blawson (u/blawson) - Readit News

blawson commented on Apple unveils 'Passwords' manager app at WWDC 2024 zdnet.com/article/forget-... · Posted by u/drx

m_a_g · 2 years ago

I wasn’t expecting this much hate towards 1Password in the comments. I was using Google Passwords, then migrated to Apple, finally to 1P7 and now 1P8. It’s one of the best software I’ve ever used and I don’t know what I’d do without it. Same goes for Fastmail as well.

blawson · 2 years ago

1Password + Fastmail integration for generating masked email is also great.

Plus a nice UI for handling OTP, notes, credit cards, IDs, bank accounts, etc, it's easily worth the annual price for me.

blawson commented on Arno A. Penzias, 90, Dies; Nobel Physicist Confirmed Big Bang Theory nytimes.com/2024/01/22/sc... · Posted by u/gumby

vmurthy · 2 years ago

Not to take away anything from Mr Penzias but here’s how I remember his name :-)

“Although Penzias and Wilson had not been looking for cosmic background radiation, didn’t know what it was when they had found it, and hadn’t described or interpreted its character in any paper, they received the 1978 Nobel Prize in physics. The Princeton researchers got only sympathy. According to Dennis Overbye in Lonely Hearts of the Cosmos, neither Penzias nor Wilson altogether understood the significance of what they had found until they read about it in the New York Times”

- Bill Bryson, A Short History of Nearly Everything

RIP Mr Penzias

blawson · 2 years ago

OT but this book is so incredible. The breadth of information covered is amazing, even for being ~20 years old at this point.

The audiobook at .7 speed is my sleep aid.

blawson commented on Google is picking ChatGPT responses from Quora as correct answer twitter.com/8teapi/status... · Posted by u/mrpatiwi

blawson · 2 years ago

FWIW I get the right answer on Google right now when I try:

Can you melt an egg?

No - if you heat an egg it's not like melting ice (or metal). Because an egg contains a substantial amount of carefully folded natural proteins in their native state.

blawson commented on Principles for building and scaling feature flag systems docs.getunleash.io/topics... · Posted by u/ferrantim

zellyn · 2 years ago

One more update. I spent a little time the other day trying to find all the feature flag products I could. I'm sure I missed a ton. Let me know in the comments!

LaunchDarkly Split Apptimize CloudBees ConfigCat DevCycle FeatBit FeatureHub Flagsmith Flipper Flipt GrowthBook Harness Molasses OpenFeature Posthog Rollout Unleash

Here's my first draft of the questions you'd want to ask about any given solution:

    Questionnaire
    
    - Does it seem to be primarily proprietary, primarily open-source, or “open core” (parts open source, enterprise features proprietary)?
      - If it’s open core or open source with a service offering, can you run it completely on your own for free?
    - Does it look “serious/mature”?
      - Lots of language SDKs
      - High-profile, high-scale users
      - Can you do rules with arbitrary attributes or is it just on/off or on/off with overrides?
    - Can it do complex rules?
    - How many language SDKs (one, a few, lots)
    - Do feature flags appear to be the primary purpose of this company/project?
      - If not, does it look like feature flags are a first-class offering, or an afterthought / checkbox-filler? (eg. split.io started out in experimentation, and then later introduced free feature flag functionality. I think it’s a first-class feature now.)
    - Does it allow approval workflows?
    - What is the basic architecture?
      - Are flags evaluated in-memory, locally? (Hopefully!)
      - Is there a relay/proxy you can run in your own environment?
      - How are changes propagated?
        - Polling?
        - Streaming?
      - Does each app retrieve/stream all the flags in a project, or just the ones they use?
      - What happens if their website goes down?
    - Do they do experiments too?
      - As a first-class offering?
    - Are there ACLs and groups/roles?
      - Can they be synced from your own source of truth?
    - Do they have a solution for mobile and web apps?
      - If so, what is the pricing model?
      - Do they have a mobile relay type product you can run yourself?
    - What is the pricing model?
      - Per developer?
      - Per end-user? MAU?

blawson · 2 years ago

Togglz is another option: https://www.togglz.org/

blawson commented on Reading SEC filings using LLMs beatandraise.com/... · Posted by u/nnechm

nnechm · 2 years ago

I have been working on getting ChatGPT to answer questions that equity research analysts, investors would like to get from SEC filings. The application uses a combination of hybrid text search and LLMs for completion and does not rely much on embedding based distance searches.

A core assumption underlying this is that LLMs are already pretty good and will continue to get better at reading texts. If provided with the right thing to read, they will do very well on 'reading comprehension'.

Open ended writing is more susceptible to errors, especially in questions related to finance. For e.g google's revenues are just as likely to be 280.2 billion vs 279 billion in a probabilistic model that guesses the next part of the sentence - Google's revenues for FY 2022 are ....

So this leaves us with the main problem to solve; Serving the right texts to the LLM aka text search.

Once the right text is served, we can generate any pretty much anything in the text, Income statements, ceo comments, accounts payable on the fly, For e.g try - `can you get me Nvidia and AMD's income statement from March 2020 ?` as in here. https://imgur.com/gallery/H8Vfd5X A few more examples, Apple's sales in China, Google's revenue by quarter: https://imgur.com/a/oCCay3o

Currently, the application supports ~8k companies that are registered with the SEC. Pdfs are still work in progress, so tesla etc don't work as well.

The stack is Nextjs on Supabase. So Postgres's inbuilt text search does a lot of heavy lifting.

If one thinks of the bigger picture, we can extend/improve this to pdfs and the entire universe of stocks and more. a.k.a a big component of what CapitalIQ, Factset, Bloomberg and Reuters do can now be generated on the fly accurately for a fraction of the cost.

Generating graphs with gross margin increasing etc are just one step further and stuff like EV/Ebitda, yet another step further, as one can call a stock pricing api for each date of the report.

I would guess a number of LLM applications follow a similar process, ask a question --> LLM converts to query --> datalakes/bases --> searching and serving texts --> answer. Goes without saying, I would appreciate any feedback, especially from those who are building stuff that looks architecturally similar :) !

blawson · 2 years ago

I tried this as a little hobby weekend project but found that after a while it would start hallucinating answers even if previously it had gotten them right. It didn’t even take that long sometimes, where I’d ask a question about revenue, then liabilities, and them to sum some revenue numbers and they would just start to be wrong.

I wouldn’t yet feel comfortable with this without some automated reconciliation which to my mind defeated the point of my hobby project but I’m curious if you’ve seen different? No doubt you’d expect this to improve over time though.

blawson commented on Nearly half of the tap water in the US is contaminated with ‘forever chemicals,’ cnn.com/2023/07/05/health... · Posted by u/pseudolus

blawson · 2 years ago

Does this mean the whole easter seaboard, should consider bottled water instead of tap?