# decision-making Decision-making is the process of formulating options, establishing preferences, and making commitments, including through technical decision analysis and AI-related methods. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building an AI-native data & insights operating system at Webflow DevFeed: [Building an AI-native data & insights operating system at Webflow]() Original publisher: [Read original article]() Author: Ashwini Chaube Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [data](), [Artificial Intelligence](), [Platform Engineering](), [Data Science](), [decision-making](), [Authorization]() Tags: [agentic](), [ai](), [data](), [data-insights](), [inside-webflow](), [permissions](), [review](), [self-service](), [skills]() ### AI overview Webflow describes how its Data & Insights team built an AI-native operating system for trusted self-service analytics. The approach combines governed data, encoded business context, reusable skills and agents, permissions, architectural controls, review practices, and human judgment, while also changing how the team works through agent-first workflows, learning, and experimentation. ### Source excerpt How we built the governed foundations for trusted self-service analytics while transforming the way our own team works. ## Beyond the data: what a Business Analyst does at Nubank DevFeed: [Beyond the data: what a Business Analyst does at Nubank]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-15T12:14:41Z Content type: opinion Language: en Sources: [Nubank]() Topics: [Data analysis](), [decision-making](), [context](), [Tech Lead]() Tags: [adoption](), [business-analyst](), [data](), [data-analysis](), [data-analytics](), [data-science-machine-learning](), [decision-making](), [manager](), [tech-lead](), [user-experience]() ### AI overview A first-person account of the Business Analyst role at Nubank, describing how BAs connect data analysis, context, and experimentation to product and business decisions. The article emphasizes clarifying trade-offs, investigating metrics, and collaborating with product and technical roles. ### Source excerpt Understand how Business Analysts at Nubank use data, context, and experimentation to guide product decisions. The post Beyond the data: what a Business Analyst does at Nubank appeared first on Building Nubank. ## Our LDX3 New York 2026 Picks - Justin Mancinelli DevFeed: [Our LDX3 New York 2026 Picks - Justin Mancinelli]() Original publisher: [Read original article]() Published: 2026-09-08T13:00:00Z Content type: opinion Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [Software Engineering](), [AI Engineering](), [Mobile](), [Kotlin Multiplatform](), [decision-making]() Tags: [2026](), [ai](), [community](), [conference](), [kotlin](), [kotlin-multiplatform](), [leadership](), [mobile]() ### AI overview Touchlab's Justin Mancinelli previews the LDX3 New York 2026 talks the company is most interested in, highlighting software engineering leadership, AI's effects on engineering and management, innovation practices, and mobile development with Kotlin Multiplatform. ### Source excerpt LDX3 is back in NYC and Touchlab will be in attendence. Here are the talks we're most excited about. ## Why Strong Candidates Are Rejected in Big Tech Interviews DevFeed: [Why Strong Candidates Are Rejected in Big Tech Interviews]() Original publisher: [Read original article]() Author: Prasad Rao Published: 2026-09-03T16:22:55Z Content type: opinion Language: en Sources: [Big Tech Careers]() Topics: [Tech Careers](), [decision-making]() Tags: [careers](), [decision-making](), [interview](), [storytelling]() ### AI overview The article argues that strong technical candidates are often rejected from big tech interviews because they underprepare for behavioral interviews and struggle to communicate their past work, impact, and decision-making clearly. ### Source excerpt The #1 reason people get rejected in big tech interviews, and the 4 week fix. ## The Art of Simplifying Decisions. DevFeed: [The Art of Simplifying Decisions.]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-08-20T12:27:37Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [decision-making](), [data analytics](), [Artificial Intelligence]() Tags: [ai-agents](), [analysis](), [data-analytics](), [decision-making]() ### AI overview The article argues that decision-making has become a bottleneck as coding becomes more scalable. It compares AI-supported options, additional meetings, and decision-making within limited scope, while emphasizing the need for context, evidence, and empowered product decisions. ### Source excerpt Decision-making is the bottleneck today. ## How to take incremental steps towards data democratisation DevFeed: [How to take incremental steps towards data democratisation]() Original publisher: [Read original article]() Author: Andy Scotland Published: 2026-08-17T13:09:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [data](), [trust](), [decision-making](), [Artificial Intelligence]() Tags: [agent-factory](), [ai](), [ai-agents](), [artificial-intelligence](), [compliance](), [conway-s-law](), [data](), [data-democratisation](), [data-engineering](), [data-platform](), [data-products](), [governance](), [incremental](), [trust]() ### AI overview This article explains how organisations can pursue data democratisation incrementally while preserving control, risk management and governance. It describes potential benefits in financial services and discusses how trusted data, data products, platforms and AI agents could support innovation, decision-making and customer service. ### Source excerpt Organisations increasingly recognise the value of making data more accessible, but concerns around control, risk and governance often stand in the way. In this post, I explore why data democratisation doesn't require organisations to sacrifice oversight, and how data products, platforms and agent factories can unlock innovation while maintaining trust, compliance and accountability. ## Unlocking your data: the value is in collaboration DevFeed: [Unlocking your data: the value is in collaboration]() Original publisher: [Read original article]() Author: Sam Perridge Published: 2026-08-12T14:59:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [data](), [datasets](), [Artificial Intelligence](), [decision-making](), [systems]() Tags: [ai](), [business-intelligence](), [claude](), [collaboration](), [data](), [data-democratisation](), [data-engineering](), [data-maturity](), [data-platform](), [datasets](), [decision-making](), [governance](), [guardrails](), [reporting](), [self-service]() ### AI overview This opinion article argues that organisations unlock more value from data when datasets are connected and insights are accessible across teams. It describes a progression from paper records and siloed systems to connected and democratised data, including self-service analytics and AI, while emphasising governance and practical adoption. ### Source excerpt Organisations often focus on collecting data and connecting systems, but the greatest value comes from helping datasets work together and making insights accessible to the people who need them. In this post, I explore the journey from siloed data to democratised access, showing how self-service analytics and AI can unlock hidden value, while strong governance provides the guardrails for confident decision-making. ## A framework for sequential decisions in daily life and beyond DevFeed: [A framework for sequential decisions in daily life and beyond]() Original publisher: [Read original article]() Author: Nisarg Suthar Published: 2026-08-04T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [decision-making]() Tags: [data-modeling](), [data-science](), [decision-making](), [model](), [operations-research](), [sequential-decision](), [structured](), [supply-chain]() ### AI overview The article introduces a Universal Modeling Framework proposed by Warren Powell for reasoning about sequential decisions under uncertainty and incomplete information. It explains how mathematical modeling can clarify decision problems and support better choices across areas such as finance, healthcare, data-center infrastructure, and supply chains. ### Source excerpt Photo by Sophia Kunkel on Unsplash Decision-making occupies a significant portion of our mental uptime. Whether we work in finance, energy, transportation, healthcare, e-commerce, foreign policy, or global supply chains, we are constantly required to make choices in the presence of uncertainty and incomplete information. As new information arrives, decisions must be revised, refined, and sometimes completely reconsidered. Every decision carries consequences -- some rewarding, others costly. The ability to consistently make better choices is often a defining factor behind successful outcomes. Yet effective decision-making remains notoriously difficult. Describing a problem as "mind-bogglingly complex" is really just a by-product of a failure to think about the problem in a structured way.-- Dr. Warren Powell This article introduces a Universal Modeling Framework for reasoning about sequential decision problems proposed by Dr. Warren Powell, an operations researcher at Princeton University. The same framework can be applied across a remarkably diverse set of problems: buying or selling financial assets, evaluating a new user experience, selecting candidate drugs for clinical trials, investing in data-center infrastructure, or managing large-scale supply chains. Here we shall undertake an approach that focusses on identifying the core elements of a decision-making process. Central to our approach is the creation of a simple mathematical model that eliminates the ambiguity of describing problems in language. Modeling is an art, guided by a mathematical framework, and results in a well-defined problem that we can put on a computer to solve. Even when the ultimate goal is not to automate the decision, the act of modeling itself often leads to deeper understanding and better choices. A dynamic model for sequential ecisions A sequential decision process can be represented as follows: Where: Sₜ is the state variable capturing our state of knowledge at time t. For example, inve ## What Is Enterprise Knowledge Management (EKM) and How Do You Actually Build It? DevFeed: [What Is Enterprise Knowledge Management (EKM) and How Do You Actually Build It?]() Original publisher: [Read original article]() Author: Janeera Published: 2026-07-30T12:23:25Z Content type: tutorial Language: en Sources: [Knowledge Management Tips, Best Practices and More]() Topics: [knowledge-management](), [decision-making]() Tags: [enterprise-knowledge-management](), [how-to](), [knowledge-management](), [knowledge-management-system](), [practical]() ### AI overview A practical guide to enterprise knowledge management (EKM), covering the knowledge organizations hold, how to assess program maturity, the components of an effective system, common failure points, and audits before investing in tooling. ### Source excerpt A senior engineer gives two weeks' notice. She mentions, almost offhand, that she's ... The post What Is Enterprise Knowledge Management (EKM) and How Do You Actually Build It? appeared first on Document360. ## A Recap of the 2026 Experimentation Conference at Booking.com DevFeed: [A Recap of the 2026 Experimentation Conference at Booking.com]() Original publisher: [Read original article]() Author: Mel JI Mueller Published: 2026-07-16T08:18:03Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [experiments](), [decision-making](), [Artificial Intelligence](), [Tooling]() Tags: [2025](), [ai](), [analysis](), [data-science](), [events](), [experimentation](), [organizational](), [parallel](), [recap](), [themes]() ### AI overview A recap of Booking.com's 2026 Experimentation Conference, which brought together more than 150 experimentation practitioners from 49 companies. The article summarizes survey findings, conference themes, and sessions on AI-assisted experimentation, experimentation quality and velocity, and organizational culture. ### Source excerpt By Kevin Anderson, Angelica Goetzen, Jorden Lentze, and Melanie Mueller On May 18, 2026, we hosted the third annual Experimentation Conference at Booking.com on our Amsterdam campus. What started in 2024 as an experiment itself -- would large-scale experimentation practitioners come together to learn from each other? -- has grown into an event which brings together over 150 practitioners from 49 companies which run experiments at scale. About one third of attendees came back a second or third time. The room collectively ran 56,000 experiments per year. It's a unique crowd, and that's exactly the point. The day opened with sharing the results of the survey data we collected from the participating companies on the state of experimentation across the room, revealing some interesting findings: most teams operate a centre of excellence model, roughly a third release over 90% of features through controlled experiments, and the top challenges are scaling, coordination, platform tooling, and culture. These shared experiences helped shape the programme. We had three sessions, grouped by the three conference themes: AI and experimentation: AI-assisted analysis, no-code experimentation Quality / velocity tradeoff: High-quality vs high-speed experimentation Experimentation culture: Build organizational buy-in and data-driven decision-making Each session followed the same format: two talks, then a panel discussion on the same topic. We closed with nine parallel breakout groups for deeper conversation. Below is a recap of the key sessions. Read the recap of 2025 | Read the recap of 2024 Session 1: AI and experimentation The conference started off with the hot topic of AI in experimentation. AI is changing how we experiment and how we support experimenters. How Experimentation Protects Decisions in an AI-Written World -- Marcel Toben Marcel Toben, Head of Engineering at Zalando, opened with a provocation he'd recently heard from software engineers in Berlin: nobody on his team had wr ## Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making DevFeed: [Devavrat Shah's research and Ikigai Labs use tabular data for real-time forecasting and decision-making]() Original publisher: [Read original article]() Author: David Chandler | Laboratory for Information and Decision Systems Published: 2026-07-14T20:25:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [decision-making](), [Structured-data](), [AI Models](), [data](), [Time Series](), [Computer science](), [Electrical engineering and computer science (EECS)](), [Temporal data](), [systems]() Tags: [ai](), [ai-and-decision-making](), [ai-in-business-planning](), [ai-models](), [algorithms](), [artificial-intelligence](), [business-and-management](), [business-modeling](), [celonis-context-model](), [computer-science-and-technology](), [data](), [data-systems](), [decision-making](), [devavrat-shah](), [electrical-engineering-and-computer-science-eecs](), [faculty](), [forecasting](), [idss](), [ikigailabs](), [laboratory-for-information-and-decision-systems-lids](), [mit-eecs](), [mit-idss](), [mit-intellectual-property](), [mit-lids](), [mit-schwarzman-college-of-computing](), [predictions](), [profile](), [real-time](), [research](), [school-of-engineering](), [startups](), [structured](), [tabular-data](), [time-series-data]() ### AI overview MIT Professor Devavrat Shah's research led to a foundation model for tabular and time-series enterprise data. Developed with Ikigai Labs, the system continuously tests predictions against real outcomes to support large-scale forecasting and decision-making. ### Source excerpt Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources. ## AI Can Amplify Misalignment in Product Development DevFeed: [AI Can Amplify Misalignment in Product Development]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-06-24T12:27:25Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Development](), [decision-making](), [Artificial Intelligence]() Tags: [ai](), [critical-thinking](), [decision-making](), [development]() ### AI overview The article argues that AI-generated strategies, roadmaps, and user stories can produce polished artefacts quickly without creating the alignment and shared understanding required for product development. It warns that relying on generated outputs can weaken decision-making and critical thinking, while presenting AI as an amplifier of existing strengths and weaknesses. ### Source excerpt "AI amplifies misalignment." Andrey Khusid, Miro CEO. ## One year with Codeberg DevFeed: [One year with Codeberg]() Original publisher: [Read original article]() Author: Ludovic Courtès Published: 2026-06-22T14:00:00Z Content type: article Language: en Sources: [GNU Guix -- Blog]() Topics: [pull-requests](), [hosting](), [issue tracking](), [decision-making](), [Git](), [forgejo]() Tags: [article](), [change](), [collaboration](), [community](), [decision-making](), [forgejo](), [issue-tracking](), [pull-requests](), [source]() ### AI overview Guix describes its migration to Codeberg for source code hosting, issue tracking, and pull requests after more than a decade using Savannah, email-based bug reports, patches, and Debbugs. The article discusses the reasons for the change, the project's collective decision-making process, and early takeaways after one year. ### Source excerpt A year ago, Guix migrated to Codeberg for source code hosting, issue tracking, and pull requests. This is a significant change for a project with more than 400 people contributing code each year, after more than decade hosting code at Savannah and dealing with bug reports and patches by email, tracked by a Debbugs instance . This article discusses the process that led to this change and lists some takeaways, a year later. The non-obvious choice For years before, the question of our choice of source code hosting and collaboration tools would regularly come up. However, with... ## Scaling Experimentation Quality at Booking.com DevFeed: [Scaling Experimentation Quality at Booking.com]() Original publisher: [Read original article]() Author: Edgar Cano Published: 2026-03-24T11:44:21Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [experiments](), [Development](), [decision-making](), [human review]() Tags: [best-practices](), [data-science](), [decision-making](), [experimentation](), [featured](), [human-review](), [metrics](), [product-development](), [quality]() ### AI overview Booking.com describes how it addressed declining experimentation quality as experiment volume grew. The article discusses arbitrary test durations, significance-seeking, and the trade-offs between enforcing standards and educating product teams. ### Source excerpt Authors: Edgar Cano, Daisy Duursma, Nils Skotara, Melanie Mueller Figure 1. Three-pillar components of Booking.com's Experimentation Quality Experimentation is at the core of product development in Booking.com, powered by our in-house platform, "ET" (Experiment Tool). At any given moment, we run approximately 1,000 parallel experiments to evaluate product changes. These experiments or A/B tests allow teams to directly compare a new version of the website against the existing one, validating hypotheses about how specific changes impact important metrics. In our organization, these pitfalls became more evident as our experiment volume grew. We observed that experimenters might set an arbitrary "two-week" duration without thinking about sufficient power, or extend a test until results "became significant" or "trended positive." Knowing that this lack of consistency leads to flawed decision-making we dedicated significant effort to increasing the quality of our experimentation process, making Experimentation Quality a key KPI for our program. However, identifying the problem was only the start; the greater challenge is how to implement these standards across a large organization. Enforcement vs. Education When deciding how to scale quality, we faced a fundamental choice: Do we enforce strict controls or we rely on education. Ultimately, we left it to product teams to decide how to conduct their experiments. This choice entailed several trade-offs: Enforcement: Ensures comparability, consistency, and reliability. However, it comes at the cost of flexibility. There is a risk that people follow "rules" blindly without understanding the rationale. Education: Aims for a culture where experimenters understand the why behind best practices. This leads to better buy-in, allows teams to challenge methods, and highlights individual responsibility. It also prevents bottlenecking. If enforcement requires human review, it slows down development. However, education requires a massive ## Reframing Healthcare Technology in the AI Era DevFeed: [Reframing Healthcare Technology in the AI Era]() Original publisher: [Read original article]() Author: Jon Wettersten Published: 2026-03-06T22:14:00Z Content type: opinion Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [Artificial Intelligence](), [Generative AI](), [Automation](), [decision-making](), [trust](), [digital]() Tags: [ai](), [automation](), [decision-making](), [generative-ai](), [governance](), [healthcare](), [healthtech](), [human-centered-design](), [safety](), [technology](), [transparency](), [trust]() ### AI overview This article argues that healthcare organizations should approach AI adoption through human-centered design, stronger governance, transparency, and alignment with clinical standards. It emphasizes that clinicians need technology to reduce administrative burden and support patient care without compromising safety, trust, or practical and ethical readiness. ### Source excerpt At 8th Light, we help healthcare organizations design and build AI-enabled systems that put people first, combining product strategy, human-centered design, and technology leadership to create innovative solutions that work for clinicians as well as patients. Why This Matters Although AI continues to dominate healthtech headlines, many frontline clinicians are still battling clunky systems, fragmented workflows, and administrative overload. The reality? Healthcare practitioners are not asking for more technology, they're asking for more time with patients. The tension between innovation and adoption continues to shape healthcare's digital transformation. As AI tools quickly evolve and adapt to revolutionize diagnosis, documentation, and decision-making, many healthcare providers remain cautious. Their questions are not about the potential of technology, but about its practical and ethical readiness for real-world care. For instance: How do we vet these tools? Can we trust the outcomes? Will this actually improve care or just add more work? Will this solution deliver measurable ROI without compromising patient safety? These are not questions of resistance, they're questions of responsibility. As healthcare systems race to deploy generative AI, predictive analytics, and automation, clinicians and administrators are demanding stronger governance, transparency, and alignment with clinical standards. Their goal isn't to slow innovation, but to ensure technology protects both patients and the licensed physicians who care for them. Behind these questions lies a workforce already under significant strain, and technology that fails to ease that burden risks making it worse. The Urgent Need for Human-Centered Healthtech The U.S. is already experiencing workforce strain across many health professions, driven by geographic maldistribution, burnout, and demographic pressures. Federal workforce projections show ongoing gaps across multiple professions, with non-metro communities ## Trusting AI agents: A reinsurance case study DevFeed: [Trusting AI agents: A reinsurance case study]() Original publisher: [Read original article]() Author: Sophia Barnes Published: 2026-01-22T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [data-processing](), [datasets](), [decision-making](), [parallel](), [file]() Tags: [agents](), [ai](), [ai-agents](), [automate](), [case-study](), [data-processing](), [excel](), [how-to](), [insurance](), [multi-agent](), [pricing](), [risk](), [workflows]() ### AI overview This case study explains how a multi-agent AI system with human-in-the-loop safeguards automates reinsurance data workflows. The system parses unstandardized Excel submission packs, matches catastrophe events with historical records, creates cedant loss records, and flags changes to existing data. ### Source excerpt Learn how to build a reliable multi-agent AI system with human-in-the-loop safeguards using Temporal. A detailed case study on automating complex reinsurance data workflows. ## Normalized Entropy or Apply Rate? Evaluation Metrics for Online Modeling Experiments DevFeed: [Normalized Entropy or Apply Rate? Evaluation Metrics for Online Modeling Experiments]() Original publisher: [Read original article]() Author: Megan Chen Published: 2025-11-11T06:16:53Z Content type: opinion Language: en Sources: [Indeed]() Topics: [recommendation systems](), [Model Development](), [decision-making](), [benchmarking]() Tags: [a-b-testing](), [data-science](), [decision-making](), [evaluation](), [experiments](), [machine-learning](), [metric](), [models](), [ranking](), [unsorted]() ### AI overview Indeed examines whether model performance metrics or product metrics should guide online modeling experiments. It discusses how optimizing individual ranking models may not align with broader business goals and considers evaluation metrics for model rollouts. ### Source excerpt Introduction At Indeed, our mission is to help people get jobs. We connect job seekers with their next career opportunities and assist employers in finding the ideal candidates. This makes matching a fundamental problem in the products we develop. The Ranking Models team is responsible for building Machine Learning models that drive matching between job [...] ## Management and Productivity Laws for Decision-Making and Organizational Behavior DevFeed: [Management and Productivity Laws for Decision-Making and Organizational Behavior]() Original publisher: [Read original article]() Author: Tom Published: 2025-08-26T00:00:00Z Content type: opinion Language: en Sources: [Tomasz Gągor]() Topics: [decision-making](), [Agile](), [meetings]() Tags: [business-efficiency](), [communication](), [conway-s-law](), [corporate-best-practices](), [decision-making](), [effectiveness](), [leadership](), [leadership-tips](), [management](), [management-laws](), [murphy-s-law](), [organizational-behavior](), [processes](), [productivity-principles](), [team-management](), [workplace-decision-making]() ### AI overview This opinion article introduces several management and productivity "laws," including Kidlin's Law, Gilbert's Law, Wilson's Law, and Falkland's Law. It explains how clear problem statements, explicit guidance, timely action, and avoiding unnecessary decisions can support workplace decision-making and organizational effectiveness. ### Source excerpt Discover the most influential management and productivity "laws"-from Murphy's Law to Conway's Law-that shape decision-making, leadership, and organizational behavior. Learn practical applications and scientific backgrounds to boost your effectiveness at work. ## How Managers Can Develop Team Leadership Through Meeting Delegation DevFeed: [How Managers Can Develop Team Leadership Through Meeting Delegation]() Original publisher: [Read original article]() Author: Ryan Murphy Published: 2025-07-14T12:00:13Z Content type: opinion Language: en Sources: [The Software Engineering Times]() Topics: [meetings](), [decision-making](), [trust](), [real-time]() Tags: [coaching](), [decision-making](), [leadership](), [manager](), [meetings](), [real-time](), [retro](), [support](), [sync](), [trust]() ### AI overview This opinion article argues that managers should avoid leading every meeting and instead give team members opportunities to run standups, retrospectives, and stakeholder syncs. It recommends providing preparation support, staying available, and debriefing afterward to develop independent leaders and reduce managerial burnout. ### Source excerpt Let's just say it straight. If you're the one leading every single meeting, you're either inexperienced, insecure, or haven't been shown how to be a real manager yet. If that stings a little, that's okay. Maybe it should. There are a few exceptions. Sure, incidents. Maybe a gnarly stakeholder review or a performance conversation. But even in those high-stakes moments, there's usually room to coach. Let someone else run the show. As long as someone is clear on decision-making, you don't need to be the one running the room every time. That's not what good leadership looks like. Good leadership is about building people who can do the job without you. Why You Keep Grabbing the Mic Most managers hog the meetings for one of a few reasons: It's faster if I do it They're not ready yet What if they mess it up? I'll be blamed if it goes wrong I'm the one accountable If that sounds like you, you're not alone. I've heard them all before. I've said a few of them too, early in my career. But you need to realise what those excuses are actually saying to your team: You're not ready. I don't trust you. I don't care enough to help you grow. Not great. What It Looks Like to Coach Instead This isn't about disappearing. It's not about sitting out. It's not delegating and walking away. It's about showing up differently. Coaching in real time. Supporting from the side, not from the front. Let them lead the standup. Let them chair the retro. Let them run the stakeholder sync. Then debrief. What worked? What would you do differently next time? What was hard? That's coaching. That's how they get better. That's how you build a team that can do hard things without you steering every move. The Real Reason You're Still Leading Everything Sometimes, it's not about fear. It's about ego. If everything runs through you If you always need to be in the room If you secretly like being the person with the answers That's ego. It's not sustainable. It's not leadership. It's how you end up with a team that ## Making Better Decisions - How we can decide better together. DevFeed: [Making Better Decisions - How we can decide better together.]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2025-06-19T00:00:00Z Content type: article Language: en Sources: [Tobias Mende]() Topics: [decision-making]() Tags: [berlin](), [collaboration](), [conference](), [decision-making](), [processes](), [quality](), [teams]() ### AI overview This talk presents collaborative decision-making methods, real-world examples, and practical strategies for improving decision quality, inclusiveness, clarity, and team commitment. It also discusses simplifying decision-making processes and using tools to improve efficiency. ### Source excerpt With the number of daily decisions increasing significantly over the past ten years, effective decision-making is crucial. Decisions need to be made as fast and as good as possible. This talk focuses on the value of collaborative decision-making, highlighting benefits such as improved problem-solving through diverse perspectives and enhanced commitment from team members. We will explore various group decision-making methods supported by real-world examples. ## A Three-Part Formula for Documentation That Converts Developers DevFeed: [A Three-Part Formula for Documentation That Converts Developers]() Original publisher: [Read original article]() Author: Emma Adler Published: 2025-06-11T00:00:00Z Content type: tutorial Language: en Sources: [Mintlify Blog]() Topics: [Documentation](), [decision-making](), [context](), [Software]() Tags: [api](), [best-practices](), [conversion](), [developer](), [docs](), [documentation](), [sdk]() ### AI overview The article presents a three-part approach to developer documentation: clarity, context, and flow. It argues that documentation should guide developers toward meaningful actions through progressive disclosure, visual hierarchy, clear decision points, and practical examples. ### Source excerpt You probably don't immediately notice the design when you walk into a well-designed store. However, you end up finding what you need without wandering, and you move from browsing to buying without getting frustrated. You may notice that you had a pleasant shopping experience, but you may not realize that every part of the environment is intentional: the lighting, the aisles, the checkout flow, even what is placed at eye level. ## Deciding Better in Organizations DevFeed: [Deciding Better in Organizations]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2025-03-29T05:00:00Z Content type: opinion Language: en Sources: [Tobias Mende]() Topics: [decision-making](), [context](), [meetings]() Tags: [collaboration-decision-making-leadership-culture](), [context](), [decision-making](), [distributed](), [improvements](), [meetings]() ### AI overview The article examines decision-making in software organizations, arguing that decision quality should be evaluated by the decision-making context and process rather than by outcomes alone. It discusses organizational problems such as centralized decisions, prolonged peer discussions, blocked work, and limited commitment, and points to collaborative and distributed decision-making methods as ways to improve. ### Source excerpt Deciding Better: How to Make Faster, Smarter, and More Effective Decisions in Your Organization The quality of your decisions influences the quality of your life. The quality of the decisions in your organization determines your company's future. ## The Leadership Dilemma - When Times Change and Generations Collide DevFeed: [The Leadership Dilemma - When Times Change and Generations Collide]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2025-03-01T05:00:00Z Content type: opinion Language: en Sources: [Tobias Mende]() Topics: [decision-making](), [FIRST]() Tags: [decision-making](), [founders](), [leadership](), [leadership-culture-decision-making-generational-differences-coaching](), [organization](), [startups]() ### AI overview The article argues that founders may apply leadership assumptions shaped by older workplace models even as organizations and younger employees increasingly value autonomy, impact, purpose, and inspiration. It focuses on decision-making problems, including evaluating decisions by outcomes, fear of poor results, and top-down interventions that can increase organizational dependency. ### Source excerpt The Leadership Dilemma - When Times Change and Generations Collide Your leadership style is heavily influenced by the leadership you have experienced yourself. The way how you run and build a company is the result of the companies you have worked in, your experiences, and your considerations. ## Focus on decisions, not tasks DevFeed: [Focus on decisions, not tasks]() Original publisher: [Read original article]() Published: 2024-10-15T00:00:00Z Content type: opinion Language: en Sources: [technicalwriting.dev]() Topics: [decision-making](), [Documentation](), [context]() Tags: [communication](), [decision-making](), [documentation](), [strategy](), [technical-writing](), [writing]() ### AI overview The article argues that technical documentation should support users' decisions, not merely help them complete tasks. It emphasizes documenting context, required decisions, consequences, and useful resources or references. ### Source excerpt Documentation needs to help people make decisions, not just accomplish tasks. [Next page]()