Files that codex and any other coding agent has access to, should be opt-in NOT opt-out. I think codex is not the right layer to solve this if you want a sane(one-click) UX. We built our own internal sandboxing-terminal…
It is very common to find tables with 1000+ columns in machine learning training sets at e-commerce companies. The largest I have seen had over 10000 columns.
easily the most memorable comment i have ever seen on hackernews so far. kudos good sir!
usually when a sharp sensation arises in an area, there is a habitual tendency to counteract - unconsciously tense surrounding muscles or antagonistic muscles or switch posture etc. the idea is to observe with clarity…
just skimmed the proposal, dont see how inline rendered f-strings are more complicated than the alternative.
gotcha! thanks for the clarification
So the writes are O(N) then - to keeps reads at O(1)?
dumb question: how do z-sets or feldera deal with updates to values that were incorporated into the max already? For example - max over {4, 5} is 5. Now I update the 5 to a 3, so the set becomes {4, 3} with a max of 4.…
i personally like nim’s approach to memory management - implicitly refcounted, but exposes clean manual memory management when needed
great job on open sourcing!
publicly funded research, but behind paywalls, was scraped to build the chatbot - by “china” not open ai, causes “people” to lose their s**t. i do think ip infringement is not cool in general - but it doesnt seem right…
duckdb is mit licensed. [1] datafusion is apache v2 licensed. [2] pg_lakehouse built on top of data fusion is AGPL v3 + business licensed. [3] [1] https://github.com/duckdb/duckdb/blob/main/LICENSE [2]…
Nim?
almost every button click is either powered by a model or guarded by a model.
We don't accept hints yet - but we determine what to cache.
To be pedantic, even in star schema - the dim tables are denormalized, fact tables are not. I agree that my statement would be much better if used snowflake schema instead.
"Imagine" is the operative word :-)
These are reconstruction of features / columns that don’t exist yet.
Normalization is overloaded. I was referring to schema normalization (3NF etc) not feature normalization - like standard scaling etc.
We do this for training data generation already. We have plans to implement this behavior for computing the batch arm of feature serving.
Snapshots can’t travel back with milliseconds precision or even minute level precision. They are just full dumps at regular fixed intervals in time.
Pardon the jargon. But it is a necessary addition to the vocabulary. To evaluate if a feature is valuable, you could attach the value of the feature to past inferences and retrain a new model to check for improvement in…
Imagine you are building a customer support bot for a food delivery app. The user might say - I need a refund. The bot needs to know contextual information - order details, delivery tracking details etc. Now you have…
We haven’t tried materialize - IIUC materialized is pure kappa. Since we need to correct upstream data errors and forget selective data(GDPR) automatically - we need a lambda system. Tecton, we evaluated, but decided…
Let’s say you want to compute avg transaction value of a user in the last 90days. You could pull individual transactions and average during the request time - or you could pre compute a partial aggregates and…
Files that codex and any other coding agent has access to, should be opt-in NOT opt-out. I think codex is not the right layer to solve this if you want a sane(one-click) UX. We built our own internal sandboxing-terminal…
It is very common to find tables with 1000+ columns in machine learning training sets at e-commerce companies. The largest I have seen had over 10000 columns.
easily the most memorable comment i have ever seen on hackernews so far. kudos good sir!
usually when a sharp sensation arises in an area, there is a habitual tendency to counteract - unconsciously tense surrounding muscles or antagonistic muscles or switch posture etc. the idea is to observe with clarity…
just skimmed the proposal, dont see how inline rendered f-strings are more complicated than the alternative.
gotcha! thanks for the clarification
So the writes are O(N) then - to keeps reads at O(1)?
dumb question: how do z-sets or feldera deal with updates to values that were incorporated into the max already? For example - max over {4, 5} is 5. Now I update the 5 to a 3, so the set becomes {4, 3} with a max of 4.…
i personally like nim’s approach to memory management - implicitly refcounted, but exposes clean manual memory management when needed
great job on open sourcing!
publicly funded research, but behind paywalls, was scraped to build the chatbot - by “china” not open ai, causes “people” to lose their s**t. i do think ip infringement is not cool in general - but it doesnt seem right…
duckdb is mit licensed. [1] datafusion is apache v2 licensed. [2] pg_lakehouse built on top of data fusion is AGPL v3 + business licensed. [3] [1] https://github.com/duckdb/duckdb/blob/main/LICENSE [2]…
Nim?
almost every button click is either powered by a model or guarded by a model.
We don't accept hints yet - but we determine what to cache.
To be pedantic, even in star schema - the dim tables are denormalized, fact tables are not. I agree that my statement would be much better if used snowflake schema instead.
"Imagine" is the operative word :-)
These are reconstruction of features / columns that don’t exist yet.
Normalization is overloaded. I was referring to schema normalization (3NF etc) not feature normalization - like standard scaling etc.
We do this for training data generation already. We have plans to implement this behavior for computing the batch arm of feature serving.
Snapshots can’t travel back with milliseconds precision or even minute level precision. They are just full dumps at regular fixed intervals in time.
Pardon the jargon. But it is a necessary addition to the vocabulary. To evaluate if a feature is valuable, you could attach the value of the feature to past inferences and retrain a new model to check for improvement in…
Imagine you are building a customer support bot for a food delivery app. The user might say - I need a refund. The bot needs to know contextual information - order details, delivery tracking details etc. Now you have…
We haven’t tried materialize - IIUC materialized is pure kappa. Since we need to correct upstream data errors and forget selective data(GDPR) automatically - we need a lambda system. Tecton, we evaluated, but decided…
Let’s say you want to compute avg transaction value of a user in the last 90days. You could pull individual transactions and average during the request time - or you could pre compute a partial aggregates and…