CLN v1.4 Engine · Feb 2026
Semantic Filtering
In traditional notes, finding info means scanning. In CLN v1.4,
it means querying. This turns your text files into a semantic search engine that
understands the difference between a task, a risk, and a definition.
01/Breaking Linear Search
- Prose
Search (Ctrl+F): String matches only. No
context. "O(N)" complexity.
+ CLN
Query: Semantic structures. Finds
! + #project. "O(1)" lookup.
FIND ! AND #critical
. Task: Fix login (Skipped)
! Risk: Database leak #critical
? Question: When is deadline? (Skipped)
! Risk: Server overload #critical
02/Building the Index
Technical
How CLN v1.4 transforms text into a database:
. Step 1:
Parsing. Indentation and symbols are converted
into Node objects.
. Step 2:
Inverted Index. Maps are built for every
operator (
!, ?, =) and
tag (#).. Step 3:
Intersection. Queries find the overlap between
these maps.
03/Example Queries
|
FIND . AND #todo → Returns all actionable
tasks.
|
FIND ! AND #legal → Returns all legal risks.
|
FIND = AND #concept → Returns your personal
dictionary.
04The Cognitive
Payoff
+ Zero
Retrieval Cost: Knowledge is never "lost" in
deep archives.
+
Focus: Get only the information needed for the
current context.
*
Note: Semantic filtering is what makes a
digital garden functional.
*
Self-Test: Can semantic filtering work on
unformatted prose? || No. It
requires the structural metadata provided by CLN
operators and tags. ||