llm-hacker-news

I had it draw four pelicans, one for each of its thinking levels (Gemini 3 Pro only had two thinking levels). Then I had it write me an <image-gallery> Web Component to help display the four pelicans it had made on my blog: https://simonwillison.net/2025/Dec/17/gemini-3-flash/ I also had it summarize this thread on Hacker News about itself: https://gist.github.com/simonw/b0e3f403bcbd6b6470e7ee0623be6... llm \ -f hn:46301851 -m "gemini-3-flash-preview" \ -s 'Summarize the themes of the opinions expressed here. For each theme, output a markdown header. Include direct "quotations" (with author attribution) where appropriate. You MUST quote directly from users when crediting them, with double quotes. Fix HTML entities. Output markdown. Go long. Include a section of quotes that illustrate opinions uncommon in the rest of the pie

wait till you try this one: https://github.com/simonw/llm-hacker-news

I hadn't heard that, but it would certainly explain why the model made a mess of this task. Tried it again like this, using a regular prompt rather than a system prompt (with the https://github.com/simonw/llm-hacker-news plugin for the hn: prefix): llm -f hn:43825900 \ 'Summarize the themes of the opinions expressed here. For each theme, output a markdown header. Include direct "quotations" (with author attribution) where appropriate. You MUST quote directly from users when crediting them, with double quotes. Fix HTML entities. Output markdown. Go long. Include a section of quotes that illustrate opinions uncommon in the rest of the piece' \ -m qwen3:32b This worked much better! https://gist.github.com/simonw/3b7dbb2432814ebc8615304756395...

Here's a summary of this conversation so far, generated using o3 after 306 comments. This time I ran it like so: llm install llm-openai-plugin llm install llm-hacker-news llm -m openai/o3 -f hn:43707719 -s 'Summarize the themes of the opinions expressed here. For each theme, output a markdown header. Include direct "quotations" (with author attribution) where appropriate. You MUST quote directly from users when crediting them, with double quotes. Fix HTML entities. Output markdown. Go long. Include a section of quotes that illustrate opinions uncommon in the rest of the piece' https://gist.github.com/simonw/a35f39b070978e703d9eb8b1aa7c0... - cost 2,684 input, 2,452 output (of which 896 were reasoning tokens) which is 12.492 cents. Then again with o4-mini using the exact same content (hence the hash ID for -f): llm -m open

"... for some reason the models still think markup languages are regular and can be handled with regular expressions" Presumably you mean this code here? https://github.com/simonw/llm-hacker-news/blob/e945c84e825f4... I reviewed it. For this particular application (turning JSON from https://hn.algolia.com/api/v1/items/43615912 into a more concise format suitable for feeding into an LLM like https://github.com/simonw/llm-hacker-news/blob/e945c84e825f4...) it's perfectly adequate (or "good enough" to quote your comment). You're welcome to convince me otherwise!