Most tech roles saw salary growth, but not all benefited equally

2025 marked a turning point for the tech industry. After several years of layoffs, cautious hiring, and uncertainty, the sector is showing signs of recovery across the region.

At the same time, companies are rethinking how they prioritise and value talent. Artificial intelligence (AI) is no longer just a talking point—it’s now embedded in hiring priorities, day-to-day workflows, and compensation, as highlighted in Nodeflair’s latest annual tech salary report.

This year’s data, released today (May 4), shows that engineers with AI skills are earning meaningfully more than their peers, with some seeing pay bumps of up to 25%. At the same time, senior engineers continue to pull ahead, as companies place greater value on judgment, architecture, autonomy, and the ability to work effectively with AI tools.

In effect, AI is reshaping the tech career ladder and rewarding stronger problem-solvers at every level. Here is a breakdown of the report and the overall compensation changes for tech roles in Singapore, detailed by role and seniority:

[Disclaimer: Salary data are derived from Nodeflair’s proprietary database of over 130,000 data points, including user submissions that are verified by documents (payslips and offer letters) as well as job advertisements from various job portals. While a majority of the entries are backed by a sizeable amount of data, Nodeflair has flagged out entries with less than 200 data points as potentially lacking accuracy.]

Data scientists are the highest-paid, earning up to S$25K per month

Based on salary data from NodeFlair’s proprietary database, lead data scientists are the highest-paid role. Despite an overall 8.3% decline, they still command a median monthly salary of S$25,000.

Meanwhile, software engineer managers earn up to S$20,100 per month, marking a 10.8% increase as compared to the previous year.

Senior solutions engineers earn a median monthly salary of S$18,500, followed closely by senior product managers at S$18,100. Other top roles include lead mobile engineers at S$16,900, lead data engineers at S$16,000, and platform (DevOps & SRE) leads at S$15,200.

Roles at S$15,000 and below include lead cybersecurity engineers, lead data analysts, systems & IT leads, and senior quality assurance engineers.

On average, senior roles saw the strongest salary growth, with pay rising 12.6%. Lead roles followed at 11.6%, while manager roles increased by 10.8%.

By contrast, mid-level and junior roles recorded more modest gains, with average salary increases of 1.7% and 5.3%, respectively.

AI skills pay off

Beyond providing salary data, NodeFlair’s report highlighted a widening pay gap between engineers with and without AI skills.

Its analysis of 50th percentile software engineers shows that AI-native talent is being paid a clear premium.

The data found that junior software engineers (zero to two years of experience) with AI skills saw a 25% pay bump at the median, earning S$6,000 per month compared to S$4,800 for those without AI skills.

Among mid-level engineers (two to five years of experience), those with AI skills earned 13% more at the 50th percentile—S$8,000 versus S$7,100 for their non-AI peers.

At the senior level (more than five years of experience), engineers with AI skills earned 18% more at the median, taking home S$10,000 compared with S$8,500 for those without.

AI fluency is no longer a nice-to-have—it’s now a salary advantage

Ethan Ang, Nodeflair founder

Advertisement

“With the rise of tools like Claude Code and a broader wave of agentic coding workflows, engineering teams are now rethinking how software gets built around AI, including placing greater value on engineers who are truly AI-native,” he added.

• Read more articles we’ve written on Singapore’s job trends here.

Featured Image Credit: Shadow_of_light/ depositphotos

Trust in online payments has never been as important as it is right now. For anyone who spends money across digital platforms, the tools we use to pay can shape the entire experience. Digital wallets stand out as more than a payment method, they’re a foundation for how fast, safe, and convenient every transaction can feel. Convenience might look like a buzzword, but for online shoppers, it’s the difference between an immediate purchase and a drawn-out checkout that breaks the flow.

Speed and flexibility are at the center of this shift. Today’s gamers, content subscribers, and e-commerce buyers have come to expect instant access, not just to their products, but to funds as well. Digital wallets answer the call, letting users transfer, store, and spend money in ways that traditional cards or slow bank payments can’t match. For those looking to stretch their value further, methods to buy Razer Gold online show how funding a wallet can help unlock exclusive game items, bonus points, or discounted content across many platforms.

Traditional payment routes come with friction. Waiting on transfers, dealing with surprise foreign transaction fees, and entering card information again for each new site can stretch a simple moment into a tedious process. Digital wallets cut this down to a few taps or clicks. They unify purchase histories, hold multiple payment options, and mask your actual card number, drastically reducing fraud risk. These perks are not only attractive, they’re quickly becoming essential for buyers who value both privacy and speed.

When looking for digital games, players often do a quick search only to find that platform stores like the PlayStation Store may have high prices or regional restrictions. Eneba gives those shopping for new titles or DLC a much wider range of game keys, often below standard store prices. Game keys are unique codes that can be redeemed for full games or content, buy a PlayStation code on Eneba, redeem it in your account, and the game appears in your library instantly. The catalog is vast, with instant code delivery, clear info about global versus region-locked content, and a support system in place. Gift cards for Xbox, PSN, and Steam are also available, turning top-ups into a hassle-free option. Crucially, Eneba verifies its merchants and maintains compliance checks so the buying experience is safe and reliable.

Digital wallets don’t just serve gamers, though. They unlock new ways to handle subscriptions, buy digital art, or access streaming services. Their real appeal is in how they make every transaction less of a process and more of a click. This shift in user expectation is subtle but profound, echoing through every part of digital commerce.

Security has become a hot topic. Payment fraud, identity theft, and data breaches drive demand for alternatives to typing out full card details. Digital wallets blend strong encryption with multi-factor authentication. These layers of protection help users manage risk without adding complexity to already busy lives. For those spending on unfamiliar sites, using a wallet adds an extra safeguard, keeping personal details out of harm’s way.

The integration of reward systems is another reason digital wallets have become staples for online buyers. Topping up with game-specific currency or third-party credits can give you cashback, bonus points, or early access offers. This builds loyalty without forcing users to stick to just one shop or ecosystem.

Digital wallets continue to grow by adapting to what users want next: more currencies, more integrations, and fewer barriers between platforms and regions. Their evolution keeps driving innovation, not just for gaming but for all forms of online spending.

Digital marketplaces like Eneba offering deals on all things digital have helped refine what buyers now expect from every online purchase: instant, secure, and tailored to their needs. As competition heats up, it’s clear that digital wallets are here to shape the future of online transactions.

A joint venture with Blackstone, Hellman & Friedman, Goldman Sachs, and General Atlantic will sell Claude into the buyout firms’ portfolio companies. OpenAI’s DeployCo arrived first; this one is bigger.

There is a kind of business school question that has been quietly answered over the past month, without anyone formally asking it. The question is: which is more valuable to a frontier-model company, the next $50bn cheque from a venture investor, or a permanent distribution channel into the operating companies of the world’s largest private-equity firms? Anthropic has been working on the second answer.

On Sunday evening, the Wall Street Journal reported that Anthropic was finalising an approximately $1.5bn joint venture with a small group of Wall Street firms, with an announcement expected as soon as Monday. A

ccording to the WSJ, Anthropic, the buyout firm Blackstone, and Hellman & Friedman are anchoring the deal at roughly $300m apiece. Goldman Sachs joins as a founding investor at about $150m, with General Atlantic and other firms making up the rest. We wrote about the outline of this venture last month, when the structure was still scoped at $1bn or so; the final figure is closer to $1.5bn.

The investors will create a vehicle that operates as something between a consulting arm and a deployment factory: helping the portfolio companies of its private-equity backers integrate Claude across their day-to-day operations.

The pitch is straightforward. Buyout firms own thousands of operating businesses across health care, logistics, manufacturing, and financial services. Each is a potential Anthropic customer. Selling to them one by one, on the standard enterprise software cycle, would take years. Doing it inside a joint venture compresses that timeline into months.

It is, in other words, less a product launch than a sales infrastructure project.

OpenAI got there first, but smaller

The structural template will be familiar. OpenAI announced a similar joint venture, DeployCo, last month, anchored by TPG, Bain Capital, Advent International, Brookfield, and Goanna Capital. The five PE firms together committed about $4bn; OpenAI itself put in $500m, with an option for a further $1bn.

The DeployCo vehicle is expected to be valued at $10bn in a round closing in early May, with OpenAI guaranteeing its PE backers an annualised return of 17.5 per cent over five years.

Anthropic’s structure is different in important ways. The total commitment is smaller in absolute dollars but more concentrated, with Anthropic itself contributing roughly the same amount as its biggest financial partner. There is no public reporting of guaranteed returns.

The investor list is heavier on prestige and lighter on breadth: Blackstone is the largest alternative-asset manager in the world, Hellman & Friedman is among the most disciplined large-cap buyout houses, Goldman is Goldman, and General Atlantic gives the venture a growth-equity stake.

Each side is, in effect, betting on a different proposition. OpenAI’s DeployCo is a numbers play: pull as many PE portfolios as possible into a captive channel, fast. Anthropic’s venture is a credibility play: anchor Claude inside a smaller number of high-profile financial firms whose imprimatur, in turn, sells the model to the rest of the market.

The timing is not accidental. Anthropic has received pre-emptive offers for a roughly $50bn round at a valuation in the $850-900bn range, with the company’s board expected to decide in May and an IPO targeted as early as October 2026.

Anthropic’s annualised revenue run rate has, by its own disclosures, gone from approximately $9bn at the end of 2025 to around $30bn by the end of March 2026. A successful public listing at those numbers requires the company to demonstrate not only model capability but durable enterprise revenue at scale.

A joint venture that pumps Claude into the portfolio companies of three or four major buyout firms creates exactly the kind of revenue ramp public-market investors prefer to model.

It also has narrative value. Claude, in this telling, is not merely a chat product or a developer API but enterprise infrastructure, embedded inside the operating businesses that move significant chunks of the real economy.

There is precedent for the strategy on Anthropic’s books already. Goldman Sachs has spent the past several months piloting Claude internally as the basis for autonomous agents in accounting and compliance, with embedded Anthropic engineers reportedly spending six months inside the bank co-developing the systems.

JPMorgan Chase and Goldman, separately, have been testing Anthropic’s Mythos model under a Project Glasswing initiative focused on AI cyber-risk. The new joint venture is the commercialisation of those experiments.

What it gives Wall Street

For the buyout firms, the calculation is similarly transparent. Private equity returns increasingly depend on operational improvements at portfolio companies rather than financial engineering at the holdco level. AI deployment, in theory, is the next great efficiency lever, and one that the largest funds have struggled to roll out consistently across diverse operating businesses. Owning a stake in the deployment vehicle for one of the two leading model companies is a hedge: it gives the firms first-mover access, preferred pricing, and, plausibly, a financial stake in Anthropic’s broader commercial trajectory.

Goldman Sachs’s $150m position is smaller in dollar terms but particularly telling. It is the same bank rumoured to be co-leading Anthropic’s eventual IPO. A $150m anchor in this venture is less an investment than a relationship deepening.

The risks the structure does not solve

Joint ventures of this kind have a chequered history in financial services. They tend to underperform the most optimistic projections, particularly when the deployed technology is changing as fast as foundation models.

Claude as it exists today will not be Claude in three years; whether the venture’s organisational structure can keep pace with model upgrades, pricing changes, and rival offerings is a real question.

The DeployCo precedent is too young to assess, and Anthropic’s vehicle is, by design, more selective in its partner roster, which limits how quickly it can absorb shocks. OpenAI’s own valuation has come under scrutiny from its investors in recent weeks, a reminder that the model side of these arrangements is not above market discipline either.

There is also a more philosophical risk. Anthropic was founded by researchers concerned about the safety of advanced AI, and has consistently positioned itself as the more cautious of the two leading commercial labs.

A consulting arm that exists primarily to embed Claude inside the operating tissue of dozens of portfolio companies, each with its own data, regulatory, and labour profile, will test that positioning more rigorously than any external benchmark.

None of these is fatal. They are simply the costs of a structure that, until last month, did not exist as a category. Anthropic has decided it would rather pay them with Wall Street co-investors than continue to compete with OpenAI through traditional enterprise sales.

If the announcement lands as expected on Monday, that decision becomes its single largest commercial bet to date, larger, in distribution implications, than any of its model launches. Whether it works will be visible in revenue figures within a year, and in the IPO prospectus shortly after.

We trust GPS like we trust gravity. It just works and gets us where we want to go. But what if someone could trick it into lying to you, and you’d have absolutely no idea?

Unlike jamming, which floods your GPS with noise and at least lets you know something is wrong, spoofing sends fake signals that look completely legitimate. You might be tracking your car or a shipment and think everything is alright when actually the shipment has been routed to someplace else, with you none being the wiser. 

That’s GPS spoofing, and it’s a bigger problem than most people realize. This is what the Department of Energy’s Oak Ridge National Laboratory is trying to solve. 

How bad can it really get?

GPS spoofing might not seem bad on an individual level, but it’s a legitimate concern for companies and governments alike. International criminal networks are already using spoofing to steal loaded long-haul trucks. 

In one case, thieves spoofed GPS to hijack multiple shipments of specialty tequila from a brand co-founded by Guy Fieri and Sammy Hagar. They pulled it off repeatedly because the GPS tracking showed the truck heading exactly where it was supposed to go. 

Now imagine that same trick applied to a truck carrying radioactive materials or pharmaceuticals.

So, what’s the fix?

Researchers at the Department of Energy’s Oak Ridge National Laboratory have built the world’s first highly sensitive, portable GPS spoofing detector that works in real time, even while moving. What makes it special is that it can detect spoofing even when the fake signals are just as strong as the real ones, something no other known system can do.

“Ours is the best in the world,” said Austin Albright, who led the team. “Trucking needs a solution that works without special conditions or dependence on a trusted reference source.”

The detector works independently, without needing a GPS receiver or knowledge of available signals. The team is now working on making it more affordable for widespread use.

Albright sums up the urgency well. Like a carbon monoxide (it’s colorless and odorless) detector catches an invisible danger before it’s too late, this device does the same for GPS, before the cargo, or worse, disappears.

Two years after a Beijing engineer started building dexterous hands inspired by a Japanese cartoon, his company holds 80% of the global market and is doubling its valuation in months.

Robotic hands are not, conventionally, the part of a humanoid robot that investors get excited about. The legs walk, the arms lift, and the head, increasingly, talks. The hands have always been the difficult part, fiddly bits of engineering with too many moving pieces and not enough commercial pull. For most of the past decade, capable, dexterous hands have been research projects, not businesses.

Linkerbot, a Beijing-based startup founded in 2023, is testing whether that has changed.

According to a Reuters report on 4 May, the two-year-old company is preparing to raise its next funding round at a $6bn valuation, double the $3bn it secured in a Series B+ closed only days earlier.

Reuters describes Linkerbot as the global market leader in high-degree-of-freedom dexterous hands, with more than 80 per cent share by volume and the only operation in the world capable of shipping above 1,000 units a month. Production, the company told Reuters, is on track to scale from roughly 5,000 units monthly to 10,000.

That is a striking pace for a category that, on most observers’ priors, was not a market until very recently.

From Doraemon to dexterity

Linkerbot’s chief executive, Alex Zhou Yong, told the South China Morning Post that the idea began in childhood with Doraemon, the Japanese cartoon cat whose magical pocket produces an inexhaustible supply of gadgets. Zhou, who studied robotics, eventually concluded that the trick was not the pocket but the hands. A robot that can manipulate objects with anything close to human dexterity does not need an infinite toolkit; it can use ours.

The company’s flagship Linker Hand series spans six to 42 degrees of freedom, the engineering measure of independent movement axes, and uses, according to Reuters, all the major actuation methods used in the field. The lightweight O6 model weighs 370g and is rated to handle a 50kg load. Higher-DoF variants, such as the L30, are aimed at research labs and humanoid integrators willing to pay for finer-grained control.

In demonstrations, the hands turn screws at speed, grasp soft objects without crushing them, thread a needle, and carry out high-precision assembly tasks. None of these is a particularly novel feat for a research prototype, earlier robot hands have managed similar tricks, and Linkerbot is far from the only firm chasing the problem; Swiss startup mimic is using generative AI to teach a humanoid hand by watching humans work. The point, in Linkerbot’s pitch, is that the demos are now happening on a production line.

Early backers include Ant Group, the financial-services arm of Alibaba, and HongShan Group, the Chinese spin-out of Sequoia Capital. The latest round added the state-backed Zhongguancun Science Park Fund, Bank of China Asset Management, and Fosun Capital. The mix, private internet capital alongside state vehicles and a major bank, is a fair summary of where Chinese deeptech funding is concentrated in 2026.
It also reflects a wider pattern.

It also reflects a wider pattern. Investor interest in Chinese humanoid robotics has surged this year, helped along by viral demonstrations including the Beijing humanoid robot half-marathon in April, where a Tien Kung Ultra unit beat the human world record by seven minutes, and a televised performance featuring Unitree’s machines.

Unitree itself filed for a Shanghai listing in March at a target valuation of around $7bn, having last raised privately at roughly $3bn. Other Chinese players, including Galbot, AgiBot, and AI2 Robotics, are clustered in the $2bn-$3bn range.

The intensity of the talent race in Chinese robotics has reached the point where rival UBTech has dangled an $18m package for a chief AI scientist. By that yardstick, a $6bn valuation for a company that makes only the hands is an aggressive bet.

It is also a bet that goes against a striking gap in the market. Figure AI, the leading US humanoid startup, raised at a $39bn valuation in September 2025, despite shipping a fraction of the volume its Chinese counterparts manage. The disparity reflects, in part, what each side is being priced as.

American investors treat humanoid companies as artificial-intelligence platforms; Chinese investors price them more cautiously, as industrial hardware businesses. Linkerbot, which sells the hands rather than the whole robot, sits awkwardly between those framings.

Hands that build hands

The company’s expansion is more concrete than most pitches at this stage of the AI cycle. Reuters reports more than 400 employees and five factories across Beijing and Shenzhen. The most quietly ambitious detail, mentioned almost in passing, is that some of those factories are being designed as intelligent production lines on which Linkerbot’s robotic hands assemble more robotic hands.

If that pans out, it would be one of the first commercial demonstrations of dexterous manipulation moving from research into closed-loop manufacturing, akin to the Siemens-Nvidia factory deployment in Erlangen but turned inward, and a useful internal proof point for would-be customers.

Linkerbot also says it has built what it calls LinkerSkillNet, described as the largest real-world dataset of dexterous manipulation skills in operation, with more than 500 captured behaviours. The figure has not been independently audited, but the broader claim, that the company is accumulating proprietary training data of the kind that will shape next-generation manipulation models, is consistent with how rivals describe the strategic prize.

Whether $6bn is too much for a hand maker, however dexterous, will depend on which market thesis turns out to be correct. If humanoid robots remain industrial niche products, Linkerbot’s component-supplier role caps its upside, however large its market share.

If, on the other hand, the field follows the trajectory China’s investors increasingly assume, with humanoids moving into logistics, services, and eventually domestic environments, as 1X is already attempting with its NEO home robots, hand suppliers with deep manufacturing capability and proprietary data could capture a disproportionate share of the value chain.

There are also softer risks: the geopolitical exposure that comes with state-fund investors and a Beijing-Shenzhen footprint at a moment of renewed US scrutiny of Chinese deeptech, and the possibility that humanoid platforms eventually integrate hand designs in-house.

Tesla, which is preparing Shanghai Gigafactory for Optimus mass production, has signalled it intends exactly that, and Meta’s acquisition of Assured Robot Intelligence hints that platform players want the whole robot, not a third-party bill of materials. Linkerbot’s bet is that being early, large, and the only volume supplier in its category will be enough to outrun those risks.

Investors will price that bet in the next round. For now, a company that started with a cartoon cat and a hunch about hands is one of the more closely watched stories in the global robotics market, and one of the few in which the headline numbers, market share, monthly volume, and employee count are not just promises.

For fifty-one weeks out of the year, I’m 100 percent not the CEO of Snap, the company behind Snapchat. That’s Evan Spiegel, the company’s billionaire cofounder. No one in their right mind would question that. But for one week out of the year, specifically last week, some people may have thought I was the social media firm’s top executive. If you looked on Wikipedia, it sure seemed like I was.

Starting on Sunday, when you clicked on Spiegel’s Wikipedia page, there was a picture of me. The same thing happened if you ran a Google Search for Evan Spiegel or asked Google Gemini about him. At the time of publication, that’s still the case.

How did this happen? Despite what the internet might have you believe, I’m Maxwell Zeff (friends call me Max). The photo on Spiegel’s Wikipedia page was taken at a TechCrunch conference last year. I’m a reporter in my twenties, and while I write about technology companies for a living, I’ve never met Spiegel and have barely ever written about Snapchat.

But now I’m the CEO—according to Wikipedia. This first came to my attention on Monday, when I was scrolling through social media and I saw a random account post “that doesn’t look like Evan Spiegel” with a screenshot of my photo on his Wikipedia page. I paused for a second, wondering if I was seeing things. I reposted the photo on Twitter and said, “Very flattering but that is indeed me, and not the CEO of Snap.” My followers were amused, responding with comments such as “Congrats on the promotion” and “when yacht invitation max.”

The next day, I was still Wikipedia Evan Spiegel. A Snap employee texted a mutual friend a screenshot of a Google search for Spiegel, saying, “Not Max being the second photo that comes up on Google now …” A day later, more colleagues, friends, and family members had started to notice. One texted me, “Why are you Evan Spiegel?” I didn’t have a good answer. Before I knew it, I had spent a whole week as Wikipedia Evan Spiegel. I decided to do some sleuthing.

On April 26, someone with the username “Artem G” changed the photo of Evan Spiegel to one of me with the comment “Newer photo,” according to the page’s revision history. Then, a few days later, someone changed it back, correctly stating: “That’s Maxwell Zeff, not Evan Spiegel.” Within hours, Artem G hopped back on and reverted the change, returning my face to the Wikipedia page saying, “Nah, new photo is better, take it to the talk page if you must.”

Artem G’s attitude and dedication piqued my interest. For the uninitiated, the talk page is where Wikipedia editors go to settle disputes. Who was this person who felt adamantly that I should be Wikipedia Evan Spiegel and was willing to throw down in the talk page to keep me there?

I scrolled a bit further down and found that Artem G had actually tried to make me Wikipedia Evan Spiegel another time, back in February, but the photo had stayed up for only a few hours. I clicked on Artem G’s contributions page to see what other Wikipedia pages he had made changes to. There were lots. He’d made hundreds of contributions to various pages—ranging from Swiss scientists to space artifacts to Claude—just in the past month.

Plenty of people scroll past cheap desk gadgets online without a second thought. One particular alarm clock shaped like the original 1984 Macintosh caught the eye of builder Wells Riley. Sold on AliExpress for around twenty dollars, the Maclock looks shockingly close to the real thing from the outside, complete with the distinctive beige case, floppy disk slot detail, and even a small screen area. Inside, though, it held nothing more than basic clock circuitry and a speaker for alarms.

First, he decided to strip out the components and replace them with parts capable of running genuine Classic Mac OS software, resulting in a tiny little gadget that yet provides a rather accurate retro computer experience. Every original button still works, the brightness knob really adjusts the display, the speaker plays the startup bells and system noises, and the entire thing can be powered by a battery. Of course, no external monitor or keyboard is required to get started, but if you do want to connect to something, use the USB ports.

Sale

Dralaw Maclock Retro Pixel Alarm Clock, 1984 Computer-Inspired USB Desk Clock with Pixel Display…

• 【Retro 1984 PC Design】 This miniature ivory-colored ABS/PMMA alarm clock revives the charm of the early computer era. A perfect nostalgic piece…
• 【Multi-Mode Pixel Display】 The high-contrast LCD simultaneously displays time, calendar, date, and room temperature. A hidden pixel smiley…
• 【Adjustable Brightness for Comfort】 Gentle, warm backlighting in the morning and dim lighting at night protect your sleep. Simple bottom dial…

Core hardware choices made the tight fit possible. A Raspberry Pi Zero 2W provides the processing power in a board small enough to hide inside the four-inch-tall case. A Waveshare 2.8-inch IPS LCD replaces the original clock face and delivers crisp pixels at a resolution suitable for the era’s software. To make the screen sit flush behind the curved plastic lens, Riley designed and 3D-printed a custom bezel that adapts the display perfectly to the Maclock’s contours. Without that printed part, the modern panel would sit too deep or sit crooked.

Wiring it all together required additional equipment. Riley created an open-source breakout board called the Macintosh Mini PCB. It may be obtained from sites such as PCBWay, and there is even a shared URL in the project files that anyone can use if they so desire. The board manages the connections for the buttons, the brightness dial, audio output via a small amp, and power. It also has an area where you may bend or remove specific pins on the Pi so that they do not interfere with the screen. The guidelines also provide a basic wiring table that shows you which pins on the breakout board connect to which on the Pi, making the entire soldering procedure much easier even if you’re new to it.

Assembly is actually a rather simple process. First, you disassemble the Maclock, which means detaching the base and exterior case. You will need to remove all of the original circuitry. Now it’s time to install the 3D-printed bezel and snap it into place. Once completed, the LCD is neatly attached behind it, and the custom PCB is inserted into its allotted location at the bottom. Wires must then be routed to the Pi Zero, which is located in the lower compartment with a small speaker module.

Getting the software up and running is also rather straightforward. Flash a copy of Raspberry Pi OS Lite onto a microSD card and boot up the Pi, and you’re almost done. Next, locate a Classic Mac OS disk image file that ends in.hda (don’t forget to obtain a compatible ROM file) then transfer those data to the card using a proper computer. Now, connect to the Pi from another computer on the network and perform a single command to retrieve a setup script from the GitHub repository. The script will install the SheepShaver emulator, configure the display drivers to work with your Waveshare panel (with a custom overlay that can be changed), start python scripts for handling the buttons and brightness control, and start all of the system services. After rebooting, the device should launch into the Macintosh environment without any problems, including that nice startup chime.