Andy Nguyen

yêu cái đẹp

[Andy Nguyen @andypodmaster]

@GuntherEagleman @grok tin này có đúng không

[Andy Nguyen @andypodmaster]

@GlobalDiss @grok thông tin này có đúng không

[taghi molavi تقی مولوی @taqimolavi]

آندری کارپاتی، یکی از هم‌بنیان‌گذاران OpenAI، تقریباً تمام چیزهایی رو که درباره ساخت هوش مصنوعی می‌دونه، رایگان روی GitHub منتشر کرده!

از ساخت GPT از صفر تا هوش مصنوعی‌ای که خودش آزمایش می‌کنه، تحقیق می‌کنه و از نتایجش یاد می‌گیره.

۶ پروژه با مجموع بیش از ۳۰۰ هزار ستاره ⭐

۱. Autoresearch | 92.7K ⭐
هوش مصنوعی‌ای که شب تا صبح خودش آزمایش انجام می‌ده، نتایج رو بررسی می‌کنه و فقط راهکارهایی رو نگه می‌داره که واقعاً جواب داده‌اند.
https://t.co/DELRy7LXDV

۲. nanoGPT | 64.4K ⭐
یاد بگیر چطور یک مدل GPT رو از صفر آموزش بدی؛ پروژه‌ای که برای خیلی‌ها نقطه ورود به دنیای LLM بود.
https://t.co/eS6fGsGNa7

۳. nanochat | 60.2K ⭐
با حدود ۱۰۰ دلار، نسخه شخصی خودت از ChatGPT رو بساز.
https://t.co/WlW6q9FMFA

۴. LLM Wiki | 50K ⭐
ایده‌ای ساده اما قدرتمند برای ساخت یک دانشنامه شخصی و قابل توسعه برای هوش مصنوعی؛ چیزی شبیه مغز دوم.
https://t.co/Z1KSvcwOCg

۵. llm.c | 31.4K ⭐
آموزش یک مدل زبانی با زبان C؛ بدون پنهان کردن پیچیدگی‌های فنی پشت فریم‌ورک‌های آماده.
https://t.co/TViNK3jeLu

۶. micrograd | 18.7K ⭐
در چند صد خط کد بفهم شبکه‌های عصبی چطور یاد می‌گیرن و گرادیان‌ها چطور کار می‌کنن.
https://t.co/Nprbsq46tS

پیشنهاد من:

از micrograd شروع کن.

بعد برو سراغ nanoGPT و nanochat.

این مسیر کمک می‌کنه به‌جای اینکه فقط با ابزارهای AI کار کنی، بفهمی پشت پرده واقعاً چه اتفاقی می‌افته.

تفاوت استفاده‌کننده از هوش مصنوعی با کسی که می‌تونه سیستم‌های هوشمند بسازه، دقیقاً از همین‌جا شروع می‌شه.

این ۶ ریپازیتوری رو ذخیره کن؛ گنجینه‌ای از دانش رایگان‌اند.

[distort @distortgeekin]

Everyone builds the agents first, then wonders why they step on each other

Draw the work first: steps, handoffs, decisions, stops, then count the agents

5 shapes, the Anthropic vs Cognition fight and a GRAPH.md you can copy ↓ https://t.co/jZut91W71X

[Dvz | Python Architect @PythonDvz]

An AI Workflow with fixed steps beats an autonomous AI Agent in 90% of real apps, yet devs force Agents.

[Jithin V Raj 💎 @jithinvraj]

AI Agent Architecture
(Only for Reference & Research)

[Kafadzhy @X1Kafadzhy]

CLAUDE CODE DESIGNED ALL OF THIS. NO DESIGNER, NO FIGMA

BOOKMARK THIS POST

the 12 skills behind it, with links:

TASTE
┣ ui-ux-pro-max · styles, palettes and font pairs that match (147K★)
┃ ┗ https://t.co/1VZWcTyov3
┣ awesome-design-md · drop in a brand's DESIGN.md, get its look (126K★)
┃ ┗ https://t.co/5gYc8SKsK7
┗ nothing-design · monochrome, type-first UI in one skill (2.9K★)
┗ https://t.co/wl2S29dtMU

BUILD
┣ frontend-design · official. anthropic's own UI skill (190K★)
┃ ┗ https://t.co/HJ8BIMsn4x
┣ stitch-skills · by Google Labs. screens straight from Stitch (8.7K★)
┃ ┗ https://t.co/PD0Qrq4UFe
┗ oil-ui · 3 different directions before it picks one (3K★)
┗ https://t.co/uqgMALacdH

POLISH
┣ impeccable · /polish /bolder /critique on any page (107K★)
┃ ┗ https://t.co/Kxbn9AJJ23
┣ unslop · turns vibecoded UI into a designed one (12.5K★)
┃ ┗ https://t.co/EZxWg4qEZr
┣ hallmark · design that refuses to look AI-made (51K★)
┃ ┗ https://t.co/AsZnjJLKsO
┗ emil kowalski skills · gestures and motion from the sonner author (55K★)
┗ https://t.co/cJ0WWxomVp

BRAND
┣ logo-design-skill · real logo process, not clipart (6.4K★)
┃ ┗ https://t.co/Q1yq1r9p4O
┗ extract-design-system · steals colors, fonts, spacing from any site
┗ https://t.co/3nmxFIvOCI

start here:
npx skills add pbakaus/impeccable
then type /critique on your ugliest page

[niko. @nikosolenne]

Si eres diseñador UI/UX, guárdate esto.

1. https://t.co/BsPEDrxmvU → más de 600 prompts de diseño y 200+ componentes con código completo para webs 3D y con scroll
2. https://t.co/1hM8CzxUpJ → 117 shaders animados en 11 estilos
3. https://t.co/FzDq5KQXiv → footers mejores
4. https://t.co/z9rlJ3109Y → inspiración para CTAs
5. https://t.co/zXbLwxoWZu → estilos de productos reales

¿Qué me he dejado?

[摸鱼巨匠🔨 @ChatGptAstra]

你的网页UI做得烂，不是你水平低，而是你没跟AI说清楚你的需求！

真正限制你的，不是想象力，而是你压根不知道怎么描述你要实现的效果。

这个项目，收录了192个带专业名称的效果库，每个名称还配有动态示例，让你直接点击Copy Prompt，即可一键让AI帮你实现你想要的效果。

落地页特效、动效、镜头移动、摄影风格等方向，全都有作业可抄！

1. 项目地址：
https://t.co/cOF5clwOrw

2. 动态画廊，所有效果均可实时播放：
https://t.co/RJz8qh3kMt

3. 也可作为 Skill 直接给 Agent 调用，让它直接帮你改好：
npx skills add showheyohtaki/motion-prompt

[Nikki Siapno @NikkiSiapno]

𝗠𝗖𝗣 𝘃𝘀 𝗥𝗔𝗚 𝘃𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀.

𝗠𝗖𝗣 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗹𝗮𝘆𝗲𝗿. It gives AI systems a standard way to connect to external systems and capabilities. MCP servers expose tools for querying databases, calling APIs, or taking actions, without requiring custom integrations for every client. But it handles connectivity, not retrieval.

That's where 𝗥𝗔𝗚 comes in. It searches your docs at query time, pulling relevant chunks into the prompt so answers can draw from your data. Keeping it current means updating an index, not retraining the model. In a basic RAG pipeline, the flow is straightforward: retrieve, then answer.

Now with 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀, the model runs in a loop: plan a step, call a tool, read the result, repeat until the goal is met. That handles multi-step tasks, at the cost of more latency, tokens, and failure points.

So the choice comes down to the gap you're closing: access to systems, knowledge from your data, or action over many steps.

But executing a task successfully doesn't necessarily mean it's still the right task.

Priorities shift, budgets change, and teams move between projects. Both humans and AI agents can keep executing work that no longer aligns with the bigger plan.

The problem is that strategy, capacity, spend, and execution data often live in different systems. By the time a quarterly review reveals the gap between plan and reality, time and budget have already been spent.

Catching that drift early means continuously connecting what was planned with the work actually happening, across both human teams and AI agents.

Tempo Loop brings together time, capacity, spend, and execution data in one unified view across human and AI-agent work.

It answers a simple question without chasing status updates: Are we still working on the right things?

When something drifts, Loop identifies the gap, recommends corrective actions with tradeoff context, and orchestrates approved changes back into the systems teams already use, like Jira.

𝗔𝗴𝗲𝗻𝘁𝘀 𝗹𝗼𝗼𝗽 𝗼𝗻 𝘁𝗮𝘀𝗸𝘀. 𝗧𝗵𝗶𝘀 𝗼𝗻𝗲 𝗹𝗼𝗼𝗽𝘀 𝗼𝗻 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝗲𝘀.

𝗦𝗲𝗲 𝗵𝗼𝘄 𝗧𝗲𝗺𝗽𝗼 𝗟𝗼𝗼𝗽 𝘄𝗼𝗿𝗸𝘀 → https://t.co/gG53bFKIYa

What else would you add?

——

♻️ Repost to help others learn AI.
🙏 Thanks to @TempoHQ for sponsoring this post.
➕ Follow me ( Nikki Siapno ) to improve at AI and system design.

[Amir Ansari @AamirAnsar94694]

𝗛𝗢𝗪 𝗧𝗢 𝗕𝗨𝗜𝗟𝗗 𝗔𝗡 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧 𝗙𝗥𝗢𝗠 𝗦𝗖𝗥𝗔𝗧𝗖𝗛 🤖

Building an AI agent is more than connecting an LLM to a few tools.

You need the right architecture, memory, integrations, orchestration, and testing to make an agent actually useful.

Here are 8 essential steps to build a reliable AI agent.

1. Define Purpose & Scope — Start With the Goal

Before building anything, identify what your agent needs to accomplish.

- Define the use case.
- Understand user requirements.
- Set success criteria.
- Identify limitations.

A clear goal prevents unnecessary complexity.

2. System Prompt Design — Give Your Agent Direction

The system prompt defines how your agent behaves and operates.

Include:

- Goals and responsibilities.
- Role and personality.
- Instructions and expected outputs.
- Safety rules and guardrails.

3. Choose an LLM — Select the Brain

Your model determines how effectively the agent understands instructions and solves problems.

Consider:

- Model capabilities.
- Context window.
- Response quality.
- Cost and latency.

4. Tools & Integrations — Give It Capabilities

Tools allow an agent to interact with external systems and perform real tasks.

Examples:

- APIs and web applications.
- MCP servers.
- Custom functions.
- Local tools and integrations.

5. Memory Systems — Help It Remember Context

Memory allows agents to retain useful information across interactions.

Important components:

- Episodic memory.
- Working memory.
- Vector databases.
- SQL databases.
- File storage.

6. Orchestration — Connect Everything

Orchestration manages how different components work together.

It handles workflows, triggers, task routing, message queues, error handling, and agent-to-agent communication.

7. User Interface — Make It Accessible

Give users a simple way to interact with your agent.

Possible interfaces include:

- Chat interfaces.
- Web applications.
- API endpoints.
- Slack or Discord bots.

8. Testing & Evaluations — Make It Reliable

A working agent isn't necessarily a good agent.

Test its performance through:

- Unit testing.
- Latency testing.
- Output quality metrics.
- Continuous evaluation and improvement.

𝗪𝗵𝘆 𝗗𝗼𝗲𝘀 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿?

A well-designed AI agent can go beyond answering questions.

It can understand goals, remember context, use tools, execute workflows, and deliver meaningful results.

The real power of AI agents comes from building the right system around the model.

Follow @AamirAnsar94694 for more AI tools & tech updates.

#AI #AIAgents #AgenticAI #ArtificialIntelligence #Automation #LLM #MCP #Tech

[Artificial Intelligence @AIGuideHQ]

Most people learn prompts.

Very few learn AI Engineering.

Here's the complete AI Engineering Master Tree

[santi @santtiagom_]

la mejor forma de aprender sobre agentes es hacer uno.

un proyecto para arrancar:
crear un agente que busque oportunidades laborales según tu perfil.

podés construirlo de a poco e incorporar un concepto en cada paso. apoyate en Claude para implementarlo y pedile que te explique qué hace cada parte antes de avanzar.

1) primero, una llamada al modelo

> pasale tu CV y una oferta copiada a mano. pedile que compare los requisitos con tu experiencia y explique si encajás. acá estás probando si puede resolver la parte central del problema.

2) separá instrucciones y contexto

> las instrucciones definen cómo trabajar: “no inventes información y señalá los requisitos que no cumplo”. el contexto es lo que necesita saber: tu experiencia y preferencias, como trabajar remoto desde X lugar, etc.

3) creá las tools

> para encontrar ofertas actuales, necesita consultar información externa en la web. creá funciones para buscar ofertas y leer publicaciones. exponelas al modelo como tools, con una descripción de qué hacen y qué parámetros reciben.

4) cerrá el loop

> devolvé el resultado de cada tool al modelo para que decida el siguiente paso: consultar otra página, cambiar la búsqueda o responder.

podés usar el Agents SDK de OpenAI para manejar este ciclo.

5) agregá límites y revisá los resultados

> configurá en código un límite de turnos y restringí las acciones disponibles. buscar ofertas no implica tener permiso para enviar tu CV.

probalo con publicaciones reales y revisá las recomendaciones cumplen los criterios que definiste. guardá los casos donde falla para repetirlos después de cada cambio.

con esto ya practicás modelo, instrucciones, contexto, tools y loop mientras construís algo que podés usar.

más adelante podés sumar evals para medir si los cambios mejoran los resultados, o subagentes que investiguen distintas empresas en paralelo.

son formas de extender el proyecto cuando ya tengas la base funcionando.

[Whitemon @0xWhitemon]

Harness, loop and graph could be the missing layers between AI models and agents that actually work.

Same model, same prompt, completely different results.

In this article, I show you how to build all three layers. https://t.co/W99aPfcSub

[Anurag Jha @anuragdotdev]

AI ENGINEER ROADMAP
│
├── 1. Programming Fundamentals
│ ├── Python
│ │ ├── Variables & Data Types
│ │ ├── Functions & Modules
│ │ ├── OOP & Exception Handling
│ │ ├── List & Dictionary Comprehensions
│ │ ├── Iterators & Generators
│ │ ├── Decorators & Context Managers
│ │ ├── Asyncio & Concurrency
│ │ ├── Type Hints & Pydantic
│ │ └── Virtual Environments & Packaging
│ ├── Data Structures & Algorithms
│ ├── Git & GitHub
│ ├── Linux & Command Line
│ ├── HTTP & Networking Fundamentals
│ ├── REST APIs
│ └── JSON, YAML & Serialization
│
├── 2. Mathematics & ML Foundations
│ ├── Linear Algebra
│ │ ├── Vectors & Matrices
│ │ ├── Dot Products
│ │ ├── Matrix Multiplication
│ │ └── Eigenvalues & Eigenvectors
│ ├── Calculus
│ │ ├── Derivatives
│ │ ├── Partial Derivatives
│ │ ├── Gradients
│ │ └── Chain Rule
│ ├── Probability & Statistics
│ │ ├── Probability Distributions
│ │ ├── Bayes' Theorem
│ │ ├── Mean & Variance
│ │ └── Statistical Inference
│ ├── Machine Learning Fundamentals
│ │ ├── Supervised & Unsupervised Learning
│ │ ├── Training & Testing
│ │ ├── Overfitting & Underfitting
│ │ ├── Loss Functions
│ │ └── Model Evaluation
│ └── Deep Learning Fundamentals
│ ├── Neural Networks
│ ├── Backpropagation
│ ├── Gradient Descent
│ └── Activation Functions
│
├── 3. Data Engineering & Processing
│ ├── NumPy
│ ├── Pandas
│ ├── Data Cleaning
│ ├── Data Transformation
│ ├── Exploratory Data Analysis
│ ├── SQL & PostgreSQL
│ ├── JSON & JSONL
│ ├── CSV, XML & Parquet
│ ├── Data Pipelines
│ ├── Web Scraping
│ ├── Data Validation
│ └── Dataset Preparation
│
├── 4. Deep Learning & Neural Networks
│ ├── Perceptrons
│ ├── Feedforward Neural Networks
│ ├── Forward & Backpropagation
│ ├── Loss Functions
│ ├── Optimizers
│ ├── CNNs & RNNs
│ ├── LSTMs & GRUs
│ ├── Attention Mechanisms
│ ├── Transformer Architecture
│ ├── Embeddings
│ └── Transfer Learning
│
├── 5. Deep Learning Frameworks
│ ├── PyTorch
│ │ ├── Tensors
│ │ ├── Autograd
│ │ ├── nn.Module
│ │ ├── DataLoaders
│ │ ├── Custom Training Loops
│ │ ├── GPU Training
│ │ └── Model Checkpointing
│ ├── TensorFlow & Keras Basics
│ ├── Hugging Face Ecosystem
│ │ ├── Transformers
│ │ ├── Datasets
│ │ ├── Tokenizers
│ │ └── Accelerate
│ └── CUDA Fundamentals
│
├── 6. LLM Fundamentals
│ ├── What Are Large Language Models?
│ ├── Transformer Architecture
│ ├── Encoder & Decoder Models
│ ├── GPT Architecture
│ ├── BERT Architecture
│ ├── Tokenization
│ ├── Vocabulary & Token IDs
│ ├── Context Windows
│ ├── Positional Encoding
│ ├── Self-Attention & Multi-Head Attention
│ ├── Pretraining & Post-Training
│ ├── Instruction Tuning
│ ├── RLHF & DPO
│ ├── Model Parameters
│ └── Open-Source vs Proprietary Models
│
├── 7. LLM APIs & Model Integration
│ ├── OpenAI API
│ ├── Anthropic API
│ ├── Google Gemini API
│ ├── Open-Weight Models
│ ├── API Authentication
│ ├── Streaming Responses
│ ├── Structured Outputs
│ ├── Function & Tool Calling
│ ├── Multimodal APIs
│ ├── Error Handling & Retries
│ ├── Rate Limits
│ ├── Token Usage & Cost Tracking
│ └── Model Routing & Fallbacks
│
├── 8. Prompt & Context Engineering
│ ├── System & User Instructions
│ ├── Few-Shot Prompting
│ ├── Chain-of-Thought Concepts
│ ├── Structured Prompting
│ ├── Prompt Templates
│ ├── Context Window Management
│ ├── Context Compression
│ ├── Context Selection & Retrieval
│ ├── Prompt Versioning
│ ├── Prompt Injection Defense
│ ├── Context Engineering
│ └── Prompt Evaluation
│
├── 9. Embeddings & Vector Databases
│ ├── What Are Embeddings?
│ ├── Dense & Sparse Embeddings
│ ├── Embedding Models
│ ├── Similarity Metrics
│ ├── Cosine Similarity
│ ├── Semantic Search
│ ├── Vector Indexing
│ ├── Approximate Nearest Neighbors
│ ├── Vector Databases
│ │ ├── Pinecone
│ │ ├── Qdrant
│ │ ├── Weaviate
│ │ ├── Milvus
│ │ └── Chroma
│ ├── Metadata Filtering
│ └── Hybrid Search
│
├── 10. Retrieval-Augmented Generation (RAG)
│ ├── RAG Architecture
│ ├── Document Ingestion
│ ├── Document Parsing
│ ├── Text Chunking Strategies
│ ├── Overlapping Chunks
│ ├── Embedding Generation
│ ├── Vector Storage
│ ├── Query Transformation
│ ├── Retrieval Strategies
│ ├── Reranking
│ ├── Hybrid Retrieval
│ ├── Contextual Retrieval
│ ├── Parent-Child Retrieval
│ ├── Multi-Query Retrieval
│ ├── Graph RAG
│ ├── Agentic RAG
│ ├── RAG Evaluation
│ └── Advanced RAG Optimization
│
├── 11. AI Agents & Agentic Systems
│ ├── What Are AI Agents?
│ ├── Agent Architecture
│ ├── Reasoning & Planning
│ ├── Tool Calling
│ ├── Function Execution
│ ├── Short-Term & Long-Term Memory
│ ├── Agent State Management
│ ├── Workflow Orchestration
│ ├── Single-Agent Systems
│ ├── Multi-Agent Systems
│ ├── Human-in-the-Loop
│ ├── Reflection & Self-Correction
│ ├── Planning & Execution Loops
│ ├── Agent Reliability
│ ├── Agent Evaluation
│ └── Agent Security
│
├── 12. AI Frameworks & Orchestration
│ ├── LangChain
│ ├── LangGraph
│ ├── LlamaIndex
│ ├── Haystack
│ ├── Semantic Kernel
│ ├── Pydantic AI
│ ├── DSPy
│ ├── CrewAI
│ ├── AutoGen
│ ├── Workflow Orchestration
│ └── Framework Selection & Tradeoffs
│
├── 13. Model Fine-Tuning & Adaptation
│ ├── Pretrained Models
│ ├── Supervised Fine-Tuning (SFT)
│ ├── Parameter-Efficient Fine-Tuning
│ ├── LoRA
│ ├── QLoRA
│ ├── PEFT
│ ├── Dataset Preparation
│ ├── Synthetic Data Generation
│ ├── Instruction Tuning
│ ├── DPO
│ ├── Fine-Tuning Evaluation
│ └── When to Fine-Tune vs RAG
│
├── 14. Multimodal AI Engineering
│ ├── Vision-Language Models
│ ├── Image Understanding
│ ├── OCR & Document Intelligence
│ ├── Speech-to-Text
│ ├── Text-to-Speech
│ ├── Audio Understanding
│ ├── Video Understanding
│ ├── Multimodal Embeddings
│ ├── Image Generation Models
│ ├── Diffusion Models
│ └── Multimodal RAG
│
├── 15. AI Evaluation & Testing
│ ├── Why AI Evaluation Matters
│ ├── Unit & Integration Testing
│ ├── LLM-as-a-Judge
│ ├── Golden Datasets
│ ├── Evaluation Datasets
│ ├── Model Accuracy
│ ├── Faithfulness & Groundedness
│ ├── Relevance & Context Precision
│ ├── Hallucination Detection
│ ├── RAG Evaluation
│ ├── Agent Evaluation
│ ├── Human Evaluation
│ ├── Regression Testing
│ ├── Online & Offline Evaluation
│ └── Evals & Observability
│
├── 16. AI Infrastructure & Inference Engineering
│ ├── LLM Inference Fundamentals
│ ├── Prefill vs Decode
│ ├── KV Cache Management
│ ├── Continuous Batching
│ ├── PagedAttention
│ ├── Speculative Decoding
│ ├── Quantization
│ ├── Model Pruning
│ ├── Knowledge Distillation
│ ├── Model Serving
│ ├── vLLM
│ ├── SGLang
│ ├── Ollama
│ ├── TensorRT-LLM
│ ├── GPU Memory Management
│ ├── Latency & Throughput Optimization
│ └── Distributed Inference Basics
│
├── 17. Backend Engineering for AI
│ ├── FastAPI
│ ├── Flask
│ ├── REST APIs
│ ├── WebSockets & Streaming
│ ├── Async Processing
│ ├── Redis & Caching
│ ├── PostgreSQL
│ ├── Background Jobs
│ ├── Message Queues
│ ├── Authentication & Authorization
│ ├── Rate Limiting
│ ├── API Security
│ ├── Microservices Basics
│ └── AI Application Architecture
│
├── 18. MLOps & LLMOps
│ ├── ML & LLM Lifecycle Management
│ ├── Experiment Tracking
│ ├── MLflow
│ ├── Weights & Biases
│ ├── Langfuse
│ ├── LangSmith
│ ├── Prompt Versioning
│ ├── Model Versioning
│ ├── Data Versioning (DVC)
│ ├── Tracing & Observability
│ ├── Cost Monitoring
│ ├── Model Monitoring
│ ├── CI/CD for AI Applications
│ └── Automated Evaluation Pipelines
│
├── 19. Cloud & DevOps
│ ├── AWS
│ │ ├── S3
│ │ ├── EC2
│ │ ├── Lambda
│ │ ├── ECS & EKS
│ │ └── SageMaker Basics
│ ├── Google Cloud & Vertex AI
│ ├── Microsoft Azure AI
│ ├── Docker
│ ├── Kubernetes
│ ├── GitHub Actions
│ ├── CI/CD Pipelines
│ ├── Terraform
│ ├── GPU Cloud Infrastructure
│ └── Deployment & Scaling
│
├── 20. AI Security & Responsible AI
│ ├── Prompt Injection
│ ├── Jailbreak Attacks
│ ├── Data Leakage
│ ├── Sensitive Data Protection
│ ├── PII Detection & Redaction
│ ├── Tool Execution Security
│ ├── Sandboxed Execution
│ ├── Model Supply Chain Security
│ ├── Adversarial Attacks
│ ├── Bias & Fairness
│ ├── Explainability
│ ├── AI Governance
│ └── Guardrails & Content Safety
│
├── 21. AI System Design & Architecture
│ ├── Designing Scalable AI Applications
│ ├── RAG System Design
│ ├── Agent System Design
│ ├── Model Routing Architecture
│ ├── Multi-Tenant AI Systems
│ ├── Caching Strategies
│ ├── Database & Vector Storage Design
│ ├── Distributed Task Processing
│ ├── Fault Tolerance & Retries
│ ├── Cost Optimization
│ ├── Latency Optimization
│ ├── Horizontal Scaling
│ ├── Reliability & Availability
│ └── AI Tradeoff Analysis
│
├── 22. Build Real-World Projects
│ ├── AI-Powered Research Assistant
│ ├── Document Intelligence Platform
│ ├── Production-Ready RAG System
│ ├── Multi-Agent Research System
│ ├── AI Customer Support Agent
│ ├── AI Coding Assistant
│ ├── Multimodal AI Assistant
│ ├── AI Meeting Intelligence System
│ ├── Enterprise Knowledge Assistant
│ ├── AI Workflow Automation Platform
│ ├── Custom LLM Fine-Tuning Pipeline
│ ├── AI Model Evaluation Platform
│ └── End-to-End Production AI SaaS
│
└── 23. Career Preparation
├── Build a Strong GitHub Portfolio
├── Create an AI Engineering Resume
├── Master Python & SQL Interviews
├── Learn ML & LLM Fundamentals
├── Practice AI System Design
├── Understand RAG & Agent Architectures
├── Learn Inference Optimization
├── Study Production AI Case Studies
├── Contribute to Open Source
├── Write Technical Articles
├── Build in Public
└── Apply for AI Engineer Roles

FINAL GOAL:
Become a Production-Ready AI Engineer

[Saurabh Dubey @SaurabhDub28465]

SYSTEM DESIGN ROADMAP — FROM ZERO TO ADVANCED

Want to learn System Design? Start with these essential concepts 👇

🔹 1. Fundamentals
→ Client-Server Architecture
→ Monolithic vs Microservices
→ Latency vs Throughput
→ Scalability & Availability

🔹 2. Core Components
→ Load Balancers
→ Caching (Redis)
→ CDN
→ DNS
→ API Gateway

🔹 3. Databases
→ SQL vs NoSQL
→ Database Indexing
→ Replication & Sharding
→ Database Partitioning

🔹 4. Distributed Systems
→ CAP Theorem
→ Consistency & Availability
→ Message Queues (Kafka, RabbitMQ)
→ Fault Tolerance

🔹 5. System Design Patterns
→ Rate Limiting
→ Circuit Breaker
→ Event-Driven Architecture
→ Service Discovery

🔹 6. Practice Real-World Designs
→ URL Shortener
→ WhatsApp
→ YouTube
→ Instagram
→ Uber
→ Netflix

Pro Tip: Don't just memorize concepts. Understand WHY a system is designed a certain way and what trade-offs each decision creates.

Save this roadmap and follow it step by step!

Repost to help others learn System Design.

Follow for more AI & Tech content.
@SaurabhDub28465

[Maya @mayaislam_ai]

AI agents are more than chatbots.

They combine LLMs, RAG, Memory, Tools, MCP, and Multi-Agent systems to turn user instructions into autonomous actions.

From understanding goals to getting work done — this is the future of AI.

Follow @mayaislam_ai for more AI insights. 🚀

#AIAgents #ArtificialIntelligence #MCP #AI

[Maya @mayaislam_ai]

𝗜𝗙 𝗬𝗢𝗨 𝗪𝗔𝗡𝗧 𝗧𝗢 𝗨𝗡𝗗𝗘𝗥𝗦𝗧𝗔𝗡𝗗 𝗔𝗜 𝗔𝗚𝗘𝗡𝗧𝗦, 𝗟𝗘𝗔𝗥𝗡 𝗧𝗛𝗘𝗦𝗘 𝟵 𝗧𝗘𝗥𝗠𝗦

AI agents are becoming more than just chatbots.

They can use tools, remember context, retrieve information, work with other agents, and execute multi-step tasks.

But first, you need to understand the language behind them.

𝗛𝗘𝗥𝗘 𝗔𝗥𝗘 𝟵 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗧 𝗧𝗘𝗥𝗠𝗦:

01 — 𝗛𝗔𝗥𝗡𝗘𝗦𝗦
The loop that turns an LLM into an agent.

Context → LLM → Tool Call → Result → Context

It connects the model with skills, memory, prompts, APIs, browsers and other tools.

02 — 𝗠𝗘𝗠𝗢𝗥𝗬 & 𝗦𝗧𝗔𝗧𝗘
Memory helps an agent remember information across tasks.

→ Short-term memory
→ Long-term memory
→ Current state
→ Open tasks

03 — 𝗥𝗔𝗚
Retrieval-Augmented Generation helps an agent find relevant information before generating an answer.

Question → Search → Relevant Data → LLM → Answer

This helps ground responses in external knowledge.

04 — 𝗠𝗖𝗣
Model Context Protocol provides a standardized way for AI systems to connect with tools and data.

Think of it as a common connection layer between AI and external capabilities.

05 — 𝗦𝗞𝗜𝗟𝗟𝗦
Reusable instructions, knowledge, scripts, and workflows that an agent can load when needed.

This makes agents more capable without putting everything into one massive prompt.

06 — 𝗚𝗨𝗔𝗥𝗗𝗥𝗔𝗜𝗟𝗦
Rules that define what an agent can and cannot do.

→ Permissions
→ Sandboxing
→ Human approval
→ Action logging

The goal is useful autonomy without uncontrolled actions.

07 — 𝗘𝗩𝗔𝗟𝗦
Evaluation systems measure whether an agent actually performs well.

Test before launch → Measure outcomes → Improve → Monitor

Good agents need more than impressive demos.

08 — 𝗔𝟮𝗔
Agent-to-Agent communication allows agents to communicate, exchange tasks, and share results.

Different agents can specialize in different jobs.

09 — 𝗠𝗨𝗟𝗧𝗜-𝗔𝗚𝗘𝗡𝗧 𝗦𝗬𝗦𝗧𝗘𝗠𝗦
One orchestrator can coordinate multiple specialized agents.

For example:

Researcher → Coder → Reviewer → Final Result

𝗧𝗛𝗘 𝗕𝗜𝗚 𝗣𝗜𝗖𝗧𝗨𝗥𝗘:

LLM + Memory + RAG + Tools + Skills + Guardrails + Evals + Agent Communication

That combination is what turns a basic AI model into a more capable agentic system.

Save this cheat sheet if you're learning AI agents.

Follow @mayaislam_ai for more AI, tools, automation and tech insights.

#AI #AIAgents #AgenticAI #ArtificialIntelligence #RAG #MCP #AITools #GenerativeAI #Tech

[Mr. Buzzoni @Mr_Buzzoni]

this PDF is a f*cking cheat code for anyone running coding agents

Diogo Almeida, founder of Jev and co-author of InstructGPT, published a PDF on building a Jev harness

the promise:
> up to 200x faster
> up to 400x cheaper

the speed and the cost both live in the harness around the model

how to use it:
> drop the PDF and the article below into Claude Code or Codex
> let it rebuild its own setup

one evening of work, and your agent runs on a harness few teams have 👇

[Gauri Tripathi @GauriTripa94282]

Git Workflow:

① "git add" → Stage
② "git commit" → Save
③ "git push" → Upload code
④ "git clone" → Copy repo
⑤ "git fetch" → Get updates
⑥ "git pull" → Fetch + merge
⑦ "git merge" → Combine branches
⑧ "git reset" → Undo
⑨ "git stash" → Save changes
#Git #GitHub