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I self-host Umami for my SaaS, ParserBee . The main reason I picked it: privacy-friendly, cookie-less, first-party analytics that adblockers supposedly leave alone because the script comes from your own domain. That last assumption turned out to be wrong, and the reason is the subdomain name itself. Writing it up because the fix is small and the failure mode is silent. The problem My Umami instance ran at umami.parserbee.com , so the tracker was loaded like this: <script defer src= "https://umami.parserbee.com/script.js" data-website-id= "..." ></script> The EasyPrivacy filter list (used by uBlock Origin, Brave Shields, AdGuard, and most other blockers) contains a rule that matches the Umami tracker by hostname pattern, along the lines of: ||umami.*/script.js It doesn't target a specific company's server. It targets any host whose subdomain is literally named umami serving a file called script.js . Which is exactly how most of us name things when self-hosting: umami.mydomain.com , plausible.mydomain.com , matomo.mydomain.com . The filter lists know this convention, and they have rules for it. The result: every visitor with an adblocker never loads the script. No errors on your side, no console noise, nothing in the Umami logs. The traffic just quietly never shows up. I only caught it because signups were arriving from campaigns that Umami claimed nobody clicked; the server logs and Stripe disagreed with the analytics, and the server logs were right. The fix Serve the same Umami instance from a second hostname that no filter list matches. No proxying, no renaming files, no changes to the Umami install itself. I added u.parserbee.com as an alias for the same service: 1. DNS record. A CNAME (or A record) for u.parserbee.com pointing at the same server as the existing umami.parserbee.com . 2. Reverse proxy. Add the new hostname to the existing Umami site config so both route to the same instance. I run Umami in Docker behind Coolify, where this is just adding a second d

Publicado originalmente en Justicia Liberal . El hecho y su lectura económica La reconfiguración de lealtades internas en el bloque peronista del Congreso que reporta LA17 podría leerse como un episodio más del folletín peronista: disputas de conducción, alineamientos con gobernadores, señales cruzadas hacia 2027. Pero desde una perspectiva económica aplicada, el fenómeno tiene consecuencias concretas sobre variables que le importan a cualquier empresa, ahorrista o asalariado argentino. La inestabilidad legislativa no es un dato político neutro. Es un factor de riesgo que los mercados descuentan en tiempo real. Incertidumbre legislativa y su precio en variables macro Cuando un bloque opositor mayoritario se fragmenta, el resultado inmediato no es la debilidad del peronismo: es la imprevisibilidad del Congreso. Y la imprevisibilidad tiene precio. El riesgo país argentino, que según datos del BCRA y operadores de mercado secundario rondó los 600-700 puntos básicos durante buena parte de 2025, es en parte una prima por incertidumbre institucional. Cada vez que el Poder Legislativo se convierte en un tablero de negociaciones opacas —donde un artículo fiscal puede ser bloqueado, modificado o aprobado según quién necesite qué favor de quién— la tasa de descuento que aplican los inversores sobre activos argentinos sube. Eso se traduce en mayor costo de financiamiento para el Tesoro y, por efecto derrame, para el crédito privado. El mecanismo es simple: si no sabés qué va a salir del Congreso la semana próxima, no invertís a largo plazo. Y si no invertís, no generás empleo formal ni capacidad productiva. El déficit como rehén de la interna El equilibrio fiscal que el gobierno de Javier Milei logró sostener durante 2024 —el primer superávit financiero en más de una década, según datos del Ministerio de Economía— depende en parte de que el Congreso no apruebe gastos que el Ejecutivo no puede financiar sin emisión. Ahí es donde la fragmentación peronista se vuelve peligrosa de

Today, I’m talking with Harvard professor and New Yorker staff writer Dr. Jill Lepore about her new book, The Rise and Fall of the Artificial State, which comes out on August 25. Jill is one of the best writers there is at identifying institutional patterns in history, and Decoder is a show about systems, so […]

AliExpress has been hit with a €550 million (about $629 million) fine for violating Europe's Digital Services Act (DSA) rules by failing to prevent illegal, unsafe, or counterfeit products from being sold on the e-commerce platform. The European Commission ruled that AliExpress didn't take effective measures to reduce the dissemination of illegal products, noting the […]

Nothing is more frustrating than spending hours, or even days, perfecting a PCB design only to discover it fails during production. The secret to avoiding this is simple: apply PCB design standards from the very beginning. These guidelines act as a blueprint for reliability, manufacturability, and long-term performance, guiding every aspect of your board—from trace widths and spacing to via placement, solder mask, and layer stackups. Following these recommended practices doesn’t just prevent manufacturing errors—you save time, reduce costs, improve quality, and minimize delays. Whether you’re building a prototype or preparing for full-scale production, designing with these rules in mind ensures your PCB performs exactly as intended. Applying these best practices sets the foundation for success in any production scenario. By starting with the right standards, you can avoid costly mistakes before they happen and bring your product to life smoothly. Why PCB Design Standards Matter (More Than You Think) PCB standards are not theoretical rules. They are: • Proven engineering practices • Built from real-world failures • Designed to reduce risk Without them, you are basically designing blindly. With them, you get: • Higher first-pass success rate • Faster manufacturing approval • Reduced rework and cost • Better product reliability Steps to Apply PCB Standards in Your Design Here’s a practical, step-by-step guide to apply PCB design standards effectively: Step 1: Start with the Right Standards (Not All Are Needed) You don’t need to follow everything. You need to follow the right ones. Key standards to consider: • IPC-2221 → General PCB design standard • IPC-7351 → Footprint design guidelines • IPC-A-600 → PCB acceptability • IPC-A-610 → Assembly quality Practical Tip: Start with IPC-2221 + IPC-7351 if you're doing design. Step 2: Apply Standards During Component Placement Most failures start here—not routing. Common Mistakes: • Random component placement • Ignoring signal

Scan a menu, pay a bill, onboard a smart plug, and you are using a piece of technology that was never meant for any of those things. The QR code was invented in 1994, and its original job was tracking car parts on a factory floor. Understanding why it was built the way it was explains why it now shows up on nearly every connected device. Who invented the QR code The QR code was created in 1994 by a team led by engineer Masahiro Hara at Denso Wave, a subsidiary of the Toyota group in Japan. At the time, the automotive industry ran on standard one-dimensional barcodes, the striped labels still seen on grocery products. Those barcodes held very little data, usually around 20 characters, and a busy assembly line often needed a worker to scan ten different labels on a single box of components. It was slow, and it was error-prone. Hara wanted a code that could store far more information and be read much faster. His answer was to go two-dimensional: instead of encoding data only in the width of vertical bars, a QR code (short for "Quick Response") stores data across a grid of black and white squares, packing in thousands of characters in a fraction of the footprint. The board game that shaped it The most famous detail of the QR code's origin is where Hara found his design inspiration. He was reportedly playing the board game Go on a lunch break, staring at the black and white stones arranged on the grid, when the idea of encoding information in a two-dimensional matrix of light and dark cells clicked into place. The harder problem was speed. A scanner needs to find the code and figure out its orientation before it can read anything, and in a factory a label might be rotated any which way. Hara's team solved this with the three distinctive square markers in the corners of every QR code. Those "position detection patterns" use a ratio of black to white areas that almost never occurs by chance in printed material, so a scanner can instantly locate the code and work out its an

According to the New York Times, federal agents have been told that the FBI will no longer be investigating confrontations involving ICE agents. The DHS and DOJ denied the change in policy to The Times. The reported change in guidance follows renewed scrutiny of violence by ICE agents who have killed two people in the […]

Originally published at htpbe.tech . The version on htpbe.tech stays in sync with the latest detection algorithm — refer to it for the canonical text. A fraud reviewer opens a PDF bank statement. The first thing many manual checks look at is the document’s Producer field — the line of metadata that records which software last wrote the file. This one says Adobe PDF Library 23.1 . To a human, and to most lightweight metadata checks, that reads as reassuring: Adobe is professional software, the kind a bank’s back office or a law firm would use. The reviewer moves on. That is exactly the reaction the forger was counting on. The document was not produced by Adobe. It was edited in a free browser-based PDF editor, then passed through a step that overwrote the Producer string to say Adobe . The metadata now lies about the file’s own origin — and it lies in the most credibility-laundering direction available, because “Adobe” is the producer string people trust most. This is producer identity forgery, and it is one of the most common ways a tampered PDF tries to talk its way past a metadata-only review. This is a case study in how that attack works at a conceptual level, why a metadata-only check waves it through, and how a structural approach — the one behind the public marker HTPBE_PRODUCER_IDENTITY_FORGED — catches the contradiction the forger left behind. If you want to see the Producer string for yourself, the free PDF metadata viewer reads it — along with every other field — straight out of any PDF. Why the Producer field is the obvious thing to forge Every PDF carries internal records about how it was made. Two fields matter most to a reviewer: producer — the software that wrote the final bytes of the file. creator — the application the content originated in. Fraud-detection lore, repeated in countless “how to spot a fake bank statement” guides, says the same thing: a real institutional document is generated by an automated back-end system, so if the producer says Mi

Organizers want the show to be the most-viewed halftime show in history. Having Shakira, Justin Bieber, Coldplay, and BTS will surely help—but do soccer fans want it?

In 1898, years before radio broadcasting existed and decades before anyone used the word "electronics," Nikola Tesla stood in front of a crowd at Madison Square Garden and did something that looked like magic. In a large pool of water sat a small iron-hulled boat. With no wires connecting them, Tesla sent commands through the air and the boat obeyed, turning, stopping, and blinking its lights on demand. Spectators were so unprepared for the idea that some accused him of hiding a trained monkey inside the hull, or of controlling it with his mind. What Tesla had actually built was the first wireless remote control, and it is the direct ancestor of every connected device we make today. A machine that took commands through the air Tesla called his invention a "teleautomaton," from the Greek for "remote" and "self-acting." The boat carried a radio receiver, a set of relays, and a battery driving its motor and rudder. From a control box on the side of the pool, Tesla transmitted radio signals that the receiver decoded into physical actions. Press a control, and a coherer-based circuit closed a relay, which in turn stepped the boat's steering and switching mechanism to a new position. The patent behind the demonstration, US Patent 613,809, "Method of and Apparatus for Controlling Mechanism of Moving Vessels or Vehicles," was granted in November 1898. Read today, it is startling how modern the thinking is. Tesla was not just wiggling a boat around a pool for show; he was describing a general system for sending control signals to a remote machine and having that machine act on them without a human physically present. That is the exact problem statement behind modern IoT , just with vacuum-era hardware. Why nobody knew what to do with it Tesla saw enormous potential. He imagined remotely piloted vessels, automated vehicles, and machines that could carry out instructions from miles away. He even pitched the concept to the US military as a radio-controlled torpedo. The receptio

Before a young Athenian took his full place in the city, he spent two years in the ephebeia. Training happened in the gymnasion, organized by tribe, the same tribes that would later send him to represent them in the Boule itself. Nobody handed him full standing first and hoped the judgment would follow. This should have been post seven. It’s showing up as sixteen because the gap only became visible once the room was real enough to test against. The Agora described what a Spec Session does. It never asked whether everyone walking into that room shares an accurate picture of what the agent can actually do. Most rooms don’t. Someone watched a demo and thinks the agent can do anything. Someone else got burned by a bad output three weeks ago and doesn’t trust it with anything real. Nobody’s intuition has been tested against the same tasks, and the spec that comes out of that room ends up too ambitious or too conservative depending on whose untested belief happened to speak first. That gap has a name in Athens. The gymnasion existed because nobody was handed a place in the city first and expected to develop judgment on the job. The ephebeia ran two years, training built around a specific fact. Physical readiness and civic judgment weren’t taught in separate places. They happened in the same space, under the same supervisors, organized by the same tribal groupings that would later structure how the city actually governed itself. The same word, gymnasion, ended up naming both the training ground for eighteen-year-olds and the buildings where Plato and Aristotle did their most serious thinking. Academy and Lyceum were gymnasia first. Nobody separated the trial from the reflection. The trial was how the reflection got earned. That’s the part worth taking seriously. Testing a tool and understanding a tool were never two different activities. The testing is how the understanding gets built. A team that reads documentation about what an agent can do has a description. A team tha

variant-confidence v0.1.0: a calibrated confidence layer for variant-effect pathogenicity scores State-of-the-art variant-effect models are accurate in cross-validation but their scores are poorly calibrated on temporal data. variant-confidence adds an auditable calibration layer on top of existing predictors — it does not train a new model. The problem: accuracy is not trust Protein variant-effect predictors (AlphaMissense, ESM-1v, EVE) report pathogenicity scores, but a clinician or researcher needs to know how much to trust the number , not just its rank. The gap is calibration, not accuracy: AnnotateMissense (2026) reports MCC 0.94 in cross-validation, dropping to 0.76 on temporal ClinVar, accuracy 0.8798. A raw score near 0.9 may not mean 90% probability. Acting on an uncalibrated score is a risk. What it does variant-confidence wraps an existing predictor's score and produces a calibrated, uncertainty-aware output: Probability calibration (AC1): Platt scaling or isotonic regression over a separate holdout. Selectable, not hardcoded. Conformal prediction (AC1b): coverage 1−α intervals, split or Mondrian by gene. ECE (AC2, AC9): Expected Calibration Error reported before/after calibration, with bootstrap CI and per-bin counts. Bins with too few samples are flagged as low-reliability. Leakage-free split (AC3): temporal split by ClinVar release date with gene isolation — the same gene never appears in both train and test. This is unit-tested. Missing-score handling (AC4): works with AlphaMissense or ESM-1v alone; emits an explicit warning instead of failing silently. Non-deceptive reporting (AC7): every result includes interval/ECE + method + threshold, never a bare calibrated score. Verification (clean clone, no network) Built under a three-party governance loop: implement → independent audit in a clean clone → merge approval. ruff check . → All checks passed. pytest tests/ → 28 passed in 8.90s (offline fixture). An honest bug we caught in audit The first ECE tes

Food producer Taylor Farms released a statement on the Cyclospora outbreak Friday, confirming that it's "voluntarily removing all iceberg lettuce sourced from central Mexico from the US market." Reuters reports that, according to a source, Taylor Farms told customers like Yum Brands owner Taco Bell and the food distributor Sysco on Thursday to pull shredded […]

Agility is opening a new training center for its Digit robots in Fremont, California.

I was tired of buzzword-heavy AI projects and marginally impactful demos. Surely we can do something more inspiring with these LLMs than build another chatbot? For me, the answer is physical AI: the moment all those breakthroughs finally reach into the real world, in robots that see, move, and figure things out for themselves. I think it is the most exciting frontier in tech right now. It is also genuinely hard to break into, because it is not one field. It is about five of them stacked on top of each other: electronics, mechanics, programming, data, and AI. Eight months ago I started my own robotics journey from scratch, and I was completely overwhelmed. How do you get from blinking an LED to a humanoid that does your dishes? There are thousands of scattered tutorials out there, with no sense of what comes first, or what any of it is building toward. So I decided to build the map. Stealing the best idea from my favorite games: If you have ever played a factory-building or strategy game like Satisfactory or Civ Six, you know the feeling. You start with almost nothing, and you unlock new tech one satisfying step at a time. Those games are proof that we will happily spend hours mastering an intimidatingly complex system, as long as it is laid out as a clear tree of unlocks. So why not point that same instinct at learning something real? That is exactly what a tech tree is: a structured, visual path where each node is a skill and each connection is a prerequisite. You start at Curiosity on the far left and work your way right, through electronics, mechanics, code, data, and AI, all the way toward autonomous robots and humanoids. The idea is simple: turn gaming time into learning time. What the tree actually is Every node on the tree is a skill to learn, and the star-shaped nodes are hands-on projects where theory finally meets a soldering iron. Nodes are color-coded by discipline, so you can see at a glance whether you are in electronics, mechanics, programming, data s

A newly renewed, $25 million-per-year contract with a subsidiary of Thompson Reuters further expands the power of ICE under the Trump administration.

I've been using Claude for a long time and mostly ignored Artifacts. Fine for a quick React demo. Not something I reached for. Then I needed to send an analysis to a few people at work, and it clicked. Or I'm just using it in a way nobody intended. Hard to say. The actual case I own the paywall backend at a Czech media house. The subscription offer on our news site is embedded as an iframe, and iframes are a bad neighbourhood: context isolation means the iframe has no access to the parent page's session, so user identity kept breaking and we kept patching it over postMessage. Every iframe is its own page view, so GA4 data was skewed and we had to build server-side tracking and session stitching to make the numbers mean anything. And ad blockers, CSP, and timeouts mean sometimes the thing just doesn't render, so we maintain a fallback UI in parallel. I wanted to propose we drop the iframe and ship a JS embed library instead, distributed through our internal npm registry. That's an architecture change, so it needs a document: what we fixed, why the iframe is still structurally wrong, what the alternative costs, what the numbers say. The numbers part came out of the same agent session, by the way. GA4 said roughly 0.14% of paywalled page views hit an error, about half of them iframe-blocked-by-browser. That's every 700th reader. Small number, real money. The boring problem You get a good answer out of the model. Now what? You paste it into a doc. Reformat it, because chat markdown does not survive the trip. Fix the tables. Decide whether it goes in Confluence or an email. Send it. Then someone asks a follow-up, you go back to the model, get a better answer, and now there are two versions of the truth and one of them is in someone's inbox. I've done the email version of exactly this document before. Outlook ate the markdown. I ended up hand-rolling plain text with unicode bullets and uppercase section headers like it was 1998. Half of that work is transport, not thinkin

Election deniers have spent years promoting the idea that the 2020 election was stolen. They believe Donald Trump’s speech finally proves them right.

Trump Media, the company behind Truth Social, is selling Wall Street faster access to the "most market-moving" posts on the US President's social media platform. On Thursday, Trump Media announced plans to launch "Truth API," a licensed real time data feed for businesses that provides "the fastest access to Truth Social's most influential accounts." It […]

The Trump administration is waging a culture war on science, and the latest salvo is in the form of a dry, bureaucratic proposal from the Office of Management and Budget (OMB) that could threaten the future of US science as we know it. The proposal would give political appointees unprecedented control over grant funding, the […]