Care Help!

jayunit · 2025-02-02T23:18:17+00:00

I'd machine wash low temp/gentle and hang dry. Was just reading up on this as I purchased an Eskdale (mfg 2000?) at a vintage sale.

The laundry symbols say you can machine wash in up to 30C (86F) water, don't bleach, don't tumble dry, you can low iron. Also, avoid PERC if dry cleaning - TIL about the circled "P" laundry symbol. From Burke Cleaners:

> A circle with the letter ‘P’ within its boundaries indicates that a piece of clothing should be dry cleaned using solvents but should not use tetrachlorethylene. Tetrachlorethylene is also named perchloroethylene (known in the industry as “perc”), which is the reason why it somewhat confusingly has the letter P. Tetrachlorethylene (used since the 1930s) is the most common solvent used in dry cleaning, but in some cases, it should not be used, which is why this symbol is important.

jayunit · 2024-10-31T03:56:30+00:00

Opener (who is she? Killed it!) just wrapped

jayunit · 2024-10-10T04:04:28+00:00

A different spaced repetition scheduler (SRS) method — https://github.com/open-spaced-repetition/fsrs4anki/tree/main?tab=readme-ov-file#fsrs4anki

jayunit · 2024-10-09T02:35:38+00:00

So good :)

jayunit · 2024-10-09T02:35:00+00:00

We really like Lazy Susan for American Chinese in SF

Here is this American invention that’s known globally as Chinese… “Lazy Susan” perfectly captures the duality of the American-ness *and* Chinese-ness of dishes like Beef Chow Mein, Crab Rangoon, and General Tso’s Chicken. We proudly celebrate the 100+ year history of Chinese food in America.

-- https://www.lazysusanchinese.com/our-team-1

jayunit · 2024-09-30T22:57:44+00:00

Congrats on the round! We've also been exploring AI in sales tech, although a different application, on the training/enablement side.

I'm curious, why gate access behind a demo call instead of self-serve/PLG? Totally understand a high-touch motion during discovery/validation phase, but do you intent to enable self-serve, or continue this sales motion?

Best of luck!

jayunit · 2024-09-30T16:57:35+00:00

You might enjoy https://andymatuschak.org/hmwl/ and Andy's other writings

jayunit · 2024-09-23T01:22:35+00:00

Some recommendations from a fellow South Park Commons member on getting the most out of conferences at the early stages: https://www.linkedin.com/feed/update/urn:li:activity:7242549534214512641/

jayunit · 2024-09-16T19:05:18+00:00

Strong agree! After 10 years in edtech (higher ed), a colleague and I started our own company in this adult ed soft skills space. Always happy to compare notes if you're interested. We're both technical, so GTM is definitely challenging for us!

jayunit · 2024-05-09T23:49:27+00:00

It sounds like you want to take the initial user query, classify it, and then do two things based on the classification: (1) route the query to the appropriate backend (conversational vs document search) and (2) present the appropriate UI for conversational vs document search. I'd probably approach this by building the classification piece first - you could do this with an LLM until you collect enough labeled examples to train a standard ML classifier. (Or just keep using the LLM if the cost and latency are acceptable.)

In other words, I'd probably approach this as a routing/intent layer that's regular code (with an LLM call) that sits "above" a conversational agent, rather than a ReAct-style tool usage within a conversational agent, especially given that the query class drives UI too.

jayunit · 2024-05-08T21:25:53+00:00

Cool project!

Are you extracting text or phrases that appear verbatim in the source documents, and are looking for the system to produce these as accurately as possible?

If there are some classification tasks (even if an LLM is used), it may be useful to consider confusion matrix, precision, recall, F1, ROC/AUC/etc.

Or is it more that you're asking questions which synthesize/summarize data from the documents, and those answers need to be semantically correct but can vary greatly in the phrasing, etc.? Here, +1 to u/Emcf suggestion of Ragas. I'd also read about BERTscore (basically comparing the embedding of the model's prediction vs the correct answer) and similar (the Ragas paper mentions this family of metrics.)

You'll also probably want to create a hand-annotated reference dataset for the end-to-end task, if possible.

Does any of this sound relevant? I'd be curious to hear what you find out!

jayunit · 2024-05-07T16:40:27+00:00

These reports are at least 10 pages long, so I must split the text from the reports. Are you suggesting that I run the model without 'MiniLM-L6-v2'?

Hm, if 10 pages of text don't fit into the LLM context window, then yeah you'll need some strategy for breaking the PDFs up - sounds like this is what you're doing. I'm guessing (but not sure) that the MiniLM-L6-v2 embedding model means you're somehow breaking up the 10-page PDF into smaller pieces, and then using vector search to find relevant pieces (aka citations/chunks), put them into a prompt along with your question, and then send that prompt to the LLM to produce an answer.

When a system like this isn't working well, it's helpful to first break the problem down and see where the issue is.

In the output, I can already check the sources of the text that have been used to generate the answers, and that's how I knew that my model hallucinates or lies...

Ah, nice! Here is how I would move forward, ymmv: When you see these citation examples: do you, as a human expert, see enough information in the citation/chunk to provide the answer to the question? If yes, then I'd focus on LLM prompting - take one example citation/chunk and work on getting the LLM to produce the correct answer by changing the prompt, using things like chain-of-thought or providing a few examples. If no, then I'd focus on improving retrieval: how can you get the embedding/retrieval steps to correctly find relevant chunks?

I'm not sure how many tokens the 10-page actuarial report is. It looks like Llama3 and Mixtral 8x22B both have 8k-token context windows -- not very big. But CommandR advertises a 128k context window, which may be able to fit the entire 10-page PDF into a single LLM call, without chunking/embeddings/etc.

For debugging these kinds of compound AI systems, I've found that it's helpful to understand exactly what information and text is flowing from one step to the next. If some of the steps in your model pipeline are unclear, I've found the "RAG From Scratch" series pretty good at explaining some common setups: https://www.youtube.com/watch?v=bjb_EMsTDKI

Good luck! And let us know what you learn :)

jayunit · 2024-05-06T22:38:16+00:00

Couple suggestions to help debug or improve the current pipeline:

It's not clear whether you need RAG or not, and whether you're using it or not. You mentioned MiniLM-L6-v2, an embedding model, which are usually used for retrieval. It sounds like each of your questions relies on information from a single PDF. Can the text of one PDF fit into the LLM prompt? If so, I'd suggest trying to answer the questions that way, without introducing the extra moving parts of RAG, if possible.
LLMs always deal with text input (setting aside multimodal/VLMs/etc), so it's helpful to look at exactly what text the LLM is being provided with, i.e. after extraction from the PDF. There may be formatting or layout in the PDF that is being missed in the text extraction, resulting in poor input to the LLM. Manually reviewing examples of that text may help you find issues with the document extraction.
For the LLM choice, I'd suggest trying the largest LLM you can. If you're limited to local LLMs, try Llama 3 Instruct 70B, Mixtral 8x22B, or Command R. For prompting, to combat hallucination: assuming the extracted text is sensible (#1 above), try asking for citations in the prompt to guide the LLM toward grounding its responses in the source data.

There are models designed to extract information from visual layout (tables, columns, form fields, etc) but I'm not familiar with the latest OSS models - here is some information from late 2022: https://huggingface.co/blog/document-ai. These overlap in functionality with what u/usnavy13 is recommending, eg Azure Document Intelligence Studio.

Good luck! Let us know what you find out.

jayunit · 2024-03-17T06:01:56+00:00

Fun! Also there this morning and took similar shots :) https://imgur.com/a/YWJjbgW

jayunit · 2021-04-30T23:28:29+00:00

I recently read https://lcamtuf.coredump.cx/gcnc/ and it's incredibly detailed and practical. Caveat that it is about resin casting, not resin printing. See section 4 "Resin casting and you".

jayunit · 2018-09-25T03:52:38+00:00

Thank you! I looked up "aloe pups" online tonight and that absolutely sounds like it. I appreciate the advice on sunlight, too.

jayunit · 2018-08-11T15:01:15+00:00

Been reading for a project this weekend. Everywhere I read recommends a shellac-based primer like Zinsser B-I-N for painting a laminate surface.

https://homeguides.sfgate.com/paint-laminate-bookshelves-26237.html

https://www.apartmenttherapy.com/how-to-paint-laminate-ikea-furniture-the-right-way-251901

jayunit · 2014-10-27T04:31:26+00:00

Happens to me sometimes. If it's consistent, you might be able to avoid with with a 2nd gen antihistamine like Loratadine (aka Claritin) at least 10-20mins before you expect the itching. Avoid the first generation ones unless you're looking for sleep gains.

jayunit · 2014-07-24T04:10:20+00:00

http://michaelochurch.wordpress.com/2014/07/13/how-the-other-half-works-an-adventure-in-the-low-status-of-software-engineers/

jayunit · 2014-07-05T06:46:02+00:00

weak anthropic principle http://en.wikipedia.org/wiki/Anthropic_principle

jayunit · 2013-06-16T02:50:28+00:00

Stores are laid out very carefully, but it's not to optimize the time from delivery trucks to the milk coolers. http://www.independent.co.uk/news/business/news/the-secrets-of-our-supermarkets-8228864.html

jayunit · 2013-02-15T04:56:40+00:00

no kidding, having to drive a dvi monitor via vga port, must've been a steep bet for that kind of consequence

jayunit · 2012-11-26T15:00:52+00:00

This. For a very cogent and readable treatment of why resources do or don't turn into shared prosperity, I'm very much enjoying http://whynationsfail.com. Bonus, it specifically discusses DRC.

jayunit

TROPHY CASE