Become An Engineer Influencer [Part 4] - Strategy #2: Social Media is a Sales Funnel

Taro Book Club: Designing Data Intensive Applications - Chapter 8 (Distributed Systems)

Topic: Chapter 8 - The Trouble with Distributed Systems

In this #taro-bookclub discussion, we'll go over Chapter 8 of [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann.

Here's the agenda:

- We’ll break down and discuss the key concepts from Chapter 8.
- There will also be time for any questions and answers you might have.

Please send your questions ahead of time to [**Rohit Katyal**](https://www.linkedin.com/in/rohitkatyal/) in the [**Taro Premium**](https://www.jointaro.com/membership/) Slack, so that they can be included in the discussion. *If you don't have the book or want more details on how this all works, check out the #taro-bookclub channel in the Taro Premium Slack as well!*

Get ready for another engaging and informative session!

Paper Reading: Mamba - Revolutionizing Sequence Modeling

Session led by: [Akshika Wijesundara, PhD](https://www.linkedin.com/in/akshika/overlay/about-this-profile/)

*Abstract:*

Explore the Mamba paper's groundbreaking approach to sequence modeling efficiency in this event. Uncover how Mamba addresses computational inefficiencies in transformers, especially for long sequences.

*Overview:*

Discover the motivation behind Mamba, highlighting its improvements over subquadratic-time architectures struggling with language tasks. Learn about Mamba's content-based reasoning and enhancements for superior performance.

*Key Features:*

1\. **Selective Structured State Space Models (SSMs):** Mamba introduces adaptive SSM parameters for content-based reasoning, allowing selective information handling for discrete modalities.

2\. **Hardware-Aware Parallel Algorithm:** Despite forgoing efficient convolutions, Mamba's hardware-aware parallel algorithm maintains fast inference and linear scaling in sequence length.

3\. **Architecture Overview:** Dive into Mamba's streamlined architecture without attention or MLP blocks, achieving efficiency without sacrificing effectiveness.

4\. **Performance Metrics:** Witness Mamba's 5× higher throughput than Transformers, with linear scaling, showcasing its real-world improvements in language, audio, and genomics tasks.

5\. **Comparison with Transformers:** See Mamba outperforms Transformers of the same size and match larger Transformers, especially in language modeling.

Join us in unraveling the secrets of Mamba, poised to redefine sequence modeling standards. Anticipate a paradigm shift in efficiency and performance in deep learning.\
\
paper link: \[[**https://arxiv.org/abs/2312.00752**](https://arxiv.org/abs/2312.00752)\]

Taro Book Club: Designing Data Intensive Applications - Chapter 7 (Transactions)

Topic: Chapter 7 - Transactions

Session lead by [**Raj Shah**](https://www.linkedin.com/in/therajshah/), [**Raghu Kalyan**](https://www.linkedin.com/in/graghukalyan/), and [**Kevin Wu**](https://www.linkedin.com/in/wkevn/) 

In this #taro-bookclub discussion, we're having a session about Chapter 7 of [**"Designing Data-Intensive Applications"**](https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321) by Martin Kleppmann.

Here's the agenda:

- We’ll break down and discuss the key concepts from Chapter 7
- There will also be time for any questions and answers you might have

Get ready for another engaging and informative session!

Paper Reading: Self-Discover - Empowering Large Language Models with Autonomous Reasoning Structures

In this talk, we delve into Self-Discover, an innovative framework revolutionizing Large Language Models (LLMs) by enabling them to autonomously discern task-specific reasoning structures, thereby empowering them to tackle complex problems without explicit prompting. 

At the heart of Self-Discover lies a self-discovery process, wherein LLMs independently select and amalgamate various atomic reasoning modules, such as critical thinking and step-by-step reasoning, into explicit structures to guide their decoding process. The broad applicability of these self-discovered structures across diverse model families—from PaLM2-L to GPT-4 and from GPT-4 to Llama2—underscores the framework's versatility and adaptability, suggesting seamless integration with an even wider range of advanced AI models. This holds promise for a unified approach to autonomous reasoning in artificial intelligence.\
\
Whether you're an AI researcher, engineer, or simply curious about this achievement in AI research, join us as we explore the intricacies of Self-Discover and its potential implications for prompt engineering and advancing the capabilities of Large Language Models in complex reasoning tasks.\
\
Paper: [**https://arxiv.org/pdf/2402.03620.pdf**](https://arxiv.org/pdf/2402.03620.pdf)

Lucia on LinkedIn: <https://www.linkedin.com/in/lucia-mocz-ph-d-35a98a1b6/>

Taro Book Club: The Fundamentals of Data Engineering - Chapter 3 (Part 2)

Session led by [Gideon Blinick](https://www.linkedin.com/in/gideonblinick/)

Chapter 3 - Part 2: Designing Good Data Architecture

In our first session on Chapter 3, we learned the 9 principles of Good Data Architecture, which are largely based on Google's Principles of Cloud-Native Architecture and Amazon's Well-Architectured Framework.

Now we get less abstract and cover Major Architecture Concepts and Examples, including Domains and Services, Distributed Systems, Data Warehouses and Lakes, the Modern Data Stack, and so much more!

Paper Reading Session: Simple Testing Can Prevent Most Critical Failures

Paper Discussion led by [Sudhi Awasthi](https://www.linkedin.com/in/sudhiawasthi/)

In this session, we will be discussing this paper titled "[Simple Testing Can Prevent Most Critical Failures: An Analysis of Production Failures in Distributed Data-Intensive Systems](https://www.usenix.org/system/files/conference/osdi14/osdi14-paper-yuan.pdf)".

Large, production quality distributed systems still fail periodically, and do so sometimes catastrophically, where most or all users experience an outage or data loss. This paper presents the result of a comprehensive study investigating 198 randomly selected, user-reported failures that occurred on Cassandra, HBase, Hadoop Distributed File System (HDFS), Hadoop MapReduce, and Redis, with the goal of understanding how one or multiple faults eventually evolve into a user-visible failure and how simple testing can prevent majority of them.\
\
Event Link: <https://www.jointaro.com/event/paper-reading-session-simple-testing-can-prevent-most-critical-failures/>

Live AMA with Casey Dai - Former Head of Engineering & Founder of Expanded Skills

[Casey Dai](https://www.linkedin.com/in/caseydai/) is a career switcher, Head of Engineering, and entrepreneur based in Canada. After getting a finance degree, he worked in consulting before switching to engineering and quickly climbed the ranks to become the Head of Engineering at Capgemini, a multi-billion dollar IT company. 

Topics covered in this Ask Me Anything (AMA):

- What are the most underrated skills for SWEs and EMs?
- How do I future-proof my skills to stay competitive in the tech industry when things are constantly changing?
- Why are there a lot of bad managers? Is there anything that can be done to prevent that?
- How do I identify opportunities to scale my impact?
- How do I become a better decision-maker when faced with uncertainty?
- What are some ways to grow aside from the company career ladder?

Casey will go **in-depth on these topics in [his upcoming course](https://www.jointaro.com/event/operate-like-an-engineering-leader-cohort-based-course/)** starting Jan 16th.

Enroll in Casey's cohort-based course Operate Like An Engineering Leader and receive a free 1:1 coaching session: [**https://www.jointaro.com/event/operate-like-an-engineering-leader-cohort-based-course/**](https://www.jointaro.com/event/operate-like-an-engineering-leader-cohort-based-course/)

This Is Why You Should Work For Big Tech

So many engineers want to work for FAANG and for good reason: There are lot of career benefits that Big Tech is exceptionally good at providing.

Here are the core scenarios where Big Tech is a slam dunk career choice for you:
- **You are earlier in career** - Big Tech will give you a rock solid foundation across all levels: Learning, compensation, job prospects - You name it. When you're younger, you will generally be more flexible with work-life balance (i.e. you're more physically able to work longer hours), so you will be better equipped to deal with the stress of Big Tech.
- **You need money** - More money is almost always useful, and if you live in a very high cost-of-living area (e.g. SF Bay Area), it becomes necessary. Big Tech gives a consistent, incredibly high income.
- **You value stability and are risk-averse** - Even with the layoffs, these companies are some of the safest places to be. Compared to peer companies, they have done far fewer layoffs across their lifespan and they have rock solid, wildly profitable business models.

Related resources:
- ["I want to land a job in a big tech as a SWE - How to do it?"](https://www.jointaro.com/question/MWJfUBnnUr8CKYFCO8bp/i-want-to-land-a-job-in-a-big-tech-as-a-swe-how-to-do-it/)
- ["What's the difference between Big Tech and startups in terms of learning and growth?"](https://www.jointaro.com/question/KWXIrFIR1EIJsvdOdo7u/whats-the-difference-between-big-tech-and-startups-in-terms-of-learning-and-growth/)

Decoding the One Big Beautiful Bill Act for Tech Workers

New tax law, same tax code for tech workers… mostly.\
\
President Trump signed the One Big Beautiful Bill Act into law on July 4, 2025.\
\
In terms of the impact to your taxes, this bill largely maintains the status quo by making permanent most of the tax changes introduced on a temporary basis through the Tax Cuts and Jobs Act in 2017.\
\
It does, however, add a few new wrinkles to the tax code.\
\
I’m hosting a live webinar next week to break it all down for tech professionals like you.\
\
🎙️ Webinar: Decoding the One Big Beautiful Bill Act for Tech Workers\
🗓️ Date & Time: July 24, 2025 from 2-2:45 PM Pacific Time\
📍 Where: Live on Zoom\
💡 Especially relevant for:\
👉 Anyone in a high-income bracket that pays a lot in state & local tax\
👉 Folk planning to make Roth conversions in the next few years\
👉Those considering retiring at 65+ in the next few years\
👉 Anyone considering taking a career break or on an ACA plan\
\
We’ll cover:\
\
✅ Changes to the deductibility of state and local taxes\
✅ How the new bill affects ISO exercises and your likelihood of paying AMT\
✅ What's up with Section 174?\
✅ Changes to QSBS\
✅ "No tax on tips/overtime"\
✅ Deductibility of new U.S. car loan interest\
✅ The new "Trump account"\
✅ Impacts to Medicaid\
✅ The new bonus deduction for seniors\
✅ Updates to clean energy and EV credits\
✅ How the new rules impact ACA health insurance subsidies

How To Properly Measure Your Data

In this lesson, we explore the essential discipline of **measuring data effectively**, a foundational step in the DMAIC process. Drawing from Amazon’s own journey, we explain why rigorous data management, real-time reporting, and a culture of confronting the brutal facts are key to achieving operational excellence.

### Key Takeaways:

- **We built a disciplined system for data measurement**: Measurement goes far beyond selecting metrics—it requires clean data, reliable pipelines, centralized data warehouses, governance, security, and standardized reporting tools like Tableau or Looker.
- **We invested in an unbiased analytics culture**: Jeff Bezos asked our CFO to form an independent analytics team whose sole job was to uncover the truth, not protect orgs or executives. These analysts had both financial and operational alignment but prioritized integrity in reporting.
- **We created mechanisms to confront reality**: Inspired by Churchill and Jim Collins, we institutionalized a culture of *brutal facts*—establishing metrics and systems that allowed leadership to face problems head-on, rather than hide or distort them.
- **We operationalized this with the Weekly Business Review (WBR)**: The WBR report was auto-generated by 7 a.m. every Monday, standardized, and packed with hundreds of metrics—enabling every department to identify trends, anomalies, and action items before the company-wide WBR each Wednesday.
- **We learned that slow is fast**: Building accurate, automated, real-time measurement systems took time and leadership buy-in. But in the long run, these systems helped us move faster and make better decisions at scale.

Ultimately, this lesson shows that **data measurement is not a tech problem—it’s a leadership imperative**. By treating it as a strategic function, we created a system that helped Amazon scale with confidence and clarity.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Amazon Case Study: Detail Pages

In this lesson, we apply everything we've learned about input metrics by walking through a real-world case from Amazon—how we evolved our **selection metric** to better align with customer needs. This story illustrates the complexity, rigor, and iteration involved in making metrics truly actionable and impactful.

### Key Takeaways:

- **We started by identifying strategic input metrics**: Inspired by Amazon’s **growth flywheel**, we identified controllable levers—like low prices, broad selection, and fast delivery—that directly improved the customer experience and fueled long-term growth.
- **We didn’t wait for perfect metrics to begin**: We initially used simple metrics like the number of product detail pages to represent selection, even if they were imperfect. Early measurement got teams moving in the right direction.
- **We learned through missteps**: Our first metric for selection encouraged teams to add lots of SKUs—regardless of demand. Sales didn’t grow, and excess inventory piled up. This forced us to iterate.
- **We refined our metric to reflect actual customer experience**: Over time, we evolved to more nuanced metrics like **detail page views for in-stock items**, and ultimately to **Demand-Weighted Coverage FastTrack In-Stock Percentage (DWC FT %)**—a metric that captured whether customers could *find*, *want*, and *get* an item quickly.
- **We aligned goals with customer value**: Category managers were now measured by how well their products met real customer demand and delivery expectations—not just raw catalog size.
- **We replaced estimates with precise real-time measurements**: When anecdotes didn’t match our metric outcomes, we realized the problem wasn’t in customer feedback—it was in how we were measuring. We moved from snapshot-based approximations to real-time software tracking, which required serious engineering investment.
- **We treated metric-building as an ongoing process**: Measurement accuracy improved over years of trial and error, and nearly every customer-facing metric eventually transitioned from rough proxies to live calculations.

### To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>

- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>

- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>

- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Get Hands-On Experience With Frontline And Customers

In addition to data and structured methods, **immersive, first-hand experiences and direct customer research** gave us some of the most impactful input metrics and ideas at Amazon. These approaches helped ground our innovation efforts in real customer needs and operational realities.

### Key Takeaways:

- **We gained critical insight from frontline experience**: During Amazon’s early years, leadership—including Jeff Bezos—regularly worked in fulfillment centers and answered customer service calls during peak seasons. These visceral experiences helped us better understand operational pain points and identify new input metrics.
- **We institutionalized hands-on learning**: The value was so great that we made it a requirement—every Amazon employee spends time in frontline roles each year. This creates empathy, sharpens customer focus, and surfaces measurable opportunities for improvement.
- **We used customer research to drive innovation**: Surveys, focus groups, and third-party data sources gave us clear, actionable feedback. A simple survey on why customers didn’t shop more often revealed four key barriers—shipping cost, delivery speed, selection, and price—which directly shaped the creation of Amazon Prime and our long-term priorities.
- **We traced key customer complaints back to inputs**: From that early research, we began measuring and improving core inputs like in-stock rate, selection breadth, delivery speed, and pricing—cementing them in our flywheel as long-term business drivers.
- **We leveraged sales and customer interactions in B2B contexts**: Especially in business-to-business scenarios, we worked closely with sales teams and met customers in person to gather input-rich feedback, helping us refine offerings and discover metrics specific to client pain points.

Together, these methods reinforce the importance of **direct, empathetic engagement with customers and operations**—not just to build understanding, but to generate input metrics that we can track, improve, and build entire services around.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Analyze

In this phase, we dive into understanding how our **inputs drive outputs**, using structured analysis to detect patterns, pinpoint issues, and reduce variability. Building a culture of disciplined, data-driven evaluation was essential to improving business performance at Amazon.

### Key Takeaways:

- **We focus on understanding input-output relationships**: The Analyze phase helps us distinguish true patterns (signal) from random fluctuations (noise) and enables us to control variability in performance.
- **We use structured mechanisms for analysis**: DMAIC demands that we move beyond ad hoc methods by creating formal processes and tools for analyzing metrics across the company.
- **We built a culture of rigorous data review**: Regular, structured reviews became part of our operating rhythm, reinforcing analytical thinking throughout our teams.
- **We institutionalized the Weekly Business Review (WBR)**: One of our most powerful tools, the WBR was developed to enable comprehensive and recurring analysis, and it remains a central part of how we stay aligned and data-informed across all business units.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>
- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>
- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>
- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Measure

In the **Measure** phase of the DMAIC process, we focused on building a consistent, reliable foundation for capturing and interpreting data. This phase was critical in enabling accurate decision-making and accountability across our rapidly growing organization.

### Key Takeaways:

- **We standardized measurements across the company**: To avoid confusion and misalignment, we worked for years to unify definitions, formulas, and data structures across multiple data warehouses and teams.

- **We eliminated self-serving metrics**: Jeff Bezos directed our CFO, Warren Jensen, to form an independent analytics team that would report the unbiased truth, rather than letting individual teams present metrics that suited their own narratives.

- **We invested in strong data infrastructure**: Reliable measurement required robust systems for data capture, storage, access, and reporting—along with clear accountability for maintaining these systems.

- **We built a culture of impartial data analysis**: By establishing dedicated analysts and leaders to oversee data integrity, we ensured that our business decisions were driven by objective, consistent information—not wishful thinking.

To dive deeper into Input Metrics Mastery and learn more from Bill and Colin, follow these links:

- Take the full course at their website: <https://workingbackwards.com/>

- Buy the book: <https://www.amazon.com/Working-Backwards-Insights-Stories-Secrets/dp/1250267595>

- Bill's LinkedIn: <https://www.linkedin.com/in/bill-carr/>

- Colin's LinkedIn: <https://www.linkedin.com/in/colinbryar/>

Taking Things Further

In Part 4 of the course, we focus on how to **embed "thinking in deltas" as a daily leadership habit**. This final module emphasizes how to continuously spot and create positive, sustainable change as a manager—not just react to problems.

- **We build the habit by journaling our daily deltas**, capturing the tangible changes we influence, however small.
- **We use the journal to reframe how we think**, making it easier over time to spot opportunities for impact in individuals, teams, and systems.
- **We improve our mental well-being as managers** by recognizing our contributions and wins in a role where feedback loops are long and ambiguous.
- **We measure our success not just by outputs, but by meaningful changes we've catalyzed**—deltas we can point to as outcomes of our leadership.

Creating a daily "delta journal" isn’t just about reflection—it’s how we grow our impact and resilience as leaders.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

Gilad’s Take: Burnt Out and Behind

In Part 3 of the course, we explored how to apply the core lesson from Part 2—creating positive and sustainable change in organizational performance—through four practical exercises. Each scenario challenged us to identify the **"deltas"** we can introduce as managers to increase impact at the individual, team, or systems level.

- **We learned to recognize that not all growth is credited to us**, and we must focus on the growth that happens *because* of our involvement—not despite it.
- **We explored supporting high performers** by making their growth more efficient (faster) and effective (more aligned with goals), rather than just letting it happen passively.
- **We tackled underperformance** by identifying root causes, offering tailored support, and protecting teammates from negative ripple effects.
- **We addressed silent group dynamics** by recognizing the power of culture and investing in small, consistent changes to build psychological safety and lasting team habits.
- **We emphasized ruthless prioritization** as a powerful delta: helping teams focus on what truly matters, not just work harder—transforming effort into sustainable outcomes.

Now, we’re ready to take this impact to the next level in Part 4.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

Burnt Out and Behind

In this final exercise, we confront a situation where a team is putting in extra hours but still failing to meet deadlines. This signals systemic issues that go beyond individual effort—and calls for thoughtful intervention.

- We recognize that **unsustainable effort without results is a red flag**—it shows that working harder isn't the solution.
- We can introduce deltas by **investigating root causes**: Are goals unrealistic? Are priorities misaligned? Is there a process breakdown?
- It’s our responsibility to **shift focus from intensity to effectiveness**—fixing the system so performance improves without burnout.
- We look at **team norms, project planning habits, and cross-functional dependencies** to identify leverage points for change.
- Our role is to help the team **recover sustainably**, improving delivery while preserving morale and long-term capacity.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

Gilad’s Take: System Design Meeting Non-Participation

In this reflection on team participation during design reviews, we explored how group dynamics can have a powerful influence beyond the individuals themselves. The challenge highlights the importance of managing team culture and fostering an environment where all voices are heard.

- We acknowledge that **group behaviors can persist even as individual team members change**—a reminder that team culture is distinct and durable.
- Our role is to **introduce small, consistent deltas** that collectively improve psychological safety and encourage open participation.
- We focus on **building psychological safety** by recognizing barriers to contribution and intentionally working to lower them.
- Though these efforts may seem small or slow, **they compound into deep, sustainable cultural change** that supports long-term performance.
- Strong team cultures—like ones where everyone contributes—require persistent effort, but the payoff is a **resilient and high-performing team**.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

System Design Meeting Non-Participation

In this exercise, we examine a common team dynamic issue: several team members remain silent during design review meetings. This scenario challenges us to improve team engagement and collaboration—core to a manager’s ability to create sustainable performance change.

- We can **create psychological safety** so team members feel comfortable speaking up. This might involve setting new norms, encouraging questions, or addressing interpersonal dynamics that suppress contribution.
- We may need to **facilitate structured participation**, such as assigning roles, using round-robin feedback formats, or pre-assigning topics so everyone has time to prepare and contribute.
- We should **investigate root causes**—whether it's confidence, lack of clarity, or a dominant voice in the room—and address them through coaching or team norms.
- This is a chance to **foster inclusion and amplify diverse perspectives**, making the team more effective and innovative in its design process.
- By creating a culture of engagement, we **raise the overall output quality** and ownership across the team—an impactful and sustainable delta.

If you want to learn more from Gilad, he shares free leadership advice daily on [LinkedIn](https://www.linkedin.com/in/giladnaor/?utm_source=taro&utm_medium=course&utm_campaign=cta) and weekly on the[ δeltas newsletter](https://posts.managementdeltas.com/?utm_source=taro&utm_medium=course&utm_campaign=cta)

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.