Rust Migration of Critical Infrastructure Libraries

Exciting milestone from Asari AI! 🥳 🥳 Asari AI"s agents migrated critical C libraries to Safe Rust, including flight software for the European Space Agency 🛰️ Our agents reasoned over system design, conducted deep verification, and even found bugs that expert reviewers missed for years. This matters because: 🔒 70% of critical security vulnerabilities come from memory safety issues in C 📊 Billions of lines of infrastructure code need migration ⚙️ Manual translation doesn't scale; existing AI struggle with the complexity Read more here: https://lnkd.in/gbkvCK7T I've always believed that the bottleneck for human progress is the massive overhead of implementation and verification. Asari AI's agents help us co-invent the future. This is just the beginning.

"Maestro" or System Engineering? I’ve seen many AI prompts that look like pure ego, but there is a significant technical reason why we give AI agents such distinct, hierarchical roles. 🤖 In a recent comment by Edmondo Porcu, the topic of "specialized agents" vs. "generalists" came up. When dealing with non-trivial tasks—like system architecture or complex backend logic—generalist models often lose context or become overly verbose. Defining a strong identity (like the "Maestro" and "Assistant" dynamic) isn't about vanity; it’s about Flow Control: State Management: Forcing an AI to close with a specific trigger like "Back to you, Maestro" acts as a deterministic signal. In multi-agent architectures, this is essential for hand-offs and knowing exactly when a task is complete. Context Anchoring: High-stakes engineering (think: building a C compiler in Rust!) requires the model to stay "on tracks." A rigid role acts as a guardrail against context drift. Specialization over Generalization: By moving away from a model that tries to "do everything," we shift toward a team of experts who know their specific boundaries and who they report to. Conclusion: Do you prefer an "authoritative" approach to keep your agents focused, or do you lean toward more neutral, structured prompts like JSON schemas for control? #AI #PromptEngineering #SoftwareArchitecture #LLM #MultiAgentSystems #BackendEngineering

I’ve been exploring a new kind of AI model that replaces attention networks found in LLMs with physics-inspired wave propagation. Most modern language models use attention to figure out how words interact with each other. Attention scales at O(n²). This wave-based approach scales at O(n log n). What does that actually mean? If we feed both systems a prompt of 1,000 tokens: • Attention requires ~1,000,000 pairwise interactions • The wave approach requires ~10,000 That’s a massive difference in both the time and cost to run an LLM. Novel ideas like this could be the difference in taking an AI bill from $400 to $40 It’s still experimental, but I love seeing architecture ideas cross domains like this. Reframing language modeling as physical wave propagation instead of token-to-token routing opens up interesting possibilities. Curious to see where it goes. Check out the Repo: https://lnkd.in/gBKSZUD4

𝐀𝐧𝐭𝐡𝐫𝐨𝐩𝐢𝐜'𝐬 $𝟐𝟎𝐊 𝐀𝐈 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭: 𝟏𝟔 𝐀𝐠𝐞𝐧𝐭𝐬 𝐁𝐮𝐢𝐥𝐝 𝟏𝟎𝟎,𝟎𝟎𝟎-𝐋𝐢𝐧𝐞 𝐂 𝐂𝐨𝐦𝐩𝐢𝐥𝐞𝐫 𝐢𝐧 𝐑𝐮𝐬𝐭 Anthropic deployed 16 Claude Opus 4 AI agents to construct a 100,000-line C compiler in Rust from scratch. This ambitious project cost approximately $20,000 in API fees, showcasing "agentic software engineering at scale." The compiler successfully handles real-world C programs, passing a substantial portion of standard test suites, marking a significant new frontier for AI in complex systems software. The $20,000 price is remarkably low for a project of this scale, hinting at transformative cost structures in software development. However, this figure covers only API compute costs, not the significant human engineering effort for design, orchestration, and oversight. The initiative highlights AI dramatically amplifying engineer productivity, rather than replacing human roles. Key enablers included rigorous test-driven development and Rust's strict type system, which acted as a critical guardrail for the AI agents. This project suggests a future where software engineering skills pivot towards designing systems, decomposing problems, and orchestrating AI agents. For enterprises, investing in clear system architecture, strong static analysis, and comprehensive test infrastructure will be crucial for effective AI adoption. #AI #SoftwareDevelopment #Engineering #Anthropic #FutureOfWork #HammadZahoor

Anthropic's artificial intelligence Claude Code creator predicts software engineering title will start to go away in 2026--- generative artificial intelligence ---

Optimizing AI-generated code for industrial applications Anthropic's Claude Opus 4.6 has successfully generated a full C compiler using a team of 16 AI agents. This breakthrough showcases the potential for AI to autonomously develop complex software tools, marking a significant shift in traditional software development paradigms. The current architecture of AI-generated code introduces several new components and challenges. The system design now includes: AI Agent Collaboration, a network of 16 AI agents working in parallel to generate the compiler; Cost Model, with a high regeneration cost of $20,000 per instance; Quality Assurance, mechanisms for ensuring consistent quality and error detection; and Maintainability, new tools and processes for maintaining the generated code. The implications for industrial automation and data management are profound but also challenging. The need for human oversight to ensure consistent quality and error detection, the generated code's slower and less reliable performance, the high cost and low maintainability of the code, and the risks to data integrity, security, and stability, especially in data-sensitive industries, are significant. As we continue to explore the potential of AI-generated code, what strategies can we implement to reduce the economic cost, improve performance, and enhance maintainability without compromising on quality and security?  Telegram: https://t.me/LLCAlliti #AIInnovation #CodeGeneration #AutomatedSoftwareDev #FutureOfProgramming #AIChallenges #TechBreakthrough #AIQualityAssurance #Claude

Working at Praetorian has given me a front-row seat to the future of development, and I’m genuinely blown away. I find myself reaching for our AI orchestration platform multiple times an hour. It’s become that integral to how I work. The deterministic approach we’ve built isn’t just another AI tool; it’s a fundamental shift in how autonomous development can work at scale. If you’re in tech or security and haven’t started integrating AI into your workflow, the gap is widening fast. Worth a read if you’re curious about where platform architecture for AI agents is headed.

Check out this post by Brij kishore Pandey RAG is no longer a “technique.” It’s a whole ecosystem — and a developer’s stack. Over the past 18 months, Retrieval-Augmented Generation (RAG) has quietly evolved from a simple “search + LLM” pattern into a complete engineering discipline. Today, building AI systems isn’t just about choosing a model. It’s about architecting the entire pipeline — from extraction to embeddings, from vector databases to evaluation, from closed models to open-source innovators.

🗂️ A FILE SYSTEM ABSTRACTION FOR CONTEXT ENGINEERING Context engineering is getting messy: prompts, retrieval, tools, memory stores, logs. Many teams build fragile pipelines where context artefacts are transient and hard to audit. This paper proposes a file-system abstraction for context engineering, inspired by the idea that “everything is a file.” The appeal is governance: uniform handling of heterogeneous context pieces through mounting, metadata, and access control. Why this matters in practice: • Persistent context improves traceability and accountability • Uniform interfaces reduce glue code and hidden dependencies • Access controls become clearer, especially for sensitive data • Audits become possible without reconstructing the entire pipeline If you are building enterprise AI, context is not just input, it is infrastructure. How are you managing provenance and access control for your context pipeline today? Sources: https://lnkd.in/g2qM4h6X #ArtificialIntelligence #RAG #MLOps #EnterpriseAI #DataGovernance

Sorry Anthropic, but generating a compiler isn't what software engineering is about. While an impressive feat and certainly nothing that could be created by a team of humans in that amount of time, it's not the normal problem our industry is faced with. Normally, we are faced with discovery. Long-term. If AI is going to be useful for software engineering it has to be good at following instructions (or finding ways) for preserving optionality in software systems. And facilitating discovery. Reproducing what is already discovered might also be useful as a foundation. But I'm not sure it's fundamental.