in cities all over the world,” he said.

• Learn more about HIDDEN here.
• Read other articles we’ve written on Singaporean businesses here.

Featured Image Credit: HIDDEN

Looking to upgrade your current desktop setup but aren’t sure where to start? Well, if your current keyboard is showing its age and just isn’t as responsive as it used to be, then getting a new keyboard is a solid way to kick things off. For the gamers and fast typers out there, you may be better suited with one of the best mechanical keyboards.

For those not in the know, mechanical keyboards use individual switches for every single key, instead of a membrane system that’s typically found on laptops and older keyboards. Although there’s no denying that mechanical keyboards are louder to type on, and probably aren’t the best option for use in a shared space, they do come with a ton of benefits.

For starters, because of the more refined and less squishy nature of the keys themselves, you can type at a much faster speed which is great for anyone who uses Microsoft Word or Google Docs for their day-to-day work. While this is a great boon for productivity, the real benefit of the tactile sensation that only mechanical keys provide can be found in gaming.

Mechanical keyboards do a far better job at keeping you locked in with the action, and when you’re playing fast-paced titles like Counter-Strike 2 or Rainbow Six Siege, which require lightning-fast reaction times, playing with anything other than a mechanical keyboard can be a disastrous idea.

Although there are some gaming laptops that come with mechanical keys built in, your best bet is to buy one separately, especially if you’re gaming at a desk. Our team of experts have put together this very guide to pair you with the right mechanical keyboard for your needs. For more buying advice, check out our breakdowns of the best gaming monitors and the best gaming mice. 

Best mechanical keyboards at a glance

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