Sahil Mangotra

📣 Simular has raised a $21.5M Series A to build AI-powered computer-use agents! When SPC Founder Fellows Ang Li and Jiachen Yang (former DeepMind scientists) joined SPC in 2023, they knew there was a way to make workflows completely autonomous. Now at Simular, they're building AI agents that handle tasks the way a knowledge worker would: autonomously. The agents navigate interfaces, complete workflows, and operate across all applications. Their breakthrough came with Agent S, an open agentic framework that hit 69.9% success on OSWorld, the industry's toughest benchmark for computer task completion. That's nearly human-level performance (72%). Now they're launching Simular 1.0: a native desktop agent that browses & operates in the background, across applications. It repeats successful tasks, learning from user corrections like a real teammate. Users can redirect the agent mid-task using natural language, and contextual triggers launch workflows automatically based on real-time activity. The technology is already processing millions of steps across insurance, recruiting, and travel workflows. Their hybrid system combines neural exploration with symbolic execution and continual learning to deliver enterprise-ready autonomy. The $21.5M Series A was led by Felicis Ventures, with participation from nVentures, Basis Set Ventures, Flying Fish Partners, SPC, and angel investor Lenny Rachitsky. Congratulations Ang Li, Jiachen Yang, and the Simular team!

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Ray has emerged as a leader in distributed AI compute because it is Python-native and designed for multimodal, heterogeneous hardware (CPU+GPU) workloads such as multimodal data processing, distributed training, and online inference. But as Ray workloads move toward production, new challenges appear. Code that worked on a single node, now breaks in a multi-node cluster. Debugging failures takes longer with logs that dissappear with cluster shutdown. And GPUs scarcity forces teams have to build workflows to run and manage clusters across clouds. This is why Ray needs a platform, one made of purpose-built developer tooling and repeatable cluster operations that ensure reliability and scalability on any cloud. See how Anyscale can be that production-ready platform for AI builders: https://lnkd.in/gNHhAMVt

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🎯 Announcing Cortex: Redefining Enterprise AI Reliability After months of R&D, we’re thrilled to unveil Cortex — a Cognitive Control Layer that makes Large Language Models trustworthy, verifiable, and enterprise-ready. 💡 The Problem: LLMs are powerful but inherently unreliable. Hallucinations, lack of provenance, and missing audit trails make them risky for critical enterprise applications. 🛠️ The Cortex Solution: We externalize trust, memory, and verification from the model. You don’t rely on the AI — Cortex guarantees reliability through a structured, auditable system. 🌟 What Makes Cortex Unique: 🔌 Model-Agnostic — Works with any LLM. Swap models without changing your logic. 🧠 Semantic Verification — Claims validated via embeddings against structured memory. 📊 Auditable Memory — Full traceability: source, confidence, timestamp. 🛡️ Enterprise Security — Built-in threat detection for prompt injection, memory poisoning, and more. ✅ Transparent Uncertainty — Every response includes explicit confidence levels. 📈 Why Enterprise Leaders Should Care: No more hoping the model “gets it right.” Every response is verified, auditable, and reliable, making Cortex ideal for production deployment. 🔗 Explore Cortex: cortex.coretexia.com 📥 Download: White Paper & Architecture Spec available Built with love & purpose by the Coretexia team. #EnterpriseAI #TrustworthyAI #CognitiveComputing #LLM #AIInnovation #TechLeadership #MachineLearning

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Yongkun's work on low code programming with LLM vs traditional support: Liu, Y., Chen, J., Bi, T., Grundy, J.C., Wang, Y., Yu, J., Chen, T., Tang, Y., Zheng, Z. An Empirical Study on Low Code Programming using Traditional vs Large Language Model Support, to appear in Journal of Systems and Software, Elsevier https://lnkd.in/gp93rbY2 Low-code programming (LCP) refers to programming using models at higher levels of abstraction, resulting in less manual and more efficient programming, and reduced learning effort for amateur developers. Many LCP tools have rapidly evolved and have benefited from the concepts of visual programming languages (VPLs) and programming by demonstration (PBD). With the huge increase in interest in using large language models (LLMs) in software engineering, LLM-based LCP has begun to become increasingly important. However, the technical principles and application scenarios of traditional approaches to LCP and LLM-based LCP are significantly different. Understanding these key differences and characteristics in the application of the two approaches to LCP by users is crucial for LCP providers in improving existing and developing new LCP tools and in better assisting users in choosing the appropriate LCP technology. We conducted an empirical study of both traditional LCP and LLM-based LCP. We analyzed developers’ discussions on Stack Overflow (SO) over the past three years and then explored the similarities and differences between traditional LCP and LLM-based LCP features and developer feedback. Our findings reveal that while traditional LCP and LLMbased LCP share common primary usage scenarios, they significantly differ in scope, limitations, and usage throughout the software development lifecycle, particularly during the implementation phase. We also examine how LLMs impact and integrate with LCP, discussing the latest technological developments in LLM-based LCP, such as its integration with VPLs and the application of LLM Agents in software engineering.

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