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If you run a team on Jira or Confluence, the deal changed on 17 August and the change was opt-out. From that date, by Atlassian’s own account, the content your team writes into its Cloud products — Confluence pages, Jira tickets, the descriptions and comments where the actual work lives — is used by default to train Rovo, Atlassian’s AI assistant. You were not asked to opt in. You were, at best, given a switch and left to find it. Answer first, because the detail matters more than the outrage: there are two settings, and they are not equal. One governs your in-app data — the text itself. The other governs metadata — the derived signals about that text. On the Free, Standard and Premium plans you can turn off the content, but the metadata switch is greyed out; Atlassian’s support page reads, flatly, “You can’t change this setting.” The full off switch, the one that also stops metadata contribution, is available only on Enterprise. Privacy, in other words, is now a plan tier. What actually changed, with the switches named Atlassian’s data-contribution documentation lays out a matrix that is worth reading slowly, because the defaults are doing the heavy lifting. In-app data contribution defaults to on for Free and Standard customers and off for Premium and Enterprise. Every tier can toggle that one. Metadata contribution is a different story: it is on across the board and can only be switched off by Enterprise. So the customer contributing the most by default — content and metadata, both on, no ability to fully stop it — is the one on the cheapest plan who never opened the settings page. The categories are broad. In-app data, per Atlassian’s materials, covers Confluence page titles and body text, Jira work-item titles, descriptions and comments, and custom status and workflow names. Metadata covers the derived layer: readability scores, task classifications (that a ticket is “sales work,” say), story points, sprint end dates, SLA values, and semantic-similarity measure

Electric-vehicle assistants rarely have just one job. A short Turkish question may ask for a nearby station, a charging-price comparison, help planning a route, or an explanation of battery health. Before an application can retrieve current data or generate an answer, it needs to identify that intent reliably. We created the Turkish EV Charging Intent Dataset as a small, transparent starting point for that routing problem. Version 1.0.0 contains 192 Turkish queries distributed evenly across eight intent classes. It is open under CC BY 4.0, includes fixed train, validation, and test splits, and is maintained by TekPedal , an EV charging map and vehicle decision platform for Türkiye. You can explore the dataset interactively , inspect the source and validation workflow on GitHub , or cite the permanent Zenodo release with DOI 10.5281/zenodo.22062688 . Why intent routing comes first An assistant should not answer every EV question in the same way. Different requests need different tools and freshness guarantees: a station request needs a map or location index; a price request needs current tariff data; route planning needs distance, range, and charging-stop logic; a battery question needs careful educational content; a vehicle comparison needs structured specifications. An intent router makes that separation explicit. It can send each query to the correct retrieval source, product page, or application workflow. This also makes evaluation easier: teams can test routing independently before measuring the quality of downstream answers. Dataset design The taxonomy contains eight balanced classes, with 24 records in each class: FIND_STATION COMPARE_PRICE ROUTE_PLANNING CHARGING_SPEED VEHICLE_COMPARISON HOME_CHARGING BATTERY_HEALTH OWNERSHIP_COST Every record includes a stable ID, the Turkish query, the intent identifier, a human-readable Turkish label, a suggested TekPedal content route, the assigned split, the language, and a provenance marker. Here is a simplified example

Enormous eruptions altered Earth’s climate and societies all over the globe.

How a tiny little electronic switch sparked the computer revolution.

Luxury water can contain different minerals and taste noticeably different. But a remote source, alkaline pH, and high price do not necessarily mean better hydration.

OVHcloud will raise prices from September, with 2026-edition gaming servers up 87 percent and other recent servers 40 to 59 percent. Founder Octave Klaba says memory cost six times more in June than a year earlier, as RAM suppliers shifted capacity toward high-bandwidth memory for AI. AWS, buying years ahead, has repriced one reserved GPU product. By Steef-Jan Wiggers

Some fortnights the gripes scatter; this one they converged. Across the forums where paying customers of the big AI tools compare notes, the same complaint surfaced against three different companies in the same window: the monthly plan buys less than it did, and nobody dropped the price to match. One firm’s temporary generosity is about to expire, another’s year-old plan has quietly tightened, and the users caught in the middle are doing the same grim sum and reaching for the same coping strategies. Quotes sourced from: Reddit — specifically the subreddits r/ClaudeCode, r/perplexity_ai and r/Anthropic. Every quote below was opened at its permalink and copied verbatim; each is listed with its handle, subreddit and date in the Sources section. We quote experiences, not verdicts — a forum post is one person’s felt reality, and we have framed it as exactly that. The limit that lapses on the 19th The loudest note this fortnight came from Claude Code users watching a date on the calendar. Anthropic had lifted weekly limits by 50% as a promotion, extended it several times, and set it to expire around 19 August — after which allowances fall back to standard. For anyone who had adjusted their workflow to the higher ceiling, the lapse reads as a cut. A user posting as EnthusiasmMountain10 laid out the worry on 14 August: “I’m seeing more tokens burned on tasks that previously felt straightforward, more meandering, and generally less output per unit of usage. At the same time, the 50% usage reduction coming on Aug 19 makes this particularly concerning.” The complaint was double-barrelled: not only is the ceiling dropping, but the same work seems to cost more against it than it used to. That second half — a sense that quality had slipped — ran through the thread. A commenter posting as Captain_Birb put it bluntly: “quality is down.. 4.6 and 4.8 were sharper. Sonnet 5 is seriously a joke — it gets roasted by Opus every time and admits his faults.” Another, TheSassyPlant , descri

You can keep Instagram from using your activity outside the app to influence the ads you get.

Hibernation cuts down on synapses, but mice seem to retain memories anyway.

Lands free of roads are under threat from the Trump administration’s proposed rollback.

Short version of a post on gex.live/research ; the full write-up, definitions and reproduce block live there. Most published dealer-gamma numbers are built from open interest : yesterday's outstanding contracts, multiplied by a convention about who holds which side. Whether the convention is right is a separate question. The prior question is simpler: how much of what trades today was already in that book this morning — and how much of tomorrow's book is being created today? Open interest and volume are enough to answer it, with no assumption about who bought. Sample: SPX and SPXW, 2022-04-14 to 2026-08-14, 1,081 trading days, every expiry within about a month (0DTE plus the 21 nearest), 8.6 million contract-days, 4.3 million with volume. Definitions Per contract (expiry, strike, right) and session D: OI(D) is open interest at the start of D, OI(D+1) at the start of the next session, ΔOI = OI(D+1) − OI(D) , vol the day's volume in that contract. |ΔOI| / vol is a lower bound on how one-sided the day's trading in that contract was — 1.0 means every lot opened (or every lot closed), 0 means opens and closes cancelled. Contracts expiring on D have no next-day OI and drop out of the ΔOI statistics; 4.1% of rows (3.8% of volume) show |ΔOI| > vol, which is impossible (OI snapshot timing) and are excluded. The book grows by 40% of what trades, every day days to expiry on D net ΔOI / volume |ΔOI| / volume (lower bound on one-sidedness) share of volume in contracts whose OI rose contract-days 1–5 37.8% 41.8% 90.3% 813,013 6–21 42.7% 53.3% 81.1% 2,206,446 22+ 42.8% 57.6% 76.5% 831,896 Across the whole book, net ΔOI is 39.9% of the day's volume on the median session (IQR 36.2–44.0%), positive in every year and every expiry bucket: the SPX book is always being built faster than it is unwound, until expiry does the unwinding. Far expiries are open-and-hold (a day's trading in a 22+ DTE contract is at least 58% one-sided); the nearest expiries churn (42% at 1–5 DTE). Per contract-

The LINK probe was supposed to use robotic arms to capture Swift and lift it into a safe orbit. Problems with its control system forced NASA to call off the attempt.

"We probably need another site that's capable of heavy and super heavy launch capability."

Cheap energy, abundant land, and proximity to Beijing have turned a city in Inner Mongolia into a crucial hub for data centers.

The DoJ said the app has made significant strides in privacy protections since the 2024 suit.

Thunderstorms make seismic waves that can be used to find sub-surface features.

Two years after the U.S. Department of Justice alleged that TikTok violated the Children’s Online Privacy Protection Act, it has reached a $400 million settlement.

LinkedIn says its AI slop button is working.

This sort of research is both exciting and terrifying: The two models in question were told to generate complete genomes for a viable bacteriophage—a type of virus able to infect and replicate itself inside bacteria, destroying them from the inside. Using an existing bacteriophage as an example—ΦX174 (pronounced “fie-ex-1-7-4”), known for its ability to infect and destroy E. coli bacteria—the models generated about 700,000 potential designs, of which the researchers picked 285 that looked most promising. The researchers then synthesised new DNA molecules using those designs and inserted them into E. coli bacteria, before waiting to see if viable bacteriophages would emerge...

Some Americans fear the FTC may be thinking about personalized pricing all wrong.