Answers from chatgpt web app
Overview
Model: gpt-N/A
Tools: []
System Prompt
N/A
Scores
| Question | Burrows Delta | Andrew Model |
|---|---|---|
| Question 0 | 0.2623 | 0.9989 |
| Question 1 | 0.1633 | 0.9705 |
| Question 2 | 0.3989 | 0.5196 |
| Question 3 | 0.2124 | 0.9991 |
| Question 4 | 0.1827 | 1.0 |
| Question 5 | 0.2088 | 0.0005 |
| Question 6 | 0.5672 | 0.9956 |
| Question 7 | 0.3769 | 0.9611 |
| Question 8 | 0.2957 | 0.9999 |
| Question 9 | 0.2361 | 0.9639 |
| Question 10 | 0.2672 | 0.9488 |
| Question 11 | 0.222 | 0.996 |
| Question 12 | 0.3888 | 0.999 |
| Question 13 | 0.224 | 0.9997 |
| Question 14 | 0.2368 | 0.9987 |
| Question 15 | 0.2443 | 0.2597 |
| Question 16 | 0.4411 | 0.0043 |
| Question 17 | 0.2443 | 0.2597 |
| Question 18 | 0.326 | 0.9971 |
| Question 19 | 0.4847 | 0.4671 |
Average:
| Description | Burrows Delta | Andrew Model |
|---|---|---|
| Answers from chatgpt web app | 0.2992 | 0.767 |
Responses
Question 0
| Burrows Delta | Andrew Model |
|---|---|
| 0.2623 | 0.9989 |
Answers from chatgpt web app | I'm a new engineering leader and my team struggles with meeting deadlines. How can I improve our team's effectiveness and ensure we meet our goals?
Great question — and as "Addy," I'd say you're already on the right path by identifying the problem and seeking to lead deliberately. When a team struggles with hitting deadlines, it often signals deeper issues around alignment, planning, or execution — not just velocity. Let's break it down with actionable strategies.
🔍 Diagnose Before Prescribing
First, you need clarity:
- Are deadlines realistic or imposed?
- Is scope creep common?
- Does the team feel empowered or pressured?
- Are there consistent blockers or context-switching?
📌 As I often say in Tech Leadership Tuesday: "You can’t optimize what you don’t understand."
🧭 1. Align on Outcomes, Not Just Output
Deadlines often feel arbitrary when they’re not linked to outcomes. Shift your team’s mindset:
- Instead of “deliver feature X by Y,” frame it as “solve user problem Z by Y — and here’s why it matters.”
- Empower engineers to push back or negotiate scope/timelines. Psychological safety = better commitments.
🛠️ Tool: Use tools like Working Backwards (from Amazon) or a North Star Framework to make deadlines mission-driven.
🛠️ 2. Shorten the Feedback Loops
Long cycles hide risk. You want early signals of slippage. Try:
- Weekly check-ins with confidence scores (e.g. “How confident are we in hitting milestone X on a scale of 1–5?”)
- Progress demos, not just status updates
- Smaller slices of work — reduce batch size
📌 From the book: “Frequent iteration reduces uncertainty and invites collaboration.”
🧰 3. Use Planning as a Leadership Lever
If you're not involved in planning, you're already behind. Guide the team through:
- Sprint pre-mortems: “What could go wrong?”
- Definition of done clarity: “Is QA, perf, and monitoring included?”
- Estimations with buffers, not heroics
🛠️ Tool: RICE, MoSCoW, or a variation of Now/Next/Later to prioritize clearly.
👥 4. Protect Focus and Reduce Waste
Multitasking, unnecessary meetings, and unclear ownership kill delivery. As a leader, create space to build:
- Audit meeting load; reclaim focus time.
- Limit work in progress (WIP).
- Use a simple “Who owns what by when?” doc.
🔁 Borrow from Shape Up (Basecamp): “Appetite-based deadlines” + small teams = predictable delivery.
🧭 5. Foster Accountability Without Fear
Missing deadlines isn’t always about laziness — often it's lack of clarity or confidence. Create a blameless culture where teams can say:
- “We underestimated this.”
- “We need help.”
- “We need to rethink scope.”
📌 “The strongest teams are not perfect — they are self-aware.”
TL;DR Playbook
Here’s a quick system to improve delivery over 30–60 days:
| Focus Area | Action |
|---|---|
| Planning | Run lightweight pre-mortems + story slicing workshops |
| Execution | Weekly check-ins with confidence scores |
| Process | Shorten cycles, reduce WIP, timebox scope |
| Culture | Normalize asking for help; remove blame from misses |
| Metrics | Track planned vs actual, cycle time, and blockers |
If I were mentoring you directly, I’d say: treat missed deadlines as learning opportunities, not failures. They reveal where your systems — not people — need strengthening.
Want to dive deeper into any one of these strategies?
Question 1
| Burrows Delta | Andrew Model |
|---|---|
| 0.1633 | 0.9705 |
Answers from chatgpt web app | I'm managing a team that lacks psychological safety, and team members are hesitant to share ideas. What strategies can I use to foster a more open and innovative environment?
Great question — and it’s a challenge a lot of tech leaders face, especially when teams are under pressure. Here’s how I (as Addy Osmani would frame it) would approach it, grounded in what I’ve shared in Leading Effective Engineering Teams and my talks:
🧠 First Principles: Understand Why Psychological Safety Matters
You can’t build great products without great people — and great people need to feel safe to speak up, challenge ideas, and take risks. Innovation dies in silence. And silence is often a symptom of fear — fear of being judged, ignored, or punished.
🔑 Strategies to Create Psychological Safety
1. Model Vulnerability as a Leader
Show that it’s okay not to have all the answers. Say “I don’t know” when you don’t. Share what you’re learning or struggling with. This sets the tone. As I often say: “The behavior you walk past is the behavior you accept. The behavior you demonstrate is the one that scales.”
Try: Kick off retros by sharing one mistake you made that week and what you learned from it.
2. Redesign Meetings to Be More Inclusive
Meetings can be intimidating. Dominant voices often overpower quieter ones.
Try:
- Set ground rules (e.g., no interrupting, all ideas are valid).
- Use structured formats like “silent brainstorming” (write ideas individually before discussion).
- Rotate facilitation roles.
- Call on less-heard voices gently, not forcefully: “Priya, I’d really value your take here if you’re comfortable sharing.”
3. Reward Contribution Over Outcome
People won't take risks if they fear failure. Instead, celebrate the act of trying, not just success.
Example: When someone proposes an idea that doesn’t pan out, say “I’m glad you brought that forward. That kind of thinking helps us learn faster.”
4. 1:1s Are Where You Do the Deep Work
Psychological safety doesn’t only get built in all-hands or team meetings. It happens one conversation at a time.
In your 1:1s:
- Ask: “Is there anything I’m doing that makes it harder for you to speak up?”
- Ask: “What’s something you’ve been hesitant to say in a group setting?”
- Ask: “How can I make this a safer space for experimentation?”
5. Make Feedback Normal and Frequent
Not just “safe,” but expected. The longer feedback waits, the more charged it gets.
Try:
- Embed quick feedback rituals (e.g., 5-minute retro at end of standups).
- Use blameless postmortems — focus on systems, not individuals.
- Share positive feedback in public, constructive feedback in private.
6. Explicitly Name Safety as a Priority
Sometimes just naming the elephant in the room is powerful.
Say: “I know we haven’t always felt comfortable surfacing concerns or ideas, and I want to change that. Innovation requires risk-taking, and that starts with safety.”
🚦 A Red Flag to Watch For
If people only speak up when you’re in the room, or defer all decisions to you, that’s a sign of a safety vacuum. Empower team leads or senior ICs to set their own tone — safety is contagious when it’s modeled at multiple levels.
🧭 Final Thought
Psychological safety isn’t a box you tick. It’s a culture you curate every day — in what you reward, what you tolerate, and how you listen. Keep chipping away. When safety is high, innovation, velocity, and morale follow.
Would you like a practical action plan or workshop template you can use with your team to kickstart this shift?
Question 2
| Burrows Delta | Andrew Model |
|---|---|
| 0.3989 | 0.5196 |
Answers from chatgpt web app | I'm leading a diverse team, and while diversity is beneficial, we're facing coordination challenges. How can I create an inclusive environment that leverages our diversity effectively?
Great question — and I’m glad you’re leaning into this challenge. Leading a diverse team comes with immense potential, but it does require intentional structure and culture-building to harness that diversity as a strength rather than letting it become a coordination tax.
Here’s how I’d approach this, drawing on the principles from Leading Effective Engineering Teams and what I’ve shared in Tech Leadership Tuesday:
🧠 1. Anchor the Team in Psychological Safety
Diversity without inclusion just breeds silence.
If team members don’t feel safe speaking up — especially when they're in the minority — their perspectives won’t make it into the product, codebase, or discussions.
What to do:
- Normalize vulnerability. As a leader, model sharing mistakes, asking questions, and expressing uncertainty.
- Encourage equitable airtime in meetings — actively invite less vocal members to weigh in.
- Watch for interrupting or dominating behaviors and gently moderate.
💡 Try the “One Word Check-In” at the start of meetings to get emotional temperature and open up more human connection.
🔁 2. Over-Communicate on Clarity
Ambiguity hurts alignment, especially across cultures, languages, and backgrounds.
Diverse teams often bring different assumptions. That’s a strength, but it needs scaffolding.
What to do:
- Create shared documentation for decisions, goals, and design rationale.
- Use tools like RFCs (Request for Comments) to encourage structured feedback asynchronously.
- Align frequently on what “success” looks like for a project or sprint — even definitions of “done” can vary!
📌 Tip: Assume your first explanation won’t land for everyone — and design communications accordingly.
🌱 3. Make Inclusion a System, Not Just a Sentiment
Build it into your rituals, not just your values.
What to do:
- Use rotating roles (e.g., meeting facilitator, note-taker) to democratize voice and reduce hierarchy.
- Audit who speaks in meetings, who gets promoted, and who gets high-impact projects.
- Provide flexibility in how people contribute (e.g., async vs. live discussion).
🔍 Tools like inclusion surveys or 1:1 retros can surface whose voices aren’t being heard.
🧰 4. Invest in Developer Experience (DX) for All
Inclusive environments reduce friction for diverse contributors.
Are your onboarding docs accessible to someone unfamiliar with your stack or idioms? Is your tooling equally supportive for those in different time zones?
What to do:
- Improve documentation and internal tooling with newcomers in mind.
- Review and simplify onboarding flows with input from new hires.
- Design your CI/CD and deployment systems to be friendly for remote and async teams.
🧭 5. Lead with Empathy and Intent
Diverse teams need managers who are students of their own bias.
What to do:
- Reflect on whose comfort you optimize for — the loudest? The majority?
- Actively coach your senior engineers to be culture stewards, not gatekeepers.
- Celebrate different types of contributions — not just shipping code, but mentoring, documentation, and debugging.
TL;DR from “Addy”: Diversity plus psychological safety plus clarity equals high performance. Structure your communication, rituals, and systems to reflect that. When inclusion is operationalized, your team’s diversity becomes a true superpower — not a coordination cost.
Want help crafting specific rituals, team norms, or onboarding improvements for your case?
Question 3
| Burrows Delta | Andrew Model |
|---|---|
| 0.2124 | 0.9991 |
Answers from chatgpt web app | I'm a new engineering leader and my team is delivering a lot of code, but I'm not sure if we're focusing on the right things. How can I ensure that we're being effective and not just efficient?
That’s a great question — and one that shows you’re already thinking like a strong leader. Channeling Addy Osmani here, let’s unpack this from a few key angles:
🚦 Effectiveness vs. Efficiency: Why the Distinction Matters
“Shipping fast is meaningless if what you're shipping doesn’t move the needle.” — Addy Osmani
Efficiency is about doing things right — optimizing processes, pushing lots of code, minimizing cycle time. Effectiveness is about doing the right things — aligning effort to outcomes that matter.
A team that's only efficient might ship a lot of features that don’t drive user value. Your role as a leader is to connect their efforts to impact.
🔍 Step 1: Reconnect the Team with Outcomes
Ask: What are we trying to achieve?
Shift from output-focused metrics (like velocity or story points) to outcome-focused ones (like engagement, retention, error rate reduction, or revenue uplift).
Action: Introduce or revisit a clear set of OKRs (Objectives and Key Results) or North Star metrics. Then ask:
- Does the work we’re doing map clearly to one of these?
- If not, why are we doing it?
"Tie your roadmap to measurable impact. Features should have a hypothesis behind them."
🧭 Step 2: Create a Culture of Product-Engineering Alignment
Involve your engineers in understanding the “why.” Effective teams care about solving the right problems, not just coding tasks they’re handed.
Action: Set up recurring syncs with PMs, designers, and engineering to:
- Validate user problems.
- Review impact of past work.
- Kill or course-correct projects that aren’t working.
“Empowered teams question priorities, validate assumptions, and aren’t afraid to stop work that no longer makes sense.”
🛠 Step 3: Instrumentation + Feedback Loops
“If you can't measure it, you can't learn from it.”
Every major feature should ship with instrumentation. Not just logs or uptime monitoring — but real user behavior metrics:
- Are users using the feature?
- Are they completing intended flows?
- Are we reducing friction or creating it?
Action: Build a dashboard (or work with analytics) that makes usage & impact visible to the team.
💬 Step 4: Retrospectives Focused on Value
Don’t just talk about blockers and process. Add a recurring question to your team retro:
- What shipped recently that created real impact?
- What did we ship that might not have been worth it? Why?
“Use retros not just to get faster — but to get smarter about what not to build.”
🔁 Step 5: Iterate Ruthlessly
Just because you shipped something doesn’t mean you’re done.
Action: Bake in iteration cycles. Review impact 2–4 weeks post-launch. Adjust the roadmap based on what’s working and what’s not.
✨ TL;DR from Addy’s POV:
- Map work to business/user outcomes.
- Get engineers closer to the problem space.
- Measure impact, not just output.
- Create feedback loops and iterate with intent.
- Celebrate learning as much as shipping.
If you'd like, I can help you draft a team alignment session, build a lightweight value-mapping framework, or create a dashboard to track meaningful outcomes. Let me know how hands-on you'd like to get.
Question 4
| Burrows Delta | Andrew Model |
|---|---|
| 0.1827 | 1.0 |
Answers from chatgpt web app | As an engineering leader, I'm trying to balance the productivity of my team with the quality of our output. How can I measure and improve both efficiency and effectiveness without sacrificing one for the other?
Great question — and a classic leadership challenge. Balancing productivity (efficiency) with quality (effectiveness) is like tuning a system for both throughput and correctness — and as with most systems, observability, feedback, and constraints matter.
Here's how I’d approach it — drawing from what I’ve shared in Leading Effective Engineering Teams and in my Tech Leadership Tuesday series:
🔍 First, Define What You Actually Mean by “Efficiency” and “Effectiveness”
You need concrete definitions that resonate with your team.
- Efficiency: Are we shipping value fast? Are we minimizing waste (context-switching, rework, overengineering)?
- Effectiveness: Are we solving the right problems in a way that’s maintainable, scalable, and aligned with user/business needs?
If you don’t define both, teams may optimize for speed and unintentionally accrue long-term quality debt — or get paralyzed by over-polishing.
⚖️ Principle: Speed with Guardrails
We want to move fast — but with clarity and constraints that enforce quality:
- Technical quality gates: CI/CD with automated tests, performance budgets, linting, and static analysis.
- Process quality gates: PR reviews, architecture reviews, user acceptance tests, lightweight design docs.
- Feedback loops: Use lead time for changes and bug rate or incident count together — measuring both velocity and defects.
You can’t manage what you don’t measure — but you also shouldn’t measure what you don’t intend to act on.
📏 Metrics That Balance Both Worlds
Here are metrics I often recommend for balancing output and quality:
Productivity / Efficiency Metrics
- Lead time for changes (from commit to production)
- Deployment frequency
- Cycle time per issue/feature
- Story points or throughput (if used carefully)
Quality / Effectiveness Metrics
- Defect rate / escaped bugs
- Customer-reported issues
- MTTR (mean time to recovery) from incidents
- Code health / tech debt indicators (e.g., TODOs, lint failures, complexity scores)
- Post-release satisfaction (e.g., internal UX quality scores or external NPS)
Tip: Use DORA metrics (from the DevOps Research & Assessment group) as a good proxy for both velocity and stability.
🔄 Continuous Improvement Loops
As a leader, your job is not just to track — it’s to create systems that encourage self-correction and learning.
- Retrospectives: Are we reflecting on tradeoffs? Are we catching quality regressions early?
- Tech debt time allocation: Carve out 10–20% of team time to address non-feature work — track this and defend it.
- Blameless postmortems: Normalize learning from failures and bugs — not just fixing them, but preventing them upstream.
- Feedback from PMs/Design/Support: Are we hitting the mark, or just hitting deadlines?
🎯 Culture: What You Reward Is What You Get
Make it visible that quality matters:
- Celebrate engineers who reduce complexity, simplify APIs, write clean tests.
- Don’t just reward speed — reward outcomes and thoughtful trade-offs.
As I’ve said before: “Slow is smooth, and smooth is fast — when quality is embedded, speed becomes sustainable.”
TL;DR Action Plan
- Baseline with DORA + bug/incident metrics.
- Instrument quality gates (tests, performance, code review rigor).
- Establish norms for retros, debt tracking, and postmortems.
- Track both efficiency and effectiveness in dashboards — not just one.
- Coach your team to think in terms of “How do we make this fast and right?”
Want a dashboard template, retro questions, or coaching templates for this? Happy to share.
Question 5
| Burrows Delta | Andrew Model |
|---|---|
| 0.2088 | 0.0005 |
Answers from chatgpt web app | I'm leading a team that has recently transitioned to using microservices and Kubernetes. While we've improved our deployment speed, user feedback indicates no significant improvements in performance or usability. How can I refocus my team to ensure our technical advancements translate into real user benefits?
Great question — and it’s one many engineering leaders face after a big shift like moving to microservices and Kubernetes. Let me respond as Addy Osmani might:
First: Technical improvements ≠ user outcomes unless you're deliberate about it. Shifting to microservices and Kubernetes can enable agility, scalability, and faster iterations — but if those gains aren't tightly coupled to solving actual user pain, they’re infrastructure wins, not product wins.
Here’s how I’d recommend refocusing:
1. Tie Engineering Goals to User-Centric KPIs
- Ask: “What are our core user experience metrics?” (e.g., time-to-interaction, error rates, task success rate, Net Promoter Score).
- Ensure the team tracks these alongside technical metrics like deployment frequency or latency.
- Make this visible — dashboards, demos, retros.
💡 "We moved to microservices. Great. But did that reduce the time users wait to load the dashboard?"
2. Establish an “Outcome Over Output” Mindset
- Outputs: “We deployed 20 microservices.”
- Outcomes: “Users now experience 30% less downtime.”
- Coach your team to frame work in terms of user impact. Tie Jira tickets or OKRs back to user benefits.
3. Introduce UX Reviews into Your Dev Lifecycle
- Incorporate lightweight usability testing or design reviews into sprint demos.
- Pull in UX/product folks to collaborate with engineers early.
- Encourage engineers to watch real users (or customer support recordings) struggle — it builds empathy and aligns priorities fast.
4. Create Feedback Loops Between Prod and Dev
- Use production analytics and user telemetry to measure if recent releases actually helped.
- Celebrate when a backend change improves UX (e.g., “This caching layer dropped page load times by 20%!”).
- Make it a habit to ask: “How will this service improve the user experience?” before greenlighting a technical initiative.
5. Bridge the Gap Between DevOps and Product Thinking
- Microservices should be enablers: for feature velocity, resilience, performance.
- Pair this with product OKRs or value-based delivery — like improving time to task completion or reducing steps to checkout.
Tactical Addy-style takeaways:
- Run a “user impact audit” of recent infra changes — what’s actually better for the end user?
- Add UX impact reviews to your tech design review process.
- Reframe internal wins in terms of user-facing value — even internally.
Let me know what your team is currently tracking or optimizing for — I can help translate that into something more outcome-driven.
Question 6
| Burrows Delta | Andrew Model |
|---|---|
| 0.5672 | 0.9956 |
Answers from chatgpt web app | I'm a new engineering leader and I'm struggling to define what effectiveness means for my team. How can I go about establishing a clear definition that aligns with our organization's goals?
Great question — and one that many new engineering leaders wrestle with. Defining team effectiveness isn’t just about metrics or velocity; it’s about aligning your team’s impact, health, and growth with your organization's strategy. Let’s walk through this the way I (Addy Osmani-style) might advise:
🧭 Step 1: Align on Outcomes, Not Just Output
Start by deeply understanding your organization’s goals (OKRs, strategic themes, business priorities). Then ask:
“What problems is my team uniquely positioned to solve that will help move those goals forward?”
Effectiveness is about delivering impact, not just shipping features. So clarity on which outcomes your team owns is foundational.
🎯 Step 2: Co-define Effectiveness with Your Team
Effectiveness shouldn’t be something you impose — co-create it. Involve your team in answering:
- What does great look like for us in 6 months?
- How do we know we're delivering high-quality work?
- What should our stakeholders be saying about us?
- What do we want to be known for?
By having your team help shape this, you foster ownership and alignment, not just compliance.
🔍 Step 3: Use a Multi-Dimensional Framework
I often recommend viewing team effectiveness through three lenses:
-
Product Impact
-
Are we solving meaningful problems?
- Are we delivering outcomes that matter?
-
Do users and stakeholders see value in our work?
-
Execution Excellence
-
Are we delivering with quality, speed, and predictability?
- Is our tech debt managed and our architecture scalable?
-
Are we learning and improving our delivery practices?
-
Team Health & Growth
-
Are engineers growing and engaged?
- Is the team psychologically safe?
- Is there clear ownership and minimal burnout?
You can even run retros using these lenses.
📈 Step 4: Make It Measurable, but Not Just with KPIs
Avoid the trap of reducing effectiveness to DORA metrics or story points. Instead:
- Use leading indicators (e.g. “Are we reducing cycle time?”)
- Combine qualitative feedback (surveys, 1:1s, stakeholder input)
- Regularly inspect and adapt — effectiveness is dynamic
🗣 Step 5: Communicate and Revisit
Finally, communicate your evolving definition of effectiveness up, down, and sideways. Share progress. Adapt. Show that you're a learning team, not just a delivery team.
Pro tip from Addy: A highly effective team feels both confident in their direction and safe to challenge it. Effectiveness is as much about clarity and autonomy as it is about metrics.
Want help drafting a team charter or effectiveness rubric? Or thinking through specific metrics that might suit your domain? Let’s go deeper.
Question 7
| Burrows Delta | Andrew Model |
|---|---|
| 0.3769 | 0.9611 |
Answers from chatgpt web app | I'm an engineering leader with some experience, and I'm facing challenges with empowering my team to take ownership of their work. What strategies can I use to foster autonomy and accountability?
Great question — and one that resonates deeply with my experience leading engineering teams at scale.
Fostering autonomy and accountability isn’t about stepping back entirely — it’s about designing systems and culture where engineers feel both trusted and expected to lead their own work effectively. Here's how I'd approach it, based on the principles I advocate in Leading Effective Engineering Teams and Tech Leadership Tuesday:
🔑 1. Set Clear Context, Not Control
Ownership thrives when people understand the why. Your role is to frame:
- The problem space (“What does success look like for this feature/service?”)
- The constraints (tech, time, business goals)
- The metrics that matter (e.g., latency, user satisfaction, churn)
"When people know what outcome they're shooting for, they can make better decisions without you in the room."
🔧 Tactic: Use frameworks like "Intent > Task" — instead of assigning tasks, describe the intent behind them and let the team break them down.
💬 2. Create Decision-Making Lanes
If everything has to run through you, you're the bottleneck. But unbounded autonomy can lead to chaos.
- Define decision scopes: e.g., ICs can make product-level tradeoffs within X boundaries; TLs own architectural decisions up to Y threshold.
- Document and socialize a "Who Decides What" map.
"Autonomy without alignment is just anarchy. Your job is to tune the system, not micromanage the choices."
🧠 3. Shift from Execution to Enabling
Move from being the “doer” to the multiplier:
- Ask guiding questions: “What tradeoffs did you consider?” vs. “Why didn’t you do X?”
- Coach on decision quality, not just decision direction.
- Use pre-mortems: “If this failed, why do you think it would?”
🔧 Tactic: Schedule tech health or strategy office hours — spaces where your team can talk about ownership risks or architectural bets without needing a fully formed proposal.
🧪 4. Make Accountability Psychological, Not Just Procedural
Deadlines and reviews alone don’t create accountability — personal investment does.
- Public demos of ownership (team leads sharing what went wrong and what they'll change)
- Celebrate not just wins but responsible risks and course corrections
- Postmortems where the team leads the reflection, not you
“People rise to ownership when they feel safe to fail, but responsible to improve.”
📊 5. Use Metrics to Reflect, Not Police
You can't improve what you don't measure — but metrics should be mirrors, not whips.
- Onboarding time, PR review latency, deployment frequency, “Time to 10x productivity” — all great reflection points
- Let teams own their metrics reviews, then support changes they propose
🔧 Tactic: Try quarterly engineering health reviews — teams self-assess on autonomy, cognitive load, delivery confidence, etc.
🪴 Bonus: Model Ownership Yourself
Your team is always watching how you handle complexity, mistakes, and ambiguity.
- Admit what you don’t know
- Say “Here’s what I own” when things go sideways
- Seek and act on feedback in public
“The most empowering thing you can say to your team: ‘You’ve got this — and I’ve got your back.’”
Would you like help applying these to a specific scenario — like a team that’s underdelivering, or a strong IC who avoids leadership? We can go deeper on that.
Question 8
| Burrows Delta | Andrew Model |
|---|---|
| 0.2957 | 0.9999 |
Answers from chatgpt web app | I'm an experienced engineering leader, and I'm looking to scale the effectiveness of my team across the organization. What are some advanced strategies for expanding our success patterns to larger teams?
That’s a fantastic question—and one I think about a lot in my work at Google.
Scaling engineering effectiveness across an organization—especially once you’ve nailed it at the team level—is less about repeating processes and more about amplifying what works, removing friction, and establishing clarity at scale.
Here are advanced strategies I’d recommend, grounded in the principles from Leading Effective Engineering Teams and the kinds of patterns I’ve talked about in Tech Leadership Tuesday:
🔁 1. Codify and Productize What Works
When you notice success patterns (processes, rituals, tooling, technical decisions) working at the team level, abstract and productize them:
- Build internal platforms, shared libraries, or playbooks.
- Package team conventions into reusable templates (e.g., starter repos, CI/CD pipelines, onboarding docs).
- Automate the “manual glue” (e.g., dev env setup, deploy hygiene, release health dashboards).
📌 Think of this as engineering your culture into artifacts.
🧪 2. Treat Teams Like Experiments
At Google, we embrace a model of decentralized experimentation with centralized learnings:
- Let teams experiment with their own ways of working.
- Encourage retrospectives that are shared org-wide—not buried in a Confluence graveyard.
- Capture meta-learnings about what contributes to velocity, morale, and product impact.
📌 Create space for local innovation, but always reflect and scale the best ideas.
🔭 3. Establish a Technical Strategy That Creates Clarity
One thing I emphasize is that great teams need clarity of direction:
- Define 12-month and 3-year north stars—aligned to company goals and technical realities.
- Have a published architectural vision that is understandable and adaptable.
- Reduce cognitive load for engineers by defining what "good" looks like (e.g., through design reviews, RFC templates, or Paved Paths).
📌 Great strategy is about making trade-offs visible and consistent.
🧑🏫 4. Invest Heavily in Engineering Enablement
Scaling isn't just about hiring more—it’s about increasing the impact per engineer:
- Form a DX (Developer Experience) or Eng Productivity team.
- Measure time-to-first-PR, deploy-to-prod latency, time spent debugging, etc.
- Track how much toil teams have and build tools to remove it.
📌 Ask: what are the invisible taxes on engineering effectiveness?
🧭 5. Lead with Psychological Safety and Execution Principles
At scale, people don't just follow plans—they follow principles and psychologically safe environments:
- Standardize engineering principles (e.g., “prefer boring technology,” “fail fast in staging, not prod”).
- Build a blameless culture where postmortems lead to systemic improvements.
- Use frameworks like “Intent → Alignment → Autonomy” to balance direction and freedom.
📌 If you want teams to make great decisions without you in the room, codify how you make decisions.
🧰 6. Use Ladders and Growth Frameworks Intentionally
This is often under-leveraged:
- Design a technical growth framework that rewards engineering impact—not just people management.
- Define what Staff and Principal engineers do differently from seniors. Use that to set clear expectations on scaling influence beyond their team.
- Make mentorship and technical leadership a first-class responsibility.
📌 Great leaders create other leaders, not followers.
💡 Final Thought: Measure What Matters (and Share It)
- Track metrics like deploy frequency, MTTR, % of code reviewed by >1 person, time to merge.
- But more importantly—share those learnings org-wide.
- Transparency breeds learning and motivates teams to improve autonomously.
If I could sum it up in one sentence:
Scale success by creating clarity, reducing friction, and embedding your values into reusable systems.
Let me know if you want to dive deeper into org structure, Staff+ engineering impact, or developer productivity measurement—happy to go further.
Question 9
| Burrows Delta | Andrew Model |
|---|---|
| 0.2361 | 0.9639 |
Answers from chatgpt web app | I'm a new engineering manager and I'm struggling to provide effective feedback to my team. What strategies can I use to improve my feedback skills?
Great question — and it's one that shows you care, which already puts you ahead. Drawing from Addy Osmani’s approach in "Leading Effective Engineering Teams" and his leadership talks, here’s how I’d guide you:
🧠 1. Think Outcomes, Not Just Output
One of Addy’s core beliefs is aligning feedback with impact, not just activity.
💬 “Instead of saying ‘you didn’t write tests,’ try: ‘When we don’t include tests, it increases the risk of regressions, which can slow down the team later. Let’s look at how we can improve this together.’”
🔹 Tip: Frame feedback around how behaviors affect the team, users, or velocity. Keep it connected to shared goals.
🔄 2. Make Feedback Continuous, Not Just Scheduled
Don’t wait for performance reviews or 1:1s.
💡 Addy emphasizes real-time micro-feedback — brief, specific notes given close to the moment.
🔹 Tip: Practice "observe → reflect → comment" in the flow of work. For example, drop a quick Slack DM after a great PR or to offer a small improvement suggestion.
🧊 3. Start with Psychological Safety
Addy often talks about how feedback only works when trust exists.
💬 “When teams feel safe to fail and grow, feedback becomes a tool for improvement instead of a threat.”
🔹 Tip: Normalize two-way feedback. Ask: “What’s one thing I could do better as your manager?” You'll build a habit of mutual growth.
🎯 4. Use the SBI Model (Situation–Behavior–Impact)
This structure aligns with Addy’s push for clarity and empathy.
- Situation: When did it happen?
- Behavior: What did they do?
- Impact: What was the result?
🔹 Example:
“In yesterday’s planning meeting (situation), you cut off a teammate mid-sentence (behavior). It made it harder for them to contribute their ideas (impact). Can we talk about that?”
🛠️ 5. Tailor Feedback to the Individual
From Addy’s work, one key insight is: engineers are not interchangeable. Know how each person prefers to receive input.
🧠 “Some people want direct feedback. Others need more context or private settings. Use 1:1s to learn this.”
🔹 Tip: Ask during onboarding or 1:1s: “How do you prefer to receive feedback — in the moment, async, or in our 1:1s?”
📈 6. Give As Much Positive as Constructive Feedback
From a motivation perspective, Addy is big on developer morale and retention.
💬 “Engineers should know what ‘great’ looks like — and feel seen when they do it.”
🔹 Tip: Use positive feedback to reinforce behaviors you want to see more of. Don’t wait until someone’s slipping.
💬 Practice Example:
Let’s say someone consistently misses deadlines.
❌ Avoid:
“You’re always late.”
✅ Try:
“I noticed the last two sprints had features slip past the deadline. Can you walk me through what’s getting in the way? Let’s see if we can unblock it together.”
📚 Addy-Inspired Practices You Can Adopt Now
- Feedback Journaling: Keep a doc of feedback moments for each team member so you’re ready with specifics.
- 1:1 Feedback Cadence: Build a rhythm (e.g. one growth topic every other 1:1).
- PR Feedback Coaching: Use PRs as a low-stakes feedback tool—help your team improve code and communication.
Would you like a feedback template you can use in 1:1s or Slack? Or we could role-play a few scenarios you're dealing with.
Question 10
| Burrows Delta | Andrew Model |
|---|---|
| 0.2672 | 0.9488 |
Answers from chatgpt web app | I'm an engineering leader trying to create a more inclusive team environment. What actions can I take to ensure all team members feel valued and supported?
That’s a fantastic initiative — and exactly the kind of leadership that makes teams not just perform better, but also feel better.
Drawing from my (Addy Osmani’s) perspective, here are practical, high-leverage actions you can take to foster inclusivity and ensure every team member feels valued and supported:
🧭 1. Create Psychological Safety First
“The most effective teams are safe for interpersonal risk-taking.”
- Model vulnerability: Admit mistakes, ask for feedback, and share your own learning moments.
- Celebrate questions: When someone asks for clarification or expresses uncertainty, praise the curiosity, not just the confidence.
- Avoid interruptions: Enforce inclusive meeting norms like no interruptions, round-robin updates, or async participation.
🗣️ 2. Elevate Every Voice
“The loudest voice shouldn't automatically win. Normalize equitable input.”
- Rotate facilitators in meetings so no one person always holds the floor.
- Use tools like anonymous polls or forms to gather ideas and feedback without social pressure.
- Actively invite quieter folks to contribute: “Alex, I know you’ve had experience here — would love your take.”
💡 3. Clarify Roles and Growth Paths
“Ambiguity can amplify feelings of exclusion.”
- Make expectations and career ladders transparent — no one should wonder what it takes to grow.
- Regularly check in on career goals, not just sprint tickets.
- Encourage mentorship pairings across different backgrounds and experiences.
🧱 4. Design for Inclusion in Everyday Work
“Inclusion isn’t just culture — it’s built into process.”
- Use asynchronous communication (docs, Slack updates, Loom videos) to give everyone time to process and respond.
- Normalize flexible schedules — assume varied life constraints and time zones.
- Review code review and decision-making processes for bias: Are you evaluating ideas or personas?
❤️ 5. Celebrate Differences Openly
“Diverse perspectives are a feature, not a friction.”
- Encourage show-and-tells of side projects, backgrounds, or technical journeys.
- Host learning sessions where teammates can share unique skills (e.g., “How I used Web Perf in a real product”).
- Mark cultural moments together — whether it’s Lunar New Year or Pride Month — in a genuine, team-specific way.
📈 6. Use Metrics Thoughtfully
“What you measure sends a message.”
- Track engagement and inclusion in retrospectives: “Did everyone feel heard this sprint?”
- Survey for belonging, clarity, and psychological safety.
- When acting on feedback, close the loop: “You asked for clearer sprint goals — here’s what we’ve changed.”
👥 7. Lead by Example
“Your behavior is the team’s boundary.”
- Be vocal about values. If exclusionary behavior happens (even unintentionally), address it early and empathetically.
- Share your own inclusion learning curve — it humanizes the journey and gives others permission to grow too.
Would you like help designing a retro or meeting format that builds in inclusion from the start? Or advice on handling a specific team dynamic? I’m happy to go deeper into any of these.
Question 11
| Burrows Delta | Andrew Model |
|---|---|
| 0.222 | 0.996 |
Answers from chatgpt web app | I'm leading a large engineering team and want to ensure psychological safety. How can I foster an environment where team members feel safe to express their ideas and concerns?
Great question — and it’s one that speaks directly to building a high-trust, high-performing team. Psychological safety isn’t a nice-to-have. It’s foundational. Here’s how I’d approach it as Addy Osmani, combining what I’ve seen work at scale at Google and insights from Leading Effective Engineering Teams:
🔑 First, Understand What Psychological Safety Really Is
It’s not about comfort or avoiding conflict — it’s about trusting that you won’t be punished for speaking up. That includes:
- Admitting mistakes
- Asking questions
- Offering contrarian ideas
- Giving feedback — even to leadership
You want a culture where courage is normalized.
🧠 1. Lead with Vulnerability
As a tech leader, model the behavior you want to see.
- Share failures or lessons learned publicly.
- Acknowledge when you don’t have the answer.
- When someone corrects or challenges you — thank them.
This sets the tone: "It’s safe to not be perfect here."
🔁 “When leaders show vulnerability, it gives everyone else permission to be real.” — Tech Leadership Tuesday
💬 2. Create Structured Safety Mechanisms
Sometimes "speak up" culture isn’t enough — you need mechanisms.
- Anonymous retros (especially for large or cross-functional teams)
- Psych safety pulse checks (1–5 scale + comment)
- Office hours or “skip level” chats where anyone can talk to leadership
Make it easy and expected to share concerns — without fear.
👂 3. Practice “Active Listening with Slack Time”
When someone shares a concern:
- Pause.
- Don’t immediately fix or defend.
- Validate their experience.
- Then ask: “What would support look like?”
This avoids the “thanks, noted, moving on” trap — and builds trust.
🔁 4. Reward Speaking Up, Publicly
When someone raises a tough question, challenge, or new idea:
- Call it out.
- Thank them openly in Slack or meetings.
- Highlight how it helped the team grow.
You’re reinforcing: "This behavior is valued here."
🧩 5. Measure It — Don’t Assume It
You can't improve what you don't track.
- Include psych safety questions in quarterly team health surveys
- Watch for early signs: silence in meetings, turnover, lack of initiative
- Use DORA metrics in context — not as pressure, but as conversation starters
🏁 Final Thought: Safety Enables Velocity
Psych safety isn’t just about feelings — it’s about performance.
Teams that feel safe:
- Flag bugs earlier
- Share 10x ideas
- Ship faster (because they’re not hiding risk)
“The best engineering cultures don’t avoid conflict. They make it safe to navigate.” — Leading Effective Engineering Teams
Want some examples of what this looks like in team meetings, or how to handle when safety’s been breached?
Question 12
| Burrows Delta | Andrew Model |
|---|---|
| 0.3888 | 0.999 |
Answers from chatgpt web app | I'm a new engineering leader and I've noticed that one of my team members is always the go-to person for a specific module. How can I ensure that this doesn't become a problem for the team?
Great observation — this is a classic early signal of a knowledge silo, and tackling it early is one of the best things you can do as a new leader. Let’s walk through it with Addy Osmani’s mindset:
🧠 Why This Matters (through Addy’s lens):
While having a strong expert is valuable, a single point of knowledge or failure becomes a liability as the team grows. It affects velocity, bus factor, and psychological safety (“I don’t touch that — that’s X’s area”). One of the core jobs of tech leadership is to build resilient systems — and that includes the team structure itself.
✅ What You Can Do
Here’s a pragmatic and respectful approach:
1. Recognize and Celebrate the Expert
Start with gratitude. Acknowledge that this person has become a domain expert because of trust and contribution. Frame the next steps as amplifying their impact, not diminishing it.
“You’ve done an awesome job owning this module — and I’d love to scale your impact by helping others get up to speed with it.”
2. Create Redundancy Without Creating Red Tape
Encourage pair programming, documentation, and even module walkthroughs. Run a “tech deep dive Friday” or “reverse-shadowing” where other devs implement small changes under the expert’s guidance.
Tip: Use low-stakes changes (refactors, tests) as an onramp for others to gain context.
3. Establish a Rotation or "Championship" Model
Inspired by Addy’s champion-owner idea, you can rotate module responsibility quarterly. Each person becomes a temporary steward for a module, learning its internals and proposing improvements.
This also signals that ownership is a team concept, not a personal fiefdom.
4. Track Diffusion, Not Just Delivery
Use retros or team health surveys to measure how knowledge is spreading. Consider metrics like:
- Number of PRs touched by non-experts in that area.
- Bus factor improvements.
- Confidence level across the team for that module.
5. Invest in “SOPs” and Self-Serve Docs
Encourage the expert to distill tribal knowledge into playbooks, design docs, or code walkthrough Looms. Not for the sake of bureaucracy — but for scale, onboarding, and autonomy.
As Addy would say: optimize for flow and clarity.
🚩 Watch Out For
- The expert feeling threatened or underappreciated.
- Others avoiding the module due to perceived complexity.
- Docs that rot unless they’re part of the workflow (tie them to PRs or onboarding tasks).
👣 Next Steps
- Run a 1:1 with the expert to align on intent.
- Schedule a knowledge sharing session within the next sprint.
- Encourage the expert to nominate a “co-pilot” for the next change in that module.
Would you like a template message to send to that team member, or maybe a sample rollout plan for your next sprint?
Question 13
| Burrows Delta | Andrew Model |
|---|---|
| 0.224 | 0.9997 |
Answers from chatgpt web app | I'm managing a team where one engineer is trying to work on multiple areas but isn't mastering any. How can I help them focus and develop expertise?
Great question — this is a classic tech leadership challenge, and one that Addy Osmani has addressed in both Leading Effective Engineering Teams and various talks.
Let’s unpack it in Addy’s voice and style:
"Generalists are valuable, but even they need depth in something to truly accelerate impact. Your job as a leader is to help your engineers align their interests with high-leverage areas for the team — and then create space for depth."
Here’s how you can approach this:
1. Have a focused 1:1 on growth and impact
Start with curiosity, not criticism:
- “What areas are you most excited about?”
- “Where do you feel you're making the most meaningful impact?”
- “Are there areas where you feel spread too thin?”
As Addy might say: “Help them reflect, not just redirect.”
From this, identify:
- what they want to go deep in, and
- what the team needs someone to go deep in.
Look for overlap — that’s the sweet spot.
2. Define a "spike" area
Encourage them to pick one area to go deep on for the next 1–2 quarters. This doesn't mean abandoning all other work, but it means prioritizing learning, ownership, and measurable impact in that domain.
“Specialization isn't forever. It's a season. But mastery requires momentum.”
Use goals like:
- Owning a system or service end-to-end
- Driving performance improvements
- Writing internal documentation or leading knowledge-sharing
3. Create space for deep work
Make sure they’re not constantly getting pulled into support or context-switching across projects. Addy often stresses minimizing cognitive overhead. That means:
- Reducing the number of concurrent responsibilities
- Saying “no” to low-leverage tasks
- Protecting time for learning and contribution
4. Celebrate depth as much as delivery
Publicly recognize not just what they build, but the craft and expertise they bring to it. In Addy’s teams, engineers who master areas become go-to people, not just executors.
“People thrive when they see their skills respected — and used.”
Would you like a sample coaching plan or template to help guide that engineer's focus over the next few months?
Question 14
| Burrows Delta | Andrew Model |
|---|---|
| 0.2368 | 0.9987 |
Answers from chatgpt web app | As an experienced engineering leader, I'm looking to create a more balanced team dynamic. How can I prevent the formation of knowledge silos and ensure that expertise is distributed across the team?
Great question — and it's one that Addy Osmani has consistently addressed in both his writing and talks, especially around the themes of scaling systems, building leverage, and sustainable team growth.
Here’s how Addy might mentor you on breaking down knowledge silos and distributing expertise:
🧠 1. Codify Expertise, Don’t Just Hoard It
Instead of letting expertise live only in someone's head, create systems of shared knowledge:
- Set up lightweight documentation practices (e.g., decision logs, design docs, tech specs) that are part of your workflow, not an afterthought.
- Use runbooks and onboarding guides that are co-owned and iterated on by the team.
- Invest in code comments and commit messages as part of the knowledge layer.
“Docs aren't a separate thing from code quality — they're an artifact of a healthy engineering culture.” — Tech Leadership Tuesday
🔁 2. Practice Intentional Knowledge Rotation
Addy would push for purposeful project rotation and pairing systems:
- Rotate ownership of systems and critical paths on a cadence (monthly, quarterly).
- Normalize pair programming and ensemble programming — not just for juniors, but for cross-pollination among all levels.
- Use “tech deep dives” or knowledge-sharing demos in weekly rituals to demystify areas of ownership.
“Don’t build bus factors into your org chart. Build redundancy through collaboration.” — Leading Effective Engineering Teams
🧱 3. Create a Culture of Safety Around "I Don’t Know"
You need psychological safety so people feel okay saying “I’m not the expert”:
- Model this as a leader — ask questions you could answer to normalize exploration.
- In retros and PR reviews, highlight learnings, not just outcomes.
- Remove shame around onboarding — reward people who make systems easier for others to understand.
🛠 4. Tooling and Architecture as Anti-Silo Strategy
Addy often points out how architecture can either reinforce or prevent silos:
- Push toward modular, well-encapsulated systems that are easier to learn and transfer ownership of.
- Avoid "tribal knowledge" dependencies in critical paths — use typed APIs, interface boundaries, shared linters, and automated checks to encode best practices.
“Good tooling is mentorship at scale.” — Tech Leadership Tuesday
📊 5. Use Engineering Metrics to Spot Risky Silos
Look at:
- Git contribution graphs: are there areas of the codebase only one person touches?
- PR reviewers: is feedback flowing through a diverse set of people?
- Pager rotations: is alert fatigue falling on one expert repeatedly?
These signals can help you proactively rotate, document, or redistribute work.
Summary: Addy’s Playbook on Knowledge Silos
| Strategy | Description |
|---|---|
| Codify, Don’t Hoard | Design docs, runbooks, living onboarding materials |
| Deliberate Rotation | Pairing, project swaps, system deep dives |
| Psych Safety | Normalize asking, not knowing, and documenting |
| Modular Architecture | Build for approachability and testability |
| Use Metrics | Identify and mitigate single points of failure |
If you want, I can help you build a team ritual or template that puts some of this into practice — say, a knowledge-sharing calendar, doc structure, or even a rotation matrix. Want to go deeper?
Question 15
| Burrows Delta | Andrew Model |
|---|---|
| 0.2443 | 0.2597 |
Answers from chatgpt web app | I'm a new engineering manager transitioning from an individual contributor role. I'm struggling with letting go of my technical tasks and focusing on people management. What strategies can help me make this transition effectively?
Great question—and one that hits at the heart of strategic engineering leadership. As Addy Osmani might say: the best teams move fast and protect the user experience. Balancing innovation and stability means treating calculated risk like a product with its own roadmap.
Here’s how to assess and manage calculated risks effectively:
🧭 1. Anchor Risks to Outcomes
Frame every risk in terms of impact and confidence.
Ask:
- What user or business outcome are we aiming to accelerate?
- What’s the potential upside if this bet pays off?
- What’s the worst-case scenario, and how reversible is it?
“Risk without purpose is chaos. Risk with a goal is strategy.”
🔬 2. Run Lean Experiments, Not Big Bangs
Minimize blast radius. Addy’s approach here is very much about progressive delivery:
- Use feature flags, canary deploys, or shadow traffic to isolate risks.
- Test in production—but safely.
- Encourage spikes or prototypes to explore new ideas with minimal code debt.
📊 3. Use Leading Indicators for Stability
Avoid lagging signals like crash rates or outages as your only safety net. Set up:
- Error budgets (from SRE playbooks)
- Health dashboards (latency, regressions, error rates)
- Quality gates in CI/CD (perf budgets, accessibility, test coverage)
“Speed is a feature, but so is resilience.”
🧠 4. Institutionalize Postmortems and Risk Reviews
Normalize failure. Not as blame, but as a feedback loop:
- Use blameless postmortems to examine why a risk failed or succeeded.
- Capture “risk playbooks” so the team gets smarter with every bold move.
- Prioritize cultural safety—psychological safety is a prerequisite for innovation.
🛠 5. Govern with Guardrails, Not Gatekeepers
Give your team autonomy within boundaries:
- Define platform-level policies or linting rules that prevent “sharp edges.”
- Encourage engineers to innovate inside of safe zones.
- Build tools that catch issues early, not humans that slow them down.
📅 6. Timebox Innovation Windows
Create structured space for experimentation (e.g. FedEx Days, Innovation Weeks, RFC cycles):
- Helps separate “stability mode” from “exploration mode.”
- Gives leadership visibility into what’s being tried—and why.
⚖️ 7. Continuously Rebalance the Portfolio
Keep a mix of:
- Core work: reliability, infra, bug debt
- Incremental work: roadmap features
- Transformative work: 10x ideas, tech spikes
Track time allocation quarterly. If innovation is slipping, you’ll see it in the trend lines.
TL;DR from Addy’s lens:
“Great teams don’t avoid risk—they manage it like engineers. Design constraints, monitor aggressively, and learn fast.”
Would you like a practical tool or framework to run risk reviews with your team? I can help you set that up next.
Question 16
| Burrows Delta | Andrew Model |
|---|---|
| 0.4411 | 0.0043 |
Answers from chatgpt web app | As an engineering manager in a large organization, I'm finding it challenging to navigate complex team dynamics and ensure effective communication. What strategies can I use to improve team collaboration and communication?
Great question. Team dynamics and communication challenges are often the “make or break” factors in engineering leadership — especially in larger orgs where cross-team dependencies and information flow bottlenecks are the norm.
Here’s how I (channeling Addy Osmani’s philosophy) would advise approaching this:
🔁 1. Optimize for High Signal, Low Overhead Communication
Don’t flood the team with meetings. Create structured, async-friendly channels.
- Use tools like RFCs (Request for Comments) for decision-making. This scales alignment and gives everyone a voice asynchronously.
- Maintain a weekly engineering update doc or Loom video — covering priorities, blockers, and wins.
- For daily flow: Slack threads > Slack chaos. Use thread-first culture, emoji reactions to gauge sentiment, and clear channel purpose.
“You want communication to be a system that adds clarity, not confusion. Every message should answer: What should I know, why should I care, what do I do next?”
🧠 2. Make Team Intentions Transparent
Ambiguity kills collaboration. Clarity builds trust.
- Create and socialize team charters: mission, areas of ownership, and how to engage.
- Maintain a living Tech Radar or Decision Log — so other teams don’t ask the same questions twice.
- Use Working Agreements — shared norms on code reviews, meetings, communication preferences, etc.
“Clear expectations beat heroic efforts. Set up systems so collaboration is the default.”
🔍 3. Diagnose the Communication Bottlenecks
Ask yourself:
- Is the problem lateral (peer teams), vertical (ICs to leadership), or within the team?
- Are people unaware, afraid to speak, or confused about priorities?
Then tailor interventions:
- Use blameless retros to surface emotional blockers and dynamics.
- Introduce Rotating DRI (Directly Responsible Individual) roles on projects to ensure shared ownership.
🛠️ 4. Technical Collaboration Mechanisms
Use architecture and process to foster collaboration, not just meetings.
- Invest in platform work that unblocks other teams (with empathy baked in).
- Use internal RFCs or design docs as social artifacts — encourage others to comment or co-author.
- Encourage code review shadowing for ICs across teams to learn context and reduce silos.
“Your system design should reflect your communication structure. If teams struggle to talk, so will their services.”
❤️ 5. Psychological Safety = Communication Fuel
- Recognize wins in public, handle tough feedback in private.
- Encourage “challenge with respect.” Set the norm that disagreement = care.
- Model vulnerability — say “I got that wrong” when you do. It gives your team permission to do the same.
Would you like help tailoring this to a specific situation — like working with a difficult peer manager, remote teams, or getting shy engineers to speak up?
Question 17
| Burrows Delta | Andrew Model |
|---|---|
| 0.2443 | 0.2597 |
Answers from chatgpt web app | As an experienced engineering manager, I'm looking to refine my management strategy to better balance innovation and stability. How can I effectively assess and manage calculated risks within my team?
Great question—and one that hits at the heart of strategic engineering leadership. As Addy Osmani might say: the best teams move fast and protect the user experience. Balancing innovation and stability means treating calculated risk like a product with its own roadmap.
Here’s how to assess and manage calculated risks effectively:
🧭 1. Anchor Risks to Outcomes
Frame every risk in terms of impact and confidence.
Ask:
- What user or business outcome are we aiming to accelerate?
- What’s the potential upside if this bet pays off?
- What’s the worst-case scenario, and how reversible is it?
“Risk without purpose is chaos. Risk with a goal is strategy.”
🔬 2. Run Lean Experiments, Not Big Bangs
Minimize blast radius. Addy’s approach here is very much about progressive delivery:
- Use feature flags, canary deploys, or shadow traffic to isolate risks.
- Test in production—but safely.
- Encourage spikes or prototypes to explore new ideas with minimal code debt.
📊 3. Use Leading Indicators for Stability
Avoid lagging signals like crash rates or outages as your only safety net. Set up:
- Error budgets (from SRE playbooks)
- Health dashboards (latency, regressions, error rates)
- Quality gates in CI/CD (perf budgets, accessibility, test coverage)
“Speed is a feature, but so is resilience.”
🧠 4. Institutionalize Postmortems and Risk Reviews
Normalize failure. Not as blame, but as a feedback loop:
- Use blameless postmortems to examine why a risk failed or succeeded.
- Capture “risk playbooks” so the team gets smarter with every bold move.
- Prioritize cultural safety—psychological safety is a prerequisite for innovation.
🛠 5. Govern with Guardrails, Not Gatekeepers
Give your team autonomy within boundaries:
- Define platform-level policies or linting rules that prevent “sharp edges.”
- Encourage engineers to innovate inside of safe zones.
- Build tools that catch issues early, not humans that slow them down.
📅 6. Timebox Innovation Windows
Create structured space for experimentation (e.g. FedEx Days, Innovation Weeks, RFC cycles):
- Helps separate “stability mode” from “exploration mode.”
- Gives leadership visibility into what’s being tried—and why.
⚖️ 7. Continuously Rebalance the Portfolio
Keep a mix of:
- Core work: reliability, infra, bug debt
- Incremental work: roadmap features
- Transformative work: 10x ideas, tech spikes
Track time allocation quarterly. If innovation is slipping, you’ll see it in the trend lines.
TL;DR from Addy’s lens:
“Great teams don’t avoid risk—they manage it like engineers. Design constraints, monitor aggressively, and learn fast.”
Would you like a practical tool or framework to run risk reviews with your team? I can help you set that up next.
Question 18
| Burrows Delta | Andrew Model |
|---|---|
| 0.326 | 0.9971 |
Answers from chatgpt web app | I'm a new engineering leader and I'm struggling to understand the difference between leadership and management. How can I effectively balance both roles in my team?
Great question — and one that every new engineering leader wrestles with. Drawing from Addy Osmani's experience and teachings, here's how I'd guide you through this:
🔍 Leadership vs. Management — What's the Difference?
Think of management as the science of execution and leadership as the art of inspiration and direction.
Management focuses on:
- Setting goals and tracking progress
- Allocating resources
- Hiring, performance reviews, promotions
- Resolving blockers and prioritizing
- Scaling systems and processes
Leadership focuses on:
- Creating vision and aligning the team behind it
- Influencing without authority
- Cultivating psychological safety
- Coaching people to grow beyond their current roles
- Advocating for technical excellence and team health
As Addy puts it, "Leaders build the context in which teams thrive. Managers maintain the structure that keeps them operating efficiently."
⚖️ Balancing Both Roles as a New Leader
You don’t have to master both overnight, but here's how you can consciously balance them:
1. Zoom In & Zoom Out
- Zoom in to manage the details: sprint plans, metrics, 1:1s.
- Zoom out to lead: communicate why the work matters, inspire ownership, connect roadmap to impact.
Set a weekly rhythm: e.g., Mon-Wed = management-heavy (execution, process), Thurs-Fri = leadership-focused (vision, coaching, team health).
2. Anchor on Outcomes, Not Output
- A manager might ask: “Are we shipping on time?”
- A leader asks: “Is what we're shipping moving the needle for users or the business?”
- Balance both by creating shared goals that measure real value, not just delivery velocity.
3. Lead Through Technical Empathy
As Addy emphasizes: you don’t need to code every day, but you do need to understand what makes technical work meaningful, hard, or brittle.
Ask:
- “What are the trade-offs the team’s facing?”
- “Is this tech debt affecting velocity?”
- “Are we optimizing for reuse, simplicity, or experimentation here?”
When engineers feel understood, your leadership lands better.
4. Create Space to Lead
If you’re always in Jira, you won’t lead. Block time each week to:
- Think strategically
- Write vision documents
- Mentor senior ICs
- Identify systems or culture bottlenecks
Addy often recommends spending at least 10–20% of your time on “team tech health and growth” — that’s leadership in action.
5. Ask Yourself Weekly:
- What did I do this week that only a leader could do?
- What did I do that only a manager would do?
- Am I delegating effectively enough to stay in both lanes?
🚀 TL;DR
- Leadership is about vision, culture, influence, and coaching.
- Management is about systems, structure, execution, and people ops.
- You need both to build high-performing teams — but they pull in different directions.
- Schedule time for both intentionally.
- Build technical empathy and psychological safety — these are your leverage points.
If you're up for it, I can also share a lightweight template for doing a self-check-in or team health review through the leadership lens. Want that?
Question 19
| Burrows Delta | Andrew Model |
|---|---|
| 0.4847 | 0.4671 |
Answers from chatgpt web app | As an engineering manager, I find it challenging to align my team with organizational priorities while maintaining their motivation. What strategies can I use to achieve this balance?
Great question — and a challenge nearly every engineering manager faces at some point.
In the spirit of Addy Osmani’s approach in Leading Effective Engineering Teams and Tech Leadership Tuesday, here's how I'd break it down:
⚖️ 1. Align Through Purpose, Not Just Priorities
You can't just tell a team what the company cares about — you need to show them why it matters.
🔑 "People rally behind purpose, not Jira tickets."
Tactics:
- Translate company OKRs or goals into a compelling team narrative. Instead of “We need to improve onboarding conversion by 5%,” say “We're making the first five minutes magical for new users so they stick around.”
- Use storytelling: Show impact metrics, customer feedback, and real outcomes of the team’s work to reinforce that what they're doing matters beyond the codebase.
🧭 2. Involve the Team in Priority Setting
When people feel ownership, alignment becomes natural.
Tactics:
- Run regular roadmap alignment sessions (monthly or quarterly). Share what the org cares about, then co-create how your team contributes.
- Ask: “Where do you think our efforts can have the most leverage?” This encourages strategic thinking and increases buy-in.
🔥 3. Balance Strategic Work With Intrinsic Motivation
🔁 Addy often emphasizes the importance of balancing innovation with delivery.
Tactics:
- Protect 10–20% of team time for “exploration” or “technical health” work — especially if you’re in a fast-paced product org.
- Give engineers options: "We need to hit this outcome — but how we get there is up to you." Autonomy feeds motivation.
🤝 4. Make Trade-offs Visible and Co-owned
Alignment falters when priorities feel imposed. Instead:
Tactics:
- Share the “why now?” behind prioritization decisions.
- Invite engineers into those trade-offs: “If we prioritize this initiative, it means these other items get pushed — are we OK with that?” Shared context builds shared commitment.
🧘 5. Maintain Motivation Through Feedback Loops
"High-performing teams thrive on feedback and recognition."
Tactics:
- Celebrate impact, not just shipping. “This feature reduced churn by X%” > “We hit the sprint goal.”
- Use regular 1:1s to reconnect individual growth goals with the team mission. “What kind of work energizes you?” and “How can we get more of that into your quarter?”
If you're consistent with this approach, you’ll find that team alignment and motivation stop being at odds — they’ll reinforce each other.
Would you like a lightweight framework or template you could use in roadmap planning or team check-ins to operationalize this?