Ask HN: Can trivial LLM calls be compiled into conventional data pipelines?
It would then generate candidate DAGs using the 41 task types as building blocks, instantiate each node with an appropriate implementation, and optimize the composition for quality, cost, and latency. Candidate pipelines would be tested on time-separated and group-separated holdouts before being deployed behind abstention and fallback.The problem is quite likely undetermined based on just the input and output contracts alone even if inferred correctly. The intermediate graph is therefore not a recovered latent reasoning trace.
- ▪It would then generate candidate DAGs using the 41 task types as building blocks, instantiate each node with an appropriate implementation, and optimize the composition for quality, cost, and latency.
- ▪Candidate pipelines would be tested on time-separated and group-separated holdouts before being deployed behind abstention and fallback.The problem is quite likely undetermined based on just the input and output contracts alone even if infe
- ▪The intermediate graph is therefore not a recovered latent reasoning trace.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,879 of its stories.
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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 | Ycombinator |
| Canonical URL | https://news.ycombinator.com/item?id=49199957 |
| Publication time | Thu, 06 Aug 2026 17:48:49 +0000 |
| Retrieval time | 2026-08-06T18:00:42.523Z |
| Last seen | 2026-08-06T18:00:42.523Z |
| 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 | BZVX5EHdT-ir · 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
We are investigating whether recurring LLM workloads can be replaced, where appropriate, by automatically constructed pipelines of regexes, deterministic parsers, traditional ML and NLP models.As an example, suppose an application repeatedly asks a frontier model to read an annual report and return all customer–supplier relationships as structured records containing a customer, supplier, and supporting evidence.A possible replacement pipeline might run named-entity recognition, entity normalization, candidate generation, entity linking, relation extraction, and schema validation.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.