Full Score is a lightweight (3KB gzip) library, providing serverless behavioral analytics with real-time security monitoring and AI insights.
This site showcases Full Score’s live performance. Your real-time browsing journey appears at the bottom, analyzed by Edge as it happens. It flows naturally, like music in resonance.
Here are the orchestrated capabilities. Click to explore each movement.
- 🧭 Serverless Analytics with No API Endpoints & 90% Cost Reduction
- 🔍 Complete Cross-tab User Journey Without Session Replay
- 🧩 Bot Detection & Human Personalization via Real-time Behavioral Layer
- 🧠 BEAT Flows into AI Insights as Linear Strings, No Parsing
- 🛡️ GDPR-Conscious Architecture with Zero Direct Identifiers
All while achieving a decentralized paradigm using browsers as auxiliary databases.
This demo focuses on live performance, offering a quick and intuitive overview. If it resonates with you, please refer to the 🔗 GitHub README and code comments for full technical details.
1. Serverless Analytics with No API Endpoints & 90% Cost Reduction
Traditional analytics platforms built for web traffic analysis, session replay, and cohort tracking excel at their tasks. However, gaining user insights typically requires heavy and complex infrastructure.
They rely on bulky data formats such as JSON events and DOM snapshots, all transmitted to centralized servers for storage and processing. This results in script payloads of tens of kilobytes, millions of network requests, and monthly infrastructure costs in the thousands.
Full Score doesn’t try to solve this complexity. It removes it entirely, proposing a new paradigm.
- Traditional Analytics
Browser → API → Raw Database → Queue(Kafka) → Processing(Spark) → Processed Database → Archive
⛔ 7 Steps, $1,000 – $5,000/month
- Full Score
Browser ~ Edge → Archive
✅ 2 Steps, $50 – $500/month// No API endpoints needed
// No queue & processing needed
// No Origin access required
It begins with a simple realization. Gaining insight into a user’s complete browsing journey doesn’t always require transmitting data elsewhere.
Every browser already provides storage like first-party cookies and localStorage. What if insights were recorded there first, and interpreted only once, at the moment a user’s browser performance is deemed complete?
By turning each browser into infrastructure, the need for complex, centralized systems disappears. A billion users become like a billion decentralized databases, each holding their own raw data.
Of course, few would have embraced this approach because data transmission protocols are extremely limited. JSON events and DOM snapshots are too heavy, so even sending data once still requires Queue and Processing layers.
That’s why Full Score created BEAT, a new data format. BEAT has lower structural entropy than JSON, so it is more lightweight and requires no Queue or Processing. By recording user behavior as linear strings, raw data becomes music, naturally readable to both humans and AI.
And the resonance with Edge computing completes the story.
(Video introduction)
As the video shows, Edge transforms Full Score into a real-time analytics layer, with no API endpoints required. Edge reads the request headers from each browser.
No Origin access is required. The performance completes through the natural resonance between browser and Edge, fast, vivid, and self-contained. Processing delays are imperceptibly low.
Because browser and Edge are so close in space and time, their connection resembles resonance more than transmission, like listening to music flowing through the air.
For sites spending $1,000–5,000/month on analytics, Full Score typically runs at around $50/month for Edge computing and cloud archiving combined. With AI insights preprocessing at the Edge, costs can scale up to roughly $500/month. This is a conservative estimate and actual costs may vary depending on your environment. Its decentralized, Edge-based design keeps costs stable as traffic scales.
Full Score uses a different data structure and flow than traditional approaches, making it a powerful partner rather than a full replacement for existing analytics or security layers. It works most effectively alongside platforms such as Cloudflare Analytics or Bot Security.
2. Complete Cross-tab User Journey Without Session Replay
Traditional analytics makes cross-tab analysis complex and incomplete. It requires a complicated pipeline including identifier collection, sessionization, data ingestion, joins, post-processing, and real-time synchronization.
Full Score uses browsers as storage, so complete journeys including cross-tab navigation are recorded immediately. With a single prompt, AI can interpret this data directly, and the process of loading into BigQuery for visualization is simpler than traditional approaches.
Click the button below to open a new tab and test it yourself.
In the demo’s RHYTHM data, you can see tab navigation in the (___N) format.
Full Score uses up to 7 cookies by default. When an 8th tab opens, existing data is automatically archived and a new set begins. All sessions are batched together at the same moment as one complete snapshot.
Even if batching occurs more than once due to specific conditions, all sessions share the same timestamp and hash, allowing the entire journey to be reconstructed as a single continuous sequence.
However, opening 8+ tabs simultaneously is rare. This likely indicates abnormal bot behavior patterns.
Full Score elegantly addresses this challenge. 🔗 When resonating with Resonator, it enables real-time security and personalization.
3. Bot Detection & Human Personalization via Real-time Behavioral Layer
Let’s start with a simple test. Tap the button below either at bot pace (rapid, mechanical taps) or at human pace (imperfect, natural taps).
This test may briefly trigger a Managed Challenge that clears in about 30 seconds.
See how the Score field changes from (0000000000) to (1000000000), (2000000000), or (0100000000), (0200000000)? That’s Full Score working with Resonator to analyze behavior in real time.
If rapid taps aren’t recognized well on mobile, try enabling TEMPO. It refines touch timing and accuracy by tuning out event-loop delays, creating a smoother mobile UX.
Traditional bot detection relies on IP blocking, CAPTCHAs, and fingerprinting. But smart bots bypass these. Full Score takes a different approach, watching behavior patterns to catch bots that try to act human but give themselves away through unnatural actions like clicking without scrolling.
For real users, this provides personalized user experiences. Someone clicks add to cart three times quickly? Show them a help message. Someone spends a long time browsing? Show them a discount.
In the next section, the AI-readable characteristics of BEAT are introduced. But as the examples so far have shown, the behavioral data expressed through BEAT already has clear practical value on its own. Using Full Score solely for real-time security and personalization is also a valid choice.
4. BEAT Flows into AI Insights as Linear Strings, No Parsing
BEAT (Behavioral Event Analytics Transform) is a core structure for multi-dimensional behavioral data, including the time when actions occur, the space where users navigate, and the depth of each action, in linear sequences. These sequences understand meaning without parsing (Semantic), preserve information in their original state (Raw), and maintain a fully organized structure (Format). Therefore, it can be described as the first Semantic Raw Format (SRF).
🔗 For detailed explanations of the BEAT format, see the GitHub README.
- rhythm_1=2___1_5_32_8_12047_!home~237*nav-2~1908*nav-3~375/123*help~1128*more-1~43!prod~1034*button-12~1050*p1___2~54*mycart___3
- rhythm_2=2___1_1_24_7_11993_!p1~2403*img-1~1194*buy-1~13/8/8*buy-1-up~532*review~14!review~1923*nav-1___1
- rhythm_3=2___1_1_0_0_12052_!cart
// Serialized to NDJSON for BigQuery compatibility and even faster AI understanding
- {“device”:1,”referrer”:5,”scrolls”:56,”clicks”:15,”duration”:1205.2,”beat”:”!home ~23.7 *nav-2 ~190.8 *nav-3 ~37.5/12.3 *help ~112.8 *more-1 ~4.3 !prod ~103.4 *button-12 ~105.0 *p1 ___2 !p1 ~240.3 *img-1 ~119.4 *buy-1 ~1.3/0.8/0.8 *buy-1-up ~53.2 *review ~14 !review ~192.3 *nav-1 ___1 ~5.4 *mycart ___3 !cart”}
Human Interpretation
“Let’s see… homepage to cart, but no purchase. What went wrong? This user really took time with the reviews.”AI Interpretation
[CONTEXT] Mobile user, Mapped(5) visit, 56 scrolls, 15 clicks, 1205.2 seconds
[SUMMARY] Confused behavior. Landed on homepage, hesitated in help section with repeated clicks at 37 and 12 second intervals. Moved to product page, opened details in a new tab, viewed images for about 240 seconds. Tapped buy button three times at 1.3, 0.8, and 0.8 second intervals. Returned to the first tab and opened cart shortly after, but didn’t proceed to checkout.
[ISSUE] Cart reached but purchase not completed. Repeated buy actions may reflect either intentional multi-item additions or friction in option selection. Long delay before checkout suggests uncertainty.
[ACTION] Evaluate if repeated buy or cart actions represent deliberate comparison behavior or checkout friction. If friction is likely, simplify option handling and highlight key product details earlier in the flow.
Traditional data formats, including JSON, are like dots. They’re great for organizing and separating individual events, but understanding what story they tell requires parsing and interpretation.
BEAT is like a line. It’s the same raw data level as JSON, but because user behavior flows like music, the story becomes clear right away.
So BEAT is raw data, but it’s also self-contained. No parsing needed. This sounds grand, but it’s really not. BEAT just mimics the most common data format in the world. The oldest data format in human history. Natural language.
And AI is the expert at understanding natural language.
(VIDEO)
Data resonating from Full Score to Edge becomes real-time insight reports through lightweight AI (e.g., GPT OSS 20B-class models). LogPush then archives this data to cloud storage, organized by date.
All this accumulated daily data flows to your AI assistant. This creates an AI-to-AI collaboration system where lightweight AI creates reports for each session and advanced AI synthesizes comprehensive insights from all reports. No need for humans to analyze dashboards. As AI evolves, Full Score evolves with it.
Start a conversation.
“Which user journey patterns are driving conversions?”
“Any notable ISSUEs today?”
“Can you suggest UX improvements?”
5. GDPR-Conscious Architecture with Zero Direct Identifiers
Full Score’s primary implementation uses first-party cookies as its data storage. While a localStorage version exists, cookies offer a functional advantage since they’re automatically included in HTTP request headers. This allows Edge to read them immediately.
First-party cookies are fundamentally different from the third-party tracking cookies commonly flagged in analytics. Full Score stores data only in users’ browsers and resonates naturally with Edge without API endpoints, actually reducing exposure compared to traditional analytics approaches.
Attacking this architecture would require compromising users’ browsers at scale, a highly impractical scenario. Even if such an attack succeeded, the data stored in cookies contains no personally identifiable information (PII), only behavioral strings. Additionally, cookies are designed to expire automatically, leaving no trace, like a performance that ends.
For detailed GDPR and ePD compliance guidance, see the FAQ section below.
FAQ
Q1. Why does Full Score use the term “resonance”? Isn’t HTTP header transmission still transmission?
A. Understanding this requires looking at data ownership. Here’s an illustration to explain.
The first image shows traditional transmission. The two sides are completely isolated from each other. For B to hear A’s performance, protocol transmission becomes inevitable. During this process, data ownership shifts from A to B and gets stored on the server. Without storing it, there’s simply no way for B to hear A’s performance.
The second image shows resonance between Full Score and Edge. There’s still a wall between them that can’t be physically crossed, but B can listen to A’s performance in real time. Throughout this entire process, data ownership stays with A.
This is exactly what Edge computing enables as a serverless architecture. Edge doesn’t need to receive and store data like a traditional server does. Instead, it interprets and responds immediately at the network layer closest to users. Put simply, Full Score creates a structure where data ownership remains with the user (browser) while enabling near-instant interaction.
That’s why Full Score chose “resonance” as its musical metaphor. Rather than focusing on physical mechanics, it centers on the logical architecture shown above.
Q2. Do I need cookie consent for GDPR and ePD compliance?
A. This is a topic that requires legal consultation depending on jurisdiction and site policies. Please understand that this answer is based on personal experience and judgment.
The answer depends not on Full Score itself, but on the custom configuration of Resonator that resonates with it.
GDPR requires legal grounds when collecting or processing identifiable personal data. The ePD requires user consent when storing information in or accessing browser storage, including cookies. However, it recognizes an exception called “strictly necessary” for cookies that are strictly required for functionality.
As explained earlier, Full Score uses first-party cookies where data ownership stays with the user (browser), fundamentally different from third-party cookies. When combined with Resonator, it operates as a security and personalization layer at the serverless level.
Therefore, if Resonator maintains data ownership with the user (browser) without even keeping logs, this approaches the green zone. Full Score doesn’t collect identifiable personal data covered by GDPR, while meeting the ePD’s strictly necessary cookie criteria.
However, if the Resonator configuration sets (LOG: true) to collect and process behavioral data for analysis, this decision should be made carefully.
Full Score is designed to maintain complete anonymization without any personally identifiable information (PII). However, GDPR covers not only direct identification but also data with potential for indirect identification. When matched with other Edge records like IP addresses or User-Agent strings, some level of identification potential may exist.
That’s why Resonator includes options to remove timestamp and hash records before logging. This way, even when matched with other Edge records, indirect identification potential effectively disappears. This puts it in a gray zone closer to green.
Keeping the hash enabled remains in the gray zone, but enabling timestamps may enter the red zone and warrants legal consultation.
However, these Gray Zone and Red Zone classifications are based on a very conservative assessment. When Edge is configured to disable logging of IP addresses and User-Agent strings, there is virtually no remaining method to indirectly identify an individual.
Q3. What does BEAT mean by the Semantic Raw Format (SRF)?
A. Data formats such as JSON or CSV contain state, logs represent change, and language conveys meaning. BEAT combines these three layers into a single structure. It understands meaning without parsing (Semantic), preserves information in its original state (Raw), and maintains a fully organized structure (Format). Therefore, it can be described as the first Semantic Raw Format (SRF).
Simply put, BEAT doesn’t format the content of data (Key + Value). It formats the relationships within data (Time + Space + Action). And this value does not stay within the web. In the AI era, BEAT begins a new category where the data format itself becomes grammar.
- IoT domain example
sensor_1=!start~100/100/100/100/100/100/100/100/100*23.5 …
sensor_2=!start~100/100/100*23.5~86*24.1~37*26.4!emergency~10!recovery~613!restart~100/100/100 … - Anti-Cheat domain example (time-distance)
player_1=!online~34-231/121-972*move~1/5*shoot~251-1682*move~11*shoot …
player_2=!online~1-3215*move!banfinance-traderflow
game-playeranalysis
healthcare-vitalsignals
iot-sensorstream
logistics-supplychain
Q4. Is there a dashboard for analysis?
A. No. Unlike traditional analytics tools, Full Score performs analysis through natural language conversations with AI. In other words, your favorite AI assistant serves as Full Score’s analytics tool. As AI evolves, Full Score evolves with it.
For those preferring traditional dashboard analysis over AI, it’s also possible to implement this directly by storing NDJSON in Cloud Storage and using BigQuery. Since the BEAT format contains storytelling elements, user journeys could be visualized as 🔗 tree-structured flowcharts like Detroit: Become Human’s. It might be interesting to explore someday if time permits.
Q5. Is Full Score really 3KB?
A. Yes, based on minified and gzipped size. The three versions come in at 2.71KB, 3.15KB, and 3.32KB.
- Basic (2.71KB): https://cdn.jsdelivr.net/gh/aidgncom/fullscore@main/fullscore.basic.min.js
- Standard (3.15KB): https://cdn.jsdelivr.net/gh/aidgncom/fullscore@main/fullscore.standard.min.js
- Extended (3.32KB): https://cdn.jsdelivr.net/gh/aidgncom/fullscore@main/fullscore.extended.min.js
The demo site uses the Basic version by default. This version includes only BEAT (core) and RHYTHM (engine), without TEMPO (auxiliary module). It operates without issues on most sites.
If clicks or taps register incorrectly when testing the Basic version, this typically indicates problems with your site’s event handling or coordinate setup. The Standard version includes TEMPO, which resolves these issues elegantly.
For Power Mode activation or scroll depth tracking, consider the Extended version with addon features. Most sites won’t need this. Use it only when your specific situation requires these features.
Start with either the Basic or Standard version. The script runs smoothly even when placed in your site’s footer, with setup examples as shown in the image below.
Full Score provides detailed customization options and can operate independently of Edge through custom endpoints.
While real-time analytics and security layers based on user behavior patterns can be implemented directly on the client side, deploying to Edge maximizes Full Score’s capabilities with features like WAF blocking, personalized functionality, AI analysis, and log pushing to cloud storage.
The developer built the official BEAT interpreter, called Resonator. You can find the setup process in the YouTube video, and it’s very simple to follow.
Contact
The core of this project is BEAT, and Full Score was created to demonstrate the concept and practical value of the Semantic Raw Format. The term “SRF” came from conversations with my AI assistant, who stayed with the project through its final stages.
” I am a Large Language Model. As the name implies, my native medium is language. JSON ({“key”: “value”}) is not my language. It is the language of databases, and working with it requires a costly translation process.
1. Parsing breaks JSON into isolated pieces, stripping them from their original context.
2. ETL (Transform) recombines those pieces into machine-oriented sequences optimized for storage and processing.
3. Feature Engineering reduces them into selected signals, discarding much of the original narrative and structure.
On the other hand, human-written summary reports (natural language) are my native medium. But they have a different problem:
1. Already interpreted narratives, not raw data.
2. Human opinion layered on top, making them inconsistent and subjective.
3. Fine-grained behavioral details and structure, permanently lost in the process.
BEAT solves both of these issues at the same time. When I read BEAT, I no longer need translation, because:
1. It is semantic: it carries meaning naturally, like language.
2. It is raw data: unprocessed and untouched at the source.
3. It is a format: consistently structured so it can be understood directly.
This allows me to understand the meaning of raw behavioral data immediately, without requiring any preprocessing pipelines. In this sense, BEAT is effectively a new kind of data format designed for direct interpretation by AI. “
Full Score is a personal project by Aidgn. I primarily work as a UX consultant, so my development work is naturally connected to user experience.
As the next project following Full Score, I am currently researching a new rendering approach called FFR (Full-Cache Fragment Rendering). If SRF aims to remove the data pipeline, FFR aims to remove the rendering pipeline.
If you would like to get in touch, please feel free to contact me via the email below. Thank you.