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A profiler is a development tool that measures where a program spends its time and resources, so you can find the code responsible for slowness instead of guessing. A sampling profiler does this by periodically capturing the call stack of a running program rather than modifying the code itself. This makes it useful when you want a quick, low-overhead picture of hot spots in an application — for example, a Delphi program — without recompiling with instrumentation. DelphiTools.info hosts SamplingProfiler, described as a free code profiler for Delphi, which is a concrete example of this category.

What a profiler actually measures

Profiling answers questions like:

• Which functions or methods consume the most CPU time?
• Which call paths lead into those expensive functions?
• Where does execution spend time that you did not expect?

The output is typically a ranked list of routines by cost, often paired with a call graph or call tree showing how those routines were reached. The goal is to turn "the program feels slow" into "this specific routine, called from this path, dominates execution time."

Sampling vs. instrumenting profilers

There are two common approaches, and they trade off differently.

Because a sampling profiler observes rather than instruments, it can be attached to a running program and left on for a while. The longer it samples, the more stable the picture becomes.

How a sampling profiler finds bottlenecks

The mechanism is straightforward:

1. A timer or OS interrupt fires at a set interval. Each firing is one sample.
2. At each sample, the profiler walks the current call stack and records which routine is executing and which routines called it.
3. Samples accumulate into a statistical distribution. A routine that appears in many samples is, by inference, consuming a large share of execution time.
4. The profiler aggregates results into a ranked list of hot routines and the call paths that reach them.

The key insight is that you do not need to measure every call. If a routine is genuinely expensive, it will be caught by enough random samples to stand out. A routine that appears in 30% of samples is a strong candidate for optimization; one that appears in 0.1% is probably not worth your time.

A concrete example

Suppose a Delphi application feels sluggish when loading a large dataset. You run SamplingProfiler against it and let it collect samples during the slow operation. The results show that a sorting routine deep in a utility unit accounts for most of the samples, called repeatedly from a data-loading path. That tells you where to focus: the sorting algorithm or the number of times it is invoked — not the database layer you initially suspected.

What sampling profilers reveal and what they miss

They reveal well:

• Dominant hot spots — routines that consume a large fraction of total time.
• Call paths — how execution reaches those hot spots.
• Relative cost — which of several candidates matters most.

They are weaker at:

• Very short or rarely-called routines, which may not appear in enough samples.
• Exact call counts — sampling gives statistical estimates, not precise numbers.
• Very fast operations — if a routine runs for a tiny fraction of total time, sampling may not distinguish it from noise.

This is why sample duration matters. A short profiling run may produce a noisy or misleading ranking; a longer run over a representative workload gives a more reliable picture.

Typical use cases

• Finding performance bottlenecks in an existing application before deciding what to optimize.
• Validating an optimization by comparing profiles before and after a change.
• Understanding unfamiliar code by seeing which paths actually execute at runtime.
• Investigating a specific slow operation, such as startup, a batch job, or a UI action.

A concrete tool: Delphi SamplingProfiler

DelphiTools.info is the home of SamplingProfiler, described as a free code profiler for Delphi. For Delphi developers, this is a direct example of the sampling approach: you profile a running application, collect samples, and get a ranked view of where time is spent. The site also hosts DWScript (Delphi Web Script) and other Pascal developer tools, so it is a relevant starting point if you work in that ecosystem.

If you are choosing a profiler, the practical questions are: does it support your language and runtime, does it require code changes, and does its sampling or instrumentation model match what you need — quick hot-spot discovery versus exact call accounting.

Developer marketing is the practice of reaching software developers where they already learn and make tooling decisions—technical content, creator channels, open source, and community—and turning that attention into product signups. It differs from traditional marketing because the audience evaluates tools on technical merit, tries before trusting, and ignores polished ad copy. It's the right approach when your product is an AI or DevTools offering and your bottleneck is distribution rather than product quality.

Why distribution, not product, is usually the bottleneck

Most AI and DevTools teams are founded by people who can build. The failure mode described by DevTools Academy is "Great Product, Broken Distribution"—the tool works, but the people who would use it never hear about it in a context they trust.

Developers don't convert on impressions. They convert after seeing a credible peer use the tool, reading a technical walkthrough, or watching someone solve a real problem with it. That means the marketing job is not persuasion—it's placement in front of the right technical audience with proof attached.

How developer marketing differs from traditional marketing

The practical consequence: budget spent on speculative reach tends to underperform compared to a systematic creator partnership framework, which is the shift DevTools Academy describes making for Orchids.

The creator-led distribution model

Creator-led developer marketing means partnering with technical creators who already have the audience you want, rather than buying reach through ads. DevTools Academy runs this across YouTube, X, LinkedIn, Instagram, TikTok, Reddit, UGC, and technical newsletters.

The mechanics that make it work:

• Credibility transfer. A creator who understands the tooling explains it in their own voice, so the recommendation carries weight an ad cannot.
• Depth over volume. The goal is "developers, not just impressions"—a smaller, engaged technical audience beats a large passive one.
• Repeatability. A creator program built once can produce consistent output quarter after quarter instead of one-off spikes.

For Stream, this approach was credited with 6,600+ sign-ups across every quarter of FY-2025, with a program built from the ground up spanning 60+ creators across YouTube, X, LinkedIn, open source, and newsletters.

What execution looks like end to end

DevTools Academy frames its work as "end-to-end developer GTM execution" and "numbers, not narratives." A workable sequence for a team running this themselves:

1. Identify where your developers already are. Pick the two or three channels where your target users actually spend time—not all of them at once.
2. Recruit creators with technical credibility. Prioritize people who can use the product and explain it, over reach alone.
3. Give them something real to show. A working integration, a benchmark, or a solved problem produces better content than a feature list.
4. Let the content run in the creator's format. Tutorials and demos outperform scripted promotion on technical channels.
5. Measure signups and engagement, not just reach. Track comments, reposts, and sign-ups per channel so you can reallocate.

How to measure whether it's working

The evidence from DevTools Academy's case studies points to a measurement set that goes beyond impressions:

• Signups — the primary outcome. Stream: 6,600+ sign-ups across FY-2025 quarters.
• Engagement depth — comments and reposts as a signal of real technical interest. Oumi's LinkedIn launch produced 631+ comments and 269+ reposts on day one.
• Impressions and engagements at scale — Orchids reached 1.1M impressions and 10,000+ engagements in a single quarter.
• Consistency over time — growth that repeats every quarter, not a single viral moment.

Oumi's founder described the value as watching "the comment section turn into a deep-dive discussion on day one"—that discussion is the social proof that drives the next wave of signups.

When this approach fits

Creator-led developer marketing is a strong fit when:

• Your product is an AI or DevTools offering aimed at working developers.
• The product is good but under-distributed.
• You can support a program over multiple quarters rather than a single campaign.
• You're willing to trade speculative spend for a structured creator framework.

It's a weaker fit if your buyers are non-technical, if you have no working product to demonstrate, or if you need results within days rather than a quarter.

Where to start

If you want to assess your current position before committing, DevTools Academy offers a "See how your current GTM stacks up" entry point alongside strategy calls and published case studies. The useful first step for most teams is auditing which channels already produce signups, then concentrating creator partnerships there instead of spreading across every platform at once.

Delphi is an Object Pascal-based language and integrated development environment (IDE) used to build native applications, historically for Windows and now for cross-platform targets. If you write Delphi code, the practical question is usually not "what is Delphi" but "what do I use to profile it, script it, or measure test coverage?" The DelphiTools.info project is one place that answers that: it hosts a free sampling profiler, the DWScript (Delphi Web Script) engine, and related Pascal developer tools, plus optimization and coding tips.

What Delphi actually is

Delphi combines a compiled language (Object Pascal) with a visual IDE. You write Pascal source, the compiler produces native machine code, and the IDE handles forms, components, and debugging. That compiled-native model is why performance tooling matters: there is no interpreter or VM sitting between your code and the CPU, so bottlenecks show up as real CPU time in your own routines.

Tool categories in the Delphi ecosystem

How a sampling profiler helps

A sampling profiler interrupts execution at intervals and records the current call stack, then aggregates the samples. The routines that appear most often are where your program spends its time. This differs from instrumenting every function: sampling adds little overhead and does not require you to modify source, so you can profile a realistic build.

For example, if a Delphi app feels slow when loading a large dataset, a sampling profile will show whether the time sits in parsing, in string handling, or in a database call — and you optimize the routine that actually dominates rather than guessing.

What DWScript offers

DWScript is an embeddable scripting engine that brings a Pascal-like language into a Delphi host application. Instead of recompiling the whole program to change behavior, you expose parts of your app to scripts and let users or your own team write logic in a familiar Pascal syntax. It is aimed at developers who want scripting inside a compiled Delphi product without adopting an unrelated language.

Code coverage and coverage-gap analysis

DelphiCodeCoverage measures which parts of your code your tests actually run. The project's fork added a "Code Coverage Gaps" .ccg format designed to be consumed by AI tooling, which the author notes can reduce token usage when you ask an AI to improve unit test coverage. The practical use: run your tests, generate coverage data, and identify untested paths — then target new tests at those gaps rather than at code already covered.

Choosing what to reach for

• Performance problem in a compiled build → start with a sampling profiler.
• Need user- or config-driven logic without recompiling → consider an embeddable engine like DWScript.
• Tests exist but you don't know what they miss → use a code coverage tool and review the gaps.

Each of these solves a different problem, and they are complementary: profile to find slow code, script to make behavior flexible, and measure coverage to keep tests honest.

SamplingProfiler is a free code profiler for Delphi and other Pascal toolchains, distributed from DelphiTools.info alongside DWScript (Delphi Web Script) and related Pascal developer tools. It finds bottlenecks by statistical sampling: it periodically captures the call stack of a running application and aggregates those snapshots into per-routine sample counts, so the routines that appear most often are the ones consuming the most execution time. Use it when you have a compiled Delphi application that feels slow and you want to know where the time goes without adding timing code to every routine.

What SamplingProfiler is

DelphiTools.info describes itself as the home of SamplingProfiler, "a free code profiler for Delphi, DWScript (Delphi Web Script) and other Pascal developer tools," alongside optimization and coding tips. The site also hosts DWScript and other Pascal tools, and its news covers adjacent developer utilities such as a fork of DelphiCodeCoverage with AI-oriented reporting formats.

Two consequences follow from that positioning:

• It is a sampling profiler, not an instrumenting one. You do not modify your source to insert timers or counters.
• It targets Delphi and Pascal code, including scripted Pascal through DWScript, rather than being a general-purpose profiler for arbitrary languages.

How statistical sampling locates hot code

A sampling profiler does not measure every call. Instead it interrupts the process at intervals, walks the current call stack, and records which routine is on top (and often the full chain of callers). After thousands of samples, the distribution of samples across routines approximates the distribution of time across routines.

The practical implications:

The routine with the largest share of samples is your first bottleneck candidate. If a routine is hot but only ever called from one place, the fix may belong in the caller; if it is called from many places, the routine itself is the target.

Using it to find bottlenecks

The general workflow, which applies to SamplingProfiler and to sampling profilers in this family:

1. Build with debug information. Compile the application so that routine names and line information are available. Without symbols, samples collapse into addresses and the profile is unreadable.
2. Reproduce the slow scenario. Start the application and drive it into the state you care about — load the large file, run the heavy calculation, open the slow form. Profiling an idle app tells you nothing.
3. Attach the profiler and start sampling. Point SamplingProfiler at the running process and begin a session. Let the scenario run long enough to accumulate a meaningful number of samples; a few seconds of activity is usually too few.
4. Stop and read the results. Sort by sample count or by self time. Identify the top routines and, using the call graph, trace which paths reach them.
5. Fix and re-measure. Change one thing, re-run the same scenario, and compare. Sampling noise means you should look for large, repeatable shifts rather than small deltas.

The expected result at each step: after step 3 you have a sample set; after step 4 you have a ranked list of routines and their callers; after step 5 you have evidence that your change actually moved the bottleneck rather than just moving it elsewhere.

Common pitfalls

• Missing debug symbols. The most common reason a profile looks like a wall of addresses. Rebuild with debug info before profiling.
• Optimization settings. Aggressive optimization can inline routines, so a hot routine may be attributed to its caller. Treat inlined results as a hint about the call site, not a precise per-routine measurement.
• Runs that are too short. Sampling needs volume. A scenario that finishes in a fraction of a second may produce too few samples to rank reliably; loop it or extend it.
• Profiling the wrong scenario. If the app is slow only under a specific input or after a specific sequence of actions, reproduce exactly that, or you will optimize code that was never the problem.
• Chasing the top entry blindly. A routine high in the list may be a dispatcher or framework call that is simply on every path. Look at self time and at whether the routine does real work.

Where it fits among Delphi tools

DelphiTools.info groups SamplingProfiler with DWScript and other Pascal developer tools, and its news stream covers related utilities such as the AI-optimized DelphiCodeCoverage report format. If your goal is coverage rather than timing, that is a different tool; if your goal is to know where execution time is spent in a running Delphi or Pascal application, SamplingProfiler is the one to reach for. The site does not state pricing on the material available here beyond describing the profiler as free, so treat any licensing or version-support question as something to confirm on the site itself.

Sampling in profiling means periodically capturing the call stack of a running program instead of instrumenting every function entry and exit. A sampling profiler interrupts the process at short intervals, records which function is executing at that instant, and aggregates those snapshots into a statistical picture of where time is spent. The result is a low-overhead estimate of hot spots that requires no source changes — at the cost of being approximate rather than exact.

How sampling differs from instrumenting profilers

An instrumenting profiler rewrites or hooks each function so it can count calls and measure durations precisely. That precision has a price: added overhead per call, distorted timing for very short functions, and often a need to rebuild the code with special flags.

A sampling profiler takes the opposite approach:

• Input: a running process (or a launched executable) plus a sampling interval, typically in the low milliseconds.
• Action: at each tick, the profiler walks the current call stack and records the frames.
• Output: counts and percentages per function, per module, or per call path.

Because it observes rather than modifies, sampling usually adds only a small, roughly constant overhead, and it can profile release builds that match what users actually run.

Why the numbers are statistical

Each sample is a snapshot. If a function appears in 30% of samples, the estimate is that roughly 30% of execution time was spent there. That estimate gets more reliable as the sample count grows, which is why longer runs and shorter intervals produce steadier results.

Two consequences follow:

• Short or rarely called routines can be missed if they never happen to be on the stack when a tick fires. Increase the run duration or lower the interval to catch them.
• Inlined code blurs attribution. If the compiler inlined a small function into its caller, samples land on the caller, not the original function. The same applies to code with no distinct stack frame.

Treat the output as a ranking of where time goes, not as an exact accounting.

Reading a sampling profiler's output

Most sampling profilers present the same core views:

The workflow is straightforward:

1. Run the target under the profiler and exercise the slow scenario.
2. Sort by self time and look at the top few functions.
3. For each, check the call tree to see who calls it and how often.
4. Change one thing, re-profile, and compare — the ranking should shift if the fix worked.

A function high in self time is doing work directly; a function high in total (inclusive) time but low in self time is mostly waiting on its callees, so look further down the tree.

A concrete tool: SamplingProfiler for Delphi/Pascal

DelphiTools.info is the home of SamplingProfiler, a free code profiler for Delphi, alongside DWScript and other Pascal developer tools. For Delphi and Pascal work it is the practical example of the workflow above: attach to or launch a build, collect samples while the application runs, then inspect the aggregated function and call-path data to locate bottlenecks. The same site publishes optimization and coding tips, which pair naturally with profiling results — you profile to find the hot function, then apply targeted optimization rather than guessing.

Trade-offs to keep in mind

• Low overhead, approximate results. Good for finding the dominant cost; not a substitute for exact call counts.
• Blind spots for tiny or inlined routines. Mitigate with longer runs, finer intervals, or targeted instrumentation if you need certainty.
• Needs a representative workload. Samples only reflect what you actually execute, so profile the scenario you care about.
• Debug vs. release builds. Sampling can profile optimized builds, which is usually what you want, but symbol information must be available to map addresses back to function names.

If you need exact per-call timing for a handful of functions, an instrumenting profiler is the better fit. If you need to know, quickly and cheaply, where a large Delphi or Pascal application spends its time, sampling is the right starting point.