It is not the job of sales to find customers with problems that we have solved, but also to find the problems that we can potentially solve. The role is one part customer relations and one part business development.

The challenge with this role is that features fall on a spectrum from nice-to-have to critical-for-business; being able to tell the difference is a key skill for anyone in sales. If we filter all the nice-to-have functionality out of the developer's backlog what remains is the high value and impactful functions that allow us to sign more deals.

Engineering teams often miss this business context and lack the skill to differentiate these features. Part of the problem stems from being a few steps removed from the customer and nice-to-have features can appear more exciting to work on but don't end up creating value.

Engineers that wish to be highly autonomous and are charged with seeking impactful work need to exercise their inner salesperson. Those that don't embrace these skills are at risk of wasting their time and working on the wrong thing.

Buy Signals

Those in sales and business dev look for hints that inform them of the project's potential to get to the deal signing stage. Focusing on projects with high potential allows us to avoid chasing cases that won't move forward or are likely to end without being closed.

If more than one customer is asking for a feature this can be an obvious indication that focusing on this can provide more impact. If you're working on internal software infrastructure, are you getting a feature request from more than one team, or is it from the esoteric data scientist that seems to have their own bespoke setup?

Sales rely on buy signals. A buy signal can indicate when a client is excited enough by a solution that they are willing to consider a purchase. The most obvious way they signal this is by giving you money. If they're trying to give you money before the solution is built, you'll know the solution is somewhat important to them.

We don't have to rely on the client putting down money for us to recognise a buy signal. Buy signals can appear from any skin a customer is willing to risk for a solution. If a customer is willing to vouch for the product to a superior this is a political stake. This allows us to move up the decision chain and provides a positive signal that a deal can make it to close. We also use this as a way of making progress in conversations without explicitly asking for money.

Software engineers should want to get closer to the projects that have the largest impact. We can use buy signals to determine the projects with the most buy-in from leadership and create the most business.

Discovery Questions

Developers can find themselves working on the right project but can end up developing the wrong thing without an accurate idea of the pain-point. We need to peel back the layers that surround a customer's pain to determine how we may best deliver.

Sales do this with discovery sessions or hearing it directly from the customer. Their goal is to avoid making incorrect assumptions about the problem which may lead to building the wrong solution. Software engineers could also benefit from this skill. If you're given a spec an engineer should ensure they're delivering the appropriate fix, otherwise they risk missing the target and have to do revisions or start again from scratch.

Sales does this with open ended questions, the more space they provide a customer to explore the problem the higher the chance we expose insight. Getting them to dig deeper on specific points can expose issues they might be having and asking how a solution might affect their business can help sales qualify this lead.

A classic technique of getting your customer to talk is called Mirroring covered in "Never Split the Difference" by Chris Voss.

Are your developers asking enough questions to expose misaligned assumptions?

Touch Points

Progress updates with customers allow you to show that you are keeping them in mind and their requests haven't been forgotten about. They also provide a heads up when new features are near release. They are used to build trust and allow the customer to feel like they are helping guide the process.

If your wins are also their wins they will feel like they have more skin in the project's success and become an internal champion across the industry and in their own business.

How often do you provide a task to a developer and only hear about it three months later when there's just two weeks until the deadline?

Regular touch points don't just serve the relationship you are building with your customers but also allow you to validate the project closer to real-time. Ensuring that you are headed in the correct direction is critical for success. If you're heading down the wrong path you'll want to know as soon as possible.

It's a shame that developers seem to be stereotyped as introverts lacking people skills that wish to be isolated from any call with the customer. The industry appears to lean into this idea by adding more barriers between the client and developer. The truth is the companies that have their developers closest to users and customers are the ones that succeed and the best way for an engineer to level up is to care more about the customer.

Determine Timelines

Software can rely on external processes and teams. Figure out what you're building and predict where you might need approvals or contracts, this will allow you to get the ball rolling in those departments. Sales already do this on their discovery calls by probing if the customer will need to bump other departments, bumping them now can lead to less delay.

Within a company we might require a third party tool for the new feature we're developing, so the sooner we loop in finance or the security team the better. We can have them work on their specifics in the background while we work on the feature. We can probe our PM before we start the project that we might require looping in these other teams.

We should also determine when something will be needed and understand the timeline. They could be aiming to deploy their MVP in a month which could affect how we prioritise building the solution. If the deadline is tight, perhaps we can determine if there's only a subset of features required for the MVP and avoid sinking time into the features that are expected at a later time.

Alignment

Developers can level themselves up by becoming more aligned with the business and not less. One way of doing this is by moving closer to the customer or learning how other roles operate. Software will continue to be a competitive industry and engineers can't afford to waste time on things that don't delight their users and/or provide impact.

We have two large tables and 100 guests coming to our wedding and we have to figure out how they will be seated. Defining the seating chart tends to be the most enjoyable part of wedding planning¹.

After drawing circles for all the seats and guests in excalidraw.com, I began connecting the circles with colourful lines to map out specific relationships. As an example, a couple at the wedding should be seated together.

It dawned on me that this is a constraint satisfaction problem (CSP); and modeling CSPs is something I've been doing for the last year.

Constraint Satisfaction Problems

CSPs are a family of problems where you are required to find the value to a number of variables given certain constraints to those variables. There are many areas that such problems occur, one such area is packing optimisations.

There are software engineering roles that rely on solving these type of questions; typically these roles will include the term "Formal Methods" under their list of responsibilities.

To solve a CSP problem, we begin by modelling the problem mathematically. There are a few notations that allow us to define the variables and constraints for these kinds of problems, the one I am familiar with is called SMT-language.

Here is a simple example where we wish to find valid values for x and y:

(declare-const x Int)
(declare-const y Int)
(assert (> x 0))
(assert (< y 10))
(assert (= (+ x y) 15))
(check-sat)
(get-model)

You may have noticed that this uses prefix notation, i.e. (> x 0) which is equivalent to (x > 0) in the familiar infix notation.

To find valid solutions we need a solver such as z3. If we provide z3 with the file above it will give us a solution for x and y that fits the constraint. E.g x = 15 and y = 0. There may be more than one valid solution. Sometimes there is not solution and it will return unsat. Short for unsatisfiable.

Knapsack problems

We can use z3 and smt to model and solve a classic knapsack problem. Given the following products:

• Product A has a size of 3 and value of 4.
• Product B has a size of 5 and a value of 7.

Pack a bag with a capacity of 16. Maximizing the total value of the bag.

(declare-const productA_count Int)
(declare-const productB_count Int)
(declare-const total_value Int)

(assert (>= productA_count 0))
(assert (>= productB_count 0))

; Product A: size=3, value=4
; Product B: size=5, value=7
; Bag capacity: 16

(assert (<= (+ (* 3 productA_count) (* 5 productB_count)) 16))
(assert (= total_value (+ (* 4 productA_count) (* 7 productB_count))))

(maximize total_value)
(check-sat)
(get-model)

Solving this tells us that if we pack two of each product we maximize the total value of the bag; this being 22.

Seating

Getting back into the seating chart. I wrote (vibed) a program that allowed me to draw different connections between each of my guests. These connections account for the different constraints we wanted to map onto the guests. So if A and B are a couple, ensuring they sit next to each other is a constraint.

Here is a complete list of the constraints that were modeled:

1. Sit next to each other.
2. Sit opposite each other. Or constraint 1.
3. Sit diagonally across from each other. Or constraint 2.
4. Not next to, not opposite and not diagonal from each other.
5. A should be next to B OR C.

After drawing all these constraints between our guests this is how the wedding looks:

Green is constraint 1, yellow 2, orange 3, dashed red 4 and dashed purple is constraint 5.

We are seating everyone at two large tables. As would have been done in a classic viking longhouse. In order to model the guests at these tables each guest will need a variable for the position, the side and the table.

The groom will have an assigned seat, in the middle of the first table facing inward, towards the guests. As guest number 1 he would have the following variables:

# table_1: int = 0
# pos_1: int = 12
# side_1: bool = true

This would seat me in the middle of the table looking out across the room. We then define these variables for all 100 guests. We also need to include a constraint that all the table_{n} variables must be 0 or 1, since there are only two tables. Additionally the pos_{n} variables have to be between [0 and 25) since there are only 25 seats on each side of the tables.

Constraints

Now we model each constraint listed above. The first constraint states that guest A and B must be next to each other. If we take guests 11 and 12 this would look like the following:

(assert (= table_11 table_12))
(assert (= side_11 side_12))
(assert (or (= pos_11 (+ pos_12 1) (= pos_12 (+ pos_11 1)))))

I.e. Same table, same side but their positions are offset by 1 or -1.

The next constraint allows the guest to not only sit next to someone but as an alternative they can be opposite each other. To do this we assign the above constraint to same_side_const and define a new constraint assign it to opposite_const and in order to satisfy either of them we say. (assert (or same_side_const opposite_const)). Here's the definition of opposite_const:

(assert (= table_11 table_12))
(assert (distinct side_11 side_12))
(assert (= pos_11 pos_12))

I.e. Same table, different sides but same position.

For the 3rd constraint we merge constraint 1 and 2 together. B needs to have a different side to A and the position needs to be offset by 1 or -1.

Using the tool above to define the graph constraints I could then export the constraints to be passed into a script that generates the smt file for z3. It then provided a solution that I could import into the same tool to see a visual representation of how it would seat all the guests. You can see that below:

As you'll notice from the colour of each line, all the constraints are satisfied.²

Results

So after 5 hours was this seating chart useful? Not really...

The guests that are on the edge of each of the clusters weren't really people we wanted to sit together. However the arrangement within each cluster was great.

Was this fun? Totally!

I'm not going to do this again, because I'm not going to be married again, but in the event that I have to create a seating chart for 100 people in the future there are certain things that might provide a better outcome.

Perhaps it would be better to rely on more flexible constraints, as an example instead of saying A and B need to be in close vicinity I would instead say A should sit next to at least B, C or D. Giving more flexibility to who A can be sat next to.

After presenting this analysis to my future wife she showed me how she had laid out the tables. You can see this below.

Since the constraints are already linking the guests, I was able to spot some improvements to the chart and get some value from this effort.

There was another improvement to the modelling I could have made when looking at the final chart.

If we wanted A to sit next to C and A was in a couple with B. I had not considered that it was probably fine for B to sit next to C instead of A. If C would get on with A there's a chance they would also get on with their partner B. So I could have relaxed this constraint and had C sit next to either A or B.

Creating a seating chart is not a recurring problem of mine, so sadly there's no need for me to refine this solution further.

Guests forgetting to tell me that they're coming has also caused me to pull some hair out, as with most math, when it attempts to model the real world.

A tech stack is more often inherited than it is chosen. On the rare occasion someone gets the chance to decide which tech the company gets to use for the next decade. I've seen this go well and I've seen this go belly up.

If you're in the right club, you know the answer; stick with boring.

The Postgres Paradox

When thinking about how long a technology will last I often use the current age of the software to determine how much longer it will be around. So if it's 2 years old, it might be around 2 more years. Postgres dates back 35 years, so it will probably be around for 35 more years.¹

We can view this in terms of experienced employees; we are more likely to find someone with 20 years of Postgres experience than we are to find someone with 20 years in a tech that's only existed for 2 years.

Taking the age of the tech into consideration allows us to earmark the hiring pool and the likelihood that fixes or workarounds exist for problems we might run into. Diving into newer tech increases the chance we are the first to come across certain problems.

The bleeding edge might appear shiny and new but we aren't here to ship blog posts we are here to ship features and delight customers.

These avoidable issues are hard to solve because of two reasons:

1. They're novel, we're the first to run into them.
2. The people that have faced this issue are expensive, because not many of them exist.

Make a Difference

Starting a business is a risk, picking fancy new tech is a risk. These risks add up. Where you decide to take the risk depends on where you are trying to drive innovation.

In 2015, London, two neobanks were founded; this is the tech they are built on and the tech's age at the time. Revolut is built on Postgres (25yo) and Java (19yo), the other is built on Cassandra (7yo) and Golang (6yo). The former has 50million customers, the later has 10million.

Risks are allowed to be taken, but we should limit the number of unknowns and not make things harder than they need to be. If we pick our entire stack on the principle of shiny and new we are going to be fighting our tech more often that we are solving customer use-cases.

Get excited about delighting users, not the fancy tech.

Don't be First, Be Fast

Early market leaders have much greater long-term success and enter an average of 13 years after pioneers.

Golder and Tellis (1993)

People make the assumption that if they're not using the latest technology they're not staying competitive, when they are instead just becoming a case-study for the industry.²

The study by Golder and Tellis tells us that first movers have a failure rate that is 6 times higher than fast followers.

If the tech stack mattered, you'd see it in advertising, but you don't, because the customer doesn't care. Your tech stack doesn't determine your product market fit³

Your investors might care (🤷).

If I had to choose between customers and investors I'd pick customers.⁴

The Right Tool for The Job

Ask a graduate what tool or language they would use for their next project and they'll give you a political answer: "I would pick the best tool for the job". Here's a list of programming languages. There are 50 that start with the letter A. Let me know when you've found the one that's best suited for the job.

Perhaps we should define what "best for the job", means. If we are in a team of 12 engineers that are maintaining 2 Postgres databases; introducing a MySQL database on the basis that it addresses a specific use-case better than postgres is not a good idea.

If we hire a DB expert in the future are we going to ensure they're versed in both Postgres and MySQL? It's niche but we could find them at a price.

We don't have to hire this DB expert, our team has built up experience with Postgres, now they'll need to familiarise themselves with the maintenance process of a new db. Just bumping your database version is now slightly tricker and you're monitoring news and bug fixes for both releases.

We could define "best for the job" as perhaps the one we are most familiar with, and this is often the best choice. If you can skip learning a new language or learning the framework step, you're going to get to market faster than others. Don't let learning the tech be the procrastination for learning the market.

In a startup we are testing our market assumptions, this is the largest risk to the company, we shouldn't slow down the speed we get to market. It's better to know we are wrong sooner rather than later.

Getting Experience

Engineers at startups might use the job as an opportunity to focus on CV driven develop instead of focusing on the needs of the business.

I've seen a graduate write every new project in a new programming language.

Anyone know Elixir? We're trying to fix a bug in production and the developer that wrote this is no longer at the company.

From a slack thread.

Starting every new project in a new language isn't a fast way to learn how to deliver. It's a fast-way to get 3 months of experience 5 times. Learn how to deal with a language's pain points and how to solve issues with a specific framework is what makes you valuable. If you're looking to stretch yourself, then use the same language and framework for larger projects.

If you've only got 3 months of experience in a language and framework you're probably not that hard to replace.

The new thing won’t be better, you just aren’t aware of all of the ways it will be terrible yet

Slide 84

Propaganda

Propaganda doesn't just occur in war or between competing states, corporate propaganda is a well established strategy available to large technical institutions.

Let's say you are working on an internal framework that holds up your billion dollar social media platform, you wish to hire more engineers to maintain and build the project. Training new hires on this framework is time consuming, wouldn't it be great if the engineers we hired were already familiar with the framework?

They can also hire developer advocates, these are the influencers of the software engineering world, corporate mercenaric missonaries that are very good at making the work they're doing exciting.

Ignore the hype, your job is to keep things simple.

Bleeding Edge

One reason you might wish to be in the bleeding edge of tech is to gain the expertise and then build around some of the fundamentals you've learnt. Perhaps your customers are others trying to enter the market, this is perhaps a safety-net for the risk you take, but the market can also flatten out and you're left holding a bag of air.

There are a few parts that make up the blog, the static site generator, the API and the repo that hosts all the HTML. This article provides an overview of these pieces and how they're all brought together to create the website.

Framework

The framework is called Pelican a python based static site generator. Each post is written in markdown which starts with meta tags such as Title and Category. Pelican then uses this and a custom theme template to create HTML. The theme uses jinja templating to construct template HTML pages.

Pelican also provides a config file which I have used for feature flags and to enable extensions. The extensions I've enabled include footnotes and code-highlighting. Additionally Pelican hosts a local version of my blog which it recompiles whenever it detects a change to any of the files.

In the config there's a list which contains all the links rendered in the sidebar. Extending or updating these links is only a matter of making changes to this list.

There are two directories inprogress/ and content/. inprogress/ stores drafts that are half complete, completed or minor thoughts for potential blog posts. When these are ready to be published they are moved over to content/ and when these are pushed to the repo, using a github action, pelican automatically renders the entire blog into HTML. The action then creates a commit for any changes and pushes the changes to a public repo.

The public repo contains the entire rendering of the blog and theme. Relying on Github pages, this is then hosted.

The CV

Within the same repo as the drafts and published content there's a CV written in Latex. This latex file can be rendered to a PDF. I have a bash script which allows me to recompile this PDF whenever changes are made to the Latex script.

The API

There's an API that contains edge functions written in Go. These serve requests for the comment sections that sit below each blog post. There are three endpoints, one fetches the count of comments given a list of article URLs. This is used on index pages.

There's an endpoint to fetch comments given an article URL and an endpoint to post a comment to an article URL. The repo containing these endpoints has an action which deploys updates on each new commit.

Finally the API handles the mailing list. It handles registering new emails, verifying the email is valid and lastly triggers an edge function to look for new posts using the blog's RSS feed in order to send updates to recipients in the mailing list.

Software is planning, and as the adage goes, failing to plan is planning to fail. This article goes into the development of Git and UNIX with the goal of dispelling the myth that the genius software engineer exists and when we hear something was written entirely over night there's key information being omitted.

Everyone likes the story of the programmer that disappears and resurfaces after a week with some revolutionising software. We all enjoy a compelling narrative but in reality great software takes time, additionally for most great projects the code is actually the smallest part of the project.

Planning

Projects don't go wrong they start wrong

How Big Things Get Done (2023)

We shouldn't view software creation from the point when coding starts. The process should include the upfront planning. In 1975 Brooke advocated that the coding portion of a software project should amount to 17% of the overall time and it should be noted that in the 1970s the most dominant programming languages were FORTRAN and COBOL.

If a software requirements error is detected and corrected during the plans and requirements phase, its correction is a relatively simple matter of updating the requirements specification.

Software Engineering Economics (1981)

When we think of planning we often think of planning permissions, regulation and endless bureaucracy. We don't have time for that, we are trying to move fast and break things. However, in software, the planning stage is usually when most of the thinking happens and the hard problems get ironed out. Thinking is hard and most people avoid it, make thinking easier and you can stand out from other developers.

The planning phase of a project is also the point in time when large changes are the cheapest.¹

UNIX

One fable within software is that UNIX was developed over the weekend by Ken Thompson. Ken is quite the mythical figure in the software world, Brian Kernighan attests to this in multiple interviews.

We must keep in mind though that Ken worked for three years on Multics which he carried many features forward to UNIX. Here's not saying Ken isn't a great programmer, but we shouldn't discount that his mind was focused on an operating system for quite an amount of time and probably made some contribution towards formulating a better system.

Imagine your best programmer said to you, "sure let me work on the project for two to three years then we will scrap it; and start again. You are bound to have the best in class software." Instead businesses want every project to have a fabled programmer and to continue the trope.

Ken Thompson developed UNIX over 3 weeks, there's a video where Brian Kernighan remarks that modern software engineers aren't as productive².

Git

At this point Git is the most popular form of version control for software, it's also famously known to have been written in a fairly short amount of time. Linus states that he wrote Git in 10 days and we mostly assume the timeline for the project was - I have an idea and ten days later we have Git.

However this is not the case. Linus wrote Git in 10 days but it took him 4 month of thinking about the problem until he had a solution that he was satisfied with. 10 days after that we had Git.

Being a Creator

When it comes to creation, sometimes we are too excited by the answer that we fail to think about the question. A lot of software gets written without getting to the root of the problem and thus fail to materialise any value.

Large projects fail when creation comes first and planning comes never. Even after you have complete knowledge of the problem, solutions may have challenges which require addressing upfront, addressing them when you are midway through a project is going to cost both time and money.

For some problems you can have an attitude of "we will work it out when we get there", but you don't want to have this attitude to all problems as when you get there you might be 95% of the way through your time and budget and you have crossed the point of no return and getting over this hurdle requires going over budget.

Use your planning to categorise problems into "it won't work unless we know this" and "we can figure this out when we get there".

Board Games

"Is it fun" is the most important question in board game design. Ideally you should find this out before you hire an artist, write storyboards or even print and design cards. In board game creation circles they advocate tearing up pieces of paper and scribbling out a prototype to answer this question. The same thing applies to software.

Determine if the question you are answering is the correct one and determine if your software will actual solve the problem, do this; Wizard of Oz style, before putting a shovel into the ground.

Assumptions

All projects start with assumptions. These are typically the first things that need to be addressed before you start coding. Sometimes asking someone if a solution exists or how something works can knock off some unknowns and you'll be in a better position to start than had you remained silent.

Design it Twice

A Philosophy of Software Design (2018)

John Ousterhout advocates that designing software once is not enough and when you are planning something out you should make multiple attempts at it before you move ahead. He's found this leads to better design.

Every project comes with its unknowns and challenges, we are probably never going to rebuild the exact same solution twice. So packing in as much learning as you can upfront about the challenges ahead will leave you at least the most prepared you could have been.

The beginning of project is the cheapest time to learn so we should maximise and front load learning. Learning midway through a project is an expensive way of learning. It's fine if we can afford these learnings but on big and critical projects you don't want to bring up the point that the work we've been doing for the last 6 months was all for nothing especially if we could have learnt this earlier on.

Break things down, explore and figure out how each piece of the project will work and that we are actually providing the right answer to the correct question, this is the only way to deliver on time and on budget.

If we jump straight into code our design strategy is hope.