标签：#ev

Last month I shared how I built 143 free online tools that run 100% in your browser — no signup, no uploads, no watermarks. That post got a great response (and a lot of "how is this free?" comments — answer: it stays free because files never touch a server, so there are no processing costs). Today's update: every single tool now has a full guide series. What's new 144 how-to articles — one per tool — live at toolfyra.vercel.app/blog : Step-by-step guides — every input explained, common pitfalls, pro tips Real competitor comparison tables (we scraped and analyzed who ranks for what, and where their tools annoy users with account walls) FAQ sections with schema markup so answers surface directly in search and AI assistants Unique generated illustrations per article Smart related-tools clusters — finish one task, the next tool is one click away Why guides for calculator tools? Because "how to use a calculator" is what people actually search for. Tools win clicks; guides win trust and rankings . Each article is built from real search-engine data: live SERP results, keyword expansions, and competitor FAQ analysis — zero guesswork. The engineering side (for the dev readers) Every tool is a single HTML page with vanilla JS — calculators run client-side, file tools use Canvas/FileReader APIs The blog is generated (Python build script): schema.org BlogPosting + FAQPage + BreadcrumbList, per-post OG images as optimized SVGs, canonical URLs, sitemap + IndexNow pings on every deploy New site-wide: instant search (type "pdf" → live results dropdown, keyboard-first: / to focus, ↑↓ to navigate), a Tools dropdown with 11 categories, and a mobile hamburger panel — all vanilla JS, no dependencies Privacy by architecture: there is literally no upload endpoint to breach What's next More waves of content (FAQ, mistakes-to-avoid, and comparison articles for every tool) A batch of new tools from our demand-research pipeline (we score thousands of real search phrases before writing a line

Introduction: The Systemic Failure in Application Security Onboarding Consider the scenario of a junior Application Security Engineer tasked with securing a 2-billion-line codebase, written in unfamiliar languages, within a one-month deadline. This is not a theoretical exercise but the lived experience of a recent graduate in India, whose public appeal for assistance reveals profound deficiencies in how organizations integrate and support junior AppSec talent. The pressure is unrelenting, the tools are insufficient, and the expectations are disconnected from practical realities. This case is not an isolated incident but a symptom of a broader organizational failure to address the complexities of application security in high-stakes environments. The engineer’s experience underscores a critical misalignment: the exponential growth in codebase complexity has outstripped the resources and guidance provided to those responsible for securing them. Absent a senior AppSec mentor, with limited proficiency in critical languages such as Laravel/PHP and C#, and equipped only with rudimentary tools like grep and Codex, the engineer is forced to navigate an environment rife with unseen risks. The consequences are twofold: individual inefficiency and self-doubt, compounded by organizational exposure to unmitigated security threats. The causal pathway is unambiguous: massive codebases + unrealistic deadlines + subpar tools + absent mentorship → overwhelmed engineers → cursory reviews → undetected vulnerabilities → systemic security compromise. The risks extend beyond individual burnout to include data breaches, financial liabilities, and reputational damage. This is not an edge case but a predictable outcome of organizational neglect. The urgency is undeniable. As software systems increase in complexity and cyber threats proliferate, the demand for competent, adequately supported AppSec professionals has never been more critical. Yet, organizations persist in failing to bridge the

Grand Theft Auto VI is nigh. Here’s what the developer revealed about its highly anticipated game.

The JVM ecosystem offers a wide range of powerful frameworks, each with its own strengths and capabilities. In a modern distributed architecture, however, applications are not always built using the same framework. Services developed with frameworks such as Quarkus, Micronaut, and Spring Boot may need to communicate seamlessly as part of the same system. This guide demonstrates how RabbitMQ can enable cross-framework asynchronous communication between JVM applications. We will build two applications using different frameworks: a Quarkus application that publishes LeaveRequest messages and a Micronaut application that consumes and processes them. The first application, built with Quarkus, publishes a LeaveRequest object as a message to RabbitMQ. The second application, built with Micronaut, receives the LeaveRequest message and processes it according to the application's business logic. By the end of this guide, you will have a practical understanding of how two applications built with different Java frameworks can communicate asynchronously using RabbitMQ. Lets begin the journey To ensure that both applications use a consistent message contract, create a separate Gradle project named common. This project will contain the shared LeaveRequest model and can be referenced as a dependency by both the Quarkus and Micronaut applications. @Introspected @Serdeable public record LeaveRequest ( String personName , String personRole , String facilityName , String wardName , String shiftName , String leaveReason , String recipientName , String recipientEmail , String recipient , String subject ) {} The dependency on the common project will be dependencies { annotationProcessor ( "io.micronaut:micronaut-inject-java:5.1.12" ) implementation ( "io.micronaut.serde:micronaut-serde-jackson:3.1.1" ) } The @Introspected and @Serdeable annotations enable Micronaut to generate the metadata required for efficient introspection and serialization. Connecting Quarkus to RabbitMQ To connect th

Hey everyone! 👋 Finding reliable, free public APIs for side projects or learning is always a hassle because many listed APIs eventually go down or become paid. To solve this, I created Awesome Free APIs Live — an open-source, curated collection of 50+ free public APIs across AI, developer tools, security, and open data. ⚙️ How It Works Daily Health Checks: Powered by GitHub Actions , an automated script tests endpoints daily and updates live status badges. Zero Dead Links: Broken endpoints are flagged automatically so developers don't waste time debugging dead services. Categorized & Searchable: Clean, categorized UI hosted on GitHub Pages. ### 🔗 Links 🌐 Live Directory: shilpshakti.github.io/awesome-free-apis-live ⭐ GitHub Repository: github.com/ShilpShakti/awesome-free-apis-live Contributions are very welcome! If you know of any great free APIs, feel free to open a PR or check out the open issues on GitHub.

What is ZeroDroid? ZeroDroid is an open-source Android toolkit that exposes the radios, sensors and connected-device capabilities already present in a phone. GitHub: https://github.com/theabhishekchandra/ZeroDroid What problems does it address? The app contains 29 tools across five areas: Wireless: Wi-Fi, BLE, NFC, Bluetooth Classic and peer-to-peer connections RF and signals: IR, UWB, SDR-device detection and ultrasonic analysis Sensors: GPS/GNSS, QR analysis, device sensors and magnetic anomalies Network: USB inspection, cell-tower information and wardriving Security: tracker scanning, hidden-camera indicators, rogue-AP detection, network scanning and deauthentication indicators Architecture ZeroDroid uses Kotlin, Jetpack Compose, Material 3, MVVM, StateFlow, Hilt and Room. Services are lazy-loaded, and scanning begins only when the user starts a tool. Important limitations A smartphone cannot guarantee that it has found every camera, tracker, bug or network threat. Several detections are heuristic and may produce false positives or miss threats. Hardware availability also differs between Android devices. The project is intended only for education, defensive security and testing devices or networks you own or are authorized to assess. Feedback wanted I am looking for: Compatibility reports from different Android phones Feedback about permission handling False-positive reports Contributions, tests and documentation improvements Repository: https://github.com/theabhishekchandra/ZeroDroid

When testing opinionated Linux distributions, the ultimate benchmark isn't how smoothly they run on a workstation with 16 cores and a high-end GPU—it's how gracefully they perform on budget, resource-constrained hardware. Enter Omarchy , the "omakase" Arch-based distribution created by David Heinemeier Hansson (DHH) . Built around the Hyprland tiling window manager and explicitly tailored for modern developer productivity, Omarchy proves that a curated desktop environment doesn't require a heavy computing footprint. Running Omarchy 4.0.0 on an entry-level laptop built around an Intel Celeron N4020 CPU demonstrates how deliberate software curation turns modest hardware into a fast, highly capable development machine. 💻 Hardware & System Overview Below is the environment breakdown from our test run: Category Specification / Details Hardware / PC Model ASUS C204M Processor Intel® Celeron® N4020 (2 cores / 2 threads) @ 2.80 GHz Graphics Integrated Intel UHD Graphics 600 Display 11" Built-in Display (1366x768 @ 60 Hz) RAM Utilization 2.69 GiB / 3.68 GiB (~73% load) Storage / Root 15.66 GiB / 27.10 GiB (~58% used) on Btrfs OS & Kernel Omarchy 4.0.0-1 (Linux Kernel 7.1.8-arch1-3) Compositor Hyprland 0.56.2 (Wayland) 🚀 The Developer Experience: What Makes Omarchy Special Omarchy isn't just an Arch installer with custom dots; it's an opinionated operating system designed to eliminate setup friction and let you write code immediately. 1. Zero-Friction Language Setup via Menus Setting up language runtimes on a fresh Linux install often involves hunting down version managers (like asdf , nvm , or pyenv ), configuring shell initialization scripts, and managing system paths. Omarchy streamlines this entirely. Through its integrated menu system, installing a programming language or developer stack is as simple as launching the system menu, picking a language (Node.js, Ruby, Python, Go, Rust), and hitting Enter. The system automatically installs the necessary version managers, conf

Claire McDonough is stepping down on October 30 to pursue a new opportunity, the company said in a filing on Thursday.

Indexar o código fora do repo: como economizar tokens sem jogar o projeto no contexto Pessoal, o agent precisava achar um símbolo. Trabalho de um minuto. Na prática, ele abria arquivo atrás de arquivo, colava dump de teste no papo e a janela sumia. Às vezes a fatura também. Não era o modelo burro. Era eu pagando o monorepo inteiro pra responder a pergunta errada. A pergunta mudou. Deixei de ser “qual tool faz o agent entender o repo?” e virei: o que é memória de domínio, e o que é só custo de ler código nesta sessão? Tem um segundo motivo, e ele não é economia. Um índice de símbolos é um mapa do seu sistema : quem chama o quê, onde está o fluxo crítico. Se esse mapa mora no git, no cache de CI ou num serviço que o agent também escreve, o blast radius não é só token. É superfície. Duas contas, um prompt Memória de domínio é política. O que pode ser lembrado, por qual porta se entra, o que é canônico. Notas, contratos, “onde a gente decide X”. Indexer de código não resolve isso. Code-read barato é custo de sessão. Achar caller e símbolo sem despejar o working tree no prompt. Isso não deveria virar a sua base de conhecimento. Eu misturava. O indexer virava KB. O vault virava grep sem porta. Os dois falhavam, e a sessão inchava igual. Economizar token aqui não é trocar de modelo da semana. É separar camada. E decidir onde o mapa vive . O que eu mudei na mesa O mapa de símbolos saiu do working tree. Cache local, fora do repo , fora do git. Reindex é operação de máquina, não de PR. O agent consulta o índice; não precisa reler o monorepo pra “quem chama essa função?”. Quatro perguntas que eu faço antes de indexar um repo (vale colar no README do setup): O índice vive na minha máquina ou sai dela (cloud, CI, cache compartilhado)? Entra em contexto de agent que também tem tool de escrita ? Como eu apago e revogo? Quem mais lê isso? Índice ≠ fonte de verdade versionada. Least privilege no que entra no contexto continua valendo. Depois, parei de mandar firehose de CLI cru. tes

Last week, it came to light Cursor had mostly finished migrating from SolidJS to React . This migration happened about seven months ago. But it became a central focus of discussion following the Solid 2.0 RC release . Then yesterday, a week later, it came to my attention that the Anthropic docs example command for their large-scale migration feature is: I admit that my gut reaction was not great. Out of all the examples they could have chosen... Years of my work became a canonical example of the thing you migrate away from — in the same week we shipped the biggest release in the project's history — stung in a way I won't pretend it didn't. My second reaction was to assume that, like the other trickle-down posts I'd seen this week, this rode the same week-old news cycle. Then I checked the Internet Archive and realized this has been there since at least April 2026 . Four months before the Cursor story broke. At this point, the whole public footprint was a mention of an experiment sandwiched between bigger updates in a Cursor blog post posted in January. The kind of thing that no one outside the industry would even really pick up on. No reasoning, no benchmarks, no argument. Stop to think about what that means. I should be careful here because I can't prove anyone at Anthropic ever read that Cursor post. Nobody can. Maybe a docs writer saw the experiment. Maybe Claude drafted its own example. But think it through. Either it traveled from a buried line in one company's release notes into another company's official docs, or it needed no origin at all. It was already assumed before any public migration existed. Our industry has quietly started broadcasting conclusions where it used to transmit arguments. We couldn't have picked a worse time, because — as I'll get to — arguments are the only source that still matters. Why This Matters More Than It Used To It would be fair to ask, hasn't it always been like this? Teams cargo cult large players. Netflix or Facebook uses thi

Every hackathon has the same 90-second moment of dread: someone hands you a codebase you've never seen, and you have to make sense of it before the clock runs out. File trees don't help. grep doesn't help. You waste the first 30–60 minutes reading the wrong files, missing a hidden dependency, and stepping straight into a production trap nobody warned you about. In other words: before writing code, you spend half your time trying to figure out where the hell the code is. The idea wasn't "AI that writes your code." It was "AI that tells you where to look before you write it." For InnovaHack Chapter-1, my team built Waypoint — a dev onboarding platform. Point it at any GitHub repo or a local folder, describe a task like "Add a new global configuration flag to app.set()", and instead of you reading the whole codebase to figure out where that even goes, it hands you a Mission Brief : exactly which files you'll touch, the traps waiting in them, what to learn first, and the order to do it in. Waypoint made the Top 50 — one of the 50 chosen to advance to Round 2. Here's how it actually works under the hood, what it took to build, what's next for it — and since I don't believe in only posting the highlight reel, what happened after we placed that made us walk away from the next round. The problem: the cold-start tax Every time a developer joins a new codebase, or picks up an unfamiliar task in one they already know, there's a tax paid in wrong files read, missed dependencies, and traps hit blind. For me, that moment came when I wanted to contribute to Forem — the open-source project that actually powers DEV. I didn't know a line of Ruby on Rails, and between learning the language, understanding the framework, and preparing for interviews, I didn't have the time to read an entire unfamiliar codebase just to figure out where one feature belonged. I never ended up making that contribution. But the problem stuck with me. It's also a pattern for our team more broadly: we delibera

We build GPTree with several coding agents working the same repository at once: Claude Code, Codex, and Cursor, each in its own git worktree. The failure that finally made us build tooling for it was small and completely silent. One session was told to replace PaymentService with a Stripe-specific implementation. Another was told to add PayPal support to PaymentService . Different worktrees. Different files. Zero textual conflict. Git merged both branches cleanly, and the second change now depended on an extension point the first had deleted. Nothing in the toolchain had an opinion about it at any moment. Git compares diffs. It cannot compare plans. Worktrees isolate files, not plans Worktrees became the standard answer to parallel agents for a good reason: two sessions editing one checkout will overwrite each other's files and poison each other's context. Isolated checkouts fix that completely. But three failure modes survive file isolation, because they were never about files: Destructive versus additive. One agent removes or replaces a thing another agent is building on. The example above. Merges clean, breaks the design. Duplicate work. Two agents solve the same problem from different angles because nothing assigned ownership. You pay twice and then pay again to reconcile. Contract drift. One agent changes an API, a schema, or a config contract while another codes against the old shape. Compiles, runs, disagrees at runtime. A shared task list helps with the second one, if every agent reads it, every time. Nothing in that setup catches the first or third, because the collision is between intentions, and intentions live in prompts, not in any file a tool can watch. Declare the work before doing it Foremerge is the internal tool we built for this, open-sourced this week. It is a coordination protocol that sits above Git: agents declare what they are about to do, before they do it, in a form precise enough to check. A declaration is an intent with one or more semant

Originally published at parvejshah.com/blog/why-browser-agents-fail-in-production-without-semantic-layers by Parvej Shah . The Fragility of Machine Vision in Modern DOMs Maybe the next evolution of frontend engineering isn't just designing interfaces for humans. It is designing interfaces that machines can reliably understand too. Browser agents don't always fail because the AI model is bad. Often, the web page itself is fundamentally hostile to machine parsers. Modern single-page applications (SPAs) render deeply nested <div> trees with ephemeral, auto-generated class names (such as Tailwind or CSS-in-JS hashes). While this provides fluid visual rendering for human users, it strips away semantic meaning for automated agents. graph TD A[AI Browser Agent] -->|Fragile Visual OCR / Coordinate Guessing| B[Opaque Div Hierarchy] B -->|Frontend Code Deploy / CSS Hash Shift| C[Broken Automation & Flaky Selectors] A -->|Direct Deterministic Query| D[Semantic Schema & data-agent Attributes] D -->|Refactor-Proof Contract| E[Deterministic Task Execution] Moving Beyond Ephemeral Selectors We already treat accessibility (a11y) as a non-negotiable contract between the frontend and assistive technologies through ARIA attributes. Why not extend that exact engineering rigor to AI agents? Imagine components exposing explicit, stable machine intent: // The machine contract: deterministic, testable, refactor-proof < button data - agent = " checkout-submit-button " data - agent - action = " complete-transaction " className = " btn-primary " > Confirm & Pay < /button > With explicit semantic attributes: Zero Layout Guesswork: The agent does not need to guess which button to click based on pixel coordinates or fragile CSS selectors. Deterministic Interaction Paths: Continuous integration (CI) test suites can validate machine contracts alongside accessibility audits. Reduced Latency & Token Costs: Vision-language models (VLMs) introduce non-deterministic latency and high token costs when in

₹85,000 per month. That was the AI API bill sitting in my client's inbox when they called me in a mild panic last quarter. They run a mid-sized e-commerce operation in Pune — about 4,000 orders a day — and had integrated AI into customer support, product descriptions, and internal reporting. The AI was working beautifully. The invoice was not. Three weeks later, their monthly bill was ₹12,400. Same tasks. Same quality. No corners cut. Here's exactly what changed. The real problem: every task was using the most expensive model When I audited their setup, the issue was obvious within five minutes. Every single API call — whether it was classifying a customer complaint into one of 8 categories or generating a 2,000-word product description — was hitting the same premium model. It's the most common mistake I see with businesses adopting AI: they pick one model during the proof-of-concept phase and never revisit that decision as they scale. You wouldn't hire a senior chartered accountant to do data entry. But that's essentially what was happening — a top-tier reasoning model answering "Is this complaint about shipping or billing?" Fix 1: Model routing — the single biggest cost lever Model routing means sending each task to the cheapest model that can handle it at acceptable quality. I categorised their ~47 distinct API call types into three tiers. 68% of calls moved to the lightweight tier, 20% to mid-tier, only 12% stayed on premium. That single change dropped the bill from ₹85K to roughly ₹38K — no quality loss, verified with two weeks of A/B testing on customer satisfaction scores before switching fully. Fix 2: Prompt caching — stop paying for the same context twice Their support bot sent the same 1,200-token system prompt with every call — policies, tone, catalogue context, all identical across thousands of daily calls. Caching processes it once and references it cheaply on subsequent calls within the window. At ~6,000 support interactions a day, this alone saved ₹8,

Originally published at parvejshah.com/blog/why-browser-agents-fail-in-production-without-semantic-layers-test by Parvej Shah . The Semantic Contract Modern web applications optimize DOM trees for human eyes with nested divs... graph TD A[Vision Model] -->|Fragile OCR| B[DOM Tree] C[Semantic Layer] -->|Deterministic Contract| B const button = document . querySelector ( " [data-agent=submit] " ); Parvej Shah is a Lead Full-Stack Web Developer & Platform Architect based in Dhaka, Bangladesh. Explore full architecture case studies and production code at parvejshah.com .

Netflix has walked back plans to put AAA games on its streaming platform. Instead, it's pivoting to marketing highly anticipated titles like Grand Theft Auto VI.

The first OpenClaw deployment is usually straightforward. You provision a machine, configure one agent, connect a few tools, and watch it complete a real task. If something breaks, you inspect the logs, fix the configuration, and restart the process. That is a valid way to prove the use case. It is not yet a production architecture. The category changes when an agency, SaaS company, consultant, or internal platform team needs to run OpenClaw for multiple clients. Every agent now belongs to a tenant, holds state, uses credentials, controls browser sessions, changes files, and can create external side effects. A failure is no longer just a failed process. It can become a missed client task, a duplicated email, a corrupted workspace, or an access-control incident. The right question is therefore not, "How many OpenClaw containers can this server run?" It is, "How many client environments can our team operate safely, recoverably, and without adding one human babysitter for every few agents?" This guide presents a practical architecture and deployment checklist for answering that question. Start with the correct unit of architecture Do not model an OpenClaw fleet as a list of processes. Model it as a list of client cells. A client cell is the complete operating boundary for one tenant or one agent. It includes: the OpenClaw process and its configuration; its resource envelope: reserved and maximum RAM, CPU cores, burst allowance, and priority; the persistent workspace and task artifacts; credentials and integration permissions; browser profiles, cookies, and active sessions; email, phone, or chat identity; logs, events, and audit history; recovery policy and human owner. This distinction matters because a process can be healthy while the client cell is broken. The daemon may still respond, but its CRM credential has expired. The container may be running, but the browser session is stuck behind a login prompt. The agent may have restarted successfully, but its workspace c

Svelte/SvelteKit Forms: The Fastest Path From <form> to Inbox with onsubmit.dev (form backend) SvelteKit makes forms pleasant to build, but a contact form still needs somewhere to send its data. If all you want is “visitor fills out <form> → message arrives in my inbox,” building and operating another server-side handler can feel disproportionate. onsubmit.dev (form backend) provides a hosted form endpoint for that job, and its Svelte integration can keep the application code small. One naming detail is worth clearing up immediately: onsubmit.dev (form backend) is a service, while Svelte has its own on:submit event directive. They are unrelated. In this article, references to the product always mean onsubmit.dev (form backend), not Svelte's on:submit . The usual SvelteKit approach SvelteKit already has a solid answer for server-side form handling: form actions. A typical contact form can POST to a +page.server.ts action, where you validate the fields and then do something useful with them. Conceptually, that gives you: Svelte <form> ↓ SvelteKit form action ↓ validation ↓ email provider / database / notification service ↓ your inbox This is a good architecture when submitting the form kicks off application-specific business logic. For a simple portfolio, landing page, documentation site, or “contact us” form, however, you also inherit the less interesting parts of owning that pipeline: delivery integration, configuration, error handling, spam controls, and maintenance. That's where using a dedicated form backend can make sense. Using svelte-onsubmit The Svelte integration is svelte-onsubmit . Rather than reproducing package code that might drift as its API evolves, use the current installation and usage snippet from the official integration documentation: https://onsubmit.dev/integrations That documentation is the source of truth for wiring the package into your current Svelte/SvelteKit project. The resulting architecture is deliberately simpler: Svelte <form> ↓ host

The lakehouse projects spent this week arguing about boundaries. Iceberg decided where conformance testing lives and started sketching the REST API shape that V4 tables will need. Polaris argued about what a committer owes a project when LLMs make pull requests cheap. Parquet pulled a feature apart because two proposals were reaching for the same mechanism. DataFusion and Iceberg Rust opened a joint thread about which repository should own their integration. Every one of those debates is a question about ownership, and the answers this week tell you a lot about how these communities plan to scale. Apache Iceberg The single biggest outcome of the week was the creation of a new repository. Neelesh Salian, working with Sung Yun and Andrei Tserakhau, called a vote to create apache/iceberg-verification , a standalone home for language-neutral conformance fixtures that every Iceberg implementation can run against. The vote passed with five binding +1s from Russell Spitzer, Sung Yun, Matt Topol, Daniel Weeks, and Amogh Jahagirdar, plus twenty-two non-binding votes. That is a wide turnout. The names on the non-binding list read like a roll call of the Rust, Python, Go, and Java maintainers, which is the point. Salian will now work with a PMC member to stand the repository up. The reason this matters goes beyond tidiness. Iceberg has at least five serious implementations today across Java, Python, Rust, Go, and C++. Each one carries its own test fixtures and its own understanding of edge cases in the spec. When two implementations disagree about how to interpret a manifest list, users find out the hard way. A shared set of fixtures that every implementation reads from one place turns spec ambiguity into a failing test rather than a production surprise. The 29 messages in the vote thread also included a fair amount of discussion about what belongs in the first batch of fixtures, and the conversation is worth reading if you maintain a client. The second major thread was about

Here is the disk layout of one mobile mapping survey — a vehicle with a LiDAR scanner and a panoramic camera, driven along a road: data/001_MMS/ 507 MB point cloud orbit/oblak/ 566 MB spherical photos trajectory/*.gpkg 108 KB the path the vehicle drove Just over a gigabyte. The database this feeds holds 2.3 GB in total — for 2.7 million road features across a hundred layers. Two more surveys and the binary data outweighs everything the database has ever stored. So the question isn't how to put a point cloud in Postgres. It's what you put in Postgres instead . The trajectory is the index Of that gigabyte, one file goes into the database: the 108 KB trajectory, a GeoPackage holding the line the vehicle drove. That line is what makes the survey findable. It draws on the map with everything else. You can ask which surveys cover a junction, which are newest, whether a stretch of road has been captured since the resurfacing. All the questions people actually ask are questions about where and when , and the trajectory answers every one of them at 0.01% of the storage. The heavy files never enter the database. The row holds paths: class Cloud ( models . Model ): name = models . CharField ( max_length = 120 , db_index = True ) path_name = models . CharField ( max_length = 120 ) # -> octree metadata JSON orbit_url = models . CharField ( max_length = 255 ) # -> spherical photo index spherical_photo = models . BooleanField ( default = False ) recording_date = models . DateField ( null = True ) source_srid = models . IntegerField ( null = True , choices = SOURCE_SRID_CHOICES ) available = models . BooleanField ( default = True ) Metadata, geometry, and pointers. That's the whole trick, and it isn't clever — it's just the discipline to not reach for a bytea column. Why not in the database Postgres will happily store a gigabyte. It's the access pattern that kills you. A browser point cloud viewer doesn't fetch a point cloud. It fetches an octree : a tree of small files, and as the