This Chatgpt Prompt= $20k growth consultant. : r/PromptEngineering
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AI/ML
Prompt Engineering | Kaggle
Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals.
Introduction · Reverse Engineering
security
A non-beginner Data Engineering Roadmap — 2025 Edition
Master advanced Data Engineering with this 2025 roadmap! Explore tools, skills, and resources to excel in Python, SQL, CI/CD, AI, cloud, and more
database
Migrating in-place from PostgreSQL to MySQL
Migrating in-place from PostgreSQL to MySQL Alex Toumazis, Software Engineer Oct 7, 2024 The Yelp Reservations service (yelp_res) is the service that powers reservations on Yelp. It was acquired along...
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Seeing Like a Programmer (LambdaConf 2024) — Sympolymathesy, by Chris Krycho
Resiliency, Limits, and Moral Hazards in Software Engineering
architecture
A Map of Sync
We can categorize sync platforms across nine dimensions: data size, data update rate, the structure of the data, input latency, offline support, numbe...
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.
Data Industry Primer - by Eric Flaningam - Generative Value
An overview of one of the most fragmented industries in tech
End-to-end data engineering project - batch edition · Start Data Engineering
Struggling to come up with a data engineering project idea? Overwhelmed by all the setup necessary to start building a data engineering project? Don't know where to get data for your side project? Then this post is for you. We will go over the key components, and help you understand what you need to design and build your data projects. We will do this using a sample end-to-end data engineering project.