🤔Everyone’s talking about AI agents. But few understand how they actually work—under the hood. If you’re building or working with intelligent systems, understanding their architecture isn’t optional—it’s foundational. This carousel breaks down the anatomy of a modern AI agent: • How user input flows through layered systems • How reasoning, planning, and tool use are orchestrated • How LLMs fit into a much larger ecosystem of intelligence For data scientists, ML engineers, and AI builders—this is the stack behind the buzzword 📅 Want to join live? Register now for the upcoming Agentic AI Bootcamp happening on 25th Nov. Don’t miss your chance to build, test, and evaluate intelligent agents! hubs.la/Q03VrFBb0 #AIAgents #AIArchitecture #LLMStack #AIAgentStack #ReasoningModels #ToolUse #AIAgentsExplained #MachineLearning #MLOps #AIEngineering #DataScienceCommunity #AIBuilders #IntelligentSystems #AIWorkflows #DeepLearning #TechExplained

🚀 Introduce Acontext: the #opensource Context Data Platform for self-learning AI #agents. 💾Multimodal context storage via a unified API 📊Real-time task status observability 📚Automatic skill learning GitHub: github.com/memodb-io/Acontex… 🥑 #AI #developer #AIAgent #LLM #llmstack

Testing new features.... 👀👾⌛️ #buildinpublic #AI #LLMs #llmstack #llmtesting #Openai Followr.ai ✅

🚀 The future is bullish for $PAAL, and here’s why you should be hyped! 💥 Despite all the FUD flying around, the $PAAL team has been grinding full-time, expanding to 11 dedicated developers led by 2 CTOs who’ve been here since day one! 🔥 These guys are building something real, not just chasing quick pumps. 🌐 As #ChatGPT approaches a billion weekly users, and #Ethereum cements its role as the backbone of the #DeFiRevolution, people will soon realize that inflation isn’t “transitory”... They’ll need to protect themselves financially, and #cryptocurrencies will be the go-to solution! 💪 Imagine a world where Large Language Models (LLMs) teach millions about #Crypto, #Web3Innovation, and the decentralized economy that’s been thriving for over a decade; all explained in simple, up-to-date language. That’s the vision, and $PAAL is leading the charge! 🚀 #Bitcoin was created to shield us from inflation and money printing with no backing, yet the centralized media labeled it a scam for years. Most people missed the real value — but not us. $PAAL is building the infrastructure to educate and empower the next billion users! 🌍 #Crypto moves fast, but Rome wasn’t built in a day, and neither will @PaalMind. They’re taking the time to do it right, ensuring that when the masses need an #llmstack to decode #Web3Culture, $PAAL will be the builder of that revolutionary #AIAgent! 🤖✨ With strong #Web2 relationships with Fortune 500s, $PAAL will deliver fast, reliable, and educational models to the world. Imagine @IBM & other giants adopting our #LLMs — the floodgates will open, and everyone will want in! 🌊 Our team is top-tier, and we’ve got a healthy runway. No wasting funds on useless marketing or moonboy buybacks; we’re focused on building the best talent, tech, and infrastructure to scale the strongest Web3 AI out there. 💎 And that’s just the beginning! $PAAL is also developing what could be the most profitable AI trading bot — capable of operating across commodities, crypto, stocks, and forex. Traders will simply copy their #TradingView bots, and our AI will analyze, optimize, and execute; all while protecting their alpha and IP! 🔥 Plus, top traders will have the option to then list their quant strategies on @PaalXOfficial, earning 10% of gains while keeping their edge private. It will be a win-win for everyone! 💰 Even @MyMemesAI is pushing the boundaries of tech; so for those questioning our legitimacy, rest assured, the team is giving 110%. This isn’t just a hype project; we have serious #Web3Builders that saw the potential in Web3 and then immediately started building for the future! 🌟 Buying and working with #cryptocurrencies early gave me the freedom to live without a 9-5 — and that was years before @PaalMind even launched! 💪 This token, this team, and this community are the real deal. Anyone telling you otherwise is probably just trying to pump their own bags and rotate out. The true winners are those building the infrastructure — imho $PAAL is leading that charge! 🚀🔥 The future belongs to those who build it. And $PAAL is doing just that. 🧠🤖 LFG! 💥🌍

$RZLV Rezolve AI - BrainPowa LLM Thread - My Findings #ECommerce #RetailTech #AI #AgenticAI #VisualCommerce #CustomerExperience #GenZ #AgenticCommerce #SmartPay #BlockChain #FinTech #DigitalPayments #Google #Microsoft #LLMs #LLMstack #Shopping 1/10. Hello and Disclaimer I have been searching and cross-tabulating anything I could find relating to the Brainpowa architecture. I do not have any access to Rezolve AI, nor do I have access to their login protected white paper or api collection meant for customers. Therefore my findings are publicly sourced and purely unverified. As an investor that may decide to grow my initial position into 2026, it was prudent for me to conduct this due diligence. “Brainpowa is not a wrapper around ChatGPT or any other third-party model. It is a completely independent, purpose-built AI engine trained exclusively on retail data using our own infrastructure.” — Daniel M. Wagner, CEO, Rezolve AI (Jan 2025) The information in this thread has not been validated with Rezolve Ai. I do not have any connection to the company or promoters of the company. I am just a boomer with too much time on my hands. RZLV is my fun play, and I am long into 2026 (1D chart). Feel free to comment and disagree - I am always ready to learn. Please excuse any typos. I am well known for typos. If you like this thread, bookmark and re-post. ⬇️ Raj

Day 8 of building the PromptOps platform. Releasing the first public poster. Wish me luck, and share your opinion. #promptops #llmstack #aiinsoftwaredevelopment

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Tool Spotlight: Perplexity Claude = Goldmine. Perplexity finds ..... Claude refines.... This duo crushed my research workflow this week. If you're using LLMs, don’t sleep on this combo. What’s your favorite AI tool pairing? #ClaudeAI #PerplexityAI #LLMStack #AItools

AI tools drop faster than I can test them. This week: - Claude for docs, - Grok for code, - ChatGPT Agents for tasks. Soo trying to build smarter, not just flashier. Whatsss your current AI tool stack? 👇 #AIAgents #ClaudeAI #Grok #LLMstack #buildinpublic

Day 31 of #TheAIandDataStreak100 Today, I Explored RAG vs Fine-Tuning: RAG retrieves knowledge at runtime. Fine-tuning bakes it into the model. Choose RAG for dynamic content. Fine-tune for static, focused domains. #GenAI #RAG #FineTuning #LLM #AIapps #LLMStack #AIengineering

Foundational skills for high impact AI Engineering: • 𝗟𝗟𝗠 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴: From supervised techniques to reinforcement-based approaches, customizing foundational models is becoming a core capability. • 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Architecting LangGraph- or AutoGPT-style agents with structured memory, function calling, and state management. • 𝗥𝗔𝗚 & 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Designing hybrid pipelines with vector databases, structured data, and dynamic evaluators for grounded generation. • 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲: Quantization, distillation, and compression are essential for latency-sensitive deployments. • 𝗘𝗱𝗴𝗲 𝗔𝗜 & 𝗢𝗻-𝗗𝗲𝘃𝗶𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Enabling real-time inference on mobile, edge, and IoT platforms. • 𝗔𝗜 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗥𝗼𝗯𝘂𝘀𝘁𝗻𝗲𝘀𝘀: Red teaming, hallucination detection, and adversarial defenses should be embedded—not bolted on. 📚 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 & 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀: → Hugging Face, TFX, PyTorch Lightning, FastAI, JAX, Keras ☁️ 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗜𝗻𝗳𝗿𝗮 → Google Vertex AI, AWS SageMaker, CoreWeave, RunPod, Modal 🧠 𝗚𝗲𝗻𝗔𝗜 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 → LLaMA 3, Mistral, OpenAI, Claude, LangChain, LlamaIndex, Ollama 🤖 𝗔𝗴𝗲𝗻𝘁 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 → LangGraph, CrewAI, Semantic Kernel, DSPy, AutoGPT, Haystack 📈 𝗦𝗲𝗿𝘃𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 → NVIDIA Triton, Ray Serve, BentoML, KServe, ONNX Runtime 🧩 𝗨𝗜, 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀, 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 → Streamlit, Gradio, Flask, Panel, Voila 📦 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 & 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 → Airflow, Prefect, Dagster, Argo Workflows, Mage AI ⚙️ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻 → TensorRT, DeepSpeed, TVM, QAT, Fireworks Inference Engine 🔐 𝗔𝗜 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 → PySyft, Flower, Opacus, CleverHans, Adversarial Robustness Toolbox 📌 Takeaway As GenAI systems move from prototypes to core production components, engineering teams need to master both the model layer and the surrounding infrastructure. The bar has been raised—not just in terms of capability, but responsibility. This is an exciting time to be building. #AIEngineering #LLMs #RAG #AgenticAI #InferenceOptimization #GenAI #MachineLearning #LLMStack #AIInfrastructure #EdgeAI #AIEthics #LangGraph #HuggingFace #FireworksAI #TechLeadership #AIinProduction

The future isn’t in *big models* — it’s in *smart memory*. @recallnet is pioneering: 🧠 Personal & team-level memory graphs 🔁 Event-linked recall across time 🧩 Modular plug-in memory for any LLM Because memory *is* the multiplier. \#AIInfra #ContextAwareAI #LLMstack

Yesterday, I promised $80 to one person. @mosesmojekwu1 was chosen DM with your USDT BEP20 Address Do you want to win big? Start yapping about @OpenledgerHQ 💡OpenLedger boosts our framework with rigorous benchmarking for AI agent evaluation. 💡ElizaOS, a popular Web3-native OS, integrates our primitives to support multi-model autonomy. 💡ARC’s Rig,with 2.5M users, cuts AI errors by 90% using our trust layer in a Rust-based LLMstack

🤖🏗️☸️ 𝘾𝙤𝙣𝙨𝙩𝙧𝙪𝙘𝙩 𝙔𝙤𝙪𝙧 𝙊𝙥𝙚𝙣-𝙎𝙤𝙪𝙧𝙘𝙚 𝘼𝙄 𝘾𝙤𝙢𝙥𝙪𝙩𝙚 𝙎𝙩𝙖𝙘𝙠: 𝙆𝙪𝙗𝙚𝙧𝙣𝙚𝙩𝙚𝙨 𝙍𝙖𝙮 𝙋𝙮𝙏𝙤𝙧𝙘𝙝 𝙫𝙇𝙇𝙈 ☸️🏗️🤖 #for_ai_architects #for_solutions_architects #for_cloud_architects #did_you_know_that the hottest DIY recipe for AI infrastructure is three layers deep-Kubernetes for cluster tenancy, Ray for distributed scheduling, and PyTorch vLLM for model magic? Today, let's discover how @anyscalecompute calls it the "open-source AI compute stack" and companies like Pinterest, Uber, and Roblox are already cooking with it 🔗 𝙏𝙝𝙚 3-𝙇𝙖𝙮𝙚𝙧 𝘾𝙖𝙠𝙚 1️⃣ Training & Inference (L1) - PyTorch handles autograd model definition; vLLM supercharges GPU memory with PagedAttention for 10× faster token throughput. 2️⃣ Distributed Compute (L2) - Ray Core schedules thousands of tasks, autoscaling pods and moving tensors across GPUs or TPUs with near-linear speed-ups. 3️⃣ Cluster Orchestrator (L3) - Kubernetes (KubeRay) grants multi-tenant isolation, node pooling, and spot/regular mix-then tears it all down with a single kubectl delete. 🚀 𝙒𝙝𝙮 𝙩𝙝𝙞𝙨 𝙨𝙩𝙖𝙘𝙠 (1) Elastic Everything - Ray grows from laptop to 1 K GPUs; K8s node-autoscaler plugs cost leaks on low-traffic days. (2) Model Velocity - vLLM's on-device KV cache means you serve 20 K tps with a single A100-tested by Ray Serve LLM APIs in v2.44. (3) Cloud-Agnostic - Same YAML runs on GKE, EKS, AKS, or on-prem OpenShift; Ray abstracts the silicon. (4) Community Muscle - 28 K GitHub stars on Ray and growing; patches drop weekly across PyTorch & vLLM integrations. 💡 𝙆𝙚𝙮 𝙏𝙖𝙠𝙚𝙖𝙬𝙖𝙮 Kubernetes gives you machines, Ray gives you distributed superpowers, PyTorch gives you the model canvas, and vLLM slams the gas pedal on inference. Glue them together and you've got a cloud-agnostic, wallet-friendly factory for training, fine-tuning, and serving any frontier LLM. Thanks to @robertnishihara for his blog post: An Open Source Stack for AI Compute: Kubernetes Ray PyTorch vLLM lnkd.in/dxNvQshV 📬 Stay tuned! lnkd.in/eYWBZ5ud #OpenSourceAI #Kubernetes #Ray #PyTorch #vLLM #LLMStack #AIInfrastructure #KubeRay #DistributedTraining #BatchInference #VertexAI #GKE #GPU #SpotSavings #DevOps #MLOps #favikon #AI #SOTA #artificialintellgence #generativeai #cloud #cloudcomputing #infrastructure #cloudnative

💬 Build your own AI assistant - without API limits | @hyperbolic_labs Tired of token caps, vendor lock-in, and closed black-box models? @hyperbolic_labs lets you launch your own LLM backend - using open-source models, full-stack control, and real GPU power. 🛠 Custom logic 🌍 Any interface 💸 Predictable costs Your AI. Your infrastructure. #Hyperbolic #LLMstack #AIbackend

We’re proud to support initiatives like this at Intel Liftoff, where startup-friendly infrastructure and open standards can actually coexist. intel.ly/3H3ywI8 #IntelLiftoff #GenAI #OPEA #OpenSourceAI #EnterpriseAI #AIInfra #LLMstack

6/9🧵Atropos is 1 of 3 core components in the Nous RL stack. - Training inference frameworks are on the way. - Together, they form an asynchronous, distributed system — the backbone of Psyche, a global decentralized training network. #Decentralized #Network #LLMStack #AI

What tools do top AI developers use in 2025? Here's a concise list: - Jupyter Notebook - LangChain LlamaIndex - Hugging Face - OpenAI API - LLMStack for agent testing - Pinecone/Weaviate for vector databases Master these tools to shape the future. #ArtificialIntelligence #developers #AI #huggingface #LLMstack #pinecone #OpenAI

🧠🧪 @nvidia 𝙇𝙡𝙖𝙢𝙖 𝙉𝙚𝙢𝙤𝙩𝙧𝙤𝙣: 𝘽𝙪𝙞𝙡𝙙𝙞𝙣𝙜 𝘼𝙜𝙚𝙣𝙩𝙞𝙘 𝘼𝙄 𝙬𝙞𝙩𝙝 𝙍𝙚𝙖𝙨𝙤𝙣𝙞𝙣𝙜 𝙖𝙣𝙙 𝙍𝙇-𝙏𝙪𝙣𝙚𝙙 𝙈𝙤𝙙𝙚𝙡𝙨 🤖📈 #for_ai_architects #for_solutions_architects #for_cloud_architects #did_you_know_that NVIDIA is leading a paradigm shift in enterprise AI agents with the open-source Llama Nemotron family, fine-tuned with scientific reasoning, curriculum RL, and instruction-following? Today, let's discover an agentic training pipeline unlocks precision reasoning and efficient model performance—from distillation to alignment. 🧱 𝘿𝙞𝙨𝙩𝙞𝙡𝙡𝙖𝙩𝙞𝙤𝙣 & 𝙈𝙤𝙙𝙚𝙡 𝙎𝙞𝙯𝙞𝙣𝙜 - Starts with Llama 3.1 (405B) - Uses Neural Architecture Search (NAS) to prune → Llama 3.1 (253B) - Distilled and pretrained using NVIDIA NeMo for efficient deployment 🎯 𝙎𝙪𝙥𝙚𝙧𝙫𝙞𝙨𝙚𝙙 𝙁𝙞𝙣𝙚-𝙏𝙪𝙣𝙞𝙣𝙜 𝙒𝙞𝙩𝙝 𝙍𝙚𝙖𝙨𝙤𝙣𝙞𝙣𝙜 - 60B tokens from curated real synthetic prompts - Prompts from Chat, Math, Code, Instruction-Following, and Function Calling - Responses vetted by Llama 3.3 70B, DeepSeek-R1, Qwen 2.5, QwQ-32B 🧠 𝙍𝙚𝙞𝙣𝙛𝙤𝙧𝙘𝙚𝙢𝙚𝙣𝙩 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙁𝙤𝙧 𝙍𝙚𝙖𝙨𝙤𝙣𝙞𝙣𝙜 - 253B SFT model refined using RL with Llama 3.3 70B as a judge - 140k H100 GPU hours across 240k reasoning Q&A samples - Curriculum-based filtering using Llama Nemotron Super (49B) 🧑‍🏫 𝙍𝙇 𝙛𝙤𝙧 𝘼𝙡𝙞𝙜𝙣𝙢𝙚𝙣𝙩 & 𝘾𝙝𝙖𝙩 - Instruction Following: 30k prompts → tuned with NeMo reward verifier - HelpSteer 2: 50k chat alignment prompts → RL with Llama Nemotron Reward 🚀 𝙁𝙞𝙣𝙖𝙡 𝙍𝙚𝙨𝙪𝙡𝙩? Llama Nemotron with Reasoning NIM (253B) — a production-grade, reward-tuned, RL-enhanced model built for enterprise agent applications. Thanks to @llm_wizard , @tanay_varshney, Zhilin_Wang, Chintan Patel, Tugrul Konuk and Jiaqi Zeng for their blog post: Build Enterprise AI Agents with Advanced Open NVIDIA Llama Nemotron Reasoning Models 🔗 lnkd.in/dwSK9wCV Subscribe & stay tuned for more: 📬 lnkd.in/dSsBFgAN #llamanemotron #reasoningai #nvidiaopensource #nemonim #sft #curriculumrl #deepseek #llama3 #qwen #instructiontuning #chatagents #aialignment #llmstack #agenticaichat #agentinfrastructure #nvidiaai #aifoundationmodels #openllm #aiops #favikon #cloud #cloudcomputing #datacenters

7) So, what can you do with these now that they are "local?" - integrate with VSCode (example: @continuedev) - experiment privately and securely - integrate with local agents (example: LLMStack from @trypromptly and @ajhai - thx!)