AI/ML Engineering & LLMOps

Training/inference, vector search, RAG, evaluation, safety, and production ML/LLM stacks.

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Inside AI/ML Engineering & LLMOps

Frameworks & Training PyTorch, TensorFlow, JAX, datasets, and accelerator workflows. Inference & Serving Serving runtimes, Triton, vLLM, ONNX Runtime, and scalable endpoints. RAG & Vector Databases Embeddings, retrieval strategies, chunking, and hybrid search patterns. Prompting, Guardrails & Eval Prompt tools, safety filters, red-teaming, evals, and regression benchmarks. MLOps & Governance Tracking, model registry, feature stores, lineage, and compliance.

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Latest in AI/ML Engineering & LLMOps


forkast.news > digitalocean-agent-droplets-first-paas-tier-managed-agent-runtime-with-predictable-pricing

DigitalOcean Agent Droplets — First PaaS-Tier Managed Agent Runtime With Predictable Pricing

16+ min ago   (380+ words) The first major cloud provider to offer a flat-fee managed runtime for AI agents bundles compute, inference, memory, and 16,000+ tools into one plan — reshaping the economics of building agents. The unit economics of Agent Droplets represent a significant shift in…...


dev.to > thiagolongom > i-used-the-icd-10-tree-as-a-scoring-function-1h5b

I used the ICD-10 tree as a scoring function

11+ min ago   (475+ words) Most medical quiz apps score you as right or wrong. That throws away the part that matters. In clinical reasoning, mistaking cholecystitis for appendicitis and mistaking it for a myocardial infarction are not the same mistake. One is a near…...


dev.to > libme > cost-attribution-before-cost-optimization-how-to-find-out-what-is-actually-charging-you-5086

Cost Attribution Before Cost Optimization: How to Find Out What Is Actually Charging You

11+ min ago   (862+ words) A commenter on an earlier post about surprise serverless bills made a point I keep coming back to: it is hard to control costs when you do not know what is actually charging you. That is the whole problem. Most…...


dev.to > javimosch > a-7m-parameter-model-that-knows-when-to-say-no-4phn

A 7M-parameter model that knows when to say no

11+ min ago   (356+ words) Most requests sent to an LLM are boring. "What's the weather in Osaka", "remind me to call mom", "what's 47 times 23". A frontier model answers them beautifully — and every call costs real money and a network round-trip. I built the opposite…...


dev.to > descope > free-saml-decoder-how-to-read-and-debug-saml-responses-5hmh

Free SAML Decoder: How to Read and Debug SAML Responses

14+ min ago   (1589+ words) This blog was originally published on Descope. This guide uses Descope's SAML Guru, a free browser-based SAML decoder, to read a response from end to end. We'll work through the fields where SSO most often breaks, and briefly cover the…...


dev.to > jackymencz > sentinel-ir-non-technical-guide-to-saving-millions-on-ai-agent-operations-2eh9

Sentinel-IR: Non-Technical Guide to Saving Millions on AI Agent Operations

11+ min ago   (286+ words) 🚀 Sentinel-IR: Non-Technical Guide to Saving Millions on AI Agent Operations This guide breaks down Sentinel-IR, a deterministic data-compression layer engineered to slash AI execution costs without sacrificing accuracy. Think of it as ZIP compression for LLM context windows. The AI…...


dev.to > javimosch > your-oldest-head-is-probably-your-worst-head-a-7m-router-post-mortem-jd7

Your oldest head is probably your worst head (a 7M router post-mortem)

14+ min ago   (232+ words) A few weeks ago I wrote about building a 7M-parameter typed dispatcher in pure machin/MFL — a tiny model that reads a request, picks a route, reports calibrated confidence, and delegates instead of guessing. Since then I spent a sprint…...


dev.to > jackymencz > we-gave-our-ai-agent-a-memory-that-forgets-on-purpose-memory-sentinel-4b1h

We Gave Our AI Agent a Memory That Forgets — On Purpose. Memory Sentinel

14+ min ago   (535+ words) How Sentinel's memory works, why it can't grow forever, and why forgetting turned out to be the hardest part to design. Every AI agent demo shows you what the agent remembers. Nobody shows you what happens after three months — when…...


medium.com > @Yash_Bhardwaj > day-3-i-built-gpt-from-scratch-162-million-parameters-zero-intelligence-696a4dd300b1

Day 3: I Built GPT From Scratch. 162 Million Parameters. Zero Intelligence.

34+ min ago   (1829+ words) Here is the exact moment my model came alive. I gave it three tokens, the opening of a short story: “I HAD”. It thought for a second and handed me back this: Three tokens in. Five brand new tokens out....


dev.to > jsvnsk-tech > why-ais-seemingly-misbehave-1410

Why AIs Seemingly Misbehave

18+ min ago   (849+ words) This article is about understanding why AI assistants and related AI systems seemingly misbehave. It explains why semantic understanding does not guarantee reliable instruction following. It examines why important constraints and factual requirements can be enforced more reliably. It offers…...