Blog
# AI Hallucination
# Artificial Intelligence
# Deep Learning
# Editor's Pick
# LLM
Hallucinations in LLMs Are Not a Bug in the Data
Subtitle: It’s a feature of the architecture
Summary: Hallucination in LLMs is not a data quality problem. It is not a training problem. It is not a problem you can solve with more [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedbac), better filtering, or a larger context window.
**It is a structural property of what these systems are optimized to do.**
I have held this position for months, and the reaction is predictable: researchers working on retrieval augmentation, fine-tuning pipelines, and alignment techniques would prefer a more optimistic framing. I understand why.
What has been missing from this argument is geometry. Intuition about objectives and architecture is necessary but not sufficient. We need to open the model and look at what is actually happening inside when a system produces a confident wrong answer. Not at the logits. Not at the attention patterns. At the internal trajectory of the representation itself, layer by layer, from input to output. That is what the work I am presenting here did.
Javier Marín · May 5th, 2026
Médéric Hurier · Apr 28th, 2026
A deep dive into the practical limitations of agent protocols like MCP and A2A for low-level tasks, and why the "Linux philosophy" of using a raw command-line interface provides a more lightweight, composable alternative for local development, paving the way for an Agent OS.
# Artificial Intelligence
# Software Engineering
# LLM
# AI Agent
# Software Development
Médéric Hurier · Apr 21st, 2026
The 5xP Framework is a practical strategy that uses five targeted Markdown files (Product, Platform, Process, Profile, and Principle) to seamlessly align AI coding assistants with your project's architecture and business goals. By defining strict context boundaries, this framework drastically reduces prompt bloat and prevents AI hallucinations, moving developers away from unstructured "vibe coding" and closer to reliable, spec-driven development.
# Artificial Intelligence
# Software Engineering
# Productivity
# AI Agent
# Coding
Vishakha Gupta · Apr 14th, 2026
The blog argues that context graphs can serve as the system of record for reasoning, capturing how decisions are made, corrected, and carried forward across humans and AI agents. It shows that making context graphs real requires more than an abstraction: it demands a technical substrate, clear processes, and cultural norms that let organizations review, refine, and preserve judgment over time. When organizations pair emerging context‑graph technology with the cultural shift required to justify decisions, annotate reasoning, and protect shared context, they unlock a future where human and agent judgment reinforce each other, decisions become auditable, knowledge compounds, and the entire company grows more intelligent over time, which is the real trillion‑dollar opportunity.
# AI Agents
# Knowledge Graph and Graph Databases
# Usability and Debugging
# Vector / Similarity / Semantic Search
Ayesha Imran · Apr 7th, 2026
This blog walks through how to build an AI agent that can meaningfully use a unified collection of multimodal assets like speaker bios, talk titles or descriptions, and eventually their PDF or video content, by pairing the right tools with the right memory design. It demonstrates how retrieval, parsing, and reasoning components must be engineered so the agent can navigate relationships, interpret metadata, and answer higher‑order questions with accuracy. By grounding the workflow in a multimodal database like ApertureDB, the agent gains reliable access to structured context, enabling richer insights across any real‑world content collection.
# AI Agents
# Knowledge Graph and Graph Databases
# Multimodal/Generative AI
# Vector / Similarity / Semantic Search
# RAG
# Dataset Preparation and Management
Ayesha Imran · Mar 31st, 2026
The blog shows how to unify scattered multimodal assets, e.g., speaker bios, talk titles, videos, and PDFs. into a single, well‑structured memory layer. It explains the metadata and schema decisions that let an agent answer richer, cross‑asset questions such as trending topics, influential speakers, or patterns across a full conference. By grounding these relationships in a multimodal database like ApertureDB, the approach generalizes to any domain where organizations need AI to reason over diverse, real‑world collections of content.
# AI Agents
# Knowledge Graph and Graph Databases
# Multimodal/Generative AI
# Vector/Similarity/Semantic Search
Subham Kundu · Mar 24th, 2026
AI coding platforms work best when you treat the AI as a junior engineer, not a replacement for your thinking. Break problems into small tasks, plan in Markdown before coding, and keep your context window lean - accuracy drops sharply past 50% capacity. Never debug in the same chat where you built the feature; the AI is biased by its own logic. For existing codebases, reference well-written code as examples. For new projects, define strict guardrails early - without them, the AI makes hundreds of arbitrary decisions that compound into a mess. The blog dives deep into all the patterns that work, the anti-patterns that silently kill your codebase, and strategies for both brownfield and greenfield projects - each illustrated with detailed diagrams. You stay the architect; the AI executes.
# AI Coding
# Software Engineering
# AI Assistants
Médéric Hurier · Mar 17th, 2026
Chaigent combines Chainlit and Vertex AI to deliver a code-first, serverless AI agent platform that avoids costly per-seat licensing fees. It empowers developers to build highly customizable, enterprise-grade agents using a scalable pay-as-you-go architecture.
# Artificial Intelligence
# AI Agent
# Generative AI Tools
# Google Cloud Platform
# Data Science
Médéric Hurier · Mar 10th, 2026
mAIdAI is a lightweight personal AI assistant built with Google Chat, Cloud Run, and Vertex AI, designed to automate repetitive micro-tasks. By grounding the model with a local markdown context file, it provides highly personalized workflow assistance directly within your chat environment.
# Generative AI Tools
# Artificial Intelligence
# AI Agent
# Programming
# Automation
Médéric Hurier · Mar 3rd, 2026
This article explores how to use "Agent Skills"—simple Markdown-based context modules—to ensure AI agents strictly adhere to your team's MLOps practices and tooling preferences. By providing explicit organizational rules upfront, developers can eliminate generic boilerplate and align AI-generated code with production-grade standards.
# MLOps
# AI Agent
# Software Engineering
# Generative AI Tools
# Coding
Axel Mendoza · Feb 24th, 2026
A hands-on beginner roadmap for learning Kubernetes, designed to walk you through core concepts (like clusters, pods, services, deployments, storage, RBAC, autoscaling, etc.) with simple explanations, CLI examples, and practical exercises. By following it you build real experience and are prepared to use Kubernetes locally or on cloud platforms like GKE or EKS.
# DevOps
# Kubernetes
# From Scratch