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I recently built WordScrambler, a free tool for unscrambling letters and solving anagrams, mostly out of frustration with existing tools being cluttered with ads or requiring sign-up just to see a result. Here's a quick look at the core technique behind how it works. The problem Given a jumbled set of letters (say, ucim), find every valid dictionary word that can be formed from some or all of those letters. The naive approach, generating every permutation and checking each against a dictionary, gets slow fast. A 7-letter input has 5,040 permutations; a 12-letter input has nearly 480 million. That's not viable for instant results. The signature trick The key insight: two words are anagrams of each other if and only if their letters, sorted alphabetically, produce the same string. For example: "listen" -> sorted -> "eilnst" "silent" -> sorted -> "eilnst" Both hash to the same signature. So instead of generating permutations, you can: Precompute a signature for every word in your dictionary and group words by signature. For a given input, generate the signature of the input (and its relevant sub-combinations, for partial-length matches). Look up matching signatures in a hash map, an O(1) lookup instead of a brute-force search. This turns "find every valid word from these letters" into a fast lookup problem rather than a combinatorial one, which is what makes results feel instant even against a large dictionary (WordScrambler checks against roughly 246,000 words). Handling partial-length matches Most real unscrambling needs go beyond "use every letter", people want every valid word of any length using a subset of the given letters. That means generating signatures for all relevant letter subsets (not full permutations, just subsets, which is a much smaller set) and checking each against the dictionary map. Try it You can play with the live version here: wordscrambler.online — it also shows word definitions and Scrabble/Words With Friends point values alongside each resu

Ich wollte kein Gesellschaftskapitel schreiben Ehrlich gesagt hatte ich es fest geplant, es wegzulassen. Das Buch sollte technisch bleiben. Guards, Skills, Crystallization-Loop. Harte Zahlen, echte Systeme. Kein Manifest, kein Aktivismus. Aber dann saß ich eines Abends vor meinem Dashboard und schaute auf eine Zahl, die mich nicht losließ: 1.087 autonom erledigte Tasks. Ohne mich. In einem einzigen Monat. Und ich dachte: Was passiert, wenn das nicht mehr mein persönliches Experiment ist, sondern die Standardausstattung eines Unternehmens? Was mein System jeden Tag beweist Mein Agentensystem läuft auf einer einfachen Architektur. Cron-Jobs feuern Tasks, Guards prüfen Ausgaben, Skills führen spezialisierte Workflows aus. Die Erfolgskennzahlen sprechen für sich: 232 aktive Cron-Jobs, die rund um die Uhr arbeiten 88,1% Aufgaben ohne menschliches Eingreifen abgeschlossen Durchschnittliche Reaktionszeit unter 4 Minuten für routinemaessige Entscheidungen Das ist kein Proof-of-Concept. Das ist mein Arbeitsalltag. Ein konkretes Beispiel: Jede Nacht um 2:00 Uhr laeuft ein Agent, der meine LinkedIn-Performance auswertet, Erkenntnisse in eine Wissensdatei schreibt und den naechsten Morgen mit priorisierten Empfehlungen vorbereitet. Ich wache auf und finde eine fertige Analyse vor. # Auszug aus meinem Cron-Setup 0 2 * * * claude --skill post-analyse --input performance/daily/ $( date +%Y-%m-%d ) .md 30 2 * * * claude --skill content-excellence --mode review Das ist Routine. Das laeuft jeden Tag. Das macht niemand manuell. Die Frage, die ich nicht ignorieren konnte Wenn eine Person mit diesem System die Produktivitaet von mehreren Vollzeitkraeften erreicht, dann stellt sich eine Frage, die ich nicht wegdefinieren kann: Was passiert mit den Arbeitsplaetzen, die diese Routinetaetigkeit bisher ausgefuellt haben? Der IMF schaetzt, dass 40% aller Arbeitsplaetze weltweit von KI betroffen sein werden. Nicht in zehn Jahren. Die Verschiebung findet jetzt statt, in kleinen Schritten, in je

Tesla has discontinued Solar Roof, its solar panels designed to look like regular roofing tiles, Electrek reports. Sources "close to the program" told the publication that Tesla has informed its third-party installer network that Solar Roof is no longer available to order, and that only conventional solar panels will be supplied going forward. While Tesla […]

Cloudflare has recently detailed how it is using AI to transform internal engineering standards from passive documentation into an actively enforced control system across the software development lifecycle. By Craig Risi

Architecture is not a fixed choice made once; fitness is a moving target driven by changing regulations, tech, and markets. Even a sound design can silently stop fitting over time without bad calls. Spanning seven articles on context stores, gateways, and topologies, this collection treats architecture as an evolving sociotechnical craft where teams deliberately shape friction, fitness, and flow. By InfoQ

The Nyrius Phoenix Home True 4K60 can wirelessly transmit games, TV shows, movies, and more across your home (even through walls).

Dan Finneran discusses the risks of unowned AI-generated code in production and demonstrates how eBPF can intercept and control AI API traffic in Kubernetes. He explains how kernel-level socket hooks enable transparent prompt filtering, model swapping, token limits, and syscall restrictions to secure AI agents without modifying application source code or restarting containers. By Dan Finneran

After two years of testing, these are the electric toothbrushes that impressed WIRED staffers the most.

As the appetite for “authenticity” grows online, content creators are buying up empty boxes for luxury goods—and resellers are cashing in for “crazy prices.”

From the best Dyson vacuum to the best wireless headphones and earbuds we’ve tested, you can get some great gadgets on sale already ahead of Labor Day.

Serverless on AWS isn't "just use Lambda." It's a design philosophy: let AWS manage the infrastructure, pay only for what you use, and build with managed services that scale independently. But the patterns that work in serverless are fundamentally different from traditional architectures — and the anti-patterns are expensive to learn the hard way. This guide covers the patterns that work in production, the anti-patterns that waste money or cause outages, and the decision framework for when serverless is the right (or wrong) choice. The Serverless Building Blocks ┌─────────────────────────────────────────────────────────────────────┐ │ AWS SERVERLESS STACK │ ├─────────────────────────────────────────────────────────────────────┤ │ COMPUTE │ Lambda | Fargate (serverless containers) │ │ API │ API Gateway (REST/HTTP/WebSocket) | AppSync (GraphQL)│ │ ORCHESTRATION │ Step Functions | EventBridge Scheduler │ │ MESSAGING │ SQS | SNS | EventBridge │ │ STORAGE │ S3 | DynamoDB | Aurora Serverless │ │ STREAMING │ Kinesis | DynamoDB Streams | MSK Serverless │ │ AUTH │ Cognito | IAM | Lambda Authorizers │ │ OBSERVABILITY │ CloudWatch | X-Ray | Application Signals │ └─────────────────────────────────────────────────────────────────────┘ Key principle: In serverless, you compose applications from managed services. Lambda is the glue between them — not the application itself. Pattern 1: Synchronous API (Request/Response) The most common serverless pattern: HTTP API backed by Lambda. Client → API Gateway → Lambda → DynamoDB / Aurora Serverless │ Response ← ─ ─ ─ ─ ─ ─ ┘ Best Practices API Gateway HTTP API (not REST API) — cheaper, faster, simpler for most cases One Lambda per route (single responsibility) — not a monolith Lambda Keep Lambda warm — use Provisioned Concurrency for latency-sensitive endpoints DynamoDB for simple access patterns — scales with traffic, no connection pooling Aurora Serverless v2 for complex queries — but use RDS Proxy to manage connections When to Choose H

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

A company that plans to beam sunlight from space to Earth on demand might unintentionally brighten the night sky for many more people than intended, according to a new study. Later this year, the US company Reflect Orbital plans to launch a test satellite called Eärendil-1 that will extend an 18-by-18-meter mirror in orbit. The…

Two numbers off my Search Console this morning, same 28 day window, same site. The free landing page roast tool: 42 impressions, average position 38.0. The blog post I wrote to support that tool: 11 impressions, average position 21.1. Six weeks earlier it was the other way round. On July 4 the tool sat at position 7.5 on 18 impressions and the article was at 17.8 on 38. So the tool has more than doubled its reach since then, and its average position has gotten roughly five times worse over the same stretch. Both of those things are true at once, and working out why changed how I plan the next tool. The tool favors.dev/roast takes a URL and gives back a conversion score out of 100. It screenshots your full public page, then grades the copy and the design together across six categories: clarity, value proposition, trust, CTA, visual design and SEO. You get back the specific issues hurting signups with a fix for each, the things the page already does well, and a one line verdict. No signup, no credit card, no email field. Paste a URL, press "Roast it", read the result. It is deliberately small, and the scoping is most of why it shipped. The cut list was: accounts and password resets, saved history, dashboards, billing and usage limits, settings and themes, support for every edge case, and an admin panel for myself. Every one of those is how a weekend build turns into a month. If a free tool needs a billing system, you have started building a second product by accident. What those numbers actually say Here is the honest read, because "my free tool beat 29 of my 32 blog posts" is technically true and a bit misleading. Reading Tool impressions Tool position Article impressions Article position Jul 4 18 7.5 38 17.8 Jul 11 20 7.1 42 16.9 Jul 19 22 10.4 39 19.1 Aug 15 42 38.0 11 21.1 Impressions climbed because the tool started matching a much wider spread of queries. Average position fell for exactly the same reason. It is not ranking better. It is ranking on more things, m

A failure message can be technically correct and still be frustratingly incomplete. Consider a timeout. It tells us something important about the failure mechanism, but not which operation encountered it. Adding the complete request target might answer that question, yet it can also expose identifiers, query parameters, access material, or other data that never belonged in a broadly visible diagnostic record. A safer middle ground is to give failures two separate coordinates: a reason code that explains how the operation failed, and a bounded operation label that explains where it failed. That distinction makes diagnostics more useful without turning failure handling into an accidental data-exposure channel. A reason code is not a location Reason codes describe failure mechanics. Generic examples might include deadline , cancelled , unauthorised , or invalid_response . These codes are valuable because they let systems group similar outcomes. A dashboard can count deadline failures across operations, while application logic can decide whether a particular reason is retryable. What a reason code cannot reliably explain is the operation being attempted. A deadline during a summary read may require a different investigation from a deadline while assembling a detailed response. Combining both meanings into one free-form message makes failures harder to query and encourages presentation text to become an informal data model. Model the two coordinates separately A deliberately generic, invented C# model might look like this: public enum OperationArea { Summary , Detail , Archive } public sealed record FailureDetail ( string ReasonCode , OperationArea ? Area = null ); The reason remains suitable for classification. The operation label adds location without carrying an unrestricted request value. An enum is not the only option. A validated value object or centrally managed set of constants can work too. The important constraint is that labels come from a small, reviewed voca

The UK government is facing calls to cancel a sprawling health care contract with Palantir. The region of Greater Manchester insists it can do a better job itself.

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Choose a text-summary API by the percentage of outputs that pass a source-grounded evaluation, then compare latency, regional controls, and cost only among the candidates that clear that bar. For a JavaScript subscription app serving US and EU users, the decisive constraint is rarely the cheapest advertised token rate. It is the complete production path: cleaning an article, fitting or splitting it, generating a summary, validating claims, and recovering safely when a request is interrupted. Short answer: use a narrow internal completion interface, test it with representative long documents, and keep the provider choice behind an adapter. A direct hosted endpoint is the simpler default for one approved backend. Add a self-hosted gateway only when routing, policy enforcement, or repeated provider comparisons justify another service to operate. I start this kind of decision in a notebook, but I don't stop at a few outputs that sound good. Fluent summaries can omit the one qualification that changes an article's meaning. The useful unit of comparison is an accepted summary, not a successful API response. What should a US and EU text summary API evaluation measure? Define acceptance before sending the first request. For a long article, I usually want a short abstract, the central claims, preserved numbers, and explicit uncertainty where the source is uncertain. Those fields form an output contract. The evaluator then asks whether each claim is supported by the input and whether any required idea disappeared. Build the corpus from document shapes the product expects: clean prose, copied navigation, tables flattened into text, repeated paragraphs, empty sections, contradictory statements, and inputs near the application's size limit. Keep a held-out slice for release decisions. Otherwise prompt tuning turns the evaluation set into a memory test. The regional review belongs beside quality, but it answers a different question. An API being reachable from Europe does not est

An e-commerce alert is not complete when an API accepts a message. It is complete when the application records a terminal delivery state, suppresses an invalid recipient, or escalates through a separately governed channel. Short answer: use a dedicated SMS API for a small critical-alert worker when template ownership and direct status control matter; keep AWS SNS when SMS belongs inside an existing cloud messaging stack, and prefer a callback-capable provider when escalation must begin in under a minute. That choice creates work. A direct API keeps the send path narrow, but polling, retries, dead-letter handling, and country-specific fallback rules remain application responsibilities. For critical alerts, those responsibilities need the same idempotency and audit discipline as a ledger entry: one intent, one durable identifier, and an append-only record of every state observation. No provider turns carrier delivery into exactly-once delivery. Implement the template control plane in Node.js Start with the contract, not the vendor. The application owns an immutable alert intent containing the business event ID, recipient, template version, jurisdiction, and escalation deadline. Template ownership is the decision axis: if compliance reviewers must approve and reproduce the exact text that was sent, keep the canonical template version in the application and treat a provider template ID as deployment metadata. If a provider must own localization or regulatory registration, record that provider template ID beside the application version rather than letting it become invisible configuration. A useful state machine separates accepted from a terminal delivery result. Persist the provider message ID after the initial send, schedule periodic status reads, and append each observation with its timestamp and request ID. A retry after HTTP 429 is transport recovery, not permission to create a second alert; honor Retry-After , use exponential backoff, and preserve the same idempote

Short answer: Choose a transactional email API only after its custom domain verification, DKIM rotation, suppression export, event history, and rollback controls let a small team explain every accepted, deferred, bounced, or blocked message. A transactional email API is only simple while delivery state stays simple. The operational constraint is recovery, not the length of the send request. That is the choice. A low send price is useful, but it can't compensate for a sender identity nobody can rotate safely or a suppression list nobody can inspect. I've been paged for missed jobs and duplicate deliveries. Email creates the same class of incident: an application retries because it can't tell what happened, then either drops a message or sends it twice. Treat the provider as one part of a delivery system, not as a Send() function with a receipt. What should a startup verify in a simple transactional email deliverability API? Start with a short proof, using a subdomain that is separate from employee mail. Verify that the service can establish the custom domain through DNS records you control, show each record's status independently, and preserve the previous signing configuration while a new DKIM selector is being rolled out. A single green "verified" badge isn't enough evidence for a runbook. Then trace one synthetic message from the application's request ID to the provider's message ID and onward to the final event. The API should distinguish request acceptance from actual delivery. Those are different states, and collapsing them makes retry policy dangerous. Check how long event data remains queryable, whether webhook events can be replayed or recovered, and whether a human can export the same data during an incident. Suppression management deserves its own test. You need to know what creates a suppression, its scope, how it is queried, and what review is required before removal. An unsubscribe, a permanent delivery failure, and an operator block may all prevent a s