The Halfway Point

After a decade in mechanical engineering, I've recently pivoted into firmware. In my spare time, I'm expanding my skills through hands-on projects including machine learning, homelab tinkering, and 3D printing.

The hottest Substack posts of The Halfway Point

And their main takeaways

The Halfway Point

39 implied HN points • 12 Jul 23
  1. The author is a mechanical engineer who recently shifted to software development for automated hardware testing, finding himself at the halfway point between software and hardware.
  2. The author enjoys working on personal projects that blend software and hardware skills, like using a 3D printer for prototyping and exploring neural networks and setting up a homelab.
  3. The author's goal is to share hands-on lessons from his projects, including specifics on budget-friendly homelab setup, problem-solving in ML and 3D printing, and troubleshooting techniques.

The best LLM quantization method no one talks about

1 HN point • 26 Aug 23
  1. The quantization method called bitsandbytes, optimized for PyTorch models to run in 8-bit or 4-bit, stands out for being original and efficient.
  2. The pros include not needing training data, compatibility with huggingface accelerate supported models, speed, and ease of use.
  3. The cons revolve around CUDA requirement, VRAM limitations, storage size of models, and lack of CPU offloading.

Simultaneous Localization and Mapping

0 implied HN points • 26 Apr 24
🕹 Technology AI Sensors Statistics Robotics
  1. Self-driving cars need to know their exact location to avoid accidents. GPS and sensors like RADAR have errors, so it's tricky to get precise positioning.
  2. The Kalman filter helps improve the accuracy of measurements by combining noisy data over time. It has two main steps: updating measurements and predicting motion.
  3. For complex situations, there are advanced versions of the Kalman filter, like the Extended and Unscented Kalman filters, which can handle non-linear data better for more accurate tracking.

Using Genetic Algorithms to solve any problem

0 implied HN points • 26 Apr 24
🕹 Technology Algorithms Data science Artificial Intelligence Optimization Programming
  1. Genetic algorithms are useful tools for solving various problems because they adapt well and can be implemented easily. They help find good solutions, even if those solutions aren't always the absolute best.
  2. When using genetic algorithms, it's important to define three key elements: the system, the cost function, and how the system should change to minimize costs. This helps organize and optimize the problem-solving process.
  3. The DEAP library for Python makes it simple to create and manage genetic algorithms. It provides tools to easily track progress and make the necessary adjustments during the optimization process.

Procedural Flow for Mechanical Design

0 implied HN points • 26 Apr 24
🕹 Technology Engineering Design Manufacturing Simulation Prototyping
  1. When designing a product, it's crucial to define the project scope clearly. This helps prevent misunderstandings and changes that can be costly later on.
  2. Using tools like design block diagrams can help visualize the design process. This makes it easier to define parts and see how everything fits together.
  3. Consider the quantity and materials needed for the design early on. This affects manufacturing choices and ultimately how well the product can be made.
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Low Cost Accurate Air Quality Sensor (PM/Temperature/Humidity)

0 implied HN points • 26 Apr 24
🕹 Technology IOT Sensors Microcontrollers Programming Air Quality
  1. You can build a low-cost air quality sensor using an ESP32 for under $120. It's a great way to monitor air quality without spending too much money.
  2. This sensor not only shows air quality data on a local website but also sends it to the cloud and alerts you when the air quality is poor. It's pretty handy!
  3. You can set everything up without soldering, making it easier and safer to use, especially in a small space where fumes might be a problem.