Baloop
אפליקציה שמלווה מדריכי נוער בשטח
תקציר
מדריכות ומדריכים בחינוך בלתי פורמלי בוחרים בתפקיד מתוך תחושת שליחות ורצון להשפיע. אולם, מאז פרוץ מלחמת חרבות ברזל, רבים מהם מדווחים על פער הולך וגדל בין המוטיבציה האישית שלהם לבין בני נוער שאינם פנויים רגשית להשתתף בפעילויות. פער זה מוביל לתחושת תסכול ושחיקה.
מתוך רצון לתת מענה למצב זה פותחה Baloop – אפליקציה תומכת הדרכה דינמית, המבוססת על דיווחים מהשטח. כל מדריך יכול לעקוב בזמן אמת אחר ההתקדמות האישית שלו לפי היעדים האישיים שקבע, לזהות הצלחות ולהעצים את חווית ההדרכה. האפליקציה מספקת תובנות מותאמות אישית למדריך, ומפעילה מנגנוני תגמול חווייתיים כגון הענקת סמלים ותגים להכרה בהישגים, התראות חיוביות והצעות לפעולה בהתבסס על הצלחות קודמות.
תהליך פיתוח האפליקציה התבסס על ראיונות עומק עם מדריכי נוער, רכזים ואנשי מקצוע. לאחר ניתוח הנתונים, התנסו המרואיינים בהדמיות של האפליקציה, והמשובים שנאספו שימשו לשיפור התוצר. Baloop פועלת תוך הנגשת כלים לשיפור תחושת המסוגלות של מדריכות ומדריכים, במיוחד ברגעים שבהם החברה זקוקה להם יותר מתמיד.
פרויקט מסכם בתואר שני בעיצוב תעשייתי,
מסלול ניהול עיצוב וחדשנות בצלאל, אקדמיה לאמנות ועיצוב ירושלים
בהנחיית הדס זמר בן־ארי
מחקר, פיתוח קונספט, UX/UI Design, פרוטוטייפינג והצגת ממצאים
The Problem
When groundcover collects all logs by default, users face two major challenges:
-
Too much irrelevant data. Many logs are noisy or unnecessary, and users need a way to filter them out.
-
Unreadable formats. Some logs arrive in unstructured text, making them difficult to parse or search.
Before this project, the only way to manage logs was by editing configuration files using a scripting format called OTTL. This method was manual, error-prone, and disconnected from the product UI. Users had the power, but not the usability.
The Goal
The design goal was to create an experience that allows users to choose which logs to keep or drop, control how logs are structured and parsed, and do everything inside the groundcover UI without writing code. The solution needed to support expert-level flexibility while remaining accessible and intuitive for daily use.
Research & Competitive Exploration
To inform the initial direction, I conducted a competitive audit of observability and log analysis tools (e.g., Datadog, Coralogix, Open Telemetry). While many platforms offer advanced rule engines, they often require steep learning curves or depend on rigid syntax. I mapped key UX patterns and pain points across these tools to identify opportunities for a more guided, intuitive rule creation experience.
Process
I identified two key user scenarios:
Reactive flow. Users are investigating an issue, encounter a problematic log, and want to fix or filter it on the spot.
Proactive flow. Users are configuring the system more broadly and need an overview of all parsing rules to manage and refine them.
I collaborated with product managers and developers to map current pain points and understand user mental models around logs and rule creation.
Iterations
Stage 1
Parsing Playground
The initial version was a playground environment where users could write and preview parsing logic. Although there was no way to save or apply rules in the UI, the playground helped validate user intent.
One feature that stood out was the addition of an LLM-assisted which helped users generate rule configuration quickly. This early success demonstrated that AI-assisted configuration could lower the barrier for working with complicated rule formats.
Stage 2
Inline editing and Parsing Pipelines settings page
In the second iteration, we introduced two key components at once. First, we added direct actions on individual log entries: "Create Drop Rule" and "Add Parsing Rule," giving users the ability to act from the exact log they were reviewing.
Simultaneously, we launched the Parsing Pipelines settings page, a central dashboard showing all active rules along with system-level metrics such as drop rate and error count. While this brought much-needed visibility, it did not yet allow users to create new rules independently from the settings view.
Upcoming
Stage 3
Query Builder for Settings
The next planned iteration will allow users to create and edit parsing pipelines directly from the Settings screen, without needing to start from a specific log entry. This will include a query builder to help users search and filter logs before building a rule. This step aims to complete the experience by supporting full rule creation from both reactive and proactive workflows.
Final Design
The final design supported both reactive and proactive workflows. Users could take action directly from the Logs view with inline log actions, such as dropping or editing logs while staying in context. The Parsing Pipelines settings page served as a centralized dashboard that displayed all existing rules with performance metrics like drop rate and error count, while also allowing users to edit or delete rules as needed. Together, these elements created a smooth and effective experience for users managing log parsing through the UI.
Reactive flow
Add Parsing Rule
Creating configuration
Place rule order in pipeline
Proactive flow
Settings page
Reorder pipelines
Empty state - not connected
Impact
This release brought log parsing directly into the groundcover UI, allowing users to manage their log configuration without needing to edit code or contact support. It turned a previously manual process into a self-serve experience.
The project improved user control, cut friction, and created a solid foundation for future features. By shipping in steps, we tested, learned, and improved quickly. Supporting both in-the-moment actions and planned changes made the system more flexible and user-friendly.
And yes!
We do have
dark mode