You're Not Paying for Compute. You're Paying for Memory Bandwidth
TL;DR— Inference cost conversations obsess over FLOPs and token prices, but the real constraint on LLM serving is memory bandwidth— specifically the c…
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От legacy до промышленной платформы: инженерная эволюция OSA в «Магнит»
Как мы провели проект через четыре «эпохи» — от ручных запусков на Windows‑планировщике до Spark + k8s на масштабе сети …
Can your AI agent actually manage ML infrastructure?
I’ve spent enough time in production environments to know that 'chatting with an AI' is a useless metric if the AI can't touch the actual hardware or …
Your Guardrails Are a Firewall. Your Failures Are a Cascade
TL;DR— Most production AI teams build safety layers using the content-moderation mental model: classify input, classify output, block or pass. But the…
Three weeks before the enterprise contract, the voice agent wasnt operator-ready.
Three weeks before the enterprise contract, the voice agent wasn't operator-ready Look. We had 99.2% uptime in staging. We had eval coverage on 1,400 …
Azure Databricks for MLOps and Feature Engineering at Scale with Apache Spark, Delta Lake, and MLflow
Raw data doesn't win model competitions. Features do. And when your raw data is tens of billions of rows sitting across multiple sources, you can't af…
What Is an Agent Registry? (And What We Broke Before We Had One)
TL;DR An AI agent registry is a centralized catalog of every agent in your organization — what each agent does, what tools it can access, what version…
MLOps for LLM: A Case Study on Dresscode
I've recently participated in the Gemma 4 challenge here on DEV.to, but fell short compared to many amazing projects. I really liked LIKAS . I encoura…
Channels-last memory format cut our conv backbone latency 22%
TL;DR: Switching our convolutional segmentation backbone to PyTorch's channels-last memory format cut inference latency by about 22% on A100s, with no…
The SDXL VAE overflow that decoded black images in fp16
TL;DR: The SDXL VAE decoder pushes activations past 65504, the max value fp16 can hold, so the last decode step overflows to inf and you get a fully b…
AI Workloads Are Reshaping Kubernetes in 2026: GPU Scheduling, MLOps, and the Platform Engineering Reckoning
How GPU scheduling complexity and MLOps integration are forcing platform teams to rearchitect Kubernetes clusters before operational debt becomes insu…
Why multi-agent orchestration is harder than it looks
One AI agent answering a question is useful. Five agents that divide a complex task, pass state to each other, and act on live enterprise systems is a…
Почему пилоты ИИ не масштабируются? У них нет системы управления
Пилот ИИ может хорошо работать на демонстрации и всё равно быть не готовым к промышленной эксплуатации. В статье разберем, какие элементы управления н…
RLAIF Is Eating RLHF — Here Are the Four Places Human Feedback Still Wins
RLAIF is having a moment. Walk through any alignment paper or vendor pitch from the last six months and you'll see the same claim: replace your human …
nvidia-smi Reports 97% Utilization While the GPU Sits Idle
TL;DR A GPU shows 97% utilization in nvidia-smi , but training throughput is a fraction of what benchmarks promise. The GPU is not computing; it is wa…
I Processed 2.4 Billion Tokens Across 52 AI Models for $0.52. Here's the Full Breakdown.
I run a production multi-agent AI system on a single M1 Mac in Jamaica. 6 autonomous agents. 26 cron workflows. 5-layer persistent memory. All contain…
I Built a Production RAG System on My M1 Mac for $0
I Built a Production RAG System on My M1 Mac for $0 Most RAG tutorials stop at "it answers questions." But answering questions is table stakes. The re…
Миф о «равных весах»: что на самом деле скрывается внутри малых моделей
Последние годы развитие LLM шло по пути экстенсивного масштабирования: считалось, что чем больше весов и данных, тем умнее модель. В индустрии даже сл…
I Built a Complete AI Infrastructure Stack from Scratch — Here's What I Learned
I Built a Complete AI Infrastructure Stack from Scratch — Here's What I Learned Most AI projects start at the top of the stack. You grab an LLM API, w…
QAT vs PTQ on our edge vision model: 6 months of A/B data
TL;DR: We ran post-training quantisation (PTQ) and quantisation-aware training (QAT) side by side on the same defect-classification model deployed on …
Part 2: Enterprise Decision Intelligence Architecture: AI Governance, Threshold Policy Engines, and Operational AI Systems
Part 1 showed how to evaluate binary classification thresholds in Python. This part asks the harder enterprise question: What happens when that thresh…
Why 91% of AI Agents Fail in Production (And What the 9% Do Differently)
Everyone is building AI agents right now. Autonomous systems that reason, plan, and act without humans in the loop. Agents that write code, manage wor…
Why your diffusion model is slow at batch size 1 (and what actually helps)
TL;DR: Single-image diffusion inference is bottlenecked by kernel launch overhead and attention memory traffic, not raw FLOPs. torch.compile with mode…
When AI Meets Reality: Why “Hello World” Isn’t Enough for LLM Systems
Most AI tutorials stop at “Hello World.” You wire up a model, send a prompt, get a response, and feel like you’ve built something. But the moment you …
What GenAI Actually Costs in Production
The first number anyone quotes when asked what generative AI costs is a per-token figure. It is a comfortable number — small, unambiguous, available o…
The Missing Engineering Stack for Production AI Agents
The "build an agent in 5 minutes" tutorials get you to a demo. They don't get you to production. Here's the field guide for the four primitives that d…
Инженерный подход к MLOps: как принципы расчётной механики ложатся в архитектуру AutoML
«Если что-то может пойти не так, это обязательно случится» . Мы не пытаемся предотвратить отказ, мы проектируем систему так, чтобы отказ одного элемен…
Beyond Monitoring: Building AI-Powered Predictive Observability for Retail Data Pipelines published
Three numbers before we start: Average detection time with traditional monitoring: 4.2 hours Average detection time with predictive observability: 11 …