Barry Warsaw

Bandwidth-Aware Traffic Engineering: First Principles for Real Networks Image courtesy: researchgate.net Traffic engineering exists because networks are finite. Links have limits, traffic competes, and assumptions eventually collide with physics. Over decades of network evolution, architectures and control systems have come and gone, but the fundamental challenge has not changed: How to move traffic through a shared infrastructure without exceeding what the network can safely deliver? In recent years, many networks have drifted away from strategic bandwidth management in favor of looser, tactical approaches....

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What are your thoughts on these blog posts from Anthropic. I'm a bit torn. On the one hand, its pretty impressive that Claude was able to create a working optimized C compiler but it feels misleading to say this was "from scratch" (quote from their blog) given the agent used GCC for comparison testing of its own output. The deeper you read into the article the more it seems like Claude resorted to trial and error for key parts of this task. https://lnkd.in/eREZy_Px Quoted from the blog: "The fix was to use GCC as an online known-good compiler oracle to compare against. I wrote a new test harness that randomly compiled most of the kernel using GCC, and only the remaining files with Claude's C Compiler. If the kernel worked, then the problem wasn’t in Claude’s subset of the files. If it broke, then it could further refine by re-compiling some of these files with GCC."

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Our work in memory for agentic systems covered this benchmark, so we gave it a go. There's a lot more to memory than measured by LongMemEval, but it's the best there is for multi-conversation benchmarking. And it exposes some key points with regard to the limits of even large context language models.

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As COO at www.jaiinfoway.com this is an important signal for where agentic AI is heading. Non technical implementations are catching up fast but the real story is iteration speed and learning loops. Building Cowork entirely with Claude Code shows how agents are becoming first class workers not just assistants. At www.jaiinfoway.com we see this shift clearly as enterprises move from copilots to autonomous task execution with guardrails. UX will evolve tooling will mature but the direction is set. Curious how others here see non technical users adopting agent driven workflows this year. Are we ready for agents as coworkers. #Jaiinfoway #AgenticAI #Claude #AIAgents #EnterpriseAI #FutureOfWork #AIProductivity #AITransformation #AILeadership

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I've released standalone executables (Windows and macOS, with Apple Silicon / MPS support) for our open-source AI-based photo-editing tool. The tool provides full control over the camera processing pipeline, supports multiple picture styles and style mixing, enables unlimited post-capture editing after saving without keeping the original DNG files, and relies heavily on AI-based models throughout the ISP pipeline. You can try it out without installing Python or any external libraries here: https://lnkd.in/gzq6x74p Feedback is very welcome, so please feel free to leave a comment or send me a direct message.

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My latest blog post: Using AI to promote divergent thinking A possible explanation for why we observe such a discrepancy between the productivity gains from AI tools of seasoned senior software engineers versus their less-experienced counterparts: Inexperienced problem solvers have a tendency to get stuck when using AI tools to solve complex problems because they bias towards convergent thinking: narrowing possibilities and looking for a specific solution which fits within their preconceptions. Experienced problem solvers who have adopted AI tools tend to use it to augment a scientific process, which includes building mental models, formulating hypotheses, and performing experiments. In this process, they use AI to accelerate divergent thinking: exploration and discovery of possibilities. (Blog post link in the first comment)

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The most impactful and important SDK release Trainiun Neuron team has worked on since inception: Native PyTorch support for Trainium, open source kernel (NKI) compiler and pre-optimized NKI kernel library, Neuron Explorer (IMO, the fastest and most intuitive ML profiler in industry), optimized inference implementation for major MoE and video models (GPT-OSS, Qwen, Pixtral….), complete vLLM v1 support !! All with full support for Trainium3. Using Trainium, whether for research, training , or large scale inference just became more intuitive and simple. https://lnkd.in/gjfhjv4j

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Thank Dani Cherkassky Great piece on the architecture shift voice AI needs. The core thesis: cloud-centric voice systems fail in real-world conditions because they are too slow, too expensive to keep always-on, and deaf to spatial context. The solution is a hybrid architecture—fast, always-on edge processing (Spatial Hearing AI and Cognition AI) handling 80% of interactions locally, with cloud LLMs reserved for complex reasoning. But the Kahneman thesis runs deeper than the article suggests. System 1 and System 2 are not just useful metaphors—they reflect an actual biological sequence. Fast, intuitive processing (System 1) evolved first; slow, deliberate reasoning (System 2) was layered on top. The brain's architecture reflects this: spatial-acoustic processing happens in milliseconds at the brainstem and primary auditory cortex, long before language centers engage. Kardome is not just borrowing Kahneman as marketing. Their architecture is neurologically correct—it replicates the hierarchical, parallel, spatially-aware processing that makes human auditory cognition so robust. Here is the deeper point: hearing preceded language in human evolution, and spatial hearing preceded both. The mammalian auditory system evolved to detect predators and locate prey—survival functions that required sub-second latency and continuous environmental awareness. Language processing was layered on top of this infrastructure. Current voice AI inverts this evolutionary logic: it starts with language models and treats spatial audio as an afterthought. Kardome restores the natural hierarchy. 🙌 Danny Shapiro, Rory Sutherland

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