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With just one more major mission upcoming, NASA's science programs need a boost.

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, the role AI is playing in it.

Originally published on xroot.dev . On-chain, a regulated fund token and a honeypot scam are the same shape. Both are Token-2022 mints. Both keep an active freeze authority. Both set DefaultAccountState to Frozen , so every new holder's account starts locked. One of them is a European money-market fund following its regulator's rules; the other is a trap built to let you buy and never sell. Every token scanner I tested reads them identically: red flags, high risk, score zero. The reason this now matters is sRFC-37, the Token ACL standard — the Solana Foundation's official mechanism for permissioned tokens. It has been live on mainnet since March 2026, real institutional money already uses it, and the entire real-world-asset wave forming on Solana is going to ship in this exact shape. This post covers how it works end to end — and the structural check that separates a compliant token from a trap, verified against the chain rather than anyone's metadata. Why Permissioned Tokens Exist at All A tokenized treasury fund, a regulated stablecoin, a security token — their issuers are not allowed to let anyone hold them. KYC requirements, sanctions screening, court orders, investor-accreditation rules: the issuer must be able to control who holds the asset and stop specific wallets, or the asset cannot legally exist on a public chain. Solana had two ways to build that before, and both hurt: Transfer hooks run issuer code on every transfer — but every DEX, wallet and protocol touching the token must implement the hook interface, so composability dies at exactly the venues that create liquidity. Manual freeze-and-thaw keeps standard transfers — but every new holder starts frozen and waits for the issuer to co-sign a thaw. Onboarding becomes a support ticket, and the issuer signs forever. Token ACL is the third path: keep the freeze mechanism — the one lever the token program already enforces everywhere — but make thawing self-service against a published rulebook. The Mechanics:

Android Auto has several essential features enabled by default, but you may be surprised at how much it can really do.

The company is testing robots on tasks that can performed by technicians.

Burnt out on wrist buzzes and notification overload? A new crop of minimalist wearables promises to collect your health data without demanding your attention.

Experience the oddly satisfying joy of labeling bins, drawers, and more with the best Bluetooth and traditional label makers.

Young adults are in more group chats than ever—sometimes more than 20 at a time. Here’s how to tame the chat madness.

Detecta y neutraliza estafas telefónicas con voces deep‑fake: guía práctica y herramientas de clonación vocal Introducción Imagina que recibes una llamada de tu jefe pidiéndote una transferencia urgente… pero la voz es una réplica perfecta generada por IA. En 2024 esa escena ya no es ficción: las estafas con voces deep‑fake están a la orden del día en EE. UU., Europa y América Latina. Gracias a plataformas como ElevenLabs , iSpeech o a modelos de código abierto como Coqui‑TTS y VITS‑OpenAI , crear una copia casi idéntica de la voz de cualquier persona cuesta menos de 100 USD y se hace en cuestión de minutos. En este artículo verás cómo funciona la tecnología , cómo identificar una voz sintética y qué medidas tomar tanto si eres usuario particular como si gestionas la seguridad de una empresa. 1. Tecnologías de clonación vocal: panorama rápido Tipo Ejemplo Precio (mensual) Necesita GPU? Privacidad Comentario SaaS (API) ElevenLabs, iSpeech, Resemble AI $0‑$49 (plan básico) No Los datos se envían a la nube Fácil de integrar, ideal para pruebas rápidas OSS (auto‑alojado) Coqui‑TTS, Mozilla TTS, VITS‑OpenAI Gratis (coste de infraestructura) Sí (GPU recomendada) 100 % bajo tu control Requiere instalación y ajuste fino 2. Detecta una voz deep‑fake en 3 pasos (y sin ser ingeniero) Paso 1 – Escucha los “errores humanos” Señal auditiva Qué indica Respiraciones muy cortas o inexistentes Síntesis sin modelo de respiración Entonación monótona en frases largas Falta de variabilidad prosódica “Cortes” o chasquidos en palabras compuestas Artefactos de concatenación de fonemas Falta de “cierre” de consonantes (p. ej. “s” muy suave) Modelo TTS de baja calidad Paso 2 – Analiza el espectro con una herramienta gratuita # Instala DeepSpeech-detect (Python) pip install deepvoice-detect # Analiza la llamada guardada como audio.wav deepvoice-detect audio.wav --output report.json El archivo report.json contiene un score de probabilidad (0‑1). Valores > 0.7 suelen corresponder a voces generad

When an internet outage hits, users immediately turn to their phones to find out if it's just them or a widespread network issue. Because they are often relying on spotty cellular data, any tracking site needs to load instantly and deliver highly localized information. I recently launched CoxOutage.us to map and track Cox Communications disruptions. Here is a breakdown of the technical and SEO strategies I used to build it. Performance & Traffic Handling Outage trackers face a unique challenge: they get zero traffic when things are fine, and massive, sudden spikes the minute a service goes down. Aggressive Caching: I implemented LiteSpeed Cache combined with Memcached for object caching. This ensures that database queries are kept to an absolute minimum when a sudden wave of users hits the site. Edge Delivery: Everything sits behind Cloudflare for DNS management and edge-level caching, ensuring the server (hosted via InterServer) doesn't get overwhelmed during regional outages. Scalable SEO & Routing Architecture The biggest hurdle was capturing local search intent accurately. Hyper-Specific URL Slugs: Initially, you might think to use a simple routing structure like /los-angeles . However, I found that using full keyword slugs—such as /cox-outage-los-angeles —significantly boosted visibility and search performance. Automated Indexing & Schema: I utilized the Google Indexing API to push new city landing pages instantly. Paired with Rank Math, the site generates precise schema markup so search engines understand the real-time nature of the status updates. Looking Forward Right now, the focus is on scaling out the localized landing pages and refining the automated reporting pipeline. If you have experience building high-traffic, real-time alert systems or handling sudden traffic spikes, I’d love to hear your approach. Check out the live project here: CoxOutage.us Feedback and suggestions are always welcome!

A Technical PM's journey from frustration to shipping a solo Android app As a Technical Project Manager, I track time constantly. Client hours, project phases, billable work. It's part of the job. But every time tracker I tried left me frustrated. Toggl is powerful — too powerful. Every time I opened it, I had to navigate through workspaces, projects, tags, and integrations I'd never use. Clockify felt the same. Harvest was built for teams, not for someone who just wants to know where their day went. And don't get me started on the design. Most of these apps look like they were built in 2012 and never updated. So I did what any slightly obsessive PM would do: I built my own. The Problem I Was Actually Solving It wasn't that existing trackers lacked features. It was that they had too many. Every morning I'd open an app, get overwhelmed by options, and either spend 2 minutes setting up a timer correctly or just give up and track nothing. By the end of the week, I had no idea where my billable hours went — which meant I was probably undercharging clients. I wanted one thing: tap a button, start tracking. That's it. Building Tempo as a Non-Developer Here's the part that still surprises me: I built Tempo without writing a single line of code. As a Technical PM, I understand systems, workflows, and user experience — but I'm not a developer. I used AI tools to go from idea to a fully functional Android app in 2–3 months. The process wasn't always smooth. There were bugs, confusing UX decisions, and moments where I questioned whether I was building something anyone else would actually use. But I kept coming back to the same question: would I use this every day? And the answer was always yes. What Tempo Does (and Doesn't Do) Tempo is deliberately minimal: One tap to start tracking **— no setup, no forms, no friction **Billable vs non-billable toggle — know exactly what to invoice Daily & weekly reports — see where your time actually goes Custom projects with icons and colors

1. Introduction Excel is much more than a spreadsheet for entering numbers. It can be used as a data-analysis tool that helps analysts inspect, validate, filter, summarize, and prepare raw data before deeper analysis begins. In typical analytics, the quality of the final work depends heavily on the quality of the data used; therefore, data cleaning is not an optional step—it is the foundation of effective data analysis. This article demonstrates key Week 1 Excel concepts _using an employee dataset containing _employee IDs, names, departments, gender, marital status, hire dates, salaries, educational level, performance score among others. The raw file intentionally contains common data-quality issues: inconsistent capitalization on the First and Last names, blank records, duplicate employee records, varying department names, currency and dates that need review. By working through these issues, the article shows how Excel’s formatting tools, text functions, filters, conditional formatting, numerical functions, conditional summaries, and date functions can turn a messy workbook into an analysis-ready dataset. 2. Why Data Cleaning Matters Data cleaning is more than just about removing errors. By standardizing formats and categories, we make datasets more transparent, usable, and valuable for management analysis and reporting purposes. Data analysis is simple – garbage in, garbage out. A dashboard or prediction can appear professional, but can be misleading if the underlying data has duplicates, blank values, inconsistent categories or incorrectly formatted text and dates. For example, “IT” “I.T.” and “Information Tech” can be viewed as different department values if naming is not standardized. Duplication of an employee ID can inflate employee counts and department totals. A blank performance score might mean that something is missing and should be looked into and dates saved as text cannot be reliably used in calculations such as employee tenure checks. A good practice

It's the week before vizcrush goes public, and I have two files open side by side. On the left, the launch copy: the JS core beats the most popular npm downsampling package by 32×, "and WASM adds another 5-10x on top." On the right, the repo's own benchmark control run: wasm/js ≈ 1.00× . One million points, same algorithm, same machine. Parity. I go looking for the measurements behind the claim. Half of it holds up: the 32× JS comparison has a result file (1.72ms against 55.52ms, real). The claimed additional 5-10× from WASM has nothing behind it, and the repo's own control run contradicts it. That afternoon set the shape of the whole launch: before anything shipped, every performance claim would either get a measurement behind it or get deleted. Three beliefs didn't survive. Each one got a public retraction, written up as an ADR in the repo. vizcrush is a set of data primitives for browser visualization (downsampling, binning, spatial indexing, streaming sketches), written in Rust, compiled to WebAssembly, with a pure-JS core behind the same API as a fallback and explicitly selectable backend. It went open source this week: the repo and the book are public, and all 11 packages are live on npm. npm install @vizcrush/core @vizcrush/downsample This is a launch story about turning benchmark results into product policy: claims, documentation, and WebGPU policy follow the measurements, while WASM dispatch stays availability-based pending further investigation. One scope note before the data. Every result here is workload-specific: LTTB (Largest-Triangle-Three-Buckets, the downsampling algorithm that picks, per bucket, the point that best preserves the visual shape of the line) is downsampling, the stats kernel is a reduction, and bin2d is histogramming. Which backend wins is algorithm- and engine-dependent, so none of what follows is a library-wide WASM-versus-JS verdict. It is three specific workloads measured on specific engines, with the claims and documentation follo

This latest lawsuit is particularly broad and homes in on accusations of illegal piracy.

A technology problem rarely stays a technology problem for very long. A platform may need to scale. A product may need to move faster. An organisation may want to introduce AI, modernise an ageing estate, improve customer experience or launch something entirely new. The first instinct is usually to look at the technology itself. Which architecture should change? Which platform should we buy? Which team should build it? Which tools should we introduce? Those questions matter. But they are often not the questions that determine the outcome. At Cralgo, one pattern keeps appearing across technology work: the harder part is frequently the system around the technology. The problem behind the problem Consider a programme that appears to have an execution issue. Delivery is slow. Priorities keep changing. Teams disagree. Decisions are repeatedly reopened. The roadmap keeps moving. It is easy to conclude that the engineering team needs to become faster. But look closer and the constraint may be somewhere else: ownership is unclear; priorities are not genuinely ordered; product and technology are working from different assumptions; architecture decisions are being made without business context; teams are executing tasks without understanding the judgement behind them; governance exists, but only as reporting; critical decisions remain dependent on a small number of people. None of these are purely technical problems. They are questions of judgement, ownership, capability, sequencing and governance. Technology simply makes them visible. Better technology does not automatically create better execution Organisations understandably invest heavily in platforms, cloud, data, automation and AI. But technology increases capability only when the organisation around it can use that capability well. A new platform cannot decide what should be prioritised. A new operating model diagram cannot create ownership. A dashboard cannot replace judgement. AI cannot resolve ambiguity that an orga

Protecting data that must stay secret ten years from now is a problem for today : an adversary can capture your encrypted traffic now and decrypt it once quantum capability exists ( harvest now, decrypt later ). Quipu is a free hybrid post-quantum encryption library for data at rest: it combines proven classical cryptography with the new kind, so that it only breaks if both fall at once. Pure Rust, and why Quipu started out aiming at several languages: a Rust core with a C ABI on top and bindings for Python, Node and Go. It worked, but the lesson was clear: maintaining a stable C interface plus four bindings, each with its own packaging and interoperability tests, was complexity that did not pay for itself against the real goal — protecting data at rest — and it widened the attack surface with unsafe we did not want. Today Quipu is pure Rust : memory safe, no garbage collector, no first-party unsafe . And for people who do not write Rust, it ships as a native Python wheel via PyO3 — the surface that non-Rust users actually need. One codebase, one thing to audit. It is the same philosophy that guides the rest: where good cryptography exists, reuse it; simplicity is a security decision, not a convenience. Installation cargo add quipu # Rust pip install quipu-crypto # Python (native wheel, PyO3) Encrypt and decrypt in Python import quipu # Symmetric, with a passphrase blob = quipu . encrypt_stream ( b " sensitive data " , " my-passphrase " ) assert quipu . decrypt_stream ( blob , " my-passphrase " ) == b " sensitive data " # Post-quantum, for a recipient pub , sec = quipu . generate_keypair () # X25519 + ML-KEM-1024 c = quipu . encode_to_recipient ( b " secret " , pub ) assert quipu . decode_as_recipient ( c , sec ) == b " secret " What is underneath Encryption: XChaCha20-Poly1305 (authenticated AEAD). Key derivation: Argon2id (brute-force resistant) + HKDF. Post-quantum: X25519 + ML-KEM-1024 for keys; Ed25519 + ML-DSA-87 for signatures. Security level: NIST category 5

Most performance optimization focuses on making each operation faster. HKD Kernel approaches a different question: What if most of those operations did not need to execute at all? I’ve been working on HKD Kernel, a native C library for exact sparse and incremental computation. The target workload looks like this: A large computation has already been evaluated. Only a small subset of the inputs changes. The dependency structure tells us which results can actually change. HKD recomputes those affected regions instead of repeating the entire calculation. The important word is exact. The optimized result must equal the result of full recomputation. What the benchmark measures The repository contains reproducible benchmarks comparing full recomputation with the HKD incremental path. Across the benchmark suite currently documented in the repository, the measured mean speedup is roughly 18,000x. That requires an important qualification: This does not mean HKD makes arbitrary programs 18,000x faster. It means that on workloads with sparse changes and reusable state, avoiding redundant computation can produce extremely large reductions in work. That distinction is important enough that I built the repository around reproducibility rather than a black-box benchmark claim. What HKD Kernel is not HKD Kernel: does not replace the macOS XNU kernel does not modify CPU microcode does not disable SIP does not change processor ALU hardware It is a user-space native computation library. Where I think this model is useful The workloads I’m most interested in include: dependency graphs incremental build systems large simulations with sparse updates optimization systems financial/risk recomputation logistics and scheduling cached numerical pipelines The real question is not “how fast is HKD?” It is: How much of your current computation is being repeated even though the inputs affecting it never changed? I’d especially like developers to try to break the benchmark assumptions or suggest w

Vijay Pande — who left a16z's roughly $4 billion biotech practice last year to start the much smaller, AI-native VZVC — talks about why biology is finally shifting from a "discovery" science to an "engineering" one, why clinical trials are still brutally expensive, and why he thinks open, shared datasets (not walled-off ones) are what will actually let AI transform medicine.

If you're wondering if CarPlay works over Wi-Fi or Bluetooth, the answer is yes. It uses both at different times.

Cloudflare KV for Session Caching in Multi-Tenant FastAPI: Reducing PostgreSQL Load Without Redis Complexity Every SaaS I've built hits the same wall: session validation on every request hammers PostgreSQL. You add Redis, suddenly you're managing another service, debugging cache invalidation, and paying for redundancy you don't need. Then I discovered Cloudflare KV sits between your users and origin server. It's not a replacement for PostgreSQL—it's a read cache positioned at the edge that auto-syncs on writes. For multi-tenant session and permission data, this eliminates 60–80% of auth-related database queries without the operational complexity of Redis. This is the approach I use in CitizenApp. Here's why it works, how to implement it, and where I nearly broke production. Why Cloudflare KV Beats Redis for Session Caching Redis requires: A separate service deployment (Render, AWS ElastiCache) Connection pooling logic in your app Cache invalidation strategies you'll get wrong Monitoring for memory leaks and eviction Cost that scales with your hot data size Cloudflare KV requires: A binding in your edge worker (one line of config) Simple key-value storage at 200+ edge locations Automatic TTL expiration Zero operational overhead—Cloudflare manages it Here's my honest take: I prefer KV because I don't have to think about it. My workers validate JWT tokens and fetch session data from KV before even routing to my FastAPI origin. Cache misses flow to PostgreSQL and write back to KV. No connection pools. No eviction policies. No debugging Redis memory fragmentation at 3 AM. The tradeoff? KV is slower than in-memory Redis (ms vs microseconds), but for session lookups happening 200+ times per second per user at global scale, edge-cached responses beat origin-fetched ones every time. Architecture: Edge Validation + Origin Sync Your flow looks like this: Request hits Cloudflare Worker Worker checks KV for session + permissions (hit = serve immediately) KV miss → fetch from Fas