Perspectives on recent AI research and what matters in practice.
Regulated industries do not get to treat AI evaluation as an academic exercise. Regulators, auditors, and downstream users care about measurable controls, reproducible evidence, and concrete limits on...
Read more →Prompt engineering libraries are often judged by convenience and features. When latency is a first-class constraint, different tradeoffs become dominant. This guide lists the practical factors that ac...
Read more →Running a product or prototype on large language models can quickly blow a solo developer's budget. Most guides focus on swapping models or quantizing weights. Those are useful, but there are subtler,...
Read more →Small language models here means models typically in the sub-20B parameter range. They are attractive because they reduce cost, allow on-prem and edge deployment, and give tighter latency and privacy...
Read more →I work with teams that take language models into production for real business problems. The arXiv paper "From Naive RAG to Deep Agentic Retrieval" (arXiv:2607.24791) grabbed my attention because it tr...
Read more →Paper: From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance
Selecting a GPU cloud provider in 2025 is a practical decision. The hardware differences narrowed, but the operational differences widened. This note evaluates the providers engineers and technical le...
Read more →High-stakes applications mean lives, money, or critical infrastructure depend on the system behaving correctly. That changes priorities. Engineers and leaders frequently adopt safety techniques that s...
Read more →LLM APIs accelerate prototyping and reduce infrastructure work. That makes them irresistible for product teams and experiments. What does not show up on a simple per-token bill are the engineering cos...
Read more →AI decisions require hard tradeoffs. When money is limited, the right choice is rarely "build everything" or "buy everything." This note lays out the pragmatic factors that actually matter, recommends...
Read more →Effective caching is one of the easiest ways to cut cost and latency for systems that call large language models. But not all caches are the same. Choices about what to cache, how to hash a request, a...
Read more →Title: Routing Decisions and Pre-Execution Critique for LLM Agents: What Works and What Still Worries Me...
Read more →Paper: Reward-Driven LLM Agent Workflows: Synthesizing POMDP Routing and Self-Correction for Autonomous Decision-Making
AI product managers must decide how language models and surrounding components will be composed, maintained, and observed in a product. Orchestration tools promise to simplify that work, but they trad...
Read more →Prompt engineering is no longer a set of tricks for toy demos. By 2025 it is an engineering discipline with architecture, observability, testing, and cost as first-class concerns. The patterns that wo...
Read more →Embeddings are the plumbing for search, recommendation, clustering, and retrieval augmented generation. For solo developers, the temptation is to pick a single off-the-shelf model and expect it to wor...
Read more →I work with teams building AI systems where correctness matters. A lot of failure modes I see come from assumptions about what a model will use from a long context. The paper "Not All Needles Are Foun...
Read more →Paper: Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs
Title: Using an Agentic LLM to Bring Heterogeneous Context into CTV Recommendations...
Read more →Paper: An LLM-powered Agentic Recommendation System for Connected TV Content Discovery
AI agents are no longer a research toy. They are production components that coordinate models, retrieval systems, external APIs, and humans. Building agents that work reliably under production load re...
Read more →AI observability is necessary for safe, reliable models, but it is not free. Teams that treat observability as a checkbox will find the financial burden shows up where it hurts: monthly cloud bills, s...
Read more →Regulated industries require agents that are auditable, controllable, and deployable under strict security and data residency rules. The software choice matters more than in consumer apps. This post r...
Read more →Title: Making Hybrid MoE Models Actually Deployable: Notes on Nemotron-Labs-3-Puzzle-75B-A9B...
Read more →Paper: Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs
Small language models are not a cheaper clone of large models. They are a different engineering choice with distinct costs, failure modes, and deployment paths. This guide gives practical rules for wh...
Read more →Startups building products that require reliable multi-step reasoning face three practical questions: which model actually reasons well on real inputs, how much will it cost, and how hard is it to run...
Read more →Engineers building or shipping AI systems often have more pressure than money. Evaluation decisions are not academic exercises. They determine whether models break in production, whether a product shi...
Read more →Title: A Practical Take on ForeAgent: Hindsight Self-Refinement for Image Forensics...
Read more →Paper: Perception, Verdict, and Evolution: Hindsight-Driven Self-Refining Forensics Agent for AI-Generated Image Detection
Large language models need memory systems for retrieval, grounding, and state management. Choosing the right system is about latency, scale, consistency, update patterns, and cost. The options below a...
Read more →Deploying AI on a strict budget forces clear tradeoffs. Choices that look cheaper at first can drive higher ongoing costs in compute, operations, or missed SLAs. This post separates the practical opti...
Read more →Engineers building reasoning systems often do not have the luxury of large inference bills or racks of A100s. The right small model plus the right engineering patterns can deliver much of the practica...
Read more →Choosing an API for production or prototype work when money is tight means balancing per-call price, model capability, latency, and operational overhead. This list prioritizes practical cost per unit...
Read more →Hallucinations are not just a technical nuisance. They are a business and product risk. Reducing them costs money and time, and different mitigation techniques trade off compute, latency, engineering...
Read more →Most guidance on caching LLM results assumes production traffic, multi-person teams, and fixed SLAs. Solo developers do not operate under those constraints. They trade off developer velocity, cost con...
Read more →Title: Queen-Bee: A Practical Architecture for Governed Multi-Agent Orchestration...
Read more →Paper: Queen-Bee Agents: A BeeSpec-Centered Architecture for Governed Enterprise MCP Orchestration
Regulated industries impose requirements that change how teams build LLM systems: auditable decisions, end-to-end data lineage, human-in-the-loop checkpoints, strict access controls, and reproducible...
Read more →When accuracy is the primary requirement, choosing an inference engine is not just about speed or cost. Different runtimes implement differently optimized kernels, numerical precisions, quantization s...
Read more →Title: Agents Auditing Agents: Practical takeaways from POIROT...
Read more →Paper: POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems
Teams are still treating AI projects like models to be tuned instead of systems to be operated. In 2026 the technology changed: foundation models, retrieval-augmented systems, multi-model orchestratio...
Read more →Title: Compiling Numeric Answers: Practical takeaways from a data-centric compiler for financial QA...
Read more →Paper: Fighting Numerical Hallucinations via Data-centric Compilation for Online Financial QA
Title: Practical trust for agentic AI: what this new survey gets right and what still needs work...
Read more →Paper: Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
Title: Training one agent to query knowledge graphs: what KG-R1 gets right and what still matters in production...
Read more →Paper: Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning
Runtime-Certified KV Cache Quantization for Safe Long-Context Inference...
Read more →Paper: Runtime-Certified Bounded-Error Quantized Attention
Title: What EngiAI Gets Right About Multi-Agent Engineering Workflows, and Where it Still Falls Short...
Read more →Paper: EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design
Title: Building Multi-Paradigm Agent Systems that You Can Operate...
Read more →Paper: Multi-Paradigm Agent Interaction in Practice:A Systematic Analysis of Generator-Evaluator, ReAct Loop,and Adversarial Evaluation in the buddyMe Framework
Title: Deliberative Multi-Agent LLMs for Trading: a Practical Look at AgenticAITA...
Read more →Paper: AgenticAITA: A Proof-Of-Concept About Deliberative Multi-Agent Reasoning for Autonomous Trading Systems
Title: Trajectory-Level Reward Models and What Plan-RewardBench Gets Right...
Read more →Paper: Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling
Title: Self-healing LLM agents in practice: what this paper gets right and what it leaves hanging...
Read more →Paper: A Self-Healing Framework for Reliable LLM-Based Autonomous Agents
Title: Episodic Context Reconstruction with E-mem: promising idea, hard systems work...
Read more →Paper: E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory
Title: Bian Que and the Practicalities of LLM Agents for Online Operations...
Read more →Paper: Bian Que: An Agentic Framework with Flexible Skill Arrangement for Online System Operations
Title: Training an on-prem product-matcher from agentic reasoning with RL...
Read more →Paper: EPM-RL: Reinforcement Learning for On-Premise Product Mapping in E-Commerce
Title: Memanto and the memory trade-offs you actually care about in production agents...
Read more →Paper: Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents
Title: Using Ontologies as an External Memory for LLMs: Practical gains and the engineering work it hides...
Read more →Paper: Automatic Ontology Construction Using LLMs as an External Layer of Memory, Verification, and Planning for Hybrid Intelligent Systems
Title: Agentic LLMs for Brain MRI Workflows: a practical take on a training-free pipeline...
Read more →Paper: Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis