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Technology companies are extending AI beyond code generation into earlier stages of the software lifecycle, including PRD validation, design inputs, and code review. Initiatives from Uber, DoorDash, and Cloudflare highlight a shift toward AI-driven governance layers that evaluate engineering artifacts before implementation while preserving human oversight across the development pipeline. By Leela Kumili

New details have emerged about Valor's latest fund, which last year announced it was raising an unspecified amount of capital.

Google’s first new smart speaker in six years is here and once again leads its competitors—now with paywalled features.

AI has made it easier to produce working software. That part is real. It can write code, draft documents, research a topic, scaffold a prototype, and debug a problem faster than most teams can finish writing a decent ticket. But faster building doesn't automatically mean better product decisions. That's the part I keep coming back to. For decades, software teams optimized around delivery. Requirements, design, development, QA, release. Waterfall softened into Agile. Agile grew into DevOps. The practices changed, but the assumption underneath stayed pretty stable: building software is expensive, so plan carefully before you start. That made sense because, for a long time, it was true. Now that assumption is breaking. AI is doing to software what calculators did to accounting. It isn't eliminating the job. It's moving the job up a level. The syntax, boilerplate, first draft, and some of the debugging are getting offloaded. The work doesn't disappear. The bottleneck moves. Learning is still expensive Here's what didn't get cheaper: understanding what people actually need getting stakeholders aligned deciding what evidence would change your mind putting something real in front of users reading the signal without fooling yourself The old question was: Can we build it fast enough? The new question is: Do we understand the problem well enough? That sounds like a small shift, but it changes the work. It changes what strong engineers spend time on. It changes what product people need from engineering. It changes how teams should define "done." If the code ships but nobody learns anything, did the team actually move forward? Sometimes yes. Often no. Users don't know until they can touch it People are not great at specifying requirements up front. Not because they're difficult. Because they're human. Most of us don't know how we feel about something until we can react to a version of it. A mockup. A prototype. A rough slice. A real workflow with sharp edges. So the fastest pat

The Jeff Bezos-backed startup finally revealed more pricing details for its debut EV, including a $29,950 starting price for the SUV variant, and boosted the base range to 205 miles.

Launching with better specs than expected, the no-frills Bezos-backed electric pickup looks set to take on Ford’s Maverick—and be an EV truck you can afford and fix yourself.

We now know the price of Slate Auto's affordable American-made electric truck, almost a year after the company warned it wouldn't hit its initial "under $20,000" target price. The no-frills pickup starts at $24,950 - matching the revised mid-$20,000 price range it promised last year, after the Trump administration announced it was putting an end […]

With its new pickup, Slate Auto is making a simple bet: price matters more than almost anything else. The company announced today that the American-made electric truck will start at $24,950, placing it squarely in the mid-$20,000 price range it had originally promised and making it the least expensive pickup truck and EV available today. […]

The common cold comes for us all—often more than once a year. And there is no way to prevent it. The best you can do is take vitamin C and stay away from people with the sniffles. Now, the payment company Stripe, founded by brothers Patrick and John Collison, says it will fund a new…

Naomi Saphra discusses 5 rules governing language model behavior, breaking down why LLMs act like populations rather than individuals. She explains how tokenization creates strange semantic blind spots and highlights the mechanics of sycophancy, showing how models leverage subtle data associations to match user biases and demographics - even guessing political views based on favorite sports teams. By Naomi Saphra

These goggles have an excellent display, solid metric tracking, and an open-water “SwimStraight” feature. But the real smart tech requires a subscription.

As record-breaking heat crushes Europe, organizers are moving events online to avoid exposing people to dangerously high temperatures.

Every quarterly earnings number for every US public company going back to 2009 is sitting in a free, well-documented JSON API run by the US government. No API key. No rate limit for normal use. No paywall. Almost nobody in the dev community seems to know it exists. It's at data.sec.gov , and it's the same data Bloomberg charges $24k/year for. What's in it The SEC requires all US-listed companies to file financial reports in XBRL — a structured XML format where every number is tagged with a standardised concept name. The EDGAR system has been collecting these since around 2009. The companyfacts endpoint exposes all of it as clean JSON: GET https://data.sec.gov/api/xbrl/companyfacts/CIK{cik}.json Where CIK is the company's SEC identifier (10 digits, zero-padded). For Apple, that's 0000320193 . The response is a large JSON object with every concept the company has ever reported, broken down by period. The other endpoint you need is the ticker-to-CIK map: GET https://www.sec.gov/files/company_tickers.json This gives you a flat list of all US-listed companies with their CIK, ticker, and name. Load it once and cache it. One gotcha: concept names vary by company Companies don't all use the same GAAP concept names to report the same thing. Apple reports revenue as RevenueFromContractWithCustomerExcludingAssessedTax . Older companies use Revenues . Some use SalesRevenueNet . If you just look up one concept name, you'll get blanks for most companies. The fix is a concept alias map: try each name in order, use the first one that has data. const CONCEPT_MAP : Record < string , string [] > = { revenue : [ ' Revenues ' , ' RevenueFromContractWithCustomerExcludingAssessedTax ' , ' RevenueFromContractWithCustomerIncludingAssessedTax ' , ' SalesRevenueNet ' , ' SalesRevenueGoodsNet ' , ], netIncome : [ ' NetIncomeLoss ' , ' NetIncomeLossAvailableToCommonStockholdersBasic ' , ' ProfitLoss ' , ], operatingCashFlow : [ ' NetCashProvidedByUsedInOperatingActivities ' , ' NetCashProvidedB

A deep dive into how Spark transforms your SQL into a physical execution plan — and how Databricks layers Adaptive Query Execution and the Photon vectorized engine on top to squeeze out maximum performance. Table of Contents Why Query Optimization Matters The Catalyst Optimizer Pipeline Stage 1: Parsing — From SQL to Unresolved Logical Plan Stage 2: Analysis — Binding to the Catalog Stage 3: Logical Optimization — Rule-Based Rewrites Stage 4: Physical Planning — Strategies and Cost Models Adaptive Query Execution (AQE) The Photon Engine Reading Explain Plans Tuning Reference Table References Why Query Optimization Matters A Spark query written by a human and a Spark query executed by the engine are often very different things. The gap between them — the optimization — is what separates a job that runs in 3 minutes from one that runs in 3 hours on identical hardware. Databricks compounds Spark's native Catalyst optimizer with two additional layers: Adaptive Query Execution (AQE) — re-optimizes the query at runtime using actual statistics collected mid-job Photon — a C++ vectorized execution engine that replaces the JVM-based Spark executor for eligible operators Understanding all three lets you write queries that cooperate with the engine rather than fight it. The Catalyst Optimizer Pipeline Catalyst is Spark's rule-based and cost-based query optimizer. Every query — whether written in SQL, DataFrame API, or Dataset API — passes through the same four-stage pipeline before a single byte of data is read. Stage 1: Parsing — From SQL to Unresolved Logical Plan # ── Catalyst Stage 1: Parsing ───────────────────────────────────────────────── # Spark uses ANTLR4 to parse SQL into an Abstract Syntax Tree (AST). # At this point column names are NOT validated — the plan is "unresolved". from pyspark.sql import SparkSession spark = SparkSession . builder . appName ( " catalyst-demo " ). getOrCreate () # Both of these produce identical internal representations df_api = ( spark .

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With the Reactive Data Layer Architecture (RDLA), you establish a clear boundary between public data APIs and private, framework-specific data-source implementations. Your presentation layer operates in a purely reactive manner, observing data changes rather than procedurally querying them. RDLA also simplifies testing by encouraging you to program to interfaces and use clean seeding patterns. By Mervyn Anthony

Your face called, and it’s low-key offended you might trust TikTok more than WIRED.

Introduction When a query runs in ClickHouse®, the database does much more than simply read data and return results. Before execution begins, ClickHouse® parses the SQL statement, analyzes it, applies optimizations, and builds an execution plan that determines the most efficient way to process the query. Understanding query execution plans is one of the most valuable skills for anyone working with ClickHouse®. They provide visibility into how queries are executed, helping you identify bottlenecks, validate optimization efforts, and troubleshoot performance issues. In this article, we'll explore how ClickHouse® generates execution plans, the different EXPLAIN modes, and how to interpret them for better query optimization. Why Query Execution Plans Matter A SQL query defines what data you want, but it doesn't explain how the database retrieves it. Consider the following query: SELECT country , count () FROM events GROUP BY country ; Although the query looks simple, ClickHouse® must determine: Which data parts to read Whether primary indexes can reduce the scan If data skipping indexes can be used How aggregation should be performed Whether parallel execution is possible How intermediate results should be merged A query execution plan provides answers to these questions, making it an essential tool for performance tuning. The ClickHouse Query Lifecycle Every query passes through several stages before producing results. The lifecycle typically looks like this: SQL Query │ ▼ Parser │ ▼ Analyzer │ ▼ Optimizer │ ▼ Query Plan │ ▼ Execution Pipeline │ ▼ Results Each stage plays an important role: Parser validates SQL syntax. Analyzer resolves tables, columns, and expressions. Optimizer applies query optimizations. Query Plan determines the logical execution steps. Pipeline distributes work across multiple threads. Execution processes the data and returns the results. Understanding this workflow makes execution plans much easier to interpret. Introducing the EXPLAIN Statement

OpenAI and Broadcom introduce Jalapeño, a custom AI chip built for LLM inference to improve performance, efficiency, and scale across AI systems.

Dein bestes Google-Ranking ist wertlos, wenn die Antwort schon vor dem Klick gegeben wurde. Genau das passiert gerade: Nutzer fragen ChatGPT, Claude oder Perplexity – und bekommen eine fertige Antwort mit drei, vier zitierten Quellen. Bist du nicht darunter, existierst du in diesem Moment nicht. Kein Ranking, kein Klick, keine zweite Chance. Die Disziplin, die das adressiert, heißt Generative Engine Optimization (GEO) . Und sie ist – anders als der Marketing-Lärm vermuten lässt – zu großen Teilen ein Engineering-Problem. Crawler-Zugang, Rendering, strukturierte Daten. Lauter Dinge, über die ein Entwickler entscheidet, nicht das Content-Team. SEO optimiert auf den Klick. GEO optimiert auf das Zitat. Der Unterschied ist nicht kosmetisch. Klassisches SEO will, dass du auf Platz eins rankst, damit jemand klickt. GEO will, dass ein Sprachmodell deinen Absatz wörtlich in seine Antwort übernimmt – inklusive Quellenangabe. Der Klick ist nur noch Bonus. Daraus folgt ein anderer Tech-Stack an Signalen: Aspekt Klassisches SEO GEO / KI-Sichtbarkeit Ziel Top-10 in Google Zitat in ChatGPT, Claude, Perplexity Relevante Bots Googlebot, Bingbot GPTBot, ClaudeBot, PerplexityBot Index-Hinweis sitemap.xml llms.txt + sitemap.xml Strukturierte Daten Rich Snippets Entity-Linking ( Organization , sameAs , @graph ) Rendering Google rendert JS (verzögert) viele KI-Bots rendern kein JS → SSR Pflicht Erfolgskontrolle Search Console, Rank-Tracker Citation- & Mention-Tracking in LLMs Die Hebel überschneiden sich – sauberes HTML, schnelle Antwortzeiten, valides Markup helfen beidem. Aber die Bots, die Index-Signale und die Erfolgskontrolle sind eigenständig. Wer GEO als „SEO mit neuem Namen" abtut, übersieht genau die Stellen, an denen es klemmt. Schritt 1: Lass die Bots überhaupt rein Bevor du über Content-Qualität nachdenkst, klär die banale Frage: Kommt der Crawler durch? Erstaunlich oft lautet die Antwort nein – und niemand merkt es, weil ein Browser die Seite ja problemlos lädt. Die drei Use