Ask HN: How is all new software not broken?
The article discusses the challenges and perceptions surrounding the use of large language models (LLMs) in software development. It emphasizes that while some critics view LLMs as ineffective, others find them valuable when used correctly. The author shares their workflow for maximizing the productivity of LLMs, highlighting the importance of context and engineering skills.
- ▪Some people believe LLMs are useless and that those who find them productive are misleading.
- ▪The author suggests that effective use of LLMs requires a solid understanding of one's workflow and codebase.
- ▪A structured approach to using LLMs can significantly enhance productivity and output quality.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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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=48270403 |
| Publication time | Mon, 25 May 2026 19:03:02 +0000 |
| Retrieval time | 2026-05-25T19:07:40.295Z |
| Last seen | 2026-05-25T19:07:40.295Z |
| 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 | zdKbRAk9WrEb |
| 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
It's uncertain what you are doing wrong.I work in Big Tech™. I will tell you that they are certainly not lying. Though maybe they are overstating how much velocity they've gotten. Or at least generously attributing llms to a refinement of their pdlc to maximize the output that llms facilitate.On hackernews, some people would have you believe that llms are essentially useless, produce only garbage, and literally everyone that says they're productive with them is a liar or has ai psychosis.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.