标签：#tech

Prime Day has brought a number of Apple deals, but one of the most useful if you’re planning to travel over the July 4th weekend or later this summer is on the new AirTags. The four-pack has dropped to a new low of $90 ($9 off) at Amazon and Best Buy, bringing the price of […]

Will Cathcart, who led WhatsApp for the past seven years, is stepping down from his role as Meta appoints a new leader. On Monday, Meta CEO Mark Zuckerberg announced on Facebook that Kunal Shah, the founder of an Indian fintech startup called Cred, is taking over as the head of WhatsApp. Cred is an app […]

Today, I’m talking with Jack Conte, the CEO of Patreon. Jack last joined me on the show almost exactly five years ago, in the summer of 2021, and a lot has changed on the internet and in the creator landscape since then, so I was very excited to talk to him again, especially since his […]

Seven months after introducing its $119.98 Ultra BodyScan smart scale, Wyze announced a cheaper $79.98 alternative available today that makes a few compromises to shave $40 off the price. There's no Wi-Fi, but you can sync the BodyScan's measurements to Apple Health and Google Fit by connecting to the Wyze mobile app over Bluetooth. And […]

Bob Starr was delighted with his vibe-coded website. "Boomberg" showed how much US tax money is going to tech companies, and Starr launched it online immediately after making it. It wasn't until months after the site went live that he realized there was a problem: a hidden SQL injection risk. It could've left the site […]

2026 AI Coding Agents Are Making Developers Forget How to Code: Why the Convenience Trap Threatens Innovation As AI‑driven platforms like Atoms, Devin, Windsurf, Cursor, and Warp reshape software engineering, the real cost may be a gradual erosion of core programming fundamentals. The latest MarkTechPost comparison shows AI coding agents moving from novelty to mainstream. Teams report faster feature cycles, fewer lines of manual boilerplate, and a shift toward intent‑first workflows. Yet beneath the productivity headlines lies a subtle trade‑off: every hour spent letting an agent write code is an hour not spent exercising the mental muscles that let us reason about edge cases, optimize performance, or invent novel algorithms. The Rise of Intent‑First Development Modern agents excel at turning a natural‑language description into a runnable diff. Atoms uses multimodal reasoning to interpret UI sketches; Devin can autonomously open pull requests after a high‑level prompt; Windsurf lets engineers edit across files with conversational commands. This paradigm reduces the cognitive load of syntax hunting and lets engineers focus on what the software should do, not how to type it. Measuring the Productivity‑Skill Trade‑off Data from early adopters shows a 38% cut in boilerplate typing and a 22% boost in sprint velocity. However, internal surveys reveal a 15% drop in self‑reported confidence when debugging low‑level concurrency bugs, and a 20% increase in reliance on agent‑generated explanations rather than personal code walkthroughs. The numbers suggest a growing dependency that mirrors the calculator effect seen in mathematics education. Second‑Order Shifts: From Craftsmanship to Orchestration As routine typing fades, engineers spend more time validating AI output, refining prompts, and orchestrating multi‑agent pipelines. Traditional code reviews evolve into “prompt reviews,” where the gatekeeper judges whether the AI captured the business intent. New roles—AI Interaction

Mach's approach differs sharply from some of its peers.

According to a Wall Street Journal investigation, Polymarket has been paying people to film themselves placing fake bets and celebrating fake wins on social media. WSJ identified over 1,100 deceptive clips and talked to creators who, despite not stating as such in their videos, confirmed the company paid them to create the clips. The videos […]

If you had a Roomba, especially in the early days of the robot vacuum, it was in many ways a fairly unsophisticated machine. It would just bump around your house, looking for something to suck up, until its battery died or its (way too small) tank filled up. Not that it mattered, though. You probably […]

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on aviation, air taxis, and Wi-Fi speeds at 30,000 feet, follow Andrew J. Hawkins. The Stepback arrives in our subscribers' inboxes on Sunday at 8AM ET. Opt in for The Stepback here. How it started Last year, […]

The cryptographic keys that secure your computer's boot sequence will start to expire on June 24. Here's what that means for you.

Most fintech breaches you read about involve a hacker, a vulnerability, and a headline. Most fintech losses I've actually seen up close involve none of those things. They involve someone who read the terms of a cashback offer more carefully than the product team did, found the one path through the workflow nobody had tested, and quietly walked away with money the system handed over willingly. That's the part standard security testing misses. A penetration test asks: can someone break in? Business logic testing asks a more uncomfortable question: what happens if someone uses every feature exactly as designed, just not exactly as intended ? In a country processing billions of UPI transactions a month, that second question matters just as much as the first — arguably more, because nobody needs a zero-day to abuse a referral program. Here's where that gap shows up most often in Indian fintech apps. Wallet Systems: Built for Speed, Tested for Function, Rarely Tested for Abuse A digital wallet sits at the intersection of multiple money-in paths — UPI, card, net banking, cashback credits — and at least one money-out path. Every intersection like that is a place where timing and assumptions can quietly fall apart. The classic version of this is a race condition: top up the wallet and spend from it in two near-simultaneous requests, and check whether the balance check happens before or after both transactions are committed. Done right, this should be impossible. Done wrong, a user can spend money that, technically, hadn't arrived yet — or spend the same balance twice. There's a quieter version of the same problem around refunds. If a refund is credited back to the wallet on a different timeline than the original debit was finalized, there's often a window where the balance briefly shows more than it should, and a fast enough user can act inside that window before reconciliation catches up. And then there's KYC tiering. Minimum-KYC wallets in India are deliberately capped at

FinOps X 2026 , terminó hace apenas una semana y concluyó con JR Storment, el Director Ejecutivo de la FinOps Foundation compartiendo uno de los anuncios más esperados, la presentación de Tokenomics Foundation . ¿Qué es? Es una iniciativa de la Linux Foundation, que busca establecer estándares abiertos, lineamientos referentes, y buenas prácticas de forma específica para el costo en Inteligencia Artificial y el uso de tokens, así como otros elementos relacionados con esta tecnología con el objetivo de guiar a las empresas y organizaciones a optimizar su consumo de IA y generar mejores resultados en el valor tecnológico. Algunas acciones: Visualización de los costos Atribución del valor Estandarización de procesos, entre ellos FOCUS La creación de esta iniciativa surge en un momento en el que la IA, se ha colocado como una de las tendencias más relevantes, desde LATAM y otras regiones, con diferentes niveles de desarrollo, y un nivel de diversidad complejo. De forma aparente el costo de la IA puede verse reflejado en los tokens, pero la realidad es que sólo es una parte de los que representa el costo de soluciones de IA, partiendo particularmente de la estructura de costos de estas tecnología, en lo global, podemos detectar 3: Costos del modelo : Engloban los costos del desarrollo e implementación del modelo Costos indirectos : Están relacionados con el funcionamiento de un modelo a nivel organizacional Costos asociados : Integran las erogaciones, relacionadas con las puesta en marcha del modelo, pero no directamente en él, por ejemplo, la infraestructura, y servicios relacionados Dentro de cada categoría de costos, los servicios y etapas del desarrollo de IA, son variados Los servicios y etapas de la creación de procesos de IA que están involucrados en cada categoría de costos, muestran la complejidad para la creación de valor en estas iniciativas. Durante FinOps X, tuvimos diferentes charlas relacionadas con IA, el principal reto: cómo monitorear, medir, e incremen

Hi, friends! Welcome to Installer No. 133, your guide to the best and Verge-iest stuff in the world. (If you're new here, welcome, happy belated Juneteenth, and also you can read all the old editions at the Installer homepage.) This week, I've been reading about Sam Bankman-Fried and PE Guy and admin nights (which we […]

Financial advisors have a dirty secret: they spend almost half their working hours not advising anyone. The culprit? Compliance documentation. After every client meeting, advisors must document what was discussed, what was recommended, whether those recommendations were suitable, and whether they followed FINRA and SEC rules — all in a format their CRM can ingest. A 45-minute meeting routinely generates 2 hours of paperwork. I built an open-source tool that does it in about 3 minutes. Here's exactly how — every architectural decision, every trade-off, and every line of code that matters. The Problem Is More Specific Than You Think When I started talking to advisory firms, I expected "meetings take too long" or "we need better CRM software." Instead, every compliance officer said the same thing: "We're not worried about the notes. We're worried about what's NOT in the notes." The real pain isn't documentation speed — it's the compliance gap. If a client says "I can't afford to lose this money" and the advisor recommends an aggressive growth fund, that's a FINRA 2111 suitability violation. But if the note-taker (usually the advisor, writing from memory hours later) forgets that quote? No record of the red flag. This changed my entire system design. It's not a transcription tool with formatting. It's a compliance engine that listens for mismatches. Architecture Four-stage pipeline: Audio → Transcription → Structured Extraction → Compliance Check → CRM Note (Whisper) (Claude via (Rule engine) (Formatter) OpenRouter) Stack: Python/FastAPI + React frontend + Whisper (local) + Claude via OpenRouter Two key design choices: Whisper runs locally. Advisory meetings contain PII and legally privileged information. Sending audio to third-party APIs isn't optional for most firms — it's a regulatory non-starter. Compliance engine is NOT an LLM. You can't have a probabilistic system making deterministic compliance judgments. The compliance check uses hardcoded rules against structur

How AI Will Shape the Technology Industry in 2027 We're roughly 6 months out from 2027, and the signals are already converging: AI is not coming — it has arrived, and the next wave will be fundamentally different from everything that came before it. For developers and tech professionals, 2027 isn't a distant horizon. It's the next major inflection point to prepare for now. Here's what the research, analysts, and industry leaders are saying about what's ahead. From General-Purpose to Task-Specific: The Enterprise AI Shift One of the clearest signals comes from Gartner (April 2025): by 2027, organisations will use small, task-specific AI models three times more than general-purpose large language models. The era of "one model to rule them all" is already ending at the enterprise level. Companies are learning that a fine-tuned, domain-specific model trained on their proprietary data consistently outperforms a generic LLM on their specific workflows. Faster, cheaper, more accurate, and harder for competitors to replicate. For developers, this has real implications: Skills in fine-tuning, RAG (retrieval-augmented generation), and model evaluation become more valuable than prompt engineering alone The ability to build and maintain internal AI pipelines on private data will be a core engineering competency Generic API integrations to OpenAI or Anthropic get replaced — or layered under — proprietary model infrastructure The companies building and maintaining these specialised models will have durable competitive advantages. The ones that don't will be running on shared infrastructure that their competitors can access equally. The Macroeconomic Wake-Up Call: AI Hits GDP in 2027 Goldman Sachs projects that AI may start to meaningfully boost US GDP in 2027 — marking the first measurable macroeconomic signal of the current AI wave. Paired with estimates that ~25% of tasks in advanced economies could be automated by 2027 (10–20% in emerging markets), the scale of workforce restr

Nothing's next budget phone is the latest victim of RAMageddon. As 9to5Google reports, Nothing co-founder Akis Evangelidis announced in a post on X that a follow-up to the CMF Phone 2 Pro won't be coming this year: We were working on a successor but with memory prices where they are right now, we can't build […]

Smart lighting company Philips Hue has launched its first wired wall modules. Installed behind existing wall switches, the new devices bring non-smart lights into the Hue ecosystem for the first time. Hue also announced new Play table and floor lamps that are more affordable versions of its Signe series, along with upgrades to its E14 […]

We've lost something in the past 15 years. Netflix, Amazon, Disney, Apple; they've all convinced us that streaming is the best way to watch movies and shows at home. With everything at our fingertips, there's no need to run to Blockbuster for the weekend's entertainment, or wait for a DVD rental to arrive in the […]

Getting the future right is now big business. But at a festival in Berkeley, forecasters worry that sports markets could take the whole industry down.