10 github repos for system design interviews
a collection of 10 github repositories covering system design fundamentals, interview questions, and specialized topics for mobile and frontend roles.
aitag machine learning
summaries filed under machine learning
why centerloss hurts ood detection and multi-scale mahalanobis wins
a new method called goen uses multi-scale features and mahalanobis distance to beat deep ensembles on out-of-distribution detection, while revealing that centerloss harms performance.
ainemotron-labs diffusion speeds text generation
nvidia releases diffusion language models that generate multiple tokens in parallel and refine them, offering faster inference and flexible modes.
aipytorch opens ambassador applications for 2026
the pytorch foundation seeks new ambassadors to lead local communities, create tutorials, and mentor users, with applications open until june 18, 2026.
aispecialization beats scale in ai procurement
a 3b specialized model outperformed frontier apis at lower cost, showing training alignment matters more than parameter count.
aitext degeneration costs more than you think
a small fraction of autoregressive model requests can loop into repetition, inflating batch inference time by over 40% and exposing a blind spot in standard benchmarks.
aigemma 4 learns to inspect files and run code
a tutorial shows how to give gemma 4 tools for local filesystem exploration and restricted python execution, making it more agentic.
ailanguage models learn to forecast research idea success
a new method trains language models to predict which research ideas will work better without running experiments, using a dataset of 11,488 idea pairs from paperswithcode.
aino feature ranking is faithful, stable, and complete under collinearity
a new proof shows that feature rankings cannot be simultaneously faithful, stable, and complete when features are correlated, and proposes an ensemble method as a solution.
aibest small language models on hugging face now
a curated look at top small language models under 7b parameters on hugging face, with benchmarks and code to get started.
aineural mutual information estimation in masked sequence models
a neural estimator learns pairwise conditional mutual information from pretrained masked diffusion models, enabling faster parallel decoding by identifying independent variable subsets.
aifully looped transformer stabilizes iterative block reuse
a new transformer variant fixes training instability in looped models by distributing signals across layers and reusing attention states, enabling deeper iteration without extra parameters.
ai10 github repos to master quant trading
a curated list of github repositories covering strategies, frameworks, and tools for systematic quantitative trading development.
aisql window functions for real business problems
practical patterns for running totals, sessionization, cohort analysis, and percentile ranking using sql window functions.
aianonymize production data with mimesis
learn to use the mimesis python library to replace sensitive personal data with realistic fake data for safe data science work.
ailearning lagrangian multipliers for milp with guarantees
new research provides statistical learning bounds for using machine learning to predict lagrangian multipliers in mixed integer linear programming, showing generalization scales with problem size and sample count.
aihellora adapts moe models by targeting hot experts
hellora fine-tunes mixture-of-experts models by adding low-rank adapters only to the most active experts, cutting parameters and compute while boosting performance.
aicalibrated uncertainty cuts llm cascade costs by 31%
a calibration-first router uses isotonic regression to map token uncertainty to error probability, enabling cost-optimal escalation thresholds without per-workload tuning.
aiolmoearth v1.1 cuts compute costs up to 3x
a new family of remote sensing models reduces token sequence length to lower inference costs while matching previous performance.
aipython libraries for data engineering in 2026
a roundup of ten python libraries that improve data pipeline speed, reliability, and maintenance across orchestration, ingestion, quality, and storage.
ai