2026-02-04 00:30:36
Cut through the noise with this engineer-friendly guide to Kubernetes observability. Save this reference for fast-track access to essential kubectl commands and critical metrics, from disk I/O and network latency to real-time cluster events. Perfect for scaling, debugging, and tuning your workloads without sifting through endless docs.
Digital services require accurate extraction of information from user-submitted documents such as identification cards, driver’s licenses, and vehicle registration certificates. This process is essential for electronic know-your-customer (eKYC) verification. However, the diversity of languages and document formats across the region makes this task particularly challenging.
Grab Engineering Team faced significant obstacles with traditional Optical Character Recognition (OCR) systems, which struggled to handle the variety of document templates. While powerful proprietary Large Language Models (LLMs) were available, they often failed to adequately understand Southeast Asian languages, produced errors and hallucinations, and suffered from high latency. Open-source Vision LLMs offered better efficiency but lacked the accuracy required for production deployment.
This situation prompted Grab to fine-tune existing models and eventually build a lightweight, specialized Vision LLM from the ground up. In this article, we will look at the complete architecture, the technical decisions made, and the results achieved.
Disclaimer: This post is based on publicly shared details from the Grab Engineering Team. Please comment if you notice any inaccuracies.
Before diving into the solution, it helps to understand what a Vision LLM is and how it differs from traditional text-based language models.
A standard LLM processes text inputs and generates text outputs. A Vision LLM extends this capability by enabling the model to understand and process images. The architecture consists of three essential components working together:
The first component is the image encoder. This module processes an image and converts it into a numerical format that computers can work with. Think of it as translating visual information into a structured representation of numbers and vectors.
The second component is the vision-language projector. This acts as a bridge between the image encoder and the language model. It transforms the numerical representation of the image into a format that the language model can interpret and use alongside text inputs.
The third component is the language model itself. This is the familiar text-processing model that takes both the transformed image information and any text instructions to generate a final text output. In the case of document processing, this output would be the extracted text and structured information from the document.
See the diagram below:
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Grab evaluated several open-source models capable of performing OCR and Key Information Extraction (KIE). The options included Qwen2VL, miniCPM, Llama3.2 Vision, Pixtral 12B, GOT-OCR2.0, and NVLM 1.0.
After thorough evaluation, Grab selected Qwen2-VL 2B as the base multimodal LLM. This decision was driven by several critical factors:
First, the model size was appropriate. With 2 billion parameters, it was small enough to allow full fine-tuning on GPUs with limited VRAM resources. Larger models would have required more expensive infrastructure and longer training times.
Second, the model offered good Southeast Asian language support. The tokenizer showed efficiency for languages like Thai and Vietnamese, indicating decent native vocabulary coverage. A tokenizer is the component that breaks text into smaller units (tokens) that the model can process. Efficient tokenization means the model can represent these languages without wasting capacity.
Third, and perhaps most importantly, Qwen2-VL supports dynamic resolution. Unlike models that require fixed-size image inputs, this model can process images in their native resolution. This capability is critical for OCR tasks because resizing or cropping images can distort text characters, leading to recognition errors. Preserving the original resolution maintains text integrity and improves accuracy.
Initial benchmarking of Qwen2VL and miniCPM on Grab’s dataset revealed low accuracy, primarily due to the limited coverage of Southeast Asian languages. This finding motivated the team to pursue fine-tuning to improve OCR and KIE accuracy.
However, training LLMs is both data-intensive and GPU resource-intensive, which brings up two important questions: how to use open-source and internal data effectively, and how to customize the model to reduce latency while maintaining high accuracy.
Grab developed two approaches to generate training data for the model:
The first approach involved creating synthetic training data. Grab extracted Southeast Asian language text content from Common Crawl, a large online text corpus that contains data from across the internet. Using an in-house synthetic data pipeline, the team generated text images by rendering this content in various fonts, backgrounds, and augmentations.
The resulting dataset included text in Bahasa Indonesia, Thai, Vietnamese, and English. Each image contained a paragraph of random sentences extracted from the corpus. This synthetic approach offered several advantages. It allowed controlled generation of training examples, enabled the creation of unlimited variations, and ensured coverage of different visual styles and document conditions.
The second approach leveraged real documents collected by Grab. Experiments showed that applying document detection and orientation correction significantly improved OCR and information extraction.
To generate a preprocessing dataset, Grab built Documint, an internal platform that creates an auto-labelling and preprocessing framework for document understanding.
Documint prepares high-quality, labelled datasets through various submodules that execute the full OCR and KIE task. The team used this pipeline with a large volume of Grab-collected cards and documents to extract training labels. Human reviewers then refined the data to achieve high label accuracy.
Documint consists of four main modules:
The detection module identifies the document region from a full picture.
The orientation module determines the correction angle needed, such as 180 degrees if a document is upside down.
The OCR module extracts text values in an unstructured format.
Finally, the KIE module converts the unstructured text into structured JSON values.
Grab conducted the model development in three distinct phases, each building on the lessons learned from the previous phase:
The first attempt at fine-tuning involved a technique called Low-Rank Adaptation, or LoRA.
This method is efficient because it updates only a small portion of the model’s parameters rather than retraining the entire model. Specifically, LoRA adds small trainable matrices to the model while keeping most of the original weights frozen. This approach minimizes computational resource requirements and reduces training time.
Grab trained the model on curated document data that included various document templates in multiple languages. The performance showed promise for documents with Latin scripts. The LoRA fine-tuned Qwen2VL-2B achieved high field-level accuracy for Indonesian documents.
However, the fine-tuned model struggled with two categories of documents:
First, it had difficulty with documents containing non-Latin scripts, such as Thai and Vietnamese.
Second, it performed poorly on unstructured layouts with small, dense text.
The experiments revealed a key limitation. While open-source Vision LLMs often have extensive multilingual text corpus coverage for the language model decoder’s pre-training, they lack visual examples of text in Southeast Asian languages during vision encoder training. The language model might understand Thai text, but the vision encoder had never learned to recognize what Thai characters look like in images. This insight drove the decision to pursue full parameter fine-tuning.
Drawing from the Large Language and Vision Assistant (LLAVA) methodology, Grab implemented a two-stage training approach:
In Stage 1, called continual pre-training, the team trained only the vision components of the model using synthetic OCR datasets created for Bahasa Indonesia, Thai, Vietnamese, and English. This stage helped the model learn the unique visual patterns of Southeast Asian scripts. During this stage, the language model remained frozen, meaning its weights were not updated.
In Stage 2, called full-parameter fine-tuning, Grab fine-tuned the entire model. This included the vision encoder, the projector, and the language model. The team used task-specific document data for this training. All components of the model were now trainable and could be optimized together for the document extraction task.
The results were significant. For example, the Thai document accuracy increased by 70 percentage points from the baseline. Vietnamese document accuracy rose by 40 percentage points from the baseline. Indonesian documents saw a 15 percentage point improvement, and Philippine documents improved by 6 percentage points.
The fully fine-tuned Qwen2-VL 2B model delivered substantial improvements, especially on documents that the LoRA model had struggled with.
While the 2B model succeeded, full fine-tuning pushed the limits of available GPUs.
To optimize resource usage and create a model perfectly tailored to their needs, Grab decided to build a lightweight Vision LLM with approximately 1 billion parameters from scratch.
The strategy involved combining the best components from different models. Grab took the powerful vision encoder from the larger Qwen2-VL 2B model, which had proven effective at understanding document images. The team paired it with the compact and efficient language decoder from the Qwen2.5 0.5B model. They connected these components with an adjusted projector layer to ensure seamless communication between the vision encoder and language decoder.
This combination created a custom Vision LLM with approximately 1 billion parameters, optimized for both training and deployment.
Grab trained this new model using a comprehensive four-stage process:
Stage 1 focused on projector alignment. The first step was to train the new projector layer to ensure the vision encoder and language decoder could communicate effectively. Without proper alignment, the language model would not be able to interpret the vision encoder’s outputs correctly.
Stage 2 involved vision tower enhancement. The team trained the vision encoder on a vast and diverse set of public multimodal datasets. These datasets covered tasks like visual question answering, general OCR, and image captioning. This stage improved the model’s foundational visual understanding across various scenarios.
Stage 3 centered on language-specific visual training. Grab trained the model on two types of synthetic OCR data specific to Southeast Asian languages. This stage proved critical. Without it, performance on non-Latin documents dropped by as much as 10 percentage points. This stage ensured the vision encoder could recognize the specific visual characteristics of Thai, Vietnamese, and other regional scripts.
Stage 4 completed the process with task-centric fine-tuning. The team performed full-parameter fine-tuning on the custom 1B model using the curated document dataset. This final stage optimized the entire system for the specific production use case of document information extraction.
The final 1B model achieved remarkable results across two key metrics: accuracy and latency.
For accuracy, the model performed comparably to the larger 2B model, staying within a 3 percentage point accuracy gap across most document types. The model also maintained strong generalization when trained on quality-augmented datasets, meaning it could handle variations it had not seen during training.
For latency, the results were even more impressive. The 1B model achieved 48 percent faster processing at the P50 latency (median response time), 56 percent faster at P90 latency (90th percentile), and 56 percent faster at P99 latency (99th percentile, representing worst-case scenarios).
These latency improvements are particularly important. Grab identified that one of the biggest weaknesses of external APIs like ChatGPT or Gemini was the P99 latency, which can easily be 3 to 4 times higher than the P50 latency. This variability would not be acceptable for large-scale production rollouts where consistent performance is essential.
The project yielded several important insights that can guide similar efforts.
Full parameter fine-tuning proved superior to LoRA for specialized, non-Latin script domains. While LoRA is efficient, it cannot match the performance gains of updating all model parameters when dealing with significantly different data distributions.
Lightweight models can be highly effective. A smaller model of approximately 1 billion parameters, built from scratch and trained comprehensively, can achieve near state-of-the-art results. This validates the approach of custom architecture over simply using the largest available model.
The choice of base model matters significantly. Starting with a model that has native support for target languages is crucial for success. Trying to force a model to learn languages it was not designed for leads to suboptimal results.
Data quality plays a critical role. Meticulous dataset preprocessing and augmentation are as important as model architecture in achieving consistent and accurate results. The effort invested in building Documint and creating synthetic datasets directly contributed to the final model’s success.
Finally, native resolution support is transformative for OCR tasks. A model that can handle dynamic image resolutions preserves text integrity and dramatically improves OCR capabilities. This feature prevents the distortion that occurs when images are resized to fit fixed input dimensions.
Grab’s journey of building a Vision LLM demonstrates that specialized Vision LLMs can effectively replace traditional OCR pipelines with a single, unified, highly accurate model. This opens new possibilities for document processing at scale.
The project shows that with strategic training approaches, high-quality data preparation, and thoughtful model architecture decisions, smaller specialized models can outperform larger general-purpose alternatives. The resulting system processes documents faster and more accurately than previous solutions while using fewer computational resources.
Grab continues to enhance these capabilities. The team is developing Chain of Thought-based OCR and KIE models to strengthen generalization and tackle even more diverse document scenarios. They are also extending support to all Grab markets, bringing advanced document processing to Myanmar, Cambodia, and beyond.
References:
2026-02-03 00:31:12
One of the clearest AI predictions for 2026: models won’t be the bottleneck—context will. As AI agents pull from vector stores, session state, long-term memory, SQL, and more, finding the right data becomes the hard part. Miss critical context and responses fall apart. Send too much and latency and costs spike.
Context engines emerge as the fix. A single layer to store, index, and serve structured and unstructured data, across short- and long-term memory. The result: faster responses, lower costs, and AI apps that actually work in production.
When we interact with modern large language models like GPT, Claude, or Gemini, we are witnessing a process fundamentally different from how humans form sentences. While we naturally construct thoughts and convert them into words, LLMs operate through a cyclical conversion process.
Understanding this process reveals both the capabilities and limitations of these powerful systems.
At the heart of most modern LLMs lies an architecture called a transformer. Introduced in 2017, transformers are sequence prediction algorithms built from neural network layers. The architecture has three essential components:
An embedding layer that converts tokens into numerical representations.
Multiple transformer layers where computation happens.
Output layer that converts results back into text.
See the diagram below:
Transformers process all words simultaneously rather than one at a time, enabling them to learn from massive text datasets and capture complex word relationships.
In this article, we will look at how the transformer architecture works in a step-by-step manner.
Before any computation can happen, the model must convert text into a form it can work with. This begins with tokenization, where text gets broken down into fundamental units called tokens. These are not always complete words. They can be subwords, word fragments, or even individual characters.
Consider this example input: “I love transformers!” The tokenizer might break this into: [”I”, “ love”, “ transform”, “ers”, “!”]. Notice that “transformers” became two separate tokens. Each unique token in the vocabulary gets assigned a unique integer ID:
“I” might be token 150
“love” might be token 8942
“transform” might be token 3301
“ers” might be token 1847
“!” might be token 254
These IDs are arbitrary identifiers with no inherent relationships. Tokens 150 and 151 are not similar just because their numbers are close. The overall vocabulary typically contains 50,000 to 100,000 unique tokens that the model learned during training.
Neural networks cannot work directly with token IDs because they are just fixed identifiers. Each token ID gets mapped to a vector, a list of continuous numbers usually containing hundreds or thousands of dimensions. These are called embeddings.
Here is a simplified example with five dimensions (real models may use 768 to 4096):
Token “dog” becomes [0.23, -0.67, 0.45, 0.89, -0.12]
Token “wolf” becomes [0.25, -0.65, 0.47, 0.91, -0.10]
Token “car” becomes [-0.82, 0.34, -0.56, 0.12, 0.78]
Notice how “dog” and “wolf” have similar numbers, while “car” is completely different. This creates a semantic space where related concepts cluster together.
Why the need for multiple dimensions? This is because with just one number per word, we might encounter contradictions. For example:
“stock” equals 5.2 (financial term)
“capital” equals 5.3 (similar financial term)
“rare” equals -5.2 (antonym: uncommon)
“debt” equals -5.3 (antonym of capital)
Now, “rare” and “debt” both have similar negative values, implying they are related, which makes no sense. Hundreds of dimensions allow the model to represent complex relationships without such contradictions.
In this space, we can perform mathematical operations. The embedding for “king” minus “man” plus “woman” approximately equals “queen.” These relationships emerge during training from patterns in text data.
Transformers do not inherently understand word order. Without additional information, “The dog chased the cat” and “The cat chased the dog” would look identical because both contain the same tokens.
The solution is positional embeddings. Every position gets mapped to a position vector, just like tokens get mapped to meaning vectors.
For the token “dog” appearing at position 2, it might look like the following:
Word embedding: [0.23, -0.67, 0.45, 0.89, -0.12]
Position 2 embedding: [0.05, 0.12, -0.08, 0.03, 0.02]
Combined (element-wise sum): [0.28, -0.55, 0.37, 0.92, -0.10]
This combined embedding captures both the meaning of the word and its context of use. This is also what flows into the transformer layers.
The transformer layers implement the attention mechanism, which is the key innovation that makes these models so powerful. Each transformer layer operates using three components for every token: queries, keys, and values. We can think of this as a fuzzy dictionary lookup where the model compares what it is looking for (the query) against all possible answers (the keys) and returns weighted combinations of the corresponding values.
Let us walk through a concrete example. Consider the sentence: “The cat sat on the mat because it was comfortable.”
When the model processes the word “it,” it needs to determine what “it” refers to. Here is what happens:
First, the embedding for “it” generates a query vector asking essentially, “What noun am I referring to?”
Next, this query is compared against the keys from all previous tokens. Each comparison produces a similarity score. For example:
“The” (article) generates score: 0.05
“cat” (noun) generates score: 8.3
“sat” (verb) generates score: 0.2
“on” (preposition) generates score: 0.03
“the” (article) generates score: 0.04
“mat” (noun) generates score: 4.1
“because” (conjunction) generates score: 0.1
The raw scores are then converted into attention weights that sum to 1.0. For example:
“cat” receives attention weight: 0.75 (75 percent)
“mat” receives attention weight: 0.20 (20 percent)
All other tokens: 0.05 total (5 percent combined)
Finally, the model takes the value vectors from each token and combines them using these weights. For example:
Output = (0.75 × Value_cat) + (0.20 × Value_mat) + (0.03 × Value_the) + ...The value from “cat” contributes 75 percent to the output, “mat” contributes 20 percent, and everything else is nearly ignored. This weighted combination becomes the new representation for “it” that captures the contextual understanding that “it” most likely refers to “cat.”
This attention process happens in every transformer layer, but each layer learns to detect different patterns.
Early layers learn basic patterns like grammar and common word pairs. When processing “cat,” these layers might heavily attend to “The” because they learn that articles and their nouns are related.
Middle layers learn sentence structure and relationships between phrases. They might figure out that “cat” is the subject of “sat” and that “on the mat” forms a prepositional phrase indicating location.
Deep layers extract abstract meaning. They might understand that this sentence describes a physical situation and implies the cat is comfortable or resting.
Each layer refines the representation progressively. The output of one layer becomes the input for the next, with each layer adding more contextual understanding.
Importantly, only the final transformer layer needs to predict an actual token. All intermediate layers perform the same attention operations but simply transform the representations to be more useful for downstream layers. A middle layer does not output token predictions. Instead, it outputs refined vector representations that flow to the next layer.
This stacking of many layers, each specializing in different aspects of language understanding, is what enables LLMs to capture complex patterns and generate coherent text.
After flowing through all layers, the final vector must be converted to text. The unembedding layer compares this vector against every token embedding and produces scores.
For example, to complete “I love to eat,” the unembedding might produce:
“pizza”: 65.2
“tacos”: 64.8
“sushi”: 64.1
“food”: 58.3
“barbeque”: 57.9
“car”: -12.4
“42”: -45.8
These arbitrary scores get converted to probabilities using softmax:
“pizza”: 28.3 percent
“tacos”: 24.1 percent
“sushi”: 18.9 percent
“food”: 7.2 percent
“barbeque”: 6.1 percent
“car”: 0.0001 percent
“42”: 0.0000001 percent
Tokens with similar scores (65.2 versus 64.8) receive similar probabilities (28.3 versus 24.1 percent), while low-scoring tokens get near-zero probabilities.
The model does not select the highest probability token. Instead, it randomly samples from this distribution. Think of a roulette wheel where each token gets a slice proportional to its probability. Pizza gets 28.3 percent, tacos get 24.1 percent, and 42 gets a microscopic slice.
The reason for this randomness is that always picking a specific value like “pizza” would create repetitive, unnatural output. Random sampling weighted by probability allows selection of “tacos,” “sushi,” or “barbeque,” producing varied, natural responses. Occasionally, a lower-probability token gets picked, leading to creative outputs.
The generation process repeats for every token. Let us walk through an example where the initial prompt is “The capital of France.” Here’s how different cycles go through the transformer:
Cycle 1:
Input: [”The”, “capital”, “of”, “France”]
Process through all layers
Sample: “is” (80 percent)
Output so far: “The capital of France is”
Cycle 2:
Input: [”The”, “capital”, “of”, “France”, “is”] (includes new token)
Process through all layers (5 tokens now)
Sample: “Paris” (92 percent)
Output so far: “The capital of France is Paris”
Cycle 3:
Input: [”The”, “capital”, “of”, “France”, “is”, “Paris”] (6 tokens)
Process through all layers
Sample: “.” (65 percent)
Output so far: “The capital of France is Paris.”
Cycle 4:
Input: [”The”, “capital”, “of”, “France”, “is”, “Paris”, “.”] (7 tokens)
Process through all layers
Sample: [EoS] token (88 percent)
Stop the loop
Final output: “The capital of France is Paris.”
The [EoS] or end-of-sequence token signals completion. Each cycle processes all previous tokens. This is why generation can slow as responses lengthen.
This is called autoregressive generation because each output depends on all previous outputs. If an unusual token gets selected (perhaps “chalk” with 0.01 percent probability in “I love to eat chalk”), all subsequent tokens will be influenced by this choice.
The transformer flow operates in two contexts: training and inference.
During training, the model learns language patterns from billions of text examples. It starts with random weights and gradually adjusts them. Here is how training works:
Training text: “The cat sat on the mat.”
Model receives: “The cat sat on the”
With random initial weights, the model might predict:
“banana”: 25 percent
“car”: 22 percent
“mat”: 3 percent (correct answer has low probability)
“elephant”: 18 percent
The training process calculates the error (mat should have been higher) and uses backpropagation to adjust every weight:
Embeddings for “on” and “the” get adjusted
Attention weights in all 96 layers get adjusted
Unembedding layer gets adjusted
Each adjustment is tiny (0.245 to 0.247), but it accumulates across billions of examples. After seeing “sat on the” followed by “mat” thousands of times in different contexts, the model learns this pattern. Training takes weeks on thousands of GPUs and costs millions of dollars. Once complete, weights are frozen.
During inference, the transformer runs with frozen weights:
User query: “Complete this: The cat sat on the”
The model processes the input with its learned weights and outputs: “mat” (85 percent), “floor” (8 percent), “chair” (3 percent). It samples “mat” and returns it. No weight changes occur.
The model used its learned knowledge but did not learn anything new. The conversations do not update model weights. To teach the model new information, we would need to retrain it with new data, which requires substantial computational resources.
See the diagram below that shows the various steps in an LLM execution flow:
The transformer architecture provides an elegant solution to understanding and generating human language. By converting text to numerical representations, using attention mechanisms to capture relationships between words, and stacking many layers to learn increasingly abstract patterns, transformers enable modern LLMs to produce coherent and useful text.
This process involves seven key steps that repeat for every generated token: tokenization, embedding creation, positional encoding, processing through transformer layers with attention mechanisms, unembedding to scores, sampling from probabilities, and decoding back to text. Each step builds on the previous one, transforming raw text into mathematical representations that the model can manipulate, then back into human-readable output.
Understanding this process reveals both the capabilities and limitations of these systems. In essence, LLMs are sophisticated pattern-matching machines that predict the most likely next token based on patterns learned from massive datasets.
2026-02-01 00:31:08
Richard Socher and Bryan McCann are among the most-cited AI researchers in the world. They just released 35 predictions for 2026. Three that stand out:
The LLM revolution has been “mined out” and capital floods back to fundamental research
“Reward engineering” becomes a job; prompts can’t handle what’s coming next
Traditional coding will be gone by December; AI writes the code and humans manage it
This week’s system design refresher:
HTTP/2 over TCP vs HTTP/3 over QUIC
How Cursor Agent Works
How Git Really Stores Your Data
How NAT Works
Building a Computer Vision App on Ring APIs
We’re hiring at ByteByteGo
HTTP/2 vs HTTP/3 looks like an HTTP upgrade. It’s actually a transport-layer rethink.
HTTP/2 fixed a big problem in HTTP/1.1: too many connections. It introduced multiplexing, allowing multiple requests and responses to share a single connection. On paper, that sounds ideal.
But under the hood, HTTP/2 still runs on TCP. All streams share the same TCP connection, the same ordering, and the same congestion control. When a single TCP packet is lost, TCP pauses delivery until it’s retransmitted.
Since packets can carry data from multiple streams, one loss ends up blocking all streams. That’s TCP head-of-line blocking. Multiplexed at the HTTP layer, serialized at the transport layer.
HTTP/3 takes a different approach. Instead of TCP, it runs over QUIC, which is built on UDP. QUIC moves multiplexing down into the transport layer itself.
Each stream is independent, with its own ordering and recovery. If a packet is lost, only the affected stream waits. The others keep flowing. Same idea at the HTTP layer. Very different behavior on the wire.
HTTP/2: multiplexing above TCP
HTTP/3: multiplexing inside the transport
Over to you: Have you actually seen TCP head-of-line blocking show up in real systems, or is it mostly theoretical in your experience?
Cursor recently shipped Composer, its agentic coding model, and shared that the agent can be ~4× faster!
We worked with the Cursor team, particularly Lee Robinson, to understand how the system is put together, and what drives the speed.
A coding agent is a system that can take a task, explore a repo, edit multiple files, and iterate until the build and tests pass.
Inside Cursor, a router first picks a suitable coding model (including Composer) to handle the request.
The system then starts a loop: retrieve the most relevant code (context retrieval), use tools to open and edit files, and run commands in a sandbox. Once the tests pass, the task is complete.
Cursor uses three key techniques to keep this loop fast:
Mixture-of-Expert (MoE): A sparse MoE architecture activates only a subset of model weights per token.
Speculative decoding: a smaller model drafts multiple tokens at once, then a larger model verifies them in parallel to reduce latency.
Context compaction: summarize older steps and keep only the active working set so the prompt stays relevant and short as iterations continue.
Ever wondered what actually happens inside Git when you run commands like add, commit, or checkout? Most developers use Git every day, but very few know what’s going on under the hood.
Git has two layers:
Porcelain (user-facing commands): add, commit, checkout, rebase, etc.
Plumbing (low-level building blocks): hash-object, cat-file, read-tree, update-index, and more.
When you trigger a Git command:
Your porcelain command is translated by Git
It calls lower-level plumbing operations
Plumbing writes directly into the .git directory (Git’s entire internal database)
Inside the .git directory: Git stores everything it needs to reconstruct your repo.
objects/ : all file content and metadata stored by hash
refs/ : branches and tags
index : staging area
config : repo configuration
HEAD : current branch pointer
The .git folder is your repository. If you delete it, the project loses its entire history.
Everything in Git is built from just four objects:
blob : file contents
tree : directories
commit : metadata + parents
tag : annotated reference
Over to you: Which Git command has confused you the most in real-world projects?
Every device in your home probably shares the same public IP, yet each one browses, streams, and connects independently.
Ever wondered how that’s even possible?
That magic is handled by NAT (Network Address Translation), one of the silent workhorses of modern networking. It’s the reason IPv4 hasn’t run out completely, and why your router can hide dozens of devices behind a single public IP.
The Core Idea: Inside your local network, devices use private IP addresses that never leave your home or office. Your router, however, uses a single public IP address when talking to the outside world.
NAT rewrites each outbound request so it appears to come from that public IP address, assigning a unique port mapping for every internal connection.
Outbound NAT (Local to Internet)
When a device sends a request:
NAT replaces the private IP address with the public one
Assigns a unique port so it can track the connection
Sends the packet out to the internet as if it originated from the router
Reverse NAT (Internet to Local)
When the response returns:
NAT checks its translation table
Restores the original private IP address and port
Delivers the packet to the correct device on the local network
Ring just announced a new Appstore. For the first time, third party developers can request early access to Ring APIs.
This changes Ring from a closed product into a programmable platform.
We are one of the first teams working with early Ring API access.
We explored what developers can build with Ring event data and how quickly we can take it to production.
We built a Driveway Derby Detector. Here is how it works at a high level:
We registered our endpoints and received client credentials for Developer APIs (Self-serve through developer.amazon.com/ring)
When the camera detects motion, we get notified on the webhook (< 30 min integration)
We pull the associated video clips (< 30 min integration)
We run the clip through YOLO based object detection model (YMMV based on your application)
We emit the data from the model to a DynamoDB database
We wrote an application which creates visuals with various graphs to detect high speeds of wild drivers in our family when they enter our driveway
If you want to try this yourself, you can request early access here
I am hiring for 2 roles: Technical Deep Dive Writer (System Design or AI Systems), and Lead Instructor (Building the World’s Most Useful AI Cohort).
We are looking for exceptional people who love teaching and enjoy breaking down complex ideas. You will work very closely with me to produce deep, accurate, and well structured technical content. The goal is not volume. The goal is to set the quality bar for how system design and modern AI systems are explained.
If you are interested, please send your resume along with a short note on why you are excited about the role to [email protected]
Job descriptions are below.
Technical Deep Dive Writer
Lead Instructor, Building the World’s Most Popular AI Cohort
2026-01-30 00:30:59
In modern software development, APIs serve as the critical communication layer between clients and backend services.
Whether we are building a web application, mobile app, or any internet-based system, the API layer acts as the primary interface through which clients access functionality and data. As our applications grow and attract more users, the ability to scale this API layer becomes increasingly important for maintaining performance and delivering a positive user experience.
API scalability refers to the system’s ability to handle increasing amounts of traffic and requests without degrading performance. As applications gain popularity, they inevitably face surges in user demand. Without proper scaling mechanisms, these traffic spikes can lead to slow response times, timeouts, or even complete system failures.
In this article, we will learn how to scale APIs effectively using different strategies.
2026-01-29 00:30:44
Bugs sneak out when less than 80% of user flows are tested before shipping. However, getting that kind of coverage (and staying there) is hard and pricey for any team.
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We’ve all been there. Our code works perfectly, passes all tests, and does exactly what it’s supposed to do. Then we deploy it to production and realize it takes 10 seconds to load a page when users expect instant results. Or worse, it works fine with test data but crawls to a halt with real-world volumes.
The common reaction is to think about optimizing later, or leaving performance tuning for experts. Both assumptions are wrong. The truth is that writing reasonably fast code doesn’t require advanced computer science knowledge or years of experience. It requires developing an intuition about where performance matters and learning some fundamental principles.
Many developers have heard the famous quote about premature optimization being “the root of all evil.” However, this quote from Donald Knuth is almost always taken out of context. The full statement reads: “We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%”.
This article is about that critical 3%, where we’ll explore how to estimate performance impact, when to measure, what to look for, and practical techniques that work across different programming languages.
One of the most valuable skills in performance-aware development is the ability to estimate rough performance costs before writing code. We don’t need precise measurements at this stage, but we just need to understand orders of magnitude.
Think of computer operations as existing in different speed tiers. At the fastest tier, we have CPU cache access, which happens in nanoseconds. These are operations where the data is already sitting right next to the processor, ready to be used. One tier slower is accessing main memory (RAM), which takes roughly 100 times longer than cache access. Moving down the hierarchy, reading from an SSD might take 40,000 times longer than a cache access. Network operations take even longer, and traditional spinning disk seeks can be millions of times slower than working with cached data.
This matters because for designing a system that needs to process a million records, the architecture should look completely different depending on whether that data comes from memory, disk, or a network call. A simple back-of-the-envelope calculation can tell us whether a proposed solution will take seconds, minutes, or hours.
Here’s a practical example. Suppose we need to process one million user records. If each record requires a network call to a database, and each call takes 50 milliseconds, we’re looking at 50 million milliseconds, or about 14 hours. However, if we can batch those requests and fetch 1000 records per call, suddenly we only need 1000 calls, which takes about 50 seconds.
Our intuition about performance bottlenecks is usually wrong. We might spend days optimizing a function we think is slow, only to discover through profiling that some completely different part of the code is the actual problem.
This is why the main rule of performance optimization is to measure first and optimize second. Modern programming languages and platforms provide excellent profiling tools that show us exactly where our program spends its time. These tools track CPU usage, memory allocations, I/O operations, and lock contention in multi-threaded programs.
The basic profiling approach is straightforward.
First, we run our program under a profiler using realistic workloads, not toy examples. The profile shows us which functions consume the most time.
Sometimes, however, we encounter what’s called a flat profile. This is when no single function dominates the runtime. Instead, many functions each contribute a small percentage. This situation is actually common in mature codebases where the obvious bottlenecks have already been fixed. When facing a flat profile, our strategy shifts. We look for patterns across multiple functions, consider structural changes higher up in the call chain, or focus on accumulating many small improvements rather than one big win.
The key point is that we should let data guide our optimization decisions.
The most important performance improvements almost always come from choosing better algorithms and data structures. A better algorithm can provide a 10x or 100x speedup, dwarfing any micro-optimization we make.
Consider a common scenario: we have two lists and need to find which items from the first list exist in the second. The naive approach uses nested loops. For each item in list A, we scan through all of list B looking for a match. If each list has 1000 items, that’s potentially one million comparisons. This is an O(N²) algorithm, meaning the work grows with the square of the input size.
A better approach converts list B into a hash table, then looks up each item from list A. Hash table lookups are typically O(1), constant time, so now we’re doing 1000 lookups instead of a million comparisons. The total work is O(N) instead of O(N²). For our 1000-item lists, this could mean finishing in milliseconds instead of seconds.
Another common algorithmic improvement involves caching or precomputation. If we’re calculating the same value repeatedly, especially inside a loop, we should calculate it once and store the result.
The key to spotting algorithmic problems in a profile is looking for functions that consume most of the runtime and contain nested loops, or that repeatedly search or sort the same data. Before we dive into optimizing such code, we should step back and ask if there is a fundamentally different approach that does less work?
Modern CPUs are incredibly fast, but they can only work on data that’s in their small, ultra-fast caches. When the data they need isn’t in cache, they have to fetch it from main memory, which is much slower. This difference is so significant that the layout of our data in memory often matters more than the algorithmic complexity of our code.
See the diagram below:
The fundamental principle here is locality: data that is accessed together should be stored together in memory. When the CPU fetches data from memory, it doesn’t fetch just one byte. It fetches an entire cache line, typically 64 bytes. If our related data is scattered across memory, we waste cache lines and constantly fetch new data. If it’s packed together, we get multiple pieces of related data in each cache line.
Consider two ways to store a list of user records:
We could have an array of pointers, where each pointer points to a user object allocated somewhere else in memory.
Or we could have a single contiguous array where all user objects are stored sequentially.
The first approach means that accessing each user requires chasing a pointer, and each user object might be on a different cache line. The second approach means that once we fetch the first user, the next several users are likely already in cache.
This is why arrays and vectors typically outperform linked lists for most operations, even though linked lists have theoretical advantages for insertions and deletions. The cache efficiency of sequential access usually dominates.
Reducing the memory footprint also helps. Smaller data structures mean more fit in cache. If we’re using a 64-bit integer to store values that never exceed 255, we’re wasting memory. Using an 8-bit type instead means we can fit eight times as many values in the same cache line. Similarly, removing unnecessary fields from frequently accessed objects can have a measurable impact.
The access pattern matters too. Sequential access through an array is much faster than random access. If we’re summing a million numbers stored in an array, it’s fast because we access them sequentially and the CPU can predict what we’ll need next. If those same numbers are in a linked list, each access requires chasing a pointer to an unpredictable location, destroying cache efficiency.
The practical takeaway is to prefer contiguous storage (arrays, vectors) over scattered storage (linked lists, maps) when performance matters. Keep related data together, and access it sequentially when possible.
Every time we allocate memory, there’s a cost. The memory allocator has to find available space, bookkeeping data structures need updating, and the new object typically needs initialization. Later, when we’re done with the object, it needs cleanup or destruction. In garbage-collected languages, the collector has to track these objects and eventually reclaim them, which can cause noticeable pauses.
Beyond the allocator overhead, each allocation typically ends up on a different cache line. If we’re creating many small objects independently, they’ll be scattered across memory, hurting cache efficiency as discussed in the previous section.
Common sources of excessive allocation include creating temporary objects inside loops, repeatedly resizing containers as we add elements, and copying data when we could move it or just reference it.
Container pre-sizing is an effective technique. If we know we’ll eventually need a vector with 1000 elements, we should reserve that space upfront. Otherwise, the vector might allocate space for 10 elements, then 20, then 40, and so on as we add elements, copying all existing data each time.
Reusing objects is another straightforward win. If we’re creating and destroying the same type of temporary object thousands of times in a loop, we should create it once before the loop and reuse it on each iteration, clearing it as needed. This is especially important for complex objects like strings, collections, or protocol buffers.
Modern languages support “moving” data instead of copying it. Moving transfers ownership of the data without duplicating it. When we’re passing large objects between functions and don’t need to keep the original, moving is much cheaper than copying.
Different container types have different allocation characteristics. Maps and sets typically allocate memory for each element individually. Vectors and arrays are allocated in bulk. When performance matters, and we’re choosing between containers with similar capabilities, the allocation pattern can be the deciding factor.
The fastest code is code that never runs. Before we optimize how we do something, we should ask whether we need to do it at all or whether we can do it less frequently.
Some common strategies for the same are as follows:
Creating fast paths for common cases is a powerful technique. In many systems, 80% of cases follow a simple pattern while 20% require complex handling. If we can identify the common case and create an optimized path for it, most operations become faster even though the complex case remains unchanged. For example, if most strings in our system are short, we can optimize specifically for short strings while keeping the general implementation for longer ones.
Precomputation and caching exploit the idea that calculating something once is cheaper than calculating it repeatedly. If we have a value that doesn’t change during a loop, we should calculate it before the loop, not on every iteration. If we have an expensive calculation whose result might be needed multiple times, we should cache it after computing it the first time.
Lazy evaluation defers work until we know it’s actually needed. If we’re building a complex report that the user might never request, we shouldn’t generate it eagerly during initialization. Similarly, if we’re processing a sequence of items and might exit early based on the first few items, we shouldn’t process all items upfront.
Bailing out early means checking cheap conditions before expensive ones and returning as soon as we have an answer. If we’re validating user input and the first validation check fails, we shouldn’t proceed with expensive downstream processing. This simple principle can eliminate huge amounts of unnecessary work.
Finally, there’s the choice between general and specialized code. General-purpose libraries handle all possible cases, which often makes them slower than code written for one specific case. If we’re in a performance-critical section and using a heavyweight general solution, we should consider whether a lightweight specialized implementation would suffice.
Some performance considerations should become second nature as we write code, even before we profile.
When designing APIs, we should think about efficient implementation. Processing items in batches is usually more efficient than processing them one at a time. Allowing callers to pass in pre-allocated buffers can eliminate allocations in hot paths. We should avoid forcing features on callers who don’t need them. For example, if most users of a data structure don’t need thread-safety, we shouldn’t build synchronization into the structure itself. Let those who need it add synchronization externally.
We should watch out for hidden costs. Logging statements can be expensive even when logging is disabled, because checking whether logging is enabled still requires work. If we have a logging function called millions of times, we should consider removing it or checking the logging level once outside the hot path. Similarly, error checking inside tight loops can add up. When possible, we should validate inputs at module boundaries rather than checking the same conditions repeatedly.
Every language has performance characteristics for its standard containers and libraries. For example, in Python, list comprehensions and built-in functions are typically faster than manual loops. In JavaScript, typed arrays are much faster than regular arrays for numeric data. In Java, ArrayList almost always beats LinkedList. In C++, vector is usually the right choice over list. Learning these characteristics for our chosen language pays dividends.
String operations are quite common and often performance-sensitive. In many languages, concatenating strings in a loop is expensive because each concatenation creates a new string object. String builders or buffers solve this problem by accumulating the result efficiently and creating the final string once.
Not all code needs optimization. We shouldn’t optimize everything, just the parts that matter. The famous Pareto principle applies here: roughly 20% of our code typically accounts for 80% of the runtime. Our job is to identify and optimize that critical 20%.
Performance isn’t the only concern. Code readability also matters because other developers need to maintain our code. Overly optimized code can be difficult to understand and modify. Developer time has value, too. Spending a week optimizing code that saves one second per day might not be worthwhile.
The right approach is to write clear, correct code first. Then we should measure to find real bottlenecks, followed by optimizing the critical paths until we hit our performance targets. At that point, we stop. Additional optimization has diminishing returns and increasing costs in terms of code complexity.
That said, some situations clearly demand performance attention. Code that runs millions of times, like functions in the hot path of request handling, deserves optimization. User-facing operations should feel instant because perceived performance affects user satisfaction. Background processing at scale can consume enormous resources if inefficient, directly impacting operational costs. Resource-constrained environments like mobile devices or embedded systems need careful optimization to function well.
Writing fast code requires developing intuition about where performance matters and learning fundamental principles that apply across languages and platforms.
The journey starts with estimation. Understanding the rough costs of common operations helps us make smart architectural decisions before writing code. Measurement shows us where the real problems are, often surprising us by revealing bottlenecks we never suspected. Better algorithms and data structures provide order-of-magnitude improvements that dwarf micro-optimizations. Memory layout and allocation patterns matter more than many developers realize. And often, the easiest performance win is simply avoiding unnecessary work.
The mindset shift is from treating performance as something we fix later to something we consider throughout development. We don’t need to obsess over every line of code, but we should develop awareness of the performance implications of our choices.
2026-01-28 00:30:31
Connecting user accounts to third-party APIs always comes with the same plumbing: OAuth flows, token storage, refresh logic, and provider-specific quirks.
WorkOS Pipes removes that overhead. Users connect services like GitHub, Slack, Google, Salesforce, and other supported providers through a drop-in widget. Your backend requests a valid access token from the Pipes API when needed, while Pipes handles credential storage and token refresh.
Sometime before 2019, Google built a system that manages permissions for billions of users while maintaining both correctness and speed.
When you share a Google Doc with a colleague or make a YouTube video private, a complex system works behind the scenes to ensure that only the right people can access the content. That system is Zanzibar, Google’s global authorization infrastructure that handles over 10 million permission checks every second across services like Drive, YouTube, Photos, Calendar, and Maps.
In this article, we will look at the high-level architecture of Zanzibar and understand the valuable lessons it provides for building large-scale systems, particularly around the challenges of distributed authorization.
See the diagram below that shows the high-level architecture of Zanzibar.
Disclaimer: This post is based on publicly shared details from the Google Engineering Team. Please comment if you notice any inaccuracies.
Authorization answers a simple question: Can this particular user access this particular resource? For a small application with a few users, checking permissions is straightforward. We might store a list of allowed users for each document and check if the requesting user is on that list.
The challenge multiplies at Google’s scale. For reference, Zanzibar stores over two trillion permission records and serves them from dozens of data centers worldwide. A typical user action might trigger tens or hundreds of permission checks. When searching for an artifact in Google Drive, the system must verify your access to every result before displaying it. Any delay in these checks directly impacts user experience.
Beyond scale, authorization systems also face a critical correctness problem that Google calls the “new enemy” problem. Consider the scenario where we remove someone from a document’s access list, then add new content to that document. If the system uses stale permission data, the person who was just removed might still see the new content. This happens when the system doesn’t properly track the order in which you made changes.
Zanzibar solves these challenges through three key architectural decisions:
A flexible data model based on tuples.
A consistency protocol that respects causality.
A serving layer optimized for common access patterns.
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Zanzibar represents all permissions as relation tuples, which are simple statements about relationships between objects and users. A tuple follows this format: object, relation, user. For example, “document 123, viewer, Alice” means Alice can view document 123.
See the diagram below:
This tuple-based approach differs from traditional access control lists that attach permissions directly to objects. Tuples can refer to other tuples. Instead of listing every member of a group individually on a document, we can create one tuple that says “members of the Engineering group can view this document.” When the Engineering group membership changes, the document permissions automatically reflect those changes.
The system organizes tuples into namespaces, which are containers for objects of the same type. Google Drive might have separate namespaces for documents and folders, while YouTube has namespaces for videos and channels. Each namespace defines what relations are possible and how they interact.
Zanzibar’s clients can use a configuration language to specify rules about how relations are composed. For instance, a configuration might state that all editors are also viewers, but not all viewers are editors.
See the code snippet below that shows the configuration language approach for defining the relations.
name: “doc”
relation { name: “owner” }
relation {
name: “editor”
userset_rewrite {
union {
child { _this {} }
child { computed_userset { relation: “owner” } }
}
}
}
relation {
name: “viewer”
userset_rewrite {
union {
child { _this {} }
child { computed_userset { relation: “editor” } }
}
}
}Source: Zanzibar Research Paper
These rules, called userset rewrites, let the system derive complex permissions from simple stored tuples. For example, consider a document in a folder. The folder has viewers, and you want those viewers to automatically see all documents in the folder. Rather than duplicating the viewer list on every document, you write a rule saying that to check who can view a document, look up its parent folder, and include that folder’s viewers. This approach enables permission inheritance without data duplication.
The configuration language supports set operations like union, intersection, and exclusion. A YouTube video might specify that its viewers include direct viewers, plus viewers of its parent channel, plus anyone who can edit the video. This flexibility allows diverse Google services to specify their specific authorization policies using the same underlying system.
The “new enemy” problem shows why distributed authorization is harder than it appears. When you revoke someone’s access and then modify content, two separate systems must coordinate:
Zanzibar for permissions
Application for content
Zanzibar addresses this through tokens called zookies. When an application saves new content, it requests an authorization check from Zanzibar. If authorized, Zanzibar returns a zookie encoding the current timestamp, which the application stores with the content.
Later, when someone tries to view that content, the application sends both the viewer’s identity and the stored zookie to Zanzibar. This tells Zanzibar to check permissions using data at least as fresh as that timestamp. Since the timestamp came from after any permission changes, Zanzibar will see those changes when performing the check.
This protocol works because Zanzibar uses Google Spanner, which provides external consistency.
If event A happens before event B in real time, their timestamps reflect that ordering across all data centers worldwide through Spanner’s TrueTime technology.
The zookie protocol has an important property. It specifies the minimum required freshness, not an exact timestamp. Zanzibar can use any timestamp equal to or fresher than required, enabling performance optimizations.
Zanzibar runs on over 10,000 servers organized into dozens of clusters worldwide. Each cluster contains hundreds of servers that cooperate to answer authorization requests. The system replicates all permission data to more than 30 geographic locations, ensuring that checks can be performed close to users.
When a check request arrives, it goes to any server in the nearest cluster, and that server becomes the coordinator for the request. Based on the permission configuration, the coordinator may need to contact other servers to evaluate different parts of the check. These servers might recursively contact additional servers, particularly when checking membership in nested groups.
For instance, checking if Alice can view a document might require verifying if she is an editor (which implies viewer access), and whether her group memberships grant access, and whether the document’s parent folder grants access. Each of these checks can execute in parallel on different servers, which then combine the results.
The distributed nature of this processing can create potential hotspots. Popular content generates many concurrent permission checks, all targeting the same underlying data. Zanzibar employs several techniques to mitigate these hotspots:
First, the system maintains a distributed cache across all servers in a cluster. Using consistent hashing, related checks route to the same server, allowing that server to cache results and serve subsequent identical checks from memory. The cache keys include timestamps, so checks at the same time can share cached results.
Second, Zanzibar uses a lock table to deduplicate concurrent identical requests. When multiple requests for the same check arrive simultaneously, only one actually executes the check. The others wait for the result, then all receive the same answer. This prevents flash crowds from overwhelming the system before the cache warms up.
Third, for exceptionally hot items, Zanzibar reads the entire permission set at once rather than checking individual users. While this consumes more bandwidth for the initial read, subsequent checks for any user can be answered from the cached full set.
The system also makes intelligent choices about where to evaluate checks. The zookie flexibility mentioned earlier allows Zanzibar to round evaluation timestamps to coarse boundaries, such as one-second or ten-second intervals. This quantization means that many checks evaluate at the same timestamp and can share cache entries, dramatically improving hit rates.
Some scenarios involve deeply nested groups or groups with thousands of subgroups. Checking membership by recursively following relationships becomes too slow when these structures grow large.
Zanzibar includes a component called Leopard that maintains a denormalized index precomputing transitive group membership. Instead of following chains like “Alice is in Backend, Backend is in Engineering,” Leopard stores direct mappings from users to all groups they belong to.
Leopard uses two types of sets: one mapping users to their direct parent groups, and another mapping groups to all descendant groups. Therefore, checking if Alice belongs to Engineering becomes a set intersection operation that executes in milliseconds.
Leopard keeps its denormalized index consistent through a two-layer approach. An offline process periodically builds a complete index from snapshots. An incremental layer watches for changes and applies them on top of the snapshot. Queries combine both layers for consistent results.
Zanzibar’s performance reveals optimization for common cases. Around 99% of permission checks use moderately stale data, served entirely from local replicas. These checks have a median latency of 3 milliseconds and reach the 95th percentile at 9 milliseconds. The remaining 1% requiring fresher data have a 95th percentile latency of around 60 milliseconds due to cross-region communication.
Writes are slower by design, with a median latency of 127 milliseconds reflecting distributed consensus costs. However, writes represent only 0.25% of operations.
Zanzibar employs request hedging to reduce tail latency. After sending a request to one replica and receiving no response within a specified threshold, the system sends the same request to another replica and uses the response from the first replica that arrives. Each server tracks latency distributions and automatically tunes parameters like default staleness and hedging thresholds.
Operating a shared authorization service for hundreds of client applications requires strict isolation between clients. A misbehaving or unexpectedly popular feature in one application should not affect others.
Zanzibar implements isolation at multiple levels. Each client has CPU quotas measured in generic compute units. If a client exceeds its quota during periods of resource contention, its requests are throttled, but other clients continue unaffected. The system also limits the number of concurrent requests per client and the number of concurrent database reads per client.
The lock tables used for deduplication include the client identity in their keys. This ensures that if one client creates a hotspot that fills its lock table, other clients’ requests can still proceed independently.
These isolation mechanisms proved essential in production. When clients launch new features or experience unexpected usage patterns, the problems remain contained. Over three years of operation, Zanzibar has maintained 99.999% availability, meaning less than two minutes of downtime per quarter.
Google’s Zanzibar represents five years of evolution in production, serving hundreds of applications and billions of users. The system demonstrates that authorization at massive scale requires more than just fast databases. It demands careful attention to consistent semantics, intelligent caching and optimization, and robust isolation between clients.
Zanzibar’s architecture offers insights applicable beyond Google’s scale. The tuple-based data model provides a clean abstraction unifying access control lists and group membership. Separating policy configuration from data storage makes it easier to evolve authorization logic without migrating data.
The consistency model demonstrates that strong guarantees are achievable in globally distributed systems through careful protocol design. The zookie approach balances correctness with performance by giving the system flexibility within bounds.
Most importantly, Zanzibar illustrates optimizing for observed behavior rather than theoretical worst cases. The system handles the common case (stale reads) extremely well while supporting the uncommon case (fresh reads) adequately. The sophisticated caching strategies show how to overcome normalized storage limitations while maintaining correctness.
For engineers building authorization systems, Zanzibar provides a comprehensive reference architecture. Even at smaller scales, the principles of tuple-based modeling, explicit consistency guarantees, and optimization through measurement remain valuable.
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