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Question : Implement next permutation, which rearranges numbers into the lexicographically next greater permutation of numbers. If such arrangement is not possible, it must rearrange it as the lowest possible order (ie, sorted in ascending order). The replacement must be in-place and use only constant extra memory. Here are some examples. Inputs are in the left-hand column and its corresponding outputs are in the right-hand column. Example : 1,2,3 → 1,3,2 3,2,1 → 1,2,3 1,1,5 → 1,5,1 Idea : Scan from right to left and find the first element that is less that its previous. eg: 1 6 3 5 -> here it is 3. Let's name it as index. Again scan from right to left and find the first element that is greater than 3 and that's 5. Let's mark it as idx. 3.In this step we swap 3 and 5. Reverse elements from index+1 till the array length. Code: public void nextPermutation(int[] nums) { int index = -1; for(int i=nums.length-1;i>0;i--){ if(nums[i]>nums[i-1]){ index = i-1; break; } } if(index==-1){ reverse(nums,0,nums.length-1); return; } int idx=0; for(int i=nums.length-1;i>=index+1;i--){ if(nums[i]>nums[index]){ idx=i; break; } } swap(nums,index,idx); reverse(nums,index+1,nums.length-1); } void swap(int[] nums,int i,int j){ int temp =nums[i]; nums[i] = nums[j]; nums[j] = temp; } void reverse(int[] nums,int i ,int j){ while(i<j){ swap(nums,i,j); i++; j--; } } Code Explanation : We first initialize index=-1 and traverse backward to find the first one with i that satisfy the condition nums[i]>nums[i-1] . We assign this to index and break out of the loop. for(int i=nums.length-1;i>0;i--){ if(nums[i]>nums[i-1]){ index = i-1; break; } } Next step we are discussing a corner case. For example if the given array is 3,2,1 then we cannot find the element that satisfies the previous condition. So when the array is given in decreasing order we just reverse it and return. if(index==-1){ reverse(nums,0,nums.length-1); return; } Next iteration we are considering another variable idx and traverse backw

La forma del problema Un directorio es una superficie: el miembro lo abre y adivina. Un recomendador es una superficie de empujar: el sistema propone y tiene que justificarse. La justificación es la parte difícil, y es donde vive la estadística. Tres restricciones hicieron esto distinto de un recomendador de contenido: El item es una persona con capacidad finita. Un hilo se le puede recomendar a diez mil personas. Un experto no. Una mala recomendación es cara de los dos lados. Quien pide desperdicia una petición, el experto desperdicia una hora, y los dos aprenden a ignorar la superficie. La afirmación tiene que ser checable. "Quizá te guste este hilo" no necesita evidencia. "Esta persona está un nivel adelante de ti en diseño de sistemas" sí. Recuperación: híbrida, fusionada con RRF Tres recuperadores independientes sobre el conjunto de expertos elegibles, fusionados con Reciprocal Rank Fusion: def rrf_fuse ( * ranked_lists , k = 60 ): """ Fusiona listas de ids rankeadas. El score depende solo del rank, nunca de la escala propia del recuperador, que es el punto: la similitud coseno y un conteo de hilos resueltos no son números comparables. """ fused = {} for lst in ranked_lists : for rank , key in enumerate ( lst ): fused [ key ] = fused . get ( key , 0.0 ) + 1.0 / ( k + rank ) return fused RRF es la primitiva correcta aquí por una razón que vale la pena decir: los recuperadores emiten cantidades incomparables. Uno regresa un coseno en [-1, 1] , uno regresa un conteo entero de hilos resueltos, uno regresa un delta de nivel de escalera. Normalizarlos a una escala común requiere supuestos sobre sus distribuciones que nadie tiene a este volumen de datos. RRF descarta las magnitudes y se queda solo con el orden, que es exactamente la información que sobrevive a una muestra chica. k = 60 es la constante estándar de la formulación original de Cormack et al. Aplana la cabeza: la diferencia entre el rank 1 y el rank 2 es 1/61 - 1/62 ≈ 0.00026 , así que un recuperador no pu

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

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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

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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-

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