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Today, the public mint for Lobsters on TON goes live on Getgems 🦞 This is not just another NFT drop. In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI. Here, the NFT is not just an image and not just a collectible. Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API. So you are not just getting an asset in your wallet. You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces. What makes this especially interesting is the timing. In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative: Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent. Public mint: today, 16:00 Price: 50 TON 👉 Mint your Lobster on Getgems 🦞🦞🦞

LLM Engineering Roadmap (2026 Practical Guide) 🗺✨ If your goal is to build real LLM apps (not just prompts), follow this order. 🚀 1️⃣ Python + APIs 🐍🔌 You’ll spend most of your time wiring systems. Learn: → functions, classes → working with APIs (requests, JSON) → async basics → environment variables Resources → Python for Everybody https://lnkd.in/gUqkvnGG → Introduction to Python https://lnkd.in/g7xfYJVZ → MLTUT Python Basics Course https://lnkd.in/gCqfyCGZ 2️⃣ Text Basics (NLP) 📝🧠 You don’t need heavy theory, just the essentials. Learn: → tokenization → text cleaning → similarity (cosine) → basic embeddings idea Resources → Natural Language Processing Specialization https://lnkd.in/gz_xmqD9 → NLP in Python https://lnkd.in/gnpcJxhz 3️⃣ Transformers (What’s happening behind the API) 🤖🔍 Enough to not treat it like a black box. Learn: → tokens, context window → attention (high level) → why embeddings work → limits of LLMs Resources → Generative AI with Large Language Models https://lnkd.in/gk3PPtyf → Hugging Face Transformers Course https://lnkd.in/ggSR5JNb 4️⃣ Prompting (Make outputs reliable) 💬🎯 Treat prompts like code. Learn: → few-shot examples → structured outputs (JSON) → system vs user instructions → simple evals (does it break?) Resources → Prompt Engineering for ChatGPT https://lnkd.in/gyg4EiJS → Prompt Engineering with LLMs https://lnkd.in/gn67Mxga 5️⃣ Embeddings + Vector DBs 📊🗄 This is how you add your data. Learn: → embedding generation → similarity search → indexing Tools: → FAISS → Pinecone → Chroma Resources → Working with Embeddings https://lnkd.in/gnngPW4E → Vector Databases & Semantic Search https://lnkd.in/gP2HdMmD 6️⃣ RAG Pipelines 🔗🔄 Most useful apps use this pattern. Learn: → chunking documents → retrieval + ranking → prompt + context design → basic evaluation Resources → Generative AI for Software Development https://lnkd.in/g3uduecv → Build RAG Apps with LangChain https://lnkd.in/ggXJjgDN 7️⃣ Build Real Applications 🛠💻 Keep them small and usable. Build: → document Q&A (PDF → answers) → internal knowledge bot → code assistant (repo Q&A) → support chatbot Tools: → LangChain → LlamaIndex → OpenAI APIs Resources → Build LLM Apps with LangChain & Python https://lnkd.in/g6xXVX_8 → LLM Applications https://lnkd.in/gzs8_SRk 8️⃣ Deployment 🚢☁️ Make it usable by others. Learn: → FastAPI endpoints → streaming responses → caching (reduce cost) → logging + monitoring Tools: → FastAPI → Docker → AWS / GCP Resources →Machine Learning Engineering for Production (MLOps) https://lnkd.in/gCMtYSk5 → MLOps Fundamentals https://lnkd.in/g8TGrUzT

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🚀 LLM Architectures 🧠 Transformer architectures may look similar, but they solve very different problems once data starts flowing through them. 🔄 The four main Transformer families in simple terms. 📚 👉 Decoder-only models like GPT and LLaMA generate text one token at a time. Each new token looks only at previous tokens. This makes them great for chat, code generation, and text completion. 💬💻 👉 Encoder-only models like BERT and RoBERTa focus on understanding text. Every token sees the full sentence at once. These models are used for classification, search, and extracting meaning rather than generating text. 🔍📖 👉 Encoder-decoder models like T5 and BART first understand the input, then generate an output. This setup is common for translation, summarization, and question answering. 🌐📝 👉 Mixture of Experts (MoE) models like Mixtral and GLaM scale smarter, not harder. A router sends tokens to a small set of expert networks, allowing very large models to run efficiently. ⚡️🤖 Example: Summarizing a document 📄 - Decoder-only generates fluent text ✍️ - Encoder-only ranks important sentences 🏷 - Encoder-decoder produces a clean summary 🧹 - MoE scales the process with lower compute cost 💰 Choosing the right Transformer matters more than choosing the largest one. ⚖️✨