Xiaoze Jin

HPC Centers to Prepare for Quantum Integration in eFTQC era High performance computing is evolving. And the intersection is with the world of quantum tech even in the early fault tolerant quantum computing era (eFTQC). A new report from Hyperion Research and Alice & Bob (https://lnkd.in/gfxAQw8P) argues that "eFTQC will accelerate solutions in critical HPC applications beyond classical supercomputing capabilities within the next five years". Transistor size, chip energy capacity, and energy production requirements have turned vendors and organizations to alternatives for future computing needs. The Hyperion "report recommends that HPC centers explore heterogeneous workloads by collaborating with quantum vendors today." Heather C. West, Ph.D of IDC recently posted (https://lnkd.in/gwB-JPfY) about this #hybrid evolution pointing to collaborations between 1) Microsoft & Quantinuum 2) Amazon Web Services (AWS) and IonQ 3) Google and NVIDIA 4) Microsoft and Atom Computing (I was honoured to be part of this announcement webinar) 5) IBM and AMD For companies looking to accelerate the hybrid evolution in computing, scanning these companies for their progress, reading the full Hyperion/Alice & Bob report and watching Heather's posts will keep your organization updated and well prepared for the hybrid future. Hilary Kaye Veronica Combs HKA Marketing Communications Dr. Martha Boeckenfeld Greg Spievak Goldy Hyder Joey Cahue Quantum Strategy Institute Reboot Communications Ltd. Cierra Choucair David Spotts Prof. Dr. Ingrid Vasiliu-Feltes ®© #quantumforbusiness #quantumtechnology #technology #computing #business #cto #ceo #cio #hybridcomputing Image source: Newswire/HKA Marketing Communications

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Voice AI is quietly hitting an inflection point 🔊 NVIDIA just published a great deep dive on scaling speech recognition for real-world voice agents - latency, streaming accuracy, robustness, and why “ASR quality” isn’t just about WER anymore. 👉 https://lnkd.in/enDUKHvG A few things that stood out: - Streaming + low latency is non-negotiable if you want natural conversations - ASR needs to handle interruptions, background noise, accents, and partial utterances, not clean demo audio - Scaling voice agents isn’t just model quality, it’s systems engineering If you want to actually try building or running voice agents instead of just reading about them, Pipecat is a great place to start: 👉 https://pipecat.ai This is the kind of progress that makes voice agents finally feel… practical. Not demos. Not prototypes. Production. Curious to see where the ecosystem lands this year 👀

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Software development is rapidly evolving from ‘telling computers what to do’ to ‘teaching computers what to do’. In my recent talk at the Silicon Valley AI Think Tank, I expressed the importance of training your own proprietary AI models and walked through the steps involved. The most important lesson was around sourcing large volumes of quality data.

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Great article by Ashpreet B. on adaptive AI agents and continuous learning AI. Highly recommended reading for agentic engineering. https://lnkd.in/gZQknWrn

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📈 More LLMs, higher quality!! 💰 Gemini, Cursor (Composer) & GPT-5 reviewed Claude plans & implementation on 27 PRs Only 35% of issues were found by multiple LLMs. The rest of the findings? Completely unique to a single tool. Claude caught a CSRF bypass and WebSocket auth gaps that nobody else touched. GPT-5 flagged flow-breaking type errors. Cursor caught race conditions and a few bugs. Dropping any one tool would leave blind spots. Claude is the strongest reviewer, however, in Claude Code it has the most context as well. Subjectively, Cursor Bugbot delivers best insights & actual bugs on Github - Composer run on local changes is not as productive. My next steps are 1) Quantify quality of Copilot & Bugbot reviews on Github 2) try claude-octopus (link in comments) & acpx for stability of the setup

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One way to get better results with LLMs is to grow the training corpus and build larger architectures. Another way that is emerging is to allocate more computation time for inference. In the following paper, the authors propose a novel inference time scaling approach by adapting particle-based Monte Carlo methods -- I really like that idea; partly because it intends to utilize probabilistic modelling for inference, partly because I favor SSMs, and partly because this kind of approach will make it possible to run LLMs locally with decent results. Here is the link: https://lnkd.in/dFUQnT3X Enjoy! #AI #LLM #Inference #ProbabilisticModeling

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Qdrant Launches Qdrant Cloud Inference to Unify Embeddings and Vector Search Across Multiple Modalities https://lnkd.in/gtYesBZi #Technologynews #AI #Technews #TechLeadership #ITStrategy #FutureOfIT #TechTrends

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Industry benchmarking of cutting-edge LLMs like Claude Opus, Gemini 3, and Codex 5.2 drives discussion on AI's current maturity in software engineering. Recent evaluations underscore rapid advancements, prompting enterprises to reassess development workflows. By 2026, strategic integration of context-aware systems will reshape engineering roles toward higher-value tasks. Early adopters leveraging retrieval-augmented generation and LLMOps platforms demonstrate measurable gains in productivity and innovation velocity. For technology leaders, proactive adaptation remains critical for sustaining competitive advantage in AI-driven... #SoftwareEngineering #AITransformation