Building your first LLM API call in Python (step by step)
aie_1.1: building itWriting your first LLM api call in Python.merakiSep 15, 20261ShareBefore going any further with this build, a quick note. If you have not, start there, it’ll make this make more sense.In the previous lesson, we established that an LLM, Large Language Model, is a prediction machine. And as a result, it depends on the context you provide.
- ▪aie_1.1: building itWriting your first LLM api call in Python.merakiSep 15, 20261ShareBefore going any further with this build, a quick note.
- ▪If you have not, start there, it’ll make this make more sense.In the previous lesson, we established that an LLM, Large Language Model, is a prediction machine.
- ▪And as a result, it depends on the context you provide.
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,016 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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 | Hacker News (AI / LLM) |
| Canonical URL | https://heymeraki.substack.com/p/aie_10-building-it |
| Publication time | Tue, 15 Sep 2026 15:05:56 +0000 |
| Retrieval time | 2026-09-15T15:11:52.751Z |
| Last seen | 2026-09-15T15:11:52.751Z |
| 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 | KDLNfaz1NeWd · 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
aie_1.1: building itWriting your first LLM api call in Python.merakiSep 15, 20261ShareBefore going any further with this build, a quick note. This build assumes you have read aie_1.0. If you have not, start there, it’ll make this make more sense.In the previous lesson, we established that an LLM, Large Language Model, is a prediction machine. It has no memory i.e. it is stateless. And as a result, it depends on the context you provide. In this build, we are going to talk to one in code. We’ll call what we build First Contact.What we are buildingA Python script that makes an API call to Anthropic, prints what it gets back and logs how many tokens (the unit a context window is measured in) it used.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
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