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Some of the world's largest tech companies and AI startups have come together to decry the current state of cybersecurity and to advertise a new solution that they say can ward off a new generation of cyber threats.

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

You self-host S3-compatible storage on bare metal by installing a single Rust binary on a Linux server and pointing any S3 client at it. RustFS installs with one script, listens on port 9000 (S3 API) and 9001 (console), and is Apache 2.0 licensed. Single-node mode is production-ready today; multi-node clustering is still under testing. Every command below is copied verbatim from the official source cited beside it. This sandbox has no Docker daemon, so none of the commands were executed here; they are marked accordingly. Key Stats Fact Source RustFS installs with one command and runs as a systemd service on x86_64 or aarch64 Linux RustFS docs (Linux quick-start) Default S3 API port is 9000; console port is 9001 RustFS GitHub README Default credentials are rustfsadmin / rustfsadmin and must be changed RustFS README + docs RustFS is Apache 2.0 licensed and S3-compatible RustFS GitHub README Single-node mode is production-ready; distributed mode is still under testing RustFS README Feature & Status What is self-hosted S3-compatible storage? A self-hosted S3-compatible storage server is a program you run on your own hardware that speaks the Amazon S3 API. Applications using AWS SDKs, the aws CLI, or MinIO's mc can talk to it without code changes, because the bucket, object, and credential model matches S3. The difference from a cloud bucket is ownership: the disks, the network path, and the uptime are yours. RustFS is one such server, written in Rust and licensed under Apache 2.0. It exposes the S3 API on port 9000 and a web console on 9001, and it stores objects on the local filesystem. Because it is S3-compatible, the same client code that targets AWS S3 also targets a RustFS node. That compatibility is the whole point of self-hosting here: you get an S3 endpoint without renting one. Why run object storage on bare metal? Running object storage on bare metal means installing the server directly on a Linux machine instead of in a container or a managed cloud. The appeal

Turning an iOS app into a React web app is no longer the hard part. Modern coding agents can generate a convincing first version quickly. The hard part is answering three less glamorous questions: Did we discover every important screen and state? Did the generated app preserve the source behavior and data? Is the result actually close to the native UI, or does it merely look plausible? I built Grounded iOS-to-Web Harness to make those questions auditable. 🔗 GitHub: https://github.com/tiezhu0415/grounded-ios-to-web-harness What it is Grounded iOS-to-Web Harness is an experimental, lightweight grounding + verification layer for migrating iOS apps into complete, interactive, mobile-sized WebApps. Claude Code remains the primary implementer. The Harness does not prescribe the React component tree, choose a state-management library, or replace the coding agent. Instead, it establishes source facts and verifies the result against evidence from the original app. iOS source + Assets + code graph + runtime states ↓ locked source facts ↓ per-screen implementation context ↓ agent builds the React WebApp ↓ coverage + truth + behavior + critical VRT ↓ bounded repair, then human review Why prompt-only migration is not enough A prompt such as “convert this iOS app to React” can produce a good demo. But on longer tasks, an agent may miss screens, implement only one state, invent data or assets, choose navigation that differs from iOS, forget earlier facts, or optimize a screenshot while breaking real interaction. This project treats the iOS source and Assets as the truth for content and behavior, while runtime screenshots provide evidence for what the result should look like. The pipeline 1. Discover and reconcile source facts Static source inspection, codebase-memory, and necessary iOS runtime exploration are combined into machine-readable facts for screens, UI states, actions, navigation outcomes, cross-screen flows, real data and asset origins, and source confidence. Facts are l

Cloudflare announced Wallets, giving agents a stablecoin balance and spending controls, though only handle claiming is live and squatting complaints have already surfaced. Payments run on x402, now hosted by the Linux Foundation. The controls bound single payments, not sequences, leaving composition to the application above. By Steef-Jan Wiggers

Report shows Meta's challenges replacing people with AI agents.

Every internal tool I need sits behind SSH. Grafana, Prometheus, the staging clusters, internal AI tooling—none of it answers on a public address, and the only door is a bastion I have a key for. That is the right setup for anything with real data behind it, and I wouldn't change it. What I did change is typing ssh -N -L 3000:localhost:3000 -J bastion prod-1 from memory four times a day across three different machines. So one weekend, I started writing SSH Config Manager . It is a native macOS app that edits ~/.ssh/config without wrecking formatting, saves tunnels as presets, and opens those tunnels in-process instead of shelling out to ssh . I wrote it for my own workflow first. Putting it on the App Store came later, once it was genuinely useful to me and I figured others were struggling with the exact same friction. The VPN Question The first thing people ask is: why not run a VPN and be done with it? Fair question, but the honest answer is that SSH is the tool I already understand inside and out. I have configured sshd enough times to know what PermitRootLogin no and PasswordAuthentication no actually change. When a connection stops working, I can usually name the exact line that broke it. A VPN introduces a whole second network layer underneath, complete with its own credentials, its own background daemon to keep patched, and its own unique failure modes to debug at 2:00 AM when production is down. SSH is already on every Linux server I touch and every developer machine I own — there is nothing new to roll out and nothing new to secure. The tradeoff is real, and I would rather acknowledge it up front. Operating without a VPN means no transparent network routing: every internal service I want to reach must be explicitly forwarded to a local port in advance, and a colleague without my config reaches none of them. Still, I would far rather maintain a clean list of port forwards than maintain another background daemon. Shell Aliases Do Not Survive Three Machines Th

"Most" Xbox One and Series X games are compatible; discs will still work after being claimed.

Messing with the genetic code might have gotten a bit easier to do.

Microsoft is officially announcing a new Xbox feature that will allow Xbox owners to digitize their existing physical game collections. The disc-to-digital feature, which The Verge exclusively revealed last month, is part of work that Microsoft is doing for game preservation and in preparation for the next generation of Xbox consoles. "Most Xbox One and […]

The special-edition translucent green Xbox finally has an official price. Preorders for the console start August 27th at 10AM ET / 7AM PT, although Microsoft says "some of XBOX's most dedicated fans" will receive early access preorder emails starting today. The console will launch on November 13th, alongside a matching controller with the same "OG […]

Bose QuietComfort headphones used to be innovative. Bose's noise-canceling research is the reason the technology is where it is today, and its headphones set the standard. They were the ubiquitous traveler's accessory. But over the past few years, it's felt like Bose has rested on that reputation, as other companies have caught up. Sony and […]

Bill Gates has been reflecting a lot on AI lately, and the process has triggered a stark awakening. Once a staunch AI optimist, the Microsoft cofounder is now deeply pessimistic about what AI means for our collective future. Having been conspicuously quiet on AI issues recently, Gates is back with a nearly 6,000-word essay seeking […]

The schema catalog for an AI assistant is the artefact that answers the question "what does this database look like right now". Whether the database is Postgres, MySQL, SQL Server or Redshift, the shape of the problem is the same: the catalog carries table names, column names, types, keys, and enough relationships to let the assistant write a query that resolves. It lives somewhere between the database and the assistant, has to stay in sync with a database that changes underneath it, and is almost always built the same weekend the team decides they want an AI assistant reading their data. It runs fine for the first three tables. The problems start around the fourth week, and none of them look like the same problem twice. The distinction worth naming early is between the connection layer (how the assistant reaches the database) and the knowledge layer (what the assistant knows about the database's shape). The connection layer receives most of the attention, because credentials, network isolation and query cost are visible failure modes and easy to argue about. The knowledge layer is where most of the actual quality of the assistant lives, and it decays quietly. The AI database context page covers why this second layer matters at all when the first one exists. Why not just point the assistant at the database Connecting the AI directly to production is the shortest path and the one most teams reject after five minutes of thinking about it. The assistant would get read access on tables it should not see, its queries can be arbitrarily expensive, its credentials would live somewhere they should not, and the audit trail becomes hard to reason about. What most teams end up building is a layer in between: a representation of the database that the assistant can read cheaply and safely without ever touching production. That layer is what this article is about. It is not the connection. It is the catalog. The five recipes teams build Ask fifteen senior developers how to build

The same zsh script could list ~/Documents when I ran it in Terminal. Started as a LaunchAgent, it failed with: ls: /Users/administrator/Documents: Operation not permitted The LaunchAgent had the same user ID, the same $HOME , and the same script. That combination makes this look like a Unix permission problem. In this test it was not. The useful discriminator was the launch context: access succeeded from Terminal, failed from launchd , and still succeeded for a path outside the protected folder. I reproduced this on macOS 15.6.1 (Darwin 24.6.0) with a LaunchAgent in gui/501 . The probe was removed after the test. Why chmod is the wrong first check The obvious suspects were file ownership, a wrong home directory, or a job running as another user. The probe printed those facts before touching the files: #!/bin/zsh print -- "user= $( id -un ) uid= $( id -u ) " print -- "home= $HOME pwd= $PWD " /bin/ls " $HOME /Documents" 2>&1 | /usr/bin/head -5 /bin/cat " $HOME /Documents/vinh/working/CLAUDE.md" 2>&1 | /usr/bin/head -1 # Negative control: outside Documents /bin/ls " $HOME /.pf004" 2>&1 | /usr/bin/head -5 The two runs produced this difference: Check Terminal LaunchAgent in gui/501 User / uid administrator / 501 administrator / 501 $HOME /Users/administrator /Users/administrator ls ~/Documents Listed entries Operation not permitted cat inside ~/Documents Read the file Operation not permitted ls ~/.pf004 Listed entries Listed entries The working directory differed, but the script used absolute paths under $HOME , so PWD=/ did not explain the denial. The negative control mattered more: the LaunchAgent could read another directory owned by the same user. Changing ownership or mode bits would not explain why only the launch context changed the result. The owning layer is the privacy context On this machine, the access decision was attached to how the process was launched, not just to uid 501. Terminal had a privacy context that allowed access to the user's Documents folder.

When you're building an AI evaluation platform with multiple microservices, the "core" services get all the attention — the evaluation engine, the scoring system, the RAG pipeline. But a platform doesn't work without the connective tissue: the workflow orchestration that keeps humans in the loop, the taxonomy engine that classifies tasks intelligently, the platform service that ties authentication together, and the evaluation suites that ensure models actually remember context. These four services don't make headlines, but they're what turned a collection of microservices into an actual platform. Here's what went into each one and why the engineering decisions mattered. Workflow Orchestration: The Human-in-the-Loop Engine AI evaluation is not fully automated — and it shouldn't be. Certain decisions require human judgment: Is this model response harmful? Does this evaluation rubric make sense for this domain? Is this edge case a genuine failure or acceptable behavior? The workflow orchestrator manages these decision points. It coordinates multi-step evaluation workflows where some steps are automated (LLM scoring, data validation) and others require human approval before the pipeline continues. The Architecture The core is a state machine built on FastAPI and PostgreSQL. Each workflow is a DAG (directed acyclic graph) of tasks, where each node can be: Automated: Runs immediately, calls another service (scoring, data enrichment), stores the result Human gate: Pauses the workflow, notifies the assigned reviewer via the notification service, waits for approval/rejection Conditional: Routes to different branches based on previous step outcomes (e.g., if confidence score < threshold, escalate to senior reviewer) State transitions are persisted in PostgreSQL with Alembic-managed migrations. Every transition is logged — who approved what, when, and with what context. This audit trail turned out to be critical for client reporting. Real-Time Updates with WebSocket The origin

If you are building an iOS app with Kotlin Multiplatform (KMP) or Compose Multiplatform, you might have opened your generated Shared.h header at some point and wondered why it is 20,000+ lines long. I ran into this recently while optimizing one of my personal KMP apps. I kept seeing Objective-C classes generated for every single theme color, dimension constant, and internal state model, even though my Swift code never touched any of them. To get a clear picture of what was actually going on, I built a small Gradle plugin called kmprofiler . It parses the generated Objective-C header, scans your Swift source files, and highlights which exported declarations have zero call sites in Swift. The numbers on my app caught me off guard, but cleaning it up took just a few minutes. Why does Kotlin/Native export so much? In Kotlin, declarations are public by default. When targeting iOS, the Kotlin/Native compiler looks at every public class, top-level function, and property in your shared module and creates an Objective-C class interface and runtime method trampolines in the framework binary. The compiler cannot dead-strip these automatically because Objective-C relies on dynamic dispatch. It has to assume Swift or Objective-C could call them at runtime. If your UI is built with Compose Multiplatform or your Swift app only interacts with a couple of high-level bridge interfaces, most of those exported Objective-C wrappers end up being dead weight. The Audit: 459 Exports, 282 Unused When I ran kmprofiler on my app (Framed), it gave me this breakdown: ### 📊 KMP iOS Export Profile Export surface: 459 Kotlin declarations exported to Objective-C. No direct Swift call site found for 282 of them (61.4% uncalled). The unused exports mostly fell into three buckets: File Facades ( *Kt classes): Top-level properties in files like Dimens.kt (38 spacing constants) or Color.kt generated synthetic Objective-C classes like DimensKt with static getters for every single constant. Internal UI St

Every AI video pipeline eventually has to answer an unglamorous question: what did we actually pay for that clip? On the main video-generation service, the answer for months had been "a hardcoded constant." That's fine until the vendor changes its own pricing, or a code path pays for the same synthesis twice, or a voice engine mints a clone, bills for it, and never sends it downstream. Over a ten-PR run I audited and rebuilt the voice and lip-sync pipeline from the billing layer up, then used the vendor's own SKU tiers to cut cost 7x without touching output quality. A cost model built from hardcoded constants isn't a cost model. It's a guess that happens to compile. Billing what the vendor actually charges PR #224 was workstream one of three from a sibling-tool audit: port the cost-accounting fixes that Presenter Generation and Variant Multiplier had already found, verifying each one against this repo's own code rather than assuming the same defect existed in the same place. Anthropic returns exact token counts on every response. Nothing in the pipeline read them — every charge was a hardcoded per-call constant, so the ledger and the vendor invoice diverged the moment usage drifted from whatever number had been typed in at launch. The same PR closed a second gap: two editor-facing routes could spend money — kicking off a generation, retrying a step — outside any run . A run is the unit everything else (budgets, audit trail, the cost ledger) is keyed to. A spend with no run attached is a spend the ledger can't even see, which is worse than a wrong number. Paying twice for a take the model returns unchanged PR #225 found the sibling bug's twin: some vendor calls return the exact same asset on a retry — no new synthesis happened — and the pipeline billed a second time anyway because "call succeeded" and "call did new work" were treated as the same fact. The fix is the boring, correct kind: hash the output, and only charge when the hash changes from the take you already

The new Designed for Xbox 25th Anniversary collection features a handful of limited edition gadgets available for pre-order today. Paying homage to the Halo special edition of the original Xbox, almost everything in the collection is translucent "OG Green," matching the Xbox Series X25 limited edition console it announced in June at E3. That includes […]

The PostgreSQL manual is unusually direct about this: When an application receives this error message, it should abort the current transaction and retry the whole transaction from the beginning. "The whole transaction" is doing a lot of work in that sentence, and it is the part that gets dropped. TypeORM issue #9806 — "Auto Retry options on error in transactions (e.g. Deadlock)" — has been open since February 2023. Thirty 👍, six comments, no implementation. Meanwhile typeorm-transactional , at 188,000 downloads a week, ships @Transactional() with isolation levels and seven propagation modes and no retry at all. So the ecosystem's actual answer to "how do I use SERIALIZABLE in Node" is: don't. Use READ COMMITTED , don't think about write skew, and hope. I spent a while building the thing that issue asks for. The short version of what I found: the feature as literally requested cannot be built correctly , and the reason is more interesting than the feature. The implementation everyone reaches for first Wrap the query. It's the obvious move — the error came from a query, so retry the query: async function withRetry < T > ( fn : () => Promise < T > , attempts = 3 ): Promise < T > { for ( let i = 1 ; ; i ++ ) { try { return await fn (); } catch ( e ) { if ( i >= attempts || ! isSerializationFailure ( e )) throw e ; await sleep ( 50 * i ); } } } await dataSource . transaction ( ' SERIALIZABLE ' , async ( em ) => { const from = await em . findOneOrFail ( Account , { where : { id : fromId } }); const to = await em . findOneOrFail ( Account , { where : { id : toId } }); await withRetry (() => em . decrement ( Account , { id : fromId }, ' balance ' , amt )); // ← here await withRetry (() => em . increment ( Account , { id : toId }, ' balance ' , amt )); // ← and here }); This does nothing. Worse than nothing — it turns one clear error into a confusing one. When PostgreSQL raises 40001 , it does not fail that statement . It aborts the entire transaction . The connection is now