标签：#t

A Ravenloft series is currently in development from executive producer Alfonso Cuarón, writer and executive producer John August, and Hasbro Entertainment, Deadline reports. It could bring to life one of Dungeons & Dragons' most iconic campaign settings, which got an update earlier this year with Ravenloft: The Horrors Within. Netflix's Ravenloft series will reportedly center […]

Researchers now have clear new targets and insights for developing a Shigella vaccine.

Shopify's engineering introduced gisting, a novel technique for compressing long LLM prompts into a smaller set of learned "gist" tokens, improving throughput and reducing inference cost. By Sergio De Simone

Open source, commercial, single-hop, multi-hop, mixnet? The array of options is dizzying.

The company is about to formally launch the gold two-seater, with no steering wheel or pedals -- a move that could change Tesla forever.

Circuit split raised the odds that SCOTUS will rule on Kalshi fight against states.

The high-profile startup's annual revenue run rate stands at over $100 million.

The grid has been straining under the weight of new AI data centers, and that has utilities courting fusion startups.

MLH AI Roadshow Bengaluru Hey Everyone! I am Sanidhya Goel, a new MLH Hackathon Community...

Pro soccer team's CTO points to "issues with the Broadcom takeover."

Hi Folks , In this article, I'm going to share my learnings in SQL . Install MySQL Workbench editor from **MySQL Downloads **found in internet. After Installation, Open MySQL Workbench Click on '+' icon to create new sql editor Click on import icon present on top in sql editor workbench After importing, See the data, schema tables present in left side panel We see the sql editor workspace, result Grid,output view. How to display data present in result table ? Use SELECT '*' as Keyword FROM table_name Ex: SELECT * FROM moviesdb.movies; Instead of moviesdb.movies we can use movies as name. Follow the steps below: Go to the left panel where movie data present, select movie data table -> Right click on default schema it highlights in bold -> Then, Write query as, SELECT * FROM movies Method - 2 USE moviesdb SELECT * FROM movies; Here we're using ' USE ' Keyword How can we check how many rows are present after writing query ? Just go to output view present in MySQL Workbench editor (present below to the result Grid) you can see there (or) Check with Excel sheet using filter option to check how many rows are present. Suppose we're writing query but we don't know recieved output is right or wrong. We can check with our excel sheet data using filter option if it matches with our output rows then our query is correct this is how we can debug and check ✅ output. There are some inbuilt clauses present in SQL 1.WHERE 2.COUNT 3.DISTINCT (for unique values) 4.LIKE 5.% - Wildcard Search Ex: %movies% Gives the movies data present anywhere in the sentence %movies - Returns movies data present starting at the sentence %movies - Returns of movies present at last in the sentence Ctrl + Scroll UP - Zoom in SQL Editor Database - Collection of rows and columns in tabular format with schema it has duplicate ids, unique ids in different formats like excel, .sql etc. How to save all written queries ? Write all queries at one place and highlight each query, do the next steps mentioned below,

Google is preparing to make Search more agentic: instead of only returning results for a query typed by a person, persistent AI agents will be able to monitor information, evaluate options and take certain actions on a user's behalf. For businesses, that raises a practical question. Is the information on your website clear enough for an AI system to understand, compare and potentially act on? In its official announcement on a new era for AI Search , Google outlined Search agents that can work in the background around user-defined criteria. The company says the first category, information agents, will monitor topics across blogs, news and social content in real time, then provide updates and trigger potential actions. Google also described agentic tasks such as booking local experiences and services, including calls to businesses on a user's behalf. This is a confirmed product direction and rollout plan, not merely a prediction about how search might evolve. It does not mean traditional search results disappear. It does mean that a growing share of discovery could be mediated by systems that do more than retrieve links. They may identify a need, gather relevant details, compare available options and move a task toward completion. What Google is rolling out Google says information agents will launch first for Google AI Pro and Ultra subscribers in summer 2026. Wider availability in the United States is planned later in the season. The agents are intended to operate continuously, rather than only when a person opens Search and enters a new prompt. The initial use case is information monitoring. A user could define a topic and criteria, then have an agent follow relevant material across the web and report back when conditions change. Google also described a broader path toward actions, including booking and transactions. Shopping is part of that path, with Google saying it intends to expand agentic capabilities so actions can be completed through providers. Search capab

Enterprise resource planning has traditionally been associated with large vendors, complex implementations, long contracts, and significant licensing investments. For decades, companies evaluating ERP systems were likely to encounter names such as SAP, Oracle, Microsoft Dynamics, and other established commercial platforms. That model is not disappearing. But the assumptions behind it are changing. In 2026, enterprises have more choices than simply selecting between competing proprietary ERP vendors. Open-source platforms such as ERPNext, Odoo, and other business application ecosystems have become credible alternatives for organizations that want greater control over their software, more flexible customization, and different approaches to total cost of ownership. At the same time, SaaS pricing, vendor lock-in, integration complexity, cloud adoption, data ownership, and increasingly capable development tools are changing how businesses think about enterprise software. This does not mean open-source ERP is automatically better. It means the ERP decision deserves to be reconsidered. The question is no longer simply: Which ERP vendor should we buy from? Increasingly, organizations are asking: How much control should we retain over the software that runs our business? What Is Open-Source ERP? Open-source ERP is enterprise resource planning software whose source code is made available under an open-source license that grants defined rights to use, inspect, modify, and distribute the software. The practical implications depend heavily on the specific project's license. For an enterprise buyer, however, the important distinction is that open-source software can provide a level of visibility and extensibility that proprietary software may not. A simplified comparison looks like this: Proprietary ERP Business │ ▼ Vendor Software │ ├── Vendor controls source ├── Vendor controls roadmap ├── Vendor controls licensing └── Vendor controls many upgrade decisions Open-Source ERP Busi

Abliteration.AI is making powerful AI models without guardrails easier to access, arguing that giving defenders the same tools as bad actors could ultimately improve cybersecurity.

TikTok backed out of a congressional committee's "public roundtable" set for this month over concerns it would be asked about child safety practices, the chair of the committee said. After negotiating "in good faith" to bring TikTok's chief security officer before the House Select Committee on China to answer questions about Chinese government access to […]

I've tried every productivity app out there. Notion, Obsidian, Todoist, Habitica, Day One - you name it. Every single one either wants my credit card, my email, or my data. Some want all three. So I built Lexis (lexisapp.xyz) - a free, local-first productivity suite that combines habits, notes, journal, tasks, documents, and an AI assistant into one app. No sign-up. No subscription. All data stays on your device. What is Lexis? Lexis is a web app (also available as a desktop Electron app) that bundles six productivity tools into one: Habits - Track daily habits with streaks, analytics, and a calendar view Notes - Rich text notes with full markdown support Journal - Daily journaling with mood tracking Tasks - Task management with priorities, due dates, and kanban-style organization Documents - Create and manage longer-form documents Noor - An AI assistant powered by three models (Ethos 4.7, Logos 4.5, Verse 4) that can chat, generate images, and help with your productivity data Everything runs in the browser. Your data is stored locally in IndexedDB. Nothing is sent to any server - not even us. Why Local-First? The local-first movement is about giving users ownership of their data. When your notes live in Notion's servers, you're at the mercy of their pricing, their uptime, and their privacy policies. With Lexis: Your data never leaves your device No account needed - just open the app and start using it Works offline - full functionality without internet (except AI features) GDPR compliant by design - we literally can't see your data because it never reaches our servers The AI Assistant (Noor) I wanted an AI that feels like it's yours, not a corporate chatbot. Noor is Lexis's built-in AI assistant with three models: Ethos 4.7 - the creative, conversational model Logos 4.5 - the analytical, precise model Verse 4 - the fast, efficient model Noor can chat with you about your tasks, habits, and notes. It can generate images. Voice dictation runs through your browser's bu

zarazhangrui/follow-builders is gaining attention for a simple reason: it focuses on the people building AI systems, not just the influencers discussing them. With 84 new stars today, the project is positioned as an AI builders digest that monitors notable creators across X and YouTube podcasts, then remixes their ideas into shorter, easier-to-scan summaries. That workflow addresses a real productivity problem. AI research and engineering conversations are scattered across long videos, fast-moving social feeds, and repeated announcements. A focused digest can reduce the time spent collecting links while preserving the practical signal: architectural decisions, implementation lessons, tools, and emerging patterns. A sensible first step is to inspect the repository locally before deciding how deeply it fits your workflow: git clone https://github.com/zarazhangrui/follow-builders.git cd follow-builders # Inspect the setup instructions and available scripts ls -la find . -maxdepth 2 -type f | sort | head -80 For an AI-assisted workflow, I would pair the project with a small review loop: Collect the generated digest. Extract claims, links, and mentioned tools. Open the original source before acting on important technical advice. Save durable findings in a project notes file or knowledge base. This keeps summaries useful without treating them as authoritative research. It also makes the tool a good companion for developers using Cursor or another AI IDE: the digest supplies discovery, while the IDE helps turn validated ideas into experiments and code. Before production use, watch for two trade-offs: Summary fidelity: compressed content can lose context, caveats, or disagreements from the original conversation. Source coverage: ranking “top builders” may introduce selection bias, so important perspectives can be missed. The strongest use case is not replacing primary sources. It is building a high-signal starting queue for developers who want to follow AI progress without

AI search is creating a new measurement problem for website owners: it can be harder to see how, where, and why content appears in an answer-led search experience. The concern is not a confirmed Google policy change or a universal loss of transparency. It is a credible industry signal that AI Overviews, AI Mode, and similar experiences may make traditional SEO visibility and attribution more difficult to verify. The discussion is timely because AI search is becoming another route by which people discover information, brands, and products. Search Engine Land's 2025 AI search optimization survey coverage provides useful context for the growing focus on GEO and AEO , terms often used to describe efforts to improve visibility in generative and answer engines. Google, meanwhile, continues to document AI-enabled Search experiences and related controls through its AI in Search materials. What remains uncertain is how consistently publishers will be able to connect AI answer visibility to traffic and commercial results. Why AI search changes the measurement question Traditional SEO has never offered perfect visibility, but it has established signals: rankings, impressions, clicks, landing-page visits, and referral data. AI-generated results can complicate that model because a search experience may synthesize an answer, cite selected sources, prompt follow-up questions, or satisfy a user without a visit to a publisher's site. This does not mean conventional SEO measurement is obsolete. It means teams should avoid treating a familiar metric as a complete picture of search performance when AI features are involved. The central question shifts from "Where do we rank?" to a broader one: Are we being represented accurately and usefully in the search journeys that matter to our customers? The practical challenge has several parts: Visibility can be contextual. An AI-generated response may differ by query wording and the information selected for the answer. Attribution may be weake

Hello, I'm Shrijith Venkatramana, and I'm building LiveReview — a blast-radius aware AI code review built for your business-critical systems. Star us to help devs discover the project, give it a try, and share your feedback to help improve the product. A modern LLM can contain hundreds of billions of parameters, run on extremely expensive accelerators, and still spend most of its inference time doing something that looks embarrassingly sequential: token 1 -> token 2 -> token 3 -> token 4 -> token 5 -> ... That is the awkward part of autoregressive generation. The model may process a whole prompt in parallel during the initial prefill, but once generation starts, the next token depends on the previous token. So generating 100 tokens looks conceptually like running the model 100 times. And for many serving workloads, that is exactly where the money goes. A family of techniques tries to break this bottleneck by asking a deceptively simple question: What if the model could predict several future tokens at once, then verify them in parallel? That idea leads to speculative decoding, Medusa-style multiple decoding heads, and the broader multi-token prediction approach used during training. The interesting part is that these are not merely "optimization tricks." They change the computational structure of decoding. This article develops that idea from first principles and then gets into the engineering details. 1. The problem: your GPU is doing an expensive sequential loop Consider ordinary autoregressive decoding. Given a prompt: The capital of France is the model predicts: Paris Then it feeds the new sequence back through the model: The capital of France is Paris and predicts the next token. Then again: The capital of France is Paris . and so on. Formally, the model factorizes the probability of a sequence as: P(x1, x2, ..., xT) = product over t of P(xt | x1, ..., x(t-1)) That conditional dependence is what makes language modeling so useful. It is also what makes decoding

I ship Codenames AI, a web game. Each game mode keeps its own save in localStorage. Reload the tab,...