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Engineers at early-stage startups operate in a high-pressure environment. With limited resources and constantly shifting priorities, their cognitive load is immense. This burden can hinder productivity, innovation, and engineer satisfaction.

Startups must make minimizing cognitive load a priority.

My previous article on Hackernoon discussed how achieving a flow state is critical for managing engineers’ immense cognitive load. I have argued that promoting a flow state reduces the cognitive burden, empowering engineers to leverage their mental capabilities for innovation and productivity fully.

The high-pressure startup environment strains mental faculties with ill-defined problems, constant context switching, and a lack of structure.

The Startup Drain on Engineering Minds

Engineers working for startups are thrown into a pot of uncertainty and complexity. Figuring out problems that aren’t well-defined and have little structure is hard on the mind. Having to deal with many unknowns while the stakes are high can be stressful.

Priorities in business change quickly, so engineers have to constantly switch contexts. Processes and infrastructure that aren’t set up well also add to cognitive load. Every choice feels big when there isn’t much to hold you up.

Cognitive Load’s Drag on Developer Velocity

Studies show that cognitive load directly cuts down on the amount of working memory that can be used for tasks. When engineers push their minds to the limit, their work suffers.

Fighting with bad tools and not knowing what you want to achieve is frustrating. When you have a lot to think about, you become exhausted quickly.

Startups can’t afford for engineers not to do as well as they could. For sustained velocity, you must be able to handle your mental load.

Cognitive Load in Deciphering Code

Understanding tangled codebases also taxes engineers’ brains. Projects with intertwined dependencies, inconsistent naming, overly clever abstractions, and ambiguous intent impose steep learning curves.

Startups should champion coding best practices focused on comprehension. Well-named variables, small focused functions, separation of concerns, and eliminating duplication avoid overloading working memory. Comments elucidate rationale and high-level architecture. Graspable code enables new hires to ramp up faster.

Code reviews play a vital role in pinpointing areas for improvement. Reviewers can call out convoluted logic and suggest simplifications. Regular refactoring keeps code clarity a priority as the system grows. Legible code complements controlled cognitive load.

Cognitive Load on Engineering Teams

In addition to individual cognitive load, teams deal with collective burden. Too many hand-offs between groups, unclear ownership, and clunky coordination accumulate. Engineers waste mental energy navigating organizational disorder.

Effective team topologies, like those proposed by Matthew Skelton and Manuel Pais, streamline work across groups and minimize coordination overhead. Platform teams provide self-service access to foundational needs. Feature teams own vertical slices with autonomy. Enabling teams offer specialized expertise. With unambiguous domains, teams can focus cognitive effort on delivering value.

Removing Distractions and Uncertainty

Leaders need to eliminate distractions and fog that engineers don’t need. Clean up your physical and digital spaces. Workflows and communication should be improved.

Set clear goals that focus on what the customer wants. Cut down on meetings and status checks that aren’t necessary. Allow more time to focus to get into flow states.

Simplify processes so that they only do what needs to be done. Automation can get rid of low-value tasks that are done over and over. Give engineers the tools they need to find and get rid of distractions.

Starting with Intuitive Tooling Foundation

Choosing simple, well-known tools and architectures cuts down on cognitive load right away. Build on proven patterns, and don’t redo the basics.

Standardized dev environments and cloud-based tools like GitHub Codespaces, Coder, Gitpod, Codeanywhere, Daytona, or Replit give developers environments that are ready to code right out of the box. This keeps you from spending mental energy setting up and fixing environments.

Use living documentation to help people understand each other. Optimize the sharing of knowledge. Use domains instead of layers to modularize.

Choose frameworks with opinions over flexibility. Take away choices. Give enough structure to reduce load, but don’t make it too hard.

Adding Complexity Intentionally

Complexity should be added purposely, not preemptively. Wait for validated needs before introducing new tools, architecture intricacy, or processes.

Engineers must drive adding complexity based on hard-won experience, not assumptions. Resist founders or investors insisting on unnecessary technology.

Leverage proof-of-concept to trial integrations with minimal investment. Measure cognitive load qualitatively.

Evaluating Cognitive Load

While cognitive load can appear vague, researchers have crafted ways to measure it. One widely used scale is the NASA Task Load Index (TLX), evaluating mental, physical, and temporal demands, performance, effort, and frustration.

Startups should leverage tools like the NASA TLX to quantify cognitive load over time. Log ratings for engineers during different stages of product evolution. Identify spikes that may signal overload. Track averages to expose pain points in workflows. Comparing load before and after process changes demonstrates impact.

Measuring and tuning metrics like time-to-productivity and satisfaction should be continuous. Quantified metrics complement qualitative feedback from engineers. Together they furnish actionable insights on refining the developer experience.

Fine-tuning Developer Experience Longitudinally

As the startup grows up, measure the engineering cognitive load at each milestone. Capture load data before and after substantial tooling, process, or architecture changes.

Watch for load indicators like lower velocity, elevated bug rates, frustration, and churn risk. Regularly survey engineer happiness.

Give engineers autonomy and mastery to shape their own experience. Remove unnecessary mental encumbrance to enable productivity.

The pressures of early-stage startups impose intense cognitive demands on engineers. Removing unnecessary mental effort through deliberate processes, simplified workflows, and intuitive tools unlocks greater productivity, innovation velocity, and job fulfillment across the startup journey.

5 Tactics for Lessening Cognitive Load

Here are 5 key ways startups can reduce the cognitive load for engineers:

  1. Eliminate distractions by minimizing interruptions and decluttering environments.
  2. Start with intuitive foundations via familiar technologies and straightforward architectures.
  3. Add complexity deliberately once validated, avoiding premature overcomplication.
  4. Quantify cognitive load at different stages to identify pain points.
  5. Continuously optimize developer experience by providing productivity tools and resources.

Minimizing unnecessary mental effort should be a top priority for early-stage startups.

This allows engineers to focus their cognitive resources on delivering maximum value during the critical formative stages.

This article was originally published by Nikola Balic on Hackernoon.


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