Nicholas Cutting

[Sahn Lam @sahnlam]

What Makes Nginx So Useful

[Oluwatobi Mustapha @Tobi_Msp]

Networking in 3 minutes

[Daffydre🎶 @Daffydre2]

This movie is absolutely 100% good 🍿🍿🎥

[Security Trybe @SecurityTrybe]

Best 3 YouTube Channels to Learn…

[Dan Neidle @DanNeidle]

This is the napkin on which a young economist called Art Laffer doodled a curve that he said showed that tax rises would reduce tax revenue.

It's the most consequential napkin in history, inspiring a movement that led to series of tax cuts in the US, UK and across the world.

[Govardhana Miriyala Kannaiah @govardhana_mk]

Not a joke, many Cloud Engineers don’t fully understand AWS Data Transfer costs, their complications, and implications.

Here, I’ve made this to help you better understand.

👋 PS - I wrote an article with visuals and a detailed breakdown of this use case in my newsletter here: https://t.co/gRuscfAYEb

🔁 Consider a Retweet if this is helpful.

[MarkoNtech @markontechcom]

** Linux tip: 5 examples how to create multiple directories at once with a single command **

1. Parent directory with multiple sub-directories

Command:
mkdir -p main-directory /{subdir1,subdir2,subdir3,subdir4}

Result:

[Dan Neidle @DanNeidle]

Our new web-based fraud-finding tool takes just a few clicks to find companies with suspicious and potentially fraudulent accounts. Here's a short demo showing quite how easy it is:

[Inferam @InferamAI]

Math made simple https://t.co/eaN4Xs9eMN

[Dave W Plummer @davepl1968]

If you know why x > y, you're a good C developer!

[Interesting STEM @InterestingSTEM]

Did you know?

[Santiago @svpino]

I built a Visual Studio Code extension to turn Visual Studio code into a custom learning environment for my Machine Learning class.

Attached, you'll see a 2-minute video of how it works.

I haven't tried this with my students yet (the February cohort will be the first to do so), but I think it will be huge.

I wish more people would offer a learning experience like this.

My next cohort starts in February and you can join at https://t.co/iZifcK7UTF. You'll get lifetime access to the best Machine Learning engineering cohort online.

[Ania Kubów #JavaScriptGames @ania_kubow]

Master CSS Grid in 2 CSS lines!

[Alex Xu @alexxubyte]

What happens when you type google .com into a browser?

[Dhanian 🗯️ @e_opore]

Frontend development Roadmap.

[Aurimas Griciūnas @Aurimas_Gr]

Here is why you need to understand 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 as an AI Engineer.

Simple naive RAG systems are rarely used in real world applications. To provide correct actions to solve the user intent, we are always adding some agency to the RAG system - it is usually just a bit of it.

It is important to 𝗻𝗼𝘁 𝗴𝗲𝘁 𝗹𝗼𝘀𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗯𝘂𝘇𝘇 𝗮𝗻𝗱 𝘁𝗲𝗿𝗺𝗶𝗻𝗼𝗹𝗼𝗴𝘆 and understand that there is 𝗻𝗼 𝘀𝗶𝗻𝗴𝗹𝗲 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 to add this agency to your RAG system and you should adapt to your use case. My advice is to think in systems and engineering flows.

Let’s explore some of the moving pieces in Agentic RAG:

𝟭. Analysis of the user query: we pass the original user query to a LLM based Agent for analysis. This is where:

➡️ The original query can be rewritten, sometimes multiple times to create either a single or multiple queries to be passed down the pipeline.
➡️ The agent decides if additional data sources are required to answer the query.

𝟮. If additional data is required, the Retrieval step is triggered. In Agentic RAG case, we could have a single or multiple agents responsible for figuring out what data sources should be tapped into, few examples:

➡️ Real time user data. This is a pretty cool concept as we might have some real time information like current location available for the user.
➡️ Internal documents that a user might be interested in.
➡️ Data available on the web.
➡️ …

𝟯. If there is no need for additional data, we try to compose the answer (or multiple answers or a set of actions) straight via an LLM.
𝟰. The answer gets analyzed, summarized and evaluated for correctness and relevance:

➡️ If the Agent decides that the answer is good enough, it gets returned to the user.
➡️ If the Agent decides that the answer needs improvement, we try to rewrite the user query and repeat the generation loop.

✅ Remember the Reflection pattern from my last Newsletter article? This is exactly that. 👆

The real power of Agentic RAG lies in its ability to perform additional routing pre and post generation, handle multiple distinct data sources for retrieval if it is needed and recover from failures while generating correct answers.

What are your thoughts on Agentic RAG? Let me know in the comments! 👇

#RAG #LLM #AI

[maharshi @maharshii]

i have noticed that LLMs like claude and gpt-4o work really well with this prompt, it instructs them to 'contemplate' for a bit before giving the final answer.

[Alex Prompter @alex_prompter]

I turned ChatGPT into my personal tutor and now I can learn anything in just 5 minutes.

Here are 8 powerful prompts I use daily ↓

[TRÄW🤟 @thatstraw]

Linux command line redirection crash course

[History Nerd @_HistoryNerd]

During WWII, the Nazis launched a secret war.

They believed Freemasons hid ancient secrets of power and mysticism.

So they raided lodges across Europe, seizing archives of ancient rituals and political influence.

Here's the untold story of the Nazis vs. the Freemasons."