# Spec Driven Development Spec-Driven Development is a software development methodology in which explicit specifications guide implementation and verification, with some approaches using specifications to generate working code. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Engineering Practices for Building and Operating AI Systems DevFeed: [Engineering Practices for Building and Operating AI Systems]() Original publisher: [Read original article]() Author: Nicole Morgan Published: 2026-08-05T00:00:00Z Content type: opinion Language: en Sources: [Harness Blog]() Topics: [Agentic development](), [Testing](), [Spec Driven Development](), [context](), [Requirements]() Tags: [agentic-development](), [requirements](), [spec-driven-development](), [testing]() ### AI overview Harness argues that AI-assisted development needs stronger specifications, testing, operational controls, and failure-mode analysis to maintain delivery quality and manage risk. ### Source excerpt Learn agentic development best practices with spec-driven development, AI system testing, and operational readiness for secure AI delivery. | Blog ## SpectrePaste: An AI-Assisted, Fileless PowerShell Malware Delivery System DevFeed: [SpectrePaste: An AI-Assisted, Fileless PowerShell Malware Delivery System]() Original publisher: [Read original article]() Author: Joshua Platt Published: 2026-07-06T18:54:30Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Artificial Intelligence](), [Malware](), [Threat Research](), [PowerShell](), [C2](), [Caching](), [Encryption](), [Resilience](), [Routing (disambiguation)](), [Polymorphism](), [Back end](), [Spec Driven Development]() Tags: [ai](), [artificial-intelligence](), [backend](), [c2](), [cache](), [cybersecurity](), [detection-engineering](), [encryption](), [malware](), [malware-analysis](), [polymorphism](), [powershell](), [resilience](), [routing](), [spec-driven-development](), [threat-intelligence]() ### AI overview The article describes SpectrePaste, a previously undocumented fileless malware delivery system that threat actors used with AI as its primary orchestrator and developer. Its early architecture included automated AMSI-bypass generation, XOR-encrypted payloads, resilient command-and-control traffic handling, caching, request queuing, and administrative priority routing. ### Source excerpt by Joshua Platt and Jason Reaves [TLP:CLEAR] Earlier this year, Google Threat Intelligence[1] reported threat actors are increasingly deploying novel, AI-enabled malware in active operations[2]. While investigating a recent OSINT article[3] on malware campaign activity reported as "DeepLoad", our threat intelligence team identified a separate, previously undocumented fileless delivery system we track as "SpectrePaste". The prior public reporting correctly suspected AI involvement in the "DeepLoad" delivery chain, but our analysis reveals a more conclusive and concerning reality. AI did not just play a supporting role. It acted as the primary orchestrator and developer behind the entire SpectrePaste system. Threat actors internally referred to the early system as "PasteFast Panel." In this initial iteration, the system was highly modular, structured, and designed specifically for resilience against high-volume bot traffic. One of the hallmarks of automated, AI-assisted development. The early architecture functioned as a centralized PowerShell payload delivery system with several notable features: Automated Evasion Generation: The paste builder module automatically prepended Anti-Malware Scan Interface (AMSI) bypass scripts to payloads upon creation, followed by XOR encryption using a custom obfuscator template. This ensured every payload served was dynamically packed. C2 Resilience & High-Load Handling: The most sophisticated feature of the early version was its custom cache manager queue system. The threat actors anticipated massive, simultaneous beaconing from compromised hosts. To prevent database exhaustion, the panel featured an automated threshold toggle. During traffic spikes, the system queued requests, cached the encrypted payloads in memory, and deduplicated IP addresses to ensure a single infected bot could not inadvertently DDoS the command server. Admin Priority Routing: Developer requirements explicitly prioritized operator access. Administrative routes ## New Open-Source Context Specs Released DevFeed: [New Open-Source Context Specs Released]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-06-24T16:58:13Z Content type: article Language: en Sources: [Capital One Tech]() Topics: [Spec Driven Development](), [AI-assisted coding](), [context window](), [Open Source](), [Development]() Tags: [ai-assisted-coding](), [context-engineering](), [context-window](), [development](), [open-source](), [software-engineering](), [spec-driven-development]() ### AI overview Capital One has open-sourced Context Specs, a spec-driven development framework that uses context engineering for AI-assisted coding. It captures domain knowledge in reusable experts and uses automated feedback signals to verify an agent's work, addressing context-window constraints such as context decay, pollution, and compaction loss. ### Source excerpt Capital One open-sources Context Specs, an SDD framework using context engineering to bridge business ideas and code. Every developer has had the same experience: The AI writes code confidently, only for you to realize that it has missed the mark. The fix isn't a better model, it's better context. Right context, right time. That's the whole game. To address this challenge, Capital One is open-sourcing Context Specs, a framework for spec-driven development (SDD) that treats context engineering-the practice of building systems that dynamically decide what your agent sees and when it sees it-as the primary lever for AI-assisted coding. The framework achieves this by capturing a team's domain-specific knowledge into reusable "experts" that you create once and compose across the entire workflow, and then using automated feedback "signals" to verify the agent's work. The real problem: Your agent's context window is finite Every AI coding agent operates within a context window, a fixed amount of information it can see at any given moment. This constraint creates three mechanical failure modes that plague every developer using AI tools today: Context decay: Older messages in a conversation get ignored, summarized or dropped entirely. That careful instruction you gave 20 messages ago? Gone. Context pollution: When an agent searches your codebase autonomously, it pulls in irrelevant files. Every irrelevant token displaces a useful one. Compaction loss: When the window fills up, the system summarizes history to make room. You don't control what gets dropped. These aren't edge cases; they're the default experience. Most frameworks for AI-assisted development weren't designed with these constraints in mind. Some frameworks generate thousands of lines of specification markdown before a single line of code is written, burning millions of tokens on ceremony. Other frameworks are so lightweight they leave everything to interpretation, forcing the agent to search blindly and pollute ## Spec Driven Development isn't Waterfall DevFeed: [Spec Driven Development isn't Waterfall]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2026-04-09T00:00:00Z Content type: opinion Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Spec Driven Development](), [Agile](), [Kiro](), [Software Engineering](), [Artificial Intelligence]() Tags: [agile](), [ai](), [blog](), [development](), [engineering](), [software-development](), [spec-driven-development](), [writing]() ### AI overview The article argues that spec-driven development is not a return to waterfall development. It presents specifications as explicit, versioned, living artifacts that evolve through user feedback and iterative development, with implementation derived from changing requirements and design choices. In this view, AI can accelerate the iteration cycle without making the process rigid or top-down. ### Source excerpt Spec Driven Development isn't Waterfall Write down what you mean. After spending a few months writing (e.g. on the Kiro Blog), and speaking (e.g. Real Python Podcast, SE Radio) about spec-driven development, I've noticed a common misconception: spec driven development is a return to a waterfall style of software development. Specification driven development (in Kiro, for example) isn't about pulling designs up-front, it's about pulling designs up. Making specifications explicit, versioned, living artifacts that the implementation of the software flows from, rather than static artifacts. This distinction is important, because software development (like all complex product development and engineering tasks) is a fundamentally iterative process. It is extremely rare for a software project to know all of the requirements up-front. It's much more common for one of the goals of the development process being to discover requirements, most frequently through engaging users in the cycle of feedback. This is a point that's missed in strict waterfall software development processes, and missed in critiques (like Dijkstra's) of natural language specification (as I have written about before). The Agile movement is often presented as a high-minded set of ideas, but I think it's more accurate to see it as a reflection of a simple fact: as software became more complex, and filled more roles in society, top-down approaches to design simply no longer work. From the Agile Manifesto: Customer collaboration over contract negotiation Responding to change over following a plan These are simple reflections of reality. Software specifications are complex, dynamically changing, internally conflicting, and invariably incomplete. In specification driven development, the specification is the thing being iterated on, rather than the implementation. The iteration cycle is the same as before, but potentially much quicker because of the accelerating effect of AI. So if specifications aren't up-front ## How I code with AI agents (spec-driven development) DevFeed: [How I code with AI agents (spec-driven development)]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-01-30T12:24:42Z Content type: tutorial Language: en Sources: [The AI Engineer]() Topics: [Spec Driven Development](), [Development](), [Code](), [AI Agent](), [Claude Code]() Tags: [agents](), [claude-code](), [code](), [guide](), [spec-driven-development]() ### AI overview An opinionated guide to spec-driven development with AI coding agents. It explains how to define a reviewed, version-controlled specification before execution, distinguishes specs from PRDs and design documents, and outlines when each document is useful. ### Source excerpt An opinionated guide to writing code with AI agents like Claude Code. ## Spec-Driven Development in Practice: How AI Simplify Full-Stack Java DevFeed: [Spec-Driven Development in Practice: How AI Simplify Full-Stack Java]() Original publisher: [Read original article]() Author: Simon Martinelli Published: 2025-12-14T03:03:46Z Content type: tutorial Language: en Sources: [Java Advent Calendar]() Topics: [Spec Driven Development](), [Specifications](), [Artificial Intelligence](), [Requirements](), [Java](), [Spring Boot]() Tags: [2025](), [ai](), [full-stack](), [java](), [spec-driven-development](), [spring-boot]() ### AI overview The article explains Spec-Driven Development and the AI Unified Process (AIUP) for a full-stack Java application using Spring Boot, jOOQ, and Vaadin. It presents requirements, an entity model, and system use cases as ordered specification artifacts from which AI can generate code, tests, and documentation. ### Source excerpt AI is changing how we build software, but many teams still work as if nothing has changed. They treat code as the only reliable artifact. Everything else slowly gets outdated: AI code generation tools can make this even worse if used without structure. They produce code fast, but the process behind the code remains the [...] The post Spec-Driven Development in Practice: How AI Simplify Full-Stack Java appeared first on JVM Advent. ## Just Launched: Neon Is Now a Kiro Power DevFeed: [Just Launched: Neon Is Now a Kiro Power]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-12-03T17:52:39Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Kiro](), [AI Development](), [ide](), [Model Context Protocol (MCP)](), [Spec Driven Development](), [backend-development](), [Deployment]() Tags: [agentic-ai-development](), [ai-development](), [backend-development](), [deployment](), [ide](), [kiro](), [mcp](), [product](), [spec-driven-development]() ### AI overview The article announces Neon as an early launch partner for Kiro powers. It describes a Neon power that lets developers deploy Postgres databases, create production-like branches, run migrations and tests in isolated environments, and revisit past states from within the IDE. It also explains how Kiro powers provide domain-specific tools and knowledge through MCP servers, steering files, and hooks. ### Source excerpt Kiro just announced powers at re:Invent, a new way for developers to access a curated set of tools (each packaged with domain knowledge and best practices) directly from the IDE. Neon is one of the first launch partners, alongside companies like Figma, Stripe, Supabase, Postman,...