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Creators who want to start earning on the platform will need at least 8,000 qualified watch hours over the past year or 20 million qualified Shorts views in the last 90 days.

Screenshot prompting has a ceiling. You paste the design, the model makes a plausible approximation, you correct it, and on the next turn it drifts again. Nothing is anchored. The model has no source of truth to check itself against between turns. A context bundle changes the contract. Instead of a pixel reference the model has to interpret every time, you get a structured, referenceable set of files — design tokens, layout IR, component inventory, UI strings — that stay in the session and stay consistent. Claude Code can read them, implement from them, and check its own output against them on demand. This post walks the full pipeline, from bundle export to a reviewed, token-verified implementation, using figmascope , a browser tool that turns any Figma file into exactly that bundle. What makes this deterministic Three things make the bundle referenceable rather than interpretable: Tokens are typed and keyed. tokens.json maps semantic names ( spacing.16 , color.7f5cfe ) to exact values. The model can check its output against the file without re-processing the design. The IR is a tree, not pixels. screens/home.json describes the layout in terms of stack/overlay/absolute/leaf nodes — the same abstraction the implementation target (Compose, React, etc.) uses. There's no visual interpretation step. The bundle is stable across turns. Once it's in the repo, every prompt in the session can reference the same files. Token drift is detectable: ask the model to compare its output against tokens.json and it can do it mechanically. Step 1: Generate the bundle Open figmascope.dev in your browser. Paste your Figma file URL. The exporter runs client-side using the Figma REST API — your Figma personal access token is stored in localStorage and never sent to figmascope's servers. Click Export Agent Context . The page exports top-level frames, resolves design tokens, builds the IR, and downloads context-bundle.zip . Step 2: Unzip into your project # from your project root unzip ~/Dow

Battery materials startup Sila will use a $1.4 billion loan from the U.S. Department of Defense to scale production at its factory in Washington State.

The two companies were once embroiled in a trade secret theft lawsuit. Now, Wisk is being absorbed into Archer.

Boeing is selling three of its electric vertical takeoff and landing (eVTOL) subsidiaries to Archer Aviation, in addition to taking an undisclosed stake in the San Jose-based company. The subsidiares to be acquired by Archer include Wisk Aero, which has been developing an autonomous electric aircraft; SkyGrid, which is building air traffic management systems to […]

By the end of this guide you will have a published Tableau dashboard and a three-point story, built on a real dataset. It lives on a public URL you can put in an application. You build four small sheets. Each one makes exactly one point. You arrange them on a single screen, then walk a reader through them in three steps that end with a recommendation. Every step says what to click and what you should see afterwards. Four small sheets, rather than a wall of charts, because a dashboard has to argue for something. A screen holding everything you could build leaves the reader to work out what matters. Most readers will not do that work. Dashboard vs Story, in one line. A dashboard puts several charts on one screen so someone can explore. A story is a sequence of views with captions, clicked through in order, so someone is walked to a conclusion. Build both: the dashboard is what a hiring manager glances at, the story is what proves you can think. The original carries a diagram here. In words: Four separate worksheets stack on the left: a big single number, a set of vertical bars, a set of horizontal bars, and a scatter of circles. An arrow points right to one dashboard panel that holds all four of them arranged on a single screen: the number across the top, the two bar charts side by side in the middle, the scatter along the bottom. A second arrow points right to three story cards numbered one, two and three, each showing one of those views with a caption line above it. The worked example. Every instruction below is written against a real, free dataset: the Telco Customer Churn file on Kaggle, 7,043 customers, one row each. A finished analysis of it, including the Python script that shapes the data, is public at telco-churn-analysis . Swap in your own dataset and the steps do not change, only the field names do. Step 1: Shape the data before you open Tableau Tableau is a display layer. Deriving something inside it takes longer than deriving it upstream in SQL, Python or

This guide gives you a repeatable loop for the days when nothing works, and four true stories showing it used on real problems. Here is what a working day actually contains. A website's firewall blocks you for no reason. A table that visibly exists tells your script it does not. A query runs for thirty minutes with no end in sight. A fix you know is correct changes nothing at all. None of that means you are doing it wrong. That is the job. What separates people who ship analyses from people who stop is technical tenacity : staying methodical when the tools fight back. It is not a personality trait you either have or lack. It is a small procedure, and you can learn it in the next ten minutes. The diagnosis loop (tenacity is a method, not a mood) Think back to the last time a tool beat you for an hour. What was the first thing you did when it failed, and what did you do second? Most people can name the first move and not the second, and the second is where the method lives. Gritting your teeth and re-running the same thing harder is not tenacity; it's frustration with extra steps. What experienced people actually run is a loop: Step Move 1. Read the actual message Not "it's broken" — the words. Error messages name the symptom precisely, even when the cause is elsewhere. 2. Form ONE hypothesis "The table isn't in the file the script reads." Specific enough to be wrong. 3. Run the cheapest test of it Prefer checks that take seconds — list the tables, count the rows, print one value. 4. Verify from a second vantage point Don't ask the tool that's confusing you whether it's confused. Check the file from outside, the data from a different program, the value with a different query. 5. Change ONE thing, re-run Change three things and you'll never know which one mattered — or which one broke something new. 6. Timebox, then change strategy If the current approach has eaten 30 minutes with no progress, stopping is a decision, not a defeat. There's usually a second road. Four tr

COUNT looks like the simplest function in SQL, and it is the one that quietly trips up the most people in interviews and on the job. The confusion is almost always the same: COUNT(*) , COUNT(column) , and COUNT(DISTINCT column) look nearly identical but count three different things. Once you can say out loud what each one counts, a lot opens up. You can verify a data migration, find duplicates, and measure how complete a column is, all with the same little function. This guide is that explanation, with lots of small examples you can copy. The one-sentence version. COUNT(*) counts rows . COUNT(column) counts rows where that column is not NULL . COUNT(DISTINCT column) counts how many different non-NULL values that column has. Everything below is just that sentence, slowed down. The three forms of COUNT and what each one counts Picture one small table, customers , with a region column where two rows were never filled in: id name region 1 Maya North 2 Jordan South 3 Alex North 4 Sam NULL 5 Taylor NULL Now run the three forms on it: SELECT COUNT(*) AS all_rows, COUNT(region) AS rows_with_region, COUNT(DISTINCT region) AS different_regions FROM customers; all_rows rows_with_region different_regions 5 3 2 COUNT(*) = 5. Every row, no exceptions. The * means "the row itself," so NULLs never matter. COUNT(region) = 3. Only the rows where region has a value. Sam and Taylor are skipped because their region is NULL. COUNT(DISTINCT region) = 2. The different values are just North and South . The two Norths collapse to one, and NULL is not counted. The NULL rule that makes them disagree Predict it first. A table has 100 rows. Twenty of them have no email address. What does counting the email column give you? Say the number before you read on. Here is the whole trick in one line: COUNT(*) counts rows. COUNT(something) counts non-NULL values of that something. So the moment a column has any NULLs, COUNT(column) comes back smaller than COUNT(*) . That gap is not a bug, it is informat

This guide walks through five steps for working out which records are the same real thing, and merging them without wrecking your data. It runs on real chart data, and it includes the two times the rules came out wrong. Here is the problem in one example. Count the distinct artists in Billboard's public chart history and the number is wrong. "Elvis Presley" and "Elvis Presley With The Jordanaires" are the same man, and so are five other credit strings. One real-world entity , seven database strings . Every dataset with human-entered names has this. Customers who signed up twice. "IBM" against "I.B.M." against "International Business Machines". The same supplier in two systems, spelled two ways. The work of fixing it is called entity resolution . Matching across two datasets is record linkage . Removing duplicates inside one is deduplication . They are the same skill pointed at different situations, and it is one of the most common tasks an analyst actually gets handed. The vocabulary map Term Meaning Entity The real-world thing: one artist, one customer, one company Entity resolution Figuring out which records refer to the same entity Record linkage The same problem across two datasets. "Is row 5 in file A the same person as row 90 in file B?" Formalized by Fellegi & Sunter (1969) Deduplication The same problem inside one dataset Normalization / standardization Transforming values toward a canonical form (lowercasing, trimming, cutting suffixes) so equal things become equal strings Match key The cleaned column(s) you actually join on Match rate The share of records that found their counterpart. This is the number that keeps the whole exercise honest Clerical review Human eyes on the records the rules could not decide. This is a formal stage of the classic framework, not an admission of failure Step 1: measure the fragmentation before fixing anything The worked example is Billboard Hot 100 history, 1958 to present. The goal is one clean row per artist. Before writing

Platforms are finally recognizing that people don’t want to consume AI slop. A growing number of sites and apps now have tools and policies to flag, label, and ban AI-generated content.

An AI interview is increasingly the first step of a hiring process. Since there’s no human on the other end, candidates are scheduling them whenever—even deep into the night.

The Laravel 13 Features That Matter in Real Projects Laravel 13 shipped on March 17, 2026, and the upgrade story is unusually simple: zero application-level breaking changes from Laravel 12, one hard requirement (PHP 8.3), and several features that are genuinely useful in production rather than just impressive in release notes. This post focuses on the features you will actually reach for on real client projects — not an exhaustive tour. For the full release overview, upgrade checklist, and breaking changes reference, see Laravel 13: Features, Upgrade Guide, and Breaking Changes . Prerequisites: PHP 8.3+, Laravel 13.x (latest stable: 13.14.0 as of June 2026), Composer 2.x. 1. PHP Attributes on Models and Controllers Laravel 13 adds PHP 8-style #[Attribute] support across 15+ framework locations. The old property-based syntax still works — this is purely additive. On Eloquent Models: use Illuminate\Database\Eloquent\Attributes\Table ; use Illuminate\Database\Eloquent\Attributes\Fillable ; use Illuminate\Database\Eloquent\Attributes\Hidden ; #[Table('posts', primaryKey: 'id', incrementing: true, timestamps: true)] #[Fillable('title', 'body', 'user_id')] #[Hidden('deleted_at')] class Post extends Model {} On Controllers: use Illuminate\Routing\Attributes\Controllers\Authorize ; use Illuminate\Routing\Attributes\Controllers\Middleware ; #[Middleware('auth')] class CommentController extends Controller { #[Middleware('subscribed')] #[Authorize('create', [Comment::class, 'post'])] public function store ( Post $post ) { } } When to actually use this: Attributes shine on large domain models where $fillable , $hidden , $casts , and relationship declarations are scattered across the class. Collocating table definition and mass assignment rules at the top of the file improves readability at a glance. On small CRUD models, the tradeoff is extra import lines for minimal gain. Common mistake: Mass-converting every existing model to attribute syntax in a single PR. It creates a lar

Kennedy Torkura discusses practical GenAI red teaming techniques to safeguard LLMs and knowledge bases against security threats like data poisoning and LLMjacking on AWS. He explains how engineering leaders and architects can bridge traditional cloud security with MITRE ATLAS frameworks to proactively identify vulnerabilities, implement guardrails, and secure production AI applications. By Kennedy Torkura

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Way back in the summer of 2017, AI researchers at Google put out a paper called “Attention Is All You Need,” in which they described a new…

Every few decades, someone announces that science has reached its end. In 1903, the revered physicist Albert Michelson wrote that the “facts of physical science have all been discovered.” In the 1980s, Stephen Hawking predicted that theoretical physics might be finished by the end of the century. With the explosive arrival of artificial intelligence, the…

Client intake often requires two types of information: searchable answers and files for review. This Vite and React example collects both in the same response. You can try the form without an account. Ask only what you need The example asks for: the client's name; a work email address; the result they need; an optional target date; up to five briefs or reference files. Each answer should help someone prepare for the first call. Leave detailed discovery questions for the call. Define the form The form schema lives in the React app: import { createClient , defineForm , FilloForm } from " @usefillo/react " ; const intake = defineForm ({ id : " vite-client-intake " , title : " Tell us about your project " , description : " Tell us what you need, when you need it and which files will help us prepare. " , pages : [ { id : " intake " , blocks : [ { id : " name " , kind : " short_text " , label : " Your name " , required : true }, { id : " email " , kind : " email " , label : " Work email " , required : true }, { id : " outcome " , kind : " long_text " , label : " What result do you need? " , required : true , }, { id : " target-date " , kind : " date " , label : " Target date " }, { id : " documents " , kind : " file_upload " , label : " Briefs or reference files (PDF, DOCX, PNG or JPG) " , accept : [ " .pdf " , " .doc " , " .docx " , " .png " , " .jpg " , " .jpeg " ], maxFiles : 5 , }, ], }, ], settings : { submitLabel : " Send project details " }, }); Keep the form and field IDs after you collect the first response. Fillo uses them as stored answer keys. You can change labels and help text without changing the IDs. The React app controls the route, layout, styles and what happens after submit. Fillo handles the schema, validation, uploads and responses. The SDK renders React controls in the page. It does not use an iframe. Send files straight to storage The browser sends each file to the storage connected to the Fillo workspace. The Vite app does not proxy the file throu

When someone says "Bitcoin has over 15,000 nodes worldwide," they mean 15,000+ independent computers are each running Bitcoin software and each maintaining their own full copy of the blockchain. No server owns the truth. Every node checks it for itself. That single fact — every node independently verifies every transaction and block against protocol rules — is the reason blockchains don't need a central authority. If one node tries to cheat, the rest simply ignore it. There's no admin account to compromise because there's no admin. Not All Nodes Do the Same Job Full Node Downloads and stores the entire blockchain, every block since genesis, and independently validates everything against consensus rules. Highest security ~500 GB for Bitcoin ~1 TB for Ethereum This is the backbone of network security. A full node doesn't trust anyone's summary of the chain; it recomputes validity itself. Light Node (SPV) Stores only block headers, not full transaction data. Uses Merkle proofs and relies on full nodes to verify transactions. Low storage, ~50 MB Trusts full nodes for verification What most mobile wallets run Mining/Validator Node A full node that also participates in block creation. Miners (Proof of Work) solve computational puzzles; validators (Proof of Stake) stake cryptocurrency as collateral. Both earn rewards for securing the network. Creates new blocks Earns rewards Requires specialized hardware (PoW) or capital at stake (PoS) Archive Node Everything a full node stores, plus historical state at every block height. Complete history ~15+ TB for Ethereum Used by explorers, analytics platforms, and enterprise tooling Why Peer-to-Peer Instead of Client-Server A traditional web service is client-server: your browser requests data from a company's servers. If those servers go down, the service is unavailable. That's a single point of failure by design. Blockchain networks use peer-to-peer (P2P) architecture instead. Every participant is simultaneously a client and a serv

I spend a lot of time in the AI space -- reading papers, building things, talking to engineers who are actually shipping. And there is a gap between what the demos show and what production systems actually look like that nobody is being fully honest about. So here is my honest take on where things actually are. The Problem With How We Talk About AI Agents Everyone is calling everything an "agent" right now. A function that calls a tool? Agent. A chatbot with memory? Agent. A script with a loop? Agent. This dilution is not just semantic. It is causing real engineering mistakes. When you do not have a precise definition for what you are building, you end up over-engineering simple pipelines and under-engineering genuinely complex ones. I have seen teams spend weeks adding "agentic" orchestration to workflows that would have been fine as a single well-structured prompt. Here is the definition I keep coming back to: an agent is a system that has an objective, not just an instruction. It decides what to do next. It handles failure. It knows when it is done. Everything else is just a fancy function call. 🟢 If your system needs a human to tell it each step, it is not an agent. It is a chat interface. 🔵 If your system can recover from a failed tool call and try a different approach, you are getting somewhere. ✅ If your system can decompose a goal into subtasks and delegate them, that is the real thing. What Is Actually Happening in Production Right Now The honest picture from teams I follow and talk to: Most real agent deployments are narrow. They do one thing well. Customer support triage. Document extraction. Code review on a specific codebase. They are not general-purpose reasoning engines. They are purpose-built pipelines with some intelligence in the decision layer. The teams getting good results are not chasing the latest model release. They are obsessing over: ☑️ Tool design -- what can the agent actually call, and how clean is the interface ☑️ Failure handling -- wh

Why Geo-Block? Not every country needs to reach your server. If you run a local business in Brazil, you don't need traffic from North Korea. If you serve customers in the EU, you probably don't need visitors from 150 other countries hitting your login page. Geo-blocking at the WAF level stops unwanted traffic before it ever reaches your application. No CPU spent. No database queries wasted. No bandwidth consumed. The Numbers from My Server After 30 days of logging, I checked where attacks came from: Traffic Source % of Total Requests % of Attacks Target countries (where my customers are) 23% 8% Non-target countries 77% 92% 77% of my traffic came from countries I don't serve, and 92% of attacks originated from those countries. Geo-blocking the non-target regions would eliminate the vast majority of malicious traffic with zero impact on real users. Setting Up Geo-Blocking in SafeLine Step 1: Go to IP Groups -> Geo Blocking in the dashboard. Step 2: Choose your approach: Option A: Allow-list mode (strictest) Block everything, then whitelist specific countries. Block : ALL Allow : United States , Canada , United Kingdom , Germany , France , Netherlands Option B: Block-list mode (targeted) Allow everything, then block specific high-noise regions. Block : Russia , China , Vietnam , North Korea , Iran Step 3: Apply the rule. Done. What Happens to Blocked Visitors Blocked IPs see a 403 Forbidden page. They can't reach your application at all — the WAF drops the connection at the proxy layer. Your app server never sees these requests. SafeLine logs every geo-blocked request to Attack Logs. You'll see: Which country the IP was from What URL they tried to access The exact timestamp Which Countries to Block Based on my 30-day log analysis and common community reports: Almost always safe to block: North Korea — 0 legitimate traffic for 99.9% of sites Iran — heavy scanner activity, minimal legitimate traffic (for non-Iranian sites) High scanner volume, consider blocking if not yo

The Problem Your login page, search endpoint, or contact form is getting hammered. Rate limiting is the fix — but implementing it in application code means finding every endpoint, writing middleware, choosing a storage backend, and deploying changes. On a WAF, you set it once and it applies everywhere. Why WAF-Level Rate Limiting Is Better Approach Code-Level WAF-Level Setup time Hours to days 5 minutes Code changes Required None Applies to One endpoint at a time All routes with one rule Storage Redis/Memcached needed Built into WAF Performance impact Hits your app server Blocked at proxy Updates Deploy new code Change a rule in dashboard Step-by-Step: Rate Limit Setup 1. Log into SafeLine Dashboard Go to https://<your-ip>:9443 . Navigate to Rules -> Add Rule -> Rate Limiting. 2. Create Your First Rule — Login Protection Name: Login brute force protection Match: URL contains /login OR /wp-login.php OR /auth Limit: 5 requests per minute per IP Action: Block (return 429 Too Many Requests) Block duration: 15 minutes This stops credential stuffing cold. An attacker who tries 5 wrong passwords in 60 seconds gets blocked for 15 minutes. That's a maximum of 480 attempts per day — vs unlimited without rate limiting. 3. Search Endpoint Protection Name: Search rate limit Match: URL contains /search OR /query Limit: 30 requests per minute per IP Action: Challenge (JS captcha) Search endpoints are expensive. A single user running a script can do 1,000+ queries per minute and degrade performance for everyone. 30/min is generous for humans but stops scripts. 4. Global Baseline Name: Global request limit Match: /* Limit: 300 requests per minute per IP Action: Throttle Catches anything that slips through specific rules. 300/min = 5/sec, which is more than any human needs. What Happens When a Limit Is Hit SafeLine logs every rate limit trigger to the Attack Log. You'll see: Which IP triggered it Which endpoint they were hitting Time of the trigger Whether they got blocked, challenge