DEV Community: Tech Talk Augusta

Three Visions of Innovation: Highlights from Tech Talk Augusta's June Meetup

Schuster Braun — Thu, 11 Jun 2026 15:38:36 +0000

Tech Talk Augusta brought together three fascinating perspectives on how technology is pushing the boundaries of what's possible. From space-based radar to animatronic flowers, here's what we learned.

Seeing Through the Clouds: Kyle McCloud on Commercial Synthetic Aperture Radar

Kyle from Umbra Space opened the evening with a compelling look at how commercial satellite radar is revolutionizing Earth observation. Synthetic Aperture Radar (SAR) technology, originally developed in the 1930s and 40s, has only recently become accessible to commercial operators—and Umbra is leading the charge.

The key advantage of SAR over traditional optical imaging is simple but powerful: it works through clouds and at night. In a world where 70% of Earth is covered by clouds or darkness at any given moment, this capability opens entirely new possibilities for monitoring our planet.

Umbra's current systems can resolve objects the size of a laptop from space, with their parabolic dish technology achieving 25-centimeter resolution. To demonstrate the scale, Kyle noted that they can cover the entire Savannah River in just 7-8 shots. The company recently released 4,000 open-source images, with 16,000 more coming soon.

The practical applications are already emerging. Commercial customers are using SAR imagery to monitor port facilities for stock market implications. Infrastructure monitoring, disaster response, maritime domain awareness, and defense and intelligence applications all benefit from this persistent, all-weather view of Earth. Umbra, headquartered in Santa Barbara with an office in DC, is also planning a hackathon to engage the local tech community around these capabilities.

Where Art Meets Science: Genevieve Lucas on Creative Experimentation

Genevieve Lucas challenged the notion that science and art are separate domains. With a bachelor's degree in computer science and a master's in progress, she's living at the intersection of both worlds as an artist in residence at Westobou, hosted by Gertrude Herbert.

Her current project transforms a local passion fruit plant into a series of animatronic sculptures—flowers that look around and blink, powered by embedded microcontrollers and mechanical precision. The technical journey reveals the hybrid nature of modern creative work:

Hardware & Modeling: Genevieve selected a XIAO ESP32 C3 microcontroller to drive her animatronics. For 3D modeling, she leveraged Autodesk 360, drawing inspiration from the puppeteer industry and open-source animatronics projects. The tight constraints of a small print bed made parallel project management challenging, but Bamboo printing got the job done.

Materials & Fabrication: Working with resin introduced unexpected challenges—managing air bubbles and paint adhesion required experimentation. The real breakthrough came from collaborating with the local maker community: she heat-sealed polyester fabric using the laser cutter at GCC's garage, then hand-airbrushed the petals.

Genevieve sees her work as part of a broader movement where artists are increasingly embracing scientific and technical tools. She highlighted emerging techniques like cellulose printing (imaging vegetables), 3D-printed textiles, and SCOBI leather as examples of this convergence. The result is art that's simultaneously aesthetically compelling and technically sophisticated.

Making Metal Printing Accessible: Shawn Matt and Trusted Metal

Shawn Matt wrapped up the evening discussing Trusted Metal, an additive manufacturing startup founded in 2023 that's tackling one of modern manufacturing's most complex challenges: making metal 3D printing accessible and reliable.

The challenge is significant. Traditional metal printing uses laser powder bed fusion (LPBF)—a process comparable to CNC forging that's expensive, difficult, and opaque. The company's approach centers on a simple framework: Process > Structure > Properties > Performance = Characterization. They're studying every variable, from how cookies bake to how metal behaves before and after heat treatment.

Equipment & Capabilities: Trusted Metal operates two laser powder bed machines that spread metal powder in fine layers, selectively melt it, and weld it into a solid structure. Beyond the printers, they've invested in the characterization infrastructure: optical and electron microscopes, hardness testers, 3D scanners, and vision measurement systems.

The core problem they're solving is vendor lock-in. Unlike CNC machining, which uses the universal G-Code standard, there's no equivalent in laser powder bed fusion. Each of the four major machine manufacturers has its own black box of parameters and workflows, leading to four different potential defect patterns. Machines have different quirks—some slow down at different speeds when making certain geometries. This fragmentation means companies are locked into specific vendors and their proprietary processes.

Trusted Metal's Solution: The company is building an open standard for metal additive manufacturing: a common build file format, common machine quality metrics, and a digital execution log that works across platforms. This democratization of metal printing is the foundation of their business model.

The presentation included striking electron microscope images showing the intricate detail of their work—a visual reminder that innovation often happens at scales invisible to the naked eye.

What's Next?

Both Umbra and Trusted Metal are actively hiring and looking to expand their local presence. The June meetup reinforced what makes Tech Talk Augusta special: access to founders and builders working on cutting-edge problems, from orbital imaging to laboratory characterization.

If you missed this month's talks, mark your calendar for the next meetup—you never know what technical frontier you'll encounter.

Exploring Numbers in Programming

Sarah Matta — Tue, 01 Apr 2025 16:18:32 +0000

Hi! I’m Sarah.

I am a self-taught programmer with a passion for where math meets programming.

This blog post represents a talk I gave at the inaugural Tech Talk Augusta.

As an introduction to my talks, I like to read the intro to Neil deGrasse Tyson’s Starry Messenger because it is applicable as well as impactful to STEM and it really sets the mood to consume and digest STEM considerations that you're about to hear (or read, in this instance). You can read it here: Starry Messenger

What is STEM? (o゜▽゜)o☆

Science, Technology, Engineering, and Mathematics. STEM builds the communities and realities that we live in. It both reflects on the past and propels us forward.

Today, let's focus on the M in STEM - Mathematics!

You Know More Math Than You Think ╰(°▽°)╯

I have always loved math, but I never progressed as far as I would have liked (and still would like to). I’ve heard many programmers say similar things. People often claim they “don’t know math.”

If you just thought to yourself:

(>﹏<)
“Yep, that’s me”

today I’m going to try to convince you that you know more than you realize.

Number Sense ♪(^∇^*)

What is Number Sense? ヾ(＠⌒ー⌒＠)ノ

I believe I have “strong number sense.” Number sense is the intuitive understanding of numbers and their relationships. It starts with grasping concepts like:

Quantities
More & less
Bigger & smaller

To understand these concepts is to understand math logic. Understanding math logic is comparable to understanding programming logic.

Math is Logic (～￣▽￣)～

How many of you know what this symbol is?

\sum

How many of you understand it?

i = 1 \sum n i^{2}

This is Sigma notation, also known as summation. It represents:

Performing an operation (right)
Starting from a defined lower bound (bottom)
Going up to a defined upper bound (top)
Incrementing at each step
Adding each result to an overall sum

How many of you know what this is?

for (let i = start; i <= end; i++) {
    sum += operation(i);
}

Yes! It’s a for loop.

Guess what? They’re the same! Sigma is a For Loop!

i = 1 \sum 4 i^{2}

i = 1 \sum 4 i^{2} = 1^{2} + 2^{2} + 3^{2} + 4^{2} = 30

for (let i = 1; i <= 4; ++i){
    sum += i*i;
}

Now, how many of you understand Sigma notation?

The Power of Number Sense in Programming (⓿_⓿)

Problem-Solving Foundation ( *︾▽︾)

Good number sense is foundational to problem-solving. And how many of us would call programming problem-solving? 🙋‍♂️

Math teaches logical reasoning and problem-solving skills that are essential in programming. It helps in breaking down complex problems into smaller, manageable parts just like writing a program or debugging code.

A friend once said:

“My high school geometry class is what helped me start to excel at programming. My teacher emphasized proof-based geometry, and it really helped me figure out how to break big problems down into smaller ones.”

Math is Logic, Programming is Logic (〃￣︶￣)人(￣︶￣〃)

This talk is focused on numbers specifically, so let’s adventure beyond bits and bytes and explore The Numeric Foundation of Programming.

Precision vs. Accuracy: A Computational Perspective

If precision vs accuracy sounds familiar to you, you have probably taken a physics class! 😆

In programming, they have distinct meanings and can have profound implications.

Precision refers to the level of detail in a measurement or calculation. Example: the number of decimal places a computation can represent.
Accuracy refers to how close a result is to the actual, true value. It’s about correctness, not just detail.

Concept	Definition	Example
Precision	Level of detail in a result	3.1415926535 (high precision, may not be accurate)
Accuracy	Closeness to the true value	3.14 (lower precision, but accurate)

Speed vs. Correctness

Speed depends on numeric type and computation method.
Context determines the right approach.

So what does precision vs. accuracy mean in programming?

Binary vs. Decimal: The Foundations of Numeric Representation ◑﹏◐

Decimal vs. Binary in Csharp

// Decimal vs Binary floating-point comparison

double binaryResult = 0.1 + 0.2;  // 0.30000000000000004 - fast

decimal decimalResult = 0.1m + 0.2m;  // Exactly 0.3 - slow

double binaryResult = 0.1 + 0.2; represents precision because it provides many decimal places but isn't exactly accurate. It’s faster but has a small error.
decimal decimalResult = 0.1m + 0.2m; represents accuracy because it gives exactly 0.3. It’s slower, but mathematically correct.

Binary Numerical Systems (Base-2)

Binary numbers are the native language of computers, representing data using only two states: 0 and 1. This fundamental representation stems from electronic circuitry's on/off nature.

Decimal Numerical Systems (Base-10)

Decimal numbers represent our natural human counting system, using ten distinct digits (0-9).

Performance Considerations

Binary is the most efficient for computer processing.
Decimal calculations are precise but the slowest for computer processing.
Decimal also requires conversion for computer storage.

Common Use Cases:

Type	Used For
Binary	Performance-critical computations, game development, memory-constrained environments
Decimal	Financial calculations, scientific measurements

Conclusion []~(￣▽￣)~*

Math is deeply embedded in programming. Understanding number sense, precision vs. accuracy, and binary vs. decimal can help programmers write more efficient and effective code.

So the next time you think, “I don’t know math”, remember - you already use it every day in programming!