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Cover image for When a Model Gets Stuck: How GPT‑5.2 Finished a 'Simple' Spinner That Opus 4.5 Couldn't
Vyacheslav Mayorskiy
Vyacheslav Mayorskiy

Posted on • Originally published at blog.ayechat.ai

When a Model Gets Stuck: How GPT‑5.2 Finished a 'Simple' Spinner That Opus 4.5 Couldn't

#programming #llm #python #ai

I have a weakness for feature requests that start with:

"This should be simple."

Because they are simple.

Right up until you try to make them correct.

This one came from a very real pain point in Aye Chat:

"Sometimes the LLM response stalls mid‑sentence. Show a basic spinner when that happens, and remove it when more text arrives."

In a web UI, a stall is annoying.
In a terminal, a stall looks exactly like a crash.

So yes - I needed a subtle ⋯ waiting for more indicator.
Not a blinking disco. Not a permanent footer. Just an honest signal: we're alive, we're waiting.

And this is where the story gets interesting, because it wasn't just a concurrency story.

It was also a model story.

Claude Opus 4.5 got us most of the way there, then got stuck in a loop of "reasonable fixes" that didn't quite land.
GPT‑5.2 came in and finished the job - not by being more verbose or more confident, but by being more precise about what the system actually was: a little real‑time UI
state machine with multiple writers.

This is the write-up I wish I had before I lost an afternoon to a spinner.

The scene: simulated streaming meets real-world stalls

Aye Chat streams responses into a terminal UI using Rich (Live).
We also have simulated streaming: even if a provider returns larger chunks, we animate output word-by-word so it feels like "typing."

Real providers behave like this:

  • you receive some tokens,
  • then there's a gap (LLM is gathering some thoughts, server-side pause, backpressure),
  • then streaming resumes.

If that gap happens mid-sentence, users freeze with it.

So the request had four deceptively clean requirements:

  1. Detect "stall" while streaming.
  2. Show ⋯ waiting for more only during stalls.
  3. Remove it immediately when new text arrives.
  4. Don't break Markdown or final formatting.

The architecture (and why it can lie to you)

At a high level the streaming UI has three moving parts:

  • update(content: str) - called when new streamed content arrives (full accumulated content, not a delta).
  • _animate_words(new_text: str) - prints newly received text word-by-word with a small delay.
  • a background monitor thread - periodically decides whether we are "stalled."

Rendering is via a helper like:

_create_response_panel(content, use_markdown=True, show_stall_indicator=False) -> Panel
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When show_stall_indicator=True, it appends:

⋯ waiting for more
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So far, boring.

But then you hit the question that decides everything:

What does "stalled" mean?

And it turns out there are two kinds of "stalled":

  • Network stall: no new content is arriving.
  • User-visible stall: nothing is changing on screen.

Those are not the same in a system that intentionally delays rendering.

Where Opus 4.5 got stuck: fixing symptoms instead of the machine

Claude Opus 4.5 did the first part quickly:

  • add a timestamp,
  • monitor elapsed time,
  • show the indicator if we exceed a threshold.

It "worked"… until it didn't.

The bug appeared very specific:

The indicator blinks briefly even when words are still printing.

That symptom is a big clue. It means the stall detector is looking at time since last network update, while the UI is still busy animating buffered words.

Opus tried the next obvious move: add an _is_animating flag and suppress the indicator while animating.

Still not fixed.

At that point, you can feel a model fall into a common trap: it keeps proposing plausible tweaks (threshold changes, different checks, "maybe we should…"), but it doesn't
fully re-frame the problem.

Because the real problem wasn't just "the flag is wrong."

The real problem was that we had two concurrent writers to the same UI.

  • the animation path calls Live.update() as it prints words
  • the monitor thread calls Live.update() as it toggles the indicator

Without serialization, you will eventually render an inconsistent intermediate frame - which, to the user, looks like blinking.

So the "didn't fix it" moment wasn't Opus being incompetent.

It was Opus being stuck in a local optimum:

  • treat it as timing
  • treat it as one boolean
  • treat it as "add one more guard"

When what we needed was: state + synchronization.

That's the moment I switched models.

What GPT‑5.2 did differently: treat it like a state machine with a single renderer

GPT‑5.2 didn't win by being clever.
It won by being strict.

It made three changes that turned the spinner from "mostly works" into "boring and correct."

1) Serialize shared state and all UI updates

First: a lock.

self._lock = threading.RLock()
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And then a rule:

If it touches shared state or calls Live.update(), it must hold the lock.

We centralized rendering into a single helper so we stopped sprinkling Live.update() in random code paths:

def _refresh_display(self, use_markdown: bool = False, show_stall: bool = False) -> None:
    with self._lock:
        if not self._live:
            return

        self._live.update(
            _create_response_panel(
                self._animated_content,
                use_markdown=use_markdown,
                show_stall_indicator=show_stall,
            )
        )
        self._showing_stall_indicator = show_stall
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This one change kills a whole class of "blinking because two threads fought for the frame buffer."

2) Redefine "stall" as "caught up and no new input"

This was the conceptual pivot.

A stall should only be possible when:

  • we are not currently animating, and
  • the animated output has caught up to what we have received.

In code:

caught_up = (not self._is_animating) and (self._animated_content == self._current_content)
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That single definition fixes the original "indicator shows while words are still printing" bug.

If the UI is still draining buffered words, you're not stalled.
You're busy.

3) Use "last receive time," not "last render time"

After the above, we hit a second, subtler bug:

When streaming is actually paused, the indicator blinks instead of staying lit.

This is a classic mistake in real-time UI code: if you update the "progress timestamp" when you redraw the indicator, the indicator becomes self-canceling.

So GPT‑5.2 separated the concepts:

  • _last_receive_time changes only when new stream content arrives
  • redraws do not touch it 
self._last_receive_time: float = 0.0
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Updated only in update() when content truly changes:

with self._lock:
    if content == self._current_content:
        return
    self._last_receive_time = time.time()
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And the monitor checks:

time_since_receive = time.time() - self._last_receive_time
should_show_stall = time_since_receive >= self._stall_threshold
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That makes the indicator "sticky" in the correct way:

  • it turns on after the threshold,
  • it stays on continuously,
  • and it turns off immediately when new text arrives.

The final monitor loop (the boring version that works)

Here's what the working logic boils down to:

def _monitor_stall(self) -> None:
    while not self._stop_monitoring.is_set():
        if self._stop_monitoring.wait(0.5):
            break

        with self._lock:
            if not self._started or not self._animated_content:
                continue

            caught_up = (not self._is_animating) and (self._animated_content == self._current_content)
            if not caught_up:
                continue

            time_since_receive = time.time() - self._last_receive_time
            should_show_stall = time_since_receive >= self._stall_threshold

            if should_show_stall != self._showing_stall_indicator:
                self._live.update(
                    _create_response_panel(
                        self._animated_content,
                        use_markdown=False,
                        show_stall_indicator=should_show_stall,
                    )
                )
                self._showing_stall_indicator = should_show_stall
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Key properties:

  • no indicator while buffered words are still animating
  • indicator appears only after no new content arrives for stall_threshold
  • indicator stays on continuously once shown
  • indicator disappears immediately when new text arrives

The spinner stops being a feature.
It becomes infrastructure.

Which is exactly what terminal UX should be.

The real theme: why swapping models is a debugging tool

I'm not interested in "model wars."

But I am interested in the practical reality of building with them:

  • Some models are great at first-pass implementation.
  • Some models are great at refactoring.
  • Some models are great at pushing through annoying edge cases.

In this case:

  • Opus 4.5 got to a plausible implementation quickly, and even cleaned up structure when asked. But it kept circling around incremental fixes.
  • GPT‑5.2 zoomed out, saw "two writers + ambiguous definition of stall," and forced the solution into a small state machine with serialized rendering.

That doesn't mean GPT is "better" in the abstract.

It means something more useful:

When you feel a model looping, change the shape of the conversation - or change the model.

In Aye Chat, switching models is cheap.
And when you're stuck on a UI race condition that only reproduces one out of ten times, "cheap" matters.

Takeaways (and the part that matches Aye Chat's philosophy)

  1. A spinner has a bigger correctness surface area than it deserves.
    Animation + monitoring + concurrent rendering is a real system.

  2. "Stall" is a state, not a timeout.
    It must mean "caught up and no new input," not "some time passed."

  3. Don't let rendering update the clock that decides whether to render.
    That's how you invent blinking.

  4. Locking isn't optional when multiple threads can render.
    Even if nothing crashes, the UX will.

  5. Model choice is part of the toolchain.
    When one model gets stuck in local fixes, another might see the global shape.

In a weird way, this tiny ⋯ waiting for more indicator is the same lesson as the optimistic workflow:

  • let the system move fast,
  • but build it so you can recover instantly,
  • and be pragmatic about the tools (including the model) that get you unstuck.

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