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Up front: this is my own project, so I'm not exactly neutral here ;-) Where I'm coming from I work completely differently than I did two or three years ago. For most of my career I wanted to write pretty much every line myself, and I was a bit proud of that. That has changed a lot. Instead of programming I now mostly write specifications and review what the AI generates. On the one hand that's great, I can turn new ideas into working software much faster than before. On the other hand there's the risk of stepping into the same traps with AI-generated code again and again. And that's where I noticed something. Every time I started a new project, I found myself explaining the same things to the AI. This goes into a plugin. That stays out of the core. No domain logic in the shell. Please don't invent a third way of doing tabs. The AI would nod, generate something that looked right, and two days later I'd find a slightly different version of the same sidebar with a slightly different bug. There are things I really don't want to explain over and over. A good, preferably deterministic base is getting more important, not less. I don't want to explain proven architectures from scratch every time. I'd rather build on established solutions where I can, ones the AI understands and can just use. So for my new projects I built exactly that, and put it on GitHub as open source. Why Angular? Well, simply because I think it's a great framework and I've had a lot of good experiences with it over the last 10 years. What it is, and what it isn't LoomWeaver is a workbench shell for Angular. Not a component library. Think of the frame VS Code gives you: a rail on the left, sidebars, a top bar, a status bar, and in the middle tabs and panes you can split and drag around. That frame is what most workbench-style products build themselves, every time, slightly differently. LoomWeaver gives you that frame, and your own domain moves in as plugins. The core contains zero domain logic. Even my

"Let me be clear: America is not only welcome—we want you there."

The US government is investigating whether the Cybercab, which lacks a steering wheel and pedals, meets vehicle safety standards.

While philosophers ponder AI consciousness, the models have ideas of their own.

Regulators will review the process and data Tesla used to self-certify the vehicle.

Elon Musk's gilded robot car barely made it on public roads before federal regulators launched an investigation into the vehicle's lack of traditional features - many of which are required by law. The National Highway Traffic Safety Administration said it was opening an audit query (AQ) into the Tesla Cybercab "to examine the process and […]

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can turn a chart that says E, W, N and S into one that says East, West, North and South, in about thirty seconds, without editing the data or writing a calculation. You'll also know exactly why the Aliases option is missing on some fields, which is the part that sends people looking for a workaround they don't need. It's about ten minutes. Here's the move. Right-click a dimension in the Data pane, choose Aliases, and type the name you want beside each value. The chart updates, the stored data doesn't change, and every view built on that field picks up the new labels. The short version: an alias renames the members of a discrete dimension. Only discrete dimensions have members, which is why measures, dates and continuous dimensions can't have one. An alias sits in a specific place, between what's stored and what's shown, and that placement explains everything else here. So it gets the picture. The original carries a diagram here. In words: Three stacked panels connected left to right. The left panel is labeled stored and holds four small cells reading E, W, N and S. The middle panel is a narrow vertical band labeled alias, holding four arrows. The right panel is labeled shown and holds four cells reading East, West, North and South. A solid arrow runs from the stored panel through the alias band to the shown panel, indicating the direction labels travel. A second arrow attempting to run backwards from the shown panel to the stored panel is crossed through with a heavy X, showing that renaming the label never changes the stored value. The stored cells still read E, W, N and S after the change. This is on the certification. Aliases sit in Section 2, Exploring and Analyzing Data, which is 37% of the Tableau Desktop Foundations exam and the largest section on it. The questions people get wrong are almost always about which field types accept an alias, which is section 2 below. 1. What

The investigation was launched just a few hours after Tesla put the first production Cybercabs on the road in Austin.

Instagram's visible AI labels are supposed to help people quickly spot synthetically generated content at a glance. Over the last few weeks, however, users have been reporting that the system has gone haywire. They say Meta has been automatically applying an "AI Content" label to images that they didn't create or edit using generative AI […]

The success of a program linking zoos across the US has created social media stars and generated a wave of interest in how to protect the endangered great apes in the wild.

Homeland Security Investigations agents hit the outdoor retailer with a controversial subpoena as part of a dragnet search for the identities of protesters who entered a Minnesota church in March.

A production postmortem. We shipped browser-side image compression (Rust → WASM + WebGPU), turned on cross-origin isolation for speed, and watched every format crash with compression worker crashed . Here's the root cause and the fix. The setup We built an image compressor that runs 100% in the browser — Rust compiled to WASM for the codec work, WebGPU for the heavy ML passes (background removal, denoise, watermark). No upload, so users' pixels never leave the device. Privacy is the whole selling point. For the multi-threaded code paths we rely on shared memory + atomics , which in the browser requires crossOriginIsolated . So we served the document with: Cross-Origin-Embedder-Policy: require-corp Cross-Origin-Opener-Policy: same-origin That gives us crossOriginIsolated === true , unlocks SharedArrayBuffer , and lets the *‑threaded WASM builds actually spawn workers. The build uses a nightly toolchain ( nightly-2025-06-01 + -Z build-std ) with: RUSTFLAGS = "--cfg=... +atomics,+bulk-memory --shared-memory --import-memory" and a custom rayon handle pool ( with_turbo_pool ) instead of build_global , so we control worker lifecycle and can abort/self-heal. The incident After flipping COEP to require-corp in production, every format started crashing with the same message: compression worker crashed Not one codec — JPG, PNG, WebP, AVIF, all of them. It was a P0: the core feature was dead for every user. What made it nasty: it only reproduced under real cross-origin isolation . Local dev without COEP was fine. Staging without the header was fine. So the bug hid until it hit production traffic. Root cause The *‑threaded WASM packages spin up nested rayon workers to parallelize the codec. Under COI + COEP require-corp , those nested workers get blocked by Cross-Origin-Resource-Policy / COEP — the spawned worker script is treated as a cross-origin response without the right CORP header, so the browser refuses it. No worker → the rayon pool never initializes → the compression c

A San Francisco “Hot List” is trying to buck stereotypes about dating in tech circles amid wider debates about the value of being perceived as attractive.

Every charting tool eventually gets the same feature request: "show me other times this stock looked like this." It sounds like a lookup. It is not. The retrieval is the easy half. The hard half is that a correct implementation can still produce results that are quietly meaningless, and nothing in the code will tell you. Here is the method, and the failure modes worth knowing before you ship it. No claims about predictive power anywhere in this piece — the last section explains why that is a deliberate choice, not a hedge. The naive version, and why it fails immediately The obvious first attempt: take the last 30 days of closing prices as a query vector, slide it across history, compute Euclidean distance, return the closest matches. import numpy as np def naive_search ( history , query , k = 5 ): m = len ( query ) windows = np . lib . stride_tricks . sliding_window_view ( history , m ) dists = np . linalg . norm ( windows - query , axis = 1 ) idx = np . argsort ( dists )[: k ] return idx , dists [ idx ] Run this and you get garbage — but instructively specific garbage. Every match comes from whatever period had a similar price level . Query a stock trading at $180 and you get back the other times it traded near $180. The shape is irrelevant to the metric; the offset dominates it. Scale is the same problem in a different coat. A stock that moved 2% over the window and one that moved 40% can trace an identical shape, and raw distance calls them unrelated. Normalize per window, not globally The fix is to z-normalize each window independently: def znorm ( x , axis =- 1 , eps = 1e-8 ): mu = x . mean ( axis = axis , keepdims = True ) sd = x . std ( axis = axis , keepdims = True ) return ( x - mu ) / ( sd + eps ) Per-window is the load-bearing part. Normalizing the whole series once preserves the relative offsets you were trying to remove. Each candidate window has to be centered and scaled on its own terms before it's compared. There's a satisfying identity waiting here.

Our dataset said the average loan was 2.3 million kroner. The number that actually mattered was 255,000. Both were correct. Only one of them was true. This is a writeup of three ways a dashboard can be arithmetically perfect and still lie, using real figures from an analysis of 1,000 Norwegian debt consolidation applications. If you build reporting for anyone, you have probably shipped at least one of these. 1. Summing a field that contains two different things A debt consolidation loan pays off your expensive credit card debt. It also, if you own property, rolls your existing mortgage into the same new loan. Same column in the database. Same loan_amount . Utterly different meaning. SELECT AVG ( loan_amount ) FROM applications ; -- 2,300,000 That query is right and the answer is useless. Of that 2.3 million, roughly 1.9 million is an existing mortgage being moved from one lender to another. The expensive debt, the part the customer actually has a problem with, averages 255,000 . So the headline figure overstates the thing you care about by a factor of nine. Nothing in the schema warns you. loan_amount is a number, AVG is a function, the result renders fine. The bug is that one column is holding two concepts and only a human who understands the domain will notice. -- what you actually wanted SELECT AVG ( unsecured_debt ) FROM applications ; -- 255,000 If a column can mean two things depending on another column, split it. Every time. 2. Reporting the mean when the distribution has a tail Income in this dataset runs from ordinary salaries up to about five million kroner. A handful of very high earners drag the mean upward: Mean income: ~635,000 Median income: 647,000 for homeowners, 550,000 for renters Look at what happens there. The mean sits between the two medians and describes neither group. Someone reading only the mean concludes the typical applicant earns 635,000. Nobody earns 635,000. It is an artefact. df . groupby ( ' housing ' )[ ' income ' ]. agg ([ ' mean

Tesla has officially launched the Cybercab, its autonomous vehicle with no steering wheel.

The happy path was never the bottleneck. I was timing successes and shipping a miss. Production traffic is full of misses. Would you trust a bench that never fails? An AI rewrite loves the clean try. It wraps a lookup in except KeyError. It logs the miss "for observability." It looks professional. It is also a tiny furnace. Exceptions are not cheap branches. Log formatters are not free either. I learned that the loud way. Cheap generation makes the trap faster. A model will emit a polite miss path before you blink. Technical debt used to wait for a human. Now it arrives as a helpful patch tonight. The debt is not the lookup. The debt is a story about speed with no miss mix in the graph. I needed variants, not vibes. I used MonkeyCode's free model access and free server option to draft those variants. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The model proposes shapes. It does not know your miss rate. If the graph disagrees, the patch dies. The lab I actually rerun This is a pocket harness. It is not a production claim. Steal the file. Change the mix. Keep your own picture. I am not posting a trophy chart from a machine you cannot see. # miss_bench.py # Lab harness. Treat printed rows as local output, not a benchmark paper. from __future__ import annotations import logging import time import tracemalloc from typing import Callable logging . basicConfig ( level = logging . DEBUG ) log = logging . getLogger ( " hot " ) HITS = { f " user: { i } " : i for i in range ( 800 )} KEYS = [ f " user: { i } " for i in range ( 1000 )] # 20% misses on purpose def lookup_except ( key : str ) -> int | None : try : return HITS [ key ] except KeyError : log . debug ( " cache miss key=%s " , key ) return None def lookup_get_quiet ( key : str ) -> int | None : return HITS . get ( key ) def lookup_get_log ( key : str ) -> int | None : value = HITS . get ( key ) if value is None : log . debug ( " cache miss key=%s " , key ) return value def run_mix (

Short answer: for marketplace password recovery, choose the delivery setup that can prove what happened to every message, then keep the suppression decision in your own system. Inbox placement matters, but an evidence trail is the decision axis. A custom sending domain with aligned DKIM and SPF, bounce events, and exportable history gives an auditor something better than a green dashboard. The decision note: which delivery shape leaves evidence? Delivery shape Evidence you can normally retain Best fit Trade-off Managed transactional service Webhooks, message ids, DNS guidance Teams that need mailbox feedback quickly Retention and event detail vary by contract Cloud notification primitive Basic accepted or failed status Small systems with an existing mail pipeline Bounce reason and suppression semantics may be thin Self-hosted MTA Full local logs and routing policy Data-residency teams with on-call capacity Reputation, feedback loops, and maintenance become yours My default is the first shape, with a local ledger beside it. The transport can change; the account-recovery policy should not. That split also makes a provider review concrete: ask for a sample event export, its retention period, and the fields that connect a bounce to a reset request. The catch is operational capacity. A self-hosted stack is not suitable for a marketplace that cannot staff reputation incidents, while a managed service is a poor fit when its export cannot satisfy your retention or residency rules. Keep the cloud primitive for low-volume internal tools, not as an automatic answer for customer recovery. What must a password reset email evidence trail capture? Begin with the reset request. Store a hash of a random, single-use token, its expiry, the account identifier, and the request time. OWASP recommends a consistent response for existing and non-existing accounts, rate limiting, and invalidation after use; those controls prevent delivery telemetry from becoming an account-enumeration signal

An invite-only event in Austin, Texas, kept online fans in the dark for hours.