What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
Optimization work never runs out. There is always another query to tune, another container to shrink, another cost line to trim. The practical problem is choosing which item to do next. A two-part test answers that: estimate how much a change saves, estimate how hard it is to fix, and do the high-savings, low-effort work first.
Why the list never ends
Any system that is running in production has more possible improvements than anyone will ever complete. Performance, cost, reliability and code quality each generate their own backlog, and new items appear faster than old ones close. Trying to finish the list is therefore the wrong goal. The useful question is whether the next item deserves attention before the others.
That is the premise of the article Every optimization list is infinite. One question sorts it, written by Vlad Z and published on DEV Community. The indexed copy shows a September posting date without a year, so the publication year is not confirmed here. The argument is short, and it is worth reading the original for its full wording.
Windows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallCrashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThe two questions that sort the backlog
The framework asks two questions about every candidate change:
#1 Best Overall
- How much does this save? Savings can be measured in money, latency, engineer hours, incidents avoided or any other unit the team already tracks. Pick one unit per comparison so the numbers are comparable.
- How hard is it to fix? Effort covers the work to build and ship the change, including the time to test it safely.
The author argues that no spreadsheet or formal model is required. A rough estimate on each axis is enough to decide the order of work, because the goal is triage rather than precise forecasting.
The four combinations
Placing each item on the two axes produces four groups. The author’s guidance for each is summarized below.
| Savings | Effort | Category | Author’s guidance |
|---|---|---|---|
| High | Low | Start here | Meaningful impact for comparatively little work. Do these first. |
| High | High | Plan carefully | Architectural or replacement work can be worthwhile. Plan and test it after the easier wins. |
| Low | Low | Cleanup for later | Worth doing once the team has breathing room. |
| Low | High | Trap | Technical interest or elegance does not make a weak return worth prioritizing. |
The trap: high effort, low savings
The article’s main warning is the bottom-right cell. Engineers are often drawn to large refactors or rewrites because they are interesting, and that appeal can hide a small return. The author’s anecdote makes the point: an engineer who spends three weeks on an optimization that saves $200 a month has, by this test, chosen poorly. The figures come from the author’s own experience and should be read as an illustration of the pattern, not as a typical result or a measured finding.
Recommended Free Tools
Running the test on your own backlog
A team can apply the framework in one planning session. The steps below are a practical sequence, not a procedure the article prescribes.
- List every candidate change in one place, such as the team’s issue tracker, with a one-line description of the change.
- Choose a single savings unit for the whole list, for example monthly cloud spend in dollars or p95 latency in milliseconds. Mixing units makes the sorting meaningless.
- Estimate the savings for each item in that unit, using the best current data you have. Write down the basis for each estimate.
- Estimate the effort for each item in days or engineer-weeks, including testing and rollout.
- Mark each item high or low on both axes using a cut-off the team agrees on in advance, then place it in one of the four groups.
- Work the groups in order: high savings with low effort, then high savings with high effort, then the low-savings groups as capacity allows. Drop or defer the low-savings, high-effort items unless a separate reason supports them.
Illustration only: suppose a team finds that caching one API response cuts a monthly bill by a few hundred dollars and takes an afternoon, while migrating a data store cuts the same bill by a similar amount over several months. The first lands in the start-here group. The migration may still be worth doing, but it moves into the planning group and waits its turn.
Where the framework stops
The two-axis test is deliberately simple, and that simplicity has limits. The article foregrounds savings and effort. It does not give a formal risk-adjusted model, and it does not account for dependencies, deadlines, security exposure or strategic value. Teams can and should weigh those factors, but they fall outside the article’s stated two questions.
The recommendations are the author’s, not a validated universal rule. Savings estimates are often rough, and effort is often underestimated. When two items look close on both axes, the framework does not decide between them, and the team must use its own judgment.
Summary of the approach
Keep one list, measure savings and effort in consistent units, and work through the groups in order. Start with high savings and low effort. Treat large, low-return projects with suspicion, however appealing they are to build.
The article is available on DEV Community under the title Every optimization list is infinite. One question sorts it, by Vlad Z.
Quick Recap
“
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
