# delivery Published articles for delivery. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Switching to Slack DevFeed: [Switching to Slack]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-11-09T23:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Slack](), [migration](), [notifications](), [systems](), [Documentation]() Tags: [administration](), [chat](), [connectivity](), [delivery](), [documentation](), [engineering](), [infrastructure](), [integration](), [migrate](), [news](), [notifications](), [organization](), [remote](), [slack](), [switching](), [team]() ### AI overview Khan Academy describes its decision to migrate team chat from HipChat to Slack after connectivity, message delivery, and notification problems affected reliability, especially for remote workers. The article outlines cultural, administrative, organizational, and integration-related costs that make chat-platform migration difficult. ### Source excerpt By Benjamin Pollack Khan Academy recently made the decision to migrate our team chat from HipChat to Slack. ... Read more ## What a Technical Program Manager actually does DevFeed: [What a Technical Program Manager actually does]() Original publisher: [Read original article]() Author: Dean Hume Published: 2026-09-14T15:54:31Z Content type: opinion Language: en Sources: [Dean Hume]() Topics: [Software Engineering]() Tags: [delivery](), [dependency](), [engineering](), [execution](), [meetings](), [ownership](), [project](), [software-engineering](), [teams](), [technical](), [technical-program-manager]() ### AI overview This article explains the role of a Technical Program Manager, focusing on the gaps between engineering teams, explicit ownership, cross-team dependencies, and proactive identification of release-blocking problems. ### Source excerpt What does a Technical Program Manager actually do? A look at the gaps they fill, the decisions they chase, and the reactive vs proactive split. ## MCP: Scope step-up is not authentication step-up DevFeed: [MCP: Scope step-up is not authentication step-up]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-01T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Model Context Protocol](), [Authentication](), [Authorization](), [MCP Server]() Tags: [authentication](), [authorization](), [browser](), [compliance](), [delivery](), [mcp](), [mcp-server](), [model-context-protocol](), [permission](), [protocol](), [scope](), [token]() ### AI overview The article distinguishes MCP scope step-up from authentication step-up. MCP's current authorization flow addresses insufficient permission by obtaining broader scopes, but it does not standardize verifying whether a human recently authenticated or is still present. It recommends binding out-of-band approval to the user and exact operation until this changes. ### Source excerpt MCP standardized how an agent asks for more permission. It has no vocabulary yet for asking whether the human behind the token is still there. ## Agile Leadership DevFeed: [Agile Leadership]() Original publisher: [Read original article]() Author: Dave Ogle Published: 2026-08-10T00:00:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Agile](), [Self-organizing Team](), [trust](), [Development]() Tags: [agile](), [delegation](), [delivery](), [frameworks](), [leadership](), [self-organizing-teams](), [software-development](), [teams](), [trust]() ### AI overview This opinion argues that organisational agility depends on leadership as well as agile frameworks and processes. It highlights empowerment, trust and delegation, and examines Agile2 and the British Army's Mission Command philosophy as relevant perspectives. ### Source excerpt Agile teams are built on more than frameworks and processes. This article explores why effective leadership, trust and delegation are fundamental to true organisational agility, drawing lessons from the British Army's Mission Command philosophy. ## How to accelerate agentic adoption DevFeed: [How to accelerate agentic adoption]() Original publisher: [Read original article]() Author: Simon Sear Published: 2026-08-06T08:37:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [Software Engineering](), [Automation]() Tags: [adoption](), [agentic-ai](), [agents](), [ai](), [ai-adoption-spectrum](), [apps](), [artificial-intelligence](), [automation](), [business-process](), [claude](), [delivery](), [engineering](), [safely](), [software-engineering]() ### AI overview This article presents a framework for accelerating agentic AI adoption by grouping initiatives into four categories: employee productivity tools, business transformation and workflow automation, AI features embedded in existing applications, and AI-enabled software engineering. It emphasizes clear direction, safe deployment, secure data access, education, and organizational change. ### Source excerpt Agentic AI is impacting every part of an organisation, from customer journeys to back office, supply chains and software engineering. Everything is changing at an accelerating speed, all at the same time, everywhere, all at once. In response, organisations don't want drawn-out analysis engagements with consultants or long strategy projects; they want action. They want to quickly and safely deploy agents to production and deliver value fast. ## Progressive Delivery with Argo Rollouts, Isovalent and Gateway API DevFeed: [Progressive Delivery with Argo Rollouts, Isovalent and Gateway API]() Original publisher: [Read original article]() Author: Christian Hernandez Published: 2026-08-06T07:01:00Z Content type: tutorial Language: en Sources: [Isovalent - The latest articles covering eBPF-based Networking, Observability, and Security]() Topics: [progressive delivery](), [Argo Rollouts](), [gateway]() Tags: [argo-rollouts](), [delivery](), [gateway](), [progressive-delivery]() ### AI overview This article explains how progressive delivery controls traffic to newly deployed Pods, focusing on header-based canary deployments with Argo Rollouts and Gateway API. ### Source excerpt Progressive delivery is not only about creating the new Pods, it is also about controlling which traffic reaches them. This blog explores Header-based canaries with Argo Rollouts and Gateway API ## Four pillars of agentic AI success DevFeed: [Four pillars of agentic AI success]() Original publisher: [Read original article]() Author: Simon Sear Published: 2026-07-29T15:47:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [Software Engineering](), [data](), [legacy](), [systems](), [context]() Tags: [accessibility](), [agentic-ai](), [ai](), [ai-accelerated-development](), [ai-agents](), [architecture](), [artificial-intelligence](), [context](), [data](), [data-architecture](), [data-governance](), [data-strategy](), [delivery](), [engineering](), [governance](), [legacy](), [legacy-modernisation](), [software-engineering](), [systems]() ### AI overview This article argues that organizations need strong foundations before scaling agentic AI. It identifies four pillars: modernizing legacy systems, building reliable data foundations and architecture, managing governance and risk, and democratizing access to data. ### Source excerpt Everyone is talking about agentic AI. That's not surprising. The promise is huge: AI agents that can plan, reason, use tools, work across systems and get real work done. In software engineering, that could mean faster delivery and better quality. In operations, it could mean complex processes moving with less manual effort, fewer handovers and better decisions. ## Shifting readiness left: What AI can (and can't) do for organisational readiness DevFeed: [Shifting readiness left: What AI can (and can't) do for organisational readiness]() Original publisher: [Read original article]() Author: Nel Mathams Published: 2026-07-27T14:48:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [data]() Tags: [ai](), [artificial-intelligence](), [building-shared-understanding](), [delivery](), [discovery](), [ethics](), [funding](), [governance](), [institute-for-government](), [objectives](), [organisational-goals](), [organisational-readiness](), [shift-left](), [transformation]() ### AI overview This opinion article argues that organisational readiness depends on shared understanding rather than only governance, funding, or delivery structures. It examines how AI can help align goals, surface hidden assumptions, and accelerate discovery while keeping human judgement in control, including ethical considerations around data. ### Source excerpt Organisational readiness is often treated as a matter of governance, funding and delivery structures. In this post, I argue that true readiness is about building shared understanding, and explore how AI can help organisations align on goals, surface hidden assumptions and accelerate discovery work, while keeping human judgement firmly in control. ## Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart DevFeed: [Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart]() Original publisher: [Read original article]() Author: Benjamin Knight Published: 2026-06-29T16:06:24Z Content type: article Language: en Sources: [Instacart]() Topics: [benchmarking](), [math](), [Algorithms, Complexity](), [Software](), [Programming](), [Routing (disambiguation)]() Tags: [benchmarking](), [bias](), [cardinality](), [data-science](), [delivery](), [efficiency](), [estimator](), [fixed-effects-model](), [instacart](), [linear-regression](), [measurement](), [memory](), [precision](), [pyfixest](), [regression](), [routing](), [speed](), [statistics](), [techniques]() ### AI overview This Instacart article explains why ordinary least squares regression becomes computationally impractical for marketplace experiments with high-cardinality categories. It presents the mathematical basis for using Fixest and Pyfixest, discusses switchback experiment designs for addressing treatment spillover, and describes benchmarks comparing processing speed, memory efficiency, and estimator precision. ### Source excerpt Benjamin S. Knight Scaling Marketplace experiments requires specialized statistical techniques. We examine why standard ordinary least squares regression (OLS) becomes computationally intractable when controlling for high-cardinality categories. We then dive into the underlying math and demonstrate how modern packages -- specifically Fixest and Pyfixest -- bypass these limitations. We conclude by benchmarking these methods to show their real-world impact on processing speed, memory efficiency, and estimator precision. At Instacart we strive to give our customers access to all the fresh foods and ingredients that they would normally get from a trip to the grocery store, but without the hassle of driving, finding parking, waiting in line, etc. Instacart's Marketplace team is responsible for surfacing customers' orders to shoppers, aligning Instacart's delivery windows with shoppers' projected availabilities as efficiently as possible. This entails a careful balancing act. If we offer delivery windows that are sooner / more popular, then we risk overextending shoppers' ability to fulfill those orders on time. If we are too conservative in our delivery option offerings, then we risk losing potential orders. Accurately measuring the impact of changes in our batching and routing algorithms requires thoughtful experiment design and software. Better predictions of future demand / time-to-fulfill allow Instacart to offer more convenient delivery windows.Experimentation on Marketplace One of our primary concerns in Marketplace is treatment spillage. For example, if we adjust our batching algorithm and increase the rate at which multiple orders are combined into batches in Brooklyn and Queens, then we face a real risk of also influencing the rate of batch creation / completion in Staten Island, the Bronx, and Manhattan. In this case the treatment impacts the control group -- a classic source of measurement bias as a consequence of violating the Stable Unit Treatment Value Assumpt ## Sustainable acceleration and the Agentic Software Development Life Cycle DevFeed: [Sustainable acceleration and the Agentic Software Development Life Cycle]() Original publisher: [Read original article]() Author: Dan Allsop Published: 2026-06-19T00:00:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [sdlc](), [AI Development](), [AI Agent](), [Software Engineering](), [Prompt Engineering](), [engineering-culture]() Tags: [agentic](), [agentic-ai](), [agile](), [ai](), [ai-agents](), [artificial-intelligence](), [asdlc](), [cognitive-entropy](), [culture](), [delivery](), [governance](), [people](), [prompt-engineering](), [technical]() ### AI overview The article discusses the transition from the traditional Software Development Life Cycle to an Agentic Software Development Life Cycle, in which AI agents participate across requirements, design, implementation, verification, and maintenance. It argues that sustainable productivity gains require governance, clear policies, human oversight, disciplined engineering practices, training, architectural clarity, and prompt engineering. ### Source excerpt A discussion of how to successfully leverage agentic AI to raise the productivity ceiling covering the topics of human oversight, governance, and disciplined engineering practices that preserve stability. ## Системное управление зависимостями DevFeed: [Системное управление зависимостями]() Original publisher: [Read original article]() Author: rustalenin (hh.ru) Published: 2025-06-25T09:26:34Z Content type: tutorial Language: ru Sources: [HeadHunter RU]() Topics: [jira](), [n8n]() Tags: [delivery](), [jira](), [n8n](), [tag-35a4c7fafefe](), [tag-7977493d1a0b](), [tag-b8b2aace08b8](), [workflow]() ### AI overview This article explains how to systematically manage dependencies and blockers that cause delays in delivery workflows. It presents the concepts, discusses a practical case at hh.ru, and provides step-by-step guidance for configuring Jira and n8n and visualizing blockers. ### Source excerpt Никто не любит очереди и ожидания. Мы не любим стоять в очередях и ещё больше не любим лезущих вне очереди. Сверхэффективный ад порой представляют как бесконечную, зацикленную саму на себе очередь... В конце концов, что есть символ неизбывной печали и тоски, как не Хатико. Знаете, кто ещё больше не любит ожидания? Бизнес. Бизнес очень не любит, когда ожидания копят важные проекты и инициативы. Согласно исследованиям средняя эффективность потока в Delivery составляет 35%, а всё остальное время - задачи ждут. (Данные на основе опросов специалистов -- ссылка. Метаанализ тысяч workflow от Nave - ссылка ) Справедливо, что ключевая точка роста для ускорения поставки -- уменьшение ожиданий. Именно об этих "фантастических" ожиданиях и пойдёт речь в статье. Я расскажу о системной работе с блокировками и зависимостями, которые повинны в значительном количестве задержек. Мы погрузимся в необходимую теорию, рассмотрим наш успешный практический кейс в hh.ru и, что особенно ценно, я поделюсь конкретными пошаговыми инструкциями по настройке Jira & n8n, а также способами работать с визуализацией блокеров в удобных плагинах, чтобы вы могли применить этот подход у себя. Этот материал будет полезен IT-менеджерам, тимлидам, руководителям проектов, delivery менеджерам и руководителям функций -- всем, кто стремится более осознанно и эффективно распоряжаться временем и ресурсами. Читать много букав ## Using Kanban Metrics and Reports to Identify Team Improvement Opportunities DevFeed: [Using Kanban Metrics and Reports to Identify Team Improvement Opportunities]() Original publisher: [Read original article]() Author: dennikolaev (hh.ru) Published: 2025-02-21T14:00:30Z Content type: article Language: ru Sources: [HeadHunter RU]() Topics: [Kanban](), [jira]() Tags: [cycle-time](), [datadriven](), [delivery](), [discovery](), [jira](), [kanban](), [lead-time](), [tag-22e9d0410dcb](), [tag-694195a2e09b](), [tag-8720e37629f7](), [tag-add1155413d1](), [tag-b92bf5906bbd](), [tag-e7d8489b7f64]() ### AI overview A project manager at hh.ru explains how to use Kanban-oriented metrics and reports to analyze team efficiency and identify improvement opportunities. The article covers lead time, cycle time, percentiles, classes of service, and reports from an internally customized Jira-based system. ### Source excerpt "Как и на какие метрики смотреть в поисках зоны роста команды?" -- на эту тему менеджеры проектов в нашей компании задумываются регулярно, так как менеджеры отвечают в том числе и за эффективность процессов. Правильный ответ на вопрос будет зависеть от контекста команды, заказчиков, продукта, рабочего процесса и много чего еще. Но можно выделить ряд метрик и отчетов, построенных на этих метриках, которые будут универсальны, и которые можно брать за основу для анализа. А поскольку hh.ru -- компания, которая несколько последних лет применяет Kanban-метод, то и метрики ниже будут сильно пересекаться с тем, что этот метод предлагает. Меня зовут Денис Николаев, и я менеджер проектов в hh. В этой статье покажу, на какие основные отчеты смотрю я. Также добавлю примеры того, на что обращать внимание в этих отчетах, чтобы в дальнейшем работать над повышением эффективности. Читать далее ## How Senior Engineers Take a Feature from Start to Delivery DevFeed: [How Senior Engineers Take a Feature from Start to Delivery]() Original publisher: [Read original article]() Author: Tiger Abrodi Published: 2024-03-28T17:06:09Z Content type: tutorial Language: en Sources: [Saiyan Growth Letter]() Topics: [Development](), [Users](), [Requirements](), [data](), [iteration](), [pull-requests]() Tags: [data](), [delivery](), [developers](), [development](), [feature-flagging](), [how-to](), [iteration](), [pull-requests](), [requirements](), [simplicity](), [users]() ### AI overview This tutorial presents an approach for taking a software feature from initial justification through delivery. It recommends validating why the feature is needed, defining a minimum viable product, choosing a simple technical solution, breaking the work into small tasks, and using feature flagging and rollout practices. ### Source excerpt How Senior engineers take a feature from start to delivery ## Amazon SNS: Cost Reduction and Reliable Delivery for Startups DevFeed: [Amazon SNS: Cost Reduction and Reliable Delivery for Startups]() Original publisher: [Read original article]() Author: ratneshmaurya2311@gmail.com (Ratnesh Maurya) Published: 2023-12-10T00:00:00Z Content type: article Language: en Sources: [Ratn Labs]() Topics: [Messaging](), [Amazon Simple Queue Service (SQS)](), [amazon]() Tags: [amazon](), [automatic](), [aws](), [backend](), [cloud-devops](), [cost](), [delivery](), [messaging](), [queues](), [sqs](), [system-design](), [system-design-backend-cloud-devops]() ### AI overview This article discusses how Amazon SNS can help startups reduce messaging costs through pay-per-message pricing and support message delivery with automatic retries, dead letter queues, and multi-region delivery. It also considers when Amazon SQS may be a better fit. ### Source excerpt How Amazon SNS helps startups cut messaging costs with pay-per-message pricing, automatic retries, dead letter queues, and multi-region delivery -- and when SQS might be a better fit. ## How continuous product discovery works for us DevFeed: [How continuous product discovery works for us]() Original publisher: [Read original article]() Author: Sören Weber Senior Product Manager @ trivago; Core Product; AI Linkedin profile Published: 2023-02-01T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Product Management](), [Self-organizing Team](), [Agile](), [User experience (UX)](), [Development]() Tags: [cross-functional-teams](), [delivery](), [development](), [discovery](), [driving](), [engineering](), [good-practices](), [product-management](), [research](), [scope](), [software](), [strategy]() ### AI overview A trivago product manager explains continuous product discovery, distinguishing it from product delivery and describing how user research, solution ideation, and solution testing help teams decide what to build. The article also discusses trivago's adoption of Teresa Torres's continuous discovery framework alongside cross-functional teams, outcome-driven OKRs, and agile software development. ### Source excerpt Hello, I am a product manager here at trivago. I have worked on different parts of the product such as apps, alternative accommodations, landing pages, and search & flow. We work in cross-fu... ## Setting your engineers up for success: how Skyscanner created greater clarity in our competencies... DevFeed: [Setting your engineers up for success: how Skyscanner created greater clarity in our competencies...]() Original publisher: [Read original article]() Author: Skyscanner Engineering Published: 2022-09-22T12:03:23Z Content type: opinion Language: en Sources: [Stories by Skyscanner Engineering on Medium]() Topics: [Framework]() Tags: [career-progression](), [culture](), [delivery](), [engineering](), [framework](), [performance](), [review](), [scope](), [skills](), [training]() ### AI overview Skyscanner describes how it clarified its engineering competency framework and performance expectations to support fairer feedback, clearer role expectations, and career progression. The company simplified the framework and created dedicated training after identifying gaps in how managers and engineers understood expectations. ### Source excerpt Setting your engineers up for success: how Skyscanner created greater clarity in our competencies and pathways to progression By Emanuel Müller Ramos, Skyscanner Engineering Director For many companies, this time of year is often appraisal or performance review season. Feedback is really important as we each seek to learn, progress and improve generally. However, making sure everyone is rated fairly and consistently can often be tricky if companies don't have robust and clear frameworks in place. This is something we've worked hard to ensure we address here at Skyscanner, and today we'd like to share our journey of making competency frameworks, and performance expectations, really clear within our Engineering org. Supporting our engineers' growth is one of the most important responsibilities of our Engineering Managers. Part of this includes helping engineers understand the expectations of their role and level, while also having clarity as to what the next stage of someone's career might look like. To do so, Skyscanner has a competency framework outlining skills and behaviours for each level, from entry level roles all the way to CEO. However, while the competency framework was regularly shared and discussed, a few years ago we noticed that we'd occasionally find gaps in understanding. For instance: Some Engineering Managers having a gut feel that a engineer had a gap in their performance, but couldn't relate that to the competency framework expectations Some Engineering Managers feeling uncomfortable to deliver feedback because they were not sure if they were being over-demanding or if their ask was within the expectations of that level Some Engineers feeling frustrated because they were not fully sure how a gap highlighted by their manager connected to the expectations in the competency framework While cases like this were isolated, leaving it unchecked could have consequences on how we set our engineers up for success in their current role and in their career pro ## Proofpoint's TA410 Report Shows How Attack Details Can Inform Better Defenses DevFeed: [Proofpoint's TA410 Report Shows How Attack Details Can Inform Better Defenses]() Original publisher: [Read original article]() Author: Adam Published: 2020-06-14T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Threat Research](), [proofpoint](), [Malware](), [Actor](), [Code](), [execution](), [file]() Tags: [actor](), [attacks](), [code](), [commands](), [defender](), [delivery](), [execution](), [file](), [malware](), [proofpoint](), [report](), [research](), [threat-research]() ### AI overview The article praises Proofpoint's report on TA410 attacks against U.S. utilities for its factual analysis and detailed explanation of the attack chain. It argues that details such as malicious macros, file renaming, cmd use, certutil execution, and delivery infrastructure can help defenders design better protections. ### Source excerpt I want to call out some impressive aspects of a report by Proofpoint. ## Threat Modeling as Code DevFeed: [Threat Modeling as Code]() Original publisher: [Read original article]() Author: Adam Published: 2019-01-23T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [engineering-culture](), [Code](), [standard]() Tags: [code](), [delivery](), [engineering](), [practices](), [productivity]() ### AI overview This commentary explores representing threat models as code and aligning threat modeling with established engineering practices. It argues that converting threat-modeling analysis into code and selecting suitable code representations can increase delivery consistency and productivity, while requiring upfront effort. ### Source excerpt Exploring threat models as code. ## Google Cloud Build for Android DevFeed: [Google Cloud Build for Android]() Original publisher: [Read original article]() Published: 2018-12-21T20:27:21Z Content type: tutorial Language: en Sources: [Blogs on Ryan Harter]() Topics: [Google Cloud Platform (GCP)](), [Android](), [CI/CD](), [CircleCI](), [Docker]() Tags: [android](), [circleci](), [continuous-integration](), [delivery](), [docker](), [google-cloud]() ### AI overview This introductory post examines Google Cloud Build as a continuous integration and delivery option for Android projects. It explains the author's limitations with CircleCI, including container memory constraints and projected costs, and introduces a series on configuring Google Cloud Build for Android. ### Source excerpt As I've written before, I'm always on the lookout for a great continuous integration and delivery system. For a long time I used CircleCI, but in the last month or so I've started hitting some limitations that I needed to work around. My requirements are pretty standard, I need something that works well with Android, requires minimal maintenance, and can handle somewhat larger projects. After a souring experience with CircleCI, and a short search for other hosted providers, I stumbled upon an early solution from Google that intrigued me. ## Starting a New Engagement as a Lead Consultant DevFeed: [Starting a New Engagement as a Lead Consultant]() Original publisher: [Read original article]() Author: Jay (noreply@blogger.com) Published: 2017-05-17T11:00:00Z Content type: opinion Language: en Sources: [Jay Fields]() Topics: [client](), [Software]() Tags: [advice](), [blog-post](), [consulting](), [delivery](), [enablement](), [lead](), [leadership](), [project](), [work]() ### AI overview A lead consultant reflects on starting new consulting engagements, arguing that there is no universal process. The article focuses on balancing client enablement with software delivery, identifying the client's actual needs, and improving people and processes without neglecting delivery. ### Source excerpt Someone recently asked me for information on starting new consulting engagements. A few years back I published Sean Doran and Scott Conley's thoughts on Being a Lead Consultant. Sean and Scott's list is great for any lead consultant, and the advice applies well for the lifetime of a project. I considered sending that list to the person looking for new engagement advice, but I'm not sure that list would be the best place to focus my attention at the beginning of a project. The beginning of a project is a special and dangerous time. You get a mix of (at least) optimism, concern, and freedom. There are a large number of ways the project can go, and as a lead consultant you'll play a major role determining it's outcome. The specific question I recently received was: did you have a process you followed when new engagements began. The remainder of this blog post contains my (slightly edited) response. I found being a lead at ThoughtWorks to be a nearly impossible balancing act. It's possible we didn't have a process because we weren't organized enough, but it's more likely that there's no general formula that works. What we ran into constantly was what we called the "enablement versus delivery" issue: If you're teaching client devs (enablement), you really don't have time to meet delivery deadlines; conversely if you're delivering software you rarely have time to teach. How to balance enablement versus delivery is something that varies based on the client's skillset and the ROI of the software being developed. What makes it worse is that the client often thinks they need one thing, but if the the company didn't have issues you (likely) wouldn't be there. Sometimes the issues are with the stakeholder and they'll tell you to focus on the wrong thing, and sometimes the stakeholder knows the deal but the other people you are forced to work with are the problem. Often it's a race to figure out what the client needs and get them to agree to fix it, before you've lost too much g ## On-Prem vs SaaS Information Security Compliance DevFeed: [On-Prem vs SaaS Information Security Compliance]() Original publisher: [Read original article]() Author: taylor@goteleport.com (Taylor Wakefield) Published: 2017-04-19T00:00:00Z Content type: opinion Language: en Sources: [Teleport]() Topics: [on-prem](), [Software as a service](), [Security](), [Shared Responsibility Model](), [Architecture & Design]() Tags: [article](), [blog](), [cloud](), [compliance](), [customers](), [data-center](), [delivery](), [environment](), [infrastructure](), [on-prem](), [operational](), [partnership](), [review](), [saas](), [security](), [support](), [vs](), [work]() ### AI overview This follow-up article examines the security and compliance implications of offering SaaS applications on-premises. Using a representative SaaS security compliance questionnaire as a framework, it argues that vendors should provide operational management while customers provide the infrastructure, a model the article calls Private SaaS. ### Source excerpt We review the Vendor Security Alliance's security questionnaire to look at the security ## A product management blueprint DevFeed: [A product management blueprint]() Original publisher: [Read original article]() Author: Map Published: 2015-02-18T20:55:56Z Content type: opinion Language: en Sources: [Craig Kerstiens]() Topics: [Product Management](), [.NET]() Tags: [delivery](), [engineering](), [focus](), [opinion](), [product-management](), [projects](), [startups](), [teams](), [trust](), [velocity]() ### AI overview The author presents a product management blueprint for startups and new teams, centered on building trust with teammates, improving shipping velocity, delivering iteratively, reducing scope, and eventually prioritizing work that has meaningful impact. ### Source excerpt I find myself having more conversations with startups - both small and large - about product management. I've blogged about some of the tools in my chest here but I haven't talked much about my "blueprint" for product management, which I find myself laying out in many conversations over coffee. What follows is this process I've used a few times over with new teams to get product and engineering moving together, shipping in a predictable manner, and tackling bigger and more strategic projects. Trust I need to know how to work with my team, what their working styles are, and how we interact. This starts by simply interacting - specifically, outside of the office. I heard a similar opinion recently from Chris Fry (who ran engineering at Salesforce and Twitter) when he remarked something to the effect of: "you can tell a good PM from a bad one based on if he goes to drinks with his team." Without getting hung up on whether it's beers or coffee, it's more about socialization with your team and time outside the office. My personal approach: expect a dinner invite over to my place when I take on running product for a new team. Velocity Once you've started to build some rapport, it's time to get down to business. If being able to quickly commit and ship something isn't a problem for you, then it's easy to just assume this is working. In reality most teams I encounter that need PM support don't have shipping nailed down. You probably already know if you fall into that category of feeling like you can commit and ship vs. not, so if you're not able to do that a few tips: There's some projects that everyone wants to ship that's been tried over and over, don't tackle that first. Shipping something is better than nothing. It doesn't have to be the right thing. Sometimes you don't have to ship something to get velocity, you can launch things you already have Kill scope Test things earlier and more iteratively, the more you can validate or try something without requiring a large in