Hackster.io's Emotion in Motion Contest Asks Machines to Read People

Ask an engineer where the useful knowledge about a microcontroller actually lives, and the answer is rarely the datasheet. It's in a project write-up with photos of a board that failed, a forum thread about an I²C bus that behaved differently on a longer cable, or a comment from someone who tried the same sensor last year and switched parts. That's the core of the argument Dianne Kibbey, Senior Director of Community Strategy at Avnet for Hackster.io, made in a recent conversation with Amelia Dalton. Even if you lean on AI tools every day, her case deserves a hearing.

Why a community still beats a chatbot for hardware questions

Kibbey's framing starts with a simple observation: communities form around almost everything people care about, from fitness apps to language courses. In electronics, manufacturers run support communities of their own, and engineers have always gravitated toward peers who face the same problems. Hackster, by her account, offers something a general-purpose AI assistant doesn't: crowdsourced insight and hands-on experience.

Her example is useful because it's specific. An AI tool can explain what a Nordic MCU is and how to program it. A community can tell you that someone built a project with it, which sensor held up in the field, what broke, and what they'd change next time. That gap is what she calls tribal knowledge: undocumented quirks, workarounds, and the small behavioural details of a part that never make it into a formal specification.

I think this is the part of her argument that practising engineers should weigh most carefully. Datasheets are authoritative about absolute maximum ratings and electrical characteristics. They're much less helpful about how a board behaves once it's enclosed, powered from a marginal supply, and running next to a motor driver. Those are the failure modes that cost days, and they're rarely written down by the people who designed the silicon.

What a 42,000-project library gives you that a datasheet doesn't

Kibbey describes Hackster as one of the largest global networks of engineers and students, with more than 2.5 million registered members and over 42,000 uploaded projects. Her point is less about the headcount and more about what a project actually contains. Members typically publish step-by-step instructions, code, schematics, and bills of materials, and most also make video tutorials. Over time, that makes the site a reference library of working designs, not just a feed of finished photos.

The key detail is how members document their process. Kibbey said the platform lets people record each stage of a build, including troubleshooting along the way. That matters because the decisions that went wrong are often more instructive than the one that worked. A project that shows three failed sensor options before the final pick tells you more about the trade-offs than a single clean schematic does.

Conversation is the other half of it. Comments on a project let readers ask detailed questions, and the original builder can answer them. Kibbey described this as learning by doing: you look at what others built, you see what worked and what didn't, and you get a direct line to someone who has already hit the problem you're facing.

Hackster also runs formal educational content, including build-alongs that walk through projects step by step. Kibbey summed up the philosophy in a few words: it's all about building.

AI and communities as complements, not competitors

Dalton pressed on the obvious question. If Gen AI tools keep getting better, does a community still matter? Kibbey's answer was that the two work together. She said AI tools are good at finding facts, and she's spoken with engineers who use them to speed up data sheet searches, data sheet analysis, and basic code writing. That's a fair description of where these tools currently earn their keep.

Where AI stops is the lived experience. Kibbey argued that thousands of engineers sharing their successes, failures, and optimisations produce knowledge that goes well beyond typical documentation or a generated answer. She also noted that Hackster is already appearing in citations from different AI assistants, mostly for prompts that ask for specific project examples or examples using particular boards.

That detail cuts two ways, and I'd read it carefully. On one hand, it suggests that community content is becoming a source these tools lean on, which strengthens the case that the knowledge matters. On the other, it means the quality of what AI systems learn about hardware depends partly on whether engineers keep documenting real builds in public. If the community thins out, the training material thins with it. Kibbey's view is that AI and communities together will continue to lower the barrier to entry for building with electronics, with AI helping people start and the community supplying the lessons learned.

The shift toward physical AI

Kibbey's view of what's coming next centres on what she called physical AI. Edge AI has been a familiar topic for years, meaning inference runs close to the device rather than in the cloud. The change she sees is that intelligence is moving into the physical device itself. She pointed to medical devices and robots that need to adapt and act in real-world environments as examples of where this is heading.

That shift changes the design problem. A classifier running on a sensor board is one thing. A machine that has to read a person's gesture, decide whether to move, and move in a way that feels appropriate is a different engineering task, with timing, sensing, and actuation all coupled together. Hackster's response is a set of design challenges over the next six months focused on robotics, drones, and intelligent devices. Kibbey said the platform gives engineers and students access to hardware for this work, and she expects many of these projects to start as community builds before some of them become commercial products.

Emotion in Motion: the contest details

The most concrete item in the conversation is the Emotion in Motion contest, which Kibbey said launches on November 10. It's sponsored by Renesas, Arduino, and Hackster, and the team describes it as one of its biggest hardware giveaways in many years. The prize allocation is 500 Arduino UNO R4 WiFi boards, which use a Renesas microcontroller.

The brief asks developers to think about how machines interact with people. Kibbey contrasted this with the usual robotics framing, where the question is how fast a machine moves, how precise it is, or which repetitive task it automates. Emotion in Motion asks a different question: how does a machine recognise human cues and respond in ways that feel natural? She said that as robots move into environments where they work alongside people, that question will matter more and more.

Here's how the process is set up, as Kibbey described it:

Applications open at launch. Developers log in, describe their plan, and say what they intend to build. Hackster will select the strongest ideas from those applications and award the 500 boards. A typical Hackster contest runs about four weeks, but this one falls across a busy holiday period, so applicants get an extra month. Hardware applications will be accepted until January 10.

The total prize pool is $14,500, shared across seven winning projects. First place receives $5,000, and there are second- and third-place awards. Kibbey also described several category prizes:

Best in Motion focuses on the machine itself: how the device moves, gestures, and physically reacts.

Best in Emotion looks at how the device responds to human signals, and how meaningfully it responds to the cues it receives.

There's also a student award, which Hackster hasn't run in a while, and a Best Use of the Arduino UNO R4 award.

Kibbey pointed out that the Arduino ecosystem gives developers a familiar starting point, so they can get building quickly. The Renesas processor underneath gives them room to build more sophisticated physical interactions. If you're deciding whether to enter, that combination is the practical hook: a known toolchain for the early prototype, and enough silicon headroom for the harder sensing and motion work.

On what wins, Kibbey was specific. Judges are looking for projects where the machine does more than detect an input. They want something that interprets a human signal and responds in a way that feels intentional. She said the team wants the reaction, "I would have never thought of that!" Strong execution counts, but originality counts too, and she put it plainly: clever beats complicated. She listed a few tests a project should pass. Is there a clear cause and effect? Does it take in more than one mode of human input, such as gesture and voice? Does it have a memorable personality?

That last point is worth taking seriously if you're building for this contest. A sensor fusion stack that technically works but produces no visible, understandable response will likely score lower than a simpler build with a clear behavioural link between what a person does and what the machine does next.

Hackster Holidays and the Junk Drawer Challenge

Hackster Holidays is an annual event that closes out the year by celebrating community members. Kibbey described weekly hardware giveaways, with boards already lined up from Nordic, Arduino, Infineon, and Particle. Many of these giveaways are bundled with practical accessories such as breadboards or microSD cards.

The event also includes an annual community survey, which Hackster uses to understand who its members are and what they want to build with in the coming year. Kibbey said the team plans year-end content looking back at the best projects and technologies of 2026, and it will recognise top contributors with new member awards.

The Junk Drawer Challenge closes the event. It asks people to take the parts they already have, including leftovers from earlier builds that never became anything, and turn them into something creative. Prizes are attached, and the challenge wraps up around January 10. Taken together, the holiday programme runs through mid-January.

For working engineers, the Junk Drawer Challenge is a useful reminder of what the community values. The projects that tend to get shared are rarely the ones built from brand-new kits. They're the ones where someone made a sensor, an actuator, and a microcontroller do something unexpected with parts that were already on the bench.

What to watch next

My read is that Hackster's bet is a practical one. Its argument is that the most valuable hardware knowledge is still generated by people building things and writing up what went wrong, and that AI tools make that knowledge more discoverable without replacing it. Whether that holds will depend on how many engineers keep publishing full build histories, including the failures, rather than polished final results.

If you're weighing an entry, the Emotion in Motion timeline is the immediate item. Applications open on November 10, and hardware applications close on January 10. Read the contest page for the full rules and eligibility details before you commit to a concept. If you're not entering, the six months of robotics, drone, and intelligent device challenges are likely to produce the most useful public project write-ups of the year, and those are the ones worth reading closely.

Hackster.io's main site is at hackster.io, and the Emotion in Motion competition page is at hackster.io/contests/emotion-in-motion.