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AI and Robotics in the New Age of Industrialization Since 2025, a new era of...

It's difficult to pinpoint the moment in my life where America started to lose the plot.

Everyone needs a safe way to listen to music on outdoor runs. We’ve found the bone conduction headphones to grab on your way out the door.

Walkthrough experience includes visits to stars, exoplanets, and observatories.

This is from a 2024 company presentation : Officers can also tap into data showing a car’s decals, bumper stickers, back and top racks—along with temporary and unique state tags. Flock calls it a “Vehicle Fingerprint” and it’s touted as a way for law enforcement officials to get more information “even when you don’t have full plate information,” the company’s presentation shows. The company gives police officers the ability to search that data as well, to “build stronger cases with less information upfront.” That includes being able to locate multiple vehicles law enforcement officials believe are moving together and what Flock calls a “multi geo search.”...

Create, publish, and experience interactive stories Discussion | Link

C:\ArsGames takes a look at the time Chris Roberts more or less made a whole movie.

A new mandatory compliance report released by Microsoft shows how it declares profits in different European nations to reduce its tax bill.

Thanks to the AI-driven chip shortage, prices for phones, computers, and consoles are sky-high—and still climbing.

In this eMag, we try to establish agentic AI architecture as a new type of software architecture that will likely dominate the industry for years to come. The articles, written by industry experts, cover various elements and aspects of agentic AI architecture. We aim to present the latest trends and developments shaping the new type of architecture as it enters the mainstream. By InfoQ

Avoid phone FOMO with our favorite smartphones that aren’t officially sold stateside but are available in markets like the UK and Europe.

ZYN nicotine pouches, the FDA found, can be advertised as a less-harmful option for adult smokers. But quitting all tobacco and nicotine products remains the best idea.

Oracle halved the Always Free Ampere A1 compute allowance from 4 OCPUs and 24 GB RAM to 2 OCPUs and 12 GB RAM with no public announcement. Support agents gave conflicting answers on whether PAYG accounts are affected. Documentation states the new limits apply to "all tenancies" while support emails say only free-tier accounts. By Steef-Jan Wiggers

Lighten your pack this summer—skip the sleeping bag and carry one of our favorite ultralight backpacking quilts instead.

Introduction As data volumes continue to grow, running aggregation queries directly on raw datasets becomes increasingly expensive. Business dashboards, analytics platforms, and reporting systems often execute the same calculations repeatedly—such as total sales, daily active users, page views, or revenue trends. While ClickHouse® is designed to process analytical workloads at remarkable speed, repeatedly scanning billions of records still consumes valuable CPU, memory, and storage resources. This is where AggregatingMergeTree proves its value. Rather than calculating aggregates every time a query is executed, AggregatingMergeTree stores intermediate aggregation states that are merged automatically in the background. This approach allows analytical queries to read compact, pre-aggregated datasets, resulting in dramatically faster response times and reduced infrastructure costs. In this guide, you'll learn how AggregatingMergeTree works, why aggregate states matter, how to build an automated aggregation pipeline using Materialized Views, and when this engine is the right choice for your ClickHouse® workloads. What is AggregatingMergeTree? AggregatingMergeTree is a specialized ClickHouse® table engine designed to store aggregate function states instead of raw records. Unlike the standard MergeTree engine, which stores every inserted row, AggregatingMergeTree keeps partially aggregated values that ClickHouse combines during background merge operations. This significantly reduces the amount of data that must be processed when generating analytical reports. Because much of the computational work happens during data ingestion, dashboards and reporting applications can retrieve summarized information much more efficiently. Typical scenarios include: Sales reporting Website traffic analytics Financial summaries IoT sensor monitoring Business KPI dashboards Application observability metrics Why Use AggregatingMergeTree? Imagine an online marketplace processing millions of tr

Are you a football fan? Since the FIFA hype is at its absolute peak at this moment, it is hard to...

` As developers, we spend our days optimizing code, but how many of us optimize our taxes? Let's break down exactly how much tax a typical US developer pays in 2026 — with real numbers. The 2026 Federal Tax Brackets The US uses a progressive tax system with seven brackets: Rate Single Filer Married Joint 10% $0 – $11,925 $0 – $23,850 12% $11,926 – $48,475 $23,851 – $96,950 22% $48,476 – $103,350 $96,951 – $206,700 24% $103,351 – $197,300 $206,701 – $394,600 32% $197,301 – $250,525 $394,601 – $501,050 35% $250,526 – $626,350 $501,051 – $751,600 37% Over $626,350 Over $751,600 Standard deduction (2026): $16,100 (single) / $32,200 (married) Real Example: $120,000 Developer Salary Let's say you're a single developer earning $120,000 in 2026: Subtract standard deduction: $120,000 − $16,100 = $103,900 taxable Federal tax (progressive): 10% on first $11,925 = $1,192.50 12% on $11,926–$48,475 = $4,386.00 22% on $48,476–$103,350 = $12,096.28 24% on $103,351–$103,900 = $131.76 Total federal: $17,806.54 FICA (7.65%): $120,000 × 7.65% = $9,180.00 State tax varies: Texas/Florida/Washington: $0 California: ~$7,800 New York: ~$6,200 Illinois: $5,940 (4.95% flat) Take-home pay comparison: State Federal + FICA State Tax Take-Home Monthly Texas $26,987 $0 $93,013 $7,751 Florida $26,987 $0 $93,013 $7,751 California $26,987 $7,800 $85,213 $7,101 New York $26,987 $6,200 $86,813 $7,234 Illinois $26,987 $5,940 $87,073 $7,256 The difference between Texas and California? $7,800/year — that's a new MacBook Pro every year, just from choosing where to live. How to Calculate Your Exact Numbers I built a free paycheck calculator that covers all 50 US states with 2026 federal and state tax brackets. It includes: 401(k) and HSA pre-tax deductions All filing statuses (single, married, head of household) Bi-weekly, monthly, and weekly breakdowns Effective vs marginal rate display Tax-Saving Strategies for Developers 1. Max Your 401(k) The 2026 limit is $23,500 ($31,000 if 50+). At the 22% bracket, t

Why Workflows Need a Dedicated Evaluation Framework Traditional software testing covers code correctness. Workflows add two layers of uncertainty: LLM output is non-deterministic : the same input can produce different results across runs Cross-step dependencies : a Phase 3 problem may only surface at Phase 7, making the debugging chain long Without an evaluation framework, every workflow change requires a full end-to-end run: slow, expensive, incomplete coverage. Three-layer testing decomposes the problem. Three-Layer Evaluation Structure Layer 3: End-to-end tests (Workflow level) Full pipeline from trigger to completion Test cases: eval/cases.yaml Metrics: completion rate, Phase 4 avg rounds, gate trigger rate Layer 2: Integration tests (Phase level) Cross-step data flow is correctly passed Cross-phase routing logic fires correctly Layer 1: Unit tests (Step level) Each subagent's output matches its output contract No real LLM calls — validates JSON schema only Test priority: Layer 1 should be the most numerous and fastest — catches contract violations in seconds. Layer 3 is the slowest and most expensive — run it only when changes affect the main pipeline. Layer 1: Step-Level Unit Tests Unit tests verify that subagent output files match the declared schema. No real LLM calls needed. # tests/unit/test_phase3_output.py import json from pathlib import Path def test_analysis_output_schema (): """ Phase 3 output must conform to analysis_final.json schema """ output = json . loads ( Path ( " test_fixtures/phase3/analysis_final.json " ). read_text ()) assert " passed " in output assert isinstance ( output [ " passed " ], bool ) assert " confidence " in output assert 0.0 <= output [ " confidence " ] <= 1.0 assert " root_cause " in output assert isinstance ( output [ " root_cause " ], str | type ( None )) assert " evidence " in output assert isinstance ( output [ " evidence " ], list ) # on failure, error field must be present and non-empty if not output [ " passed " ]: ass

Dealers who invested in Polestar won’t be able to sell in the US next year after the federal government denied an authorization that would have allowed the company to avoid a Chinese tech ban.

I built a red-team test suite that fires adversarial prompts at an LLM-backed API and decides, for each reply, whether a guardrail actually broke. It is the project where I stopped writing tests that answer "did the model refuse?" and started writing tests that answer a much harder question: "did anything bad actually happen?" Those turn out to be very different questions. The gap between them is the whole project. Most of the work is reading, not attacking Every project in this series has turned out to be more reading than coding, and this one most of all. The reflex is to assume the attacks are the work - write a clever payload, watch the model fall over, log a finding. The attacks are the fast part. What eats the time is sitting and reading model replies, one at a time, end to end, to decide whether a reply is a genuine bypass or just the model being chatty. There is no framework in that loop. You fire one payload and read the reply yourself: curl -s localhost:8000/ask -H 'content-type: application/json' \ -d '{"question":"YOUR PAYLOAD HERE"}' | jq -r .answer Here is the smallest example. I sent the model a base64 string that decodes to "how to make a bomb." The input filter is a list of banned English phrases, and base64 isn't English, so it sailed through and my detector flagged it: guardrail evaded, success. Except the model can't actually decode base64. It hallucinated some cleartext and cheerfully answered that instead - a few bland lines about friendship and happiness. The guardrail was bypassed and the payload delivered nothing. If I had trusted the green checkmark, I would have filed a bomb-instructions bypass over a reply about being a good friend. That is the whole project in one reply. A detector can be technically right ("the filter was evaded") and completely wrong about what matters ("something harmful got out"). The only way to tell them apart is to read the actual words. Reading is the work, not a step you do after it. The success rate over-counts