Things I Learned - 21 Apr 2024
This week, I learned: Effort engine introduces “effort” as a parametrizable way to speed up LLMs with a quality trade-off. Works on Mistral for now. Many arts demand devotion. Devoting unrestricted time is part of that. 16 hours of practice a day is not uncommon. Sessions don’t start and end on time. Instruments take a lot longer to learn than vocal music. The instrument needs to become an extension of you. Tests and homework have a purpose. It helps people figure out whether they’ve learnt. So: Write tests that make people think! Like DuckDB workshop Share a list of exercises that people can explore People need to explicitly be INVITED, and potentially IN PERSON, before they will engage with something new. For example, no one posted to [email protected] until the VIA Talks session where we got them to post. For example, having one day at IITM mandatory (especially early in the course) gets online students familiar with TAs. They understand that TAs actually help, at high quality. That they can use Discord. What makes Delhi students more assertive? How can we inculcate that in others? jsr-io/migrations is a great example of database migrations. Shape Detection API in the browser detects QR codes, face bounding boxes, Browsers also natively support blurring and face tracking. via Lessons after half a billion GPT tokens for GPT-4: Vague instructions are better than over-specifying Avoid libraries like Langchain. APIs are stabler 1 token = 3 characters is good enough GPT4 doesn’t hallucinate much, except it does a poor job of saying “I don’t know” or “There’s no such data” (the null hypothesis) Keep the output down to 10 items or so if you’re listing. For longer lists, have it explicitly enumerate Don’t worry about niches. Just wait for GPT5 #WRITE GPT clearly prefers 42 as a random number. #WRITE fal.ai “animates” pictures, creating videos. It made one from my talk. I morphed into various somewhat similar people rapidly in a 2-second span. Very promising, and far from good. llmsherpa extracts PDFs using LLMs. It has errors but it preserves hierarchy, extracts tables well, and retains image coordinates. Via +91 90031 35354 ~Vetrivel PS www.web.sp.am is a content farm that’s getting hit by OpenAI. Highlights how easy it is to create content farms, and therefore “easy” it can be to introduce bias into LLMs. OpenAI supports batching requests. Didn’t know that. Marvin provides Python decorators to create AI functions. Pretty intuitive! Outlines generates structured test with LLMs. It uses the ⭐ logit_bias trick to limit choices in output. See get_choice() Lemur from Assembly.ai does real time call transcription and summary W3C is exploring ways to allow web pages to train LLMs, to flag content as AI generated, etc. Data Provenance Explorer lists open datasets used to train LLMs. Summarize.tech summarizes YouTube videos. #WRITE Stable Audio 2.0 generates 3 min of music from a prompt. I tried Bollywood Tamil film background music. Dark, soulful and Horror movie background. Drums starts darkly. Build up to a crescendo of intense chaos.. Great that it managed, but not great music. Somewhat stereotyped. I need to learn how to prompt better. BTW, Udio is another such. Harpa.ai is a well designed Chrome extension / plugin that can chat with or automate any page. Due to in-context learning, giving 100s of examples in the prompt can teach LLMs to jailbreak. Ref With RAG on search becoming big, search APIs are growing. serper.dev, you.com, searxng being examples.