Curated artifact mapping
Resolve source-native packages, repositories, and providers to the relevant product surface, company, category, and competitive market.
03 / Product adoption
Developer activity data for software investment research.
Track product adoption, competitive mindshare, and programmatic ecosystem activity through source-native artifacts mapped to software products, companies, and markets.
The SoftwareIQ difference
Turn public ecosystem activity into research evidence.
Raw download counters do not identify the product, company, competitive market, or economic meaning of the activity. SoftwareIQ maintains those relationships and the processing required to compare them.
Dependency-aware analysis
Separate independently adopted packages from shared components so mechanical dependency downloads do not become false product or company growth.
Comparable, controlled series
Distinguish interval flows from cumulative counters, protect continuity around resets and collection changes, and retain versioned transformations.
Product-surface analysis · Amplitude
Are newer products gaining adoption within an established technology portfolio?
Separate portfolio mix from underlying scale.
Package-level histories can show both how observed activity is distributed across a product portfolio and the absolute volume behind that mix. The Amplitude exhibit provides a 100% activity-mix view for Analytics, Experiment, and Session Replay, then lets the user switch to trailing eight-week download totals.
SoftwareIQ selects independently installable, product-facing artifacts and removes shared dependencies throughout. The mix view makes portfolio composition comparable over time; the downloads view shows whether a share change reflects product momentum or movement in the broader baseline. Neither view treats package downloads as common users, attach rate, customers, or revenue.
Research outputProduct-surface monitoring, new-offering diligence, and evidence for business-mix research.
Amplitude · Portfolio adoption
Product activity mix
Selected product-facing npm activity · each period totals 100%
ExperimentSession ReplayAnalytics
Aug 26 · 100%Experiment 13.8%Session Replay 22.3%Analytics 63.9%
Category analysis · AI coding agents
How is developer attention shifting inside a defined competitive cohort?
Measure share changes without letting a source artifact become the signal.
A maintained cohort converts comparable package activity into a repeatable view of competitive developer mindshare. Claude Code, Codex, and Gemini CLI provide a real example of how the same framework can track category formation and share shifts over time.
The analysis uses an eight-week run rate and retains the documented adjustment to two isolated Codex observations. Raw values, effective values, cohort membership, and methodology stay attached so a collection anomaly cannot silently dominate the market view.
Research outputCompetitive share monitoring, category formation analysis, and product-momentum diligence.
AI coding agents · Defined npm cohort
AI coding activity share
Normalized eight-week run rate
Claude CodeCodexGemini CLI
Aug 26 · 100%Claude Code 48.3%Codex 50.1%Gemini CLI 1.6%
Technology mindshare · MongoDB and PostgreSQL
Is developer activity moving between competing technology choices?
Define the cohort before calculating the share.
Competitive activity is most defensible when the artifacts perform a similar role and come from the same ecosystem. The exhibit compares the MongoDB and PostgreSQL Node.js client packages using both cohort share and trailing eight-week download totals.
SoftwareIQ aligns dates, applies the same trailing window, and fixes the denominator to the named two-package cohort. The result measures observable Node.js developer activity and mindshare—not database installed base or commercial market share.
Research outputTechnology-selection research, competitive momentum tracking, and ecosystem-specific share analysis.
Operational databases · Node.js
Developer activity share and mindshare
MongoDB and PostgreSQL client packages only
MongoDBPostgreSQL
Aug 26 · 100%MongoDB 26.9%PostgreSQL 73.1%
Programmatic adoption · Application software
Is usage extending beyond the visible application interface?
Track the developer surface around application software.
SDK and client-library activity can show how software platforms are being integrated, automated, and extended outside their primary user interface. The exhibit follows official monday.com, Smartsheet, and ServiceNow npm artifacts as indexed series.
A common npm source measure and identical trailing window improve comparability without implying that raw package volumes represent the same user population. Maintained artifact-to-company mapping turns scattered package activity into a consistent view of programmatic ecosystem development.
Research outputAPI ecosystem monitoring, integration-platform diligence, and evidence of usage beyond seats and UI workflows.
Application software · Official SDKs
Programmatic activity beyond the application UI
Comparable npm series indexed for directional analysis
monday.comSmartsheetServiceNow
Aug 26monday.com 365.9Smartsheet 137.2ServiceNow 704.9
Index: first complete eight-week window = 100762
monday.commonday-sdk-js · npmSmartsheetsmartsheet · npmServiceNow@servicenow/sdk · npm
Methodology
From source-native activity to comparable product evidence.
Observe
Collect package, container, and provider activity on a daily operating schedule while preserving the source-native artifact and metric type.
Map
Associate artifacts with maintained projects, product surfaces, companies, categories, and competitive cohorts without erasing source identity.
Normalize
Apply metric-appropriate continuity controls, trailing windows, dependency rules, and versioned adjustments before comparison.
Research delivery
Use product and market context with every observation.
Company research
Follow individual products and developer surfaces as additional evidence alongside operating results and hiring.
Competitive intelligence
Build defined market cohorts and monitor changes in activity share, growth, and product participation.
Data science
Construct controlled daily or weekly features using stable artifact, project, company, ecosystem, and taxonomy context.
Find the signal earlier