If you’ve ever worked with PlantUML or Mermaid before it’s easy to forget the domain specific language used to build the diagrams. You sketch out your whiteboard diagram, discuss it with coworkers and are ready to start on your architecture document only to sit there stumped trying to remember how to convert your drawing into PlantUML.
Well the good news is that Claude can do it for you. Just take a picture of your whiteboard and Claude can convert it into Mermaid or PlantUML for you. I tried both and Claude is better at Mermaid but can do PlantUML.
Unfortunately, Claude’s first attempt had an error. I fiddled with it a bit in the editor, but Claude was able to fix it faster than I was.
Claude fixed the issue and I was able to generate the following PlantUML visualization.
Here is the original image compared against the PlantUML Claude generated. The relationships are correctly mapped even if the physical placement is a bit different.
Lastly, I asked Claude to do the same thing with the Mermaid diagram visualizer. Mermaid does the same things as PlantUML, it’s just newer and easier to use.
To my surprise Claude not only managed to do it in one attempt. But Mermaid is built into the Anthropic UI.
If you’re ever stuck trying to remember the PlantUML DSL just ask Claude to do it.
Interesting talk on how modern systems on a chip differ from how we imagine computers to actually work. Over the last two decades hardware has gradually abstracted away many features from the actual operating system. The computer board is now a collection of sub-computers which cooperate to mimic the operation of an actual traditional computer.
The system describe here is reminiscent of a micro service environment where you need to communicate with many different protocols to get the job done. I wonder if we can explore migrating ‘cloud’ microservice techniques down to the CPU level.
Passive Radar for finding meteors
You can detect meteors using a set of clock synchronized radios. The way it works is you monitor a reference frequency at perhaps 180MHz and can detect changes in it as meteors burn up in the atmosphere.
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.
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
KPIs need to be quantifiable. It should be easy to number how many of X someone completed
Verifiable
There should be impartial systems tracking KPI completion. We want to use systems like PagerDuty, JIRA, Github, etc to source our KPI data.
Valuable
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.
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!
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.
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.