Chris (Bashismashi)

Networking and gaming nerd. Aspiring #CCIE candidate.

#forthehorde #rolltide #thelantamer

Never stop learning and improving!

[Yarchi @undefinedKi]

This is an actual goldmine for every AI nerd

7 free AI courses on GitHub, from building your first AI agent to building your own LLM.
They teach more than most paid ones:

1. LLMs-from-scratch (107k stars). Build a ChatGPT-like model in PyTorch, step by step, from tokenizer to finetuning.
https://t.co/MdauFyYLaT

2. Microsoft's AI Agents for Beginners (79k). 18 lessons with code: tools, RAG, multi-agent, memory, security.
https://t.co/mpVL03CCPZ

3. Hugging Face Agents Course (32k). From your first agent to a GAIA final project, with a certificate at the end.
https://t.co/ypARyPni9r

4. 12-Factor Agents (27k). 12 principles for agents that hold up in production.
https://t.co/fmhPyFuoxV

5. Anthropic's courses (23k). API basics, prompt engineering, evals and tool use, straight from the Claude team.
https://t.co/C6Sr2DJJj8

6. Agents Towards Production (22k). 24 code-first tutorials: memory, MCP, security, tracing, deployment.
https://t.co/Qt5UXAl5sw

7. Learn Harness Engineering (21k). Why agents fail on real tasks and how to make them finish.
https://t.co/kaenMWWL4O

> If you're a complete beginner, start with Microsoft's AI Agents for Beginners.
> If you already build agents, go to 12-Factor Agents and Learn Harness Engineering.
> If you want to know how LLMs work inside, take LLMs-from-scratch.
> If you use Claude every day, Anthropic's courses are for you.

[Brucky @danikkk_wqs]

It's f*cking wild: Google, Cloudflare, Obsidian's CEO and the guy building Claude Code all put their AI configs on GitHub for free, and most people are still starting from zero

1. Matt Pocock's skills · 318K stars
18M+ installs. the exact folder of AI instructions he uses every day: tests first, clear specs, real reviews
https://t.co/AmtWH9JDco

2. Addy Osmani's skills · 92K stars
engineering skills from the guy working on Claude Code at Anthropic
https://t.co/1f2tlQ4NO0

3. Cloudflare's audit · 63.9K stars
the starting point of what now scans 128 of Cloudflare's own repos. one team of agents hunts for bugs, another tries to prove them wrong
https://t.co/qCsWTtnkUq

4. Obsidian CEO's skills · 51K stars
teaches any AI to write real Obsidian notes instead of messy text
https://t.co/SG7k1XpYrK

5. Google's goldfish · 14.8K stars
2 weeks old. the plugin Google's own engineers use to catch AI coding mistakes. the agent writes a design doc, then fresh AI sessions that never saw it try to tear it apart
https://t.co/mJQYjqMlFb

the biggest teams in tech already stopped writing prompts from zero. their configs are one click away

save it and steal one tonight

[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

[NVIDIA GeForce @NVIDIAGeForce]

We're giving away a Hogwarts Legacy custom wrapped NVIDIA GeForce RTX 5090 to celebrate its official release with DLSS 5 🪄

To enter:
🟢 Share this post
🟢 Comment #RTXON

Terms and Conditions: https://t.co/45T3R732hf

[Chris (Bashismashi) @cfaertech]

@NVIDIAGeForce #RTXON

[Chris (Bashismashi) @cfaertech]

@vxunderground @mrgretzky Meowing hacker

[Chris (Bashismashi) @cfaertech]

@vxunderground @chaeynz_ Toeby, Victoria, Tristan, and Oreo would love a chance to try the magical VPN!

[Chris (Bashismashi) @cfaertech]

@vxunderground @DJRorok Cmon.. highrolls!

[Magicuz @MagicuzGold]

🎉 Giveaway

🎁 1.000.000 Gold

🫶 Follow + Retweet

😎 Buy my gold guide: https://t.co/6GQCJUoSb5

#worldofwarcraft #wow

[Method @Method]

Our friend @MagicuzGold is giving away 1x Midnight Epic Edition to celebrate us defeating Nexus-King 🥳

🧡 Like & RT this post
🙋 Follow @MagicuzGold @Method
💰 Check out: https://t.co/bMEWP6DltQ

-Ends in 24hrs-

[Chris (Bashismashi) @cfaertech]

@DragonBoy_VA_ @snuffyowo I'm sorry but for pizza, that is "Stufft".. it is confusing i know! 🫠

[Chris (Bashismashi) @cfaertech]

@SkyAboveMe_Art Oh my goodness, I just saw the news! Huge congrats Sky!!!

[Chris (Bashismashi) @cfaertech]

@TheShadeVT Big oof

[Chris (Bashismashi) @cfaertech]

@KumiVT Oddly enough, not that long ago really, I had started waking up completely flipped. To where my head is at the foot of my bed and my feet where my head would be. 🤔😅 Weird times..

[Chris (Bashismashi) @cfaertech]

@FinnyaVT Why is it that my takeaway from all of this is "wait.. I thought Gawr Guras clothes was a funny face of a shark".. a shark bite makes so much more sense now. 😅

Either way, easier said than done but Shylily comparisons get thrown around everywhere.. keep your mental health up!

[Chris (Bashismashi) @cfaertech]

@HaraRaeSol @KumiVT The C word... why does this confuse me more? 🤔😆

[Chris (Bashismashi) @cfaertech]

@shylilytwitch @SinderVTuber Oh man.. if Lilly got involved like this, then you know it is pretty bad. This whole thing made even me feel terrible and I'm waaaaaaaay on the outside looking in. I'm wishing and hoping that everyone is able to eventually heal with time.

[Chris (Bashismashi) @cfaertech]

@moonahoshinova @Daniel7iao I'm glad I'm not the only one who immediately noticed that!

[Chris (Bashismashi) @cfaertech]

@dizzyembervt Beep, boop, ___?

[Chris (Bashismashi) @cfaertech]

@MoriViruss I think he biggest thing is the fact that she never stops pushing forward! Just think about how much she has done in her life so far..