general updates.

demo_testing/10x_engineers.py

@@ -0,0 +1,52 @@
+import unify
+unify.activate("10x_engineers")
+import random
+from datetime import datetime, timedelta
+
+num_employees = 20
+ages = [
+    random.randint(20, 50)
+    for _ in range(num_employees)
+]
+catchphrases = random.choices(
+    [
+        "hello... friend",
+        "hell no",
+        "did you ask o3?",
+        "will do it tomorrow",
+        "ask the intern",
+    ],
+    k=num_employees,
+)
+last_logins = [
+    (datetime.now() - timedelta(
+        days=random.randint(0, 5)
+    )).isoformat()
+    for _ in range(num_employees)
+]
+open_task_progress = [
+    {
+        task: random.random()
+        for task in random.sample(
+            [
+                "add NextJS loader",
+                "SQL migration",
+                "refactor life",
+            ],
+            random.randint(0, 3),
+        )
+    }
+    for _ in range(num_employees)
+]
+
+for age, catchphrase, last_login, otp in zip(
+    ages, catchphrases, last_logins, open_task_progress
+):
+    unify.log(
+        age=age,
+        how_10x=random.random()*10,
+        catchphrase=catchphrase,
+        last_login=last_login,
+        open_task_progress=otp,
+        will_to_live=random.choice([True, False]),
+    )

interfaces/tables.mdx

@@ -103,7 +103,7 @@ Rows are sorted in the order of the column sorts. For example, sorting by column
 
 <img class="dark-light" width="100%" src="https://raw.githubusercontent.com/unifyai/unifyai.github.io/refs/heads/main/img/externally_linked/sorting_dark.gif"/>
 
-### With Grouping [Coming Soon! 🚧]
+### With Grouping [🚧]
 
 When grouping is applied, the contents of each group are sorted as usual.
 However, the groups themselves are *also* sorted based on the reduction metric that is set in the bottom left.
@@ -119,7 +119,7 @@ So far, everything we've considered has been about *viewing* data.
 Filtering enables you to *select* a subset of the data that you want to view.
 Each column type has a different set of filter options.
 
-Let's consider the following data for self-proclaimed "10x" engineers:
+Let's consider the following data for self-proclaimed "10x" engineers on the team:
 
 ```python
 num_employees = 20
@@ -192,7 +192,7 @@ Everything else is column specific.
 
 - `==`, `!=`, `>`, `>=`, `<`, `<=`: standard comparison operators
 
-GIF
+<img class="dark-light" width="100%" src="https://raw.githubusercontent.com/unifyai/unifyai.github.io/refs/heads/main/img/externally_linked/table_numeric_filtering_dark.gif"/>
 
 ### DateTime
 
@@ -216,15 +216,15 @@ GIF
 
 GIF
 
-### Groups [Coming Soon 🚧]
+### Groups [🚧]
 
 Groups can also be directly filtered. This is useful if you want to select a specific subset of experiments, for example.
 The group filtering is applied *independently* to the standard column-wise filtering.
 If you want to propagate the group filters to the columns filters, you can simply press "propagate" in the group filter dropdown.
 
 {/* GIF */}
 
-### Global [Coming Soon 🚧]
+### Global [🚧]
 
 Filters at the column level apply the filter directly to that column, and each column filter is effectivrely stitched together with an `AND` operator.
 However, you can write your own expressive filter expressions by the global filter, using basic Python syntax, using the column names as variable names.
@@ -265,8 +265,7 @@ Filtering, grouping, and sorting all work on derived columns, just like any othe
 
 GIF
 
-
-## Joins [Coming Soon 🚧]
+## Joins [🚧]
 
 We will soon support creating new tables by joining existing ones 🔗