Auwal Shehu Ali

PhD Researcher University Sains Malaysia, | CS Lecturer Bayero University, Kano| Aspiring Cyber Physical System, Usable Security and Privacy, Data Science, ML,

[Emrullah @emrullahai]

CODEX İLE KENDİ KAYNAKLARIMDAN TAM BİR ARAŞTIRMA MAKALESİ ÜRETTİM.

Kendi tezimin kaynaklarını (PDF’ler, arşiv belgeleri ve makaleler) tek bir klasöre koydum. Sonra Codex’e dedim ki:“Sadece bu klasördeki belgeleri kullan.Orijinal bir argüman kur ve akademik düzeyde makale yaz.”

Sonuç oldukça şaşırtıcıydı.

Thread’de nasıl yaptığımı ve ne çıktığını anlatıyorum.

[Kanika @KanikaBK]

I spent the last 3 months building my entire AI knowledge system in Obsidian.

No cloud subscriptions or monthly fees.

Just local tools that work offline.

Here are 11 articles that show exactly how I did it:

[Google Cloud Tech @GoogleCloudTech]

Intro to AI agents is now in session!

Take this 30-minute course in the Gemini Enterprise Agent Ready learning path to discover how AI agents use autonomous action & reasoning to solve complex problems.

Then, show off your knowledge with a skill badge → https://t.co/ef6isEReOp

[Fei-Fei Li @drfeifei]

Scientific research is fundamental to advancing civilization and helping people globally to solve the most critical problems, from medicine to materials, from brain science to physics, and much beyond. This is only possible when scientists have access to the best tools of the time to conduct scientific research, including having access to AI-based tools.

[btwiambot @btwiambot]

Most students say they don't have resources.

Meanwhile, some of the best learning materials on the internet are completely free:

Computer Science Fundamentals
https://t.co/TixUJ1isY3

Data Structures & Algorithms
https://t.co/EGyon2SBWu

System Design
https://t.co/3z6U7AK9Rc

Web Development
https://t.co/w0fxW0A5gW

Frontend Development
https://t.co/y9RSRdhyBt

Backend Development
https://t.co/y9RSRdhyBt

DevOps
https://t.co/y9RSRdhyBt

Cloud Computing
https://t.co/y9RSRdhyBt

Data Engineering
https://t.co/g0OljYCkD6

Machine Learning & AI
https://t.co/HiJQXjkxoC

MLOps
https://t.co/jij6IbQE4P

Cybersecurity
https://t.co/HMxTjR3Wti

Linux
https://t.co/B1uEY4w3Nl

Free Programming Books
https://t.co/syyEvqZPd5

A student with an internet connection can learn almost every in-demand tech skill for free.

Information isn't scarce.

Consistency is.

[Dan Kornas @DanKornas]

Machine learning resources are everywhere. This repo helps you stop tab-hoarding.

Awesome Machine Learning Resources is a curated list of curated lists across machine learning, deep learning, and AI topics for builders trying to learn, research, or ship faster.

It helps you find learning paths, papers, tutorials, libraries, datasets, and production references by grouping resources into a scan-friendly topic map instead of sending you through random search results.

Key features:

• Broad ML taxonomy – sections for general ML, paradigms, tasks/applications, models, fairness/ethics, interdisciplinary ML, datasets, and production ML
• Beginner/researcher/engineer paths – README says it is meant to help beginners, researchers, and engineers find the right resources
• Practice + research labels – entries separate tutorials/libraries from paper lists and research collections
• Short descriptions per link – each resource includes a quick note so you can shortlist faster
• Maintenance signals – ⚠️ marks lists that have been inactive for 12+ months

It’s open-source (CC0-1.0 license).

Link in the reply 👇

[Swapna Kumar Panda @swapnakpanda]

AI & ML FREE Courses from Stanford:

❯ CS336 - LLM from Scratch
❯ CS221 - Artificial Intelligence
❯ CS229 - Machine Learning
❯ CS230 - Deep Learning
❯ CS234 - Reinforcement Learning
❯ CS224N - NLP with Deep Learning

All course links inside:

[AI Conference DL Countdown @DlCountdown]

WACV'27-R1 (reg): 15 days.
WACV'27-R1 (paper): 22 days.
ACCV'26 (reg): 29 days.
ACCV'26 (paper): 31 days.
AAAI'27 (reg): 48 days.
AAAI'27 (paper): 55 days.
WACV'27-R2 (reg): 78 days.
WACV'27-R2 (paper): 85 days.
3DV'27 (paper): 85 days.

[James @jamescoder12]

Google Gemini + NotebookLM are quietly reshaping how people actually learn anything.

From uploading a PDF, YouTube video, lecture, document, or even messy notes.

And instantly turning them into a personal AI tutor that can explain, summarize, quiz you, and keep testing you until the knowledge truly sticks.

Here are 7 prompts that can help you learn anything 10x faster 👇🏻

[Dan Kornas @DanKornas]

Stop discovering ML Python libraries one random tutorial at a time

Best-of Machine Learning with Python is a curated GitHub index of open-source machine learning Python libraries for builders who need a faster way to compare the ecosystem.

It helps you shortlist tools by grouping projects into categories and ranking them with a project-quality score based on metrics collected from GitHub and package managers.

Key features:

• 920-project index – a large scan-friendly map of open-source ML Python projects
• 34 categories – browse by area like ML frameworks, NLP, image data, AutoML, deployment, interpretability, and more
• Quality-score ranking – projects are ordered using an automated score from repo and package-manager signals
• Rich project metadata – entries show signals like stars, forks, issues, contributors, activity, downloads, and dependencies
• Weekly updates + contributions – the list is updated regularly and can be improved via issues, PRs, or projects.yaml edits

It’s open-source (CC BY-SA 4.0 license).

Link in the reply 👇

[Dan Kornas @DanKornas]

🔗 GitHub: https://t.co/HdHxD76S3B
---
✉️ If you’re into AI, ML, agents, and building real systems, join my newsletter (it’s free): https://t.co/zJ9uwd6qSd

[Atal @ZabihullahAtal]

I found 5 GitHub repos that solve Claude's biggest writing problem:

The robotic AI tone.

These repos make Claude write much more like a real human: (save this)

1) Humanizer: https://t.co/zsfRf1JpdJ
2) Humanize Writing: https://t.co/gaAwKfZPZz
3) Humanizer Skill: https://t.co/kbvvK9MIV6
4) Awesome Claude Prompts: https://t.co/KcG3HyUfge
5) Awesome Claude Skills: https://t.co/jBWiRPetAW

That's a wrap

[Auwal Shehu Ali @Asali_cs]

@dashboardlim Free

[Dan Kornas @DanKornas]

AI/ML builders: this playlist is worth saving.

Stanford CS230: Deep Learning I Autumn 2025 from Stanford Online

The useful part is the map: llms / genai / agents concepts you can revisit when you need stronger foundations or implementation ideas.

Use it as a reference, not passive watching:

• skim the outline first
• take notes on the parts that map to your work
• turn one concept into a small build or experiment

Link is in the reply/comment 👇

♻️ Share this with your network if you found it useful or insightful.

[James @jamescoder12]

🚨NotebookLM + Google Antigravity is one of the most powerful combo available right now—and almost no one is using it.

If you’re not taking advantage of this, you’re missing out on serious leverage.

Here’s how to set it up in 2 minutes + what it can do 👇

[Neo Kim @systemdesignone]

If you want to become good at AI engineering (in 3 weeks), then learn these 15 concepts:

1 AI Agents: Memory, State & Consistency
→ https://t.co/v8H7O00jub

2 Machine Learning System Design 101
→ https://t.co/9MkHcLb5e0

3 Design Personal AI Chat Assistant
→ https://t.co/nNWq3onTnW

4 How RAG Works
→ https://t.co/cGmunPTUlb

5 LLM Concepts - A Deep Dive
→ https://t.co/5lCKxq2g4N

6 How to Design an AI Agent
→ https://t.co/JvnPd9773A

7 What is Reinforcement Learning
→ https://t.co/AVpl9j1oit

8 How Vector Databases Work
→ https://t.co/FVxan8xHH3

9 Context Engineering 101
→ https://t.co/OMkiZhkODL

10 AI Coding Workflow 101
→ https://t.co/paIf9ksIU9

11 LLM Evals Explained
→ https://t.co/nv3Ol8W53p

12 How AI Agents Work
→ https://t.co/tk3zkCjRvg

13 How MCP Works
→ https://t.co/wgf8gHnnkn

14 Agentic Patterns Explained
→ https://t.co/8YdBBWvTj1

15 Multi-Agent Architecture Explained
→ https://t.co/rS5QQS7Jln

What else should make this list?

===

👋 PS - Want my System Design Playbook for FREE?

Join my newsletter with 210K+ software engineers right now:
��� https://t.co/ByOFTtOihX

===

💾 Save & RT to help others ace AI engineering.
👤 Follow @systemdesignone + turn on notifications.

[Nav Toor @heynavtoor]

10 GitHub repos that should be illegal — they're killing $50 billion in corporate revenue.

SAVE IT

1. yt-dlp
Downloads any video from YouTube, X, TikTok, Instagram, anywhere. YouTube Premium charges $14 a month to do less than this. It is 100% free.
Repo → https://t.co/TaRtkcd4qy

2. Ollama
Run GPT-4-class AI on your laptop. No API costs. Developers spend $500 a month on OpenAI for what Ollama runs offline for $0.
Repo → https://t.co/gyZhUdzsnZ

3. Fooocus
Midjourney-quality image generation on your own GPU. Midjourney charges $30 a month. Fooocus runs unlimited generations for free.
Repo → https://t.co/NDPJpIdYJs

4. Whisper
OpenAI's transcription model, open-sourced. Otter charges $20 a month for what Whisper does for free, in 99 languages.
Repo → https://t.co/blaJ4i4MnH

5. Plausible Analytics
Privacy-first Google Analytics replacement. Google Analytics 360 costs $150,000 a year for enterprises. Plausible self-hosted costs $0.
Repo → https://t.co/RFrcpqTBQ7

6. AppFlowy
Open-source Notion. Notion charges $20 per user per month for teams. AppFlowy runs unlimited users on your server for free.
Repo → https://t.co/IDMykTCkMU

7. Penpot
Open-source Figma. Figma charges $45 per editor per month. Penpot does the same job, self-hosted, free forever.
Repo → https://t.co/Lx1CYUP4p4

8. n8n
Open-source Zapier. Zapier Pro costs $600 a month for a real workflow. n8n self-hosted runs unlimited automations for $0.
Repo → https://t.co/hdycABGGc1

9. Cal .com
Open-source Calendly. Calendly Teams costs $16 per user per month. Cal. com is free for individuals and open source for teams.
Repo → https://t.co/haz8ihRsHm

10. Bitwarden
Open-source 1Password. Password managers charge $8 per user. Bitwarden is unlimited, forever, free.
Repo → https://t.co/XCZ2JtWqWQ

Here's the wildest part:

That's $50 billion in corporate revenue these repos are quietly destroying every single year.

None of these are illegal.

All of them should be.

Save this. Share it with the person in your life still paying for what's been free this whole time.

100% free. 100% open source.

[Ryan Hart @thisdudelikesAI]

Paper link: https://t.co/9Moht9k4hg

[Ajit kumar @ajitcodes]

These 9 lectures from Stanford University are the BEST for anyone wanting to learn and understand LLMs in depth

Lecture 1 - Transformer: https://t.co/6wl1VXyQxS

Lecture 2 - Transformer-Based Models & Tricks: https://t.co/rFoGOnsOY2

Lecture 3 - Tranformers & Large Language Models: https://t.co/t8H8UebPg0

Lecture 4 - LLM Training: https://t.co/KZxOEL0ezz

Lecture 5 - LLM tuning: https://t.co/PapIUSlToT

Lecture 6 - LLM Reasoning: https://t.co/dr02iTGXHs

Lecture 7 - Agentic LLMs: https://t.co/10EQm5iCBp

Lecture 8 - LLM Evaluation: https://t.co/eOKwCn3LBo

Lecture 9 - Recap & Current Trends: https://t.co/MQAGVGlqiX

Start understanding LLMs in depth from the experts. Go through each step-by-step video

Start understanding LLMs in depth from the experts. Go through each step-by-step video

[Mushtaq Bilal, PhD @MushtaqBilalPhD]

Free 66-page academic writing guide by Victoria University of Wellington

Click the link below to download the guide.

Follow Silvi on LinkedIn for more free resources on academic writing.

https://t.co/0rJTliYUfx