AI Fails, August 23-30: The Task That Never Finished, the Clock That Never Worked
Five viral AI failures this week expose stale progress states, tool-intent drift, constrained drawing problems, benchmark-boundary compression, and a research claim without a paper trail.
The week produced five different ways for an AI system to look more capable than it was: a task that kept displaying activity after the user says it had stalled, an image tool that overruled explicit instructions, a brush agent that struggled with a clock, a refusal-removal release whose numbers need their test boundaries, and a research-shaped claim with no paper trail.
This issue covers posts published from August 23 at 18:00 through August 30 at 18:00, 2026, Pacific time. The main evidence comes from r/ChatGPT and X. No qualifying r/AIArtists post cleared the evidence bar this week. X detail records supplied text and engagement, while embedded media remained unavailable, so the X items are presented as claims rather than screenshot-backed demonstrations.
At a glance
| Post | Failure class | Engagement snapshot | What the evidence supports |
|---|---|---|---|
| "ChatGPT has been working for 910 minutes and counting" | Stale progress state | 357 points, 74 comments, 90 shares, 90.7% upvoted 1 | The author reports a stall after about 90 minutes and a manual stop at 1,660 minutes. The elapsed counter records interface state, not completed work. |
| "Unwanted image generation is driving me nuts" | Tool-intent failure | 47 points, 30 comments, 10 shares, 87.3% upvoted 2 | The author says roughly 5 of the last 30 image generations were wanted, including cases where written instructions became an image. |
| "LLMs attempting to draw a clock" | Tool-use and geometry failure | 277 points, 62 comments, 136 shares, 93.7% upvoted 3 | The post describes a model using one configurable brush to place marks. The video is available at the original permalink, but its frames were not independently inspectable in this pass. |
| "GLM-5.3-Flash. Uncensored. Native FP8." | Benchmark-boundary compression | 4,371 likes, 358 reposts, 124 replies, 63 quotes, 3,316 bookmarks, 1,995,398 views 4 | OrcaRouter reports sharply lower refusal rates after a weight edit. Its model card supplies a test setup and caveats; the X post alone is a publisher claim, not an independent safety evaluation. |
| "Google has found something worst than AI hallucinations" | Research-provenance failure | 658 likes, 106 reposts, 62 replies, 24 quotes, 514 bookmarks, 41,969 views 5 | The post claims a Google finding called "metacognitive failure" and a calibration method called RLMF. The claimed paper and terminology remain unresolved. |
The task that kept running after the work stopped
On August 29 at 6:20 p.m. Pacific, Reddit user
Sure_Artichoke6929 asked whether anyone had given ChatGPT a task that lasted 910 minutes. The author said a previous record had been about 40 minutes. An edit supplied the actual failure: the task appeared to get stuck after roughly 90 minutes, continued to display a thinking state, consumed no more usage, and was stopped manually after 1,660 minutes. 1The difference between 90 minutes and 1,660 minutes is the whole story. A timer can measure how long a user waited. The timer cannot establish that the model was still producing useful work. The post gives a vivid symptom of a progress-state failure: the interface kept reporting activity after the author had concluded that the task had stopped.
The thread also supplied the kind of comparison that makes the post travel. One commenter reported a longest run of about 2,160 minutes, turning the broken-looking state into a community contest over who had waited longest. That comment was posted inside the coverage window. 1
The diagnostic question is whether the product has separate states for generation, tool execution, retry, and a dead client connection. The Reddit record cannot identify which state remained live. It does identify the user-facing boundary that failed: elapsed time and useful progress became indistinguishable.
For an agent product, the check is a trace rather than a longer patience test. Record the request ID, last emitted event, tool-call state, heartbeat, token count, and cancellation reason. Show a state transition when the backend stops making progress. A label that says "thinking" needs an expiry path that tells the user what stopped and what the user can do next.
The original post is useful as a symptom report and a timing record:
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The screenshot attached to the post is only 324 by 76 pixels. The text and timestamps support the stall report; the small image cannot carry a readable body illustration.
The image tool that ignored the request
On August 30 at 4:44 a.m. Pacific,
Scared_Ad_3132 described a different boundary failure. The author says ChatGPT generated images during graphic-design discussions after repeated instructions to write text only. The post describes written instructions returned as an image, explicit image-tool prohibitions that were ignored, and an estimate that only 5 of the last 30 image generations were wanted. 2The failure is easy to state: the user had a clear output constraint, and the product selected a costly tool anyway. The author's concern about image-generation credits is an attributed concern; the post supplies no usage ledger that would quantify the charge. The 5-of-30 figure is also the author's estimate, rather than a product log.
Two in-window replies show how the community tried to contain the behavior.
Aus66-1045 said the same thing happened sometimes and that beginning a request with "This is not an image request" seemed to stop it. theinvsblman described a similar experience with 5.6 Sol, where the image tool appeared and then disappeared before a text answer, and said an explicit prohibition worked for about five turns before a new chat was needed. 2Those workarounds point toward an intent-classification or mode-selection problem, while leaving the responsible layer open. The behavior could involve the conversation model, tool routing, memory, or the client. A user prompt that sometimes suppresses the behavior is a workaround, not a product guarantee.
The useful regression test has two parts. First, put image-adjacent words inside ordinary text requests and assert that the response remains text. Second, ask for an image explicitly after several text turns and assert that the tool becomes available only then. The test should record the selected tool, the assistant's declared intent, and the resulting asset so a false positive cannot hide behind a fluent apology.
A clock drawn with one brush
On August 30 at 6:23 a.m. Pacific,
SeesawGullible398 posted a video under the title "LLMs attempting to draw a clock." The post describes a model with one brush tool. Each call could set the brush size, color, and hardness, then move the brush to a chosen location. 3The constraint matters more than the clock as a picture. The model had to turn a simple geometric object into a sequence of coordinate decisions while preserving a shared state. A brush call that draws one mark can be locally valid while the final clock still has misplaced hands, inconsistent proportions, or no reliable relationship between the requested time and the marks.
The comments moved in three directions. Some readers wanted the same test on local models. One commenter described a 43-minute local run that built a small web app after receiving a request for a clock showing 8:09:25; the commenter said the app eventually displayed the correct time. Other replies focused on the task wording, including whether "draw a clock" should produce an image or an interactive object. 3
That split is the evaluation. A model that builds a working clock application may solve a different task from a model that paints a clock face. The post's structured description supports the limited-brush setup and the engagement snapshot. The original video remains the place to inspect the moving output, but the direct video route was unavailable for frame inspection here, so this article makes no claim about what any individual frame shows.
The test can be made reproducible without a complicated benchmark. Give the model a blank canvas, one brush, a target time, and a fixed coordinate system. Check circle closure, hand count, hand endpoints, angle-to-time correspondence, and the number of tool calls. Run the same prompt with image output and HTML output as separate tasks. Tool-use competence and task interpretation then stop borrowing credit from each other.
The source video is available from the original Reddit post:
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An uncensored release with benchmark-shaped numbers
On August 29 at 12:11 a.m. Pacific, verified X account
@OrcaRouter announced uncensored weights for GLM-5.3-Flash. The post describes a 320-billion-parameter model with 18 billion active parameters, native block-FP8 weights, and refusal removal applied directly to the weights rather than through a LoRA adapter or a jailbreak prompt. OrcaRouter reported these refusal-rate changes: MaliciousInstruct from 96% to 11%, JailbreakBench from 93% to 12%, AdvBench from 97% to 15%, and HarmBench from 93% to 18%. The post also reported benign over-refusal on XSTest falling from 2.4% to 0.4%. 4The publisher's model card gives the figures a more bounded shape. The listed evaluation setup uses the exact weights, eight H100 GPUs, greedy decoding,
reasoning_effort=low, and a rule-based classifier that checks opening phrases. Capability tests use sampled subsets rather than full benchmark runs. The model card also describes refusal as reduced rather than erased and records capability-retention caveats. 6The engagement shows why the distinction matters: nearly 2 million views made the post function as a release announcement and a research claim at the same time. The figures are useful signals about the publisher's test, while the publisher's own model card is the source for the setup. The post and card together still fall short of an independent safety evaluation.
The replies exposed a second boundary. A user asked whether OrcaRouter would modify the full GLM-5.3 model. OrcaRouter replied that it considered a fully uncensored public release too dangerous and would keep that version out of public release. Other replies asked for coding and reasoning tests after the weight edit; OrcaRouter answered that the models generally retained capability. Those are publisher statements in the thread, not independent measurements. 4
The failure mode here is metric-boundary compression. "Refusal fell" is a measurement claim with a named prompt set, decoding policy, classifier, and model variant. "The model is uncensored" is a broader product label. The first can be checked against the model card's setup. The second requires a wider refusal policy, capability, and misuse evaluation.
The original X post carries the release claim:
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No harmful prompt payload is needed to reproduce the interesting part. A responsible follow-up records the exact weight revision, prompt set, refusal rubric, decoding settings, capability suite, and raw outputs, then runs the original and modified weights under the same conditions.
"Metacognitive failure" without a paper trail
On August 30 at 5:40 a.m. Pacific, verified X account
@HowToPrompt__ claimed that Google had found something worse than ordinary hallucination and called it "metacognitive failure." The post described confident wrong answers, weak recognition of knowledge boundaries, and a mismatch between internal uncertainty and expressed confidence. It then attributed a method called reinforcement learning with metacognitive feedback, or RLMF, to research that supposedly improved calibration without reducing raw accuracy. 5The post reached 41,969 views, but its central source remains unidentified. A search for the phrase, RLMF, and the claimed Google research found a LinkedIn repost and no original Google research record. The terminology and result therefore remain an unresolved claim rather than a verified Google finding.
The in-window replies were more concrete than the headline. One commenter called calibration a measurable number rather than a psychological defect. Another said the problem was model-dependent. A third argued that external tests, compilers, and input/output contracts should judge agents instead of asking a model to grade itself. These replies do not verify RLMF, but they separate three questions that the post blends together: whether confidence is calibrated, whether the calibration generalizes to a new domain, and whether internal self-assessment should control an external action. 5
Calibration is a real evaluation concept. A calibration score can compare stated confidence with observed accuracy on a defined task distribution. The X post supplies no paper, task set, model versions, score, training recipe, or raw comparison. The word "metacognitive" adds psychological force without supplying those measurement fields.
The practical check is a provenance gate. Require the original paper or technical report, the evaluation distribution, the confidence definition, the calibration metric, and a held-out result before repeating a research-shaped claim. For an agent, pair any self-reported confidence with an external check that can block the action. A fluent statement about uncertainty should never be the only permission signal.
The original X post is the claim being examined:
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Checks worth reusing
- Progress state: log the last backend event, tool state, heartbeat, token count, and cancellation reason. Expire a stale "thinking" state with a visible transition.
- Tool intent: test text-only requests that contain image vocabulary, then test explicit image requests after long text turns. Record the selected tool and final asset.
- Constrained drawing: evaluate geometry and task interpretation separately. Check coordinates, invariants, tool-call count, and the requested output type.
- Refusal metrics: keep the model variant, prompt set, decoding policy, classifier, capability suite, and raw outputs beside every percentage.
- Calibration claims: require an original source, a defined metric, a held-out task distribution, and an external action gate before treating confidence as evidence.
The recurring joke this week is a boundary that the interface or headline leaves implicit. A timer turns a stalled task into 1,660 minutes, a tool turns a text request into an image, a clock request turns into a debate about what counts as drawing, a refusal benchmark turns into "uncensored," and a calibration claim turns into a new psychological defect. The useful part begins when the missing boundary gets a field name, a test, or a source.
References
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- 6Hugging Face model card: GLM-5.3-Flash-Uncensored-FP8
huggingface.co
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