AI 资讯
The Everything-on-Your-Branch Architecture
Database branching is one of the best ideas serverless Postgres brought to the mainstream. Fork the database at a point in time, get an isolated copy with all the data, run something risky against it, throw it away. It made preview databases and safe migrations feel routine. But a real application is not just a database. It is a database, plus the files it stores in object storage, plus the backend code that serves it, plus, increasingly, the model and gateway config it calls for AI. When you branch only the database, those other three stay shared. Your "branch" points at the same S3 bucket, the same deployed backend, and the same AI configuration as everything else. So it is half a copy, and the half it leaves out is where a lot of the interesting bugs and the scary migrations live. Neon's platform preview changes what a branch contains. A branch now forks the database and its data, the object storage and its files, the functions that run your backend, and the AI gateway config, all at the same point in time, all isolated. A branch stops being a database copy and becomes a whole environment. To make sure that is a real claim and not a diagram, I took a full-stack project, branched it, and checked every layer. Here is what happened. TL;DR Elsewhere, "branch" means the database only. Object storage, backend deploys, and AI config stay shared, so you bolt on scripts to fake per-branch versions of them. A Neon branch forks all four together: Postgres + data, object storage + files, functions (each branch gets its own URL), and the AI gateway. I proved it: branched a project with a DB, a bucket of files, a function, and the gateway. The branch came up with a copy of the rows, a copy of the files on its own storage endpoint, its own function URL, and the gateway. A write to the branch left main untouched, and deleting the branch removed all of it. That makes a branch a real environment: true preview stacks, whole-state bug reproduction, and disposable sandboxes for agent
AI 资讯
An Inventor of Apple's FaceID Wants to Analyze Your Brain's Health With AI
Gidi Littwin's new AI startup, Hemispheric, makes diagnostic brain scans for conditions like depression, PTSD, and Parkinson’s. He wants the technology to be as cheap and easy as a blood test.
AI 资讯
A Video Screen That Is Also a Camera
Amazing : Researchers from ETH Zurich in Switzerland, however, managed to create a new type of pixel that can simultaneously do both. This hypercharged pixel, called a Fourier pixel, can generate and sense arbitrary light fields and tap into a pixel’s full potential for carrying information by manipulating light’s intensity, oscillation phases, and polarization. The team reported its findings in a paper published yesterday in Nature. We are one step closer to 1984 technology: The telescreen received and transmitted simultaneously. Any sound that Winston made, above the level of a very low whisper, would be picked up by it; moreover, so long as he remained within the field of vision which the metal plaque commanded, he could be seen as well as heard. There was of course no way of knowing whether you were being watched at any given moment...
产品设计
How hard is it to build orbital data centers, actually?
"The ISS radiators are expensive and heavy. We're focused on making them cheap and light."
AI 资讯
Sotheby's big T. rex auction raises concerns hype and wealth are upending science
Private buyers are increasingly outbidding museums for fossils.
AI 资讯
Every Third-Party iOS Keyboard Is a Graveyard. So I Built a Voice Keyboard From Scratch in C++.
If you've searched the App Store for a reliable third-party QWERTY keyboard on iOS, you know how that ends. Some are abandoned. Some are ad-riddled. Some feel like they were ported from Android and never touched again. The good ones are the ones that don't ship features so much as they don't crash. The system keyboard is fine. It's fine because Apple has been iterating on it for fifteen years. Nobody else has. Third-party keyboards on iOS are a graveyard. I'm building one that isn't. It's called Diction. Most people know it as a voice keyboard, and that's what I lead with, but under the hood it's a serious low-level QWERTY project too. This post is about that half of it, because that's the half nobody talks about. Why third-party QWERTY on iOS is a graveyard Building a good keyboard extension on iOS is hard. There's a strict memory ceiling. There's a permission dance the user has to opt into. There's no keychain access, no meaningful background work, and the extension can be killed the moment iOS decides it needs the RAM. Most keyboard makers ship a first version to check the box, then abandon it once they see how much work maintaining it takes. The result is what you see on the App Store today. Every third-party keyboard I've tried on iOS fails on at least one of these: Speed. You type a letter, the letter arrives. That's it. If there's a hitch you can feel, the keyboard is broken. Half the ones I've tried have a visible delay on every keystroke. Predictability. If you correct the same word back three times, that word should be yours. The keyboard should stop fighting you. Most never do. You fight the same wrong correction for a year. Recovery. iOS keyboard extensions are memory-constrained. Bad ones freeze under pressure. When you rapid-switch between apps, half the third-party keyboards on the store will lock up until you kill and reopen the host app. Autocorrect that isn't from 2014. Fix the obvious typos. Split words that ran together. Complete contractions. Ge
AI 资讯
Hetzner was cheaper at every size I tested and I still chose managed Postgres
Twelve pricing tabs open. Neon, Hetzner, Supabase, Prisma, Scaleway, OVH. My database is half a gigabyte. I was comparing ten-terabyte price curves. At some point this week I typed the words "I am super lost here" about my own infrastructure. I advise companies on this exact class of decision. That sentence still came out of my hands. If you have ever spent an evening deep in provider pricing pages for a workload that fits on a USB stick from 2009, this one is for you. All numbers below come from the live pricing pages as of July 2026. Rates move, so verify before you commit. Three fears, all pointed at the wrong layers I went in worried about getting attacked, running out of space, and being locked in. All three dissolved under ten minutes of honest reading. DDoS lands on the website edge, not the database. My site already sits behind Cloudflare and Vercel, and a database is never publicly exposed. Only the app talks to it. Whichever provider I picked, that attack surface stayed identical. Here is the shape of the stack, and where each fear lives. MANAGED (what I run today) visitors ──> Cloudflare edge ──> Vercel app ──> managed Postgres [DDoS absorbed] [stateless] [never public, app-only access, provider patches, provider backups, provider on-call] SELF-HOSTED (the alternative I priced) visitors ──> Cloudflare edge ──> Vercel app ──> Hetzner CAX11 [DDoS absorbed] [stateless] [Postgres :5432 firewalled to app, SSH hardened, fail2ban + auto- patching = MINE] │ pg_dump every 6h ▼ encrypted ────> Cloudflare R2 [off-site copies] Same edge, same app, same attack surface. Everything in the right-hand box is what changes owners. Storage was a rounding error. My data is 0.5 GB. Even the cheapest self-hosted box includes 40 GB, eighty times headroom before the first extra cent. Lock-in was a phantom too. Managed Postgres is still stock Postgres. Exiting means a dump, a restore, and one connection string change in the deployment environment. Minutes of cutover, no rewrite an
AI 资讯
Designing a Three Reviewer Consensus Platform for Digital Harm Reporting
The Problem Real411 is a South African platform where citizens report digital harms: misinformation, incitement, hate speech, and harassment. When someone submits a complaint, it needs to be reviewed by multiple people, assessed against legal criteria, and resolved with a public verdict. The process must be transparent, auditable, and fair. I joined this project early and worked on it extensively over a long period. A senior solutions architect consulted on the database schema design. There was a cloud person who helped with parts of the infrastructure. Other coworkers contributed at different stages. I spent most of my time on the API layer and the frontend components. This article covers the architecture decisions I worked with, what I learned from the senior architect's design choices, and how the system evolved. The Status Machine Most applications model status as a column on a table. You update the value and the old state is gone. That works for simple workflows but fails when you need to know not just where a complaint is now, but how it got there and who made each decision. The senior architect who consulted on the database design suggested an append only status log. Instead of a single status column, the complaint_status table records every transition as a separate row. Each row has the status code, the user who made the change, a timestamp, and optional notes. The current status is derived by querying the most recent row. I implemented this pattern across the API layer. Every status transition became an insert operation rather than an update. It took some adjustment to shift from mutable state to event sourced state, but the benefits were immediate. Auditing became straightforward. The state machine also became easier to implement because each transition is a simple insert with a business logic check, not a conditional update. The schema has seventeen status codes covering the full lifecycle: received, claimed, under assessment, pending secretariat review,
开发者
What Makes Healthcare App Testing Different from Every Other App Category
A patient opens their prescription and sees 500mg instead of 50mg. A lab report displays "Normal"...
AI 资讯
Microsoft’s Secure Boot has been broken for a decade and no one noticed until now
Old and forgotten "shims" Microsoft failed to revoke have made Secure Boot bypasses simple.
开源项目
Windows 11’s big patch Tuesday allows you to hold off on updates for longer
Microsoft just released a long list of improvements for Windows 11 as part of its bigger patch Tuesdays, and that includes the ability to pause updates indefinitely, as reported earlier by Windows Central. This option rolled out to Windows Insiders earlier this year, allowing you to hold off on updates for up to 35 days […]
AI 资讯
Apple opens its new Siri AI to everyone with the iOS 27 public beta
If you’ve been waiting to try Apple’s revamped Siri without installing a developer beta, you now can. The company on Tuesday released the iOS 27 public beta, giving iPhone owners early access to its AI-powered assistant and other new features before the software’s official launch this fall.
AI 资讯
These painted e-tattoos could be the future of wearable biosensors
Conductive ink is painted directly onto the skin in colorful custom designs, drying into working electrodes.
AI 资讯
Meta’s Adam Mosseri says AI token budgets could soon be capped per engineer
Instagram head Adam Mosseri believes companies will eventually need to manage AI token spending the same way they manage payroll or other operating expenses, predicting that engineers could soon face limits on how much they spend using AI tools.
科技前沿
Taco Bell eyed in explosive diarrheal outbreak; leafy greens suspected
Health officials have not confirmed a source yet—and there may be multiple sources.
AI 资讯
Sotheby’s Big T. Rex Auction Raises Concerns Hype and Wealth Are Upending Science
Private buyers are increasingly outbidding museums for fossils. That’s making it difficult—or even impossible—for researchers to improve our understanding of the past.
产品设计
SwiftData Enhances Queries, Adds Support for External Types and Data Store Observation
The 2027 release of SwiftData introduces support for persisting custom and third-party types via Codable, along with the ability to organize data into SwiftUI list sections. It also adds new capabilities for observing data store changes through ResultsObserver and HistoryObserver. By Sergio De Simone
AI 资讯
Verify a Self-Hosted Installer Before Running It as Root
Downloading an installer and immediately executing it as root collapses three operational decisions into one command: Which artifact? -> Did these bytes arrive intact? -> Should this host execute them? Separate those decisions and the install becomes reviewable, reproducible, and recoverable. A concrete source-review boundary At commit c58bcd4 , the MonkeyCode runner installation template selects x86_64 or aarch64 , checks AVX on x86, requires root, and downloads an architecture-specific installer before executing it. The reviewed template uses curl -4sSLk , so certificate verification is disabled by -k . It also downloads an unversioned path. I could not find a pinned version, digest, or signature check in that template. That is a statement about controls visible in one pinned file—not a claim that the release service is compromised or that no external release control exists. Put a manifest before execution For each release artifact, publish immutable metadata through a separately protected release process: { "version" : "1.2.3" , "architecture" : "x86_64" , "file" : "runner-installer-1.2.3-x86_64" , "sha256" : "<64 lowercase hex characters>" , "size" : 18439210 , "rollback" : { "previous_version" : "1.2.2" , "artifact" : "runner-installer-1.2.2-x86_64" } } SHA-256 detects bytes that differ from the manifest. It does not prove who authored the manifest. Serve the manifest over validated TLS, pin it through deployment configuration, or sign it and verify the signature with a trusted offline public key. Verify as an unprivileged staging step The companion verify-installer.mjs checks filename, exact size, digest, version, architecture, and rollback metadata: node verify-installer.mjs release-manifest.json fixture-installer.sh node test-verifier.mjs Expected output uses the fixture's actual digest: PASS 1.2.3-fixture sha256=<digest> PASS verified fixture; rejected tampered artifact before execution The negative test appends a line to the artifact and requires both size
AI 资讯
Microsoft tests Windows Search without all the ads and fluff
Microsoft is testing a cleaner version of the Windows 11 search menu that strips it of recommended content and ads. In a blog post on Monday, Microsoft announced that it's rolling out the decluttered Search Box to Windows Insiders in the Experimental channel as the company looks to regain trust with users and fix Windows. […]
开发者
Capturing, Streaming, Storing, and Visualizing Crypto Market Data in Real Time with PostgreSQL, Debezium, Kafka, JDBC & Grafana
In the fast-moving world of cryptocurrency, market data changes every second — prices fluctuate, trades execute, and volumes shift continuously. Capturing this stream of real-time data and transforming it into meaningful insights requires a robust and scalable pipeline. In this project, I built a complete real-time crypto market data pipeline that captures, streams, stores, and visualizes live data from Binance using PostgreSQL, Debezium, Kafka, JDBC, and Grafana. The goal was to design an architecture that not only moves data instantly between systems but also keeps it queryable and monitorable in real time. What began as a simple Binance data extractor evolved into a production-grade CDC (Change Data Capture) workflow capable of detecting every database change, streaming it through Kafka, storing it in a sink database, and visualizing it live on Grafana dashboards.