CarbabaAI, a UAE-based B2B SaaS company, pioneers document intelligence using AI-driven compliance systems and QA validators to optimize enterprise workflows. As Founder, contributions include innovative solutions that reduced document processing time by 40%, showcasing a commitment to operational efficiency and transformative technologies.  

With a biomedical degree major with a data science minor from Swinburne University of Technology, expertise spans machine learning, AI systems, and data analytics. Previous roles, such as Software Developer at CliniNote, focused on real-world data solutions for healthcare. Additionally, mentoring at Monash University bridged academic concepts with practical applications, reflecting a dedication to responsible AI-driven industry innovation.

The agents that actually work aren’t using LangChain. They’re doing this 👇 

Most builders hide behind tools.

Frameworks feel powerful.

But they hide real mechanics.

You ship features.
You miss understanding.

Build the core yourself.

This free, 0$ open-source repo
teaches AI agents from scratch.
Using Python and local LLMs.

No frameworks.
No abstractions.
Just first principles.

	1.	Basic LLM Chat
Send prompts to a local model.
See raw input and output.

	2.	System Prompts
Define behavior with constraints.
Control the agent precisely.

	3.	Structured Output (JSON)
Force reliable, parsable data.
Turn text into code-ready output.

	4.	Routing Logic
Classify user intent cleanly.
Choose specific execution paths.

	5.	Tools & Capabilities
Call external Python functions.
Give the agent real power.

	6.	The Agent Loop
Implement Observe-Decide-Act.
Autonomy through tight loops.

	7.	Memory Management
Persist context across sessions.
Maintain coherent conversations.

	8.	Planning
Break goals into clear steps.
Execute with structured intent.

	9.	Atomic Actions
Isolate tool execution safely.
Prevent crashes from single failures.

	10.	Atom of Thought
Use dependency graphs for reasoning.
Handle complex multi-step tasks.

Real agency is not magic.
It is loops and state.
It is structured decisions.

Stop renting intelligence.
Start engineering it.
Master the fundamentals.

Link to repo (in the comments) 👇

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Ghadeer  Al Ruwaishedi

Document-to-Decision Architect | Building Enterprise AI Systems that Turn Documents into Trusted Decisions

🚨 BREAKING: Someone just open-sourced the Claude Code playbook engineers were gatekeeping.

If you’re building AI agents, stop what you’re doing.

This repo is insane.

I just stumbled on what might be the most valuable Claude Code resource on GitHub right now.

Not tutorials.
Not theory.

Actual production workflows.

Inside you’ll find:

→ Clean sub-agent architectures for real AI teams
→ The only MCP servers you actually need (DeepWiki, Context7, Playwright)
→ Battle-tested workflows from AI legends like Andrej Karpathy and Boris Cherny
→ Hidden slash commands that aren’t even in the docs

This isn’t just a repo.

It’s basically a blueprint for building your own AI workforce.

Instead of prompting one model…

You orchestrate specialized agents that collaborate on tasks.

Research agent.
Coding agent.
Testing agent.
Documentation agent.

All working together.

The crazy part?

You could spin up a fully customized AI team tonight.

No paid course.
No gatekeeping.

Just pure open-source gold.

I dropped the repo link in the comments 👇

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🚨BREAKING: MIT just killed the AI course industry. (Bookmark this 👇)

Most AI courses are overpriced

People feel behind on AI.
They buy expensive courses.
They still don’t know where to start.

MIT removed the confusion.

Here’s what they released for free.

1️⃣ AI 101
Learn AI from absolute zero.
Perfect for true beginners.
https://lnkd.in/dtJ47_Nk

2️⃣ Introduction to Deep Learning
Neural networks explained clearly.
Strong technical foundation.
https://lnkd.in/dV652vnD

3️⃣ Artificial Intelligence
How machines reason and learn.
Classic AI problem-solving.
https://lnkd.in/d8KJgztk

4️⃣ Introduction to Machine Learning
Core ML algorithms simplified.
Beginner-friendly approach.
https://lnkd.in/dCYxsqui

5️⃣ How to AI (Almost) Anything
Apply AI to art and music.
Hands-on creative projects.
https://lnkd.in/dFwdMnvD

6️⃣ Understanding the World Through Data
Decode reality with data.
ML meets real-world problems.
https://lnkd.in/d7V8anyu

7️⃣ Artificial Intelligence in K-12 Education
Foundations of AI explained.
Where education is heading.
https://lnkd.in/drhYa8Nj

8️⃣ Introduction to Algorithms
Learn to think efficiently.
Critical for serious AI work.
https://lnkd.in/dYGsgRPt

9️⃣ Foundation Models and Generative AI
Modern AI systems explained.
Yes, the tech behind ChatGPT.
https://lnkd.in/dFMym7EW

The best AI education is free.
The best source is MIT.
The only cost is effort.

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Ghadeer  A.

No alternative text description for this image

NVIDIA quietly dropped the best free AI curriculum available. I mapped the smartest path through it 👇

Most will still waste it.

Everyone is buying AI courses.
Stacking certificates.
Missing execution.

They learn tools.
They skip architecture.
Meetings expose the gap.

That’s not a skills problem.
It’s a direction problem.

Start with FOUNDATIONS.

Generative AI Explained
https://lnkd.in/gGzipKBq
Build real mental models first.

Building A Brain in 10 Minutes
https://lnkd.in/giHj9tCM
Understand how models are wired.

AI for All: From Basics to GenAI
https://lnkd.in/gGzipKBq
Connect basics to GenAI reality.

Then get TECHNICAL.

Getting Started with AI on Jetson Nano
https://lnkd.in/gbP9QUQZ
See how AI runs on the edge.

Introduction to AI in the Data Center
https://lnkd.in/gq4GZ5SP
Understand scale and infrastructure.

An Even Easier Introduction to CUDA
https://lnkd.in/gq4GZ5SP
Learn how GPUs do the work.

Accelerated Computing with CUDA C/C++
https://lnkd.in/g9F5aKzS
Performance changes outcomes.

Then focus on DEPLOYMENT.

NVIDIA NIM Microservices
https://lnkd.in/gJSZW6Dc
From model to production systems.

Generative AI with NeMo
https://lnkd.in/gFaDpab8
Enterprise-grade customization.

Finally, see REAL APPLICATION.

Generative AI in Digital Health
https://lnkd.in/gApPyj5W
Healthcare use cases in action.

AI Weather Models with NVIDIA Earth-2
https://lnkd.in/gCCtcUNt
AI at planetary scale.

The people getting promoted
aren’t the most certified.

They connect skills to systems.
They fix stalled pilots.
They explain execution clearly.

Pick one course. Finish it.
Tie it to business impact.

Ignorance is now optional.

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🚨 BREAKING: You can run Claude Code completely free now.

No API costs. No rate limits. 100% local on your machine.

Just Claude Code running locally fast, private, and 100% yours.

Here’s how to set up Claude Code on your own machine (free + fully private): 👇

STEP 1: Install Your Local AI Engine

Ollama runs AI models locally on Mac and Windows.

STEP 2: Download a Coding Model

Pick based on your hardware:

Powerful machine → qwen2.5-coder:32b
Regular laptop → qwen2.5-coder:7b
Lightweight → gemma:2b

STEP 3: Point Claude Code to Your Machine

This is the key step.

Set your base URL to local:

export ANTHROPIC_BASE_URL = http ://localhost:11434/v1

STEP 4: Start Coding Locally

Navigate to any project folder and run:

claude-code --model qwen2.5-coder:7b

Why this matters:

- No $20/month API costs
- No rate limits (run 24/7 if you want)
- Complete privacy (code never leaves your machine)
- Works offline after model download
- Customize with any Ollama-compatible model

This is how coding agents should work.

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🚨Most developers use Anthropic’s Claude. Almost none use it like this. 

This 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 file will make you 10x engineer 👇

It combines all the best practices shared by Claude Code creator:

Boris Cherny (creator of Claude Code at Anthropic) shared on X internal best practices and workflows he and his team actually use with Claude Code daily. 

Someone turned those threads into a structured 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 you can drop into any project.

It includes:
• Workflow orchestration
• Subagent strategy
• Self-improvement loop
• Verification before done
• Autonomous bug fixing
• Core principles

This is a compounding system. 

Every correction you make gets captured as a rule. 

Over time, Claude's mistake rate drops because it learns from your feedback.

If you build with AI daily, this will save you a lot of time.

Comment “BUILD”

And I’ll send you:

- The full CLAUDE.md file.
- A step-by-step install guide.
- How to wire it into any repo.
- How to use subagents properly.

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🚨BREAKING: Anthropic Drops AI Dev Gold For $0.00. Most devs will ignore this 👇

AI feels overwhelming.
Too many tools. No path.
Anthropic built one.

Claude Code mastery. 
Ship production-ready AI.
Build with real workflows.

API building simplified.
Auth, requests, scaling.
From prompt to product.

Python MCP deep dive.
Control context like a pro.
Smarter outputs. Less chaos.

Free courses.
Free certificates.
Massive career leverage.

Claude Code + APIs + MCP.
Anthropic just raised the bar.

Link to courses: https://lnkd.in/d6uDYTpX

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graphical user interface, text, application, chat or text message

🚨 BREAKING: An Anthropic hackathon winner just gave away his entire Claude Code system for $0.00.

He beat 100+ builders at the Anthropic × Forum Ventures hackathon.

Built zenith. chat in 8 hours using Claude Code.

Walked away with $15K in API credits.

Then did something even crazier.

He open-sourced the entire system he used.

Inside the repo:

→ 14+ agents
→ 56+ skills
→ 33+ commands
→ MCP configs, hooks, and rules
→ AgentShield security scanner
→ Continuous learning system
→ Full cross-platform support

50,000+ stars. MIT licensed.

One install command.

This is basically a plug-and-play AI agent infrastructure.

If you’re building with Claude Code or AI agents…

this is a goldmine.

I’ll send the repo + setup guide.

Comment “ZENITH” and follow me.

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undefined undefined

Anthropic

Stanford University just put the AI playbook online. Most people will never open it.

Everyone talks about AI.
Few study it seriously.
Even fewer build it.

The gap is widening fast.
Access is no longer the excuse.

Here is the 7-course roadmap 👇
From zero to AI engineer.
In the right order.

	1.	Artificial Intelligence (CS221)
Search, logic, planning, decisions.
The foundations of intelligent systems.
https://lnkd.in/gM892TxC

You learn how machines reason.
Not prompts. Principles.
	2.	Machine Learning (CS229)
Supervised vs unsupervised learning.
Core algorithms and intuition.
https://lnkd.in/gWgtbwZc

You stop copying tutorials.
You understand why models work.

	3.	Deep Learning (CS230)
Neural networks, CNNs, RNNs.
Practical training best practices.
https://lnkd.in/gYyD2Fwa

This is modern AI infrastructure.
Vision, speech, generation.

	4.	Machine Learning Theory (CS229M)
The math behind generalization.
Why models succeed or fail.
https://lnkd.in/gN4v2u9w

No more black boxes.
You see the mechanics.

	5.	Reinforcement Learning (CS234)
MDPs and Q-learning.
Systems that learn from actions.
https://lnkd.in/gKhtZYsV

Agents improve through feedback.
Like humans. At scale.

	6.	Natural Language Understanding (CS224U)
Meaning, intent, dialogue systems.
How machines interpret language.
https://lnkd.in/gg2BAW9k

Beyond autocomplete.
Into structured understanding.

	7.	NLP with Deep Learning (CS224N)
Translation, Q&A, summarization.
Sentiment and large language models.
https://lnkd.in/gWvnNXB2

This is how ChatGPT-class systems run.
Under the hood.

These are Stanford courses.
Now free. Online. Public.

The barrier is not money.
It is commitment.

Consumers scroll.
Engineers build.

Decide who you become in 2026.

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Stanford University

🚨10 GitHub repos with 600k+ combined stars. Most AI builders haven’t touched one

You save posts.
You bookmark threads.
You never build.

That is the problem.
Noise feels like progress.
But it builds nothing.

So I filtered the hype.
Only practical notebook-first repos.
Only structured learning paths.

Here are 10 that matter 👇:

	1.	microsoft/generative-ai-for-beginners
~105k stars. Structured GenAI course.
https://lnkd.in/daVemJpw

Full Microsoft curriculum in notebooks.
Build real GenAI apps step by step.

	2.	rasbt/LLMs-from-scratch
~83k stars. GPT built line by line.
https://lnkd.in/d8-e9pPs

Educational LLM implementation.
You understand transformers by coding.

	3.	microsoft/ai-agents-for-beginners
~49k stars. Agent systems explained.
https://lnkd.in/dyWxyqpR

Tools, memory, planning, workflows.
From theory to working agents.

	4.	microsoft/ML-For-Beginners
~83k stars. 26 ML lessons.
https://lnkd.in/dc-aSusG

Classic machine learning foundations.
Clear notebooks. Real exercises.

	5.	openai/openai-cookbook
~71k stars. Production patterns.
https://lnkd.in/dsCeebWa

Official API examples in notebooks.
Recipes you can ship to users.

	6.	jackfrued/Python-100-Days
~177k stars. Discipline over hype.
https://lnkd.in/dzJErdpW

100 days of Python exercises.
Brutal consistency builds fluency.

	7.	pathwaycom/llm-app
~54k stars. Real RAG systems.
https://lnkd.in/dpi3JaUc

Deployable LLM app templates.
Production-ready data pipelines.

	8.	jakevdp/PythonDataScienceHandbook
~46k stars. Data science core.
https://lnkd.in/dcKM-tQH

NumPy, Pandas, Matplotlib, SKLearn.
Foundations that never expire.

	9.	CompVis/stable-diffusion
~72k stars. Model-level insight.
https://lnkd.in/dkXWksnT

Original text-to-image codebase.
Learn diffusion by reading source.

	10.	facebookresearch/segment-anything
~53k stars. Vision breakthrough.
https://lnkd.in/d9uKEiku

Interactive image segmentation model.
Understand modern CV systems.

Most people consume AI content.
Few build with structured repos.
The difference compounds daily.

Serious builders study code.
Then they ship relentlessly.

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🚨 I regret paying for deep learning courses after finding this.

I’ve spent money on “premium” AI courses.
Some were good.
Most were… slides + buzzwords.

Then I found this full Deep Learning course by Professor Bryce.

And it exposed a hard truth:

Most people don’t struggle with deep learning 
They struggle with how it’s taught.

This course does what almost none do:

• Starts from first principles
• Explains the math visually (without ego)
• Connects why things work, not just how
• Covers the full stack: NN → CNN → RNN → LSTM → Attention → Transformers → GANs → AlphaGo
• Zero hype. Zero shortcuts. Pure understanding.

You don’t just learn models.
You learn how deep learning thinks.

If deep learning ever felt like:
“Magic”
“Black boxes”
“Copy-paste notebooks”

You skipped this layer.

And no amount of new tools will fix that.

This is the kind of foundation that:
• makes papers readable
• makes architectures intuitive
• makes you dangerous (in a good way)

It’s free.
It’s timeless.
And it’s better than most paid programs.

Link to playlist: https://lnkd.in/dajKU8Hk

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🔥Claude Code processed 10,000 files in minutes. I used to do this manually for weeks

Speed just replaced effort. 👇

Manual workflows do not scale.
Humans become the bottleneck.

Weeks lost to repetitive tasks.
Energy drained on low-leverage work.
Momentum destroyed by busywork.

Let agents handle the execution.
You focus on direction.

1️⃣ Total system authority

Claude Code works inside your machine.
Not trapped in a chat window.

It runs shell commands natively.
It manages repos and dependencies.
It reads and rewrites entire directories.

2️⃣ Your stack. One interface.

No more jumping between tools.
Everything connects at the terminal.

Update repos and pull requests.
Sync tickets and notify teams.
Move from docs to deployment instantly.

3️⃣ Speed compounds daily

Top builders remove friction.
They engineer momentum.

Switch models based on workload.
Compress context when needed.
Stop processes without losing state.

4️⃣ Memory that persists

Context no longer resets.
It evolves with your project.

Define build and test standards.
Lock in your code patterns.
Document structure once, reuse forever.

5️⃣ Prompt with precision

Clear constraints drive clean output.
Vague input creates chaos.

Break work into stages.
Approve plans before execution.
Chain analysis into action.

6️⃣ The new reality

Engineering is orchestration now.
Syntax is automated.

Stop being the typist.
Start being the architect.

Leverage beats effort.
Direction beats repetition.
Speed beats grind.

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You’re NOT behind on AI. You just haven’t seen this list yet. (Bookmark this 👇)

Harvard. Google. Anthropic. MIT.

All free.
All in one place.
All starting today.

Most people scroll past gold like this.

The ones who save it?
They’re the ones getting promoted in 12 months.

Here’s the full roadmap. Beginner to expert. $0.

🟢 BEGINNER — Start here. No excuses.

→ Anthropic Academy: Claude 101
https://lnkd.in/d6uDYTpX

→ Google AI Essentials
https://grow.google/ai

→ AWS Generative AI Essentials
https://lnkd.in/djzk-_jY

→ Elements of AI (University of Helsinki)
https://lnkd.in/dzgMRKCU

🟡 INTERMEDIATE — Build real skills.

→ DeepLearning.AI Short Courses
https://lnkd.in/dXjnT-tZ

→ Harvard CS50: AI with Python
https://lnkd.in/dncUUZss

→ Hugging Face AI Agents Course
https://lnkd.in/dPSAKrz7

🔴 EXPERT — Separate yourself.

→ Anthropic: Claude Code in Action
https://lnkd.in/d6uDYTpX

→ Practical Deep Learning for Coders (Fast.ai)
https://course.fast.ai

→ Google Generative AI Learning Path
https://lnkd.in/d5i2A-Q3

AI will not replace you.

But the person who spent their Sunday on this list?

They will.
Save this post.
Tag someone who keeps saying “I’ll start learning AI soon.”

Soon is today.

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🚨BREAKING: GitHub is the new university.
Most people are still paying for the old one

Degrees won’t teach modern AI.

AI moves faster than classrooms.
Courses are outdated on arrival.

Theory without building fails.
Real skill comes from shipping.

These repos teach the real game👇:

1.	OpenAI Cookbook
https://lnkd.in/dsCeebWa
Real examples using modern AI APIs.
Production patterns you can copy.

2.	OpenAI Agents SDK
https://lnkd.in/daunkrqS
Learn to build real AI agents.
Tools, orchestration, workflows.

3.	OpenAI Evals
github.com/openai/evals
Measure model performance properly.
Serious AI builders test everything.

4.	PydanticAI
https://lnkd.in/dThGCfFu
Structured outputs for LLM apps.
Cleaner, safer AI pipelines.

5.	Hugging Face Agents Course
https://lnkd.in/d3KW4c5K
Step-by-step agent building course.
Learn by shipping real projects.

6.	AI for Beginners
https://lnkd.in/e3nY2tbb
Microsoft’s full AI learning path.
Concepts explained with projects.

7.	Hugging Face 101 Course
https://lnkd.in/dcB786Mc
The fastest intro to the HF stack.
Models, datasets, and tooling.

8.	Hugging Face Smol Course
https://lnkd.in/dqpXjDJd
Learn small model development.
Perfect for efficient AI systems.

9.	AI Engineer Handbook
https://lnkd.in/dgqyfiR8
Covers the modern AI stack.
RAG, agents, evaluation, deployment.

10.	AI Engineering Field Guide
https://lnkd.in/dbUPTMv3
Practical patterns from real teams.
What AI engineers actually ship.

The best AI education is public.
You just have to build.

Bookmark this list.
Start shipping real projects.

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DeepSeek AI didn’t build a better AI. They stole one. Anthropic just released the receipts 👇

Silicon Valley applauded.

We worship speed.
We ignore sourcing.

24,000 fake accounts at scale.
That is not growth.

16 million prompts harvested.
That is not efficiency.

Claude’s best capabilities targeted.
Call it what it is.

The timeline cheered.
Investors celebrated.
No one asked questions.

Burn the illusion.

A dev responded differently.
They released 155k of their own
Claude Code conversations.

Generated with Opus 4.5.
On purpose. In public.

Then open sourced the tooling.
Export your own data.
Redact sensitive details.

Make it searchable.
Upload it to Hugging Face.
Audit what trains the models.

If AI feeds on us,
we audit the feast.

This is not about drama.
It is about POWERRR.

Data is leverage.
Leverage decides winners.

Comment “LIBERAL”
and I’ll send the steps
to liberate your own data.

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> Claude is FREE
> ChatGPT is FREE
> Gemini is FREE
> Grok is FREE
> Perplexity is FREE
> DeepSeek is FREE
> n8n is FREE (self-hosted)
> LangChain is FREE
> LangGraph is FREE
> CrewAI is FREE
> Hugging Face is FREE
> LlamaIndex is FREE
> Ollama is FREE
> Cursor is FREE (starter)
> VS Code is FREE
> GitHub is FREE
> Replit is FREE
> Supabase is FREE
> Vercel is FREE
> Cloudflare Workers is FREE
> Zapier is FREE (starter)
> Make. com is FREE (starter)
> Notion is FREE
> Google Colab is FREE
> Kaggle Notebooks is FREE
> Youtube is FREE

You have access to the exact same tools used by the engineers building frontier AI.

The gap between you and them isn’t money.

It’s 90 days of focused execution.

And yes…

your first weeks will probably look like this 👇

Everyone goes through this phase.

The difference between observers and builders is simple:

They kept going.

Start today.

Meme credit Eduardo Ordax 

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95% of “AI agents” aren’t agents. 
They’re prompt wrappers with dreams.

Real autonomy isn’t magic.
It’s layers. And almost everyone is missing three of them.

Here’s the stack the top 3% of builders actually use: 👇

🔹Layer 1: LLMs — The Raw Intelligence

Great at language. Useless at autonomy.
	•	Tokenization + inference
	•	Prompt shaping
	•	Direct API access

This is where most teams stop — then wonder why nothing is reliable.

🔹Layer 2: AI Agents — Intelligence That Acts

A brain finally gets hands.
	•	Tools + function calling
	•	CoT / ReAct reasoning
	•	Task decomposition
	•	Long-term memory

One agent is smart. But one agent alone can’t run a real workflow.


🔹Layer 3: Agentic Systems — Intelligence That Cooperates

This is where autonomy becomes repeatable.
	•	Multi-agent communication
	•	Roles + specialization
	•	Routing + scheduling
	•	Multi-agent RAG for shared context

This is the leap from “bot” → “operation.”

🔹Layer 4: Agentic Infrastructure — Intelligence That Survives Reality

The layer everyone forgets… until their system fails.
	•	Observability + traceability
	•	Error handling + retries
	•	Security + compliance
	•	Human-in-the-loop control
	•	Workflow automation

Without this layer?
Your agents are demos.
With it?
Your agents become deployable systems.

Most teams think they’re building agents.
They’re not.
They’re stacking prompts and praying.

Real autonomy lives at Layer 4.
Real reliability comes from architecture, not clever prompting.

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🚨BREAKING: Qwen3.5 Small just made your GPU bill a product decision, not a technical one 

Big models are officially on notice.

Compute costs are exploding.
Edge AI felt impossible.
Small models meant compromise.

Not anymore.

Meet Qwen 3.5 Small.
0.8B. 2B. 4B. 9B.
Same Qwen3.5 foundation.

Native multimodal built in.
Improved architecture.
Scaled RL for real reasoning.

0.8B / 2B → tiny and fast.
Made for edge devices.
Low latency. Low burn.

4B → lean agent base.
Multimodal and production-ready.
Way stronger than it looks.

9B → compact powerhouse.
81.7 on GPQA.
87.7 on OmniDocBench.

84.5 on Video-MME.
Closing the gap with giants.
Without giant infrastructure.

This is the shift.
Efficiency is strategy.
Performance per dollar wins.

Small is no longer cute.
Small is competitive.

Check it out links in the comments 👇

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🚨BREAKING: A $380B company just proved your marketing team is too big 

One person ran growth.

Most companies build bigger teams.
More managers.
More dashboards.

But execution keeps slowing.

Anthropic tried something different.

For 10 months

Their entire growth marketing team = 1 person.

Paid search.
Paid social.
SEO.
Email.
App stores.

Here’s the exact system he used 👇

Step 1 — Export your ads

Download every ad as a CSV.

Include:

CTR
Conversions
Spend

Upload it to Claude.

Claude instantly finds weak ads
and generates better variations.

Step 2 — Split AI into specialists

Agent #1 → Headlines only
(max 30 characters)

Agent #2 → Descriptions only
(max 90 characters)

Now you have hundreds of ideas.

Step 3 — Automate the creatives

He built a Figma plugin.

It pulls the copy.
Finds templates.
Generates full ads automatically.

100 ads in 0.5 seconds.

Step 4 — Connect Claude to Meta

He built an MCP server.

Claude reads the Meta Ads API.

Ask:

“Which ads converted best?”
“Where am I wasting spend?”

Claude answers instantly.

Step 5 — Add memory

Every test gets logged.

Next ads automatically use
what worked before.

The system improves every cycle.

The results

Ad creation:
2 hours → 15 minutes.

10x more experiments.

One marketer.
One AI system.
A $380B company.

This is AI-native marketing

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♻️ Repost to help others in your network
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Claude_code_guide

🚨عــاجــل Qwen3.5 Small خلّى فاتورة الGPU
قرار منتج مو صداع تقني.

النماذج الضخمة؟
صار عليها ضغط رسميًا.

تكاليف الحوسبة قاعدة تولّع.
Edge AI كان شكله مستحيل
والنماذج الصغيرة كانت دايم تعني “أداء أقل”.

مو بعد اليوم.

تعرّفوا على Qwen 3.5 Small:
0.8B · 2B · 4B · 9B
بنفس أساس Qwen3.5 القوي.

متعدد الوسائط من الأساس.
معمارية أقوى.
وتعزيز بالتعلم المعزز عشان استدلال فعلي مش مجرد كلام.

0.8B / 2B → صغير وسريع جدًا.
مثالي للأجهزة الطرفية.
Latency قليل. تكلفة تشغيل قليلة.

4B → قاعدة خفيفة لبناء Agents بجد.
Multimodal وجاهز للإنتاج.
أقوى بكثير مما تتوقع.

9B → قوة مدمجة.
81.7 على GPQA
87.7 على OmniDocBench
84.5 على Video-MME
يقرب من الكبار
بس بدون بنية تحتية تكسّر ميزانيتك.

هنا التحول الحقيقي:
الكفاءة صارت استراتيجية.
الأداء مقابل كل دولار هو اللعبة.

الصغير مو “كيوت” بعد.
الصغير صار منافس

الرابط في التعليقات 👇

➕ تابعوا Ghadeer لمزيد من رؤى الذكاء الاصطناعي
♻️ أعد نشرها لتفيد شبكتك
📩 احفظها للرجوع لاحقًا

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