Find related topics. | Discover entities. | See connections. | Build a topical map.
Building topical map: Starting…
SlickEdit: History, Features & Overview
SlickEdit, previously known as Visual SlickEdit, is a cross-platform commercial source code editor, text editor, and Integrated Development Environment developed by SlickEdit, Inc. SlickEdit has integrated debuggers for GNU C/C++, Java, WinDbg, Clang C/C++ LLDB, Groovy, Google Go, Python, Perl, Ruby, Scala, PHP, Xcode, and Android JVM/NDK. SlickEdit…
Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.
More settings
SlickEdit topic overview
The analysis highlights History, Features and Overview as prominent areas in the source structure around SlickEdit.
Related topics
49
Source areas
5
Connected nodes
54
Extracted relationships
65
Concept neighborhoods
28
Bridge connections
54
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Features · 19 topics
Overview · 17 topics
History · 10 topics
Reception · 2 topics
Related products · 1 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Developers
SlickEdit, Inc.
License
Proprietary
Operating system
Linux, macOS, Windows
Size
~642 MB
Stable release
2024 (2024)
Type
Source code editor
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How SlickEdit connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around SlickEdit shows recurring relationship patterns in the source. For example, SlickEdit → AIX, AWK, Google Go, Groovy, HP-UX, HTML, INI, Java, JavaScript, Like, Linux, MacOS, Makefiles, Objective-C, PHP, Solaris, Some, SPARC, Visual Studio, Windows Another extracted example is SlickEdit → Ayers, Inc, ISSN, June, Larry, Linux Journal, Lock-gray-alt-2, Lock-green, Lock-red-alt-2, Seattle, Specialized Systems Consultants, USA, Visual SlickEdit, WA, Wikisource-logo. Use these groups to spot repeated connection types before inspecting the individual relationships.
SlickEdit
Top relations
related to Features· 22
SlickEdit → AIX, AWK, Google Go, Groovy, HP-UX, HTML, INI, Java, JavaScript, Like, Linux, MacOS, Makefiles, Objective-C, PHP, Solaris, Some, SPARC, Visual Studio, Windows
related to Further reading· 15
SlickEdit → Ayers, Inc, ISSN, June, Larry, Linux Journal, Lock-gray-alt-2, Lock-green, Lock-red-alt-2, Seattle, Specialized Systems Consultants, USA, Visual SlickEdit, WA, Wikisource-logo
related to history· 13
SlickEdit → At, CEO, Clark Maurer, DOS, He, IBM, IBM's Watson, MicroEdge Inc, MS-DOS, OS/2, SlickEdit Inc, SlickEdit's, This
related to Reception· 5
SlickEdit → Game Developer, In, Linux Journal, Shawn Powers, Tom Plunket
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 65 structured relationships around SlickEdit. Examples in this analysis include SlickEdit → Developers → SlickEdit, Inc. and SlickEdit → License → Proprietary. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
SlickEdit
Developers
SlickEdit, Inc.
1.00
infobox
SlickEdit
License
Proprietary
1.00
infobox
SlickEdit
Operating system
Linux, macOS, Windows
1.00
infobox
SlickEdit
Size
~642 MB
1.00
infobox
SlickEdit
Stable release
2024 (2024)
1.00
infobox
SlickEdit
Type
Source code editor
1.00
infobox
SlickEdit
Website
www.slickedit.com
1.00
infobox
SlickEdit
related to Features
Windows
0.60
section
SlickEdit
related to Features
MacOS
0.60
section
SlickEdit
related to Features
Linux
0.60
section
SlickEdit
related to Features
AIX
0.60
section
SlickEdit
related to Features
HP-UX
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around SlickEdit bring nearby vocabulary together. In this analysis, examples include Go, Google and Groovy. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
SlickEdit
Go
Google
Groovy
Java
Php
Code
Editor
Android
Development
Html
Supports
User
slickedit
Go
Google
Groovy
Java
Php
Code
Editor
Android
Development
Html
Supports
User
integrated development environment
Gnu
Jvm
Known
Ndk
Perl
Python
Ruby
Text
Windbg
Android
Development
Integrated
android software development
Gnu
Jvm
Ndk
Perl
Python
Ruby
Windbg
Xcode
Go
Google
Groovy
Java
source code editor
Source
Known
Editor
Macos
Operating
Text
Windows
Systems
Visual
Slickedit
Integrated
Linux
google go
Go
Google
Groovy
Java
Php
Jvm
Ndk
Perl
Python
Ruby
Windbg
Android
groovy
Go
Google
Java
Php
Jvm
Ndk
Perl
Python
Ruby
Windbg
Android
Html
java
Go
Google
Groovy
Php
Jvm
Ndk
Perl
Python
Ruby
Windbg
Android
Html
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For SlickEdit, one of the stronger structural bridges in this analysis connects SlickEdit with Features. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
SlickEdit
Nodes55
Edges54
Triples65
Avg. degree1.96
Density0.036364
Components1
Source & methodology
TTTA analyzes the structure around SlickEdit to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Features & Overview, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
