This reminds me of the antics of streamer DougDoug, who often uses LLM APIs to live-summarize, analyze, or interact with his (often multi-thousand-strong) Twitch chat. Most recently I saw him do a GeoGuessr stream where he had ChatGPT assume the role of a detective who must comb through the thousands of chat messages for clues about where the chat thinks the location is, then synthesizes the clamor into a final guess. Aside from constantly being trolled by people spamming nothing but “Kyoto, Japan” in chat, it occasionaly demonstrated a pretty effective incarnation of “the wisdom of the crowd” and was strikingly accurate at times.
I had an LLM create a playlist for me. 我让一个LLM帮我创建了一个播放列表。 I’m tired of the bad playlists I get from algorithms, so I made a specific playlist with an Llama2 based on several songs I like. I started with 50, removed any I didn’t like, and added more to fill in the spaces. The small models were pretty good at this. Now I have a decent fixed playlist. It does get “tired” after a few weeks and I need to add more to it. I’ve never been able to do this myself with more than a dozen songs. 我厌倦了算法推荐的糟糕歌单，所以我用基于 Llama2 的模型制作了一个特定的歌单。我从 50 首歌曲开始，删除我不喜欢的，并添加更多歌曲填补空白。小型模型在这方面表现不错。现在我有一个不错的固定歌单。它在几周后会“疲倦”，我需要添加更多歌曲。我以前从未能够自己制作超过十首歌曲的歌单。 https://git.sr.ht/~jamesponddotco/llm-prompts/tree/trunk/data/playlist-generator.md
Before ollama and the others could do structured JSON output, I hacked together my own loop to correct the output. I used it that for dummy API endpoints to pretend to be online services but available locally, to pair with UI mockups. For my first test I made a recipe generator and then tried to see what it would take to “jailbreak” it. I also used uncensored models to allow it to generate all kinds of funny content. 在 ollama 和其他人能够生成结构化 JSON 输出之前，我编写了一个循环来校正输出。我用它来模拟 API 端点，假装是可本地访问的在线服务，以便与 UI Mockup 配合使用。我的第一次测试是一个食谱生成器，然后我尝试看看需要什么才能“越狱”它。我还使用了未经审查的模型，使其能够生成各种有趣的内容。 I think the content you can get from the SLMs for fake data is a lot more engaging than say the ruby ffaker library. 我认为从 SLM 获取的假数据内容比 ruby ffaker 库更具吸引力。