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Benjamin Ramey
2024-08-08 17:21:24 -05:00
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commit 2638be3d58
10 changed files with 164 additions and 159 deletions
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2
.obsidian/graph.json vendored
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.obsidian/workspace.json vendored
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@@ -37,7 +37,7 @@
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@@ -127,7 +127,7 @@
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@@ -150,7 +150,7 @@
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@@ -173,14 +173,32 @@
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"Topical studies/Leaving a church/Scriptural Reasons for Leaving a Church.md",
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"Work/Switching jobs pros and cons.md",
"Chapel/Announcements 1.md",
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6
2024-08-08 (Thursday).md
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@@ -11,3 +11,9 @@ Nic Hoza knows most of what's going on here. Basic desire for users:
## Commerce backlog grooming meeting
#michael-fisher #ricardo-blanco #nate-merritt #kate-neale
## Meeting with Kyle K.
#bigcommerce #kyle-kennaw #replatform
It's very unlikely we'll move away from BigCommerce. Currently painful, for sure, because of the dependence on our systems. But, perhaps we can map out the future of the migration to get less and less dependent and entangled.
I need to read more about BigCommerce and what it can do to get a better, informed picture of how we can move to it.

12
Bible Study/1 Peter Flow Outline.md
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@@ -70,17 +70,17 @@ things into which angels long to look.
Therefore,
preparing your minds for action,
and being sober-minded,
**set your hope fully** on the grace that will be brought to you at the revelation of Jesus Christ.
set your hope fully on the grace that will be brought to you at the revelation of Jesus Christ.
As obedient children,
**do not be conformed** to the passions of your former ignorance,
do not be conformed to the passions of your former ignorance,
but as he who called you is holy,
you also **be holy** in all your conduct,
you also be holy in all your conduct,
since it is written, “You shall be holy, for I am holy.”
And if you call on him as Father who judges impartially according to each one’s deeds,
**conduct yourselves with fear** throughout the time of your exile,
conduct yourselves with fear throughout the time of your exile,
knowing that you were ransomed from the futile ways inherited from your forefathers,
not with perishable things such as silver or gold,
but with the precious blood of Christ,
@@ -93,7 +93,7 @@ so that your faith and hope are in God.
Having purified your souls
by your obedience to the truth
for a sincere brotherly love,
**love one another** earnestly from a pure heart,
love one another earnestly from a pure heart,
since you have been born again,
not of perishable seed but of imperishable,
through the living and abiding word of God;
@@ -102,7 +102,7 @@ for “All flesh is like grass and all its glory like the flower of grass. The g
So put away all malice and all deceit and hypocrisy and envy and all slander.
Like newborn infants,
**long for the pure spiritual milk**,
long for the pure spiritual milk,
that by it you may grow up into salvation—
if indeed you have tasted that the Lord is good.

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Reading/Read Later/2020-01-31 Tech Talk - Michael Sterling - Royalties - Faithlife Coders - Amber.md
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@@ -1,3 +1,3 @@
| | |
|---|---|
| ----------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| ![thumbnail](Exported%20image%2020240808113925-0.png) | \| \|<br>\|---\|<br>\|## 2020-01-31 Tech Talk - Michael Sterling - Royalties - Faithlife Coders - Amber\|<br>\|[https://amber.faithlife.com/shares/921WWa1mhUUOlewF](https://amber.faithlife.com/shares/921WWa1mhUUOlewF)\|<br>\|Royalties Tech Talk Slides: [https://docs.google.com/presentation/d/1UERbx5Op1upek32_TCBuXRPgIKtZkXjmWQM6IyEzCSA/edit#slide=id.g7d0408af6d_2_93](https://docs.google.com/presentation/d/1UERbx5Op1upek32_TCBuXRPgIKtZkXjmWQM6IyEzCSA/edit#slide=id.g7d0408af6d_2_93)...\| |

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Reading/Read Later/Real World Recommendation System – Part 1.md
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@@ -4,7 +4,7 @@ Training a collaborative filtering based recommendation system on a toy dataset
- High-level paradigms (like collaborative filtering, content based recommendations, vector search, model based recommendations)
- ML algorithms (e.g., GBDTs, SVD, Multi tower neural networks, etc.)
- Modeling libraries (e.g., PyTorch, Tensorflow, XGBoost)
- Data management (e.g., choice of DB, caching strategy, reuse primary database or copy all the data in another system optimized for recommendation workload, etc.) [[1]](https://fennel.ai/blog/real-world-recommendation-system/#fn1)
- Data management (e.g., choice of DB, caching strategy, reuse primary database or copy all the data in another system optimized for recommendation workload, etc.)
- Feature management (e.g., offline vs online, precompute vs serve live)
- Serving systems (performance, query latency, distribution model, fault tolerance, etc.)
- Deployment system (e.g., how does new code get updated, build steps, keeping caches working after processes restart, etc.)
@@ -41,7 +41,7 @@ How does Retrieval work? Retrieval is done by writing a few heuristics, also cal
- Find 5 contents that user “liked” in the past, and for each such content, find 5 more “related” items
- Find the most relevant topics for a user and find the freshest content from each of the topics.
Retrieval can be powered by ML (e.g., trained embeddings), but more often than not, a larger % of generators are mere heuristics that encode some “product thinking” about what content is likely to create a good recommendation experience. And by writing a few of these and taking a union of all their candidates, we ensure that the system is able to at least consider all sorts of interesting inventory. Retrieval has only two jobs — 1) get all the interesting things (or at least as many as possible) [[2]](https://fennel.ai/blog/real-world-recommendation-system/#fn2) and 2) get as few total things as possible so that we can score/examine each candidate using the power of ML.
Retrieval can be powered by ML (e.g., trained embeddings), but more often than not, a larger % of generators are mere heuristics that encode some “product thinking” about what content is likely to create a good recommendation experience. And by writing a few of these and taking a union of all their candidates, we ensure that the system is able to at least consider all sorts of interesting inventory. Retrieval has only two jobs — 1) get all the interesting things (or at least as many as possible) and 2) get as few total things as possible so that we can score/examine each candidate using the power of ML.
## 2. Filtering
@@ -50,7 +50,8 @@ After retrieving a few hundred candidates, recommendation systems typically filt
1. Some products try to filter out content that the user has already seen before
2. Some products expose some controls to the users to hide away topics or authors or other sources of content
In short, most real-world recommendation systems develop a long list of filters over time, which once again encode some product thinking about what creates a good experience. [[[3]](https://fennel.ai/blog/real-world-recommendation-system/#fn3)
In short, most real-world recommendation systems develop a long list of filters over time, which once again encode some product thinking about what creates a good experience.
Filtering and retrieval have a very interesting relationship. Some filters are pushed down to the generators themselves — for instance, if you’re building a dating product, filters for location and sexual preferences may be a part of each generator itself. But more often than not, it is physically impossible to have each generator respect each filter at the source, and so a whole layer of filtering is needed.
## 3. Feature Extraction
@@ -65,7 +66,7 @@ So far, we have narrowed down the full inventory to a few hundred candidates and
There are two key ideas that are very successful and present in the scoring of most real-world recommendation systems (and we’d write dedicated posts about both in the future — stay tuned):
1. Multi-stage scoring — not all ML models are equal, and some are lot “heavier” than others. And it is usually not possible to run the heaviest ML models on hundreds of candidates. So instead, scoring itself is broken down in two substages — 1st stage scoring (which uses a relatively lighter ML model like GBDTs on all 500 candidates and emits out, say, top 100 candidates) and the 2nd stage scoring, which runs the heavy model (say deep neural network) on just the top 100 candidates.
2. Combining many models — ML models can only learn whatever we teach them to learn. And typically, they are taught to predict the probability of user engaging in a single action, say like. Sorting all content by what gets clicked is a good start but has lots of issues — for instance, it might only distribute clickbaity content. To make the recommendations more balanced, usually, multiple models are trained - say, one for predicting clicks, one for predicting comments, one for user reporting the content, etc. And the final score of a candidate is a weighted average of all these models. While this makes the recommendations better, this can also increase the amount of computation that needs to be done. [[4]](https://fennel.ai/blog/real-world-recommendation-system/#fn4)
2. Combining many models — ML models can only learn whatever we teach them to learn. And typically, they are taught to predict the probability of user engaging in a single action, say like. Sorting all content by what gets clicked is a good start but has lots of issues — for instance, it might only distribute clickbaity content. To make the recommendations more balanced, usually, multiple models are trained - say, one for predicting clicks, one for predicting comments, one for user reporting the content, etc. And the final score of a candidate is a weighted average of all these models. While this makes the recommendations better, this can also increase the amount of computation that needs to be done.
## 5. Ranking

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Reading/Read Later/httpsblackgirlbytes.deva-beginners-guide-to-prompt-engineering-with-github-copilot.md
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![[Attachment.pdf]]

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Reading/Read Later/httpsworld.hey.comdhhcloud-exit-pays-off-in-performance-too-4c53b697.md
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![[Attachment.pdf]]

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Book club notes/Excellence Wins.md → Work/Logos book club notes/Excellence Wins.md
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Reading/Book notes/Radical Candor.md → Work/Logos book club notes/Radical Candor.md
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