Integrate LLM into your Python runtime
Invent attributes, methods, anything out of thin air; an LLM of your choice (local or hosted) fills in the blanks, with confidence attached. There are no prompt strings, no message arrays, and no output parsing: you write ordinary classes, attributes, and method calls, and the model's capabilities are available wherever you left a blank, always priced with a probability. Everything you write yourself (code, bare values, your own words) is certain: probability 1.0, and the model can never touch it.
- ▪Invent attributes, methods, anything out of thin air; an LLM of your choice (local or hosted) fills in the blanks, with confidence attached.
- ▪There are no prompt strings, no message arrays, and no output parsing: you write ordinary classes, attributes, and method calls, and the model's capabilities are available wherever you left a blank, always priced with a probability.
- ▪Everything you write yourself (code, bare values, your own words) is certain: probability 1.0, and the model can never touch it.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,176 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/MiskaKan/thinair |
| Publication time | Sat, 01 Aug 2026 12:31:52 +0000 |
| Retrieval time | 2026-08-01T13:03:30.204Z |
| Last seen | 2026-08-01T13:03:30.204Z |
| 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 | BECv9Pio01Ew · 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
thinair Probabilistic Python objects. Invent attributes, methods, anything out of thin air; an LLM of your choice (local or hosted) fills in the blanks, with confidence attached. Code the certain, imagine the rest. The entire public surface is one class, and the whole idea fits in one line: a Thing is a value with a probability. color = Thing("rusty red", confidence=0.4) # the value the imagined read # in the demo handed back +color # 'rusty red': the value (free, no inference) ~color # 0.4: the probability (free, no inference) The interface is Python itself. There are no prompt strings, no message arrays, and no output parsing: you write ordinary classes, attributes, and method calls, and the model's capabilities are available wherever you left a blank, always priced with a probability.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.