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How we reduced CI time by 35% with Nx Caching
How we reduced CI time by 35% with Nx Caching
Sentry is a very fast-moving company. In just one month we merged 165 pull requests from 19 authors and changed over 800 files, with a total of over 22,000 additions and almost 10,000 deletions. By updating to Lerna 6 with Nx caching, we were able to reduce our CI run times by about 35%.
·sentry.engineering·
How we reduced CI time by 35% with Nx Caching
Home | envio
Home | envio
A Modern And Secure CLI Tool For Managing Environment Variables
·envio-cli.github.io·
Home | envio
envio-cli/envio: Envio is a command-line tool that simplifies the management of environment variables across multiple profiles. It allows users to easily switch between different configurations and apply them to their current environment
envio-cli/envio: Envio is a command-line tool that simplifies the management of environment variables across multiple profiles. It allows users to easily switch between different configurations and apply them to their current environment
Envio is a command-line tool that simplifies the management of environment variables across multiple profiles. It allows users to easily switch between different configurations and apply them to th...
·github.com·
envio-cli/envio: Envio is a command-line tool that simplifies the management of environment variables across multiple profiles. It allows users to easily switch between different configurations and apply them to their current environment
gvergnaud/hotscript: A library of composable functions for the type-level! Transform your TypeScript types in any way you want using functions you already know.
gvergnaud/hotscript: A library of composable functions for the type-level! Transform your TypeScript types in any way you want using functions you already know.
A library of composable functions for the type-level! Transform your TypeScript types in any way you want using functions you already know. - GitHub - gvergnaud/hotscript: A library of composable f...
·github.com·
gvergnaud/hotscript: A library of composable functions for the type-level! Transform your TypeScript types in any way you want using functions you already know.
Making Tanstack Table 1000x faster with a 1 line change
Making Tanstack Table 1000x faster with a 1 line change
A few months back I was working on a Javascript frontend for a large dataset using Tanstack Table. The relevant constraints were: Up to 50k rows of content Grouped by up to 3 columns Using react and virtualized rendering, showing 50k rows was performing well. But when the Tanstack Table grouping feature was enabled, I was seeing slowdowns on a few thousand rows, and huge slowdowns on 50k rows.
·jpcamara.com·
Making Tanstack Table 1000x faster with a 1 line change
0x6b/libgsqlite
0x6b/libgsqlite
A SQLite extension which loads a Google Sheet as a virtual table.
·github.com·
0x6b/libgsqlite
whoiskatrin/sql-translator: SQL Translator is a tool for converting natural language queries into SQL code using artificial intelligence. This project is 100% free and open source.
whoiskatrin/sql-translator: SQL Translator is a tool for converting natural language queries into SQL code using artificial intelligence. This project is 100% free and open source.
SQL Translator is a tool for converting natural language queries into SQL code using artificial intelligence. This project is 100% free and open source. - GitHub - whoiskatrin/sql-translator: SQL T...
·github.com·
whoiskatrin/sql-translator: SQL Translator is a tool for converting natural language queries into SQL code using artificial intelligence. This project is 100% free and open source.
Analyzing multi-gigabyte JSON files locally
Analyzing multi-gigabyte JSON files locally
I’ve had the pleasure of having had to analyse multi-gigabyte JSON dumps in a project context recently. JSON itself is actually a rather pleasant format to consume, as it’s human-readable and there is a lot of tooling available for it. JQ allows expressing sophisticated processing steps in a single command line, and Jupyter with Python and Pandas allow easy interactive analysis to quickly find what you’re looking for. However, with multi-gigabyte files, analysis becomes quite a lot more difficult.
·thenybble.de·
Analyzing multi-gigabyte JSON files locally
Prompt Engineering
Prompt Engineering
Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics. This post only focuses on prompt engineering for autoregressive language models, so nothing with Cloze tests, image generation or multimodality models.
·lilianweng.github.io·
Prompt Engineering
The quantum state of a TCP port
The quantum state of a TCP port
If I navigate to https://blog.cloudflare.com/, my browser will connect to a remote TCP address from the local IP address assigned to my machine, and a randomly chosen local TCP port. What happens if I then decide to head to another site? Is it possible to establish a new TCP connection from the same local IP address and port?
·blog.cloudflare.com·
The quantum state of a TCP port
How Ahrefs Saved US$400M in 3 Years by NOT Going to the Cloud
How Ahrefs Saved US$400M in 3 Years by NOT Going to the Cloud
Clouds for IT infrastructure are so popular lately that moving into the cloud has become a trend. Infrastructure as a service (IaaS) cloud provides multiple advantages: flexibility, low time for…
·tech.ahrefs.com·
How Ahrefs Saved US$400M in 3 Years by NOT Going to the Cloud
One big, one little
One big, one little
I often feel overwhelmed deciding what to do with my spare time. It’s a problem with volume moreso than ambition. When the feeling hit the other weekend I scribbled down seven ideas rattling around in my head and stared at the list blankly. I’ve used different prioritization systems in the past like the Eisenhower matrix (Urgent vs. Important) and the Six Sigma matrix (Effort vs Impact); and while those are good for evaluating required tasks… none of those work well for voluntary activities self-created by a serial project starter.
·daverupert.com·
One big, one little