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yazaddaruvala commented on Lessons learned from building a sync-engine and reactivity system with SQLite   finkelstein.fr/sqlite-syn... · Posted by u/engmarketer
yazaddaruvala · 13 days ago
FWIW, I think you might be better off with immutable rows and lamport clocks.

Everything is a full new row because it is “a message” including read receipts. Some messages like read receipts just don’t render in the chat.

Edits can work the same way by rendering over a previous message, even though the local and remote DB have multiple rows for the original and edited messages.

yazaddaruvala commented on Index 1.6B Keys with Automata and Rust (2015)   burntsushi.net/transducer... · Posted by u/djoldman
VivaTechnics · 17 days ago
Impressive! This approach can be applied to designing a NoSQL database. The flow could probably look something like this? Right?

- The client queries for "alice123". - The Query Engine checks the FST Index for an exact or prefix match. - The FST Index returns a pointer to the location in Data Storage. - Data Storage retrieves and returns the full document to the Query Engine.

yazaddaruvala · 16 days ago
What you’ve described is the foundation of Lucene and as such the foundation of Elastic Search.

FSTs are “expensive” to re-optimize and so it’s typically done “without writes”. So the database would need some workaround for that low write throughput.

To save you the time thinking about it: The only extra parts you’re missing are what Lucene calls segments and merge operations. Those decisions obviously have some tradeoffs (in Lucene’s case the tradeoff is CRUD).

There are easily another 100 ways to be creative in these tradeoffs depending on your specific need. However, I wouldn’t be surprised if the super majority of databases’ indexing implementations are roughly similar.

yazaddaruvala commented on Gemini Embedding: Powering RAG and context engineering   developers.googleblog.com... · Posted by u/simonpure
stillpointlab · a month ago
> the embeddings can be reused by the model

I can't find any evidence that this is possible with Gemini or any other LLM provider.

yazaddaruvala · a month ago
Yeah given what your saying is true and continues to be,

Seems the embeddings would just be useful for a “nice corpus search” mechanism for some regular RAG.

yazaddaruvala commented on Gemini Embedding: Powering RAG and context engineering   developers.googleblog.com... · Posted by u/simonpure
stillpointlab · a month ago
> Embeddings are crucial here, as they efficiently identify and integrate vital information—like documents, conversation history, and tool definitions—directly into a model's working memory.

I feel like I'm falling behind here, but can someone explain this to me?

My high-level view of embedding is that I send some text to the provider, they tokenize the text and then run it through some NN that spits out a vector of numbers of a particular size (looks to be variable in this case including 768, 1536 and 3072). I can then use those embeddings in places like a vector DB where I might want to do some kind of similarity search (e.g. cosine difference). I can also use them to do clustering on that similarity which can give me some classification capabilities.

But how does this translate to these things being "directly into a model's working memory'? My understanding is that with RAG I just throw a bunch of the embeddings into a vector DB as keys but the ultimate text I send in the context to the LLM is the source text that the keys represent. I don't actually send the embeddings themselves to the LLM.

So what is is marketing stuff about "directly into a model's working memory."? Is my mental view wrong?

yazaddaruvala · a month ago
At least in theory. If the model is the same, the embeddings can be reused by the model rather than recomputing them.

I believe this is what they mean.

In practice, how fast will the model change (including tokenizer)? how fast will the vector db be fully backfilled to match the model version?

That would be the “cache hit rate” of sorts and how much it helps likely depends on some of those variables for your specific corpus and query volumes.

yazaddaruvala commented on Triangle splatting: radiance fields represented by triangles   trianglesplatting.github.... · Posted by u/ath92
mjfisher · 3 months ago
And in case it helps further in the context of the article: traditional rendering pipelines for games don't render fuzzy Gaussian points, but triangles instead.

Having the model trained on how to construct triangles (rather than blobbly points) means that we're closer to a "take photos of a scene, process them automatically, and walk around them in a game engine" style pipeline.

yazaddaruvala · 3 months ago
Any insights into why game engines prefer triangles rather than guassians for fast rendering?

Are triangles cheaper for the rasterizer, antialiasing, or something similar?

yazaddaruvala commented on The Document Culture of Amazon (2021)   justingarrison.com/blog/2... · Posted by u/Tomte
malcolmgreaves · 5 months ago
To me, reading a document in a meeting that was supposed to be read beforehand is unprofessional. It’s creating a culture that says it’s ok to disrespect other people’s time by not being prepared. Meetings are already quite expensive.

> If you ask people to give feedback on something without time to think, you will get noise.

Insightful. I have also observed that such processes induce more noise as participation (regardless of having a good effect) becomes the thing that managers notice for promotion/ demotion.

yazaddaruvala · 5 months ago
Fwiw at Amazon it’s expected that the first 33%-50% of the meeting is reading time.

The rest is time for feedback, discussion, and ideally a decision or collecting action items.

yazaddaruvala commented on The Document Culture of Amazon (2021)   justingarrison.com/blog/2... · Posted by u/Tomte
alphazard · 5 months ago
I don't disagree that reading comprehension is essential to be a good engineer. But empirically, many people with a title like engineer or manager or product somethingrather have poor reading comprehension and blend in just fine.

Any process has to take this into account, you can't design a process that only works when competent people participate and incompetent people don't and then blame the incompetent people when it fails. You have to design a process that works despite some number of incompetent participants.

Document cultures do this because writing shines a bright light on the merit of an idea. My criticism is that the in-meeting read through does not have a similar effect.

yazaddaruvala · 5 months ago
The reality is that competition at Amazon is intense.

Amazon corporate is roughly broken down like this:

L4: 45%, L5: 45%, L6: 7%, L7: 2.5%, L8+: 0.5%

Only the first promotion has guardrails (L4 to L5). Typically you’re expected to be able to write at all. From there many people end their careers at L5.

Going from L5 to L6 requires being the best person on your team for multiple years running. Compared with some really smart, focused, and motivated people. You’re also expected to be able to write well, read, and kinda poorly coach other people’s writing.

Going from L6 to L7 is very difficult. One of the biggest differentiators really is scale. If you still need help writing docs - that’ll slow you down and you won’t scale. If you’re slow to read and provide valuable feedback again that will hold back scale (many people compensate by adding more time to their work days).

However, the funny thing is the doc writing culture at Amazon is built by and for L8+ leaders. Everything else was just training and weeding people out.

Going from L7 to L8 is where “in-meeting reads” really start showcasing the differences between leaders. I’ve known smarter people that made better decisions but had their careers stagnate while watching other “80th percentile” decision makers grow because of speed to grok information and deliver valueable feedback 9 of 10 times is more important than the incremental benefit of being right 10 of 10 times.

So I get your concerns about in-meeting reads throughs but just keep in mind who and what it is defacto built for.

yazaddaruvala commented on The Document Culture of Amazon (2021)   justingarrison.com/blog/2... · Posted by u/Tomte
huntaub · 5 months ago
It's interesting, after my time at Amazon (8 years) -- I struggled to visualize decisions without a document, because you get so used to reviewing things in that way. However, it's EXTREMELY heavy-handed. People will give you comments on the structure of your document (to "raise the bar") instead of the content of the document, and often you can't get documents reviewed until they are aesthetically in the right place for people to digest them. Ultimately, I think it makes sense when you're at a large organization with high attrition (such that you need to keep track of all of the decisions which are made), but otherwise, it's probably not worth it to do a formal document writing process.
yazaddaruvala · 5 months ago
The real benefit of doc writing isn’t for decision making it’s for education. It allows everyone at Amazon to evaluate the author’s ability to refine their “chain of thought”.

The nice side effect is the author taking 10x more time to save 10x L+1 and L+2 leaders (ie more expensive people) from spending that same time trying to understand it.

yazaddaruvala commented on Australian man survives 100 days with artificial heart   theguardian.com/australia... · Posted by u/n1b0m
xpl · 6 months ago
How does the device regulate the heartbeat speed? I mean, how does it know that it needs to pump blood faster or slower?
yazaddaruvala · 6 months ago
Blood oxygen sensors seem relatively cheap and low power.

I wonder if they could use that as the feedback mechanism.

Ideally if the sensors are small, low power, and cheap enough CO2 and lactic acid levels would also be good to check on to increase bloodflow.

u/yazaddaruvala

KarmaCake day3220May 31, 2013View Original