Jacob Tiab

Software Engineer ☀️😈

[freeCodeCamp.org @freeCodeCamp]

In the fast-paced world of fintech, you need quick market context.

You need to know why a stock moved and what to watch next.

Here, Nikhil shows you how to use LangChain, APIs, and StreamLit to generate market briefs from natural language queries.

https://t.co/3S9KaiA1T5

[veles @velesxbt]

A Stanford mathematician who spent 10 years as a professional magician just described the market in one sentence:

"I've spent my life on two tricks: making a rigged deck look random, and making a random one look rigged. The market is the first trick, and almost nobody catches it."

That's Persi Diaconis. He has a free lecture that asks one question: does anything actually happen at random? The answer is: far less than you think.

The market is his first trick in the wild. It looks like pure chance. Buried inside is a faint rig, a 50.75% tilt no eye can see. Your gut reads a losing week as a broken system and a hot streak as skill. Wrong both times. The tilt is invisible to human intuition, which is exactly why funds hand the decision to the math.

None of it is hidden. Diaconis has taught it for decades. The probability goes back to 1713. The lecture is free.

Here's the trap: you feel every win and every loss, but you cannot feel the average. And the average is the only thing that pays. It takes thousands of trades for a 51% edge to separate from luck, and almost everyone quits long before then.

The math is free. The patience to trust it past your own eyes is the edge.

[Yun-Ta Tsai @yunta_tsai]

I would like to offer a counterargument that LLMs (or maybe AIs) cannot jump.

Before AlphaGo, the AI field had the same argument for Go: there are 2.08 × 10^170 possibilities, nothing fits in the computer, and there is no way AI could possibly predict the outcome of the next 50-60 moves.

It turned out most moves do not lead to a win. Combined with clever use of Monte Carlo Tree Search, the sampling becomes quite manageable. The same can be said for physics, where equations are just another form of compression.

Einstein did not start with relativity. That was not his first paper. He spent years understanding the properties of light before concluding that the speed of light is constant across the universe, which unlocked his discovery of relativity. During his thought process, he also interacted with other physicists (e.g., sub-agents) to enrich his thinking.

Currently we have not run an agent for years of compute. The sessions are often fragmented and disoriented, so every new session is almost a fragmented memory of the past, but it may not be for long.

[Zenith @ZenithcoreAi]

🚨Stop wasting hours figuring out Claude on your own.

Most people open Claude...

Ask a few random prompts...

Get average answers...

Then conclude:

> "Claude isn't that good."

The truth?

They never learned how to use it.

Here's a FREE Claude Learning Library with 17 resources that can take you from beginner to power user.

👇

1. Claude 101
https://t.co/gAnRX0vNjx

2. Claude Code
https://t.co/fq4zpC0AWz

3. Claude Skills
https://t.co/xX1YaIJija

4. Claude Design
https://t.co/vpy8qcaE4h

5. Claude for Excel
https://t.co/i9FmRPcUs0

6. How to Prompt
https://t.co/po0NYzw39b

7. Claude + LinkedIn
https://t.co/oKyhkZUaId

8. Be Good at Claude
https://t.co/RZYMqWbwv4

9. Stop Writing Like AI
https://t.co/QhP9eNdGxW

10. Claude Certificates
https://t.co/FQbvIRe9tT

11. Claude for Your Team
https://t.co/JuIsd0i8Lu

12. Claude Connectors
https://t.co/uAn4i8w6Wc

13. Set Up Claude Cowork
https://t.co/BOAX4QTQhT

14. Stop Prompting Claude
https://t.co/BOAX4QTQhT

15. Make Claude Sound Like You
https://t.co/FQbvIRe9tT

16. Stop Hitting Claude Limits
https://t.co/7DcfQD5OGC

17. Stop Using Claude at Work
https://t.co/OMepjJ902t

The AI gap is getting bigger every month.

People who know how to use AI will replace people who don't.

Not because AI is smarter.

Because they know how to work smarter.

📌 Bookmark this.

You'll keep coming back to it.

♻️ Repost to help someone master Claude for free before everyone else.

[Jack Dawson @PromptWi]

🚀 If you're using Claude Code without these GitHub repositories...

You're missing out.

Here are 9 must-have repos every AI developer should know:

[Python Programming @PythonPr]

Junior vs Senior

[AI_Explorer @ai_explorer25]

Best accounts to follow from each frontier lab to stay constantly up to date

Anthropic

@karpathy
- must-follow account for AI; recently joined Anthropic

@bcherny
- Claude Code creator, always shares great tips

@trq212
- also a Claude Code developer; writes amazing articles on CC

OpenAI

@polynoamial
- works on reasoning research, shares a lot of technical details

@gabriel1
- Sora developer, great career path

@jxnlco
- works on dev experience, shares a lot about Codex

Google AI

@OfficialLoganK
- all the major Google Gemini and AI Studio updates

@ammaar
- product and design; shares great things about vibe-coding in Google AI Studio

@fofrAI
- cool use cases for generative models

Cursor

@leerob
- the loudest voice behind Cursor updates

@ericzakariasson
- shares great insights on using Cursor

@mntruell
- Cursor’s CEO; major releases and usage updates

xAI

@milichab
- recently joined xAI, shares updates on Grok

@skcd42
- also covers major Grok releases

@ai_explorer25
- covers all ai content and free resources

[Elon Musk @elonmusk]

Anyone can visit the Starship factory and launch site in Texas, as it is right next to on the public highway. It is incredibly inspiring to see!

[Dan Kornas @DanKornas]

Introduction to Machine Learning (Tübingen)

Introduction to Machine Learning The course serves as a basic introduction to machine learning and covers key concepts /Untitled.png)

[Anatoli Kopadze @AnatoliKopadze]

Google Brain founder, Andrew Ng:

"100% of my tasks are done by ai agents, self-improving loops are next.

Give it 3-6 months and prompting is gone."

31 minutes of clear explanation on building self-improving agents from scratch.

Worth more than any $500 agentic course.

Watch it, then read the full guide on loops below.

[freeCodeCamp.org @freeCodeCamp]

There are many fields in which AI can streamline processes and automate tedious tasks.

And one area with many applications for AI tools and technologies is agriculture.

In this book, Vahe details how AI can help increase crop yields, use water more efficiently, predict and react to stormy weather, and more.

https://t.co/0HswPOr3iW

[freeCodeCamp.org @freeCodeCamp]

Docker Compose isn’t just for local dev anymore.

In this guide, Balajee shows how to use modern Compose features like profiles, watch mode, and GPU support for real production workloads.

You’ll learn how to manage multiple environments from one compose.yaml, speed up container-based development, and more.

https://t.co/3sqNiaazxd

[Rahul @sairahul1]

Andrew Ng just dropped a 3-hour course on how to become an AI engineer in 2026:

00:00 - How to build agentic AI systems
04:25 - Future of AI engineering
23:38 - AI prompting full course
2:52:17 - Creating an app with AI in 30 minutes

This 3-hour watch could replace 10 AI engineering courses on the internet.

Watch it today.

Then read this.

[Codez @0xCodez]

Andrej Karpathy just dropped a 6-hour course on how to build LLMs from scratch:

• 00:00 - Deep dive into LLMs like ChatGPT
• 03:31:23 - Building ChatGPT from scratch in live
• 05:27:43 - How to use LLMs (Karpathy method)

This course will replace a $90K Stanford LLM master’s degree.

Start watching today, then read how to become an AI engineer in article below.

[Rahul @sairahul1]

How to become AI engineer in next 6 months:

By the end, you want to be able to:

- build LLM apps end-to-end
- use APIs from OpenAI / Anthropic / open-source stacks
- design prompts and context properly
- add tool calling and structured outputs
- deploy real projects

So, let’s discuss your roadmap month by month

Month 1: Get solid enough in coding and fundamentals

What to learn:

- Python really well
- Git + GitHub
- CLI / terminal basics
- JSON, APIs, HTTP, async basics
- basic SQL
- basic data handling with pandas
- virtual environments, package management, error handling
- FastAPI or Flask

Month 2: Master LLM app development

What to learn:

- prompting fundamentals
- system vs user instructions
- structured outputs / JSON schemas
- function/tool calling
- streaming responses
- conversation state
- cost / latency / token basics
- failure handling
- prompt injection awareness

Month 3: Learn RAG properly

What to learn:

- embeddings
- chunking
- vector databases
- metadata filtering
- reranking
- retrieval quality issues
- hallucination reduction
- citations and grounding

Month 4: Agents, tools, workflows, evals

- agent loops
- tool selection
- state management
- retries
- when NOT to use agents
- multi-step workflows
- evaluation harnesses
- task success metrics

Month 5: Deployment, product thinking, and reliability

What to learn:

- FastAPI production patterns
- Docker
- background jobs
- queues
- auth + API key security
- logging
- observability
- prompt/version management
- eval dashboards
- cost monitoring
- rate limits
- caching

Month 6: Specialize and become hireable

these knowledge and skills you gained can be applied in three directions

you need to choose one of them and focus on practice

although everything mentioned above is also best learned purely through practice

Direction 1: AI product engineer

Best if you want startup jobs fast

Focus on:

- LLM apps
- RAG
- agents
- deployment
- product UX

Direction 2: Applied ML / LLM engineer

Focus on:

- fine-tuning
- when to fine-tune vs prompt
- evaluation
- inference optimization
- open-source models
- training pipelines

Direction 3: AI automation engineer

Focus on:

- workflow orchestration
- business process automation
- multi-tool systems
- CRM, docs, email, support, ops use cases

This roadmap will help you go through a practical path, and the key is to study each of these points and then test them in real work

By month six, you will already have several built products or examples of completed tasks

And it will be much easier to get a job as an AI engineer

Save it so you don't lose it and can return to study later

[Jacob Tiab @kaizen_satori]

@jaysmith_ai AI

[Jay @jaysmith_ai]

ChatGPT + laptop + internet connection + 60 minutes a day = $349 per day.

Usually, I charge $81 for this guide, but today you get it 100% for free.

Like + comment 'AI' and I'll send you my step-by-step guide for FREE.

Must follow me to get guide in DM.

FREE for 48 hours.

[codila @0xCodila]

Ex-Google engineer just dropped 1-hour course: loops, self-improving AI, memory systems - from scratch:

00:00 - the self-building agent
03:01 - soul.md runs everything
30:16 - RAG memory: pull 20 messages, not 2,000
31:48 - the loop that knows when to stop
35:14 - find the bug, fix the prompt
50:22 - how Claude compresses your memory

1 hour of his guide beats any paid agent course

watch & bookmark - then read Karpathy's loop method below

[Nikki Siapno @NikkiSiapno]

I wrote 35 articles for 35 system design concepts:

1) gRPC: https://t.co/QwgTXr1N9z

2) Redis: https://t.co/YmMKgSvyVk

3) Event-driven architecture: https://t.co/QNUuf1JOy7

4) OAuth: https://t.co/TgQdzGO2Kr

5) ACID vs BASE: https://t.co/a7nOyylUxk

6) Microservices: https://t.co/1CpY04nNxb

7) Rate limiting: https://t.co/wr0UAh4sJm

PS - if you want a structured path, get my free 142-page System Design Handbook when you join my free weekly newsletter → https://t.co/rKYuAkfCSL

8) Observability: https://t.co/VjfECfyB9d

9) System design quality attributes: https://t.co/v9WJoUPevt

10) CAP theorem: https://t.co/ONwpduTOD1

11) Idempotency: https://t.co/2sItwlz1oe

12) REST APIs: https://t.co/GSjtaCU2Y8

13) Sync vs Async: https://t.co/ZXCxP7XrAb

14) Network protocols: https://t.co/tx7MlZQIwE

15) Change Data Capture (CDC): https://t.co/tgwwoTitCA

16) CI/CD pipelines: https://t.co/SM2YvhioIX

17) SSO (single sign-on): https://t.co/sNCN5cdt6V

18) JWT: https://t.co/Kuv7DAj6B9

19) Circuit breakers: https://t.co/1U81SrBokz

20) Health checks vs heartbeats: https://t.co/r5SalP6CCh

21) API gateway vs load balancer vs reverse proxy: https://t.co/Tg3EhT60tU

22) HTTPS: https://t.co/wc3CQOsmPS

23) Load balancing algorithms: https://t.co/VCLCKOZzni

24) Database caching: https://t.co/23QdZATj2o

25) API protocols: https://t.co/2CEu4Wnhsv

26) CDN: https://t.co/MbaSzBnZPQ

27) Database types: https://t.co/T0tUF1xYPI

28) Message Queues: https://t.co/7Tz5sevNA8

29) Hashing vs encryption vs tokenization: https://t.co/6eN9I2YAej

30) Service Discovery: https://t.co/rcKkXkWWcX

31) Pub/Sub: https://t.co/HF0Zr5R4SK

32) Connection pooling: https://t.co/39SsEo4kk3

33) Forward proxy vs reverse proxy: https://t.co/0P6NM8kh8u

34) Consistent hashing: https://t.co/8d8o74EsaS

35) SQL vs NoSQL: https://t.co/oDTRpsnQUn

👋 PS: Want to improve at system design? Download my free System Design Handbook and join 34,000+ engineers who get my free weekly newsletter → https://t.co/LybPLdor9s

——

🔖 Save it for later.

➕ Follow me ( Nikki Siapno ) to improve at system design.

[freeCodeCamp.org @freeCodeCamp]

If you want to run an LLM, you don't have to rely on cloud-based APIs anymore - you can do it right on your local machine.

And in this tutorial, @chairahalkar helps you build your own local AI agent.

You'll use the Qwen 3 family of LLMs along with Ollama which simplifies running LLMs locally.

https://t.co/JgLjzAbTo0