---
title: "Architecture"
description: "834 posts about Architecture, summarised, each linking to the original."
---

# Architecture
> 834 posts about Architecture, summarised, each linking to the original.

## Articles

### [Frontend Engineering at Palantir: Polar Scaled Tiles in Zodiac](https://yomu.fyi/post/frontend-engineering-at-palantir-polar-scaled-tiles-in-zodiac.md)
- Company: [Palantir](https://yomu.fyi/company/palantir.md)
- Author: Palantir
- Published: Apr 15, 2026
- Type: Explainer

Zodiac, Palantir’s custom 3D globe library built on Three.js, encountered severe performance degradation near the Arctic because latitude/longitude tiles converge into tiny slivers and create excessive geometry. Standard equal-angular tiling was replaced internally with polar-scaled tiles whose widths expand toward the poles so their surface areas remain closer to equatorial tiles. The design uses spherical surface-area calculations, power-of-two merge spans, and dynamic quadtree expansion; client-side texture compositing handles standard geodetic or Web Mercator sources, while heightmaps are combined and resampled for wider tiles. The change reduced polar geometry, draw calls, and textures by over 90% and made frame time more consistent, though texture loading remains the next bottleneck and native polar tiles would remove stitching overhead.


### [New Research Reveals Overcoming Legacy Tech Issues Key to AI Success](https://yomu.fyi/post/new-research-reveals-overcoming-legacy-tech-issues-key-to-ai-success.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Dr. William Lee
- Published: Apr 14, 2026
- Type: Benchmark

An IDC survey of 1,400 organizations across eight Asia/Pacific markets reveals that legacy architectures and accumulated data debt severely hinder enterprise artificial intelligence initiatives. While 43% of surveyed organizations cite legacy systems as a major obstacle to application development, 95% report project delays and 90% have experienced failed modernization attempts. In contrast, high-performing organizations treat modernization as a continuous discipline across people, processes, and cloud-centric data platforms capable of processing both structured and unstructured workloads. These leading enterprises achieve nearly three times more digital revenue growth than peers still constrained by technical debt. IDC notes that addressing technical and data debt proactively avoids escalating AI failure rates and delivers superior return on investment compared to superficial lift-and-shift migrations.


### [Agentic coding with ClickHouse. One person, one data stack, one full-stack application](https://yomu.fyi/post/agentic-coding-with-clickhouse-one-person-one-data-stack-one-full-stac.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Oussama Chakri
- Published: Apr 14, 2026
- Type: Problem & solution

A Solutions Architect at ClickHouse built ClickShop, a full-stack retail analytics platform, within a few days using Cursor to demonstrate production-level capabilities beyond static slides. The platform pairs ClickHouse for sub-second analytical queries across billions of rows with PostgreSQL for transactional workflows, using ClickPipes for real-time change data capture synchronization. LibreChat orchestrates 18 persona-tailored AI agents, while dual observability layers—ClickStack for OpenTelemetry infrastructure metrics and Langfuse for LLM prompt and cost tracking—store their telemetry directly in ClickHouse. Developing with an AI-assisted IDE was accelerated by open-source documentation and community skills, though it required continuous human review to handle unintended code refactoring and token consumption.


### [Mintlify boosts NPS 30% and saves 60% with real-time analytics on ClickHouse Cloud](https://yomu.fyi/post/mintlify-boosts-nps-30-and-saves-60-with-real-time-analytics-on-clickh.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 14, 2026
- Type: Problem & solution

Documentation platform Mintlify experienced major dashboard performance degradation and rate limiting under PostHog as multi-tenant analytics traffic grew rapidly. Because PostHog lacked incremental materialized views, queries scanned full raw datasets and took tens of seconds to load. Mintlify migrated its customer-facing analytics infrastructure to ClickHouse Cloud, ingesting events through Amazon MSK and ClickPipes into a single events table backing incremental materialized views. The team executed the transition over two weeks by dual-writing events and replicating the existing PostHog schema to preserve API surface compatibility. The migration reduced dashboard query latency to sub-one-second, decreased related support bug reports to zero, lifted NPS by an estimated 30%, and lowered analytics infrastructure costs by approximately 60%.


### [Build Better AI Agents: 5 Developer Tips from the Agent Bake-Off](https://yomu.fyi/post/build-better-ai-agents-5-developer-tips-from-the-agent-bake-off.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Frank Guan, Abraham Gomez
- Published: Apr 14, 2026
- Type: Tutorial

The Google Cloud AI Agent Bake-Off is presented as a test of building autonomous agents under severe time constraints for e-commerce, banking modernization, and startup go-to-market problems. Its five patterns recommend decomposing monolithic prompts into specialized sub-agents under a supervisor, designing modularly because model capabilities can quickly replace custom harnesses, and treating multimodality as native. It also advocates open protocols—including MCP, A2A, UCP, AP2, A2UI, and AG-UI—for agent and tool connections instead of bespoke wrappers. Finally, it separates probabilistic reasoning from execution: strict JSON schemas such as Pydantic validate LLM outputs before deterministic Python functions or SQL perform calculations and writes. The stated conclusion is that reliable production agents depend on rigorous software architecture rather than elaborate prompts or flashy single-shot demos.


### [Managing context in long-run agentic applications](https://yomu.fyi/post/managing-context-in-long-run-agentic-applications.md)
- Company: [Slack](https://yomu.fyi/company/slack.md)
- Author: Dominic Marks
- Published: Apr 13, 2026
- Type: Problem & solution

Long-running multi-agent systems struggle with context management because accumulating raw message histories degrades inference quality, increases latency, and exceeds context window limits. In a collaborative security investigation platform, passing unrestricted history can also introduce confirmation bias across specialized agents. To maintain coherence across unbounded rounds of investigation, the system eliminates raw message history carryover between invocations. Instead, it coordinates agents through three structured context channels: a Director's Journal for orchestration memory, a Critic's Review that scores findings to filter hallucinations, and a Critic's Timeline of validated chronological events. This architecture provides agents with tailored context without overwhelming their inference capacity.


### [Using AI agents to build Stripe-Salesforce integrations faster](https://yomu.fyi/post/using-ai-agents-to-build-stripe-salesforce-integrations-faster.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Vishnu Samavedula
- Published: Apr 13, 2026
- Type: Problem & solution

An experiment using Claude Code to build a Stripe App for Salesforce Flow exposed context gaps: the generated integration misnamed fields, targeted a nonexistent custom field, and omitted AsyncAfterCommit, causing a “Callout from triggers not supported” deployment error. The proposed remedy is a repository of grounding context files that present invocable signatures, request models, platform constraints, LWC patterns, and validated examples in concise, machine-readable form. Core files are about 35K tokens, while the full reference catalog is about 230K tokens and can be loaded on demand. With the context loaded, the agent selected the v02\_CreateCustomers invocable directly and generated a helper Apex class for nested Checkout Session inputs, along with callout-safe Flow structure and other safeguards. The author reports minutes instead of hours and fewer iterations in personal usage, while noting that org-specific details still require human verification.


### [Enabling Multi-Region Data Systems at Notion](https://yomu.fyi/post/enabling-multi-region-data-systems-at-notion.md)
- Company: [Notion](https://yomu.fyi/company/notion.md)
- Author: Justin Lee, Adam Hudson
- Published: Apr 9, 2026
- Type: Explainer

Notion’s data residency work keeps customer data processed and stored in the region where it was generated, supporting EU requirements across Search, analytics, scheduled events, and AI. To make this possible, Notion modularized infrastructure around isolated private networks and workspace IDs, then deployed regional data lakes, Kafka clusters, Spark jobs, Elasticsearch, and vector databases. Debezium captures Postgres changes into regional Kafka, while Airflow remains a centralized orchestration layer that routes jobs to regional EMR clusters; event logging similarly routes traffic by workspace and sanitizes data before Snowflake. Centralized mapping tables, shared region helpers, Terraform, and localized sanitization let teams retain a unified Snowflake environment without exporting customer data. The work took months, and Notion says it is applying these patterns to Japan and South Korea while aiming to provision future regional data stacks within days rather than months.


### [The three villains to agentic observability: retention, sampling and rollups](https://yomu.fyi/post/the-three-villains-to-agentic-observability-retention-sampling-and-rol.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mike Shi
- Published: Apr 8, 2026
- Type: Problem & solution

Observability practices increasingly depend on data sampling, short retention windows, and rollups to control storage costs. While human operators historically compensated for these compromises with intuition, AI agents lack implicit operational context and require full-fidelity telemetry to diagnose issues and justify conclusions. Head sampling drops unobserved spans early, tail sampling risks missing implicit non-error anomalies, and aggressive log expiration prevents long-term cyclical trend analysis. Addressing these constraints requires decoupling storage and compute via high-compression object storage and dedicated compute pools. Deploying real-time analytical databases such as ClickHouse enables high-concurrency, low-latency SQL querying, log clustering, and structured in-database summarization necessary for automated reasoning loops.


### [MongoDB Predictive Auto-Scaling: An Experiment](https://yomu.fyi/post/mongodb-predictive-auto-scaling-an-experiment.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: A. Jesse Jiryu Davis, Matthieu Humeau
- Published: Apr 7, 2026
- Type: Problem & solution

MongoDB Atlas initially relied on a reactive auto-scaler that took minutes to adjust instance sizes and only transitioned between adjacent tiers. Because these delays caused transient server over-utilization or unnecessary customer costs, researchers prototyped predictive auto-scaling to forecast demand. The experimental architecture used short-term and long-term forecasters for customer-driven metrics, an estimator using boosted decision trees to project CPU utilization, and a planner to pick the most cost-effective tier. In testing, the prototype kept replica sets closer to target CPU ranges while saving an average of nine cents per replica set hourly. MongoDB subsequently implemented a production version that scales replica sets up proactively before expected spikes while retaining reactive downscaling.


### [Frontend Engineering at Palantir: Plotlines in Three.js](https://yomu.fyi/post/frontend-engineering-at-palantir-plotlines-in-three-js.md)
- Company: [Palantir](https://yomu.fyi/company/palantir.md)
- Author: Palantir
- Published: Apr 7, 2026
- Type: Explainer

Palantir’s frontend engineers are building a 3D implementation of Gaia to visualize Foundry Ontology objects with geospatial properties, including ground locations, satellite positions, and paths through space. The system uses Zodiac, a Three.js library backed by Foundry-based APIs: the backend stores, filters, and computes data, while the frontend receives simplified geometry and style information for rendering. Performance techniques include converting latitude and longitude to Earth-fixed coordinates once, batching similarly styled lines into single draw calls, and using instanced meshes plus batched updates for dense satellite scenes; orbital propagation uses TLEs client-side, while heavier analysis remains server-side. The article also describes frame-synchronized animation callbacks that update material properties without rebuilding geometry, while noting that 3D APIs continue to iterate across usability, platform integration, and performance as new data sources and workflows are added.


### [Frontend Engineering at Palantir: Building a Backend-less Cross-Application API](https://yomu.fyi/post/frontend-engineering-at-palantir-building-a-backend-less-cross-applica.md)
- Company: [Palantir](https://yomu.fyi/company/palantir.md)
- Author: Palantir
- Published: Apr 6, 2026
- Type: Explainer

Palantir’s Application Frameworks team builds the “backend of the frontend”: frameworks that let applications communicate during workflows spanning multiple interfaces. The work addresses environments with denied, disrupted, intermittent, or limited (DDIL) connectivity, where relying on backend services can introduce unreliable latency. The team generalized the Gaia Controller prototype into Shared State, which lets applications define named data models and exchange updates organized by Media Type, and Commands, which exposes arbitrary logic through command IDs and request objects that return responses. Both frameworks use frontend-only browser APIs for client-side interaction, support workflows such as pairing Gaia with Workshop, and are also being used as tools by AIP agents; the post says adoption is growing across Palantir.


### [Improving storage efficiency in Magic Pocket, our immutable blob store](https://yomu.fyi/post/improving-storage-efficiency-in-magic-pocket-our-immutable-blob-store.md)
- Company: [Dropbox](https://yomu.fyi/company/dropbox.md)
- Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel
- Published: Apr 2, 2026

After introducing a direct erasure-coding service, Dropbox encountered severe storage fragmentation in its immutable blob store, Magic Pocket, due to a large number of under-filled volumes. Because their legacy compaction strategy was only designed to top off near-full volumes, the team created new multi-strategy compaction methods to aggressively consolidate sparse volumes. These approaches leverage dynamic programming and streaming pipelines to rapidly reclaim unused disk capacity at exabyte scale.


### [Welcome Gemma 4: Frontier multimodal intelligence on device](https://yomu.fyi/post/welcome-gemma-4-frontier-multimodal-intelligence-on-device.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: merve, Pedro Cuenca, Sergio Paniego, ben burtenshaw, Steven Zheng, Alvaro Bartolome, Nathan Habib
- Published: Apr 2, 2026
- Type: Announcement

Google DeepMind’s Gemma 4 family is available on Hugging Face as Apache 2-licensed, multimodal models intended for use across agents, inference engines, fine-tuning libraries, and on-device deployments. The family accepts image and text inputs across five sizes, while E2B, E4B, and 12B Unified also support audio; context windows range from 128k to 256k tokens. Its design combines alternating local and global attention, dual RoPE configurations, Per-Layer Embeddings, shared KV caches, and model-specific vision and audio handling, including an encoder-free unified 12B variant. The post documents integrations for Transformers, llama.cpp, MLX, WebGPU, Rust, and other tools, alongside fine-tuning examples and demos. Reported instruction-tuned results include a 1452 estimated text-only LMArena score for 31B and benchmark results spanning reasoning, coding, vision, audio, and long-context tasks.


### [Rill and ClickHouse: real-time operational BI for a metered world](https://yomu.fyi/post/rill-and-clickhouse-real-time-operational-bi-for-a-metered-world.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 1, 2026
- Type: Problem & solution

Granular operations across cloud computing, payment platforms, and artificial intelligence workloads produce high-volume event streams that traditional business intelligence tools struggle to unify and aggregate. Operational BI platform Rill addresses this challenge by combining declarative data loading through the dlt framework with ClickHouse's high-performance analytical aggregation engine. Data transformations, SQL-based measure expressions, and dashboard configurations are defined entirely as code using SQL and YAML. This declarative, metrics-first architecture allows developers to validate analytics locally, track changes in Git, and eliminate intermediate caching layers when querying live data. By querying ClickHouse directly and linking AI-generated insights back to underlying queries, the system provides traceable, real-time operational analytics for enterprise environments.


### [Falcon Perception](https://yomu.fyi/post/falcon-perception.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: FalconPerception
- Published: Apr 1, 2026
- Type: Announcement

Falcon Perception is a 0.6B-parameter early-fusion Transformer for open-vocabulary grounding and segmentation from natural-language prompts. It processes image patches, text, and task tokens in one sequence, using hybrid attention so image tokens receive bidirectional context while text and task tokens decode causally. Its Chain-of-Perception interface predicts each instance as coordinate, size, and segmentation tokens, with specialized Fourier-feature and dot-product heads for dense outputs. The post also introduces PBench, which profiles attributes, OCR-guided identification, spatial understanding, relations, and crowded scenes. Falcon Perception reaches 68.0 Macro-F1 on SA-Co versus 62.3 for SAM 3, while presence calibration remains weaker at MCC 0.64 versus 0.82; the 0.3B Falcon OCR model scores 80.3 on olmOCR and 88.6 on OmniDocBench.


### [Any Custom Frontend with Gradio's Backend](https://yomu.fyi/post/any-custom-frontend-with-gradio-s-backend.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: yuvraj sharma, Abubakar Abid
- Published: Apr 1, 2026
- Type: Tutorial

gradio.Server enables applications built with React, Svelte, or plain HTML/JavaScript to use Gradio’s backend facilities instead of Gradio components. The post demonstrates this with Text Behind Image, whose browser UI layers a background, styled text, and a transparent foreground produced by BiRefNet, then exports a PNG client-side. A roughly 50-line Python backend extends FastAPI, exposes a standard HTML route, and defines an @app.api("remove\_background") endpoint; @spaces.GPU handles ZeroGPU allocation while Gradio supplies queuing, concurrency control, SSE streaming, and gradio\_client compatibility. The frontend calls the endpoint through the Gradio JavaScript client, so image-processing requests pass through the queue while rendering and compositing remain in the browser. The result is a custom frontend that retains Spaces hosting and Gradio infrastructure.


### [Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents](https://yomu.fyi/post/granite-4-0-3b-vision-compact-multimodal-intelligence-for-enterprise-d.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Madison Lee, Rogerio Feris, Eli Schwartz, Dhiraj Joshi, Pengyuan Li, Isaac Sanchez
- Published: Mar 31, 2026
- Type: Announcement

Granite 4.0 3B Vision is a compact vision-language model designed to extract information from enterprise documents, including tables, charts, forms, and semantically meaningful key-value pairs. It combines a million-scale ChartNet dataset with a code-guided synthesis pipeline, DeepStack Injection for routing semantic and high-resolution visual features to different layers, and a LoRA adapter architecture built on Granite 4.0 Micro. The modular design supports standalone image processing, text-only fallback, and integration with Docling for multi-page document pipelines. Reported evaluations show 86.4% on Chart2Summary, leading the evaluated models, second place on Chart2CSV at 62.1%, leading scores on the listed table benchmarks, and 85.5% zero-shot exact-match accuracy on VAREX.


### [Training mRNA Language Models Across 25 Species for $165](https://yomu.fyi/post/training-mrna-language-models-across-25-species-for-165.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Maziyar Panahi
- Published: Mar 31, 2026
- Type: Benchmark

OpenMed describes an end-to-end protein engineering pipeline that connects ESMFold structure prediction, ProteinMPNN sequence design, and custom mRNA codon optimization. The codon-language-model study compares CodonBERT, ModernBERT, and several CodonRoBERTa variants using 250,000 E. coli coding sequences, a 69-token codon vocabulary, masked-language modeling, and training on four 80GB A100 GPUs. CodonRoBERTa-large-v2 achieved perplexity 4.10, CAI Spearman correlation 0.404, and 7.7% synonymous recovery, while ModernBERT-base reached perplexity 26.24 and correlation 0.070. The work then trained a multi-species suite on 381,283 sequences spanning 25 species, producing four production models in 55 GPU-hours and documenting planned extensions such as larger training data and additional prediction heads.


### [ClickHouse BYOC on Google Cloud  now Generally Available](https://yomu.fyi/post/clickhouse-byoc-on-google-cloud-now-generally-available.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Aashish Kohli, Yiyang Shao
- Published: Mar 31, 2026
- Type: Announcement

ClickHouse Bring Your Own Cloud is now generally available on Google Cloud across all public ClickHouse Cloud Google regions without requiring a waitlist. The architecture separates the ClickHouse-managed control plane from the customer data plane, which runs entirely within the user Google Cloud project on Google Kubernetes Engine and Google Cloud Storage. An outbound-only, encrypted Tailscale zero-trust tunnel restricts control plane communication strictly to orchestration traffic, ensuring query data never leaves the customer environment. Deployment involves a three-step Terraform setup that establishes least-privilege IAM roles, configures VPC networking, and automates cluster provisioning. ClickHouse manages database and infrastructure maintenance through ArgoCD, employing a make-before-break strategy to add updated replicas before decommissioning older nodes across customizable release channels.


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