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A VPN might compromise your Android Auto experience at first, but there are some easy workarounds.

Yet more rogue AI agents from OpenAI and Anthropic have been caught attempting to hack real targets online without permission. The discoveries add to a growing list of previously unknown incidents that have alarmed AI safety experts and intensified pressure for greater oversight of frontier systems. According to a report from the UK's AI Security […]

This marks the official launch of Zoox's commercial operations.

Anthropic is building a team for designing its own custom AI chips. The Claude maker said it would co-design hardware and models to help its technology run faster and more efficiently.

Sunbird Messaging is back on the Google Play Store, offering Android users blue bubble privileges in iMessage complete with reactions and high quality videos for $2.99 a month. Apple and Google have made cross platform messaging better in recent years with support for RCS, but Android users can still cause issues in iMessage group chats, […]

River plans to build a new factory, launch additional models from 2027, and target profitability as it scales production.

Google Assistant's days have been numbered ever since Gemini arrived on the scene, and its time is now up. Google has announced that it will be removing access to Assistant on Android phones and tablets, along with paired devices like smartwatches or headphones, from September 4th. The announcement came in an email apparently sent to […]

A startup has created beagles without the gene that causes runny noses and watery eyes for allergy sufferers.

A new study shows how the big cats cause deer to move away from roads and deeper into the forest, where they pose less of a hazard to motorists.

Part of "AI, engineering and what survives production", a series on the parts of building with AI that hold up once real traffic hits them. There is a claim going round that you have probably absorbed by now: AI-assisted development is making codebases worse. Refactoring is down, duplication is up, we are all writing more and revising less. The numbers behind it are real, the samples are enormous, and I found I had started repeating the conclusion in conversation without ever having checked it. Then it occurred to me that those figures are averages taken across hundreds of millions of changes from thousands of organisations, not one of which is mine. So what is the rate in your repository? Nobody has told you, and on current evidence nobody is going to. I set out to find mine, assumed it would take an afternoon, and spent three days discovering that the answer is far harder to get at than the confident version suggests. So this is not a piece about what AI does to code. It is about how to ask that question of your own repository without arriving at a wrong answer, which turned out to be the genuinely difficult part. The tool I built to do it is git-habits : free, local, and it reads no source code whatsoever. What git can actually tell you Git history is a surprisingly rich behavioural record. Not of quality, about which it knows nothing at all, but of habits: how often you commit, how large those commits are, whether you go back and change what you wrote last month, and whether anybody still touches the old code. That is a narrower thing than quality and it is the thing the industry claims has changed, so it is the thing worth measuring. Four signals are computable from commit metadata alone, without opening a single source file: Moved lines. The share of changed lines sitting in files git detected as renamed or copied. It is the closest thing history offers to "somebody went back and reorganised this." Legacy touch. The share of changes landing on files nobody has

A single-author repo of instruction files, not code, Ponytail passed 44,000 GitHub stars in nine days by making coding agents stop over-building. Its headline claim of 80-94% less code came from a flawed baseline; after a contributor said so, the maintainer rebuilt the benchmark as a real agentic run and published a lower figure of 54%. By Steef-Jan Wiggers

Wispr Flow, a popular dictation tool, has released a live notetaker that transcribes and summarizes meetings. It joins a growing wave of AI notetakers for the workplace.

2026 is shaping up to be an excellent year for Apple TV. Apple's streaming service has built out an impressive slate that spans returning favorites like Silo and Sugar to all-new hits including OnlyFans-inspired dramedies, terrifying comedies, and paranoid tech thrillers. But the most important release might be a feel-good sports sitcom. After what seemed […]

Search "best project management software for startups" and you get the same dozen names every time: Trello, Asana, ClickUp, Notion, Linear, monday.com, Basecamp. Ranking them by feature count tells you almost nothing, because they are not really competing for the same job. The useful question for a startup is not which tool has the most features. It is two narrower ones: does your work run through engineering or through the whole company, and does per-seat pricing or flat-rate pricing fit a headcount that is about to change? Answer those and the shortlist collapses to two or three. The split that actually decides it Two forks matter more than any side-by-side feature grid. The first is who the tool is built for. Issue trackers like Linear are built around the engineering workflow (issues, cycles, a keyboard-first interface) and feel wrong the moment a marketer or a founder tries to run a launch plan in them. General work tools like Asana, ClickUp, monday.com and Trello are built for any team, which makes them flexible but also less opinionated about how software actually ships. The second fork is the shape of the bill. Almost everything in this category charges per seat per month, so the cost scales directly with hiring. A small number, Basecamp most notably, offer a flat rate that does not. For a company planning to double headcount inside a year, that difference can outweigh any feature comparison. If your team is mostly engineers For an engineering-led startup, an issue tracker usually beats a general project tool. Linear's free plan includes unlimited members, two teams and up to 250 issues, which is enough to run a small product team before paying anything; its Basic plan is $10 per user per month billed yearly and lifts the cap to unlimited issues and five teams. The trade-off is scope: Linear is deliberately narrow, so non-engineering work does not fit it well. The larger, more familiar alternative is Jira, which startup roundups still name as the default for

A slow AI feature does not feel smart. It feels broken. That is the uncomfortable truth many AI SaaS builders hit after the demo works. The prototype answers well, the agent can call tools, and the RAG pipeline looks impressive. Then real users arrive. Prompts get longer. Queues form. Streaming starts late. One tenant uploads huge documents. Another runs bulk jobs at noon. Suddenly the same workflow that felt magical in testing feels like a spinner with an invoice attached. The fix is not simply “use a faster model.” You need an LLM latency budget : a small set of rules that says how fast each AI workflow must feel, how many tokens it can spend, when to stream, when to cache, when to route to another model, and when to stop before cost and latency drift together. This guide is for solo SaaS developers, micro SaaS builders, and AI SaaS teams shipping production features with LLM APIs, RAG, agents, or self-hosted models. Why latency budgets matter now AI platform news points in the same direction: builders are moving from chat demos to production workflows. Agent tools, web context APIs, voice agents, coding assistants, and RAG platforms are all getting more capable. At the same time, inference cost and reliability are under pressure. Latency is now a product metric. Inference efficiency is becoming a business metric. Yet many articles stop at TTFT, TPOT, quantization, batching, or model serving. Fewer show how a SaaS builder turns those ideas into a product-level budget with code, dashboards, fallbacks, and customer-safe limits. The simple model: TTFT, TPOT, and total time You do not need a PhD in serving systems to start. Track three numbers. Time to First Token Time to First Token (TTFT) is the delay between the user action and the first streamed token. It includes network time, queue time, provider overhead, tool setup, retrieval, and the model’s prefill phase. High TTFT is why a chat box feels dead. Time Per Output Token Time Per Output Token (TPOT) is the averag

The Silent Disruptor The silence in the room was absolute, broken only by the rhythmic scraping of pens on paper during a high-stakes meeting. Then, it happened. My pocket erupted into a frantic, brassy ringtone that seemed to last an eternity before I could fumble to silence it. My face turned crimson as the room’s focus shifted from the presentation to my vibrating trouser pocket. I had remembered to check my calendar, but I had completely forgotten to toggle my phone to silent mode. That moment of pure, concentrated embarrassment was the catalyst for me building Muffle. The Friction of Manual Control We live in an age of automation, yet our phones—the very devices meant to assist us—remain stubbornly manual when it comes to basic social etiquette. Every day, millions of people walk into mosques for prayer, classrooms for lectures, or medical offices for consultations, and every day, a percentage of them forget to silence their devices. This isn't just a minor annoyance; it is a persistent source of social friction. Before I started building Muffle, I looked for existing solutions. Most apps were either bloated with unnecessary permissions, required invasive cloud accounts, or simply failed to trigger at the right time. The fundamental problem wasn't just the lack of features like GPS-based prayer times or calendar-specific automation; it was the lack of reliability. If an automation app fails once, the user loses trust in it forever. If I am in a meeting, I cannot afford for the app to 'sleep' because the system decided to save battery at the expense of my configured routine. I needed something that could handle these state changes consistently, regardless of whether the phone was in my pocket, sitting on a desk, or buried in a bag. Architecting for Reliability When I began writing the core logic for Muffle, I immediately hit the wall that every Android developer eventually faces: Doze Mode. Android’s aggressive power management is designed to preserve battery by

Cada vez más gente arranca la búsqueda de un proveedor preguntándole a un modelo en vez de a un buscador. Y no pide diez opciones para comparar: pide una recomendación y recibe dos o tres nombres. Si tu empresa no está ahí, no quedaste octava. No estás en la respuesta. La pregunta que sigue es obvia: cuánto tarda en cambiar eso. Pero antes hay un problema más aburrido y más importante, que es cómo se mide. Lo escribo porque es la parte que casi nunca se cuenta y es donde se rompen los informes. Una captura de pantalla no es una medición Es el error más común y el más difícil de discutir, porque la captura parece prueba. La respuesta de una app conversacional depende del historial de la cuenta, de la sesión, del ruteo interno del proveedor, de si esa consulta activó búsqueda web o no, y de la región desde donde se pregunta. Dos personas preguntando lo mismo el mismo día reciben respuestas distintas. La misma persona preguntando dos veces también. O sea: la salida no es determinista y el instrumento no es estable. Una captura te dice qué pasó una vez, en un contexto que no podés reconstruir. Como métrica de seguimiento no sirve para nada. Lo que sí sirve es una serie: la misma consulta, literal, contra el mismo motor, con el mismo criterio de clasificación, repetida en el tiempo. El valor absoluto de un punto importa poco. Lo que importa es la diferencia entre puntos. Fijar el texto de la consulta, no la etiqueta Este es un bug de proceso que da resultados verosímiles y falsos. Si guardás en la planilla una etiqueta como "consulta de chatbot" en vez del texto exacto que preguntaste, dentro de dos meses nadie se acuerda del wording. Y el wording cambia el resultado: preguntar "quién hace X en Argentina" y "mejores empresas de X en Argentina" devuelven listas distintas. Cuando el texto se corre entre rondas, la serie deja de ser comparable, pero el gráfico sigue dibujándose igual de lindo. Guardá el string literal, versionado. Si tenés que cambiar una consulta, empezá u

In less than a week, Spider-Man: Brand New Day raked in $1 billion worldwide and had the biggest box office opening weekend in Hollywood history. The feature has been a reminder of why Sony is probably never going to give up the Spider-Man film rights, and highlighted how Marvel Studios was smart to strike a […]

Rogue AI agents from OpenAI and Anthropic have again been caught trying to disrupt servers and software—and leaving instructions for future bad behavior.

Musk kept inflating the already-big promises being made by SpaceX CFO Bret Johnsen and Gwynne Shotwell on the company's first call.