Alignment-Driven Development: The Orchestration Framework AI-Paired Coding Forgot to Build
AI-assisted development doesn't just speed up your output — it speeds up your drift. ADD (Alignment-Driven Development) is the process framework that ensures every decision in your pipeline is owned, documented, and traceable back to business intent before the damage compounds. Read All
105 reads
by
Andrew Schwabe
March 19th, 2026
Your browser does not support the audio element.
Speed
Voice
About Author
I’m a serial entrepreneur and full-stack engineer with 25+ years in EdTech, AI, and data science.
TOPICS
Related Stories
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
alignment
Obsolescence without hostility: optimization, uniformity, and the erosion of human meaning in a post-AI world
Most contemporary discussions of artificial intelligence focus on misalignment, loss of control, or catastrophic harm. This paper examines a different and comparatively neglected possibility: that advanced AI may erode the social conditions under which human meaning has historically been generated, without conflict, coercion, or displacement. The central question is not whether AI dominates humanity, but whether human participation remains causally significant once AI systems outperform humans across core instrumental domains. The argument is conditional and long-horizon in scope. It proceeds from the observation that existing limits on AI superiority are primarily technological and economic rather than principled. If these constraints are progressively overcome, and AI systems come to out
Causal Scene Narration with Runtime Safety Supervision for Vision-Language-Action Driving
arXiv:2604.01723v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models for autonomous driving must integrate diverse textual inputs, including navigation commands, hazard warnings, and traffic state descriptions, yet current systems often present these as disconnected fragments, forcing the model to discover on its own which environmental constraints are relevant to the current maneuver. We introduce Causal Scene Narration (CSN), which restructures VLA text inputs through intent-constraint alignment, quantitative grounding, and structured separation, at inference time with zero GPU cost. We complement CSN with Simplex-based runtime safety supervision and training-time alignment via Plackett-Luce DPO with negative log-likelihood (NLL) regularization. A multi-town closed-loop CA
APITestGenie: Generating Web API Tests from Requirements and API Specifications with LLMs
arXiv:2604.02039v1 Announce Type: new Abstract: Modern software systems rely heavily on Web APIs, yet creating meaningful and executable test scripts remains a largely manual, time-consuming, and error-prone task. In this paper, we present APITestGenie, a novel tool that leverages Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering to automatically generate API integration tests directly from business requirements and OpenAPI specifications. We evaluated APITestGenie on 10 real-world APIs, including 8 APIs comprising circa 1,000 live endpoints from an industrial partner in the automotive domain. The tool was able to generate syntactically and semantically valid test scripts for 89\% of the business requirements under test after at most three attempts.
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Frontier Research
PRISM: Robust VLM Alignment with Principled Reasoning for Integrated Safety in Multimodality
arXiv:2508.18649v2 Announce Type: replace Abstract: Safeguarding vision-language models (VLMs) is a critical challenge, as existing methods often suffer from over-defense, which harms utility, or rely on shallow alignment, failing to detect complex threats that require deep reasoning. To this end, we introduc PRISM (Principled Reasoning for Integrated Safety in Multimodality), a System 2-like framework that aligns VLMs through a structured four-stage reasoning process explicitly designed to handle three distinct categories of multimodal safety violations. Our framework consists of two key components: a structured reasoning pipeline that analyzes each violation category in dedicated stages, and PRISM-DPO, generated via Monte Carlo Tree Search (MCTS) to refine reasoning quality through Direc
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!