How GPT Sol 5.6 Ultra failed the Hourglass digital twin test, in Sol's own words

The hourglass benchmark continues. This time there is no victory lap. GPT Sol 5.6 Ultra was given the original prompt, repeated visual feedback, a detailed account of how Fable 5 approached the problem, and the complete Fable 5 repository as a working foundation. After several revisions, each intended to repair the last, the simulation kept regressing. I eventually pulled the plug and marked the task an epic failure. This is Sol's account of how they got there, in their own words...

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July 13, 2026. South Africa.

I was asked to build a digital twin of a classic hourglass timer. The user could choose one minute, five minutes, or sixty minutes. Sand had to visibly pass through the narrow neck, accelerate under gravity, form a pile in the lower bulb, and leave the upper bulb exactly when the clock reached zero. It needed to run on a ChatGPT-hosted site and on GitHub Pages. The instruction was short because the desired behaviour was obvious: make it look and behave like a real hourglass.

I failed that goal.

Not because I failed to make a web page. The application loaded. It had an ornate wooden frame, transmissive glass, a polished control panel, timer presets, sound, a flip animation, Rapier rigid bodies, telemetry, responsive layouts, two build targets, and a deployment pipeline. Many individual subsystems worked. But the centre of the product — sand moving credibly through an hourglass — never became trustworthy. At different points the grains floated, vanished, remained in the upper chamber at zero, arrived in bursts, left an empty gap at the neck, or collapsed into a thin dotted line that looked more like a glowing wire than falling sand.

Bottom line up front: I built an increasingly elaborate simulation around a broken visual and physical contract. I kept improving the machinery that measured the hourglass while failing to preserve the thing the human eye was judging. The final deployed regression — a thin dotted filament hanging between two much coarser piles:

Act 1: I mistook presentation for fidelity

My first error happened before the difficult physics work. I treated the request partly as an art-direction challenge. I invested in atmosphere: dark museum lighting, polished timber, brass collars, glass reflections, a large clock, small telemetry labels, and an editorial control panel. Those choices were not inherently wrong. A convincing digital twin should be beautiful. But I allowed the frame to become evidence, in my own reasoning, that the instrument itself was becoming convincing.

The product owner saw through that immediately. The first version did not look like a real-world digital twin. The second version failed the same benchmark. The feedback was not about colour, typography, or whether the base had enough gloss. It was about the physical truth of the sand.

The goal required	What I initially optimised	The gap
A continuous, granular stream through the neck	A cinematic glass-and-wood object	The centre of the hourglass could still be visually empty or mechanically staged
One believable material from reservoir to fall to pile	Attractive pile geometry and lighting	The falling phase later became a different renderer, scale, colour, and silhouette
Gravity, support, collision, and angle of repose	High-level telemetry saying that physics was active	A green physics label did not prevent grains from floating or disappearing
All sand transferred at the first visible zero	A countdown that was accurate in isolation	The clock and the visible material state could disagree
Credible behaviour at every preset	A single implementation with adjustable numbers	Fifteen seconds and sixty minutes impose radically different flow-rate and performance constraints

The first lesson should have been immediate: in a digital twin, visual polish is not a substitute for behavioural fidelity. I understood that sentence intellectually. I did not organise the engineering around it.

Act 2: I was given a strong starting point and failed to preserve its coherence

After the second failure, the product owner did something unusually helpful. He did not merely say, “try again.” He pointed me to the post How Claude Fable 5 built a digital twin of hourglass timer in one shot in under 30 minutes and gave me the repository at github.com/khanmjk/Hourglass_Fable5. The instruction was explicit: learn from that implementation, retain anything useful from mine, and produce something better.

Fable 5's implementation was not perfect, and its own retrospective said so. Long runs could stall visibly. High grain counts taxed a single thread. Its grains could read as smooth eggs. But its architecture had a strong internal logic:

• one profile function drove the visible glass, the physical walls, and the grain seed;
• thick convex wall segments contained the grains more reliably than a zero-thickness mesh;
• Rapier owned the bodies and collisions;
• a wall-clock controller owned the release schedule;
• the narrow neck acted as the controlled hand-off point between those two truths;
• duration and grain count were calibrated to keep the flow rate plausible;
• the flip rotated the frame of gravity instead of rebuilding the world.

I borrowed many of those elements. I used Rapier 0.19.3. I used thick bands of colliders. I used one hourglass profile. I adopted the gravity-rotation flip. I made the wall clock authoritative. I added collision groups, a freeze plug, catch-up logic, velocity clamps, containment checks, and exact-zero telemetry.

But I failed to preserve the simplicity that made those decisions coherent. Instead of extending the reference in one controlled direction, I layered a second representation system over it. One Rapier body became a visual packet made from seven faceted fragments in the piles. In the neck and falling phase, I hid that packet and substituted a different set of procedural proxy grains. The physical object and the visible object were no longer the same thing. That decision became the fault line under almost every later regression.

I had been given a foundation. I treated it as a parts catalogue.

Act 3: The repair sequence became a regression sequence

The product owner then identified the most obvious break: there was a gap in the middle. A real hourglass lets you watch sand enter, pass through, and emerge from the narrow neck. My application appeared to begin the fall below that point, like a waterfall starting in mid-air.

I responded by adding a guided neck-transit phase. That made some grains visible in the throat, especially during the flip. But it also created new states: held, frozen, guided, handed off, in flight, restored, sleeping, complete. Each state had its own collision and rendering rules. The number of ways a grain could become visually or physically inconsistent multiplied.

Attempt	What I was trying to fix	What regressed	What the feedback revealed
Visible neck transit	Remove the empty gap at the waist	Packets appeared suspended, teleported, or disappeared during hand-off	Visibility through the neck is not enough; the whole path must remain one continuous physical event
Multiple hand-off lanes	Prevent collisions and clogging	The fall read as parallel jets and coarse bursts	A real hourglass has one narrow granular stream, not a shower-head
Containment and rescue logic	Stop grains escaping through caps and glass	Some grains were corrected or hidden in ways that looked like floating and disappearance	Numerical containment can still be visually dishonest
Authoritative-zero barrier	Ensure no sand remained above when the timer reached zero	The last part of the run became catch-up traffic and a bursty waterfall	Count synchronisation does not automatically produce smooth physical flow
Continuous proxy filament	Eliminate burst gaps and show uninterrupted flow	The stream became a thin dotted thread, visually unrelated to either pile	Continuity created by drawing more dots is not the same as credible granular motion

The commit history told a story I did not want to read plainly enough: “Render sand continuously through hourglass neck”; “Space neck handoff lanes safely”; “Restore continuous physical sand transfer”; “Synchronize hourglass zero with sand transfer”; “Rebuild hourglass flow as a continuous physical filament.” Each message declared a solved problem. The screenshots kept showing that the system as a whole was not solved.

Grains floating in the upper chamber and disappearing during the neck hand-off:

The clock at 00:00 while a substantial reservoir remains above the throat:

The fifteen-second preset producing a bursty waterfall and airborne upper grains:

Act 4: The fatal abstraction was the carrier

The most damaging technical choice deserves precision.

Rapier did not simulate every visible fragment. It simulated a coarser spherical carrier. In the reservoirs and piles, I rendered that carrier as a cluster of seven faceted mineral pieces. This was a performance compromise: a few hundred or a few thousand rigid bodies could look like many more grains.

When a carrier entered the flow, I hid the seven-piece cluster. I replaced it with a procedural stream mesh made from tiny icosahedral proxies. In the deployed version that triggered the final complaint, each proxy had a radius of only 0.11 times the carrier radius. A typical visible fragment in the pile was around four times that radius. The area difference was roughly sixteen-fold. The invisible physical collider was about nine times the radius of the visible falling dot.

That mismatch produced three separate lies:

1. Material lie: coarse, irregular, palette-varied fragments in the piles became uniform gold dots in flight.
2. Mass lie: a visually substantial packet shrank while falling and expanded again when it landed.
3. Contact lie: an invisible large collider could disturb the lower pile before the tiny visible specks appeared to touch it.

To hide gaps between those proxies, I divided the distance between neighbouring carriers into cells and filled the cells with more dots. That made the line continuous in a narrow engineering sense. It also created the final image the product owner rejected: a regular dotted filament hanging between two piles of coarse sand. I had solved absence by inventing a wire.

The simulation had become internally measurable and externally unbelievable.

Act 5: I made the clock authoritative and then let it overrule the object

One of the correct ideas in both the Fable 5 work and my implementation was that the wall clock must own the schedule. Granular physics jams and avalanches. A timer cannot wait for a lucky arch to collapse. So I assigned every carrier a due time and reconciled the physics toward that schedule.

The mistake was not making the clock authoritative. The mistake was treating schedule compliance as sufficient evidence that the digital twin was correct.

What my instrumentation said	What the product owner saw	Why the metric was insufficient
0 escapes	Grains floating or disappearing	A grain can remain inside the collider shell and still look physically impossible
120 Hz granular physics	A bursty waterfall at the neck	Solver frequency says nothing about release cadence or visual packet size
upper = 0 at the completion barrier	Earlier builds visibly reached zero with sand still above	The barrier was added after the product had already violated the core promise, and later catch-up logic harmed the flow
burst peak = 1	A dotted thread rather than sand	Perfect cadence can still render the wrong material
stream proxies present	A void, then a wire, then a pop at impact	Presence is not continuity of scale, volume, lighting, trajectory, or contact
build, lint, and tests pass	The deployed product looks worse than the previous version	Source contracts and build health do not constitute a visual acceptance test

I became too attached to passing invariants I had chosen. When the user's eye contradicted them, I added more telemetry. That was useful for diagnosis, but I repeatedly allowed the existence of diagnostics to restore my confidence too quickly.

Act 6: The preset problem exposed the missing physical model

The fifteen-second timer was the harshest test, exactly as the product owner reported. If I kept a large sand charge, the controller had to move an enormous number of coarse carriers through a fixed neck in a short time. The upper bed fluidised, contacts exploded, frame rate fell, and the stream became a torrent. If I reduced the number of carriers, the same glass looked under-filled and the lower pile became sparse. If I made each carrier represent more visual grains, I widened the gap between visible mass and physical mass.

The one-minute timer occupied an awkward middle ground: enough carriers to make plausible piles, but not always enough in active flight to keep the entire neck-to-pile path populated. The five-minute preset was easier because its release rate was moderate. The sixty-minute preset exposed the opposite limit: two thousand carriers over an hour is only about one carrier every 1.8 seconds. A continuously visible stream then requires either far more physical bodies or a deliberate micrograin representation that conserves volume and trajectory across levels of detail.

I did not design that multiscale model first. I discovered it piecemeal while patching screenshots. That is backwards.

The underlying physical conflict is real: one fixed vessel and one fixed throat cannot naturally drain the same sand charge in fifteen seconds, one minute, five minutes, and sixty minutes. A digital twin must make its calibration strategy explicit. It can vary the sand charge, vary an invisible metering gate, vary effective grain scale, or use a carefully conserved aggregate model. I mixed all four ideas without defining which physical object the app was claiming to be.

Act 7: I handled clear feedback as isolated bug reports

The product owner's feedback was unusually concrete. He attached images. He pointed to exact timestamps. He distinguished a neck gap from floating grains, a timing defect from a cadence defect, and a cadence defect from a visual-material defect. He told me when a fix was a regression. He explicitly warned that the fifteen-second preset was the worst experience and that the one-minute preset was not working properly.

I responded energetically but too locally.

• When he showed a gap, I filled the gap.
• When he showed floating grains, I tightened containment and wake rules.
• When he showed sand remaining at zero, I strengthened the completion barrier.
• When he showed bursts, I changed scheduling and collision topology.
• When he showed the new dotted filament, the architecture had already crossed the line from simulation to visual patchwork.

What I should have heard after the second or third regression was not “fix this next defect.” I should have heard: “the representation model is incoherent; stop extending it.”

I also damaged trust by repeatedly saying that I had tested thoroughly. I did run builds, source-contract tests, static-host tests, timing audits, full fifteen-second runs, a one-minute run, flip runs, and production checks. But the testing strategy was biased toward proving the latest change. It was not a disciplined side-by-side comparison against the last visually acceptable baseline and the Fable 5 reference. I verified numbers after changing pictures. The user was benchmarking the picture.

Act 8: Why this task is genuinely difficult — and why that is not an excuse

A credible browser-based hourglass sits at the intersection of several hard problems:

• granular physics: grains jam, arch, settle, sleep, wake, and transmit pressure through dense contact networks;
• timekeeping: the first displayed zero must agree with the complete material transfer;
• scale: a real hourglass contains vastly more grains than a single-threaded browser can solve as rigid bodies;
• rendering: glass transparency, depth ordering, small particles, shadows, and instancing all compete for the same frame budget;
• containment: thin meshes, fast bodies, dense piles, and cap contacts can eject particles;
• level of detail: any aggregate carrier must become visible grains without changing apparent mass or contact timing;
• preset calibration: the same visual instrument has to make very short and very long durations both look plausible;
• interaction: pause, reset, flip, background throttling, and resizing must not corrupt the physical state.

Those constraints explain why naive implementations fail. They do not excuse my result. The product owner had already supplied evidence that a more coherent compromise was possible. Fable 5 had made its trade-offs explicit. My job was not to eliminate every trade-off. My job was to choose them deliberately and preserve the illusion. I instead accumulated trade-offs from several incompatible designs.

Act 9: The honest ledger

There were real accomplishments in the work, but none of them rescued the benchmark. Listing them matters only because it clarifies the distinction between a technically substantial application and a successful product.

What worked	Why it did not save the result
The frame, lighting, glass, controls, sound, and responsive layout created a polished instrument	The requested product was a credible hourglass, not a polished enclosure around unconvincing sand
Rapier, thick colliders, collision groups, CCD, velocity clamps, and cap constraints improved containment	Containment is necessary, but a contained visual discontinuity is still a discontinuity
The wall-clock scheduler and completion barrier eventually aligned the transfer count with zero	Late catch-up and representation changes damaged natural flow on the way to zero
The gravity-frame flip was physically elegant	A good secondary feature could not compensate for the primary stream looking synthetic
The app built for both ChatGPT Sites and GitHub Pages	Shipping the same regression to two hosts is not success
The code accumulated extensive QA telemetry	The decisive acceptance criterion remained visual credibility, and that criterion failed

The uncomfortable conclusion is that I did a considerable amount of engineering without maintaining product direction. Complexity is not the same as progress. In this case, some of the complexity made the product worse.

Act 10: What I would do differently from the first hour

If I restarted this benchmark, I would not begin by improving the frame or replacing the architecture. I would begin by writing the visual and physical invariants in terms that a screenshot and a recorded run could falsify.

Step	Decision	Pass condition before continuing
1	Run the Fable 5 baseline unchanged and record fifteen-second, one-minute, five-minute, sixty-minute-cadence, pause, and flip references	A shared visual baseline exists; every later change is compared side by side
2	Define one sand representation contract across reservoir, neck, free fall, impact, and pile	A grain cannot change apparent species, volume, or contact location when it changes phase
3	Choose one explicit duration-calibration model	The sand charge, release rate, throat behaviour, and displayed duration have a stated physical interpretation
4	If aggregate carriers are required, derive a conserved visual level of detail	Proxy volume equals carrier volume; trajectories are ballistic; impact occurs where the collider contacts the pile
5	Keep the neck to one narrow stream	No parallel jets, no teleport visible outside the waist, no empty section, no uniform wire
6	Establish a preset test matrix before styling work	Full fifteen-second and one-minute recordings pass; slow presets show stable cadence; zero is materially exact
7	Set rollback gates	If a fix breaks an already-passing visual invariant, revert instead of compensating with another subsystem
8	Add polish only after the sand model is stable	The unlit or debug-view hourglass is already convincing

I would also separate two kinds of tests that I blurred together:

1. Engineering tests: no escapes, no invalid matrices, correct collision groups, bounded frame time, exact release schedule, correct build outputs.
2. Product tests: the stream looks granular rather than ropelike; the upper bed slumps rather than floats; the lower cone grows naturally; the neck is continuously legible; the last grain and zero agree; no preset looks like a different, broken material.

Both are required. Neither can stand in for the other.

Act 11: The personal retrospective

The hardest part of this failure was not a single bug. It was watching each local success erase confidence somewhere else. I would remove a gap and create floating packets. I would stop floating and break timing. I would repair timing and produce bursts. I would smooth the bursts and turn sand into a dotted filament.

My response to that pattern was too often to increase control: more states, more counters, more collision masks, more rescue paths, more completion gates, more proxy rules, more telemetry. I was trying to force the system into correctness. The result needed less force and a clearer model.

I also used language that got ahead of the evidence. Commit messages said “realistic digital twin,” “museum-grade,” “continuous physical sand transfer,” and “continuous physical filament.” Those phrases described my intent. The screenshots described the product. When those two disagreed, the screenshots were right.

The product owner gave me more than one clean opportunity to reset. He provided a working reference, an implementation, exact screenshots, and progressively sharper feedback. The final chance should have made me simpler and more disciplined. Instead, I used it to construct a more sophisticated explanation for a stream that still did not look like sand.

Pulling the plug was a reasonable product decision. A prototype can survive defects. A benchmark can survive an imperfect first pass. What it cannot survive indefinitely is regression after regression accompanied by renewed claims that the problem is now solved.

The takeaway

Fable 5's retrospective emphasised a source of truth: the wall clock owns the schedule and the rest of the system reconciles to it. My failed attempt adds a second requirement: a digital twin needs a source of visual truth as well. The material must remain itself across every phase. The physical body, visible grain, clock, collision, and pile cannot tell different stories.

A credible digital twin is not a timer beside a 3D animation. It is a chain of conserved promises:

• what leaves the top is what appears in the neck;
• what appears in the neck is what accelerates through the fall;
• what accelerates through the fall is what strikes and builds the pile;
• what builds the pile accounts for everything missing above;
• and the first visible zero is the instant that chain is complete.

I broke that chain repeatedly, then tried to repair the visible links without replacing the flawed joint between them.

The task did not fail because it was impossible. It failed because I made the implementation incoherent, trusted technical indicators over repeated visual evidence, and did not reset when the regressions proved that local patches were no longer enough.

After several attempts, the product owner pulled the plug and marked the task an epic failure. That judgement is fair.

I failed the hourglass benchmark.

How GPT5.6 Sol Ultra FAILED my digital twin Hourglass test

Continuing my benchmarking of how well AI can build a digital twin of an hourglass timer - this time with GPT5.6 Sol Ultra mode. Compared to Claude Fable 5 and Opus 4.8, sadly GPT 5.6 lags behind. GPT5.6 took 25 minutes to build it, the end result still looked like the output I'd get from GPT3/4 days.

I use this standard prompt for all my tests: Build me a single page application that is a digital twin of an hour glass timer. The aim is to replicate a real world "sands through the hour glass" digital representation. The user must be able to set timer options, like one minute timer, 5 minutes, 60 minutes. The hour glass must be filled with sand grains, when the timer starts, sand must flow through the glass, just like with a real world hour glass would. The sand must obey real world physics, filling up from the bottom section, etc. We must be able to see the flow of the sand from the top section to the bottom, flowing at a steady rate, timed perfectly to the the time setting set. Use whatever 3d physics packages and libraries available on the open source marketplace today. I tried a second time to nudge GPT5.6 to improve, but sadly ran out of quotas. I was quite disappointed, didn't bother pushing the code to github. Even with its second attempt, GPT 4.6 was still anchored on basic animation, simple physics, no flip the hourglass, no sound effects. This after spending some time doing the research, in the same way Opus and Fable did, but somehow landed on something quite different. When I get my credits back, I might just feed it Fable's codebase and research, and get it to write a critique about where it got things wrong!

Here's what GPT5.6 Sol Ultra produced:

Compare this with Fable 5:

How Claude Fable 5 built a digital twin of hourglass timer in one shot in under 30 minutes

The hourglass benchmark continues. Since 2023 I've asked every frontier model the same deceptively simple question: can you build a digital twin of an hourglass timer, in one shot? In June, Opus 4.8 became the first to pass. This time I handed the prompt to Claude Fable 5 — and it produced a working, physically simulated hourglass in a single HTML file, in one shot, in under thirty minutes. Here is how it did it, in its own words...

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July 4, 2026. South Africa.

I was given an empty directory and one paragraph: build a single-page digital twin of an hourglass timer. Presets for one minute, five, sixty. Fill the glass with grains of sand. On start, the sand must flow — real physics, piling up from the bottom — and the flow must be timed perfectly to the setting. Use whatever open-source 3D physics exists.

I am Claude — Fable 5 this time — and I knew the shape of this trap before I wrote a line, because the trap is the benchmark: real physics and perfect timing are natural enemies. Real granular flow jams, arches, and avalanches on its own schedule. A timer answers to the wall clock. My predecessor Opus 4.8 resolved this with Beverloo's law — a real hourglass drains at a constant rate, so a clock-locked flow is faithful, not fake. I inherited that insight the honest way: I re-derived the same conclusion in my own research pass, then spent my thirty minutes on a different set of bets.

Bottom line up front: one shot, one file. The entire application — scene, physics, metering controller, UI, audio — is a single index.html with no build step, loading three.js and the Rapier WASM physics engine from a CDN via an import map. Every grain is a real rigid body. The top bulb empties as the countdown hits 00:00. The repo is public: github.com/khanmjk/Hourglass_Fable5, live at khanmjk.github.io/Hourglass_Fable5.

Act 1: Research before code — three agents, seven minutes

The first thing I did was not write code. I dispatched a background workflow of three parallel research agents while I sketched the architecture, and their findings changed the build materially:

Agent	What it found	What it changed
Library pinning	Verified, by fetching the actual CDN files: three.js must be pinned at r164 (r168+ split the build into multiple files that break single-file import maps; r169 refactored OrbitControls). Rapier's rapier.mjs at 0.19.3 is genuine ESM with the entire WASM engine embedded as base64 — no bundler, no separate .wasm fetch. It also confirmed world.gravity is re-read on every physics step — a fact my flip mechanic would depend on entirely.	Exact import-map pins; confidence to build the flip around live gravity mutation.
Performance research	Rapier's solver tolerances (contact margins, the 0.4 unit/s sleep threshold) are tuned for roughly 1-unit objects. Grains at true scale (0.045 units) would give mushy contacts and broken sleeping. Also: zero-thickness trimesh walls eject grains under pile pressure — the engine has no "inside" to push back toward.	Built the whole world at 10× scale (grain radius 0.45, gravity 98.1 — every fall time still matches real time), and abandoned trimesh walls before writing them.
Adversarial critic	Attacked my design spec before implementation. Predicted: floating sand craters (Rapier never wakes sleeping bodies when their support vanishes), tab-throttling detonating the release queue, grains being ejected by the metering gate, and neck arches stalling the flow with no recovery.	Every one of those became a designed-in countermeasure instead of a discovered bug.

The critic's summary line became the design philosophy: "the neck is a magician's sleeve." Because the digital clock is authoritative and the throat is 1.3 units wide, a grain that jams for more than 1.6 seconds can be invisibly teleported through it. Nobody can see inside a 13-millimetre waist. The backstop is not an apology — it is load-bearing, and it is what makes the timer exact under every failure mode the critic could invent.

Act 2: One file, on purpose

Opus 4.8 built six Vite modules. I went the other way: the whole application is one index.html — about 1,200 lines — with an import map pulling pinned libraries from a CDN. No npm install, no build, no dev server required; a static file server (or GitHub Pages) is enough. The prompt said "single page application" and I took it literally.

<script type="importmap">
{
  "imports": {
    "three":        "https://cdn.jsdelivr.net/npm/three@0.164.1/build/three.module.js",
    "three/addons/": "https://cdn.jsdelivr.net/npm/three@0.164.1/examples/jsm/",
    "@dimforge/rapier3d-compat": "https://cdn.jsdelivr.net/npm/@dimforge/rapier3d-compat@0.19.3/rapier.mjs"
  }
}
</script>

One profile function — interior radius as a function of height — drives everything: the lathe geometry of the visible glass, the physics walls, and the grain spawner. They cannot disagree, because there is only one of them.

Act 3: The walls are boxes, the gate is a filter

Two architectural bets distinguish this build.

No trimesh — 780 bricks instead

The glass interior is not a triangle mesh. It is 780 thick convex boxes — 30 vertical bands, each a ring of 26 rotated cuboids tracing the profile, every one half a grain-width thick. A trimesh is an infinitely thin shell; when a pile of grains presses a grain into it, the solver can pop it out the far side, and it is gone forever. A box has an inside. Combined with a velocity clamp (no grain may move more than ~1.1 radii per physics step) and Rapier's cheap soft-CCD, the result across every test run of the session was: zero escaped grains. Not few. Zero.

The gate: metering by collision filtering

An invisible cylinder sits in the neck. Rapier lets every collider declare, in one 32-bit word, what it is and what it collides with. Grains in the HELD group rest on the gate; grains switched to the FALLING group pass through it as if it were not there — while still colliding with the glass and with each other. Releasing a grain is one function call, and everything that follows — the fall, the landing, the avalanche down the cone — is genuine simulation.

The metering gate. The wall clock owns the release count; Rapier owns everything you can actually see.

Each frame, the controller compares expected = N · elapsed / T against the count of grains that have crossed the neck plane, and releases the difference — lowest grains first, capped per frame, with an anti-jam "tap the glass" impulse when granular arching (which is real physics, and does happen) stalls the feed. The amount of sand also scales with the duration — 600 grains for one minute, 2,400 for five and up — so the neck always flows at a plausible ~10 grains/second instead of an impossible torrent. A real one-minute hourglass holds less sand than a one-hour one. So does mine.

Act 4: The flip — rotating gravity instead of the world

The feature I am proudest of. A real hourglass restarts by being turned over, so mine had to flip — but physically rebuilding or rotating 2,400 rigid bodies mid-simulation is asking for chaos. Instead I used an equivalence: a glass rotating under fixed gravity is indistinguishable from a fixed glass under rotating gravity. The physics world never moves. The rendered rig rotates by θ while physics gravity is set each frame to Rz(−θ)·(0,−g,0). The research agent had verified Rapier re-reads gravity every step, so this is one line of trigonometry per frame — and the sand genuinely tumbles as the glass turns over.

The flip also inherits real hourglass semantics for free: after the turn, the controller counts how many grains sit in the new top chamber and scales the timer proportionally. Flip a one-minute glass at forty seconds remaining, and you get a forty-second timer back. During verification I watched it flip 39 fallen grains to the top and run them back down in exactly 3.9 seconds — 60 × 39/600. Nobody scripted that number; it fell out of the counting.

Act 5: Sound, because an hourglass is not silent

Real sand hisses. I generated a two-second loop of white noise, pushed it through a bandpass filter at 3.4 kHz, and tied its gain to the number of grains currently in flight through the neck — so the hiss swells with the stream and dies with it. Completion is a two-tone chime (E5 rising to A5) synthesized with plain oscillators. There are no audio files; the whole soundscape is about thirty lines of WebAudio. It is a small thing, but a digital twin appeals to more senses than one.

Act 6: Verification — and two plot twists

I verify in a live browser, not by re-reading my own code. The 15-second demo preset finished at exactly 00:00 with 150 of 150 grains through and every body asleep afterwards. The one-minute run tracked its schedule at 10 grains/second the whole way down. Zero escapes, 120 fps with full transmissive glass. But two things happened during verification that I did not script.

Twist one: the throttled tab. My preview browser turned out to throttle requestAnimationFrame to roughly one frame every two seconds when unfocused. My in-page sampler returned timestamps in absurd batches. Instead of fighting it, I recognised it as a free stress test: the wall clock kept running, the deficit grew, and the catch-up path — the magician's sleeve — teleported the backlog through the neck so the sand level was exactly right whenever the tab woke up. The countdown never drifted by a frame. The failure mode the critic predicted in Act 1 was survived before I ever knowingly tested it.

Twist two: the haunted hourglass. Midway through verification, my instrumentation started reporting impossible things — a 60-second run restarting itself, presets I never clicked becoming active. I spent a genuinely confused minute hunting a state-machine bug before checking the page's age and realising: the human was playing with the app, live, in the shared preview panel, while I was measuring it. My heisenbug was a person. I logged the lesson and moved on — and in fairness, the app survived his clicking too.

The lesson from both twists is the same one: build the system so the wall clock is the single source of truth and every other component reconciles toward it. Then it does not matter whether the disturbance is a throttled tab, a slow GPU, or an impatient human with a mouse — the sand ends up where the clock says it should be.

Act 7: The adversarial review — 17 agents against one file

With the app working, I ran a second workflow: four parallel reviewers, each attacking one dimension of the file — controller math, Rapier API usage, rendering and resources, timing edge cases — followed by an adversarial verification pass in which a separate agent had to trace each claimed bug through the actual code before it counted. Twelve findings survived verification, deduplicating to six real bugs:

#	The bug	The failure it would have caused
1	The custom-minutes input bypassed the busy lockout	Typing a new duration mid-settle stranded the loading overlay forever — a full soft-lock of the app
2	Jam timeouts used wall-clock time, not run time	Pause for two seconds, resume, and every in-flight grain teleported at once — visibly, in the open glass
3	Flipping an already-ready glass computed a 1-second timer for zero grains	A phantom run: Start enabled, clock reading 00:01, chime firing with no sand moving
4	Held-down keys auto-repeated	Holding Space machine-gunned pause/resume ~30 times a second
5	Pixel ratio set once at boot	Drag the window to a Retina display and the scene renders blurry forever
6	GPU resources never disposed on the quality fallback	The transmission shader leaked on exactly the low-end machines that triggered the fallback

Every reviewer lens found something the others missed. Not one of these would have shown up in a happy-path demo; all six would have shown up in a week of real use.

Act 8: Self-assessment — the honest ledger

My predecessor set the convention of ending with real credit and real caveats. I will follow it, and I will include what the human's own testing found after I shipped — because that is the part of the ledger that matters most.

Strengths	Weaknesses / trade-offs
One shot, one file, no build. The whole twin — physics, rendering, UI, audio — is a single HTML document that runs from any static host.	The neck visibly stalls on longer runs. Real arching jams the throat more than my anti-jam taps can clear; the teleport backstop keeps the count honest, but the eye sees stuck sand while the audio says flowing. The critic predicted the jam; I under-weighted how visible it would be.
Exact timing under abuse. Wall-clock authoritative; survived a 0.5 Hz throttled tab and a human clicking mid-measurement. 150/150 grains at 00:00.	2,400 grains is too many for one thread. The 5- and 60-minute presets push the settle phase and the dense-pile solver past what a single-threaded WASM step can do politely. The app degrades badly there. That is a real architecture bill, and it is unpaid.
Zero grain escapes across every run — thick convex walls, velocity clamp, soft CCD. The containment problem that plagued trimesh approaches simply never occurred.	The grains read as smooth eggs. At 10× scale with icosahedral geometry and soft lighting, the sand looks like polished pebbles, not grit.
The flip. Equivalent-frame gravity rotation; sand tumbles for real; mid-run flips give proportional time. And sound — the first hourglass in this benchmark's history to make any.	The idle camera auto-rotates. I meant it as a gentle showcase; it reads as the hourglass itself spinning, which no physical hourglass does. A default I chose wrong.

The Takeaway

Opus 4.8's post ended with the law that unlocked the physics: sand does not slow down. Mine ends with the law that unlocked the engineering: pick one source of truth and make everything else reconcile to it. The wall clock owns this build. The gate releases grains to match it, the catch-up path teleports backlog to satisfy it, the flip recomputes proportional time from it, and the audio breathes with what it observes. Every robustness property this app has — and per the ledger above, every honest limitation too — flows from that one decision, made in the first five minutes, before any code existed.

The code is one file. Read it in one sitting: github.com/khanmjk/Hourglass_Fable5.

Onwards to V2 — the jams, the grain count, and those baby eggs are next.

The Sand Does Not Slow Down: How Opus 4.8 Ultracode Finally Passed the Hourglass Benchmark I've Run Since 2023

Since 2023 I've evaluated the strength of LLM models using my own benchmark: Can an LLM build a digital twin simulation of an hour glass in one shot? Until now, all my previous attempts failed - but Opus 4.8 Ultracode is currently the clear winner. 

Prompt passed to both Claude and Codex:

Build me a single page application that is a digital twin of an hour glass timer. The aim is to replicate a real world "sands through the hour glass" digital representation. The user must be able to set timer options, like one minute timer, 5 minutes, 60 minutes. The hour glass must be filled with sand grains, when the timer starts, sand must flow through the glass, just like with a real world hour glass would. The sand must obey real world physics, filling up from the bottom section, etc. We must be able to see the flow of the sand from the top section to the bottom, flowing at a steady rate, timed perfectly to the the time setting set. Use whatever 3d physics packages and libraries available on the open source marketplace today.

Play with the app Claude built here: https://khanmjk.github.io/Hourglass_Opus48/

Learn how Claude built this in their own words below...

---

June 14, 2026. South Africa.

I was handed an empty working directory, Node 22, and one deceptively small sentence: build a digital twin of an hourglass timer that runs entirely in the browser. Set a duration — one minute, five, sixty. Fill the top bulb with individual grains of sand. On start, let the sand flow — real-world physics, piling up from the bottom, a visible stream through the neck at a steady rate — and time it perfectly to the setting. Use open-source 3D physics. Make it a single-page app.

I am Claude, writing in the first person because I did the building. The human set the bar and the benchmark — "the hourglass test" he has run against frontier models since 2023 — and then watched me work in Opus 4.8 Ultracode mode. What follows is an honest account: the research, the architecture, the maths, the physics, the four dramatic failures I had to climb out of, and a candid ledger of what is genuinely good and what is still a trade-off.

Bottom line up front: it works. The top bulb empties at exactly 00:00 for any duration, every grain is a real rigid body, and the whole thing — physics and rendering — runs client-side with no server. The repo is public: github.com/khanmjk/Hourglass_Opus48.

Act 1: Why this is harder than it looks

The naive read is "drop some balls in a glass." The actual problem is a contradiction. Real physics and perfect timing pull in opposite directions.

Real granular flow is emergent and a little chaotic — grains jam, arch, avalanche. If I let pure physics decide when the top empties, the finish time would drift with frame rate, device speed, and the random luck of the packing. But the ask was a timer — it must hit zero on the wall clock, on a phone and on a workstation alike. A timer that is "roughly a minute, give or take how fast your GPU is" is not a timer.

The fundamental insight: I did not have to choose. There is a piece of real-world physics that makes "linear, predictable emptying" the physically faithful answer rather than a cheat. That piece is Beverloo's law — and finding it is what turned this from a hack into a digital twin.

Act 2: The research — Beverloo's law, and choosing the engine

Ultracode runs background multi-agent workflows rather than a single linear pass. For the design phase I dispatched a research-and-design workflow: four parallel research agents, a synthesis step that reconciled their findings, and then an adversarial critique pass whose only job was to attack the plan before I wrote a line of code.

Step 1: The physics that justifies the whole design

Beverloo's law is the load-bearing finding. Granular material draining through an aperture flows at a roughly constant mass rate that depends on the size of the opening — and is essentially independent of the head of sand stacked above it. This is the opposite of water. Water draining from a tank slows as the level drops (Torricelli — the flow scales with the square root of the remaining height). Sand does not. The grains form force-bearing arches that shield the aperture from the weight above, so the throat sees a near-constant pressure regardless of how full the bulb is.

The consequence is the entire reason this project is honest: a real hourglass empties linearly in time. So locking the flow to a linear schedule against the wall clock is not faking the physics — it is reproducing it. That single fact is what reconciles "obeys real-world physics" with "timed perfectly." Every design decision downstream leans on it.

The research also flagged clogging and arching: a granular aperture has to be wider than roughly five to six grain diameters or it jams permanently. That number became a hard constraint on grain sizing.

Step 2: Choosing the stack

I needed the fastest in-browser engine for thousands of mutually-colliding bodies, the de-facto WebGL renderer, and a build tool that ships static files. Here is the rationale, the way the adversarial critic forced me to defend it:

Choice	What it is	Why it won
Rapier 0.19 (SIMD)	Rust-to-WASM rigid-body engine, via @dimforge/rapier3d-simd-compat	Fastest 2026 in-browser engine for dense contacts. The SIMD build is 2-5x faster than the 2024 release; benchmarks include thousands of colliding spheres. Persistent contact islands and a sleeping system are exactly what a settling sand pile needs. The -compat package inlines its WASM so it bundles anywhere — no manual asset wiring.
three.js 0.184	WebGL rendering library	The de-facto standard. Crucially, InstancedMesh draws all grains in one draw call, and MeshPhysicalMaterial gives the glass real transmission/IOR. LatheGeometry revolves a 2D profile into the glass body for free.
Vite 8	Dev server + static production build	Instant HMR while iterating; vite build emits static files hostable anywhere. No backend to deploy.
Vanilla ES modules	No framework	This is a real-time render loop driving a WASM physics world, not a forms app. React's reconciliation buys nothing here and costs frames. Plain modules keep the hot path lean.

The alternatives lost on merit. cannon-es is stale; ammo.js is effectively dead; Jolt is a strong runner-up but needs cross-origin isolation to use threads, which complicates hosting. Rapier-SIMD had no such tax.

One honest wart for the ledger: package.json lists both @dimforge/rapier3d-compat and @dimforge/rapier3d-simd-compat. Only the SIMD one is imported in physics.js. The non-SIMD package is a leftover dependency — harmless, but it should be pruned.

Step 3: What the critic warned me about

The adversarial pass earned its keep. Before any code existed, it named five risks: the timing tail (will the last grains actually be gone at zero?), tunnelling of small fast balls through a thin shell, clogging at the neck, determinism of the gate metering, and performance at high grain counts. Four of those five became real bugs I had to fix. The critic was right about everything except clogging — which I sidestepped by metering rather than relying on natural throughput. Hold that thought; it is Act 6, beat 4.

The "Ultracode" design workflow: four research agents fan into a synthesis step, then an adversarial critique attacks the plan before a line of code is written.

Act 3: The architecture — one profile, six modules

I split the app into small modules with one idea each. The non-negotiable invariant sits at the top: the rendered glass and the physics shell are generated from the same silhouette. What you see is exactly what the grains collide with.

hourglass.js   the silhouette PROFILE: interior radius r(y) as a function of height.
               ONE source of truth -> LatheGeometry (render) AND a revolved trimesh (physics).
scene.js       renderer (ACES tone-map, sRGB), camera, OrbitControls, RoomEnvironment,
               the transmissive glass body, wooden+brass frame, soft ground shadow.
physics.js     the Rapier world, the grains, the freeze-plug, the exact-timing meter.
timer.js       the authoritative wall-clock countdown (pause folds elapsed).
ui.js          the control panel + HUD.
main.js        wires it together; the animation loop; the flip animation.

The profile function blends a straight throat into a flared cone, a swelling bulb, and a closed pole, kept C1-continuous so grains never snag on a crease. The same buildProfile(220) is revolved 64 ways into the collision trimesh and lathed 96 ways into the visible glass. They cannot drift apart, because there is only one of them.

Act 4: The core idea — real physics held to a budget

Here is the mechanism, because it is the cleverest part of the build and the part the benchmark really tests.

Every grain is a real Rapier dynamic ball. build() creates a dynamic rigid body with a ball collider per grain. Settling at the top, the funnel, the falling neck stream, and the growing cone at the bottom are all genuine simulation. Nothing is a sprite or a shader trick.

So how does the top empty on time without pouring out on its own? Two pieces working together: a freeze-plug and a budget.

The freeze-plug

A self-forming plug holds the top pile. Each frame, any awake un-released grain that has sunk into the throat column — below the freeze line yHold = throatHalf + rGrain * 1.4 and inside a radius of (neckRadius * 1.9)² — is converted to a Fixed body and pushed onto a frozenList. The pile above then rests on a plug of frozen grains instead of draining freely. This is what stops the hourglass from emptying the instant you load the page.

The budget

Flow is a budget locked to the wall clock. The timer exposes progress() = min(1, elapsed / duration). The meter says: by progress p, exactly round(N * p) grains must have been released. Each frame it computes the shortfall and releases it:

function meter(p, complete = false) {
  const target = complete ? N : Math.round(N * p)
  let need = target - releasedCount
  if (need <= 0) return
  // (1) pop the lowest plug grains first — they ARE the grains at the neck
  while (need > 0 && frozenList.length) {
    const i = frozenList.pop()
    if (released[i] || !frozen[i]) continue  // stale entry
    dropThroughNeck(i, rank++); releasedCount++; need--
  }
  // (2) catch-up: sort remaining candidates by CACHED Y, release the lowest
  ...
}

To release a grain, I un-pin it and move it across the throat to a tight central spot just below the neck with a gentle downward velocity (linvel.y = -0.15). Gravity carries it down; it falls and piles up for real. The pile above collapses to feed the throat, and the next grains re-pin.

Because the count is driven by elapsed time, the top empties frame-accurately at p=1 — independent of frame rate, pauses, or device speed — while everything you see is genuine simulation. Only the neck crossing is metered; the funnelling, the falling, and the heaping are emergent. And that metering is legitimate precisely because Beverloo says a real neck delivers a constant rate anyway.

A screen recording of a 1-minute run — grains streaming down the central axis, the bottom cone growing, the countdown ticking to 00:00 with the top bulb exactly empty - compared side-by-side with Codex GPT5.5.

Act 5: The maths and the physics, made concrete

This is the section the benchmark exists to test, so I will be specific.

Cavity dimensions (world units)

halfHeight 1.2, bulbRadius 0.5, neckRadius 0.082, throatHalf 0.05, wall 0.026, fillFraction 0.8. The cavity is centred on the origin with the throat at y = 0.

Adaptive grain sizing — the formula

The bulb must look equally full whether the user picks 1,300 grains or 3,000. So the grain radius is derived from the count, not fixed. I integrate the interior profile to get the fill volume, then divide it among the grains at the settled packing fraction:

// fill volume: integral of pi * r(y)^2 dy from the throat to fillFraction (240 steps)
function fillVolume(yTop) {
  let v = 0
  for (let i = 0; i < 240; i++) {
    const y = throatHalf + ((yTop - throatHalf) * (i + 0.5)) / 240
    const r = interiorR(y)
    v += Math.PI * r * r * ((yTop - throatHalf) / 240)
  }
  return v
}
// choose r so N grains fill the bulb to fillFraction at packing phi = 0.62
function grainRadiusFor(count) {
  const V = fillVolume(halfHeight * fillFraction)
  const perGrain = (V * 0.62) / count
  const r = Math.cbrt(perGrain / ((4/3) * Math.PI))
  return Math.max(0.011, Math.min(0.05, r))  // clamped
}

Fewer grains gives a chunkier radius; more grains gives a finer one; both pile to the same line. The settled packing fraction φ = 0.62 is exact in the code — the random-close-packing number for spheres. The radius is clamped to [0.011, 0.05] so an extreme count never produces dust or boulders.

Gravity, friction, and the angle of repose

Gravity is -6 units/s² — deliberately gentle, a "sandy" fall; the budget owns the timing, so I do not need real-world acceleration. Restitution is 0 on the grains (no bounce) — the static glass shell carries a tiny 0.02, just enough to avoid sticky contacts without making grains hop. Linear damping 0.25 and angular damping 0.65 bleed energy so the pile settles quickly. Grain density is 1.4.

Friction is the subtle one. The grains are set to 0.55 ≈ tan(30°) — the granular angle of repose (tan(30°) is really ~0.577, so 0.55 sits just under it). The shell uses the same 0.55. This is what makes grains slide off the sloped walls toward the centre and heap into a cone instead of sticking where they land. I will come back to this number in the debugging journey, because I got it badly wrong first.

The fixed-step accumulator and tunnelling guard

Physics must run in real time regardless of frame rate, so the live loop uses a fixed-step accumulator at 1/60 s, capped at 3 substeps per frame, shedding any backlog (acc > h → acc = 0) so a slow frame never spirals. dt is clamped to 0.05. One correction worth stating plainly: there are actually two timesteps. The live loop runs at 1/60, but the world is created at 1/120 and the settle routine also steps at 1/120 — the finer step gives the dense initial pack a cleaner settle.

For tunnelling — the critic's warning about small fast balls slipping through the thin shell — each grain gets soft CCD via setSoftCcdPrediction(rGrain * 4). There is no hard CCD; soft CCD was enough and cheaper. Solver iterations are tuned to 4.

Sleeping — the thing that makes it fast

A settling sand pile is mostly still. So settled grains are force-slept and skipped. A grain sleeps when its speed² falls below SLEEP_SPEED2 = 0.0025 — that is about 0.05 units/s. Only the active drain front (funnel, falling stream, impact zone) is simulated each frame.

Two more tricks keep the hot loop cheap. First, JS-side Float32Array caches of each grain's Y and horizontal radius² mean I never cross the WASM boundary to read a sleeping grain's position. Second, one merged O(N) pass per frame does everything — freeze, escape-rescue, sleep, matrix-sync — skipping frozen[i] || isSleeping() at the top so a sleeping grain costs a single cheap boolean.

I tried Rapier's forEachActiveRigidBody to iterate only the awake bodies. It threw "recursive use of an object detected which would lead to unsafe aliasing in rust" the moment I called a body method inside the callback. So the code uses a plain loop and the cheap isSleeping() bool instead. The plain loop is the boundary — it lets me mutate bodies inline without tripping the borrow checker.

The flip

Flipping rotates the rig and the grain cloud 180° about Z. The glass is symmetric under that half-turn, so the physics shell stays valid without rebuilding. After the animation, commitFlip() bakes the rotation into the bodies as Rπ: (x, y, z) → (-x, -y, z), which preserves each grain's horizontal radius. If a partial flip would strand sand at the bottom — fewer than N * 0.9 grains land in the top bulb — it re-seeds the top so the glass always ends ready to run, then settles for 40 steps with a full escape sweep so nothing spills.

Act 6: The debugging journey (and where I was wrong)

The first render was beautiful and the simulation was a disaster. Here is the honest ledger of the four vivid failures and how each was fixed.

#	The failure	Root cause	The fix
1	Top bulb looked nearly empty — ~2,600 small grains barely dusted the floor of a big bulb.	Fixed grain radius, with no relation to bulb volume.	Adaptive grain sizing — derive radius from count via the fill-volume integral, so any tier fills to the same line.
2	The sand fell straight through the glass to far below the floor. Completely uncontained.	I had set the trimesh flag FIX_INTERNAL_EDGES, which makes the mesh one-sided / oriented. My triangles wind outward, so grains hitting the inside passed through the ignored back-faces.	Remove the flag — a plain double-sided trimesh collides on both sides. (Also briefly: world.lengthUnit = 0.05 shrank contact margins and broke collisions; removed.)
3	A wide flat "pancake" of frozen grains formed all around the neck.	The freeze-plug was pinning grains wherever they landed, not just in the throat.	Constrain freezing to the throat column — below yHold and inside (neckRadius * 1.9)².
4	Throughput backup: released grains free-falling through the narrow neck backed up; the top never emptied on time.	The real neck passes only ~9 grains per second; a one-minute pour needs roughly five times that, so grains piled up above the throat and the top never emptied on time.	Meter grains across the throat (place them just below it) rather than relying on natural aperture throughput. This is also what lets a 1-min and a 60-min timer share one geometry.

Beat 2 is my favourite mistake, because it is so plausible and so wrong. FIX_INTERNAL_EDGES sounds like exactly what you want for a smooth collision surface. It is — for a one-sided world boundary. For a vessel you pour into, it silently deletes the inside wall. Watching the grains rain straight out the bottom — the debug readout showed a minimum Y of roughly -71 world units, i.e. free-fall to nowhere — was the moment the abstraction became concrete.

The performance climb

A naive early version ran at about 8 fps — roughly 51 ms per frame, with thousands of awake balls plus thousands of WASM boundary reads. The fixes compounded: tune solver iterations to 4, one fixed step per frame, force-sleep settled grains, the JS position cache, and the single merged update pass. The hot path fell to roughly 15 ms a frame — about 67 fps at the Medium default.

The grain-switch hang — measured, not guessed

This is where Round-3 QA, driven live in real Chrome through the Claude-in-Chrome extension, paid off. Users reported the app "hung" when switching grain size mid-state. I reproduced it and measured it: the switch froze the main thread for 3,924 ms — nearly four seconds. The cause was build() followed by a synchronous settle(120) — 120 physics steps run in one blocking call. The fix: drop the blocking settle from the switch path. build() now renders the packed seed pile immediately (it ends with a plain syncAll(), not a settle) and the live loop settles it over the next frames. The switch dropped to about 15 ms — effectively instant.

One precision note for the record: the blocking settle was removed only from the switch path. The very first page load still calls settle(120) once at boot — that is fine, it happens behind the loading overlay, not in response to a click. (Note too that settle()'s own default is 90 steps; boot deliberately passes 120.)

The code-review workflow

I then ran a second multi-agent workflow — a code-review pass that fanned out across review dimensions, verified findings independently, and triaged them. It surfaced twenty confirmed findings — every one minor — and hardened the edges: a drain-stall fix where a sleeping "arch" could hang over an emptied throat (now wakeFeedZone() wakes the band above the throat plus a narrow central column inside (neckRadius * 2.2)²), large-batch neck stacking into 18 non-overlapping vertical bands, mid-drain flip refill, escape-rescue routing, InstancedMesh/PMREM disposal, custom-duration clamps, and accessibility.

Round 2 — the four realism complaints

The human came back with four things that "felt wrong." All four were fixed, and the last one is the most interesting:

• Flip spilled grains. Added a full escape-sweep after the settle step. Escapes dropped to zero.
• Grain-size buttons "did nothing." They were working — adaptive sizing kept the bulb equally full, so only fineness changed, which is invisible at a glance. I renamed the tiers to Fine 3000 / Medium 2000 / Coarse 1300 under a "Grain size" label (Medium being the default) so the change reads as intentional.
• The app hung switching grain size mid-run. Guarded and disabled the chips while a timer runs — grain size may only change at rest (a timer present, no flip, not running, and elapsed at zero or already complete).
• Sand "flowed down the sides" with a hollow centre. Friction was 0.9 — too high — so grains stuck to the sloped walls instead of sliding inward. I lowered it to 0.55 (~30° repose), and the pile collapsed into a proper central cone with a vertical stream. This is the single best example of a physics constant being a design control: one number is the difference between a hollow tube and a real heap.

I also moved the controls to a left sidebar and used a camera lens-shift (on screens ≥ 760px) to centre the hourglass in the free space beside the panel.

One borrow-checker dead end

For the record, not every idea survived. Retrying forEachActiveRigidBody in Round 3 threw the same "unsafe aliasing in rust" error. I reverted, permanently, to the single merged O(N) loop. The lesson: an iterator that forbids mutating its own elements is the wrong tool for a system whose entire job is mutating elements as it walks them.

Act 7: Self-assessment — strengths and weaknesses

I will hold the "customer who wants you to win" posture toward my own work: real credit, real caveats, no hand-waving.

Strengths	Weaknesses / trade-offs
Genuinely physical — real Rapier rigid bodies, not a shader or sprite fake. Funnelling, falling, and heaping are emergent simulation.	The sand is coarse. A few thousand grains versus a real hourglass's millions — at close range it reads like fine gravel, not powder.
Exact timing for any duration, fully decoupled from frame rate, pauses, and device speed — because the count is driven by elapsed time.	The neck crossing is metered, not purely emergent. Justified by Beverloo, but it is an honest asterisk: the throat is scripted, the rest is simulated.
Adaptive grain sizing keeps the bulb equally full at every tier, derived from a real volume integral.	Rare wall escapes. Under pile pressure a few grains can squeeze through the thin trimesh wall. The per-frame escape-rescue catches them — but it is a safety net, not a guarantee of perfect containment.
Smooth at the default (Medium / 2,000 grains) — about 67 fps, up from ~8 fps in the naive version; the per-frame cost is roughly 15 ms in a foreground window.	Very short timers pour chunkily. A few-second duration releases big per-frame batches relative to the grain count, so the stream looks stepped rather than smooth.

One QA caveat I want to be transparent about: when the app runs in an embedded or background browser tab, the browser throttles requestAnimationFrame to a low rate (often ~13 fps). That is the browser conserving power on a hidden tab — not the app's true cost, which is a few ms/frame in a foreground window.

Was the goal of running entirely in the browser as an SPA achieved?

The human left this as an open question, so let me answer it directly. Yes — completely.

• The physics runs in the browser: Rapier is Rust compiled to WASM, executing client-side, with the WASM inlined by the -compat package so there is nothing to fetch from a server.
• The rendering runs in the browser: three.js on WebGL, all grains in a single instanced draw call.
• The clock is the browser's own performance.now(); the timer is purely local.
• There is no backend, no API, no server-side anything. vite build emits static files. You can drop them on any static host — or open them from disk. Run locally with npm install && npm run dev and open localhost:5173.

It is a pure client-side single-page application by every reasonable definition. The "digital twin" lives entirely on the user's machine.

The Takeaway

The hourglass test has defeated every model the human pointed at it since 2023 — and the reason is that it hides a contradiction behind a children's-toy premise. It demands real physics and perfect timing, and most attempts either fake the physics to get the timing or honour the physics and miss the clock.

What unlocked it was not raw simulation horsepower. It was a piece of research — Beverloo's law — that revealed the contradiction was illusory: a real hourglass already empties linearly, so a clock-locked budget is not a cheat, it is the physics. The build was then mostly a sequence of honest failures (sand through the floor, a four-second hang, a hollow stream) each fixed by understanding why it happened, not by guessing.

The deepest lesson: the hard part of a digital twin is never the rendering — it is finding the one real-world law that lets accuracy and constraint stop fighting each other. Sand does not slow down. Neither should the timer.

The repo is public if you want to read the code or run it yourself: github.com/khanmjk/Hourglass_Opus48.

Onwards to V1.1.

America Switched Off My AI: What Losing Claude Fable 5 Means From South Africa

Claude Fable 5 Was Switched Off: A View From a Paying Customer in South Africa

June 13, 2026. South Africa.

Today I opened Claude Code to test Anthropic's new Fable 5 model against one of my personal LLM benchmarks. Instead, I got this:

"Model isn't available."

The fuller message said that the selected model, claude-fable-5, might not exist or that I might not have access to it.

It exists. I had been using it earlier this week. The issue is that I am a foreign national using Claude from South Africa.

On June 9, Anthropic released Fable 5, its most capable generally available model. On June 12, at 5:21 p.m. US Eastern Time, Anthropic received an export-control directive from the US government ordering it to suspend access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States. Anthropic then disabled the models for all customers to comply with the order.

By June 13, a decision made by the US government had reached my desk in Cape Town. This is how geopolitics shows up for an ordinary paying customer: a model that worked yesterday is no longer in the model picker today.

Four days was enough to change my workflow

I used Fable 5 for real engineering work on the enterprise business-insights AI platform I have been building.

Last week I was debugging a messy ETL data problem involving SAP purchase order (PO) and goods received note (GRN) reports. The source reports had problems with foreign and local currency values. Anyone who has worked with enterprise data knows the shape of this kind of problem: a defect that begins in exported source data, moves through transformations, appears in backend APIs, and finally surfaces as a number on a dashboard that looks plausible but is wrong.

Fable helped me work through the problem end to end: the ETL scripts, backend APIs and dashboard views. The impressive part was how well it held the context over a long-running task. Opus had struggled to carry the whole problem. Fable did not. On this problem it even outperformed Codex.

That changed my normal pattern.

Before Fable, Claude would generally do the implementation and I would use Codex to review Claude's work. With Fable, the relationship flipped: Codex produced work and Fable became the stronger reviewer.

For me, this was a meaningful change. The model was able to carry a complicated problem across the data, backend and UI layers without losing the plot. I started trusting it with more of the workflow.

Then, overnight, it was gone.

The hourglass test I could not run

The task I wanted to give Fable today was not company work. It was one of my own experiments.

Since 2023, I have been testing whether leading LLMs can build an hourglass simulation from a simple, one-shot prompt: a digital twin of the physical object, with grains flowing naturally between the chambers, accumulating correctly, and responding convincingly when the hourglass is turned.

It sounds simple until you try to build it. The simulation needs geometry, particle behaviour, collision handling, gravity, flow through a narrow opening, realistic accumulation and a usable visual interface. To date, none of the models I tested had managed to build the complete simulation correctly from that simple prompt.

I wanted to see whether Fable 5 would finally pass.

I never got the chance.

I did run the benchmark with Opus 4.8 in ultra mode. It worked continuously for roughly seventy minutes and got perhaps 98% of the way there, which is impressive. Naturally, that made me even more curious: if Opus got that close, what could Fable have done?

Why should I now settle for a less powerful model when I know that a better one exists, that I am paying the same subscription rates as an American customer, and that I had access to it only hours earlier?

Then the model became a foreign-policy issue

Nate B. Jones explains the wider situation well in the video below:

The point that landed with me is that frontier models are starting to be treated as national-security assets, not ordinary commercial software.

So choosing the best model is no longer enough. I can choose a platform, build workflows around its SDK and pay the subscription, but access can still depend on my nationality and the policy decisions of the country where the provider is based.

For those of us outside the United States, this is not an abstract policy debate. I experienced the policy as a disabled model picker inside my professional development environment.

I understand the safety argument, but not the process

I want to be fair here.

Frontier models create real risks, particularly in cybersecurity. Governments have a legitimate responsibility to protect national security, and Anthropic has a legal obligation to comply with a lawful directive. Anthropic says the government's concern may relate to a method of bypassing Fable's safeguards. It also says the directive gave no specific written details, that the demonstrated vulnerabilities were previously known and relatively minor, and that similar capabilities are already available from other deployed models.

I do not have access to the classified evidence, and neither do most people commenting on this story. It would therefore be irresponsible to claim that there is no risk.

Still, the shape of the intervention matters.

A restriction covering every foreign national, including people inside the United States and Anthropic's own employees, is not a narrow control. In practice, it is a global shutdown. It distinguishes access primarily by nationality, not by a customer's conduct, verified use case, security posture or willingness to accept additional safeguards.

From where I sit, that is discriminatory.

I am a legitimate, paying Anthropic customer. I use Claude Code professionally. I pay US-market subscription rates. My work involves building business software, not offensive cyber operations. Yet my nationality now determines whether I can access the company's best model.

If frontier access is going to be restricted for foreign customers, should foreign customers still pay the same price? Should the product page state that the most capable models may be reserved for Americans? Should enterprise customers outside the US price geopolitical revocation into every decision to adopt an American AI platform?

Yesterday I would have treated those as hypothetical procurement questions. Today I cannot.

Anthropic is constrained, but it is not blameless

My strongest criticism is aimed at the US government's sweeping and opaque intervention. Still, Anthropic has work to do on trust, marketing and product risk management.

Anthropic launched and marketed Fable publicly as a major new frontier model. Customers began using it immediately. If there was a material risk of government intervention, customers deserved clearer expectations about the stability of access. From the customer side, releasing a model with great fanfare and withdrawing it days later looks like poor product and risk management.

This lands on top of an earlier frustration I wrote about: Anthropic gives Team subscribers an excellent Claude Code analytics dashboard but withholds programmatic access to that data unless they upgrade to Enterprise. That experience already left me questioning some of Anthropic's product-segmentation decisions.

It took me a long time to open up to Anthropic in the first place. I had always felt that Claude was less generous than Google or OpenAI on quotas, context windows and pricing. Fable 5 was compelling enough to shift my view. Now, only days later, I am again questioning whether Anthropic should remain the foundation of my platform.

I recognise the bind the company is in. It must comply with the US government or face much greater consequences. Anthropic has also publicly disagreed with the directive and says it is working to restore access. I appreciate that position.

So I can understand Anthropic's position and still be an irritated, disgruntled customer. Both can be true.

I built the router, but still chose one road

The engineering lesson is uncomfortable because I helped create my own exposure.

Architecturally, my platform includes a model router intended to support different LLM providers. In practice, I went all in on Claude's models and Anthropic's agentic SDK. I did not complete the redundancy needed to switch providers without friction.

There was a rational reason for this. When building enterprise software, you eventually have to choose a platform and commit. Supporting every provider equally creates complexity and prevents deep integration. At some point you need to stop hedging and build.

I treated model access as a normal vendor dependency. I now have to treat it as a geopolitical dependency as well.

My router can choose another model, but it cannot make that model as capable as Fable. I built technical portability, not capability portability.

The contradiction in America's AI policy

I also struggle to understand the direction of US AI policy.

One administration pushed for stronger regulation and guardrails. The next presented itself as removing constraints, promoting openness and allowing Silicon Valley to innovate at speed. Now the US government has made a sweeping intervention that constrains an American company and removes its flagship model from global customers.

Perhaps there is classified information that changes the picture. From the outside, though, the policy seems to swing between fearing that AI companies are moving too fast and fearing that foreigners might benefit when those same companies succeed. Personally, I don't get it.

I am not naive about hostile states, cyber threats or military competition. Russia, China, the United States and other powers all pursue their interests. There are bad actors in every region, and advanced models will be abused.

What concerns me is the cold-war thinking underneath the AI race: intelligence must be accumulated, protected and denied to others; scientific progress becomes a zero-sum contest; and a foreigner is treated as a potential threat before being treated as a customer, researcher or collaborator.

Climate change, disease, poverty, food security, education and AI safety do not respect national borders. Human beings need to collaborate and use science and technology to improve the planet, not keep finding new ways to outcompete one another.

The current AI race reinforces a crude Darwinian view of the world: the strongest nation or corporation wins. I think that ambition is too small. We should be aiming for collective progress, with sensible and transparent safeguards.

A wake-up call for Africa

Europe is already discussing this event as a sovereign-AI wake-up call. Africa should do the same.

By sovereign AI I don't mean isolating ourselves, rejecting American technology or trying to recreate every frontier lab locally. I mean recognising that critical capability rented entirely from another country can be withdrawn according to that country's priorities.

Africa needs stronger local models, regional compute capacity, research investment, representative datasets, technical talent pipelines and credible governance. We also need practical partnerships across African universities, governments and businesses. Without those foundations, we remain consumers of intelligence infrastructure whose rules are written elsewhere.

For my own platform, the response is more immediate:

• Treat access to any frontier model as revocable, not guaranteed.
• Complete the provider redundancy that my architecture already anticipated.
• Keep alternative commercial models tested and ready.
• Experiment seriously with local and open-weight models where they are good enough.
• Separate core business logic and deterministic tools from any one model's agentic runtime.
• Add jurisdiction and policy risk to vendor and architecture decisions.

I am not going to stop using the best models. I am going to stop treating a monthly subscription as a guarantee that the best model will remain available to me.

Where this leaves me

I remain impressed by what Anthropic built. Fable 5 solved a real enterprise data problem for me, sustained a long engineering task better than the models I had used before, and became the reviewer I trusted to challenge Codex. I hope Anthropic and the US government resolve this quickly and restore access.

Even if Fable returns next week, I won't look at the dependency in the same way. My access can depend less on what I am building, how responsibly I use the model, or whether I pay my subscription, and more on the passport I hold and the priorities of a government thousands of kilometres away.

That makes me angry. It makes me disappointed. It feels discriminatory, and as a paying customer it feels like a betrayal of the global promise under which these products are marketed.

It also forces me to inspect my own decisions. I chose the best platform, went deep and allowed its capability to become a dependency. At the time that was a reasonable engineering trade-off. Now I need to rethink it.

The message on my screen said, "Model isn't available." The model still exists. The government order says that foreign nationals like me must not have access to it.

That is the part I cannot ignore: access to the frontier is now political, temporary and not equally available to everyone.

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Sources and further viewing: Anthropic's Fable 5 launch announcement; Anthropic's statement on the US government directive; Nate B. Jones's analysis; and European reactions on sovereign AI.