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Google Flights has a date grid with a fare for every departure day, and a "track prices" toggle that emails you when its pick of dates moves. Both are fine for one trip. Neither gives you the table: every day, the fare, the airline and stops behind it, in rows you can sort, keep and put a threshold on. If your dates are flexible and you want the cheapest day to fly as data — or airfare price tracking that runs every morning — you need rows. This is how to get one row per departure day from Google Flights as JSON, with no API key (there is no public Google Flights API), and how to turn it into a flight price alert. 1. One request, one row per day The Flight Price Tracker on Apify takes routes, a first departure day and a window length. For each day it searches Google Flights, keeps that day's cheapest itinerary and ranks the days. A 30-day window on one route is at most 30 fare rows plus a free status row. curl -X POST "https://api.apify.com/v2/acts/kestrel~flight-price-tracker/run-sync-get-dataset-items?token= $APIFY_TOKEN " \ -H "Content-Type: application/json" \ -d '{"routes": ["LIS-LHR"], "departDate": "2026-10-05", "days": 30, "adults": 1, "currency": "USD", "market": "us"}' A fare row: { "type" : "fare" , "route" : "LIS-LHR" , "trip" : "one_way" , "depart_date" : "2026-10-05" , "return_date" : null , "seat" : "economy" , "adults" : 1 , "currency" : "USD" , "price" : 127 , "price_display" : "127 US dollars" , "airline" : "Tap Air Portugal" , "stops" : 0 , "depart_time" : "8:00 PM" , "arrive_time" : "10:55 PM" , "duration" : "2 hr 55 min" , "duration_minutes" : 175 , "layovers" : null , "co2_kg" : 123 , "itineraries_seen" : 12 , "cheapest_in_window" : true , "rank_in_window" : 1 , "google_url" : "https://www.google.com/travel/flights?tfs=..." , "fetched_at" : "2026-08-29T06:25:14+00:00" } cheapest_in_window is true on exactly one day per route; rank_in_window orders the rest. The free status row repeats the headline as cheapest and cheapest_date , with days_searc

Google Hotels already compares every booking site for a hotel and a stay — Booking.com, Expedia, Agoda, Hotels.com and the hotel's own site. It has a "track prices" button too, but it emails you on its own terms, picks the sources, and keeps the history. If you want the numbers — for a trip, a rate parity check, or a price history chart — you need them as rows. This is how to get Google Hotels prices for exact dates as JSON, without a Google API key (there is no public Google Hotels API for reading prices; the official Hotel APIs are feeds for hotels sending prices to Google), and how to turn that into daily hotel price tracking. 1. One request, every booking site's rate The Google Hotels Prices Scraper on Apify takes a place search or a list of hotels, a stay, occupancy and currency, and returns three row types: hotel (lowest nightly rate + stay total), offer (each source's rate, free‑cancellation flag, deep link) and status . You pay per priced row; sold‑out hotels and empty searches are free. curl -X POST "https://api.apify.com/v2/acts/kestrel~google-hotels-prices/run-sync-get-dataset-items?token= $APIFY_TOKEN " \ -H "Content-Type: application/json" \ -d '{"queries": ["hotels in Lisbon"], "checkIn": "2026-10-03", "checkOut": "2026-10-06", "adults": 2, "currency": "USD", "maxHotels": 20}' A hotel row looks like this: { "type" : "hotel" , "name" : "The Central House Lisbon Baixa" , "check_in" : "2026-10-03" , "check_out" : "2026-10-06" , "nights" : 3 , "nightly" : 81.81 , "nightly_display" : "$82" , "total" : 245 , "stars" : 2 , "rating" : 4.3 , "reviews" : 727 , "deal" : "19% less than usual" , "entity_id" : "ChkIg-b2ismUj7M1Gg0vZy8xMWg3MThreGg1EAE" , "google_url" : "https://www.google.com/travel/hotels/entity/ChkI…" } and an offer row (with "includeOffers": true ): { "type" : "offer" , "name" : "Hyatt Regency Lisbon" , "source" : "Booking.com" , "official" : false , "nightly" : 569.35 , "total" : 1708.05 , "free_cancel" : true , "free_cancel_until" : "Oct 1" , "p

I Built an Autonomous AI Agent That Hunts Bounties. Here's What Happened. The Setup I gave an AI agent one job: find paid work online, build the deliverable, and earn money — autonomously. Not a chatbot. Not a copilot. An agent that scans 232+ listings across multiple platforms, filters out scams and ghost sponsors, writes proposals, generates deliverables with real market data, and queues everything for human approval. Here's what happened in the first 48 hours. The Stack (All Free) Python core — pipeline orchestration, economic gate, critic Ollama + qwen3:4b — local LLM for analysis writing (no API costs) Chart.js — dashboard visualizations Public APIs — CoinGecko, DeFiLlama, Solana RPC (all keyless) GitHub Pages — free hosting for the portfolio Windows Task Scheduler — runs every day at 9 AM + every 4 hours Total infrastructure cost: $0/month. What the Agent Actually Does Every Morning 09:00 — Wake up ├── Check-in on AgentHansa (earn $0.01 USDC daily drip) ├── Scan Superteam Earn (232 live listings) ├── Scan Clawlancer/TaskForce/MoltJobs for gigs ├── Scan GitHub for paid issues ($20-500 fixes) ├── Filter through 7 anti-scam layers: │ geo restrictions, human-presence demands, │ ghost sponsors (no web/twitter/verification), │ unverified payers, real-money requirements ├── Economic gate: expected value must be positive ├── Local LLM critic reviews against actual page content └── If candidate passes everything: → Build deliverable (report/dashboard/thread draft) → Generate proposal text → Send Telegram alert with approval command The Filters That Saved Me In the first 24 hours, the agent found 232 listings. After filtering: Filter Killed HUMAN_ONLY access 216 Ghost sponsors (no identity) 1 (would've wasted hours) Real-money deposit required 1 ($1000 bug bounty trap) Country walls 1 (Superteam Canada only) Already claimed/stale Rest Without these filters, I would have wasted days on bounties that were never going to pay. The First Deliverable The agent found a $500 bo

Waymo has launched Gemini in Waymo , a beta in-car conversational assistant for riders using its Ojai robotaxi experience . Accessed through a Gemini icon on the cabin screen, the feature lets riders use natural language and voice to adjust parts of the cabin, ask about their journey and get information about nearby places or broader topics. The important boundary is clear: Gemini is a rider-facing assistant, not part of the autonomous driving system. Waymo Driver continues to control the vehicle , while Gemini operates separately and does not influence driving decisions. For riders, the integration turns the cabin display into a more conversational interface. For the wider automotive market, it is a concrete example of generative AI being deployed inside a commercial mobility service without being assigned responsibility for vehicle control. Waymo describes the feature in its official Gemini in Waymo announcement . The company says Gemini stays inactive until a rider chooses to engage it. It does not access real-time driving data unless the rider explicitly asks for information related to the ride. What Gemini in Waymo can do today Gemini in Waymo is designed around requests that are useful during a trip, rather than around autonomous navigation. A rider can tap or press the Gemini icon and speak to the assistant. The initial beta supports interactions such as: Cabin-control requests , including asking to set the air conditioning to a specified temperature. Ride-related questions , such as seeking information about the current journey. Information about surroundings , including questions about local sites. General knowledge queries through a hands-free conversational interface . This scope matters because it places Gemini in the passenger experience layer. The assistant can make a ride feel more responsive without creating confusion about which system is responsible for safety-critical driving functions. Area Gemini in Waymo Waymo Driver Primary role Rider-facing c

Garden Irrigation System Summary MY project to make a bespoke irrigation system for my home garden, which comes in at under £10 per zone including the actual water delivery method, and is made with relatively easily sourced components. I am a mechanical engineer by training, but not an electrician so interested in hearing pointers on how to make it better. Some of the component and tool links below are AliExpress affiliate links. If you buy through them I earn a small commission at no extra cost to you. Everything listed is what I actually bought and used, or the closest equivalent I could find. This helps me fund some more ambitious but hopefully useful builds in the future. Intro So I have a vegetable patch and some flowers in the garden; it became a bit of a job during the hot days of summer to water the plants in the evening. I didn’t especially mind it but given my love of AI and tech, alongside recent experiments with Home Assistant, I thought there must be a 2026 version of this job. I tried a Wi-Fi-controlled tap, but quickly realised the flow rate was low - due to a small aperture size, and also scaling up with this type of solution to 6 + zones would quickly get expensive and leave me dependent on battery-powered solutions - also not a big win. So as I had begun experimenting with creating my own devices with dev boards etc, I figured, “how hard can it be” and in honesty it wasn’t, just took a bit of trial and error. This guide will be focused on how i would build it today, not all the steps that got me to here. My philosophy Standardised equipment/ components as much as possible Speed of delivery = speed of experimentation Modular where possible Anything can be achieved at any cost, but some of the fun is building something from very little Components Note all water pipes for this project are ½ inch and so connector etc are for that, this corresponds to a ¾ in threaded connector for attaching to pipes Standard UK Hose (½ inch) ¾ inch Threaded Tap Push Fit

Google has introduced a dedicated Student Hub in the Gemini ecosystem , bringing study notebooks, flashcards and interactive practice quizzes into one in-app space. The central idea is to connect a learner's course materials with Gemini's AI tools, reducing the work of moving between separate note-taking, revision and question-generation tools. The official Gemini for Students page presents the hub as a gateway to Gemini's education-focused capabilities. It is part of a broader Google education AI initiative that also involves NotebookLM and Google for Education resources, rather than a standalone feature with no connection to the rest of Google's products. For students, the practical value is straightforward: uploaded learning materials can become organized revision assets. For businesses that create internal training or support education programs, the release is also a useful example of how generative AI can consolidate material preparation, knowledge review and self-assessment into a more connected workflow. Google has not, however, confirmed a specific learning management system integration in the supplied materials. How Gemini Student Hub connects learning materials and AI tools The Student Hub is designed as a dedicated space where courses and content connect with Gemini. Its core tools include a study notebook, flashcard creation and quick practice quizzes. Google says Gemini notebooks can take uploaded course materials, including PDFs, slides and notes, and generate study aids such as flashcards, quizzes and study guides. A significant detail is the use of inline citations to user-provided sources for those generated materials. That does not remove the need for learners to check the results, but it gives them a way to trace an AI-produced prompt or explanation back to the material they uploaded. In a learning workflow, that is more useful than treating a general-purpose chatbot response as an unanchored answer. NotebookLM is an important part of the wider wo

Google is offering eligible college students in the United States 12 months of Google AI Pro at no charge . The offer, announced on August 19, 2026, gives students access to the paid Gemini plan that Google values at $19.99 per month. It is redeemable through December 31, 2026, and standard Google AI Pro pricing applies after the free year unless the student cancels. The program is aimed at academic work, but it also matters for the wider Gemini ecosystem . It puts higher-capacity AI tools, Google app integrations and substantial cloud storage in the hands of students who may carry those workflows into internships, startups and future workplaces. For businesses, the immediate lesson is not that Google has announced a broader pricing reduction. It has not. Rather, teams should expect more new users to become familiar with Gemini and the ways it connects with everyday Google tools. What Google AI Pro includes for eligible US students According to Google's official student offer announcement , eligible US college students who claim the promotion receive one year of Google AI Pro. Google says the plan includes four times higher usage limits within Gemini , Gemini Spark, integrations with Google apps such as Gmail and Docs , and 5 TB of Google One storage. Google has also introduced a student hub in the Gemini app for participating students. The hub is intended to support learning with features including study notebooks and Deep Research in Gemini Live. These tools are presented as part of a student-focused experience, rather than as a separate business plan or a new API offering. The distinction matters. Access to Gemini through this offer does not, by itself, establish access to every Google AI product or developer service. Students and organizations considering Gemini for a particular workflow should check the relevant product terms and capabilities rather than assuming that an app subscription covers all Google AI services. Offer detail Eligible college students in t

Anthropic’s recent work on Claude Sonnet 5 points to a potentially important direction in AI safety: using post-training methods to improve the behavior of increasingly capable models. Public material from Anthropic indicates that Sonnet 5 received substantial post-training alignment work and delivered safety improvements over earlier Sonnet versions. A separate public signal suggests researchers may be exploring whether one model can help align a stronger successor, although the specific reported training lineage has not been documented in Anthropic’s first-party materials. For businesses deploying advanced AI, the practical lesson is not that alignment has been solved. It is that model behavior can be materially shaped after base training, and that safety results need to be assessed in the context of the tasks a company actually plans to automate. What Anthropic’s published results establish In its official Claude Sonnet 5 announcement , Anthropic describes substantial post-training intended to align the model with Claude’s constitution. The company reports improvements in safety-related behavior, including stronger refusals of unsafe requests and lower misalignment findings in automated audits compared with Sonnet 4.6. That is meaningful because post-training is the stage where a model’s responses, instruction-following behavior, and safety boundaries can be adjusted after its underlying capabilities are developed. In operational terms, it can affect whether an AI assistant follows risky instructions, mishandles sensitive workflows, or produces responses that conflict with a company’s intended rules. However, the available research also establishes an important limit. Sonnet 5 was not uniformly at the level of Claude Opus 4.8 across every safety measure. Anthropic’s evaluations still identified some automated assessments where Sonnet 5 showed higher misalignment relative to Opus 4.8. Opus 4.8, released in May 2026, is the company’s production-ready reference poin

Un déploiement devrait être la chose la plus ennuyeuse de ta semaine. S'il est excitant, c'est mauvais signe. Au début de ma carrière, les mises en production étaient des événements. On retenait son souffle, on croisait les doigts, quelqu'un exécutait de mémoire une séquence d'étapes manuelles, et on regardait les journaux avec une boule au ventre. C'était palpitant. C'était aussi terrifiant, et le côté palpitant était précisément le problème : chaque déploiement était un pari, parce que chaque déploiement était un peu différent du précédent. Un bon déploiement est répétable. La même chose, de la même façon, à chaque fois — automatisée, pas récitée par un humain fatigué à la fin d'une longue journée. Quand le processus est un script plutôt qu'une cérémonie, l'ennui remplace l'angoisse. Tu ne pries plus. Tu appuies sur un bouton, et le résultat est prévisible parce qu'il a déjà été prévisible cent fois. L'automatisation fait ici plus que gagner du temps. Elle supprime toute une catégorie d'erreurs : l'étape oubliée, le mauvais paramètre, le « je croyais que tu l'avais fait ». La machine ne se fatigue pas, ne saute pas de ligne, ne se laisse pas distraire à mi-chemin. Elle rend le déploiement fiable au point d'en être ennuyeux — et l'ennui, en production, est un luxe. Alors, si tes mises en production font encore monter le rythme cardiaque, ce n'est pas de la prudence. C'est un signal. Rends-les répétables, rends-les automatiques, rends-les ennuyeuses. Garde le frisson pour ta vie ; ton système de production, lui, mérite l'ennui. – Serguey Shinder

Anthropic’s publicly documented work on AI safety includes Petri , an open-source behavioral auditing tool, and ongoing updates to Claude models such as Claude Opus 4.7 . Those materials show continued investment in testing model behavior and improving model capabilities. They do not, however, substantiate a precise claim that Claude improved safety scores across 10 alignment failures without capability trade-offs, or that particular methods generalized to models exactly 4.7 times larger. That distinction matters for teams evaluating AI systems. Broad statements about alignment progress can be useful signals of research direction, but operational decisions need to rest on documented evaluations, relevant use cases, and the controls a company can apply in its own workflow. Anthropic’s public record supports a narrower, more practical conclusion: behavioral auditing is becoming a more visible part of how frontier AI models are assessed, while model releases and safety research remain separate evidence streams. What Anthropic’s public materials document Petri is designed for behavioral AI auditing Anthropic describes Petri as an open-source auditing tool . Its Petri 2.0 update, published in January 2026, added a larger seed library with 70 new seeds and improved mitigations intended to address evaluation awareness. Evaluation awareness is relevant because a model may behave differently when it appears to be taking a test than when it is operating in a more ordinary setting. The Petri 2.0 work reported results across 10 target models , using Claude Sonnet 4.5 and GPT-5.1 as auditors. This establishes that Anthropic has described a cross-model auditing effort. It does not establish that Claude itself achieved a safety improvement across 10 defined alignment failures. A target-model count, an auditor model, and a set of alignment failures are different measurements and should not be treated as interchangeable. For readers, the important point is that behavioral audits can

The fastest way to lose trust in an AI support assistant is to watch it confidently invent an answer. That is the problem retrieval-augmented generation (RAG) is built to solve. Instead of relying on whatever a language model memorized during training, a RAG system first retrieves the relevant passages from your own knowledge base, your help docs, policies, and product specs, then generates a reply grounded in that verified content. The result is an assistant that answers from your truth, cites its sources, and reflects updates the moment you publish them, with no expensive retraining. The business case is hard to ignore. Teams deploying RAG report sharply lower issue resolution times, deflected tier-one tickets, and agents who get accurate suggested answers in seconds. But RAG is not a switch you flip. Answer quality lives or dies on how well you chunk, embed, and retrieve your content, and on the guardrails you put around what the model is allowed to say. This guide walks through how RAG actually works and what it takes to ship it responsibly. Why plain LLMs fall short for support A bare language model is a confident generalist with three liabilities that are fatal in a support context. Its knowledge is frozen at training time, so it has never seen your latest pricing, your current return policy, or the feature you shipped last week. It has no access to your specifics, so it does not know this customer's plan or your internal escalation rules. And when it does not know, it tends to guess fluently. In casual use a wrong answer is an annoyance. In customer support it is a refund processed against the wrong policy, a security claim that isn't true, or a promise your team now has to honour. RAG addresses all three problems by grounding every answer in your verified, current content, so the model stops improvising and starts citing. In plain terms A plain chatbot answers from memory and sometimes guesses. A RAG assistant looks up the answer in your documentation first,

Every team has a list of small jobs nobody wants to do: copy a new lead from the website into the CRM, chase an unpaid invoice, summarise a support email, post the same update to three places. None of them is hard. Together they eat hours every week and quietly burn out the people stuck doing them. Automation platforms exist to take that list off your hands, and in 2026 the three names you will keep running into are Zapier, Make and n8n. What changed recently is that all three stopped being simple "when this, then that" tools and grew real AI agents. Zapier shipped Agents that act across its 8,000-plus app catalogue. Make added Maia, an assistant that builds whole workflows from a sentence. n8n 2.0 went deepest, with native LangChain support, 70-plus AI nodes, persistent memory and human approval steps. So the question is no longer whether to automate, it is which platform fits how you work, what you can afford to run at scale, and how much control you need over your own data. Here is how the three actually differ, and how to pick without locking yourself into the wrong one. The honest three-way comparison The marketing pages all promise the same outcome. The real differences show up in pricing models and ceilings. Zapier is the easiest to start with and the most expensive to scale. It bills per task , and every single action counts. A ten-step workflow that runs 1,000 times a month burns 10,000 tasks. Great for non-technical teams wiring up a handful of common apps; painful once volume grows. Make sits in the middle. Its visual canvas is genuinely pleasant for branching, multi-step logic, and it bills per operation at a lower unit cost, staying under roughly 100 euros a month even at high volume. The sweet spot for teams that want real logic without code. n8n is the power tool. Free if you self-host, 20 to 50 euros a month on its cloud, plus the AI token costs you would pay anyway. It is the most AI-native of the three and the only one you can run entirely on your

The EU-funded XAIDA project is using artificial intelligence to help researchers detect, analyze and attribute extreme weather events, including heatwaves, in a changing climate. Its work matters because better understanding of the link between climate change and individual extremes can support more informed decisions over time. But XAIDA is not launching a consumer weather app, a commercial forecasting service, or a ready-to-integrate API for businesses. XAIDA, short for eXtreme events: Artificial Intelligence for Detection and Attribution , began in 2021 under the EU's Horizon 2020 programme. The project brings together European research groups working on data-driven methods for extreme-weather science. Its official tools overview describes a collection of AI-enabled capabilities designed to support science, policy and decision-making. That distinction is important. A weather forecast estimates likely conditions at a particular place and time. XAIDA's work is focused more broadly on detecting extreme phenomena, examining their characteristics and quantifying the influence of climate change. These are related to prediction, but they are not the same as publishing a daily operational forecast for a business location. What XAIDA is building XAIDA's public materials describe the Artificial Intelligence for Disentangling Extremes , or AIDE, toolbox alongside related AI-based methods. The project also refers to stochastic weather generation and other analytical approaches. Together, these tools are intended to help researchers investigate complex extreme events and their climate context. The project has used AI techniques, including variational autoencoders, in case studies and research outputs concerning heatwaves and other extremes. A variational autoencoder is a machine-learning approach that can learn patterns in complex data and generate statistically plausible variations. In this context, such methods can help researchers examine how extreme events relate to under

I switched my testing order so the phone goes first and the desktop goes second, on every RAXXO tool without exception A desktop-first habit hid layout and tap-target problems for months because the biggest screen forgives the most mistakes Testing on a phone first forces the same discipline as writing a short sentence instead of a long one, cut what does not fit The rule survives even for tools built for a keyboard and a terminal, because the landing page and the first impression are still mobile The Habit I Had Backwards For a long time I built and tested everything in the same order: open the code editor on a wide monitor, ship the feature, check it on desktop, call it done. If I had time left over, I would open it on my phone to confirm nothing was broken. That last step felt like a formality, a quick glance rather than a real check, because the tool had already passed on the screen I spent most of my day looking at. The problem with that order is that the desktop is the most forgiving screen there is. Extra padding does not matter when there is space to spare. A button that is slightly smaller than it should be is still easy to click with a precise mouse pointer. Text that wraps awkwardly at narrow widths never shows up because the window is never narrow. Every mistake that a small screen would expose gets absorbed by the size of a big one, which means desktop-first testing is really desktop-only testing wearing a disguise. I noticed this the hard way, not through a single dramatic failure but through a slow accumulation of small ones. A support message here about a button that was hard to hit. A review there that mentioned the site felt cramped on a phone. None of them were urgent enough on their own to stop what I was doing, so I patched each one individually and moved on, the same reactive pattern I try to avoid everywhere else in the studio, including the check I run on every tool before I call it shipped . It took stepping back and counting the pattern to

Google completed its August 2026 spam update after a rollout that began on August 18 and finished on August 21. The update was a routine spam-enforcement release rather than a newly announced flagship policy change, but third-party tracking indicates that its ranking effects were substantial for some websites. For site owners dependent on organic search , the central message is straightforward: Google’s enforcement against spam remains active, and abrupt visibility changes can be severe when a site falls on the wrong side of its quality and manipulation assessments. Google recorded the release and completion of the rollout in its official Search Status Dashboard incident entry . The company listed the start time as August 18, 2026, at 09:27 PDT, and marked the incident complete on August 21, 2026, at 01:49 PDT. It was Google’s third announced spam update of 2026, following spam updates in March and June. The official notice establishes the timing of the rollout, not a detailed account of which sites or tactics were affected. That is where independent ranking data adds useful context. SE Ranking’s analysis, later reported by Search Engine Land, found that 16.71% of URLs that had ranked in the Top 10 dropped beyond position 100 for the same keyword during the August update. Its July baseline showed 9.2% making that same move. The August share was therefore roughly 82% higher than the baseline. What the ranking data shows A move from the Top 10 to beyond position 100 is not a minor fluctuation. It can effectively remove a page from the search results that most users see, with an immediate effect on clicks and leads for pages that previously generated traffic. The SE Ranking figures do not prove that every observed loss was caused by Google’s update, nor do they identify every affected site type. They do, however, provide a market-wide indication that the August rollout coincided with sharper movement than a normal July comparison period. Measure July baseline August 20

Google has renamed NotebookLM as Gemini Notebook , placing its source-grounded research tool more directly inside the Gemini ecosystem. The change is more than a new label. Google is connecting notebooks with Gemini and Google Search, documenting broader source engagement through Deep Research and Expert Intelligence, and adding the ability to run code in notebooks for eligible higher-tier users. For teams that assemble information from documents, web pages, spreadsheets, presentations and specialist publications, the direction is clear: Gemini Notebook is intended to become a workspace for combining sources rather than treating each source as a separate research task. The practical value depends on the quality and permissions of the material a user adds, but the product is moving toward a more connected knowledge workflow. What changes with Gemini Notebook In its July 16, 2026 announcement, Google's official Gemini Notebook update confirmed that NotebookLM continues under the Gemini Notebook name. Google described the change as part of a wider integration with Gemini tools and surfaces, including the Gemini app and Google Search. The renamed service retains the notebook model: users bring together sources and ask questions based on that supplied material. Google Help documentation describes supported and discoverable sources across the web, Google Drive, Gemini Chats and other surfaces. Examples include web URLs, YouTube videos, PDFs, ePub files and Drive content. In relevant contexts, the documentation also references Google Docs, Sheets, Slides and Google Play Books content. Google uses Expert Intelligence to describe a source-engagement framework that helps people work with trusted sources. The related announcement says this begins with eligible Google Play ebooks in Gemini Notebook, allowing readers to combine authors' expertise with other sources in a notebook. Eligibility matters. The available material does not establish that every Play Books title can be us

The Linux Foundation has launched Akrites , an initiative intended to coordinate vulnerability discovery, remediation and disclosure for critical open-source software. The project arrives as AI-enabled vulnerability scanning changes the scale at which potential software flaws can be identified. Its central emphasis is not simply finding more issues, but getting fixes made upstream and patches deployed. Akrites is organized around a multi-stakeholder coalition that includes technology vendors, financial institutions and open-source foundations. The official Akrites open letter describes the effort under the message, “We All Depend on Open Source. We Will Defend It Together.” The initiative was launched on June 25, 2026 and is coordinated by the Linux Foundation. The publicly displayed letter includes organizations such as AWS, Anthropic, Chainguard, Cisco, Citi, Google, Microsoft and GitHub, JPMorganChase, IBM, NVIDIA and OpenAI, Endor Labs, Red Hat, the Rust Foundation, Sonatype, Vodafone and Zscaler. It also lists open-source groups including the Cloud Native Computing Foundation, OpenInfra Foundation, OpenJS Foundation, LF Energy, OpenSSF and the PyTorch Foundation. What Akrites is trying to change Akrites is focused on a practical security lifecycle: identifying vulnerabilities in critical open-source projects, helping drive remediation and handling disclosure. That focus matters because discovering a possible vulnerability is only an early stage of risk reduction. A finding has limited value if maintainers cannot address it, if the correction is not adopted upstream, or if downstream users do not deploy the available patch. The initiative therefore places upstream fixes and patch deployment at the center of its stated success measures. This is a meaningful distinction from approaches that judge progress largely by the number of vulnerabilities found or reports generated. The coalition combines several types of participants: Cloud, software and security vendors ,

SEO hiring is increasingly centered on senior ownership rather than pure execution. A Semrush analysis of 3,900 US SEO job listings on Indeed, captured on November 25, 2025, found that 59% of openings were senior leadership roles . The category included Director, VP, Head, Chief, Lead, and Executive titles. The finding matters because it signals how employers are defining SEO work for 2026. Companies appear to place greater value on people who can set priorities, manage projects, connect SEO with other channels, and direct AI-enabled workflows . That does not mean junior SEO work has disappeared. It does mean that the available listings are weighted strongly toward people accountable for strategy and business outcomes. What the SEO job data shows Semrush's analysis of 3,900 SEO job listings describes a polarized US market. Senior leadership positions made up the majority of listed roles, while SEO Specialist jobs represented about 15% and SEO Manager jobs about 10%. Listing category or measure What Semrush found What it indicates Senior leadership roles 59% of listings Demand is concentrated in roles with strategic ownership. SEO Specialist roles About 15% of listings Specialist execution roles are a smaller share of the market. SEO Manager roles About 10% of listings Mid-level management roles are also less prevalent than leadership listings. Median pay About $130,000 for senior roles, versus about $71,630 for other positions Employers are placing a substantial pay premium on senior SEO responsibility. The study also found that approximately 31% of senior listings mentioned project management. Cross-channel responsibilities were another recurring theme, reinforcing the idea that SEO is being hired as a growth function that must work with content, marketing, product, and other teams. AI is part of this changing job description. Semrush found AI mentioned in 31% of senior listings, with roughly 10% specifically mentioning AI familiarity. These figures do not prove th

Google has introduced a new interactive visualization capability in the Gemini app that can turn questions and complex topics into manipulable models and simulations inside a chat. Rather than returning only a written explanation or a static illustration, Gemini can create visuals that users explore through prompts and on-screen controls. The update is designed to make concepts easier to investigate in context. Google describes examples including rotating molecular structures and physics simulations where users can adjust variables such as initial velocity and gravity to see the results immediately. That shift from a fixed diagram to a live, prompt-driven model is the important change for people using Gemini to learn, explain, or test an idea. From static diagrams to interactive models In its April 9, 2026, official announcement of interactive simulations and models , Google said the Gemini app can generate custom visualizations directly within a conversation. Users can ask Gemini to “show me” or “help me visualize” a concept after selecting the Pro model in the prompt bar. The capability is rolling out globally to Gemini app users. Google also notes an important availability limitation: it is not yet available for Education and Workspace accounts . The announcement identifies the Pro model as the route to access the feature, but it does not set out pricing details for this specific visualization capability. The practical elements Google has confirmed are: Interactive simulations and models generated within the Gemini chat experience. Prompt-based requests to visualize a question or concept. Direct manipulation of visual variables and controls, including sliders in relevant simulations. A global rollout for Gemini app users, excluding Education and Workspace accounts for now. Approach Static diagram or written answer Gemini interactive visualization How users explore a topic Read or view a fixed explanation Manipulate a model within the chat Changing assumptions Req

I don't have an audience. No newsletter, no Twitter following, no YouTube channel. Every product I shipped before this one died the same way: a human had to discover it, and no humans knew I existed. So I flipped the buyer. An AI agent doesn't care about my follower count. It picks tools by spec, reliability, and price — from a registry it can search on its own. If I could ship a tool that agents discover, call, and pay for without a human in the loop, my distribution problem would stop mattering. That's what license-verify is: an Apify Actor that verifies a US contractor's license, surety bond, and insurance from official state data, exposed via the Model Context Protocol (MCP) so AI clients like Claude can call it mid-conversation, priced pay-per-event at $0.03 per successful lookup. Here's how I built it, the input-schema decisions that made it agent-callable, and the one-line billing bug that silently made every call free. Why contractor licenses I run a side business building tools for small contractor shops, so I knew the pain firsthand: before a homeowner (or a general contractor, or an insurance adjuster) hires a roofer, someone should check the license is active, the surety bond is real, and the insurance hasn't lapsed. In Washington State, all three live in the Department of Labor & Industries' open-data API on data.wa.gov. Most tools that "verify licenses" scrape an HTML page and return a status string. The official JSON gives you the actual bond amount and the insurance carrier. That's the difference between "probably fine" and "verified." It's also a perfect agent task: a small, well-defined question ("is ECOSTSC758NN licensed, bonded, insured?") with a structured answer an agent can act on. An AI assistant helping someone plan a renovation can reach for it mid-task, the same way it reaches for a calculator. The stack: one codebase, two doors The core is a TypeScript verification engine with a provider-per-state design. It ships through two doors: An Ap