标签：#AR

A new $20 billion valuation would be a giant step up from the $10 billion valuation it reached in October.

iPhone Ultra or the iPhone Fold. No matter what you call it, here are all the rumors and leaks we've gathered on Apple's most anticipated iPhone in years.

Claude subscribers must soon pay usage-based fees to access Anthropic’s best consumer AI model—a sign that the golden era of AI subscriptions is ending.

"Show a smile, hide the blade." — The 36 Stratagems, Conceal a Dagger in a Smile Previously on...

If you’re looking for a relatively affordable way to cut down on cooling costs, Google’s Nest Thermostat can help. It’s packed with smart controls and energy-saving features, and right now it’s on sale in white for $79 ($50 off), which is its best price of the year, at Amazon. The smart thermostat is quick to […]

The company is taking a modular approach to designing these chips, anticipating that their needs will change as AI evolves rapidly by the time the chips are in production.

The soft, oddly intimate home-chore robot has been given some very tactile hands.

Every system that does "semantic" anything — RAG pipelines, recommendation engines, image search, dedup — boils down to one operation: given this vector, find the closest ones out of millions. The vectors are embeddings, a few hundred to a couple thousand numbers each, and "closest" means closest in meaning. You'd assume the database either scans all of them (slow but correct) or uses some clever tree to jump straight to the answer. It does neither. Instead it deliberately settles for the approximately closest vectors — and that compromise is the entire reason vector search is fast enough to exist. Two algorithms do almost all the heavy lifting in practice, in pgvector, Qdrant, FAISS, and the rest: IVF and HNSW . Here's what they're actually doing under the hood, and how to choose between them. Why "exact" is off the table The natural objection is: why approximate? Just find the real nearest neighbor. In two or three dimensions you could — a k-d tree or similar structure prunes away big regions of space and finds the true closest point quickly. The trouble is that embeddings live in hundreds of dimensions, and high-dimensional space is deeply weird. It's called the curse of dimensionality . As dimensions grow, the distance to your nearest point and the distance to your farthest point drift toward being almost the same. Formally, the contrast (d_max − d_min) / d_min shrinks toward zero. When everything is roughly equidistant from everything else, a tree can't confidently say "skip this whole branch, it's too far" — the bounding regions all overlap, every branch looks plausible, and the search degrades into checking nearly everything. Exact indexes quietly collapse back into brute force. So we change the question. Instead of "prove you found the nearest," we ask "quickly find something very probably among the nearest." That's approximate nearest neighbor (ANN) search, and it swaps a guarantee for speed. The quality knob becomes recall : of the true top-k neighbors, wh

How I moved from a traditional web application mindset to exploring local-first architecture I wanted to build a simple software tool for my personal use. Nothing complicated. Something in the category of tools people build for themselves: A personal expense tracker A budgeting application A private knowledge management tool A personal organization system The important characteristic was this: The data belonged to one person. It was not a social application. It was not a collaboration platform. It did not need users interacting with each other. There was no requirement for: Public profiles Sharing updates Real-time collaboration Social features It was simply a tool that helped one person manage their own information. When I started thinking about building it, my first instinct was the most natural one for me. I am a web application developer. My comfort zone is building web applications. So my first thought was: "Why not build a Ruby on Rails application?" Something like: User | Web Application | Ruby on Rails API | PostgreSQL Database This is an architecture I have worked with many times. The workflow is familiar: Create models Build controllers Add authentication Store data in a database Deploy the application Access it from anywhere This is a proven architecture. For many products, this is exactly the right approach. But while thinking about this project, I asked myself a different question: Am I choosing this architecture because the problem requires it, or because it is the architecture I already know? That question changed the direction completely. Understanding The Actual Problem Before choosing technology, I wanted to understand the nature of the problem. What kind of application was I actually building? There is a big difference between building: A social network A marketplace A collaboration platform A communication application versus building: A personal tool A private utility A single-user productivity application In the first category, the server is the

TL;DR Three unrelated repos, one recurring theme: derive from a single source of truth instead of duplicating it. Shipped a registry-driven sidebar section switcher (public), converged a multi-system password flow, and pushed on a customer-data identity engine. Details on the first two live in their own posts today. 1. Registry-driven sidebar switcher (public) Added a section switcher to the kickoff starter kit. The sidebar, the switcher, and breadcrumbs all read the same config/menu.php list, and the active section is picked by longest URL-prefix match — so a detail page like /admin/roles/42/edit keeps its parent selected. Full write-up in the focused post. 2. One canonical password flow Converged two apps that each rolled their own password-change/reset logic onto a single shared engine, with a fixed order (directory → external DB → local app) and no config-toggle to skip backends. A password that syncs to some systems is worse than one that fails outright, so partial success is now impossible by construction. Also fixed a subtle status bug — an unreachable backend reports skipped (a runtime fact), not disabled (a config state that no longer exists) — and added an audit log so "did it sync?" is a query, not a guess. Separate post today goes deeper. 3. Identity resolution engine (customer data work) Steady progress on a CDP-style identity layer: an idempotent, header-versioned ingest endpoint that queues incoming records, then a resolution engine that can resolve, merge, unmerge, and quarantine profiles. Two things I care about here: PII handling: sensitive identifiers are encrypted at rest with a blind index for lookups, and masked in audit trails — you can search on a value without storing it in the clear. Right-to-erasure: an erasure cascade plus an erasure log, so a deletion request actually propagates and leaves a defensible record that it did. ingest -> queue -> resolve -> profile | +-> merge / unmerge / quarantine No code from this one here — it's teaching t

TL;DR Two apps each had their own password-change/reset logic, plus config toggles to enable/disable backends. That combination quietly allowed partial syncs. Fix: one shared engine, one canonical order , backends mandatory (no config-disable), and every attempt written to an audit log. Lesson: for a write that spans several systems, "configurable steps" is a footgun. Make the flow fixed and make failure loud. The setup A user changes their password. Behind the scenes that single password has to land in several systems — a directory, an external database, and the app's own store. Two separate apps were doing this, each with slightly different code, and each with config flags like sync_oracle => true|false to turn backends on and off. Sounds flexible. It's actually a trap. Why configurable backends are a footgun TL;DR: a password that updates 2 of 3 systems is worse than one that updates none — because now the systems disagree and nobody gets an error. The moment a backend is optionally skippable, "skipped" and "failed" blur together. Someone flips a flag in one environment, forgets it in another, and now prod and staging run different flows. Debugging a login failure means first reverse-engineering which steps actually ran. Before After Each app had its own reset logic One shared engine, both apps call it Backends toggle via config Backends are mandatory, always run Order implicit / differed per app One canonical order: New directory → external DB → local app "Did it sync?" answered by guessing Every attempt logged with per-service status The canonical order The order isn't cosmetic. It runs most-authoritative-first, so if a downstream step fails you haven't already told the user their new password works. change/reset request | v [ New Directory ] --ok--> [ External DB ] --ok--> [ Local App store ] | | | fail fail fail | | | +----> stop, record per-service status, surface the failure Same engine, same order, both apps. A change API and a reset flow are just two entr

The Schlage Sense Pro is a beautiful smart lock. Sleek, discreet, and simple to use, it's Schlage's smartest lock to date. Thanks to ultra-wideband (UWB), it unlocks as I walk up to my front door; I don't need to enter a code or tap my phone or press my finger against it. I've tested several […]

Oddly, it wasn't the first time this had happened to the man.

"You promised me that you would never take power in your name."

The 4.8 L eight-cylinder generates more than 1,000 hp and 1,000 Nm, Ruf says.

PR volume went up, ticket quality didn't, and the gap got filled with LLMs on both sides of the review: bots reviewing, bots replying, bots occasionally arguing with bots about priorities that only existed in a teammate's head. Our CEO named the actual problem, and it's bigger than code review.

I asked AI to help me write this article. Then I sat there for a second, thinking about how ironic...

The Bezos-backed automaker building America’s cheapest electric truck is teaming up with the crayon company in a bid to brighten its rides. Make ours Razzmatazz.

State agency's delay could mean free robotaxi rides in company’s new Ojai vehicle for a few months.

In an interesting twist that takes advantage of the company's core product, users can chat with these shows' characters, ask them questions, and even roleplay different storylines.