Alexey Grigorev ✓

@Al_Grigor · Build and ship together: · joined 20 Jan 2020

Founder @DataTalksClub | Teaching engineers to build production AI systems | AI agents, LLMs, ML, data engineering | 100,000+ learners

29 670Followers

431Following

8 029Posts total

36.5KViews on collected posts

Neueste Beiträge

Alexey Grigorev ✓@Al_Grigor19 Sep

I hope I'll be able to do the same with .claude soon too https://t.co/1grVEyOi0A

893 views · 7 likes · 0 reposts · 0 replies Open on X →

Alexey Grigorev ✓@Al_Grigor19 Sep

I built a Django to-do app in one prompt. Cost: less than $1. Here's what the coding agent did: 1. Created the project structure - Generated Django project and app - Set up settings and URLs - Configured Tailwind CSS for styling 2. Built the data model - Created a Todo model

1.1K views · 21 likes · 4 reposts · 8 replies Open on X →

Alexey Grigorev ✓@Al_Grigor18 Sep

How about .agents for skills? 1.8K views · 5 likes · 0 reposts · 0 replies Open on X →

Alexey Grigorev ✓@Al_Grigor18 Sep

Jev is a new kind of model that doesn't generate text. It only makes decisions. You send it a state (text or JSON) plus a set of typed questions, and it returns structured JSON with probability distributions. I replaced my gpt-4o-mini setup for routing tasks to my 3 agents and

1.2K views · 23 likes · 1 reposts · 6 replies Open on X →

Alexey Grigorev ✓@Al_Grigor10 Apr

@Al_Grigor i am currently working on enterprise data analysis agent, pretty interesting. https://t.co/QjpgQfMaJ7 282 views · 1 likes · 0 reposts · 0 replies Open on X →

Alexey Grigorev ✓@Al_Grigor9 Apr

If you found this post helpful, follow me for more content like this. I publish a weekly newsletter where I share practical insights on data and AI. It focuses on projects I'm working on + interesting tools and resources I've recently tried: https://t.co/oh7DBWuGEE 793 views · 4 likes · 0 reposts · 1 replies Open on X →

Alexey Grigorev ✓@Al_Grigor9 Apr

So you follow one evolving system, practice on separate mini-projects, explore other examples, and build something of your own in parallel. That is the core of Buildcamp. The course starts in less than 5 days. You can sign up for the course here: https://t.co/REMfPMnikB https:/

1.1K views · 6 likes · 1 reposts · 0 replies Open on X →

Alexey Grigorev ✓@Al_Grigor9 Apr

Optional projects: 8. FAQ Assistant: Add search integrations and turn it into a support chatbot. 9. YouTube Transcript Summarizer: A system that extracts summaries and chapter structure from YouTube videos using structured output. 10. PDF Book Processor: Similar tasks often ht

1.2K views · 4 likes · 0 reposts · 1 replies Open on X →

Alexey Grigorev ✓@Al_Grigor9 Apr

Homework projects: 3. Document processing with AI. You download books, extract PDF text, chunk documents, and build a full RAG pipeline. 4. Wikipedia Agent. You implement search and page-fetching tools and build an agent using a framework of your choice. 5. Testing and https:/

1.1K views · 4 likes · 0 reposts · 1 replies Open on X →

Alexey Grigorev ✓@Al_Grigor9 Apr

2. Capstone project Your own AI application, built step by step during the course, from first RAG version to a more complete system with tools, testing, monitoring, evaluation, and deployment. https://t.co/6dYwGSnjWZ

1.3K views · 7 likes · 1 reposts · 1 replies Open on X →

Alexey Grigorev ✓@Al_Grigor9 Apr

1. A running example project A Documentation Agent that evolves throughout the course. It starts as a RAG system and gradually becomes an agent that is tested, monitored, and evaluated. https://t.co/62CSPLgE4j

1.5K views · 7 likes · 0 reposts · 2 replies Open on X →

Alexey Grigorev ✓@Al_Grigor9 Apr

AI Engineering Buildcamp is project-driven. You learn AI engineering by building. 15 projects you can build during the course 👇🏼 (You get lifetime access to the course if you sign up) https://t.co/VUDgEMhMLG

24.2K views · 371 likes · 74 reposts · 9 replies Open on X →

Im Vergleich zu Konten gleicher Größe

4 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. genau im Median seiner Follower-Klasse.

Medianaufrufe1 130dieses Konto924Median für 10K–100K

Reichweite, %3.81%dieses Konto3.62%Median für 10K–100K

Interaktion, %1.69%dieses Konto1.52%Median für 10K–100K

Kennzahl	Dieses Konto	Median für 10K–100K	Verhältnis
Medianaufrufe pro Beitrag	1 130	924	1.22×
Reichweite (Aufrufe ÷ Follower)	3.81%	3.62%	1.05×
Interaktionsrate	1.69%	1.52%	1.11×

Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

24.2K9 Apr

1.5K

1.3K

1.1K

1.2K

1.1K

793

28210 Apr

1.2K18 Sep

1.8K

1.1K19 Sep

893

Last 12 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.87%9 Apr

0.61%

0.68%

0.45%

0.42%

0.63%

0.35%10 Apr

2.59%18 Sep

0.33%

3.00%19 Sep

0.78%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes41.4%460 in total

Reposts7.3%81 in total

Replies2.6%29 in total

Quotes0.1%1 in total

Bookmarks48.6%540 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Neueste Beiträge

Im Vergleich zu Konten gleicher Größe

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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