This open-source app turned my Android phone into a portable physics laboratory


Your phone already uses its sensors more than you probably think. They count your steps, point maps in the right direction, adjust screen brightness, detect motion, and rotate the display when you turn the phone sideways. Phyphox is an open-source app that lets you use those sensors directly, so you can explore the world around you instead of only seeing the features they power.

Your phone has more sensors than you realize

It sees more of world than you do

Your phone is packed with sensors constantly measuring the surrounding environment. It can detect motion, rotation, sound, light, magnetic fields, location, and occasionally air pressure. With the right app, you can turn it into a portable physics laboratory.

One of my favorites is Phyphox. Phyphox, shorthand for “physical phone experiments,” is a free, open-source app developed at RWTH Aachen University. It can read and display information from your phone’s accelerometer, gyroscope, microphone, magnetometer, light sensor, GPS, and—if your phone has one—the barometer.

t graphs data in real-time, runs built-in analysis, and can export the data you collect as a CSV or Excel document for later use. If you’ve ever wanted to learn more about the physics of everyday objects and actions, it is a great place to start.

Phyphox turns your phone into a scientific instrument

See sounds as a graph

The microphone is a great place to start. Phyphox offers around ten audio experiments, from simple volume meters to full spectral analysis.

Open the waveform experiment and clap your hands. The clap appears as a sharp spike, showing you something visually that normally only lasts an instant. After that, you could try whistling or humming a steady note. The audio spectrum experiment breaks sound into component frequencies using a Fourier transform. That allows you to compare a low hum with a high whistle, see the harmonic overtones in a note, or chart how difficult it is to hold a steady pitch.

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There’s also a precise pitch detector using autocorrelation (which can be used to tune a guitar), a sound-level meter that reads in actual decibels (at least as accurately as a phone can), and a tone generator that turns your phone into a signal source for testing how sound behaves in a room.

Measure motion you normally only feel

Motion is where phyphox obviously connects to features you probably already use daily. The accelerometer helps count steps, detect movement, and contribute to crash detection on phones with the feature.

Try recording in an elevator. As it starts, stops, and changes direction, the accelerometer captures the subtle shifts your body feels. If your phone has a barometer, you’ll also see air pressure change as you move between floors.

The accelerometer is what enables your phone to work as a pedometer—each footfall shows up as a distinct peak in the accelerometer data, and you can watch the algorithm distinguish real steps from noise.

You aren’t limited to detecting footsteps either. Between the gyroscope and the accelerometer, your phone can very accurately collect information about your motion regardless of what you’re doing. I was able to very obviously see an unbalanced load in my washing machine because of its motion; you’re only limited by your imagination.

Map magnets, light, and air pressure

There are several other interesting sensors available in your phone besides those used to measure motion. One interesting example comes from the magnetometer.

The magnetic field strength of some magnets.

Open a magnetic field experiment and move your phone near a fridge magnet. The field strength rises and falls across all three axes. Move slowly around the magnet, and you start to see why textbook field-line diagrams are drawn the way they are—the data traces the same curves. If you have multiple magnets, you can use the “Absolute” tab to see which one has the strongest magnet.

Out of curiosity, I placed my phone on top of my microwave (not inside) to see if the phone could measure the activity of the magnetron that creates the microwaves. It was more sensitive than I expected—it even managed to pick up the way the magnetic field changes with time.

The barometer measures changes in air pressure. It can give you an elevation profile that is determined by measuring change in atmospheric density (and therefore pressure) as you go up and down hills. Alternatively, if you happen to know a thunderstorm will be rolling in, you can use it to measure the decrease in pressure that causes the formation of rain clouds and hurricanes.

Build your own experiments

Phyphox also makes it easier to put the data from your sensors (or experiments) to use. It can run a small web server on your phone, letting you start, stop, and monitor experiments from any browser on the same network—essential when the phone needs to sit undisturbed or be attached to something moving.


Lucas Gouveia/How-To Geek


I Use Python, but I’m Learning R and the Tidyverse for Data Analysis Too

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Every experiment exports data in formats ready for Python, a spreadsheet, Excel, or any other analysis approach. If the built-in experiments don’t cover what you need, phyphox has an experiment editor that allows you to choose sensors, set sampling rates, and design the output layout. Custom experiments can be saved and then shared as XML files, making it easy for a teacher to design one and distribute it to a whole class.


Your phone is a scientific powerhouse

Phyphox proves that physics isn’t confined to a classroom. Our everyday world is littered with examples of physics in action that we often walk by without ever considering, and we have the ability to casually take measurements that would have made any scientist alive 120 years ago envious.

Even if you don’t use it every day, keep Phyphox on your phone and the next time you notice something interesting or odd, see what you can find out.



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  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

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McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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