And seven that won't fit
inside a page.
The nine above run inside this page. These seven can't. The first is six deployed services and two Postgres databases, so instead of a demo I'll just give you a login and let you settle a real match on it. The second only proves anything when real processes crash, flood and get redeployed on real sockets, so you get the recorded runs instead, every 100 milliseconds of them. The third needs Quantinuum's quantum emulator, which is a Rust program, so it gets the same treatment. The fourth is a replicated control plane whose real numbers only exist when processes crash on real sockets, so its page replays a recorded run and also runs the same Go code in your tab as WebAssembly. The fifth is an image index built by a Node pipeline around a 150 MB vision model, so the page ships the finished index and only pulls the model into your tab when you type your own search. The sixth is 47 tumor-segmentation models trained over two and a half hours of CPU time, so its page shows their real predictions instead of training anything in your tab. The seventh needs a local language model and PyTorch, so you get a recorded session plus every benchmark output to browse.
A score doesn't count
until both teams agree.
A charity beach volleyball app, plus the competition platform underneath it. Both teams submit the score from their own phones and the system compares hashes instead of trusting either side. Payouts come out of a pure function, and a human has to close the contest behind a frozen preview before any of it moves. The ledger is append-only because the database role it runs as genuinely cannot update or delete a row.
OPEN THE WRITE-UP
The origin recovered.
Its own retries kept it down.
An edge gateway: a proxy, five cache nodes and three origins, with the parts a CDN leans on. Consistent hashing, request coalescing, retry budgets, circuit breakers, load shedding and restarts that hand the open socket to the new process. Every experiment runs twice on identical traffic with one mechanism switched off, so you can watch a cache node die, a page go viral and an origin get buried by retries nobody is waiting for, side by side. The page replays recorded runs. The gateway itself is on GitHub, and it runs on Linux and macOS in front of any plain-HTTP server.
WATCH THE REPLAYS
The test passed.
The program was wrong.
A CI gate for quantum programs. Their right answer is random, so each test checks the shape of thousands of outputs with statistics that cap both flaky failures and missed bugs at 1 in 1,000, and stops spending shots once the answer is clear. Three of the eleven bugs I planted pass every normal measurement every time, and one extra check in a different basis catches each of them in 250 shots.
WATCH A BUG GET CAUGHT
The leader died mid-rollout.
Every region still landed once.
A deployment control plane for cloud regions that drop off the network, or never had one. Three replicas keep one Raft log of what every region should run. Connected regions pull from it, and an air-gapped one only accepts signed packages carried in by hand. Kill the leader, cut a region off, crash a worker between installing and reporting back, or tamper with a package in transit, and every region still ends up on the right version exactly once. The page replays a recorded run and runs the Go code in your tab, compiled to WebAssembly. The failover times come from real processes on real sockets.
TRY TO BREAK IT
CLIP matched the look.
The board matched the taste.
Visual search the way Pinterest does it, small enough to inspect. Every image and query goes through a real CLIP model, then a graph of visual neighbors and board-mates re-ranks the results with a personalized PageRank walk. Flip one switch to compare against CLIP alone: for a brand-new pin with its board hidden, the graph puts 84% of its board-mates in the top ten, against 70% for CLIP. Drag a box around any object to search with just that region.
COMPARE THE RANKINGS
Two labeled scans
did the work of about six.
A generator that paints realistic fake tumors into real mouse micro-CT scans, so a tumor-finding model can learn from a handful of hand-outlined ones. With two labeled scans, adding the fakes did about as well as six real ones and cut tumor-volume error from 75% to 35%. Simple spheres are the control: they help a little at first, then make the model worse once there are eight labeled scans. By then the fakes stop helping too, so it's a cold-start tool.
CHANGE THE LABEL BUDGET
Forcing valid JSON
made the answers worse.
Type plain English at a mouse CT scan, and a 3-billion-parameter model on a laptop drives the viewer through tool calls while the tools produce every number. I benchmarked five ways of getting structured output from small local models. Grammar-constrained decoding made every call valid and still came out 11 points behind the models' built-in tool calling, because they padded their answers with actions nobody asked for. A schema repair took the 8B model from 89% to 96%.
REPLAY THE SESSION
One prompt caught 43%.
The pipeline caught 99%.
A doctor's visit conversation becomes proposed changes to the patient's medication list, each linked to the sentence that justifies it, and nothing is filed until the clinician signs. Before the visit, the patient reads their pill bottles to a voice assistant that only asks about what the chart can't settle. The language model only pulls out what was said; a rules engine decides. On 140 held-out synthetic visits the pipeline found 99.4% of the medication changes; one big prompt found 43%. Speech recognition and voices run on CPU.
TRY THE LIVE DEMO