Jie Li

great post on the intersection of AI workloads and Open Container Initiative "The State of OCI Artifacts for AI/ML" https://lnkd.in/gbXZykMq

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I just open-sourced langgraph-factory — a reference implementation of a multi-agent code generation pipeline built with LangGraph and locally-hosted LLMs. The pipeline takes a natural-language spec and produces a complete, buildable Next.js project. No cloud APIs at runtime. No per-token costs. Everything runs on Docker Model Runner. The architecture uses two models with different roles: • gpt-oss 20B as the "foreman" — architecture planning, manifest review, build failure diagnosis • qwen3-coder 80B as the "coder" — file generation and targeted fixes What made this interesting to build wasn't the generation itself — it was the recovery infrastructure. Build failures are inevitable when an LLM generates 20+ files. The pipeline handles them in layers: deterministic pattern-matched fixes first (no LLM call), then reviewer-guided fixes, then spin detection, then failure-informed regeneration. Most runs succeed in 1-2 build attempts. Sample run on a blog platform with markdown rendering, admin CRUD, and comments — 14 app-specific files generated on top of a 13-file scaffold, including API routes, admin pages, and a comment system: → 266 seconds total → 24 files generated → 1 build failure (2 ESLint errors) → Reviewer diagnosed in 3.6s, coder fixed in 24s → BUILD OK The repo includes the full pipeline, two working specs, a complete sample run log, and a breakdown of the hardware economics. For teams working with proprietary codebases: your prompts and generated code never leave your infrastructure. Nothing sent to a third-party API, nothing logged by a provider. One small detail I'm proud of: check the Contributors section. https://lnkd.in/eNRmnCNj #LangGraph #LocalLLM #AIEngineering #MultiAgent #CodeGeneration #OpenSource #DGXSpark #NVIDIA

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AI agents are only as powerful as their memory. Redis has been named the #1 data storage choice for AI agents in 2025. The reason? Redis delivers what AI truly needs: - Real-time vector storage - Semantic caching - Long-term memory for LLMs At Bassirah, we see this as validation of why Redis is central to the next generation of AI-native data platforms powering everything from agentic RAG systems to enterprise decision engines. 🔗 Read more: https://lnkd.in/db47k6zG #AI #Redis #RAG #LLM #DataEngineering #AIAgents #ArtificialIntelligence

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Introducing Tunix: A JAX-Native Library for LLM Post-Training Tunix(Tune-in-JAX) is a JAX based library designed to streamline the post-training of Large Language Models. It provides efficient and scalable supports for: Supervised Fine-Tuning Reinforcement Learning (RL) Knowledge Distillation https://lnkd.in/gB9W9bMS https://lnkd.in/gkDt9jx2

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Redis 8 is GA and packed with new features, including new data types, faster commands, higher throughput, and faster replication. Learn more about our most performant and scalable version yet: https://lnkd.in/eFU5qPDs

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Autonomous Code Evolution Meets NP-Completeness Authors Cunxi Yu Rongjian Liang CHIA-TUNG (Mark) HO Haoxing Ren NVIDIA AI Abstract Large language models (LLMs) have recently shown strong coding abilities, enabling not only static code generation but also iterative code self-evolving through agentic frameworks. Recently, AlphaEvolve \cite{novikov2025alphaevolve} demonstrated that LLM-based coding agents can autonomously improve algorithms and surpass human experts, with scopes limited to isolated kernels spanning hundreds of lines of code. Inspired by AlphaEvolve, we present SATLUTION, the first framework to extend LLM-based code evolution to the full repository scale, encompassing hundreds of files and tens of thousands of lines of C/C++ code. Targeting Boolean Satisfiability (SAT), the canonical NP-complete problem and a cornerstone of both theory and applications. SATLUTION orchestrates LLM agents to directly evolve solver repositories under strict correctness guarantees and distributed runtime feedback, while simultaneously self-evolving its own evolution policies and rules. Starting from SAT Competition 2024 codebases and benchmark, SATLUTION evolved solvers that decisively outperformed the human-designed winners of the SAT Competition 2025, and also surpassed both 2024 and 2025 champions on the 2024 benchmarks. #ai #code #solvers #satlution H/T Cecile G Tamura Link to paper: https://lnkd.in/dHtNUJCQ Kelly Cohen Zack Huhn Summer Crenshaw Lohith Srikanth Pentapalli Scott Phillips

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📍 Great Asia AI Summit happening live now: https://lnkd.in/gavKb_-C Just experienced Multi-Agent Systems: The Foundations Powering Tomorrow’s Enterprise AI with Prof. Michael Wooldridge, at the Great Asia AI Summit 2026. He shared how intelligent systems make decisions, collaborate with humans, and operate in complex enterprise environments. Key Takeaways: 1️⃣ Agents can proactively assist humans by automating tasks and supporting decision-making. 2️⃣ Standards like Model Context Protocol enable smoother interaction between agents and tools. 3️⃣ Agentic systems can coordinate workflows and tackle complex problems at scale. Onward to more ideas, conversations, and inspiration.🌟 #GreatAsiaAISummit #EnterpriseInnovation #FutureOfWork #AppliedTechnology #Leadership Salesforce

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Learn how to implement the Maximum Marginal Relevance (MMR) algorithm with Elasticsearch and Python, which includes code examples for vector search reranking: https://gag.gl/q0iEvL #ElasticSearchLabs

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