Forem: Abhishak Bhatnagar

How C++ Can Take Us to Mars and Back

Abhishak Bhatnagar — Tue, 06 May 2025 06:22:04 +0000

In the modern era of space exploration, software is as critical as rocket fuel. The spacecraft of the future—especially for interplanetary missions to Mars—won’t just be metal and circuits; they’ll be powered by millions of lines of highly reliable code. Among the top languages trusted for these missions is C++.

Why? Because C++ offers the performance, precision, and control needed to build the mission-critical systems that must function without fail in the unforgiving vacuum of space.

But C++ isn’t perfect. It comes with its own set of challenges—such as undefined behavior, memory issues, and hardware sensitivity. This article explores how C++ can not only take us to Mars and back, but also how its challenges can be solved with modern techniques, tools, and best practices.

Why C++ Is Chosen for Space

Before diving into the problems and solutions, let’s understand why C++ is still a favorite among aerospace engineers:

Building a Mars Mission Software Stack in C++

A real Mars mission software system might include:

Navigation System

Autonomous AI Decision Engine

Communication System

Power Management

Thermal & Life Support

Diagnostics & Recovery

Each component must be fast, fault-tolerant, and radiation-aware.

Let’s now go module by module, write real C++ code, and examine the challenges and solutions for each.

1. Undefined Behavior Risks

The Problem:
C++ gives low-level control, but that means undefined behavior (UB) can occur if:

Memory is accessed out of bounds
Null pointers are dereferenced
Integer overflows happen unchecked

In space, UB could mean mission failure.

The Solution: Use Static Analysis Tools + Safe Subsets of C++
Use tools like Clang-Tidy, CppCheck, or MISRA C++ guidelines

Avoid dangerous constructs (e.g., raw pointers, uninitialized vars)

Use modern C++11/14/17 features like constexpr, enum class, nullptr, etc.

Example:

// Safe use of enum class avoids accidental conversions
enum class ThrusterStatus { OFF, IDLE, FIRING };

void fireThruster(ThrusterStatus status) {
    if (status == ThrusterStatus::FIRING) {
        std::cout << "Thruster is firing\n";
    }
}

Static analysis would catch any misuse like comparing ThrusterStatus to an integer.

2. Pointer Bugs and Memory Leaks

The Problem:
C++ allows direct memory access. This can lead to:

Memory leaks
Dangling pointers
Double deletes

Memory bugs are very hard to debug in space, where logs may be delayed or unavailable.

The Solution: Use Smart Pointers + RAII (Resource Acquisition Is Initialization)

Replace new and delete with std::unique_ptr and std::shared_ptr
Use std::vector instead of raw arrays
Encapsulate resource handling in classes

Example:

#include <memory>
#include <iostream>

class Sensor {
public:
    Sensor() { std::cout << "Sensor initialized\n"; }
    ~Sensor() { std::cout << "Sensor destroyed\n"; }
};

void testSensor() {
    std::unique_ptr<Sensor> sensor = std::make_unique<Sensor>();
    // Auto-deletes when out of scope (RAII)
}

This ensures automatic memory cleanup, preventing leaks.

3. Portability Across Embedded Architectures

The Problem:
C++ must run on various microcontrollers or real-time operating systems (RTOS) with:

Limited RAM/CPU
No standard library support
Different instruction sets (ARM, RISC-V)

The Solution: Use Embedded C++ Subsets + Cross-Compilers

Avoid exceptions, RTTI, dynamic memory (where unsupported)
Use cross-platform frameworks (e.g., RTEMS, FreeRTOS with C++)
Cross-compile using GCC/Clang with appropriate flags

Example (Embedded-safe loop):

// No STL used
void monitorBattery(float* readings, int count) {
    float total = 0;
    for (int i = 0; i < count; ++i)
        total += readings[i];

    float avg = total / count;
    if (avg < 21.0f)
        std::cout << "Battery level low\n";
}

Also, use hardware abstraction layers (HALs) to isolate hardware-specific code.

4. Harsh Radiation Causing Bit Flips

The Problem:

Radiation in space can flip bits in:
Program memory (code)
RAM (data)
Registers (instruction execution)

This can crash or corrupt a system.

The Solution: Use ECC Memory + Software Fault Tolerance

Use Error-Correcting Code (ECC) RAM (corrects 1-bit errors)
Add software redundancy: checksums, watchdog timers, and self-diagnostics
Implement triple modular redundancy (TMR) in critical modules

Example (Simple checksum validation):

bool validatePacket(const std::string& data, uint32_t checksum) {
    uint32_t sum = 0;
    for (char c : data) sum += c;
    return (sum == checksum);
}

This kind of redundancy catches silent data corruption.

Mars Mission Software (Integrated Code Sample)

#include <iostream>
#include <memory>
#include <string>
#include <vector>
#include <cmath>

class Navigation {
    double x, y, velocity;
public:
    void update(double nx, double ny, double vel) {
        x = nx; y = ny; velocity = vel;
        std::cout << "Location: " << x << "," << y << " | Vel: " << velocity << "\n";
    }

    double timeToTarget(double targetX, double targetY) {
        double dist = sqrt(pow(targetX - x, 2) + pow(targetY - y, 2));
        return dist / velocity;
    }
};

class Comm {
public:
    void send(const std::string& msg) {
        std::cout << "[Comm] Sending: " << msg << "\n";
    }
    bool receive() {
        return rand() % 10 != 1; // Simulate signal loss
    }
};

class LifeSupport {
    double o2;
public:
    LifeSupport(double level) : o2(level) {}
    void check() {
        if (o2 < 20) std::cerr << "WARNING: Oxygen low!\n";
        else std::cout << "O2 OK\n";
    }
};

int main() {
    Navigation nav;
    nav.update(0, 0, 100);
    std::cout << "Time to Mars base: " << nav.timeToTarget(500, 500) << "s\n";

    Comm comm;
    comm.send("Telemetry nominal");
    if (!comm.receive()) std::cerr << "Signal lost!\n";

    LifeSupport ls(19.8);
    ls.check();
}

Conclusion

Despite its challenges, C++ remains one of the most powerful languages to take us to Mars and back—because it gives us:

Performance for real-time reactions
Control over hardware behavior
Determinism and precision
Strong community and legacy tools

By following modern practices, using safety features, and architecting carefully, we can overcome every challenge—turning C++ into the backbone of our journey across the solar system.

Final Thought:

“We used C to land on the Moon. We’ll use C++ to walk on Mars.”

Also read: How I Used Python to Almost Crash ChatGPT

How I Used Python to Almost Crash ChatGPT

Abhishak Bhatnagar — Mon, 05 May 2025 07:33:49 +0000

As AI continues to evolve, developers and researchers are exploring its boundaries. One such experiment I undertook involved using Python to test the limits of ChatGPT's response generation capabilities. This blog post documents how I structured the experiment, what I observed, and the key takeaways from the process.

Objective

The goal was to explore:

How ChatGPT handles dynamically generated prompts at scale.

How it deals with recursion, large inputs, and nested logic.

What practical limitations (length, processing, memory) exist in the interaction model.

Setup

I used Python to generate structured prompts that grew in complexity, size, or logical depth. The experiments were run using both the ChatGPT web interface and API, where applicable.

Environment
Python version: 3.11

ChatGPT version: GPT-4 (via web)

Libraries: None required beyond standard Python

Experiment 1: Recursive Prompt Generation

I wrote a Python script to create recursive text prompts, where each prompt builds on the previous one with increasing complexity.

def generate_recursive_prompts(count):
    prompts = []
    base = "Explain recursion."
    for i in range(1, count + 1):
        base = f"Explain recursion based on: [{base}]"
        prompts.append(base)
    return prompts

Test Details

Generated prompts ranging from 1 to 500 levels of nesting.

Copied the most complex prompt (character count > 8000) into the ChatGPT interface.

Observations

ChatGPT truncated or summarized after a certain complexity level.

At around 10–15 levels deep, it began to generalize rather than expand on each level.

Beyond a certain character limit (~4,096 tokens for web interface), it failed to process the full prompt.

Experiment 2: Bulk Prompt Submission

I used Python to simulate sending multiple prompts programmatically.

prompts = [f"Explain the concept of item {i}" for i in range(1000)]

While I didn’t use the API to send them automatically (due to OpenAI usage limits), I manually tested the effect of batch processing multiple related prompts back-to-back.

Observations
The model maintained context well up to ~10–15 prompts in a thread.

After ~20 prompts, earlier context began to degrade unless explicitly restated.

Repeated similar prompts sometimes triggered output repetition or optimization attempts by the model.

Experiment 3: Token Overflow Test

I used Python to construct a prompt exceeding the token limits.

text = "This is a test sentence. " * 2000
print(len(text))  # Character count

Results

At ~8,000 characters (approx. 3,000–4,000 tokens), ChatGPT returned a "message too long" error.
The model did not crash, but it refused to process the input.
Token limits are strictly enforced; longer prompts are automatically rejected.

Key Learnings

1. ChatGPT Has Hard Limits
The web interface caps token limits around 4,096 tokens (input + output combined).

API limits are higher (up to 8,192 or 32,768 tokens depending on the model), but still finite.

2. Recursive or Nested Prompts Are Abstracted
ChatGPT recognizes recursion but will not endlessly expand recursive logic unless specifically instructed—and even then, only within safe limits.

3. Context Management Is Strong but Not Infinite
ChatGPT can handle sequential prompts but begins to lose accuracy and detail as the thread length increases.

Resetting context or summarizing helps manage longer sessions.

Conclusion

While I did not "crash" ChatGPT in a literal sense, these experiments clearly revealed its processing limitations. Using Python to automate prompt generation is an effective way to test the robustness and practical boundaries of large language models.

For developers, researchers, or curious users, this type of testing provides useful insights into:

Designing better prompts
Understanding AI constraints
Identifying when to switch from chat-based interactions to API-based solutions for scalability