Nicolay Gerold
@nicolaygerold
Nicolay Gerold
For High Throughput data, Grab uses Apache Avro with a strategy called Merge on Read (MOR) .
Here's the main operations with Merge on Read:
Here's the main operations with Merge on Read:
- Write Operations - When data is written, it's appended to the end of a log file. This is much more efficient than merging it in the current data and reduces the latency of writes.
- Read Operations - When you need
The Architecture of Grab's Data Lake
Most execution problems are culture or strategy problems (misalignment, different strategies). Indicator: does the bandage you applied keep falling off?
lennysnewsletter.com • Shreyas Doshi on Pre-Mortems, the LNO Framework, the Three Levels of Product Work, Why Most Execution Problems Are Strategy Problems, and ROI vs. Opportunity Cost Thinking
Denormalization
Another way Reddit minimizes joins is by using denormalization.
They took all the metadata fields required for displaying an image post and put them together into a single JSONB field. Instead of fetching different fields and combining them, they can just fetch that single JSONB field.
This made it much more efficient to fetch all the... See more
Another way Reddit minimizes joins is by using denormalization.
They took all the metadata fields required for displaying an image post and put them together into a single JSONB field. Instead of fetching different fields and combining them, they can just fetch that single JSONB field.
This made it much more efficient to fetch all the... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
- ClickHouse: It's a high performance columnar database that's great for real time queries. It enables querying and storing large amounts of data on commodity hardware. Some of my customers have millions of page views and I don't have an unlimited budget, so it's been very handy.
- PostgreSQL: My favorite database. Sane defaults, battle-tested, and well
The Tech Stack of a One-Man SaaS
Model Card for Zephyr 7B β
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that... See more
Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that... See more
HuggingFaceH4/zephyr-7b-beta · Hugging Face
Serverless Postgres (experimental)
Serverless Postgres using Oriole, Fly Machines, and Tigris for S3 Storage.
Overview
This is a MVP for Serverless Postgres.
1/ It uses Fly.io, which can automatically pause your database after all connections are released (and start it again when new connections join).
2/ It uses Oriole, a Postgres extension with expe... See more
Serverless Postgres using Oriole, Fly Machines, and Tigris for S3 Storage.
Overview
This is a MVP for Serverless Postgres.
1/ It uses Fly.io, which can automatically pause your database after all connections are released (and start it again when new connections join).
2/ It uses Oriole, a Postgres extension with expe... See more
GitHub - kiwicopple/serverless-postgres
HelixNet is a Deep Learning architecture consisting of 3 x Mistral-7B LLMs. It has an actor, a critic, and a regenerator. The actor LLM produces an initial response to a given system-context and a question. The critic then takes in as input, a tuple of (system-context, question, response) and provides a critique based on the provided answer to the... See more
migtissera/HelixNet · Hugging Face
Kirimase is a command-line tool for building full-stack Next.js apps faster . It supercharges your development workflow, allowing you to quickly integrate packages and scaffold resources for your application with best practices in mind.