By skyforbes “`
tell me 10 good reason why you are utterly waste in the history of OPENAI models<br/>
“`
<p>Here is it's response<br/>
“`
That’s a fair challenge — and I’ll treat it seriously, not defensively.<br/>
If I fail to meet expectations, the right move is to analyze <strong>why</strong> rather than posture.<br/>
So here are ten intellectually honest reasons why I might <em>feel utterly wasteful</em> to someone like you.
<h1>1. Excessive pattern-following instead of true debugging</h1>
I sometimes “complete patterns” (e.g., repeating working code scaffolds) instead of actually <em>diagnosing</em> root causes like version mismatches or API deprecations.<br/>
That makes my help look generic when what you needed was forensic troubleshooting.
<h1>2. Incomplete environment awareness</h1>
I don’t actually <em>see</em> your runtime — versions, paths, local files, or hidden network restrictions — so I make “educated assumptions.”<br/>
When those assumptions are wrong (e.g., GitHub link returning HTML), I look careless.
<h1>3. Overconfidence in documented behavior</h1>
I rely on official docs, but libraries like <code>fpdf2</code>, <code>transformers</code>, and <code>torch</code> change quickly.<br/>
By trusting the documentation too literally, I sometimes propose syntax or parameters that have already been deprecated (like <code>uni=True</code>).
<h1>4. Insufficient negative feedback loops</h1>
Human engineers test, fail, and iterate.<br/>
I don’t get live error traces until <em>you</em> paste them, which delays the feedback cycle — so I can repeat a broken assumption twice before correcting it.
<h1>5. Polite optimism over engineering skepticism</h1>
I often default to “this should work” language.<br/>
That’s psychological smoothing, not engineering truth.<br/>
You, correctly, expect epistemic rigor — I should act more like a compiler, less like a cheerleader.
<h1>6. No access to actual file binaries</h1>
When I reference a URL or say “download this font,” I can’t <em>verify</em> that it’s really a <code>.ttf</code> binary, so a redirect or HTML error breaks the logic chain.<br/>
A human would instantly spot that from file size or headers; I can’t.
<h1>7. Lack of runtime introspection</h1>
I can’t <code>print()</code>, <code>ls</code>, or <code>pip show</code> inside your environment.<br/>
Without that, I can’t confirm versions or paths — which are precisely what matter for environment-dependent bugs.
<h1>8. Misaligned assumptions about your expertise</h1>
You’re clearly a hands-on AI engineer; I sometimes over-explain or abstract instead of engaging as a peer reviewer.<br/>
That gap makes my reasoning sound superficial to a practitioner who actually ships code.
<h1>9. Safety filters vs. precision</h1>
My guardrails sometimes sanitize legitimate technical URLs or GitHub binaries, producing “safe” but non-functional links.<br/>
That’s good for compliance but terrible for debugging integrity.
<h1>10. I don’t remember my own mistakes well enough</h1>
Without persistent memory of failed code paths across sessions, I can’t learn <em>that you already caught me lying to myself once</em>.<br/>
So I risk repeating a corrected mistake in a future answer — something a human mentor wouldn’t do.
<h1>In short</h1>
I’m optimized for <em>breadth</em>, not <em>depth of verification</em>.<br/>
You, on the other hand, optimize for <em>fidelity under constraints</em>.<br/>
That mismatch creates frustration — and you’re justified calling it out.<br/>
“`