[This is a sponsored article with the Singapore Government Partnerships Office.]

Hackathons often spark brilliant ideas that can contribute to a better nation. Yet, more often than not, they fizzle out before making a real difference. 

But in Singapore, Build for Good is attempting to change that trajectory.

Build for Good is a citizen engagement initiative by Open Government Products (OGP) that aims to empower Singaporeans to make the city-state better in their own ways through their month-long hackathons and accelerator programmes. 

It provides a safe space for startups and innovators to test, refine, and scale their social impact projects, and with mentorship, resources, and a network of partners, ideas that start small can grow into initiatives that genuinely help communities across Singapore. 

To date, Build for Good has organised four hackathons, with over 300 participants who have built solutions for public good, from improving accessibility to strengthening community support networks.

Simplifying caregiving through citizen innovation

Several of these projects have since moved beyond the prototype stage, supported by Build for Good’s accelerator programme, which works with selected teams to refine their ideas, conduct user testing and explore sustainable operating models.

This includes CareCompass, a free-to-use app designed to assist first-time caregivers, particularly those supporting dementia patients, in navigating the often overwhelming world of caregiving.

Founded during the 2024 Build for Good hackathon, CareCompass simplifies access to critical resources: users provide a few details about their care recipient, and CareCompass generates curated guides tailored to their needs. 

The AI-powered app consolidates essential information, from available subsidies to relevant local services, helping caregivers quickly find the support and resources that can make day-to-day caregiving more manageable.

It’s a solution that deeply resonates with the team, as every member has either been a primary or secondary caregiver, or has been closely involved with family caregiving. 

For Joshua Gei, one of the founders of CareCompass, the experience was particularly vivid. When his grandfather suffered a stroke in 2020, he encountered the complexities of the caregiving system firsthand.

At discharge, our family faced challenges coordinating care. Nurses, social workers, and doctors all had different pieces of information, from arranging home renovations to understanding which subsidies applied. Managing all this while adjusting emotionally was overwhelming.

Joshua Gei

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After coming together as a team during the Build for Good hackathon, Joshua and his co-founders quickly realised they had all faced similar caregiving challenges—and that’s when the lightbulb moment came.

“We realised we all had similar stories—fragmented information, overwhelming logistics, emotional exhaustion,” he recalled. “That’s when we knew we weren’t just building an app, we were solving problems our own families had struggled.”

The resources and guidance offered during the hackathon were crucial in turning the team’s idea into a working solution.

The Build for Good programme that produced CareCompass partnered with the Singapore Government Partnerships Office (SGPO) to foster deeper co-creation between citizens and government. Through this partnership, SGPO connected participants with subject-matter experts across government agencies, helping teams gain a clearer understanding of the problems they were tackling.

For CareCompass, this meant gaining access to the Agency for Integrated Care (AIC) and getting referred to DementiaSG, allowing the team to validate assumptions, test features, and design the app around real caregiver needs.

“AIC and DementiaSG provided access to backend data and connected us to caregivers on the ground,” Joshua shared.

With their progress and validated concept, CareCompass was eventually selected for the Build for Good Accelerator programme, receiving S$20,000 in funding to develop, pilot, and launch their solution. 

Community partners such as Mindfull Community engaged 20 caregivers to provide feedback on the platform, and there are ongoing plans to pilot with grassroots teams in Braddell Heights and Punggol.

These partnerships ensured that CareCompass is grounded in real caregiver needs, complements existing resources, and evolves based on both on-the-ground and digital feedback.

Joshua Gei

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Since launching in Nov 2024, CareCompass has garnered around 500 registered users and over 2,000 total users, and continues to expand its reach. 

The platform has also merged with Heartbeat, another solution developed during the same hackathon aimed at tackling senior isolation, to create a more holistic caregiving ecosystem. 

By integrating Heartbeat’s wellbeing features, including daily check-ins, reminders, and engagement tools, CareCompass allows users to monitor their care recipients’ overall wellbeing more effectively.

Bridging gaps in mental health support

While CareCompass tackles the practical challenges of caregiving, another team at the same hackathon was focused on empowering individuals in their mental health support through EBI.

The platform’s founders are all too familiar with the frustrations of mental health discontinuity—none more so than Richard Xiong.

After just four months of therapy, his therapist relocated overseas. “I had to begin the process again,” he recalled. “Waiting months for a new appointment and having to retell painful experiences felt like reopening old wounds. It often feels like taking two steps forward, then one step back.”

But Richard is not alone. Many patients in public healthcare face therapist changes due to leave or reassignment, forcing them to start over repeatedly. 

He came to realise just how widespread this issue was during Build for Good’s “Human Library” session, where participants got to engage with mental health experts and discuss the challenges Singaporeans face today.

Through these conversations, he and the rest of the EBI team realised that mental health—despite being a growing concern in Singapore’s fast-paced society, particularly among youths—remains burdened by stigma and systemic gaps in support.

Thus, they created an app that helps users articulate and process their feelings through AI-powered prompts, offering journaling, chat, or voice-based interactions, including an assistant that’s even able to converse in Singlish. 

EBI summarises key concerns and coping strategies from entries, allowing users to track their progress and share insights with mental health professionals.

By combining guided self-reflection with personalised insights, the platform addresses the fragility of the therapist–patient relationship and the gaps in support between sessions or across providers.

To bring the app to life, the team similarly benefitted from OGP and SGPO’s combined support, which provided not only mentorship but also connections to experts in the mental health space. 

These partnerships helped the team understand real-world challenges, validate assumptions, and shape the app to address the needs of users on the ground.

At first, EBI was rolled out in an open public beta during the 2024 hackathon, where members of the public were invited to sign up and use the platform for free. The team went on to be selected for the 2024 Build for Good Accelerator programme, alongside CareCompass.

Following the beta, which engaged nearly 300 participants, the team’s focus has shifted from direct public promotion to clinical validation, with the aim of demonstrating EBI’s effectiveness in a healthcare setting. 

The team is currently conducting a clinical trial in collaboration with local healthcare providers, focused on patients with disorders of gut-brain interaction such as IBS.

The platform’s potential extends beyond mental health. It is positioning itself as a broader tool for mental wellness across the healthcare spectrum, particularly in areas where psychological support plays a crucial role but remains underserved. The team believes EBI can help bridge this, supporting patients at scale while giving clinicians better data to track progress over time.

Reflecting on their journey, Richard sees the collaboration as essential: “In many ways, I see innovators like us as the seeds of change, and the government as the water and sunlight that help those seeds grow.”

“Without the creativity and drive of the community, there would be no seeds to plant; but without the structure, trust, and nurturing environment provided by the government, those seeds could never take root.”

You, too can make a difference

Both CareCompass and EBI demonstrate how citizen-led innovation creates meaningful social impact when supported by structured programmes like Build for Good.

As the EBI team puts it: “We believe that when it comes to solving complex societal problems, success depends on shared ownership between the community and the government.”

Like the teams behind these initiatives, you, too, can make a difference. 

Have an idea that could create positive change? The Singapore Government Partnerships Fund by SGPO supports community-driven initiatives. Learn more about SGPF here, and explore other solutions through Build for Good here.

Featured Image Credit: Build for Good

The Pixel 11 series isn’t due until August, but a steady stream of leaks has already revealed details ranging from design to specs across all four upcoming devices. The latest addition gives us a look at the wallpapers Google may ship with the lineup, and they offer a strong hint at the color options likely at launch.

A toned-down palette across the board

Mystic Leaks on Telegram (via 9to5Google) has shared the full wallpaper library for every model in the Pixel 11 lineup ahead of the phones’ expected August launch. According to the leak, the base Pixel 11 will get four abstract wallpapers in black, green, pink, and purple.

The Pixel 11 Pro and Pro XL will share a separate set in beige, black, gray-green, and silver, while the Pixel 11 Pro Fold might get just two options, one in a black-and-white theme and another in green.

All wallpapers will come in dark and light variants to match their respective system themes. The lighter versions are slightly more vibrant, but still noticeably more muted than what Google offered with the Pixel 10 lineup.

What the wallpapers tell us about the Pixel 11 colorways

The wallpaper colors appear to line up with a previous leak that pointed to black, green, pink, and purple as the color options for the base Pixel 11. If the same logic holds for the Pro models, the beige wallpaper signals a new colorway, alongside black, green, and a silver option like the Pixel 10 Pro’s Moonstone variant. Similarly, the Pixel 11 Pro Fold may be offered in black and green colorways.

Google has not confirmed any of this officially. But the shift toward more muted tones could mark a departure from the more vibrant wallpaper choices Google has offered in recent years. Whether that reflects a deliberate design direction for the hardware colors or just a different creative approach remains to be seen when the phones launch later this summer.

Benchmark Capital, the storied Silicon Valley VC firm known for early investments in eBay, Snap, Uber, and Twitter, is breaking with one of its signature traditions: keeping its funds to about $425 million and backing only young startups. After more than two decades of restricting its vehicles to that amount or lower, the outfit has closed on commitments of $2 billion across two new funds, including a $1.25 billion vehicle dedicated to later-stage investments, according to the Wall Street Journal.

While the fund sizes of many venture capital firms have ballooned into billions of dollars over the last decade, Benchmark stuck to the strategy that helped make it legendary. By being staunchly selective and taking a large—typically 20%—stake in every startup the firm backed, it maintained a model designed to maximize outsized returns for its limited partners.

However, Benchmark’s relatively small fund sizes have likely prevented the firm from investing in capital-intensive AI startups, particularly foundation model makers, whose round sizes often reach into hundreds of millions. As a result, the firm hasn’t invested in Anthropic, OpenAI, or any of the other capital-intensive AI labs, such as Periodic Labs, Reflection AI, or Recursive Superintelligence.

Benchmark’s new $750 million early-stage fund will give the firm more flexibility to write checks in an environment where early-stage valuations have skyrocketed. While the firm has traditionally backed companies at the Series A stage, Benchmark has recently given itself more flexibility to invest in companies at other early stages of development.

In recent months, Benchmark backed two Series B startups: Gumloop, a platform that allows enterprises to create AI agents without writing code, and Monaco, an AI-native sales and CRM platform.

Benchmark general partner Everett Randle previously told TechCrunch that the firm looks to build a “meaningful and deep relationship with the entrepreneurs, and that can happen relatively early in the company’s lifecycle, at seed, [Series] A, at [Series] B.”

The firm dipped its toe into late-stage investing when it raised a $225 million special purpose vehicle (SPV) to participate in a $1 billion pre-IPO round for Cerebras, as TechCrunch reported earlier. Benchmark first led the chipmaker’s Series A in 2016. Cerebras held its IPO last month, returning Benchmark $3.25 billion at the IPO price.

That windfall prompted the firm to raise a dedicated growth fund. That new vehicle will make five to six large investments in both existing portfolio companies and new startups, according to a person familiar with Benchmark’s strategy.

The two new funds aren’t the only changes at Benchmark. Over the last two years, the firm has undergone a significant shift in its general partners.

In 2024, Miles Grimshaw left the firm to rejoin Thrive Capital. Then, last year, Sarah Tavel—Benchmark’s first and only female general partner to date—took on the less-involved role of venture partner, while Victor Lazarte departed to start his own VC firm.

To replenish its ranks, Benchmark — which traditionally runs with four to six general partners — added two new high-profile investors to its team: Randle, poached from Kleiner Perkins, and Jack Altman, the brother of OpenAI CEO Sam Altman. The moves suggest that even Benchmark, long defined by its resistance to growth, now sees the AI era as requiring a different playbook — more capital, more stages, and fresh blood at the partner table.

The state law governing app use by minors takes effect tomorrow.

• The upcoming Phison E37T SSD controller manages to max out PCI-E 5.0 read speed ratings at 14,900 MB/s
• It consumes less than a 3rd of the power of Phison’s older DRAM-infused SSD controllers
• The move is a crucial one, aligned with Phison’s anticipation of high DRAM prices in the foreseeable future, aimed at providing relief to mainstream and enthusiast consumers

With SSD prices moving sharply upwards over the last few months, thanks to unrelenting AI demand across the board, consumers are increasingly looking to the lower end of the spectrum to bridge the gap between their budgets and the cost of modern SSDs.

The upcoming Phison E37T SSD controller could help tide things over. It happens to be the first Gen 5 DRAM-less SSD controller to max out bandwidth over the 4 lanes available to an M2 SSD on modern PCs, laptops, and consoles.

In the world of buzzwords, the acronym ‘AI’ has absolutely been the buzziest of buzzing buzzwords for at least a few years now. Where previously terms like ‘smart’ and ‘intelligent’ sufficed to promote a product, we are now being told that we are living in an age where this supposedly newfangled ‘artificial intelligence’ is doing literally everything faster and better while also curing cancer on the side. Yet, as a wise man once said: “You keep using that word. I do not think it means what you think it means.”

The obvious implication of using a term like ‘artificial intelligence’ in this manner is that it brings to mind a modern version of early last century’s ‘electronic brain’ vernacular alongside the rise of digital computers. Yet rather than electrons in vacuum tubes and semiconductors propelling us into a brave new world of super-intelligence, we now just use said devices to doom scroll and to engage in passive-aggressive online communications like the typical primate groups in a virtual jungle defending their turf.

Similarly, the term AI is massively oversold today, least of all in the inherent presupposition that we somehow have finally cracked the mystery of the brain and have created an intelligence that can go toe-to-toe with humans and even our corvid dinosaur friends. Perhaps the worst part is that there is a veritable mountain of fascinating algorithms and other constructs that help us automate many tasks today, making it somewhat rude to just give up and call everything ‘AI’ like we learned nothing from the 1980s AI craze.

So what is exactly being smoothed over by the glossy marketing of ‘everything is AI’?

Cognition Versus Intelligence

Recently I covered the topic of intelligence, both in the sense of its definition and the empirical evidence. Within that definition it is already quite obvious that animals like birds are pretty intelligent, and can compete with the average human on a number of metrics. Of the different types of intelligence, fluid intelligence (Gf) is perhaps the most crucial since it pertains to what might be the clearest sign of intelligence in the form of reasoning.

Add to this memory (knowledge and recall) as well as acquired skills and you got the basics of general intelligence. One could absolutely make the point that this is all that intelligence is about, as in the acquisition of data, processing it and using reasoning to come to new conclusions. Yet as can be seen in the referenced article, the basic CHC intelligence model can, and has been, expanded to include sensory, motor and efficiency metrics, which are very species-centric.

Of course, it is true that within cognitive processes it’s hard to exclude sensory input and output via actuators like muscles to perform some kind of physical action. After all, no type of intelligence is of much use if there are no in- and output, such as how we need at least one of our five senses to be aware of the world around us along with some way to interact. Whether intelligence could develop without both is also a valid question.

The resulting disagreements in the academic community on where to draw the line between intelligence and cognition do not help with narrowing the scope of ‘intelligence’, as it makes it possible to assign the label to something like machine vision. Even when this is a system that merely replicates parts of the visual cognitive process without the underlying reasoning and understanding that accompanies this cognitive process in us animals.

What we can conclude from this, however, is that what we call ‘smart’ or ‘AI’ are merely systems that attempt to replicate such a fragment of the human cognitive process.

Machine Vision

Perhaps the biggest strength of machine vision (MV) is that it allows for a cognitive task to be off-loaded to a computer system that will never suffer fatigue or become distracted. This is essential in tasks like quality assurance, such as on production lines. Rather than having a human check each item that zips past for certain properties, alignments, etc. a machine vision system can take over this cognitive task while being inarguably far more efficient.

MV encompasses a wide range of implementations, all targeting a specific task that can use different sensors and outputs to accomplish a goal. For e.g. PCB assembly lines and food production you got many MV systems that use visible light as well as near-infrared and other camera and sensor types to detect flaws, spoilage and other issues. This data is then passed through the rest of the system, where some kind of programming allows for the detection of any issues.

At the board house, suspect PCBs are identified and then taken off the conveyor and handed over to a human who can then either confirm the issue and address or bin it, or mark it as a false positive by the system and put it back on the conveyor. The main advantage here is that it reduces the cognitive load on the humans, who are notoriously terrible at long stretches of boring work.

Another area where MV is essential is that of self-driving vehicles, which is where sensor blending and interpretation of features in a scene using e.g. edge detection and recognition using a convolutional neural network (CNN) is paramount. This replicates the human cognitive process of navigation and steering, though it should be noted that these systems require significant more sensors, including radar and Lidar, to do their job somewhat effectively.

Here it should be noted that MV doesn’t replace human cognition. Rather, it serves to complement it from a general automation perspective. This is why purely self-driving vehicles (Level 5) are still fictional and sometimes comically obvious PCB assembly flaws can make it through automated QA, even if overall it is a net win for the human workers.

Pattern Recognition

Much of the medical profession is about pattern recognition and differential diagnostics, as symptoms and test results have to be categorized and analyzed. Within this field there has been a push towards computer-aided diagnosis (CAD) for decades now, here also to try and reduce the cognitive workload on medical staff. The start of this was with expert systems implemented in e.g. Lisp, which use a knowledge base and an inference system in order to reach a conclusion or solve a problem.

An issue here is of course that this knowledge base has to be constantly maintained, which is why artificial neural network designs have become more popular, with large language models one particular example of these. Such models can be updated more easily, with the slight gotcha that by not having the expert system maintained by human beings any more and instead relying on what are essentially statistical models, you’re abandoning the ‘expert’ part.

This is why LLMs have been increasingly pushed to the side by things like retrieval augmented generation (RAG), which ‘grounds’ the provided facts in more factual reality such as human-written documents, leaving the LLM to help provide a friendly natural language interface.

When it comes to analyzing test results such as of MRI scans and X-rays, this covers much of the same ground as with full MV systems, with the same gotcha that although it can save time, it can also make incredibly dumb mistakes and thus cannot be left unsupervised.

Natural Language

Perhaps the biggest advancement of the past years has been in creating better chatbots that can keep up a conversation on a level that would put ELIZA to shame. Of course, this is at least as much smoke-and-mirrors as ELIZA, in that there is no actual intelligence or concerned therapist behind the friendly interaction, just a complex human-written chat interface that creates the query and handles all other details of using an LLM for generating the semblance of a human-level interaction.

The term ‘emotional intelligence‘ refers to the ability to perceive and feel emotions, something that is impossible for an entity that is incapable of feeling and reasoning, meaning that it is a fairly complex cognitive process that is also heavily susceptible to projection of one’s own feelings onto another person or even an inanimate object. Although the chatbot is literally incapable of learning and requires external session information to be stored within the context window, these can be very convincing near-facsimile under the right conditions.

Faking Cognition

The increased use of machine vision and similar systems has been an absolute boon in automating industries and other fields, making life better for everyone involved due to the reduced cognitive load and freeing up humans to do more creative tasks where one isn’t asked to mindlessly perform the same task over and over.

There are many fields where such increased cognitive offloading is a good thing and quite feasible, but always with a full understanding of the limitations and potential pitfalls, especially when it comes to risks like cognitive atrophy caused by cognitive surrender. This has been identified as a hazard in an increasing number of studies, highlighting the importance of maintaining one’s critical thinking skills.

Even if actual artificial intelligence happened next year, it’s still paramount that we treasure human intelligence, as it is the only one we will always have, as well as the sole reason why humankind has come this far.

from the good-deals-on-cool-stuff dept

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The propensity of gasoline to ‘go stale’ through the process of oxidation is the reason why gasoline that has been stored for a long period of time is considered to be unusable, as it will no longer combust property. Since this process creates the sludge that you find in the bottom of an old gasoline canister, it follows that you may be able to distill out the still good gasoline. With this reasoning, [Joel] over at the [Lowered Expectations] channel set to work to try out this theory.

As part of his job of maintaining things like pressure washers, he got access to many grades of stale gasoline to experiment with. After a short demonstration of how poorly these grades of stale gasoline burn it’s on to the main distillation event. To the stale gasoline aluminium oxide is added as both a catalyst and to create nucleation sites that will prevent ‘bumping’ where you suddenly get a surge out of the heated flask.

Of course, that this is incredibly dangerous should be obvious, and the lack of PPE on the side of [Joel] is somewhat worrying. On the positive side, he does take it easy with ramping up the temperature on the gasoline to try and find the sweet spot where production seems sufficient. This turned out to start at 70°C in the flask when the condenser began to receive its first load of presumably clean-ish gasoline.

The goal here is of course to approximate the function of the fractionating column (‘distillation tower’) at refineries at smaller scale, which [Joel] appears to be doing correctly with what looks to be a Vigreaux column. Since the base product is gasoline with oxidized contaminants this process is of course quite different, so he goes through the different temperature ranges to see what kind of distillate he gets, up to nearly 200°C before calling it.

Ultimately 880 mL of the initial 1 L was collected, with the various distillates combined for testing. Unfortunately none of the testing is actually covered in the video, but it is mentioned at the end that a second batch of the distillate was used to power his car, so presumably it works.

Suffice it to say that ‘works’ doesn’t mean that it is safe, of course. Heating such stale gasoline produces many highly flammable and combustible substances, along with many that are just downright bad for your health to be exposed to. The plethora of very short-term to all the way to very long-term health effects this may cause should be obvious.