Serve DiffusionGemma-Jev (Djev) on a TypeSafe AI Compatible API
djev-run Serve DiffusionGemma-Jev (djev) on a TypeSafe AI compatible API on Cloud Run with an NVIDIA RTX PRO 6000 Blackwell or NVIDIA L4 GPU, or run directly inside Google Colab Pro (A100 40GB/80GB or L4 24GB) via colab.ipynb, built on mmastrac/djev. Built-in demo apps: /snake: mizorewww/laya-coreml /dino: virajbhartiya/laya-vs-jev /tetris: trungdq88/jev-tetris /vision: 1-step 6x6 spatial semantic segmentation (36 cells in ~73 ms) and 16-sensor System-1 visual radar (gemma4_vision 280 soft tokens) Deploy on Google Cloud Run Follows Cloud Run GPU best practices. Pricing 1 NVIDIA RTX PRO 6000 GPU (20 vCPU, 80 GiB RAM) costs $3.19 per hour while active and scales to $0 when idle with --min-instances=0.
- ▪djev-run Serve DiffusionGemma-Jev (djev) on a TypeSafe AI compatible API on Cloud Run with an NVIDIA RTX PRO 6000 Blackwell or NVIDIA L4 GPU, or run directly inside Google Colab Pro (A100 40GB/80GB or L4 24GB) via colab.ipynb, built on mmas
- ▪Built-in demo apps: /snake: mizorewww/laya-coreml /dino: virajbhartiya/laya-vs-jev /tetris: trungdq88/jev-tetris /vision: 1-step 6x6 spatial semantic segmentation (36 cells in ~73 ms) and 16-sensor System-1 visual radar (gemma4_vision 280 s
- ▪Pricing 1 NVIDIA RTX PRO 6000 GPU (20 vCPU, 80 GiB RAM) costs $3.19 per hour while active and scales to $0 when idle with --min-instances=0.
Hacker News (AI / LLM) files mainly under ai. We currently carry 6,154 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/taeold/djev-run |
| Publication time | Wed, 23 Sep 2026 16:53:37 +0000 |
| Retrieval time | 2026-09-23T16:59:30.854Z |
| Last seen | 2026-09-23T16:59:30.854Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | XU4FQr4jacnp · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
djev-run Serve DiffusionGemma-Jev (djev) on a TypeSafe AI compatible API on Cloud Run with an NVIDIA RTX PRO 6000 Blackwell or NVIDIA L4 GPU, or run directly inside Google Colab Pro (A100 40GB/80GB or L4 24GB) via colab.ipynb, built on mmastrac/djev. Built-in demo apps: /snake: mizorewww/laya-coreml /dino: virajbhartiya/laya-vs-jev /tetris: trungdq88/jev-tetris /vision: 1-step 6x6 spatial semantic segmentation (36 cells in ~73 ms) and 16-sensor System-1 visual radar (gemma4_vision 280 soft tokens) Deploy on Google Cloud Run Follows Cloud Run GPU best practices.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.