# Requirements Published articles for Requirements. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why human oversight is shifting from writing code to defining requirements DevFeed: [Why human oversight is shifting from writing code to defining requirements]() Original publisher: [Read original article]() Author: Naseeb Ahmed Mian Published: 2026-09-17T13:00:00Z Content type: opinion Language: en Sources: [The New Stack]() Topics: [Requirements](), [AI Agent](), [Code](), [Availability]() Tags: [agent](), [agents](), [ai-agents](), [ai-engineering](), [andela](), [automated](), [availability](), [code](), [pipeline](), [post-contributed](), [requirements](), [review](), [software-development](), [sponsor-andela](), [sponsored-post-contributed](), [tests]() ### AI overview The article argues that human oversight of AI-assisted software development should focus on defining and reviewing requirements, not only checking whether generated code conforms to them. It illustrates the risk with a flawed availability-related requirement that passed specification review, generated six passing tests, traceability checks, and automated QA while violating the feature's intended outcome. ### Source excerpt This walks through the pipeline our agents operate inside--from a recorded scoping meeting through unit specs, spec review, generated code, The post Why human oversight is shifting from writing code to defining requirements appeared first on The New Stack. ## Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers DevFeed: [Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers]() Original publisher: [Read original article]() Author: Josh Parker Published: 2026-09-16T13:00:33Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [data centers](), [AI Infrastructure](), [Google](), [Nvidia](), [Artificial Intelligence](), [datacenter](), [benchmarking](), [Requirements]() Tags: [ai](), [ai-data-centers](), [ai-infrastructure](), [capacity](), [corporate](), [data-centers](), [energy](), [google](), [infrastructure](), [launch](), [metrics](), [nvidia](), [performance-metrics](), [requirements](), [resource](), [systems]() ### AI overview Emerald AI, Google and NVIDIA announced the AI Energy Management Alliance, a coalition focused on flexible AI data centers that can dynamically adjust electricity use in response to grid conditions. The article describes technology-neutral, performance-based requirements covering response speed, duration, predictability and emergency behavior. ### Source excerpt AI factories are the infrastructure of the intelligence era. Scaling them responsibly will depend as much on innovation across the grid as inside the data center. Today, Emerald AI, Google and NVIDIA announced the launch of the AI Energy Management Alliance (AEMA), a first-of-its-kind coalition advancing data centers that can dynamically manage their electricity use [...] ## Using acceptance tests to catch regression bugs in legacy code before production DevFeed: [Using acceptance tests to catch regression bugs in legacy code before production]() Original publisher: [Read original article]() Author: Valentina Jemuović Published: 2026-09-16T07:33:58Z Content type: tutorial Language: en Sources: [Optivem Journal]() Topics: [Testing](), [Requirements](), [legacy]() Tags: [automated](), [bugs](), [e2e](), [legacy-code](), [regression](), [requirements](), [testing](), [ui](), [unit-tests]() ### AI overview This hands-on article explains why automated unit and end-to-end tests may still allow production bugs in legacy code. It presents acceptance test-driven development (ATDD), where requirements and tests describe the same observable behavior, making coverage clearer and reducing dependence on UI steps. ### Source excerpt You automated, and manual regression testing is still there. Those two facts have the same cause. ## MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications DevFeed: [MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Generative AI](), [AI Models](), [Algorithm](), [Requirements](), [Computer vision](), [Robotics]() Tags: [ai](), [ai-models](), [algorithm](), [algorithms](), [artificial-intelligence](), [computer-science-and-technology](), [computer-vision](), [diffusion-models](), [electrical-engineering-and-computer-science-eecs](), [flow-matching](), [generative-ai](), [hard-constrained-sampling](), [hardflow](), [idss](), [kaveh-alim](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [mechanical-engineering](), [mit-schwarzman-college-of-computing](), [navid-azizan](), [optimal-control](), [paper](), [requirements](), [research](), [robotics](), [safe-ai](), [school-of-engineering](), [trajectory-optimization](), [zeyang-li]() ### AI overview MIT researchers developed a deployment-time technique that lets pretrained generative AI models explore solutions while enforcing hard constraints on final outputs. Experiments in robotics, physical-process control, and computer vision found that the method satisfied required constraints and identified better solutions than existing techniques. ### Source excerpt The "HardFlow" algorithm could help generative AI models produce high-quality outputs that obey strict requirements when "pretty close" doesn't cut it. ## Italy's email open tracking pixels rules are changing DevFeed: [Italy's email open tracking pixels rules are changing]() Original publisher: [Read original article]() Author: Denis O'Sullivan Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Twilio Blog]() Topics: [data](), [Forms]() Tags: [2026](), [communications](), [compliance](), [data-protection](), [email](), [industry-insights](), [law](), [legal](), [pixel](), [regulatory](), [requirements]() ### AI overview Italy's data protection authority has issued new rules for email open-tracking pixels. Senders must disclose their use, obtain prior explicit consent for many uses, update opt-in forms, and provide opt-out options. The rules include different requirements for subscribers who joined before and after April 29, 2026, with a compliance deadline of October 28, 2026. ### Source excerpt Italy has followed France and introduced rules around email open tracking pixels. Here's what you need to know. ## Bullshit Management Didn't Die. It Got Automated. DevFeed: [Bullshit Management Didn't Die. It Got Automated.]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-09-03T12:27:11Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Product Management](), [Requirements](), [Artificial Intelligence](), [Large Language Model](), [User Stories]() Tags: [2026](), [generate](), [llms](), [product-management](), [requirements](), [user-stories]() ### AI overview The author revisits the concept of "bullshit management" and argues that practices such as gathering requirements, maintaining extensive backlogs, and producing management reports have persisted while AI has automated or amplified parts of them. The supplied text presents this as an opinion-based critique, emphasizing that polished outputs may conceal limited customer understanding and learning. ### Source excerpt Four years ago I named it. A million reads later, it's back with better tools. ## ISO 42001 Evidence: What Auditors Ask For DevFeed: [ISO 42001 Evidence: What Auditors Ask For]() Original publisher: [Read original article]() Author: info@goteleport.com (Preet Dhatt) Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [Teleport]() Topics: [audit](), [AI Development](), [Deployment](), [CI/CD](), [data-governance](), [Monitoring](), [Requirements]() Tags: [ai](), [ai-development](), [article](), [audit](), [ci-cd](), [data-governance](), [deployment](), [eu](), [iso](), [monitoring](), [requirements]() ### AI overview This article explains what auditors look for in ISO 42001 evidence and how organizations can connect access, approvals, infrastructure, CI/CD activity, releases, and production changes into an evidence trail. It also discusses monitoring, human oversight, third-party access, and data governance within an AI Management System. ### Source excerpt Prepare ISO 42001 audit evidence that proves your controls work in practice. ## A Model Portfolio for cost-efficient AI across the software development lifecycle DevFeed: [A Model Portfolio for cost-efficient AI across the software development lifecycle]() Original publisher: [Read original article]() Author: Praveen Sidda Published: 2026-09-01T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [AI Development](), [AI Models](), [Model Routing](), [sdlc](), [Agile](), [Developer experience](), [Compression](), [Requirements](), [Testing](), [Deployment](), [Code review]() Tags: [agentic-sdlc](), [ai](), [ai-cost-optimization](), [ai-models](), [code-review](), [compression](), [deployment](), [github-copilot](), [llm](), [overhead](), [requirements](), [software-development]() ### AI overview The article examines whether routing software development tasks across a portfolio of AI models can reduce costs compared with using one premium model. It reports that lower-cost models handled well-defined tasks, while premium models were reserved for architecture, implementation, and code review; context compression reduced token usage but risked losing important technical details. ### Source excerpt Image generated by AIWhat one controlled experiment taught me about matching model capability to developer work Topic: Can intelligently routing developer tasks across different AI models outperform relying on a single premium model? In this article, I put that question to the test by mapping software development lifecycle (SDLC) stages to a portfolio of AI models and comparing the outcomes. Motivation As AI becomes embedded throughout the AI-Native Development Lifecycle (AIDLC), an evolution of the traditional Software Development Lifecycle (SDLC), its cost is no longer tied to a single prompt. A single developer task can involve multiple model calls, each carrying source files, conversation history, tool definitions, and generated output. Applying the most capable model to every interaction is straightforward, but it also consumes premium model capacity on tasks that less expensive models can often complete just as effectively. This raises an important question for engineering organizations: How can teams reduce the cost of AI-assisted development without compromising quality, reliability, or the developer experience? My first instinct was to reduce token consumption. Context compression appeared to be the most direct path to lowering inference costs by shortening prompts. Although it reduced token usage, it also introduced risk. Important constraints and technical details could be lost, affecting downstream tasks. Source code, stack traces, and active instructions proved to be especially poor candidates for lossy compression. That experience shifted my focus. The objective was not to process fewer tokens, but to complete developer tasks successfully at a lower overall cost. I then experimented with model allocation. Lower-cost models handled well-defined tasks such as requirements synthesis, planning, routine test generation, deployment artifacts, and final summaries, while premium models were reserved for architecture, implementation, and code review. This appro ## Complete guide to sending email to Google: Rules, signals & setup DevFeed: [Complete guide to sending email to Google: Rules, signals & setup]() Original publisher: [Read original article]() Author: Tony Williams Published: 2026-09-01T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [Google](), [Authentication](), [Security]() Tags: [authentication](), [deliverability](), [email](), [google](), [guide](), [how-to](), [industry-insights](), [metrics](), [requirements](), [security]() ### AI overview Twilio's guide explains how Google evaluates incoming email, covering SPF, DKIM, DMARC, spam-rate management, sender reputation, user engagement, and Google Postmaster Tools. It distinguishes consumer Gmail from Google Workspace and describes differences in filtering and administrator controls. ### Source excerpt Learn how Google filters incoming email: SPF, DKIM, DMARC requirements, spam rate thresholds, and how to use Google Postmaster Tools to protect deliverability. ## Design and Planning Are the Key Skills for Building Software with AI Coding Agents DevFeed: [Design and Planning Are the Key Skills for Building Software with AI Coding Agents]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-08-14T12:12:26Z Content type: article Language: en Sources: [The AI Engineer]() Topics: [AI-assisted coding](), [AI Development](), [Software Engineering](), [Development]() Tags: [agents](), [ai](), [ai-agents](), [ai-coding](), [coding-agents](), [design](), [development-process](), [requirements](), [verification]() ### AI overview The article argues that AI coding agents can handle much of software implementation, testing, and deployment. It says engineers should focus most on design and planning: defining requirements, making technical decisions, and breaking work into tasks agents can execute reliably. ### Source excerpt How to design better software and turn it into tasks AI agents can execute reliably. ## Navigating SAMA, ADGM & DFSA Requirements with Teleport DevFeed: [Navigating SAMA, ADGM & DFSA Requirements with Teleport]() Original publisher: [Read original article]() Author: info@goteleport.com (Mukund Cadambi) Published: 2026-08-12T00:00:00Z Content type: article Language: en Sources: [Teleport]() Topics: [Requirements](), [Cybersecurity](), [Cloud](), [audit trail](), [data-governance](), [Architecture & Design]() Tags: [audit-trail](), [cloud](), [compliance](), [cybersecurity](), [data-governance](), [requirements](), [security]() ### AI overview This article explains how SAMA, ADGM, and DFSA requirements affect cloud service providers operating in Saudi Arabia and the UAE. It describes challenges involving data sovereignty, cybersecurity, identity and access controls, privileged and third-party access, and audit evidence, and presents a unified identity layer as an approach to operationalizing compliance. ### Source excerpt Learn how Teleport helps CSPs meet SAMA, ADGM, and DFSA compliance. ## 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 ## Introducing AI-powered container standardization DevFeed: [Introducing AI-powered container standardization]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-07-27T15:04:11Z Content type: article Language: en Sources: [Capital One Tech]() Topics: [Containers](), [Vulnerabilities](), [Security](), [Artificial Intelligence](), [supply-chain-security](), [sdlc]() Tags: [ai](), [ai-security](), [cloud-native](), [containers](), [requirements](), [security](), [software-development](), [software-engineering](), [software-supply-chain](), [vulnerabilities]() ### AI overview The article presents AI-powered container standardization as an approach to automate vulnerability detection, prioritization, remediation, testing, and patch deployment. It recommends avoiding exposure through curated base images and vetted dependencies, then automating ongoing remediation within the software development life cycle. ### Source excerpt AI-powered container standardization enables hands-free security remediation without sacrificing development velocity. In today's cloud-native world, containers have become the foundation of modern software delivery. Managing container security at scale can present significant challenges, thousands of vulnerabilities to track, manual remediation processes, inconsistent base images across teams and compliance requirements that slow everything down. AI-powered container standardization is a comprehensive approach that leverages artificial intelligence (AI) to automate vulnerability detection, prioritization and remediation across your entire container ecosystem. This transformative solution significantly reduces the manual toil traditionally associated with keeping containers secure and compliant. Development teams can focus on building features while the system automatically identifies vulnerabilities, generates fixes, tests changes and deploys patches, minimizing human intervention for routine updates. This approach simplifies the coordination of vulnerability remediation across hundreds of teams while ensuring that security and compliance are embedded throughout the software life cycle. Organizations can empower their developers to focus on innovation rather than patching, resulting in faster releases, stronger security and more productive engineering teams. The strategic framework: avoid and automate Our strategy for managing vulnerabilities focuses on two core principles: avoiding exposure and automating remediation, with governance baked into the software development life cycle. Avoid: Secure the foundation to minimize risk entering your environment. The most effective vulnerability remediation happens prior to the vulnerability reaching your digital ecosystems. By controlling what enters the software supply chain through curated base images and vetted dependencies/packages, you dramatically reduce the attack surface before code ever runs in production. This inc ## The foundation: Why Temporal for a data pipeline? DevFeed: [The foundation: Why Temporal for a data pipeline?]() Original publisher: [Read original article]() Author: Houman Kargaran Published: 2026-07-23T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [data](), [Orchestration](), [pii](), [Requirements](), [Resilience](), [consistency](), [Availability](), [observability](), [AI search](), [Embeddings]() Tags: [auditability](), [community](), [compliance](), [data](), [data-pipeline](), [embeddings](), [execution](), [foundation](), [requirements](), [resilience](), [search](), [temporal]() ### AI overview This tutorial explains how to justify Temporal as the orchestration layer for a PII-compliant complaint-ingestion pipeline. It covers requirements for resilience, consistency, availability, observability, redaction, internal classification, and semantic search over complaint data. ### Source excerpt How to justify Temporal for a regulated data pipeline: the requirements, the naive approach, and why Durable Execution wins on resilience and auditability. ## Kan een Amerikaans bedrijf met encryptie de Amerikaanse overheid buiten de deur houden? DevFeed: [Kan een Amerikaans bedrijf met encryptie de Amerikaanse overheid buiten de deur houden?]() Original publisher: [Read original article]() Published: 2026-07-21T13:20:00Z Content type: opinion Language: nl Sources: [Bert Hubert's writings]() Topics: [Encryption](), [sensitive data](), [Microsoft](), [servers](), [Malware]() Tags: [encryption](), [microsoft](), [requirements](), [sensitive-data](), [server]() ### AI overview This Dutch commentary examines whether encryption can prevent the United States from accessing data held or processed by partly American companies. It argues that services such as Microsoft Double Key Encryption are difficult to use for ordinary data because servers need access to plaintext for searching and other operations, and that administrators ultimately retain significant control over server data. ### Source excerpt Het is inmiddels duidelijk dat zelfs maar deels Amerikaanse bedrijven met allerhande middelen gedwongen kunnen worden om onze data te overhandigen aan de Amerikaanse overheid. Ook kan de dienstverlening gestaakt worden na een enkel briefje op Whitehouse.gov, wat zonder enige rechterlijke toetsing geplaatst kan worden. Tegen deze beide dingen is met papier niets te doen. Geen speciale afspraak zal je redden uit dit probleem (dit ook volgens de Landsadvocaat). Maar, er is dan soms nog de hoop om de data nog wel privé te houden met speciale encryptie. ## Why the traditional product manager role is changing DevFeed: [Why the traditional product manager role is changing]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-07-16T12:29:08Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Requirements](), [Artificial Intelligence](), [User Stories]() Tags: [ai](), [requirements](), [software]() ### AI overview The article argues that the traditional product manager role is becoming less relevant as teams adopt Product Engineers and AI-augmented workflows. It says PMs should focus less on exhaustive backlog management and detailed requirements, and more on strategic decisions and critical thinking. ### Source excerpt Most of the role is dying. The part that survives is the part nobody is teaching. ## Essential Non Functional Requirements DevFeed: [Essential Non Functional Requirements]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-07-07T08:33:34Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [Requirements](), [Architecture & Design](), [Scalability](), [Availability](), [Latency](), [reliability](), [Replication](), [Users]() Tags: [availability](), [data-replication](), [failover](), [haproxy](), [kubernetes](), [latency](), [nginx](), [requirements]() ### AI overview This tutorial explains non-functional requirements as quality attributes that determine how software systems behave under real-world conditions. It covers availability, latency, scalability, and durability, with implementation approaches including load balancers, health checks, failover, CDNs, data replication, horizontal scaling, Kubernetes, and concurrent API design. ### Source excerpt Don't miss them ## Your Architecture Doesn't Rot Overnight DevFeed: [Your Architecture Doesn't Rot Overnight]() Original publisher: [Read original article]() Author: Valentina Jemuović Published: 2026-07-07T06:00:37Z Content type: opinion Language: en Sources: [Optivem Journal]() Topics: [legacy](), [systems](), [Software](), [Test-driven development]() Tags: [architecture](), [business-logic](), [code](), [dependency](), [legacy](), [refactor](), [requirements]() ### AI overview The article explains how repeated convenience-driven decisions gradually weaken boundaries between business logic and infrastructure. It argues that architectural decay often remains hidden while the application works and tests pass, eventually making seemingly simple changes difficult to locate and implement. ### Source excerpt As deadlines become tighter, convenience starts winning. ## You Don't Need to Build It to Design It DevFeed: [You Don't Need to Build It to Design It]() Original publisher: [Read original article]() Author: Humayun Tanwar Published: 2026-07-06T11:05:34Z Content type: tutorial Language: en Sources: [Mindorks - Medium]() Topics: [systems](), [data-architecture](), [Latency](), [Requirements]() Tags: [business](), [code](), [compliance](), [data-architecture](), [design](), [latency](), [machine-learning](), [patterns](), [requirements](), [software-engineering](), [systems](), [tech-stack](), [technology]() ### AI overview This article explains why solutions engineers can contribute meaningfully to system design without writing the implementation. It connects integration scoping, data ownership, real-time communication choices, and failure handling to core design concepts, then presents five questions about scale, constraints, dependencies, failure tolerance, and success criteria. ### Source excerpt System design is one of those topics that feels like it belongs exclusively to engineers who write the code. The diagrams, the architecture reviews, the database choices , it all looks like backend territory from the outside. You're Already Doing This Solutions engineers sit at an interesting intersection. You're deep enough in the technical conversation to understand how systems connect, and close enough to the customer to understand what they actually need from those systems. That combination gives you a view that's genuinely useful in design conversations, and one that's worth developing deliberately. When you scope an integration and map which system owns which data, that's data architecture. When you recommend a webhook approach over polling because the customer needs real-time updates, that's a latency and reliability trade-off. When you ask a customer how they handle failures before a build starts, that's systems thinking. The formal vocabulary of system design is mostly a structured way of describing what you're already doing in the field. Building that vocabulary, and understanding the patterns underneath it, makes it easier to contribute in technical conversations, ask the right questions earlier, and connect your field experience to the broader design decisions being made around you. The Five Questions That Structure Any Design Good design conversations don't start with a diagram. They start with these. Scale. What volume are you designing for, not today but at the ceiling that changes the architecture? A thousand requests a day is a different system from a thousand per second. That number changes your database choice, whether you need a queue, and how retries need to behave. Constraints. What's already fixed? Existing tech stack, compliance requirements, vendor contracts. Every real system gets built inside constraints. Name them before drawing anything. Dependencies. What does this system talk to, and what talks to it? Systems that look simple in a conv ## How to Meet EU Cyber Resilience Act (CRA) Requirements DevFeed: [How to Meet EU Cyber Resilience Act (CRA) Requirements]() Original publisher: [Read original article]() Author: info@goteleport.com (Maximilian Heck, Waldemar Kindler) Published: 2026-07-01T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [cyber resilience act](), [Requirements](), [Security](), [audit trail](), [Encryption]() Tags: [audit-trail](), [ci-cd](), [compliance](), [cyber-resilience-act](), [cybersecurity](), [encryption](), [eu](), [how-to](), [requirements]() ### AI overview This tutorial explains how identity, access, encryption, and audit controls relate to EU Cyber Resilience Act requirements. It also discusses fragmented security toolchains, a compromised Trivy release, and infrastructure patterns such as short-lived cryptographic identities, hardware-rooted device trust, and unified audit trails. ### Source excerpt Learn how to implement identity, access, and audit controls that meet EU Cyber Resilience Act compliance requirements. ## A Problem Framing Kernel DevFeed: [A Problem Framing Kernel]() Original publisher: [Read original article]() Author: Morteza Pourmohamadi Published: 2026-06-04T02:51:27Z Content type: opinion Language: en Sources: [UX Magazine]() Topics: [User experience (UX)](), [Artificial Intelligence]() Tags: [concepts](), [design-thinking](), [requirements](), [space](), [ux]() ### AI overview This commentary examines the essential structure of problem framing in design. It argues that effective framing begins by collecting relevant concepts, needs, requirements, stakeholders, constraints, assumptions, and unknowns, then connecting those elements to generate insight; it identifies the kernel as "collect, connect, commit." ### Source excerpt What you need before you have a problem worth solving A few weeks ago, I caught up with my good friend Lucas Mara, a thoughtful design leader. We were deep in a conversation about the value of design in the age of AI when he mentioned Richard Rumelt's classic, Good Strategy Bad Strategy. It had The post A Problem Framing Kernel appeared first on UX Magazine. ## EU VAT for SaaS in 2026: Thresholds, OSS, and Common Mistakes DevFeed: [EU VAT for SaaS in 2026: Thresholds, OSS, and Common Mistakes]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-01T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service](), [Shared Responsibility Model]() Tags: [2026](), [b2b](), [compliance](), [eu](), [guide](), [how-to](), [merchant-of-record](), [mistakes](), [requirements](), [saas](), [tax](), [vat]() ### AI overview A practical guide to EU VAT for SaaS founders in 2026, covering OSS registration, sales thresholds, B2B and B2C treatment, place-of-supply rules, customer-location evidence, and invoice requirements. ### Source excerpt Complete EU VAT guide for SaaS founders in 2026. Thresholds, OSS registration, B2B vs B2C rules, invoice requirements, and how to stay compliant globally. ## 130 Release-Roadmap Issues, 818 Agent Runs: A Mid-Iteration Reality Check DevFeed: [130 Release-Roadmap Issues, 818 Agent Runs: A Mid-Iteration Reality Check]() Original publisher: [Read original article]() Author: Eugene Petrenko Published: 2026-04-28T00:00:00Z Content type: article Language: en Sources: [Eugene Petrenko]() Topics: [AI Agent](), [Claude](), [codex](), [Artificial Intelligence](), [issue tracker](), [Orchestration](), [Command-line interface](), [Graphs](), [Requirements]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-agents](), [ai-coding](), [cli](), [code](), [eugene-petrenko](), [graph](), [issue-tracker](), [jonnyzzz](), [mcp](), [mcp-steroid](), [multi-agent](), [orchestration](), [pipeline](), [requirements](), [run-agent](), [tag-41199d53f463]() ### AI overview The article describes using a swarm of Claude, Codex, and Gemini agent CLIs to research 130+ release-roadmap issues and produce implementation specifications. The process used 818 agent runs over four days at an approximately $576 cost, followed by a mid-release comparison of the research with the team's actual work. ### Source excerpt We turned one release's roadmap -- 130+ umbrella-epic issues -- into fully-researched implementation specs using a swarm of three different agent CLIs (Claude, Codex, Gemini): 818 runs, ~155M+ tokens, ~$576, four days. Then, mid-release-cycle, we ran a snapshot to compare the research against reality -- with no expectation that anything was "done" yet. Here is the pipeline, the numbers, and the honest verdict. ## Early and late-stage hypergrowth. DevFeed: [Early and late-stage hypergrowth.]() Original publisher: [Read original article]() Published: 2026-04-27T13:00:00Z Content type: opinion Language: en Sources: [Will Larson - Irrational Exuberance]() Topics: [Scalability](), [Requirements](), [Users]() Tags: [requirements](), [scalability](), [support](), [users]() ### AI overview The article distinguishes early-stage from late-stage hypergrowth. Early-stage companies can focus serially on their biggest specific problems, while late-stage companies must address many simultaneous requirements from a more skeptical market. It argues that adding a new leader can be preferable to expanding an existing leader's scope during late-stage hypergrowth. ### Source excerpt Last week, a colleague asked why I'd hired an additional new leader onto an important area rather than expanding an existing leader's scope to incorporate that area as well. The existing leader was a known quantity and doing well, so why not keep expanding them? It's a good question, and depending on the circumstances I might have done either, but explaining why I specifically brought in a new leader this time depends a bit on a distinction I think of as early versus late-stage hypergrowth. In Cross the Chasm's world, early-stage is when you've proven product market fit, have won the early adopters, and are just starting to win the early majority. In this phase, there are specific problems, and the most important problem is to solve those specific problems. For example, you might be having scalability issues, and solving that is the company's almost sole focus for a few weeks. After scalability is fixed, next you'll need to work on onboarding flows to convert for less technical users, and so on. Not only the executives, but much of the company, serially hunts down solutions to their biggest problem. When you reach late-stage hypergrowth, you are starting to encounter the late majority and laggards cohorts. This reorients the company and executive teams away from only creating an exceptional product, to also having to solve the numerous concerns and checkboxes that a skeptical audience introduces. Sure, your product might save hours a day for our team, but how does your compliance paperwork look? How stable are you? What contractual commitment will you make regarding customer support resolution? At this point, you'll still be in an extremely competitive environment to retain the innovators and early majority, while also having to solve the long list of skeptic-driven requirements. Instead of hunting down solutions, the company-and the executive team-now has to solve everything, everywhere, all at once. Going back to my colleague's question, in early-stage hypergrowth [Next page]()