Ashutosh Kulkarni’s Post

3. Core ComponentsQuantum Algorithm Library – Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), quantum neural nets. – On-demand Jupyter and notebook integrations.AI/ML Platform – Prebuilt pipelines (data ingestion, feature engineering, model training) using Kubeflow and MLOps best practices. – Automated retraining triggers based on data drift metrics.Automation Framework – Declarative YAML configurations for environments, experiments, and deployment targets. – CLI tools (qai-deploy.sh, qai-monitor.sh) and REST endpoints for CI/CD integration.Data & Knowledge Services – Graph-based metadata store with SPARQL and GraphQL endpoints. – Data quality rules and observability via embedded tests.Observability & Compliance – Grafana-powered dashboards, Prometheus metrics, and alerting. – Policy-as-code enforcement (OPA) and secure audit logs.4. Typical WorkflowDeveloper writes a quantum-AI experiment spec in YAML.Commits to Git; CI pipeline runs unit tests, style checks, and security scans.On merge, qai-unified-master.sh --train spins up a Kubernetes namespace, mounts datasets, launches quantum simulator + GPU nodes.Results and model artifacts register in the model registry; drift monitors activate.Automated canary deployment and guard-rail checks before full production rollout.5. Governance & SustainabilityESG Integration: Measure carbon footprint for each quantum and GPU job. Optimize placement for renewable-powered datacenters.Ethical AI: Bias detection checks in data and model outputs.Patents & IP: Central repository tracking invention disclosures, patent filings, and licensing terms.6. Roadmap & ExpansionFederated Quantum Networks: Cross-organization QPU sharing via secure multi-party protocols.Edge Quantum-AI Agents: Lightweight agents running inference on specialized photonic quantum accelerators.Autonomous Research Assistant: LLM-driven virtual researcher for experiment design, code generation, and automated documentation.This unified overview captures the end-to-end design philosophy, technical building blocks, operational workflows, and strategic vision of QAI Digital Quantum Aura AI Ecosystems—positioning it as a world-class quantum-AI platform and an emerging digital empire.

🚀 A Trillion-Parameter Leap: Ring-1T Sets New Open-Source Standards What if AI power at trillion-scale was open to everyone—not just tech giants? Meet Ring-1T, the world's largest open-source model: • 1 trillion parameters MoE architecture (50B active) • Outperforms Gemini 2.5 & many closed models on major reasoning/coding benchmarks • Fully open weights & code—researchers, engineers, and startups can build anything • State-of-the-art RLVR + Mixture of Experts • Handles 128K context windows Why it matters: - Democratizes massive-scale, high-reasoning AI; boosts innovation for all - Empowers anyone to advance research in math, code, logic, and more 📎 The attached image breaks down key highlights. Links in the comments. How do you see trillion-scale open AI changing real-world applications?

It doesn't make much sense to see this visual, but GitHub uses it to track activity of commits on a repository. There's a long no touch zone that happened on Neural Profit: it had to do with it made $, but the vision for the next step was architecturally non-defined. One day that vision clicked and the hardest parts of the platform are automated; it also had to do with scope creeping on the neural networks...when you have a new AI architecture pattern for finance that nobody else has; it's your edge and that seems like the best place to focus; but once its working...the $ has to go to the right places on the platform.

Huawei open-source AI development platform gets implementation details at HUAWEI CONNECT 2025, including CANN compiler specs, Mind series toolchains, and December 31 release timeline for PyTorch integration.

𝐈𝐟 𝐲𝐨𝐮 𝐚𝐫𝐞 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 - 𝐭𝐡𝐢𝐬 𝐩𝐨𝐬𝐭 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐜𝐡𝐞𝐚𝐭 𝐬𝐡𝐞𝐞𝐭. LangChain has become the *de facto framework* for building AI agents. But most engineers still get lost in its ecosystem of tools and terminologies. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝟐𝟎 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 𝐭𝐞𝐫𝐦𝐬 𝐞𝐯𝐞𝐫𝐲 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐬𝐡𝐨𝐮𝐥𝐝 𝐤𝐧𝐨𝐰 🧠👇 From Chat Models and Memory - to RouterChain, RAG, and LangServe - each term represents a building block in how modern Agentic AI systems are designed and deployed. If you understand these 20, you’re no longer “just integrating OpenAI.” You are *architecting intelligence* - building systems that can reason, act, and scale. LangChain is not just a framework anymore. It is the backbone of the AI agent era. ♻️ Repost this to help your network catch it before it blows up. ➕ Follow Anurag(Anu) Karupartifor more.

Building onchain just got a major upgrade 📈 AI tools aren't just experimental, they're actually core infrastructure for developers who want to ship faster and build better. Whether you're debugging smart contracts or querying cross-chain data, the right AI setup can make all of the difference (seriously). Here are 6 of our favorite tools for onchain development 👇 Questions about building onchain? We're here to help: https://lnkd.in/dE-ZCcuF

Most of us have hit a wall: foundation models are impressive but don't understand your lab or your company's domain language. The "almost but not quite" problem gets expensive fast. Mira Murati's new company, Thinking Machines Lab, raised $2B to tackle this. Their approach (Tinker) gives you 90% of algorithmic control while removing 90% of infrastructure pain. You write Python, they handle the distributed GPU headaches. Real example: one healthcare group cut clinical documentation time by 70% by fine-tuning on their institution's specific terminology and workflows. Generic models knew medicine, but not their medicine. Wrote up a longer analysis on when fine-tuning makes sense versus RAG or prompts, and why this abstraction level matters: https://bit.ly/46Dn8gu Worth a read if you're thinking about customizing models for your specific domain.

🔥 Techniques I’d Master If I Wanted to Make LLMs Faster + Cheaper I’m starting a new series diving deep into how to optimize large language models — the exact techniques used to make them run faster, cheaper, and more efficient in production. Over the coming weeks, I’ll share breakdowns of key ideas — how they work, when to use them, and why they matter. Here’s the lineup: 1. Quantization 2.KV-Cache Quantization 3. Flash Attention 4. Speculative Decoding 5.LoRA 6. Pruning 7. Knowledge Distillation 8. Weight Sharing 9. Sparse Attention 10. Batching & Dynamic Batching 11. Model Serving Optimization 12. Tensor Parallelism 13. Pipeline Parallelism 14. Paged Attention 15. Mixed Precision Inference 16. Early Exit / Token-Level Pruning 17. Operator Fusion 18. Graph / Compiler Optimization (ONNX, TensorRT, TVM, XLA) 19. Memory-Efficient Attention (xFormers, Flash-Decoding) 20. Prompt Caching / Response Reuse 21. Mixture-of-Experts (and Expert Merging) If you care about LLM performance, scalability, or cost optimization, you’ll find this series useful. #LLM #AI #DeepLearning #Optimization #MLOps #AIEfficiency #ModelServing

A recent debugging experience reminded our team that sometimes, the most complex issues come down to a single overlooked word. While integrating an AI model recently, one of my colleague encountered a persistent compiler error: ❌ “model gemini-1.0 not supported” The team did what most modern engineers do — we relied on AI assistance, refactored multiple modules, and revisited the logic several times. Yet, the issue persisted. After two days of iteration and confusion, we revisited the official documentation. The fix turned out to be remarkably simple: ✅ replacing "gemini-1.0" with "gemini-2.5-pro". A small detail — one version string — had halted progress for nearly 48 hours. In today’s AI-driven development environment, it’s easy to skip foundational debugging steps and overtrust automated tools. But effective engineering still starts with careful observation — reading, understanding, and questioning the exact error before reacting. AI can accelerate problem-solving, but it can’t replace engineering intuition. Sometimes, the difference between two days of debugging and two minutes of clarity is one line — or even one word. Every engineer has that one moment that humbles them. For us, this was a quiet reminder: Before asking AI for solutions, ask the error what it’s really saying. #AI #SoftwareEngineering #Debugging #TechLeadership #ProblemSolving #DeveloperMindset #LearningByBuilding