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    <title>Forem: suresh kadam</title>
    <description>The latest articles on Forem by suresh kadam (@suresh_kadam_39ff9a7c43d3).</description>
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      <title>Forem: suresh kadam</title>
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      <title>ChatGPT vs. Logic: Why AI Code is Slower</title>
      <dc:creator>suresh kadam</dc:creator>
      <pubDate>Fri, 06 Feb 2026 19:13:49 +0000</pubDate>
      <link>https://forem.com/suresh_kadam_39ff9a7c43d3/chatgpt-vs-logic-why-ai-code-is-slower-526o</link>
      <guid>https://forem.com/suresh_kadam_39ff9a7c43d3/chatgpt-vs-logic-why-ai-code-is-slower-526o</guid>
      <description>&lt;p&gt;I recently set a simple task: Find the &lt;strong&gt;lowest value in a list&lt;/strong&gt; and return &lt;strong&gt;all its indices&lt;/strong&gt;. The rules were strict:&lt;br&gt;
No built-in min() function.&lt;br&gt;
No setdefault().&lt;br&gt;
No hardcoding.&lt;br&gt;
Clean output: No "unwanted" keys in the final dictionary.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Round 1: The ChatGPT Approach&lt;/strong&gt;&lt;br&gt;
ChatGPT typically provides a very readable, "textbook" solution. It separates the problem into two logical steps.&lt;br&gt;
&lt;/p&gt;

&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight plaintext"&gt;&lt;code&gt;lst = [44,4,4,1,1,1,1,1,1,1,12,4,3,2,4,5,6,4,3,44,556,6,6,6,6,22,2,2,1]

# Step 1: Find lowest value manually
lowest = lst[0]
for num in lst:
    if num &amp;lt; lowest:
        lowest = num
# Step 2: Find all indexes of lowest value
result = {}
indexes = []
for i in range(len(lst)):
    if lst[i] == lowest:
        indexes.append(i)
result[lowest] = indexes
print(result)

&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;



&lt;p&gt;The Performance Cost:&lt;br&gt;
This code is O(2N). It has to walk through your data twice. If you have a list of 10 million items, you are performing 20 million checks.&lt;/p&gt;
&lt;h1&gt;
  
  
  Round 2: The Optimized logic
&lt;/h1&gt;

&lt;p&gt;By being a bit more clever with our logic, we can achieve the same result in a single trip through the list.&lt;br&gt;
&lt;/p&gt;

&lt;div class="highlight js-code-highlight"&gt;
&lt;pre class="highlight plaintext"&gt;&lt;code&gt;
lst = [44, 4, 4, 1, 1, 1, 1, 1, 1, 1, 12, 4, 3, 2, 4, 5, 6, 4, 3, 44, 556, 6, 6, 6, 6, 22, 2, 2, 1]

final_dict = {}
temp = float("inf")
for i, v in enumerate(lst):
    if v &amp;lt; temp:
        temp = v
        final_dict.clear()
        final_dict[v] = [i]
    elif v == temp:
        final_dict[v].append(i)

print(final_dict)


&lt;/code&gt;&lt;/pre&gt;

&lt;/div&gt;



&lt;p&gt;O(N) Complexity: It only loops through the list once.&lt;/p&gt;

&lt;p&gt;The "Short-Circuit" Effect: Once the code finds a 1, it ignores every other number that is higher (like 12, 44, or 556). It doesn't even enter the dictionary logic for those items.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The 2026 Developer Verdict&lt;/strong&gt; &lt;br&gt;
In the age of AI, it’s easy to settle for "code that works." But high-performance engineering is about looking at two loops and asking:"Can I do this in one?"&lt;br&gt;
What’s your take? Do you prioritize "Step-by-Step" readability, or do you always optimize for the single pass? &lt;/p&gt;

&lt;h1&gt;
  
  
  python #performance #algorithms #codingchallenge
&lt;/h1&gt;

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      <category>python</category>
      <category>performance</category>
      <category>ai</category>
      <category>algorithms</category>
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