Ravi Singh

[Peng @pengsonal]

PEOPLE ARE BUILDING SOME WILD STUFF WITH JEV

20 mind blowing projects worth checking out:

1. JEV-Ultrafast → browser agent
https://t.co/k5Ws7iHDsP

2. Fast-JEV-Compaction → context compression
https://t.co/kJUJJB1XjK

3. JSON-Render → generative UI
https://t.co/k5DFGE5D8b

4. Typesafe-MCP → use JEV with any client
https://t.co/jrnnGSpDEW

5. JEV-MCP → judgment toolkit
https://t.co/RXNDnhveC6

6. Semdecide → CLI classifier
https://t.co/JxW9sjqRr1

7. JEV-Codex-Router → model routing
https://t.co/AbqD0oiWbr

8. Winnow → context garbage collection
https://t.co/HXR8XyE9C3

9. JEV-Review → code review triage
https://t.co/aE2mh8ptd1

10. Blink → repo navigator
https://t.co/uBTzSfV28O

11. Agent-Desktop → desktop automation
https://t.co/aYi1CGdP3n

12. Typesafe-Mario → plays Super Mario
https://t.co/4ZW9fjcBVA

13. JEV-Drone → drone control
https://t.co/fdZOudVuJT

14. OneVOneJev → browser FPS
https://t.co/MyEYixaEYT

15. JEV-Trader → HFT market making
https://t.co/zleh9r6BQi

16. Prism → liquidity signal detection
https://t.co/4tH8uygInX

17. Neo4Jev → knowledge graph traversal
https://t.co/DnzqYO84Lq

18. JEV-Curate → training data screening
https://t.co/GpG0ROpXKR

19. Canny → completion verification
https://t.co/hmJp64jjBN

20. KillMyIdea → startup idea scoring
https://t.co/dqrDCj3Zk9

if you're using JEV, these are worth exploring

pick one and start building

[darkzodchi @zodchiii]

Jev Founder, Diogo Amogo, just released a PDF on building a Jev Harness for coding agents

this is a blueprint on how to make your coding agents 200× faster and 400× cheaper

Send this PDF and the article below to your Claude

Code or Codex instance and start shipping 200× faster 👇

[Bilgin Ibryam @bibryam]

Jev feels like a programmable web moment for AI.

AI that plugs into application logic. Route a request. Rank candidates. Evaluate an action. Small decisions developers can combine into much bigger systems.

Intelligence as a software building block.

[Arti Shah @TechByArti]

Don't waste 2 years learning to become an AI agentic engineer in 2026.

Andrew Ng, the godfather of AI, gave the complete playbook to become one from scratch.

1 hour course. Free:

• 00:00 - AI agent basics
• 12:12 - AI Agentic workflows & design patterns
• 53:27 - Practical tips for building AI agents
• 1:20:30 - self-improving AI agent loops
• 1:30:19 - multi-agent AI systems

I watched it last night.

Halfway through, I realized I could get into Anthropic in weeks, not years.

Bookmark now. Watch it. Then build your own AI agent

[rody @0x_rody]

Jev has been exploding in popularity recently.

If you already have access to the Jev API but aren’t sure how to start experimenting with it, just copy this checklist:

1. agent-desktop
Desktop automation. Read the system's accessibility tree, judge which button, menu, or input field to click next. https://t.co/ZtSEjUSPBF

2. typesafe-mario
Have Jev play Super Mario. No screenshots—just read the structured state in the emulator's RAM, then decide to run, jump, or dodge. https://t.co/GHttIjWQ3p

3. jev-drone
Use Jev to control a drone. The underlying flight control still handles stability and safety; Jev just does higher-level judgments like climbing, braking, and navigating obstacles. https://t.co/z0lYh9ykJq

4. OneVOneJev
1v1 FPS in the browser. Every decision tick, judge movement, view angle, aiming, firing, and jumping. https://t.co/aJiU0aaNcI

5. jev-trader
High-frequency market making on Monad testnet. Jev judges the next buy or sell based on spreads and trade direction, with model latency around 81ms. https://t.co/DaDRIrkJpO

6. Prism
Doesn't directly have Jev place orders. It judges states like toxic flow, market pressure, mean reversion, etc., then hands off to the original strategy. https://t.co/aim9lGRAP8

7. neo4jev
Stuff Jev into a knowledge graph. At each node, judge the most worthwhile edge to take next, then follow it all the way. https://t.co/9h0KXKdWj9

8. jev-curate
Use Jev to screen training data. For JSONL / Parquet, first judge quality, relevance, and risk, then decide which ones go into the next training round. https://t.co/yYV6aEdUtG

9. Canny
Prevents Coding Agents from stubbornly claiming they're done. Look at tool outputs, code diffs, and test results, then judge if the completion claim is reliable. https://t.co/H4jFT8hV0E

10. killmyidea
Input a startup idea, and Jev scores it from multiple angles, finally giving you KILL, FIX, or SHIP. https://t.co/XWo7JPOb6y

Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

[Amit Kumar Pandey @1006_amit7481]

BREAKING NEWS!

The engineer who built Claude Code from scratch just released a 28-minute video that's pure gold: how to write prompts that deliver ridiculously good results.

I've seen $300 courses that don't even come close to what he explains in the first 10 minutes.

CLAUDE.md files, memory shortcuts, parallel sessions, and prompting patterns that almost no one uses...

All in one single video. Completely free. No fluff.

Whether you're a developer, just starting, or have already been using Claude for months, this changes the game for you starting today.

Save. Watch. Share.

[Jami @expertwith_AI]

🚨 BREAKING: Claude can now BUILD a YouTube channel from scratch—and hit monetization in just 90 days. 100% free.

Here are 8 prompts to make it happen:

[Ravi Singh @_ravisingh8]

@RahulSi86183566 @makemytrip @DGCAIndia @makemytripcare a cus has paid extra for “Free Date Change,” the benefit should be usable when genuinely req. Ur app showed ₹3,510 but then gave “Something Went Wrong,” while ur exec gave a completely diff explanation.
Plz clarify the T&Cs and resolve this fairly. @DGCAIndia

[Shraddha Bharuka @BharukaShraddha]

🚨 Cambridge just dropped 10 FREE AI & ML textbooks (quietly).
University-level. Zero cost. Absolute gold for builders & learners.

Here’s the list with direct links 🧵👇

1️⃣ Understanding Machine Learning
Theory meets algorithms
https://t.co/ewZ2T2WYEz

2️⃣ Mathematics for ML
Linear algebra → calculus made intuitive
https://t.co/0qMXxhScrM

3️⃣ Mathematical Analysis of ML
The theory behind the code
https://t.co/N37Lax8YyY

4️⃣ Deep Learning Principles
Neural networks explained clearly
https://t.co/JjezENkzex

5️⃣ ML with Networks
Neurons → backpropagation
https://t.co/4ui5bFADNV

6️⃣ Deep Learning on Graphs
Graph Neural Networks & modern architectures
https://t.co/jHUQdmBSLi

7️⃣ Algorithmic ML
Complexity & optimization theory
https://t.co/h2fTXMXBi9

8️⃣ Probability Theory
Statistical foundations with examples
https://t.co/fyNggGGiJu

9️⃣ Elementary Probability
Beginner-friendly + real-world use
https://t.co/6VG47bsJFu

🔟 Advanced Data Analysis
Statistical learning for production systems
https://t.co/3LMKSrVq22

💡 Free textbooks. Cambridge quality.
Perfect for students, engineers & AI builders.

Save 🔖 | Repost ♻️ | Follow for more AI resources 🤝

#AI #MachineLearning #DeepLearning #FreeResources #DataScience #StudyAI

[𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰 @techwith_ram]

RAG System
├── Data Ingestion
│ ├── PDF / DOCX / PPT / Notion / Web
│ ├── Chunking strategies
│ ├── Cleaning and deduplication
│ ├── Metadata tagging
│ └── Versioning

├── Embeddings
│ ├── OpenAI / Cohere / BGE / Instructor
│ ├── Vector normalization
│ ├── Domain adaptation
│ └── Re-embedding strategy

├── Vector Database
│ ├── Pinecone / Weaviate / Qdrant / FAISS
│ ├── Index types (HNSW, IVF, Flat)
│ ├── Filtering and metadata search
│ └── Sharding and scaling

├── Retrieval
│ ├── Semantic search
│ ├── Hybrid retrieval (BM25 + vector)
│ ├── Re-ranking (cross-encoders)
│ ├── Query rewriting
│ └── Multi-hop retrieval

├── Prompt and Context Engineering
│ ├── System, user and tool prompts
│ ├── Context window management
│ ├── Citation and grounding
│ ├── Answer style control
│ └── Safety and refusal rules

├── LLM Layer
│ ├── GPT / Claude / Mistral / Llama
│ ├── Temperature and decoding
│ ├── Function calling and tools
│ └── Cost and latency optimization

├── Generation
│ ├── Context injection
│ ├── Structured output (JSON)
│ ├── Multi-step reasoning
│ └── Tool-calling agents

├── Evaluation
│ ├── Retrieval metrics (Recall@K, MRR)
│ ├── Answer quality (faithfulness, relevance)
│ ├── LLM-as-a-Judge
│ └── Regression testing

├── Feedback Loop
│ ├── User ratings
│ ├── Click-through
│ ├── Failure logging
│ └── Data refresh triggers

├── Caching
│ ├── Query caching
│ ├── Embedding caching
│ └── LLM response caching

├── Security and Guardrails
│ ├── Prompt injection protection
│ ├── Data isolation
│ ├── PII filtering
│ └── Tool permissioning

├── Observability
│ ├── Token usage
│ ├── Latency per step
│ ├── Retrieval quality
│ └── Hallucination tracking

├── Orchestration
│ ├── LangChain / LlamaIndex / custom pipelines
│ ├── Async flows
│ └── Retry and fallback models

├── Infrastructure
│ ├── API gateway
│ ├── Vector DB hosting
│ ├── Model routing
│ └── Cost tracking

└── Programming Language
└── Python or TypeScript (start here)

AI generated !!

[Jaydeep @_jaydeepkarale]

📂 Machine Learning Engineer
├─ 📂 Foundations (START HERE)
│ ├─ 📂 Python Basics
│ │ ├─ Variables, loops, functions
│ │ └─ Writing clean Python code
│ │
│ ├─ 📂 Math for ML
│ │ ├─ Linear Algebra (vectors, matrices)
│ │ ├─ Probability
│ │ └─ Statistics
│ │
│ ├─ 📂 Data Handling
│ │ ├─ Pandas
│ │ ├─ NumPy
│ │ └─ Data Cleaning
│ │
│ ├─ 📂 Data Visualization
│ │ ├─ Matplotlib
│ │ └─ Seaborn
│ │
│ └─ 📂 SQL
│ ├─ Joins
│ ├─ Aggregations
│ └─ Window Functions
│
├─ 📂 Core Machine Learning
│ ├─ 📂 ML Fundamentals
│ │ ├─ Bias vs Variance
│ │ ├─ Overfitting / Underfitting
│ │ └─ Train / Test / Validation
│ │
│ ├─ 📂 Algorithms
│ │ ├─ Linear & Logistic Regression
│ │ ├─ Decision Trees
│ │ ├─ Random Forest
│ │ └─ XGBoost
│ │
│ └─ 📂 Model Evaluation
│ ├─ Accuracy, Precision, Recall
│ ├─ ROC-AUC
│ └─ Cross Validation
│
├─ 📂 Deep Learning (LATER)
│ ├─ 📂 Neural Network Basics
│ │ ├─ Perceptron
│ │ ├─ Activation Functions
│ │ └─ Backpropagation
│ │
│ ├─ 📂 PyTorch / TensorFlow
│ │ ├─ Model Building
│ │ └─ Training Loops
│ │
│ └─ 📂 Specializations
│ ├─ NLP
│ └─ Computer Vision
│
├─ 📂 Production & MLOps (LAST)
│ ├─ 📂 Model Deployment
│ │ ├─ FastAPI / Flask
│ │ └─ REST APIs
│ │
│ ├─ 📂 MLOps
│ │ ├─ Experiment Tracking
│ │ ├─ Model Versioning
│ │ └─ Monitoring & Retraining
│ │
│ └─ 📂 Cloud
│ ├─ AWS / GCP / Azure
│ └─ Docker & CI/CD
│
└─ 📂 Career Layer
├─ 📂 Projects
│ ├─ End-to-end ML projects
│ └─ Real datasets
│
├─ 📂 Interview Prep
│ ├─ ML theory
│ ├─ Case studies
│ └─ System design (ML)
│
└─ 📂 Portfolio
├─ GitHub
└─ Blog / Case Studies

[Vipin Gautam (Viipin I Gautam) @viipin8]

GROK + YouTube = $$$$$
Even a 9-year-old can do it.

Use these prompts to make money with YouTube:

[Akshay 🚀 @akshay_pachaar]

You’re in an ML Engineer interview at Google.

Interviewer: We need to train an LLM across 1,000 GPUs. How would you make sure all GPUs share what they learn?

You: Use a central parameter server to aggregate and redistribute the weights.

Interview over.

Here’s what you missed:

[Tom Yeh @ProfTomYeh]

Here's my beginner's lecture series for RAG, Vector Database, Agent, and Multi-Agents: Download slides: 👇
* RAG: https://t.co/O3OvTXVWUI
* Agents: https://t.co/BeJedAKwC9
* Vector Database: https://t.co/UZw5Lieszp
* Multi-Agents: https://t.co/iEMJKeMYAE

---
100% original, made by hand ✍️
Join 47K+ readers of my newsletter: https://t.co/fFt8roc8D9

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

💥 Top 50 LLM Interview Questions & Answers 🤖📘

Preparing for an AI / ML / LLM interview?
This guide covers everything you need — from basics to advanced concepts ⚡

✅ Prompt Engineering
✅ Fine-tuning & RAG
✅ Transformer Architecture
✅ Tokenization & Attention
✅ Real-world LLM Scenarios

Get the full “Top 50 LLM Interview Questions” PDF 👇
1️⃣ Follow me (@daievolutionhub)
2️⃣ Repost this post 🔁
3️⃣ Comment “LLM” 💬

Follow @daievolutionhub for more AI, ML & Interview Prep content ✨

#AI #LLM #MachineLearning #PromptEngineering #InterviewPrep #DeepLearning #AIcommunity

[Dhanian 🗯️ @e_opore]

LLMs (Large Language Models) Learning Roadmap

|-- Foundations of Machine Learning
| |-- Linear Algebra, Probability & Statistics
| |-- Supervised, Unsupervised & Reinforcement Learning
| |-- Model Training, Validation & Evaluation
| |-- Overfitting, Regularization & Generalization
| |-- Feature Engineering & Data Preprocessing

|-- Deep Learning Essentials
| |-- Neural Network Basics (Perceptron, MLP, CNN, RNN)
| |-- Activation Functions, Loss Functions & Optimizers
| |-- Backpropagation & Gradient Descent
| |-- Batch Normalization, Dropout & Initialization
| |-- Frameworks: TensorFlow, PyTorch

|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- Bag-of-Words, TF-IDF & Word Embeddings
| |-- Sequence Models (RNN, LSTM, GRU)
| |-- Attention Mechanisms & Seq2Seq Models
| |-- Transformer Architecture Fundamentals

|-- Large Language Model Architecture
| |-- Transformer Layers: Encoder, Decoder, Self-Attention
| |-- Positional Encoding & Multi-Head Attention
| |-- Pretraining vs Fine-Tuning
| |-- Causal & Masked Language Modeling
| |-- Popular Architectures: GPT, BERT, T5, LLaMA, Mistral

|-- Pretraining & Fine-Tuning
| |-- Data Collection & Cleaning
| |-- Tokenization Techniques (BPE, SentencePiece)
| |-- Pretraining Objectives & Corpus Selection
| |-- Instruction Tuning & RLHF (Reinforcement Learning from Human Feedback)
| |-- LoRA, QLoRA & Parameter-Efficient Fine-Tuning

|-- Inference & Optimization
| |-- Model Quantization & Pruning
| |-- Knowledge Distillation & Compression
| |-- Efficient Inference with GPU/TPU Acceleration
| |-- Prompt Engineering & Context Management
| |-- Handling Large Context Windows & Memory

|-- Retrieval-Augmented Generation (RAG)
| |-- Vector Databases (Pinecone, FAISS, Chroma)
| |-- Document Embedding & Similarity Search
| |-- Combining Retrieval with Generation
| |-- Building RAG Pipelines for Knowledge-Enhanced LLMs
| |-- Query Expansion & Context Optimization

|-- Multi-Agent Systems & Orchestration
| |-- Building Agent Pipelines & Task Chains
| |-- Memory, Planning & Tool Integration
| |-- Coordinating Multiple Models or Tools
| |-- LangChain, AutoGPT & Agent Frameworks
| |-- Task Decomposition & Reasoning Strategies

|-- Safety, Ethics & Alignment
| |-- Bias Detection & Mitigation
| |-- Safe Response Generation & Red Teaming
| |-- Alignment Techniques & Value Learning
| |-- Content Moderation & Ethical Guardrails
| |-- Transparency, Interpretability & Model Governance

|-- Scaling & Deployment
| |-- Model Serving with FastAPI, Flask or Triton
| |-- Distributed Training (DeepSpeed, Megatron-LM)
| |-- Model Checkpointing & Version Control
| |-- API Hosting & Cloud Deployment (AWS, GCP, Azure)
| |-- Monitoring, Logging & Performance Optimization

|-- Evaluation & Benchmarking
| |-- Perplexity, BLEU, ROUGE, Accuracy Metrics
| |-- Human & Automated Evaluation Methods
| |-- Contextual Quality Assessment
| |-- Evaluating Multi-turn Conversations
| |-- Continuous Improvement Loops

|-- Real-World Projects
| |-- Building a Custom Chatbot with LLMs
| |-- Fine-Tuning GPT Models for Domain Tasks
| |-- Implementing a RAG Knowledge Assistant
| |-- Developing AI Coding or Writing Assistants
| |-- Deploying Enterprise LLM Solutions

|-- Continuous Learning & Research
| |-- Reading AI Papers & Open Model Releases
| |-- Contributing to Open-Source LLM Projects
| |-- Experimenting with Emerging Architectures
| |-- Joining AI Communities & Forums
| |-- Staying Updated on Model Trends & Safety

Get the LLMs Developer Handbook here: https://t.co/myobWOFPY7

This ebook provides a complete hands-on guide to building, fine-tuning, and deploying large language models with real-world projects, architecture insights, and advanced optimization techniques.

[Yash @_Ai_Yash_]

𝐇𝐨𝐰 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭:

1️⃣ System Prompt Define your agent’s personality, capabilities, and boundaries. This is its brain’s instruction manual.
2️⃣ LLM (Large Language Model) Choose the engine: GPT-4, Claude, Mistral, or an open-source model — pick based on reasoning needs, latency, and cost.

3️⃣ Tools Equip your agent with tools: API access, code interpreters, database queries, web search, etc. More tools = more utility.
4️⃣ Orchestration Use frameworks (like LangChain, AutoGen, CrewAI) to manage reasoning, task decomposition, and multi-agent collaboration.

5️⃣ Memory Implement both short-term (context window) and long-term memory (Vector DBs like Pinecone, Weaviate, Chroma).
6️⃣ UI (User Interface) Design an intuitive chat UI or workflow interface that enables smooth interaction with your agent.

7️⃣ AI Evals Test your agent's performance with real-world tasks. Use tools like TruLens, Rebuff, or custom evals to measure effectiveness, reliability, and safety.
#AIAgents #LLM #GPT4 #LangChain #AutoGen #SemanticKernel #AIProduct #PromptEngineering #ArtificialIntelligence #TechStack

Save it for later✅
Follow @_Ai_Yash_ for more updates.
#ai #artificalintelligent #Trending

[Min Choi @minchoi]

OpenAI literally dropped ChatGPT Prompt Packs for Any Role

11 roles. Bookmark this.

[Dhanian 🗯️ @e_opore]

The Complete LLM & Generative AI Tech Stack

Foundation Models & Core Technologies
├── Large Language Models
│ ├── OpenAI: GPT-4, GPT-4o
│ ├── Anthropic: Claude 3
│ ├── Meta: Llama 2, Llama 3
│ └── Google: Gemini, PaLM 2
├── Open Source Alternatives
│ ├── Mistral AI models
│ ├── Falcon, Vicuna
│ ├── CodeLlama, StarCoder
│ └── Custom fine-tuned models
└── Model Architectures
├── Transformer-based
├── Attention mechanisms
├── Encoder-decoder models
└── Auto-regressive models

Development Frameworks & SDKs
├── Python Libraries
│ ├── LangChain, LlamaIndex
│ ├── Transformers
│ ├── PyTorch, TensorFlow
│ └── JAX, Flax
├── API Integration
│ ├── OpenAI Python SDK
│ ├── Anthropic API
│ ├── Google AI Python SDK
│ └── REST API wrappers
└── Development Tools
├── Jupyter Notebooks
├── VS Code with AI extensions
├── Google Colab, Kaggle
└── Weights & Biases

Prompt Engineering & Optimization
├── Prompt Design Patterns
│ ├── Zero-shot, Few-shot learning
│ ├── Chain-of-Thought
│ ├── ReAct
│ └── Self-consistency, Tree of Thoughts
├── Prompt Management
│ ├── Prompt templates
│ ├── Version control for prompts
│ ├── A/B testing frameworks
│ └── Prompt optimization tools
└── Advanced Techniques
├── Function calling
├── Tool use & planning
├── Memory management
└── Multi-modal prompting

RAG & Knowledge Enhancement
├── Retrieval-Augmented Generation
│ ├── Vector databases
│ ├── Document loaders & parsers
│ ├── Embedding models
│ └── Hybrid search systems
├── Vector Databases
│ ├── Pinecone, Weaviate
│ ├── Chroma, Qdrant
│ ├── Milvus, Vespa
│ └── PostgreSQL with pgvector
└── Data Processing
├── Text chunking strategies
├── Embedding generation
├── Semantic search
└── Knowledge graph integration

Application Patterns & Architectures
├── Common Patterns
│ ├── AI Agents
│ ├── Conversational AI
│ ├── Content generation systems
│ └── Code generation
├── System Design
│ ├── Chat interfaces
│ ├── Streaming responses
│ ├── Caching strategies
│ └── Rate limiting
└── Integration Patterns
├── API gateways for AI services
├── Middleware for LLM call
└── Circuit breakers

Model Management & Deployment
├── Model Serving
│ ├── Hugging Face Inference Endpoints
│ ├── AWS SageMaker, Google Vertex AI
│ ├── Azure Machine Learning
│ └── Custom model servers
├── Fine-tuning
│ ├── LoRA,
│ ├── Parameter-efficient fine-tuning
│ ├── RLHF
│ └── Custom training pipelines
└── Optimization
├── Quantization
├── Pruning
├── Model compression
└── Hardware acceleration

Monitoring & Evaluation
├── Performance Metrics
│ ├── Latency
│ ├── Cost per request tracking
│ ├── Token usage analytics
│ └── Quality metrics
├── LLM-specific Monitoring
└── Evaluation Frameworks

Security & Responsible AI
├── Safety Measures
│ ├── Content moderation
│ └── Ethical guidelines
├── Privacy
│ ├── Data anonymization
│ ├── GDPR
│ └── Audit trails
└── Risk Management
├── Bias detection
├── Fairness metrics
├── Transparency reports
└── Response plans

Emerging Technologies & Future Trends
├── Multi-modal AI
│ ├── Vision-Language models
│ ├── Audio processing
│ ├── Video understanding
│ └── Cross-modal retrieval
├── Advanced Architectures
│ ├─MoE
│ ├── Retrieval-based models
│ ├── Neuro-symbolic AI
│ └── World models
└── Production Considerations

Get my Complete LLM and Generative AI Development eBook here: https://t.co/DBXPEOcHrI

[Mr Sinha @Mrsinha]

"If destroyed runways and burnt-out hangers look like victory to Pakistani PM, then Pakistan is free to enjoy it" - Indian Diplomat Petal Gehlot at UNGA 😂😂😂