Thermacell has launched Liv 2.0, the next generation of its Wi-Fi-connected smart mosquito protection system. It features new hardware and can cover a larger area, and Thermacell says its formula can now deter no-see-ums. But it's also more expensive and requires professional installation. Liv 2.0 uses the same setup as the original Liv - a […]

Nintendo is trying to bring back Fox's mojo with a beautiful remake of the series' best game.

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Four days into a new supplier's first batch, my invoice extraction agent had filed 31 documents with amounts shifted by a decimal. Nothing raised an error. The downstream system accepted every record. The agent returned a 200 each time. The demo had run on five clean PDFs. Clear fonts, properly formatted dates, consistent layout. The extraction agent pulled vendor name, amount, due date, line items. Every field populated, every output valid. I ran it for the stakeholder meeting and it looked exactly like something you would ship. Three months in, the agent had processed around 800 invoices without complaint. Then a new supplier switched to scanned documents. Slightly rotated, thin fonts, OCR doing what it could on degraded source material. The model found text that resembled amounts and dates, and returned confident structured output. 1,247.50 read as 12,475.0. A due date resolved to a valid date three years in the future. The confidence was the problem. The model had no mechanism to say it was uncertain. It just answered. Nobody caught it for four days. What I built after The problem was not the model. The model did what it was designed to do. Find structure in text and return it. The straight pipeline from input to output had no gate in it. The fix was not more prompting or a better model. I added a validation layer between the agent output and the downstream system. It runs synchronously, takes about 80ms, and checks four things: Every required field is non-null. Amounts parse as positive numbers within a configured range for that supplier type. Dates fall within a 90-day future window. Extracted totals are consistent with line item sums, within a small tolerance. Anything failing a check routes to a review inbox instead of the queue. A human looks at it, corrects it if needed, marks it resolved. The system logs which check triggered and what the input looked like. In the first week after deployment, the layer caught 23 documents out of about 1,400. Eleven were b

Why I built an alternative to Kubernetes overkill. Let’s talk about infrastructure efficiency. Kubernetes is the undisputed industry standard for container orchestration, and for massive, complex enterprises, it’s irreplaceable. But for small-to-medium deployments or distributed multi-host edge environments, it often feels like an operational nightmare. You install a massive orchestration layer, only to realize it's still missing the basics for actual operations. To get it production-ready, you have to layer on external tools for Ingress routing, plus a heavy stack for observability, health metrics, and SLOs. Suddenly, your infrastructure consumes more resources than your actual applications. I believe in radical software minimalism. That’s why I’ve been developing Gubernator (gbnt), an open-source, lightweight distributed container orchestrator written entirely in Go. Unlike traditional platforms, Gubernator bakes essential Site Reliability Engineering (SRE) and traffic management right into its core architecture: Native Reverse Proxy: No complex third-party Ingress controllers. Gubernator automatically manages routing—like dynamically hooking up web containers to an Ingress layer (e.g., Caddy)—making exposure seamless and native. Built-in SRE & Observability: Out-of-the-box support for health monitoring, metrics, and SLO tracking natively leveraging OpenTelemetry and Prometheus. Zero-Bloat State Management: It swaps out heavy external key-value stores for an embedded, rock-solid SQLite architecture, ensuring multi-host consistency with a near-zero footprint. Gubernator is designed for engineers who want robust, predictable, and resilient orchestration without the overhead and cognitive load of K8s. If you are passionate about minimalist backend architecture, systems engineering, or streamlined DevOps, I’d love for you to take a look at the blueprint and documentation: Explore the project: https://mario-ezquerro.github.io/gubernator/ How do you handle container orc

TL;DR : Crypto arbitrage windows on liquid pairs now close in under 100 ms. A REST polling loop typically takes 1–1.5 seconds round-trip. WebSocket delivers the same data in 20–100 ms. If you're still polling REST endpoints for orderbook data in 2026, you're missing the majority of opportunities — not because your strategy is wrong, but because your data plane is fundamentally too slow. This post walks through the math, shows a benchmark I ran on a handful of major exchanges, and provides production-grade Python code for a WebSocket client that handles reconnects, heartbeats, and orderbook reconstruction. 1. The numbers that broke REST polling When I started writing crypto arbitrage bots a few years ago, polling Binance's REST API every 500 ms was perfectly acceptable. Spreads were wide, arbitrage windows lasted multiple seconds, and the orderbook for BTCUSDT moved slowly enough that a half-second-old snapshot was still tradeable. In 2026, the same approach doesn't work. Here are the numbers as they stand today: Metric Value Median crypto arbitrage window on liquid pairs 30–80 ms Window closes in under 100 ms ~90% of cases REST round-trip latency (request → response → JSON parse) 1.0–1.5 seconds WebSocket update delivery latency (push from exchange to client) 20–100 ms The math is brutal. A 100 ms window cannot be caught by a 1500 ms poll. By the time your REST response arrives, the orderbook you're reading is 15 cycles stale. You're not "slow" — you're not even in the same temporal universe as the event you're trying to react to. 2. Why REST is fundamentally slow REST APIs over HTTPS carry overhead that adds up: TCP handshake — three packets to establish, typically 50–150 ms on intercontinental hops. TLS handshake — another full round-trip, 30–100 ms. HTTP request/response — the actual data exchange. JSON parse — depending on payload size, 5–50 ms. Rate-limit budget — most exchanges cap REST to 10–20 requests per second per IP. Polling faster gets you banned. Yes,

Kyle Lexmond explains how to handle the high-pressure environment of severe production outages. He discusses the critical distinction between mitigation and root-cause resolution, sharing personal experiences from harrowing incident rooms. He shares valuable operational strategies on overcoming cognitive overload, establishing blameless cultures, and optimizing systems for faster recovery. By Kyle Lexmond

Meta-Optimized Continual Adaptation for coastal climate resilience planning with zero-trust governance guarantees It started with a nagging feeling of inadequacy. I was deep into a research project on adaptive AI for infrastructure planning, studying how reinforcement learning agents could optimize sea-wall placements and evacuation routes. The models worked—beautifully, in fact—on static datasets. But the moment I fed them real-time satellite imagery of a rapidly eroding coastline or a sudden storm surge, they stumbled. They forgot previous strategies, overfit to the new event, or, worse, made decisions that violated basic safety constraints. I realized then that the problem wasn't just about better AI; it was about trust and adaptation in the face of chaos. My exploration of this challenge led me down a rabbit hole of meta-learning, continual learning, and cryptographic governance. What emerged was a framework I now call Meta-Optimized Continual Adaptation (MOCA) with zero-trust governance guarantees—a system designed not just to learn, but to learn how to learn in dynamic, high-stakes coastal environments, all while ensuring that every decision is auditable and tamper-proof. This article shares that journey, the technical breakthroughs, and the hard-won lessons from my experiments. Technical Background: The Three Pillars of MOCA The core insight behind MOCA is that coastal climate resilience planning requires three seemingly contradictory properties: Continual adaptation – The system must update its models as new data streams in (e.g., sea-level rise, storm frequency, erosion patterns) without catastrophic forgetting. Meta-optimization – It must learn the learning algorithm itself, so that adaptation becomes faster and more sample-efficient over time. Zero-trust governance – Every model update and decision must be cryptographically verifiable, with no single point of failure or authority. In my research, I found that existing approaches tackled these individually

We Scanned 100 AI Repos on GitHub. Here's What We Found. A drone firmware project with 3× more stars than the real one. A crypto protocol that turned GitHub into a points farm. A README with 6,289 stars and 2 commits. As a developer turned architect, I used to treat GitHub stars as a proxy for trust. More stars meant more legitimate, fewer reasons to question before cloning. That instinct got me thinking. So I built TrustStar , audited hundreds of repos, and found that some people had figured out that instinct before me. Here's what the data showed. Case 1: The Airdrop Farm (QuipNetwork) 🔴 DANGEROUS Repository Stars Forks Fork/Star ratio hashsigs-py 11,200 9 0.0008 hashsigs-rs 11,300 42 0.0037 hashsigs-ts 11,300 31 0.0027 hashsigs-solidity 11,300 33 0.003 quip-protocol 11,645 159 0.014 ethereum-sdk ~11,400 72 0.006 cpp-sdk ~11,300 44 0.004 Six repos in completely different languages (Python, Rust, TypeScript, Solidity, C++) all converging on exactly ~11,300 stars. Projects with genuinely different audiences don't do that. The mechanism was on their own website: "Each GitHub repo star earns 5 QUIP points." QuipNetwork launched a crypto airdrop in early February 2026. Users who wanted QUIP tokens starred every repo in the organization. 11,000 stars in 48 hours, after five months of zero activity. The tell: dashboard.quip.network has 2 stars. nodes.quip.network has 2 stars. The repos they forgot to include in the airdrop show the real numbers. This is the first documented instance of a crypto airdrop using GitHub as a gamification layer. These aren't bots. They're real users who just wanted tokens. Case 2: The Typosquat (ShlkOfTheRa/scarab-osd) 🔴 DANGEROUS. The most dangerous case in this dataset. ShikOfTheRa/scarab-osd is a legitimate drone flight controller firmware project. 468 stars, built over 10 years. ShlkOfTheRa/scarab-osd , one character different, was created March 3, 2026. Byte-for-byte identical code. Twelve days later, 1,485 stars purchased in a 90-minute

Third in a series of papers tracking learning rules vs. human fMRI (THINGS dataset, V1–IT, N=3 subjects). Previous finding: untrained CNNs match backprop at V1. This paper asks: when does training break that, and does the learning rule matter? Setup: RSA alignment measured at 8 checkpoints (epochs 0, 1, 2, 5, 10, 20, 30, 40), 5 seeds per rule, same architecture throughout. Main findings: BP drops 90% of V1 alignment after one epoch (r: 0.102 → 0.011, p = 0.031, consistent across all 5 seeds). FA drops 49%. PC and STDP drop only 25–31% and stabilise. By epoch 40: PC (r = 0.064) > STDP (0.059) >> BP (0.022) ≈ FA (0.019). Cohen's d > 5 for PC/STDP vs BP: extremely consistent across seeds. Opposing trend at LOC: BP shows a small increase in object-selective cortex alignment (+0.011) while local rules show nothing. Suggests a fundamental trade-off: global error signals build higher representations but destroy early ones. Degradation rate tracks error signal globality: exact gradients (BP) > random feedback (FA) > local prediction errors (PC, STDP). Limitations worth noting: 5 seeds caps permutation test resolution at p ≈ 0.031 Training on 32×32 CIFAR-10, evaluated on 224×224 THINGS, resolution/domain shift is a confound LOC increase not tested for significance, treated as suggestive Paper: arxiv.org/abs/2605.30556 Companion: arxiv.org/abs/2604.16875 Code: github.com/nilsleut Curious whether anyone has seen similar dynamics in larger architectures, the prediction would be that deeper models show the same pattern but more slowly. submitted by /u/ConfusionSpiritual19 [link] [留言]

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Installs your API into a customer's codebase in one command Discussion | Link

Turn photos into 3D Gaussian Splats on your Mac Discussion | Link

There are only 3 LLM API protocols, but unlimited providers running the same protocol. Separate protocol from identity — protocol is code, provider is data — and complexity drops from N×M to N+M. 300 lines of TypeScript. Zero dependencies. The problem isn't "it doesn't work." It's "it won't tell you it broke." In May, I built a Claude Code skill called unblind . I use DeepSeek as my daily driver, but it can't see images. So unblind forwards images to Mimo and OpenAI's vision APIs. The MVP had two providers. A few dozen lines of if-else. It worked. Then I noticed something more unsettling: an expired API key — no warning. A network hiccup — no retry. A missing permission — silently skipped. This tool didn't fail. It quietly stopped working without telling you. I added Phase 0 self-healing, circuit breakers, persistent caching, and a security sandbox. Now unblind wouldn't fail silently. But then I noticed something else. The circuit breaker doesn't care if you're calling a vision API or a translation API. The cache doesn't care if the response is an image description or OCR text. The error normalization doesn't care whether the other end is Mimo or OpenAI. A universal provider infrastructure, trapped inside a vision skill. First attempt: follow the ecosystem, hit the ceiling The largest similar project in the ecosystem is vision-support, with 19 providers. The pattern is standard—base class + subclasses, GoF Template Method. I followed it for v2.0. class BaseProvider { async analyzeImage ({ image , prompt , options }) { const { url , body , headers } = this . _buildRequest ( image , prompt , options ); const res = await apiRequest ( url , { body , headers }); return { content : await this . _parseResponse ( res ), model : this . _model }; } } class MimoProvider extends BaseProvider { ... } // 54 lines class OpenAIProvider extends BaseProvider { ... } // 45 lines class GeminiProvider extends BaseProvider { ... } // ~50 lines One subclass per provider. I expanded unblin

Amazon bucked its usual tradition of having Prime Day in July. Prime Day 2026 is happening in June, kicking off in just a few weeks. Prime members will get access to many deals starting June 23rd at 3:01AM ET through June 27th at 3:01AM ET. As with Amazon’s previous events, you don’t need to be […]

According to every product demo from the last four years, planning a trip is a killer use case for AI. Just tell it where you're going, they all promise, and your chatbot / agent / other buzzword will exhaustively search travel options, read up on all the fun things to do, check all the local […]

Review AI edits like stacked PRs at generation time. Discussion | Link

Blockchain performance is determined by consensus rules. There is no acceleration layer within the protocol. One of the most common misconceptions about blockchain technology is the belief that transaction speed can be dramatically increased through special tools, hidden settings, or external services. While applications can improve user experience and optimize how information is presented, they cannot change the fundamental rules that govern how blockchain networks process transactions. At the core of every blockchain is a consensus mechanism. Consensus is responsible for ensuring that independent participants agree on the validity and order of transactions before they become part of the permanent ledger. Whether a network uses Proof of Work, Proof of Stake, or another consensus model, transaction processing remains tied to the protocol rules that all participants follow. Every transaction moves through a structured lifecycle: submit → validate → confirm Submission introduces the transaction to the network. Validation ensures that the transaction complies with protocol requirements and contains legitimate data. Confirmation establishes agreement across the network and records the transaction as part of the blockchain. These stages are not optional. They are essential to maintaining consistency and trust within decentralized systems. Because blockchain performance is governed by consensus, there is no protocol-level acceleration layer that can bypass validation or force immediate finality. No application can override consensus. No service can remove verification requirements. No external process can alter the execution sequence established by the protocol. What users often interpret as slow performance is usually the result of network conditions such as congestion, validator workload, transaction prioritization, or fee market activity. These factors can influence confirmation times, but they do not change the underlying rules of the system. Blockchain networks are d

Keyboard accessibility is one of the most important — and most neglected — aspects of web accessibility. An estimated 2.5 million Americans have motor disabilities that prevent mouse use. If your site can't be operated entirely by keyboard, you're excluding them completely. The Four Core Principles WCAG 2.2 Principle 2 (Operable) contains the keyboard requirements: 2.1.1 Keyboard (AA): All functionality must be operable via keyboard 2.1.2 No Keyboard Trap (AA): If focus moves into a component, it must be possible to move it out 2.4.3 Focus Order (AA): If page can be navigated sequentially, order must be logical and predictable 2.4.7 Focus Visible (AA): Any keyboard-operable UI must have a visible focus indicator 2.4.11 Focus Appearance (AA, new in 2.2): Focus indicator must meet size and contrast requirements Testing Without Automated Tools Start with the basic keyboard test: Unplug (or ignore) your mouse Press Tab to move forward through interactive elements Press Shift+Tab to move backward Use Enter/Space to activate buttons, links, checkboxes Use arrow keys for radio groups, menus, sliders Use Escape to close dialogs and menus Any element you can't reach or activate? That's a WCAG 2.1.1 failure. The Most Common Keyboard Failures Custom dropdowns and menus // ❌ Keyboard inaccessible function Dropdown ({ items }) { return ( < div onClick = { toggle } className = "dropdown" > { items . map ( item => ( < div onClick = { () => select ( item ) } > { item . label } </ div > )) } </ div > ); } // ✅ Fully keyboard accessible function Dropdown ({ items }) { return ( < div role = "combobox" aria-haspopup = "listbox" aria-expanded = { isOpen } tabIndex = { 0 } onKeyDown = { handleKeyDown } // handles Enter, Space, Arrows, Escape className = "dropdown" > < ul role = "listbox" > { items . map (( item , i ) => ( < li key = { item . id } role = "option" tabIndex = { - 1 } aria-selected = { i === activeIndex } onKeyDown = { e => e . key === ' Enter ' && select ( item ) } > { item

I've been running AI infrastructure for startups long enough to know one painful truth: when you're iterating fast, GPU costs will eat your runway before your product finds product-market fit. Last quarter alone, I watched a promising seed-stage company burn through $12,000 on self-hosted inference before they had 100 paying users. That's not scale — that's a funeral. Let me share what I've learned about making open-source models production-ready without bleeding cash. This isn't theory. This is what I've deployed across three startups, and it's saved us roughly 70% on inference costs while keeping our iteration speed at hyperscale. The Real Cost of Self-Hosting (Spoiler: It's Not Just GPUs) Here's the thing nobody tells you about self-hosting. The GPU rental is just the headline number. The real cost — the one that kills startups — is the hidden infrastructure tax. Model GPU Requirements Cloud Rental (Monthly) On-Prem (Amortized) 7-9B 1× A100 40GB $400-800 $200-400 13-14B 1× A100 80GB $600-1,200 $300-600 27-32B 2× A100 80GB $1,000-2,000 $500-1,000 70-72B 4× A100 80GB $2,000-4,000 $1,000-2,000 200B+ 8× A100 80GB $4,000-8,000 $2,000-4,000 Cloud pricing based on Lambda Labs / RunPod / Vast.ai reserved instances. But here's the kicker — and I learned this the hard way after two months of burning cash on a 32B model that got 50 requests per day: Hidden Cost Monthly Estimate GPU servers (idle or loaded) $400-8,000 Load balancer / API gateway $50-200 Monitoring & alerting $50-200 DevOps engineer time (partial) $500-3,000 Model updates & maintenance $100-500 Electricity (on-prem) $200-1,000 Total hidden costs $900-4,900/month That DevOps line alone is brutal. At scale, you need someone who can handle model updates, handle crashes at 3 AM, and optimise inference. At a startup, that's either your CTO (me) or a contractor who costs $150/hour. Neither is sustainable when you're trying to ship. The Break-Even Math That Changed My Architecture Decisions I ran these numbers befor