Decide where your AI workload should run (local/edge/cloud)
AI Workload Placement — Decision Engine Core question: Given an AI workload, where should it run — device, edge, or cloud — and which model should it use to meet quality, privacy, latency, cost, energy, and hardware requirements? This isn't a claim to have invented edge/cloud AI — that space is mature, with solid tools like RunAnywhere, AWS Greengrass, and Azure IoT Edge already solving deployment and fleet management. What's missing is a fast, no-commitment decision layer: something that tells you where a workload should run before you commit to any SDK, platform, or hardware.
- ▪AI Workload Placement — Decision Engine Core question: Given an AI workload, where should it run — device, edge, or cloud — and which model should it use to meet quality, privacy, latency, cost, energy, and hardware requirements?
- ▪This isn't a claim to have invented edge/cloud AI — that space is mature, with solid tools like RunAnywhere, AWS Greengrass, and Azure IoT Edge already solving deployment and fleet management.
- ▪What's missing is a fast, no-commitment decision layer: something that tells you where a workload should run before you commit to any SDK, platform, or hardware.
Hacker News (AI / LLM) files mainly under ai. We currently carry 7,388 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/Keerthana0309/ai-workload-placement |
| Publication time | Fri, 02 Oct 2026 21:19:18 +0000 |
| Retrieval time | 2026-10-02T21:36:16.344Z |
| Last seen | 2026-10-02T21:36:16.344Z |
| 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 | IwsWVMqAp_vO · 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
AI Workload Placement — Decision Engine Core question: Given an AI workload, where should it run — device, edge, or cloud — and which model should it use to meet quality, privacy, latency, cost, energy, and hardware requirements? This isn't a claim to have invented edge/cloud AI — that space is mature, with solid tools like RunAnywhere, AWS Greengrass, and Azure IoT Edge already solving deployment and fleet management. What's missing is a fast, no-commitment decision layer: something that tells you where a workload should run before you commit to any SDK, platform, or hardware. That's the gap this project explores. What's here decide.py — a CLI decision engine.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.