A clear sign you are overdoing micro services 

Fine grained services

Microservices have been the thing for over 15 years. They are great in large companies with CI/CD environments. But as your situation drifts farther away from the ideal microservice use case traps abound. 

Building a new service for one endpoint 

If you find yourself having a conversation where you need to create a new endpoint somewhere, but adding it to any of your existing services would break the concept of that microservice. Turning it instantly into a ball of mud with no clear purpose. You have fallen into this trap. microservice does not mean each service has only one HTTP endpoint. That use case is better served with Cloud functions like AWS lambda. 

The problem here is that we have gone too far in splitting up the monolith. Splitting a monolith with 100 HTTP endpoints into a dozen or so services with eight endpoints each is great. Splitting up a monolith with 100 endpoints into 100 services is counter productive. Instead of having an actual purpose the single endpoint microservice becomes the xyz endpoint microservice. 

Endpoints are things that microservices empower. An endpoint in of itself should never justify the creation of a microservice. 

KPIs for Software Engineers

Key Performance Indicators are a common business practice. They are a quantifiable measure of performance for a specific objective. Occasionally, I am asked to create my own KPIs as an individual contributor. I think it is a bit strange, after all I work for the company, you’d think they would tell me what the KPIs are!

Ideally we want to avoid metrics which are created arbitrarily by team members. Hours worked is a great example of this especially in remote work environments. In hourly workplaces employees check in and check out to validate hours worked. I have never seen a software company do anything comparable. Typically, the software company employee is asked to fill in timesheets based on their personal memory with zero accountability. That scenario does not make for a good KPI. 

For our KPI metrics we have the following criteria:

  • countable 
  • verifiable by external system
  • valuable 
  • Individually attributable 


KPIs need to be quantifiable. It should be easy to number how many of X someone completed


There should be impartial systems tracking KPI completion. We want to use systems like PagerDuty, JIRA, Github, etc to source our KPI data. 


KPIs should relate to valuable activities for the company. We want our staff focused on things that are important. 

Individually attributable

KPIs should be based on individually attributable work. We want to avoid subjective judgements of how much of task X engineer 1 did vs engineer 2. We also do not want to encourage infighting over who gets credit for which tasks. 

Here are a few measurable things we could use for KPIs. 

  • lines of code 
  • commits 
  • bugs fixed
  • tests written 
  • Pull requests opened | accepted / rejected
  • Pull request comments
  • Pull requests reviewed | approved / rejected
  • story tickets completed
  • stories estimated 
  • Projects lead 
  • Projects delivered / Epics delivered 
  • architecture documents published
  • architecture documents reviewed / comments / approved / rejected
  • Documentation pages published
  • oncall response time when paged
  • pages recieved daytime/off hours
  • Prod deployments 
  • meetings attended
  • Pair programming sessions attended
  • Junior dev questions answered (answers documented in wiki / private stack overflow)

KPIs are a useful concept for businesses to track their performance. But they are often really ideal for business groups to examine their performance. Individual contributors rarely can claim responsibility for things like increasing the subscription renewal rate from 1% to 10%.

While this list is not exclusive it should include most of the trackable numerical things that software engineers do in their job. Then if you need to come up with some KPIs for yourself you can just pick from this list.

If you can think of some more good metrics for software engineers let us know!

Up in the air

We are in a phase where planning becomes quite difficult. ChatGPT has started a capitalistic AI war. Microsoft swept in to shepherd commercialization. Google is on the back foot for now. Amazon will launch something I have no doubt. ChatGPT style tech would make Alexa viable by solving the fractal conversation problem. 

The players are moving, immense amounts of capital has been unleashed. But for us on the outside it’s a difficult time. You can’t really plan for the future. Because the technology is advancing rapidly and is already transforming jobs in various industries.

GPT-4 has been in the news, but Midjourney has quietly advanced to the point where it is transforming job tasks in the graphic design industry. I read a complaint by a graphic designer this weekend describing how his job has become more prompt engineering than graphic design. Instead of needing to draw things he and his peers can now use AI image generation and then clean it up in photoshop. 

Video created by demonflyingfox using MidJourney V4.

In 2022 I ordered physical versions of two AI generated images that I thought were incredible examples of what AI could do. In 2023 these images are somewhat quaint. AI image generation can do so much more now. 

We don’t really know where things are going. How do you prepare exactly when the potential paths are so divergent? 

Some people claim AI will replace programmers. Others say we will never not need people to dig deep into the technical details. Personally, I lean towards the second. If AI coding hasn’t peaked yet we will likely see a 1000x increase in the amount of code being written. ChatGPT is quite good at explaining things but will it be useful at explaining interactions between multiple programs it has written? We can’t know at this point. 

Image of a line going exponential. Credit to Luke Muehlhauser who created and watermarked this image.

We are in the straight line at the far right now. We’ve discovered something about meaning in these large language models. A mapping between language and image, and mappings between language and language. It’s not AGI, but much like Deep Blue its obviously eclipsed human capabilities in some way. 

Neal Stephenson’s ‘The Diamond Age’ is a book I was intrigued by in my younger years. In it a girl is given a AI powered book which acts as her tutor from a very young age.  Much like that fictional book ChatGPT likely will become every child’s tutor going forward. Much like the iPhone, you won’t be able to buy a better one. Children have already used ChatGPT to make homework and writing assignments obsolete. The education system likely will not survive this advancement. 

The sum total of human knowledge has been put into this machine. Everyone who ever wrote anything is part of it. Buckle up. Don’t panic. Hold on. Let’s see what happens next.

Agile has gone from ‘people over process’ to ’no process’

In practice ‘agile’ means ‘we have no process in place and each team does whatever random thing the manager wants to try next’. Sometimes that is SAFE sometimes its SCRUM, usually it’s a combination of different things. This isn’t necessarily a bad thing, but there are trade offs. 

The first is standardization. If every team follows a different process it’s difficult to understand what is going on at a management level. Which teams are productive? Which teams are in downward spirals? If you don’t have a standard to judge against you can’t find out. 

Secondly, away team work is much harder. Working with a team that uses the same development process, pipeline setup, programming language and frameworks is easy. On the other hand working in the code base for a team which uses a different language, framework, architecture, etc is very difficult. Not supporting away team work severely limits your ability to integrate internal software components.

Thirdly, Estimates are not possible in this kind of environment. Since the process changes constantly historical data becomes useless. In response to this most companies don’t even keep historical data. The main use for estimates is ensuring that ‘burn down’ charts follow the 45 degree angle managers love. 

Subjective expert predictions are a valid form of estimating software tasks. But if you don’t have historical data to calibrate against estimates devolve into gaming the system. 

When you change how estimates are made, when tickets are considered done and the sprint cadence every 3-6 months there is no way you can have cross company data on productivity. The lack of process empowers management to obfuscate the productivity of their teams. The pursuit of the best process gives technical organizations a great excuse as to why they have no idea if their processes have improved over the last two years or not. 

In this type of environment all judgements have to be made based on subjective gut feelings and corporate politics. You don’t know which VP’s processes are better than the other because neither has any accountability. You don’t know which technical department is more efficient because neither the estimates or logged hours can be trusted. 

‘We’re being agile’ has become the excuse to follow whatever process you want. Instead of ‘people over process’ it has become  ‘no process’. 

When creating processes (mechanisms) its important to consider who benefits

A business process is any procedure that is done manually by employees. In the software engineering environment this includes everything from code reviews to standup and 1 on 1s. 

Compliance with processes involves education, incentives and punishments. People need to know about the process to follow it. And if you don’t attach incentives to the process, negative or positive, people won’t feel a need to follow it. 

A common cycle I see is that management will think of a new process which could solve a problem the development organization is facing. They then declare the new process and are surprised when later on no one is following the new process. 

When designing new processes it’s important to consider who benefits from the process. Additionally, it’s helpful to contrast who benefits with who is required to implement the process. Are the same people benefiting from the process as the people expected to implement the new process? 

Your job as management is to create and enforce the business processes the team follows. Typically, this is best done with buy in from the team but the buck stops with middle and senior management. So it’s important to review what processes you tried to implement and how it went. 

What pain points did we try to address with new manual processes this year? How clearly did we define the new process we wanted to implement? Did the team follow the process as expected? Who’s pain point did the new process address? Was it the development team? Was it the product team? Was it the management team? 

Typically, people do not need incentives to follow processes which benefit them. But getting people to follow processes which do not benefit them is much more difficult. 

Take the example of sprint burn down charts. These charts provide utility to project and management team members. But they rely on the engineering team members to update tickets consistently. 

The conflict comes In because burn down charts provide zero benefit to engineers but all the work to support them has to be done by engineers.

This creates a dynamic where engineers are always incentivized to reduce their time commitment to updating tickets while management is frustrated that nobody is updating tickets.

You can of course make updating tickets the primary job task for engineers to fix this issue. But then instead of developing features, updating tickets becomes the primary job responsibility for the engineering team. Not what you want in a development organization.

The problem is that you have a fundamentally extractive business process. You need some group to take on extra work duties to improve the situation for another group. Having good reporting on the state of development tasks is an important thing for engineering organizations. But when designing processes to achieve that goal it is important to think about the process in the correct context. 

Being one sided is not a bad thing in general. Businesses are fundamentally about generating profit. If we need an extractive process to make money then it is justified in the context of the business. 

However, ideally we want to avoid these kinds of processes. We want to create processes which align the value produced with those doing the work. Because people enjoy following processes more when there is something in it for them. It makes their jobs easier and as a manger it makes your job easier because you don’t need to police whether people followed the process.