A compact pane for fine-tuning parameters and monitoring value changes
panproto | schematic version control
One engine for schematic version control within and across any schema language.
Startup Program: Use Mixpanel for free for the first year - Mixpanel Docs
Read more about Mixpanel documentation
10 Linux Tweaks I Make on Every New Home Lab Server
These are the Linux configuration changes I make on every new home lab server to improve security, reliability, monitoring, logging, and long-term maintenance.
Reading a Performance Profile: Field Guide
An interactive field guide to the Chrome DevTools Performance panel: waterfall, flame chart, LCP, CLS, INP, and the Insights panel.
toolify.blue
A collection of metrics and tools to take your Bluesky experience to the next level.
panproto/panproto
One engine for schematic version control within and across any schema language.
Thireus/GGUF-Tool-Suite
Produce your own Dynamic 3.0 Quants and achieve optimum accuracy & SOTA quantization performance! Input a target size and the toolchain will create a GGUF recipe tuned to your hardware within seconds — flexible model sizing and lowest achievable perplexity/kld for GGUF enthusiasts seeking precise and automated dynamic quant production.
CUE
Configure Unify Execute Validate, define, and use dynamic and text‑based data Learn more Get started with CUE CUE makes it easy to validate data, write schemas, and ensure configurations align with policies. Get started learning about CUE with these links ..
Fine-Tuning LLMs is a Huge Waste of Time
People think they can use Fine-Tune for Knowledge Injection. People are Wrong
panproto/crates/panproto-lens-dsl at main · panproto/panproto
One engine for schematic version control within and across any schema language. - panproto/panproto
TanStack Table
Supercharge your tables or build a datagrid from scratch for TS/JS, React, Vue, Solid, Svelte, Qwik, Angular, and Lit while retaining 100% control over markup and styles.
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models | Hubert Banville
🧠 NeuralBench is now open source. Today we're releasing NeuralBench, a unified framework for benchmarking foundation models of brain activity, developed by the Brain & AI team at FAIR, Meta. 💻 Code: https://lnkd.in/dNJsgBgM 📄 White paper: https://lnkd.in/dvWMg7rx Brain foundation models are starting to show positive transfer to a range of downstream tasks, from brain-computer interfacing to clinical classification. But systematically evaluating them is hard: heterogeneous preprocessing pipelines, input structures, and adaptation methodologies make results difficult to compare. Most prior work also focuses on a narrow set of downstream tasks. NeuralBench addresses this by defining each task end-to-end with config files (data source, preprocessing, splits, optimiser, metrics, architecture) so all models can be evaluated on the same footing. What's in our first release, NeuralBench-EEG v1.0: ⚡ 36 EEG tasks across 94 public datasets, spanning motor imagery, clinical classification, cognitive decoding, and phenotype prediction. 🤖 Task-specific deep learning architectures (EEGNet, Deep4, EEGConformer, CTNet, ...) benchmarked side-by-side with recent EEG foundation models (BENDR, LaBraM, BIOT, CBraMod, LUNA, REVE). 🧩 Extensible to other neuroimaging modalities: the framework already runs MEG and fMRI tasks, leveraging our NeuralSet ecosystem for accessing brain imaging data and the broader neuroscientific software stack. 📜 Released under the MIT license. Help us make it better. Through our white paper, we invite the community to contribute new tasks, datasets, and models, especially for fMRI, MEG, and iEEG. The long-term goal is a fully unified benchmark across neuroimaging tasks and modalities. 🙏🙏🙏 This was a big team effort with Stéphane d'Ascoli, Simon Dahan, Jérémy RAPIN, Marlène Careil, Yohann Benchetrit, Jarod Lévy, Saarang P., Antoine Ratouchniak, Lucy (Mingfang) Zhang, Elisa Cascardi, Katie Begany, Teon Brooks, and Jean-Rémi King. Special thanks to Alexandre Gramfort, Thomas Moreau, Arnaud Delorme, Bruno A. and Pierre Guetschel for feedback and support. #Neuroscience #AI #NeuroAI #Python #OpenSource
At Bluesky, we measure what matters. As CIO, I’ve built a dashboard to track the key performance indicators we use for innovation. 📈 cio-command.fly.dev
Chief Innovation Officer — Command Center
cio-command.fly.dev