Author: ewernli

Expert Knowledge isn’t Always Better

Technology people come in two flavours: generalist and experts. I’m definitively a generalist. I’ve a good grasp of the core concepts being the technologies we use. I’m however lacking the expertise about the details of using them. For this, I rely on experts.

This lack of expertise of the details may even turn out to be an advantage in my position. Technologies have core concepts that support some primary technology use case. With enough knowledge about technical details, it’s possible to use the technology to support other use cases. But it’s almost never a good idea in the long term. The implementation will be fragile to subtle changes in details of the technology, and few people in the organization have enough expertise to maintain such an implementation. If your use case is not supported easily, rethink your problem at some other level. Not knowing too much about the technology means I’m limiting its use to what’s maintainable.

The last example of this kind that I encountered was about container dependencies on openshift. In our current system (not openshift-based), we start the containers using “run levels”. Such a concept doesn’t exist in openshift. You could recreate something similar using init containers and some deep technical knowledge, but it wouldn’t be trivial. Rather than misusing the technology, we will have to solve our problem at another level. The containers should be resilient to random startup order at the application-level.

Other examples from the past include details of object-relational mapping or distributed caching. Here too I believe that beeing “too smart” about using the technology isn’t good in the the long term. It’s better to stick to the common use cases and change the design at the application-level if necessary.

Sometimes some part of the design may leverage deep technological details. I’m not completely forbidding it. Used in strategic places and encapsulated in proper technology component, this is a viable strategy. We have for instance engineered a library for leader election based on the solace messaging middleware, that relies on a deep expertise of the semantics of queues in solace. This was a strategic decision and the library is maintained by a team of technology experts with proper knowhow and the support from solace itself. But such situations should be exceptions rather than the norm.

It’s hard to resist the appeal of a clever technology-based solution, but as engineers we absolutely should. When we misuse technologies, we paint ourselves in a corner.

What Is It Like to Be a Robot?

In “Metazoa“, Peter Godfrey-Smith explores the rise of consciousness in animals – from simple multicellular organisms to invertebrates like us.

Consciousness is concept that’s not so easy to capture. It’s about a sense of self, about a perception of the environment and oneself, about a subjective experience of the world. When does an animal qualify as conscious? Godfrey-Smith postulates that consciousness is a spectrum, not something one has or doesn’t. The analogy he uses for this is sleeping, or the state right after waking up. We are conscious, but with a different level of consciousness as when fully awake.

The nature of consciousness can be explored by taking extreme positions:

  • can you be conscious without any perception of the environment (a “pure mind”)?
  • does reacting to what happens around you without any emotion qualify a conscious?
  • do you need to have a nervous system and feel pain to be conscious, or is having a mood enough?
  • could you be conscious, but act indistinguishably from as an unconscious animal?

I would have described consciousness as being aware of one’s own existence, something related to mortality, and rather binary. Godfrey-Smith equates consciousness more to having a sense of self and feelings, which makes it something less demarcated. He’s using consciousness more like “awareness“, whereas I would use it more like “self-awareness“. (That said, even self-awareness isn’t maybe so binary. Between being aware of deadly dangers and being aware of your own existence, it’s hard to say when we transition from instinct to consciousness.)

The book focuses on the relationship between senses and consciousness. Godfrey explains in the book how various animals sense the world and which kind of consciousness they might have. Some animals have antennas (Shrimps), some have tentacles (Octopus), some feel water pressure (fish). Many animals have vision, but the eye structure can differ. Some animals feel pain (mammals, fishes, molluscs) , but some don’t (insects) – it’s however not so clear to define when pain is felt or not. Not feeling pain doesn’t mean the animal is unaware of body damage, just like you don’t feel pain for you car but notice very well when something is broken when driving.

The book reminded me of “What it’s like to be robot?” from Rodney Brooks. This article, unsurprisingly, references the previous book from Godfrey-Smith “Other Minds”. The article from Rodney makes parallels between the perception of octopus and artificial intelligence systems. Many of the questions raised by Godfrey-Smith about the animal world can indeed be translated directly to the digital world. Computer systems have sensors, too. The have rules to react to inputs and produce outputs. They can learn and remember things, and develop an individual “subjective” perception of the world. They don’t “feel” pain, but can be aware of malfunctions in their own system. Does this qualify as a very limited form of consciousness?

The book touches at the end on the question of artificial intelligence, but very superficially. Rather than wondering whether an artificial intelligence could be conscious, he focuses on refuting the possibility of human-like artificial intelligence. His argument is basically that neural networks do model only a subset of the brain’s physical and chemical processes and can’t thus match human intelligence (there are other physical and chemical processes at play in the brain besides synapse firing). He also argues that an emulation of these processes still wouldn’t cut it, since it wouldn’t be the real stuff.

Artificial intelligence will not have a human-like intelligence, though. Each system (biological or digital) has its own form of intelligence. Because of his anthropomorphism of artificial intelligence, Godfrey-Smith doesn’t explore the alley of consciousness in AI systems much deeper. This is unfortunate, because with his consciousness-as-spectrum approach, it would have been an interesting discussion.

Practices vs Principles

It struck me when reading Scaling the Practice of Architecture that people often use the term “principle” in a sloppy way:

There is a great deal I could write here about bad architectural principles but I’ll stick to the key aspects. Firstly, they are not practices. Practices are how you go about something, such as following TDD, or Trunk Based Delivery, or Pair Programming. This is not to say that practices are bad […] they’re just not architectural principles.

I’ve probable been using the term in a wrong way more than once. Principles don’t tell you exactly how to do something. They are just criterions to evaluate decisions. All things being equal, take the decision that fulfills the principle the most. Examples of well-known design principles are for instance

  • Single-responsibility principle
  • Keep it simple, stupid
  • Composition over inheritance

A practice, on the other hand, is a way of doing something. Examples of practices are:

  • Pair Programming
  • ​​​​​​​Shift left with CI/CD
  • Limit Work in Progress (WIP)

A lot of documents confuse the two. For instance, the SAFe Lean-Agile principle are actually mostly practices.

It could look like principles are for software design and practices are for software delivery. But you can have principles for software delivery, too. For instance, “maximize autonomy” could be a delivery principle. It doesn’t tell you how. It just tell you that if you have two options to design the organization, you should go the the one that maximizes autonomy. On the other hand, a software design practice could be to “model visually”.

Another confusion in this area come with another term similar to principles and practices: values. A value is a judgment of what we consider important. Usually they define behaviors and are then adjective (but “profit” could be a value yet isn’t an adjective). “Autonomy”, could be for instance a value. A value embodies implicitly the principle of favoring this value over others. For instance, if you value “autonomy”, you will automatically follow the principle “maximize autonomy”. If you adhere to a value, the corresponding principle comes for free.

Finally, there are “conventions” and “guideline”. Conventions tell you how to do things exactly and are mandatory. You can check if you adhere to a convention or not. This is unlike principles or practices, which have room for interpretation. A guideline is like a convention, but optional. Examples of convention or guidelines are:

  • Interfaces are versioned
  • Sanitize all inputs
  • Limit WIP to 3

Using a full example of value/principle/practice/guideline with in one area, we could have

  • value: resilience
  • principle: tolerate failures
  • practice: chaos testing
  • guideline: use tolerant reader

Granted, no matter how we try to distinguish the terms from one other, there will be some overlap in some cases. Natural language is messy. But I think it’s worth using the terms in the most appropriate ways if possible. It helps create a mental model that works. If you mix practices, principles, value and guidelines together, people might not notice immediately, but it creates a cognitive friction that makes it harder to actually apply underlying ideas.

SAFe: The Lean Mindset

An interesting aspect of the SAFe framework is that it tries to combine two agile mindsets. The first mindset is the iterative mindset of methods like Scrum. It’s a cornerstone of agile development and SAFe “scales” it from the team-level to the program-level, for instance with the PI Planning.

Another mindset in SAFe is the lean mindset. The lean mindset is not about iteration, but about optimising the flow of value.

Lean came initially from manufacturing where the goal is to (1) reduce the time to produce physical good, and (2) reduce the “inventory” needed in the process, and (3) reduce the “waste” produced during manufacturing. In manufacturing, managing inventory requires warehousing and logistics, this costs money. Materials that end up as waste cost money too but do not produce value. To reduce delivery time, each step in the delivery process must be optimised and wait time be reduced to the minimum.

These ideas can be translated to the software world if we consider that features under development are “inventory” and the development process is a pipeline that can be optimised. Features under development are “inventory” since they don’t produce value but must be managed. Waste is a bit harder to map but it represents all the unnecessary work that end up not being used (think of unused design document, analysis, etc.). The development pipeline can take many forms but is always a variation of define, build, verify, and release. The quicker a feature can transition in the pipeline the faster you produce value.

Lean in itself doesn’t require iteration. Iterations are needed to manage uncertainty and course-correct the product development in the face of new information. Lean is about optimising a delivery process. But the delivery process could be about the delivery of a similar item every time, like cars in the manufacturing world.

But Lean is also a great complement to iterative approaches like Scrum. In this case, the goal of the lean mindset is in a way to optimise the iteration speed. Rather than having several features with long delivery time, focus on few features and short delivery time.

SAFe emphasises the lean mindset with concepts like the continuous delivery pipeline and value stream mapping. Besides presiding over the process, the RTE are also charged to improve the flow of value in the organisation.

The lean mindset isn’t as established as the iterative mindset. I find it interesting that SAFe integrates it and promotes it. We conducted a value stream mapping session at work, and it was very enlightening. Thinking in waiting time, inventory, waste does indeed work in the software world, too.

It’s a simple way to highlight process and organisational issues. It gives clarity to what should be optimised and not get lost in organisation design. Chances are, if you want to reduce waiting time, you will have to solve a bunch of other problems first. The lean mindset positions these problems not as end in themselves, but as bottlenecks to short delivery time. It helps you prioritize these problems. It’s a bit like Test-driven Development (TDD). Making things testable requires that you figure out a good design first. But assessing testability is easier than assessing “good design”. In the case of Lean, minimising “waiting time” requires that you figure out a good organisation first, but measuring “waiting time” is easier than measuring “good organisation”.

Silly Product Ideas that Win

When Twitter appeared more than a decade ago, I though it was silly. I saw little value in a service that only allowed sharing 140-character long text messages. I registered on a bunch of social media platforms and created nevertheless a twitter account. Some years later, the only social media platform I’m actively using is… twitter.

There’s a lesson here for me and it’s that it’s hard to predict what will succeed. A lot of products can appear silly or superficial at first. They may appear so in the current time frame, but this can change in the future. Initially, twitter was full of people microblogging their life. It was boring. But it morphed in a platform that is useful to follow the news.

A startup like mighty can look silly now – why would you stream your browser from a powerful computer in the cloud? But as applications are ported to the web, maybe the boundary between thin client and server will move again.

We prefer to endorse project that appear profound and ethical, like supporting green energy, or reducing poverty. Product ideas that are silly or superficial don’t match these criterion and it’s easy to dismiss them. But innovation happens often because of such products. No matter how silly or superficial you think they are, if they gain traction, they need to solve their problem well at scale. These products are incubators for other technologies that can be used in other contexts. Twitter, for instance, open sourced several components. If Mighty gains traction, it might lead to new protocols for low-latency interactive streaming interfaces. An obvious candidate for such a technology could be set-top TV boxes.

These products might appears superficial at first and might lack the “credibility” of other domains, but here too, the first impression might be misguiding. A platform like twitter can support free speech and democracy (sure, there are problems with the platform, but it at least showed there are other ways to have public discourse). A product like Mighty might in turn make it more affordable to own computers for poor people, since it minimizes hardware requirements. Because these product don’t have an “noble” goal initially attached to them, doesn’t mean they don’t serve noble cause in the long term.

There are of course silly ideas that are simply silly and will fail. But the difference between products that are superficially silly and truly silly is not obvious. I took in this text the example of twitter and mighty. In retrospect, the case for twitter is clear. For mighty, I still don’t know. The idea puzzles me because it’s at the boundary.

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Perfect Alignment is Unnecessary

Few years ago, I would have described a good organization as one where everyone is on the same page. By it, I would have meant exactly on the same page. I realize now that I was wrong. You don’t need to be perfectly on the same page. Being mostly on the same page is enough, and a little bit a chaos is ok.

Engineers are very well positioned to understand why: to be on the same page you need to coordinate, and coordination is expensive. This holds for actors in a software system (threads, processes) but also actors in an organization (person, teams, units). Coordinating between actors takes time, and as such slows the system. You should first try to design your system so that the need for coordination is reduced, and then if necessary, balance coordination with consistency (being on the same page).

The analogy works surprisingly well (maybe it’s not an analogy but a property of system in general?). Take optimistic locking in software systems: it’s a tradeoff between consistency and performance. Rather than lock the resource on each change, you only check when you do the final write if you’ve been working on the most up to date information. If not, you do a retry. In this case, there’s a performance hit, but overall the system is faster this way. The equivalent in an organization would be to accept that some people somewhere have outdated information. They will work based on this outdated information until a synchronization point happens and they realized the information is outdated. Some work will have to be corrected or redone. It may be upsetting, but should happen rarely.

The art of organization design is to reduce coordination and when needed use the right synchronization points. The goal is to prevent catastrophic mistakes. Some inconsistencies here and there, if timely resolved and with small consequences, are fine. Do not synchronize on everything (it’s way too expensive) but synchronize often enough to keep the risks small. Prefer many small risks than looming, large big risks.

There are lots of patterns in software system to synchronize and coordinate actors in the system. There are also a lot of patterns to synchronize and coordinate actors in an organization: all-hand sessions, company memo, internal trainings, review boards, formal processes, team meetings, etc.

Interestingly, software systems and organizations have different profiles when it comes to the tradeoffs between consistency and speed. For software systems, relaxing consistency beyond simple techniques like optimistic locking is usually hard. Transactional systems are still a lot easier to build than systems with relaxed consistency. On the other hand, an organization will always work with relaxed consistency somehow: it’s impossible for an organization to update the “collective brain” in a transaction. It’s the nature of people to misunderstand information, forget things, or simply take vacations or be sick.

Speaking of coordination and alignment, Elon Musk put it like this:

“Every person in your company is a vector. Your progress is determined by the sum of all vectors.” – Elon Musk.

What this analogy does not consider is the time needed to align. If lots of time is lost on coordination, the vectors are smaller. You then have to choose between an expensive perfect alignment, or some inexpensive imperfect alignment. Given that organizations constantly course-correct, vectors accumulate projects after projects (or task after task) and there are plenty of opportunities to adjust the alignment, even each time in an imperfect manner. This is why in a good organization, a little bit of chaos is ok.

What’s My Exposure to Data Lock-out?

My computer died a few days ago. Fortunately, I had a backup and could restore my data without problem on another laptop. Still, I’ve been wondering in the meantime: what if the restore hadn’t worked? How easily could I be locked out of my data ?

I have data online and data offline. My online data are mostly stored by google. If say, my account is compromised and due to a misbehavior from the hacker, my account is disabled. Would I ever be able to recover my online data? Not sure.

My data offline are stored on the harddrive, which I regularly backup with time machine. If a ransomware encrypts all my data, the backup shouldn’t be affected. Unless the ransomware encrypts slowly over months, without me noticing, and suddenly activates the lock out. Am I sure ransomeware don’t work like this? Not sure.

My laptop suffered a hardware failure. It hanged during booting, and no safe booting mode made it through. The “target disk” mode seemed still to work, though. It would have been a very bad luck, to not be able to access either the data on the harddisk or the backup. Both should fail simultaneously. But can we rule out this possibility? Not sure.

Harddisks and backup can be encrypted with passwords. I don’t make use of this option because I believe it could make things harder in case I have to recover the data. I could for instance have simply forgotten my password. Or some part could be corrupted. Without encryption I guess the bad segment can be skipped; with encryption I don’t know. Granted, these are speculative considerations. But are they completely irrational? Not sure.

Connecting my old backup to the new computer turned out to be more complicated than I thought. It involved two adapters: one for firewire to thunderbolt 2 adapter and one thunderbolt 2 to thunderbolt 4 adapter. Protocol and hardware evolve. With some more older technology, could it have turned out to be impossible to connect it to the new world? Not sure.

The probability of any of these scenario happening is small. It would be very bad luck and in some case would require multiple things to go wrong at once. But the impact would be very big—20 years of memory not lost, but inaccessible. There’s no need to be paranoid, but it’s worth reflecting on the risks and reduce the exposure.

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The Superpower of Framing Problems

Some problem we work on a concrete. They have a clear scope and you know what has to be solved exactly. Sometimes, problems we need to address are however muddy, or unclear.

When something used to work, but doesn’t work any more, the problem is clearly framed: the thing is broken and must be repaired. However, if you have someting like a “software quality problem”, the problem isn’t clearly framed. Quality takes many form. It’s unclear what you have to solve.

To explore solutions you need first to frame the problem in a meaningful way. With this frame in place, you can explore the solution space and check how well the various solutions solve the problem. Without a proper frame, you might not even be able to identify when you have solved your problem, because the problem is defined in such a muddy way.

The “quality problem” mentionned previsouly could be reframed more precisely for instance as a problem or reliability, usability, or performance. It could be framed in terms of the number of tickets open per release, or about the time it takes to resolve tickets.

Depending on how you frame your problem, you will find different solutions. Using the wrong frame limits the solution space, or in the worst case, means you will solve the wrong problem. It’s worth investing the time to understand the problem and frame it correctly.

If I had an hour to solve a problem I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions.– Albert Einstein

I’ve talked up to now about framing problems. Framing does however work even in a broader sense and can be used each time there is a challenge or an open question. Each time you should come up with a solution, there is some framing going on.

Something interesting about framing is, that in itself, it isn’t about proposing a solution. It’s about framing the solution space. As such, people are usually quite open to reframing problems or explore with new frames. Whereas if you propose solutions, you can except heated discussions, when it’s only about framing, usually the friction with other people is pretty low. While framing in itself is not a solution, it does however impact the solution that you will find. When people don’t agree on some solution, usually, people have different implicit frames for the problem. Working on understanding the frames is sometimes more productive than debating the solutions themselves.

A second thing interesting about framing is that you don’t need to be an expert in the solution to help framing problems. You need to be a an expert in the solution space, but not the actual solution. Going back the the example of “software quality problem”, you can help with framing if you know about software delivery in general. You don’t need to be a cloud expert or or process expert. This means that good framing skills are more transferable than skills about specific solutions.

I wrote long time ago about using breadth & depth to assess whether a thesis we good. In essence, this is a specific frame for the problem of thesis quality. Finding good frames for problems helps in many other cases. Framing problems is a great skill to learn.

SAFe: What’s a Release Train Engineer?

SAFe introduces a new role in the industry: the release train engineer (RTE). A RTE is, according to the framework:

The Release Train Engineer (RTE) is a servant leader and coach for the Agile Release Train (ART). The RTE’s major responsibilities are to facilitate the ART events and processes and assist the teams in delivering value. RTEs communicate with stakeholders, escalate impediments, help manage risk, and drive relentless improvement.

The role is designed like a scrum master at the ART level. At a minimum, a RTE ensures that the process is followed. But a good RTE helps teams improve their performance – that’s the essence of the job. A RTE doesn’t have any authority over the content in the backlog. The focus on only on improvement at the organisational level. As such, the wording “assist the teams in delivering value” leaves quite some lattitude in how impactful an RTE can be.

What do you expect from a RTE? I am wondering how this role will establish itself in the industry. Here are my personal expectations.

Level I – The Organizer. The RTE ensures that the process is followed. He/She ensures that information flows between the teams using the elements of the framework. The RTE helps resolve problems related to the work environement as they appear. Example of such problem could be: tools to communicate, organisation of the program backlog, running the ART events. He/She makes sure people can work.

Level II – The Moderator. The RTE is able to create plattforms or use existing plattforms to encourge discussions in the ART / Solution. With some moderation talent, he/she can help instill change, support improvements, or create alignment. The RTE helps resolve problems about team performance as they appear. Example of such problem could be: interpersonal issues, improving the collaboration with a specific provider, managing morale in challenging time, ensuring transparency, suggesting a feature stop to address the existing bugs first.

Level III – The Influencer. The RTE identifies systemic performance issues in the organisation and work towards resolving them by instilling change at the organisation, technical, or product management levels. Example of such issues could be: addressing systemic quality issues due to the work culture, working with the system architects/teams/system team to make the continous delivery pipeline faster, encouraging decentral decision-making (while managing risks), improving feedback loops.

The higher the level, the more interdisciplinary the RTE will have to work. While little knowledge of product management or architecture is needed to be proficient at level I, problems at level II and III will require a good understanding of how engineering works and how product management, technology and processes influence each others. On the technology front, the RTE is also a key stakeholder to support mindset like DevOps, which means he must also have some good understanding of how technology supports delivery and operations.

The RTE role ressembles that of the more established delivery manager. Both focus on similar sets of issues.

The big difference between both roles lies I think in the mindset. A RTE is a coach and as such has little formal authority in itself. He leads by helping other take the right call. A delivery manager will typically have more formal authority. For instance, a RTE has no authority over the priorisation of backlog in itself. The PM and PO have formally this responsability. The RTE coaches the PM/PO in priorizing work.

The higher the level, the more the RTE works at the level of the engineering culture. It’s easy to define values and visions that nobody follows. Culture is defined by how people effectively behaves. It’s hard to be a good RTE. Just like it’s hard to be a good scrum master. Changing how people work isn’t easy.