1 files changed, 7 insertions, 34 deletions

diff --git a/_log/neo4j-a-star-search.md b/_log/neo4j-a-star-search.md
index 9f4a7d2..3105662 100644
--- a/_log/neo4j-a-star-search.md
+++ b/_log/neo4j-a-star-search.md
@@ -1,25 +1,15 @@
 ---
-title: That time I forked Neo4J to implement A* search
+title: Neo4J contribution
 date: 2018-03-06
 layout: post
 ---
 
 Written in 2026, backdated to 2018. 
 
-Our vessel tracking system hit a wall. We needed shortest paths through a
-13,000-vertex graph. But the system slowed to a crawl after 4,000.  Kristian,
-our CTO, suggested replacing Dijkstra's search with the faster A* search—an
-algorithm I had encountered as an undergraduate.
+Before v3.4.0, Neo4J shipped with Dijkstra's shortest path search. The
+algorithm was too slow for our marine vessel tracking application.
 
-The database we used, Neo4J, employed a plug-in system for graph algorithms—a
-mature open-source project developed by experts. Forking it for our unassuming
-start-up was bold. Often, application developers are reluctant to step into
-lower layers.
-
-It took me a week to set up the toolchain, build the project, and run the
-tests. I reviewed Dijkstra's implementation, separating logic from
-infrastructure. I then refactored the algorithm to use the haversine function
-to steer the search:
+Forked and added A* search. Used the haversine function to steer the search:
 
 ```
 private double computeHeuristic(
@@ -43,8 +33,7 @@ private double computeHeuristic(
 }
 ```
 
-And revised the core search loop to update the costs when a better path is
-found:
+Core search loop updates costs when a better path is found:
 
 ```
 private void updateCosts(
@@ -59,25 +48,9 @@ private void updateCosts(
 }
 ```
 
-I remember celebrating a 300x speed up, and comfortably scaling to the target
-13,000. 
-
-I was even more thrilled when the upstream project accepted my changes, and
-invited me to contribute. In hindsight, I'm a little surprised that my changes
-were accepted in their original form. The heuristic was tightly coupled to our
-use case. Decoupling it would require significant refactoring and interface
-changes.
-
-It was, for a time, the best outcome I could have imagined.
-
-My celebration of success, however, was short-lived. In a matter of months, as
-the database grew larger and denser—to 16,000 vertices, if memory serves—A*
-search too began to struggle. We fell back on a far less elegant PostgreSQL
-cache. I learnt my first bitter lesson about optimization: dumb caches beat
-clever algorithms. I briefly wondered if I should have stayed with pure
-mathematics.
+Upstreamed the changes.
 
-Source code: <a
+GitHub release: <a
 href="https://github.com/neo4j-contrib/neo4j-graph-algorithms/releases/tag/3.4.0.0"
 class="external" target="_blank" rel="noopener noreferrer">Neo4J v3.4.0</a> |
 <a