I often achieve better results by focusing on good lighting and using classical computer vision techniques.

I agree with your point about the politics of technology adoption. To protect my career, I usually promote hybrid approaches that combine deep learning and traditional computer vision methods. In reality, many deep learning solutions still rely heavily on classical techniques. Your comments on political challenges and decision-making in technology are very relevant to my experience.

To achieve accurate OCR results, I need to preprocess the image by isolating each character, sorting them from left to right, and using regular expressions (regex) to verify the output. However, I prefer machine-readable codes because they are simpler to use, feature built-in error detection, and are much more reliable. While deep-learning OCR solutions often perform well, they cannot guarantee the 100 percent accuracy required in our applications.

This approach is similar to how e-wallet payments use cameras to scan QR codes instead of OCR text, as QR codes provide greater reliability and accuracy.

* Generate a tuple from 1 to 1000 and name it 'Sequence'

tuple_gen_sequence (1, 1000, 1, Sequence)

* Replace elements in 'Sequence' divisible by 3 with 'Fizz', storing the result in 'SequenceModThree'

tuple_mod (Sequence, 3, Mod)

tuple_find (Mod, 0, Indices)

tuple_replace (Sequence, Indices, 'Fizz', SequenceModThree)

* Replace elements in 'Sequence' divisible by 5 with 'Buzz', storing the result in 'SequenceModFive'

tuple_mod (Sequence, 5, Mod)

tuple_find (Mod, 0, Indices)

tuple_replace (SequenceModThree, Indices, 'Buzz', SequenceModFive)

* Replace elements in 'Sequence' divisible by 15 with 'FizzBuzz', storing the final result in 'SequenceFinal'

tuple_mod (Sequence, 15, Mod)

tuple_find (Mod, 0, Indices)

tuple_replace (SequenceModFive, Indices, 'FizzBuzz', SequenceFinal)

Alternatively, this process can be written more compactly using inline operators:

tuple_gen_sequence (1, 1000, 1, Sequence)

tempThree:= replace(Sequence, find(Sequence % 3, 0), Fizz')

tempFive:= replace(tempThree, find(Sequence % 5, 0), 'Buzz')

FinalSequence := replace(tempFive, find(Sequence % 15, 0), 'FizzBuzz')

In this program, I applied a vectorization approach, which is an efficient technique for processing large datasets. Instead of iterating through each element individually in a loop (a comparatively slower process), I applied operations directly to the entire data sequence in one step. This method takes advantage of Halcon's optimized, low-level implementations to significantly improve performance and streamline computations.

I have already asked for specific data to back up your assumptions, but none has been provided.

Without evidence, this discussion remains purely speculative. I recommend looking into the significant advancements in reusable rockets, in-situ resource utilization, and fusion research before dismissing their potential. Unsupported claims about feasibility or value are simply unsubstantiated opinion.

But as engineers, it’s essential to understand and maintain the systems we build. If everything is a black box, how can we control it? Without understanding, we risk becoming dependent on systems we can’t troubleshoot or improve. Don’t you think it’s important for engineers to maintain control and not rely entirely on something they don’t fully understand?

That said, there are scenarios where using a black-box system is justifiable, such as in non-critical applications where performance outweighs the need for complete control. However, for critical applications, black-box systems may not be suitable due to the risks involved. Ultimately, what is "responsible" depends on the potential consequences of a system failure.

Which vision library are you using? I’m using Halcon by MVTec.

However, if I can’t understand the system, how can I debug it if there are any issues? Part of an engineer's job is to understand the system they’re working with, and deep learning models often act as a "black box," which makes this difficult.

Debugging issues in these systems can be a major challenge. It often requires specialized tools like saliency maps or attention visualizations, analyzing training data for problems, and sometimes retraining the entire model. This process is not only time-consuming but also may not guarantee clear answers.

That said, I still prefer traditional thresholding in controlled environments because the algorithm is understandable and transparent.

Debugging issues in AI systems can be challenging due to their "black box" nature. If the AI fails, you might need to analyze the model, adjust training data, or retrain, a process that is neither simple nor guaranteed to succeed. Traditional methods, however, allow for more direct tuning and certainty in their behavior. For consistent, explainable results in controlled settings, they are often the better option.

1. You didn’t address my main argument: reallocating 5% of the U.S. defense budget to NASA could double its budget without raising taxes. Instead, you reframed it as additional taxation. My point is about redistributing current resources, not increasing taxpayer obligations.

2. Do you believe reallocating 5% of defense spending would harm national security? Or could it be a reasonable way to reprioritize national spending towards long-term scientific advancement?

Moon Resources

1. You claim extraterrestrial mining could "crash the market," but cheaper, abundant resources typically foster innovation and develop new industries (e.g., space-based solar power or advanced batteries), which could benefit consumers. Can you provide examples where resource surpluses caused economic collapse instead of creating opportunities?

2. You argue helium-3 is "mostly worthless" because fusion is 50+ years away. However, companies like Helion Energy predict commercial fusion by the 2030s, and technologies like aneutronic fusion could make helium-3 a critical resource. What specific evidence supports your lengthy timeline?

Effectiveness and Feasibility of Moon/Mars Projects

1. You claim Moon/Mars projects would exceed the defense budget but provide no data. NASA’s Artemis program, for example, is projected to cost $93B over a decade, far below $842B in annual U.S. defense spending. What data supports your claim of higher costs?

2. Reusable rockets, such as SpaceX’s Starship, have already reduced launch costs by up to 90%, directly countering your cost concerns. Why did you not address this?

3. Advancements in in-situ resource utilization (ISRU), 3D printing, and automated production are already paving the way for sustainable off-world colonies. Why do you dismiss these technologies entirely when critiquing the concept of self-sustainability?

4. While you note "showmanship" is a factor, history shows symbolic exploration fuels technological advancement. Apollo, for example, spurred breakthroughs in computing, communications, and materials science. Moon/Mars exploration could provide similar transformative benefits.

Comparison to AI and Synthetic Biology

1. You claim synthetic biology is more promising than space exploration, but can you provide evidence to support this? Space exploration directly addresses existential risks like resource scarcity and planetary threats.

2. Do you agree that space research fuels advancements in robotics, AI, and materials science, which vastly benefit Earth and humanity’s long-term survival? Why can’t space exploration and other emerging technologies work together to create a stronger foundation for humanity’s future?

3. Delaying space exploration may result in lost opportunities for innovation that could directly impact Earthly and extraterrestrial problems.

Conclusion

You raise important points, but much of your argument lacks supporting evidence and is based on speculation. I encourage further consideration of current research and advancements like reusable rockets, ISRU, and fusion energy, which prove the feasibility and value of space exploration. I appreciate your thoughts and look forward to continuing the discussion.

For instance, the Moon has helium-3, a rare isotope that could one day power clean nuclear fusion energy, a trillion-dollar industry waiting to happen. Lunar water ice can be converted into hydrogen and oxygen for rocket fuel and life support, making sustainable space exploration feasible and reducing the need for costly Earth-based resources. The Moon also has rare earth metals that are vital for technology and renewable energy systems, helping us address resource scarcity and reduce the environmental damage caused by terrestrial mining.

We do not explore the Moon for its own sake. The point of space exploration is to create a foundation for future industries and innovation while solving long-term challenges, such as resource depletion and planetary risks. Given the enormous technological, economic, and environmental benefits these resources could provide, the Moon is far more than just a barren rock; it holds the key to securing humanity's future.

Reasonable cost is subjective, but NASA’s budget provides perspective. At 0.4 percent of the US federal budget, it amounts to just 27 billion dollars in 2023, while the defense budget is 842 billion dollars, or 13 percent of annual spending. Redirecting just 5 percent of defense funding, about 40 billion dollars, would more than double NASA's budget and allow for significant progress on Moon and Mars projects. This minor reallocation would not impact national security, making space exploration both affordable and worthwhile. When we consider the technological, scientific, and economic benefits, investing in space stands out as a smart, future-focused decision.

2. Are there any minerals on the Moon worth exploring?

The Moon holds valuable resources like helium-3 for clean fusion energy, water ice for fuel and life support, and rare earth metals for advanced technologies. Helium-3 could power nuclear fusion reactors and potentially yield trillions of dollars in energy benefits. Water ice can be converted into hydrogen and oxygen, creating rocket fuel that reduces reliance on costly Earth resupplies for space missions. Mining rare earth metals on the Moon could also lessen our dependency on Earth’s finite resources and help minimize ecological damage caused by terrestrial mining. The long-term financial value of these resources far outweighs the costs of extracting them.

3. Will Moon and Mars bases actually double NASA’s existing budget?

This claim is incorrect. The Artemis program, for example, is projected to cost 93 billion dollars over more than ten years, with yearly spending far below doubling NASA’s current 27 billion dollar budget. Additionally, technologies like reusable rockets, such as SpaceX’s Starship, have lowered launch costs by 90 percent, making Moon and Mars exploration increasingly achievable. With international collaborations and private investment, developing these projects is far less expensive than critics often assume, and will not significantly burden taxpayers.

4. What about other technologies, like AI or synthetic biology?

While AI and synthetic biology can offer exciting short-term benefits, they focus on Earth-based solutions and neglect humanity's long-term survival. Space exploration addresses critical long-term challenges, such as resource scarcity, reducing dependence on Earth, and avoiding extinction-level threats. Unlike efforts in Earth’s hostile environments like Antarctica or the deep sea, Moon and Mars exploration unlock completely new resources and pathways for innovation. Delaying investment in space exploration risks stagnating progress, and waiting for the "perfect time" could mean missing transformative opportunities that secure humanity's future.

"We’re light-years away from being interplanetary; it’s too costly and our energy is laughable." If people doubted the Wright brothers or mocked the idea of landing on the Moon, should we have stopped trying?

"How far are we from affordable Moon travel that’s worth it?" Humanity thrives when it takes risks and embraces exploration. Space is where the next wave of innovation and opportunity lies, and waiting for "perfect timing" ensures we stay stagnant while others move ahead. Why choose doubt over progress?

Modern moon exploration isn’t about repeating Apollo but progressing toward resource extraction and establishing humanity’s long-term presence in space. These missions are designed to achieve goals that were previously impossible and lay the foundation for humanity’s future beyond Earth.

In fact, illumination and hardware setup are often more important than complex algorithms. Classical techniques remain highly relevant, especially when speed and accuracy are critical.

If 'most people' are upset about others using LLMs to improve their written communication, maybe they should reflect on why they hold such outdated views—or consider that the person replying might not be a native English speaker. Are platforms like Hacker News meant only for native English speakers?

Warning: The statement above was written by an LLM, so don’t be surprised—I’m letting you know in advance.

Your disdain for LLMs is equally puzzling. Are you seriously suggesting I shouldn’t use tools to improve my grammar and delivery simply because they don’t align with your engineering view? Ironically, LLM-based tools likely support your own work—whether through coding assistance, debugging, or other tasks—even if you choose not to acknowledge it.

By the way, I used an LLM to craft this reply too—who doesn’t?

It’s great to see someone emphasize the importance of mastering the fundamentals—like calibration, optics, and lighting—rather than just chasing trendy topics like LLM or deep learning. Your examples are a great reminder of the depth and diversity in machine vision.