Demos

Every demo below is a live app built on Layer that reimplements nothing. Each composes shipped gateway features — routing, hybrid text fusion, fuzzy matching, local embedding serving, pipelines, snapshots, and the function runtime — over a different corpus, and makes the gateway’s behavior legible in the UI. They are also the fastest way to see what the gateway does without standing up a cluster.

Demo	What it shows	Corpus
shelf	The query router, made legible	Books
chart	Query routing on clinical search, with a number	PMC-Patients case reports
hybrid-text	Hybrid text fusion, proven with qrels	BEIR/SciFact abstracts
wiki	Auto routing plus CPU-only Lattice embedding, at scale	Simple English Wikipedia
lens	Text-to-image search with zero GPUs	Wikimedia Commons Quality images
shop	Everything together — an end-to-end app	Amazon product catalog

shelf — book search that shows its routing

Live: shelf.hevlayer.com · Source: github.com/hev/shelf

One search box, three routes. Type an author, a title, or a vibe; the gateway’s Auto rank expression picks keyword (hybrid_text), semantic, or a fused blend from the shape of the query, and shelf renders that decision as a badge with the reason. The routing policy keys on token count, so the canned chips visibly change route as the query gets longer. This is the text-native routing showcase: it makes the query router decision the hero, not a footnote.

Built on the query router (Auto), hybrid text fusion, and fuzzy matching.

chart — clinical patient-notes search that shows its routing

Live: chart.hevlayer.com

The same routing hero on the corpus with the sharpest bimodal query distribution there is: clinicians search both by exact token (metformin 500mg, CABG, aspirn) and by clinical picture (elderly woman with progressive dyspnea and bilateral lower-extremity edema). chart is the first Layer demo with real relevance judgments — PMC-Patients ReCDS qrels — so the routing and hybrid claims are measured, not asserted. Behind the search box, an open-weight Gemma cascade (vLLM, scale-to-zero on the GPU pool) reads each note once and extracts clinical events and facet labels: the function runtime showcase.

The corpus is published, de-identified case reports (PMC-Patients, CC-BY-NC-SA). It is a search demo — not raw EHR, and not clinical advice.

Built on the query router, hybrid text fusion with fuzzy matching, pipelines, the function runtime, and snapshots.

hybrid-text — hybrid text fusion over SciFact

Live: hybrid-text.hevlayer.com · Source: github.com/hev/hybrid-text-fusion-demo

The eval-shaped sibling of the routing demos, over ~5,000 scientific abstracts from BEIR/SciFact. One query string fans out into a full-input BM25 leg plus one fuzzy leg per token, fused by reciprocal rank fusion — so results survive typos and morphological variants without losing BM25’s signal. It is purely lexical: no embeddings, no GPU, no vector index. SciFact ships qrels, so the UI flags known-relevant abstracts and the demo scores nDCG@10 / recall@10; every search also shows its gateway round-trip time and a fusion inspector (tokens, legs, RRF constant).

Built on hybrid text fusion and fuzzy matching.

wiki — all of Simple English Wikipedia, routed and embedded on CPU

Live: wiki.hevlayer.com · Source: github.com/hev/wiki

The routing hero at corpus scale: one Auto query over all 283,997 Simple English Wikipedia articles (1.74M paragraph rows) routes each search to full-text, semantic, or a fused RRF blend, and the UI renders the gateway’s routing echo beside every result. The semantic leg is the Lattice showcase — the whole corpus embedded through prefer: lattice, an ~8 MB int4 lookup-table artifact served in-process on the gateway CPU, with performance.embedding_ms and embedding_tokens echoed whenever the chosen route embeds. No GPU anywhere in the write or query path.

Built on the query router, hybrid text fusion, and local embedding serving (prefer: lattice).

lens — text-to-image search with zero GPUs

Live: lens.hevlayer.com · Source: github.com/hev/lens

Cross-modal search over Wikimedia Commons Quality images: type sunset over water, get sunsets. The schema is two lines — a string image_url attribute with a local CLIP embedding profile. The gateway fetches each image and runs CLIP’s image tower in-process on CPU at write time, then embeds query text with the same checkpoint’s text tower at query time. The app posts writes and queries and renders the echo; it contains no embedding, tokenizer, or image-preprocessing code, and there is no GPU worker or autoscaler pool anywhere in the path. Every result pairs the fixed serving contract (prefer: local, gateway CPU) with the live performance.embedding_ms echo, and carries its Commons attribution and license.

Built on local CLIP serving (serving.prefer: local, modality: image) and schema-attribute embedding.

shop — semantic shopping, everything together

Live: shop.hevlayer.com (formerly hev-shop.com, which redirects) · Source: github.com/hev/shop

The end-to-end application workload: an indexing pipeline, semantic search, recommendations, facets, and observability in one app. shop embeds product images with CLIP ViT-L/14 and writes one vector per product through Layer pipelines into turbopuffer; the storefront serves image-native semantic search, nearest_to_id recommendations, facet exploration from namespace snapshots, and Layer freshness signals. KEDA scales workers from pipeline metrics and Karpenter scales nodes next to the workload that creates the demand. Where shelf is the text-native routing showcase, shop is the image-native one — and the demo that exercises the most of Layer at once.

Built on pipelines, the write path, query (nearest_to_id), snapshots, search history, and autoscaling.