Benchmarking should be a public good. Today, we are releasing a major update to Metriq, our platform for open, community-driven quantum computer benchmarking. 📰 And we've put out a paper describing the platform! Check it out: https://lnkd.in/es8jnYZn As the field moves toward quantum advantage, Metriq provides a shared, reproducible record of performance across the diverse hardware landscape. This release introduces a new collaborative workflow: 🔹 metriq-gym: An open-source Python toolkit to run benchmarks across providers. 🔹 metriq-data: A public, versioned dataset of results. 🔹 metriq-web: Interactive dashboards to track performance over time. Join us in the effort: Run benchmarks, peer-review data, or propose new suites via open RFCs. See you on GitHub! ⭐️ More details in the comments. 👇 #QuantumComputing #OpenSource #Benchmarking #Metriq #UnitaryFoundation

Today we are releasing a major update to Metriq, the platform for open, community-driven quantum computer benchmarking. It's been built by Unitary Foundation with a lot of help from the external community of open source contributors. Blogpost: https://lnkd.in/ePiPx7sX This release introduces metriq-gym, a new open-source toolkit for defining and running benchmarks across hardware providers, metriq-data, a public dataset of benchmark results, along with a new Metriq website, where results can be tracked and shared: http://metriq.info/ We invite the quantum community to suggest improvements, extend the benchmark suite, run experiments, and upload new results. As quantum computers evolve over time, the Metriq platform will evolve with them. Check out our new paper describing the platform, and see you on GitHub!

The Demo Gap Your demo works perfectly. On your laptop. On your GPU. On your data. In your Jupyter notebook. With your Python version. Ship it to production and it falls apart in ways that take engineers days to debug. This is the demo gap. And it exists because the development and deployment environment are two completely different technology stacks held together by Docker and hope. #NeuralEcosystems closes the gap. Same language. Same runtime. Development to production. No translation layer. What works on your laptop works everywhere. https://lnkd.in/d2EqRgzc #NeuralOS #NeuralSCRIPT #NeuralSCRIPT++ #NeuralCPU #NeuralGPU  #NeuralFUSE #NeuralRV #NeuralEDGE #NeuralDB #NeuralPIPE  #NeuralSENSE #NeuralAUTO #NeuralFUZZY #NeuralIP #NeuralSDR  #NeuralMESH #NeuralUI #NeuralZONE #NeuralGAURD #NeuralSHARE #NeuralGHOST #NeuralBIO #NeuralHEALTH #NeuralNAV #NeuralWEB  #UAE #Innovation

This week, I focused on a core problem in high-performance data pipelines: Broadcasting. The goal was to normalize delivery costs across multiple cities and weeks. In a typical Python environment, this would involve nested loops or redundant memory allocations to "match" data shapes. In NumPy, I used dimension alignment to trigger a "Zero-Copy" operation. By reshaping a 1D multiplier into a (5, 1) column vector, the C-engine "virtually" stretches the data across the 2D grid. Hardware Alignment for Engineering: Memory Efficiency: No actual copies of the multiplier were created in RAM. SIMD Acceleration: The operation runs at the silicon level, processing multiple data points per clock cycle. Clean Architecture: High-dimensional transformations expressed in a single, readable line of code. Mastering these "under-the-hood" mechanics is what allows Python to scale for heavy ML workloads. #DataScience #Python #NumPy #PerformanceEngineering #MachineLearning

LLMs have HuggingFace, LangChain, and Ollama. World models have... nothing. Every architecture — DreamerV3, TD-MPC2, Diffusion WM, V-JEPA2 — lives in its own repo with its own config format, its own training loop, its own data pipeline. Switching between them means rewriting everything from scratch. We built WorldFlux to fix this. One Python API. Every world model. Parity-tested. → Unified interface across architectures → Reproduces original paper results (verified) → Pluggable 5-layer design — swap components without forking The same abstraction layer that transformed LLM development is now available for world models. "pip install worldflux" Watch the 30-second teaser below ↓ What would you build if switching world models took one line instead of one week? #WorldModels #ReinforcementLearning #MachineLearning #AI #Python

We have been talking about how great the web-based notebooks are… But they do hit real computational limits. Try something simple like computing eigenvalues of a 1000×1000 matrix. You’re looking at seconds of runtime and ~40 MB of memory consumption just for this single experiment. And now imagine stacking multiple experiments on top of that. That’s where things start to break.   Now with the new compute notebooks you can: • Run multiple notebooks in parallel • Handle heavy numerical workloads • Terminate sessions instantly and reclaim memory • Keep your interactive apps separate from computation   In the short video-demo below: • Interactive notebook apps • Eigenvalue computation of a1000x1000 matrix   #Python #Engineering #DataScience #WebAssembly #Simulation #Computing #concurrency

Tired of complex infrastructure setup? The expanded #EOSCEUNode Tools Hub is your one-stop shop for research software, ready for instant deployment. From data processing to advanced analytics, access powerful tools like Galaxy for biomedical research and DaskHub for parallel computing in Python. ✅ One-Stop Access: A curated catalogue of reliable applications. ✅ For All Skill Levels: Deploy easily as a beginner or use TOSCA templates for advanced, reproducible workflows. ✅ Cost-Covered: Computational resources are provided by the European Commission. Ready to get started? We have everything you need: Watch the Demo Video: See how to allocate a Virtual Machine and set up tools in your User Space. Follow the Tutorial: "Tools Hub: Introduction for Researchers" Take the Course: "How to use the EOSC EU Node Tools Hub: A Complete Guide" 🔗 Explore the Tools Hub: https://lnkd.in/epvSbBEj

✅ Day 72 of 100 Days LeetCode Challenge Problem: 🔹 #3868 – Minimum Cost to Equalize Arrays Using Swaps 🔗 https://lnkd.in/gwbcmecy Learning Journey: 🔹 Today’s problem involved making two arrays identical with the minimum number of cross-array swaps. 🔹 Swapping within the same array is free, but swapping elements between arrays costs 1 operation. 🔹 I used Counter to count the frequency of elements in both arrays. 🔹 Then I combined the counters to check the total occurrences of each element. 🔹 If any element has an odd total frequency, it’s impossible to distribute it equally between both arrays. 🔹 Otherwise, I calculated the difference in counts between the two arrays to determine how many elements must be swapped. Concepts Used: 🔹 Frequency Counting (Counter) 🔹 Hash Maps 🔹 Greedy Counting Logic 🔹 Swap Balancing Key Insight: 🔹 For the arrays to become identical, every element must appear an even number of times across both arrays. 🔹 The imbalance of each element indicates how many swaps are required, and dividing appropriately accounts for pairwise swaps. Complexity: 🔹 Time: O(n) 🔹 Space: O(n) #LeetCode #Algorithms #DataStructures #CodingInterview #100DaysOfCode #SoftwareEngineering #Python #ProblemSolving #LearningInPublic #TechCareers

Hugging Face tokenizers now has riscv64 wheel support merged upstream, thanks to BayLibre's work on PR #1951. Once the next release ships, anyone on RISC-V can just pip install tokenizers without building from source. That's one of the most requested ML packages ticked off the list. On the WebAssembly front, two more PRs landed in Ocre Runtime this week (the Cloud Native Computing Foundation (CNCF) edge container project). A wasi-sysroot refactor and a follow-up fix, both tested on a banana pi Open Source Project F3. WebAssembly on RISC-V is getting real CI coverage now. 34 releases went out across my forks. The Docker stack hit v29.3.1 with containerd 2.2.1 and runc 1.4.0. DuckDB v1.5.1 was built on a native RISE (RISC-V Software Ecosystem) riscv64 runner on Scaleway. And Mistral AI Vibe v2.7.0 brings the Mistral AI coding CLI to riscv64 with a new conversation rewind feature. The riscv64 Python wheel index keeps growing: 50+ packages now, including PyTorch 2.10, cryptography 47.0, tiktoken 0.12, pydantic-core, numpy 2.5, and llama-cpp-python. All built natively on a BananaPi F3. OpenSCAD pushed 6 daily builds with .deb and .rpm packages for riscv64, arm64, and amd64. SDKMAN hit a snag: two PRs for riscv64 state support were closed due to an ongoing backend migration. Tracking the issue for when that settles. What's your experience with pip install on RISC-V? Curious how many packages just work for you out of the box. #RISCV #RISCVEverywhere #OpenSource #Python #Docker #EdgeAI #WebAssembly #DockerCaptain #devEco

Headline: AI Agents on 4GB RAM? I’ve spent the last few days pushing the limits of my 7-year-old laptop (Intel i5-7200U, 4GB RAM). Running Fedora 43 and Python 3.14, I realized that modern AI frameworks are simply too heavy for older hardware in that, they leak memory, crash on new Python versions, and struggle with small models. So, I built "NOEL." (Native Operations & Execution Layer) NOEL is a lightweight, "Direct-to-Shell" AI Assistant. Instead of relying on bloated Python agents, it uses a custom Bash bridge to communicate directly with local LLMs via Ollama. Key Project Highlights:  Bypassed Python 3.14 compatibility issues with a custom metadata shim.  Optimized for the 1.5B Qwen2.5-Coder model to ensure fast inference on 2 CPU cores.  Native Fedora integration: Noel handles journalctl logs and DNF package management seamlessly. 100% Private & Local: No data leaves my SSD. This project proves that the "Small Language Model" (SLM) revolution isn't just for high-end servers—it's for anyone with a terminal and a curious mind. Plans to do a test run to convert it to a full personal assistant AI is considered when i get a hold of better hardware resources. So at the moment we remain on the testing and breaking stage. Any support is appreciated 😁. Check out the full technical breakdown and the source code on my GitHub! https://lnkd.in/dyM37XhD #AI #Linux #Fedora #OpenSource #Ollama #EdgeComputing #Python #HardwareHacking #SelfHosted