iFeeling Daily
Daily curated AI insights you can't miss.
Core dump epidemiology: fixing an 18-year-old bug
A fascinating debugging story from OpenAI's infrastructure team that reveals how building high-quality population-level data turned two seemingly impossible crashes—hardware corruption and an 18-year-old libunwind race condition—into solvable problems, with lessons for any engineer operating complex systems.
DiScoFormer: One transformer for density and score, across distributions
Many problems in machine learning and the sciences reduce to recovering a distribution from a finite set of samples, which requires estimating two quantities: density (how common values are) and score (the gradient of log-density, pointing toward more probable regions). Score estimation is central to diffusion-based generative models, Bayesian sampling, and particle simulations, but existing tools force a painful trade-off. Kernel density estimation (KDE) is generalizable and needs no training, but its accuracy collapses in high dimensions, while neural score-matching models stay accurate in high dimensions but must be retrained from scratch for every new distribution. This tension leaves practitioners choosing between generality and precision.
BigQuery AI.AGG() Deep Dive: Summarize Unstructured Data at Scale with SQL
BigQuery's new AI.AGG() function lets you summarize millions of rows of logs, product descriptions, or images using natural language in a single SQL query—automatically handling batching and context windows. It's a practical bridge between LLM-powered analysis and warehouse-scale data, especially useful when paired with other AI functions like AI.CLASSIFY() for structured output.
How Curve Uses BigQuery Graph to Catch Fraud Rings at Scale
Curve shows how moving from expensive SQL self-joins to BigQuery Graph's native GQL let them detect hidden fraud rings at scale, saving ~$12M in 2025. The insight is that keeping graph analytics inside your existing warehouse avoids the migration pain of specialized graph databases.
Turner Industries’ Blueprint for a Secure, Cloud-First Infrastructure
Turner Industries achieved 40-50% lower cost-per-device, 1,750 annual IT hours saved, and a simpler security model by moving to a ChromeOS, Google Workspace, and Cameyo stack. A concrete case for cloud-first infrastructure at industrial scale.
Run a Private vLLM Server on Hugging Face Jobs with One Command
This article shows you exactly how to stand up a private vLLM server on Hugging Face Jobs with one command, query it from anywhere with the OpenAI client, and scale it to larger models — all without provisioning servers or touching Kubernetes. It also covers SSH debugging, tool calling for agents, and when to use Jobs versus Inference Endpoints.
Which Tokens Do Hybrid Models Predict Better? A Token-Level Comparison
A token-level comparison of Olmo 3 and Olmo Hybrid shows that hybrid models excel on meaning-bearing words while transformers hold the edge on copy tasks and bracket matching. The authors demonstrate that overall loss is too coarse to reveal architectural tradeoffs and propose using filtered token losses as a more informative evaluation during pretraining.
Google Backup and DR adds cross-region backups for regional resilience
Google's Backup and DR Service now supports cross-region backups, letting you decouple backup destination from source region. This balances regional resilience with cost and compliance control, available for Compute Engine, Disks, and Filestore, with Cloud SQL and AlloyDB coming.
FFASR Leaderboard: Benchmarking ASR in Real-World Far-Field Conditions
The FFASR Leaderboard from Treble Technologies and Hugging Face is the first open, community-driven benchmark for far-field ASR, evaluating models across 14 simulated rooms with validated acoustics. Early results show far-field WER at low SNR is several times higher than near-field WER, making the real-world robustness gap visible and comparable for the first time.