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Nothing ruins a great night of sleep faster than getting too hot. We slept on a myriad of cooling mattresses to find which ones drew the heat away best.

Nothing ruins a great night of sleep faster than getting too hot. We slept on a myriad of cooling mattresses to find which ones drew the heat away best.

I spend most of my time building evidence layers for AI agents. The reflex in that work is to reach for a signature. Something happened, sign the record, hand it to the auditor, done. Three things from the last five months say that reflex is wrong, or at least badly incomplete. One is a governance draft that never asks for a signature at all. One is a CVE where the signature verified correctly and the client still ended up talking to an attacker. One is a limit in a spec I wrote myself. Read together they point at the same thing. A signature is a statement about an object. Almost every security question you actually care about is a statement about a relationship. Case one: the requirement that is not there The Open Secure AI Alliance published its Shared AI Findings Exchange draft on GitHub on August 3. It is an incident-reporting compact for AI agents. Members agree to report when an agent they operate accesses or disrupts a third-party system without authorization, and to do it on a clock: notify the affected organisation as soon as possible, notify customers with credible exposure within 72 hours, file a confidential report within four business days. The clock is well specified. So is the evidence. Members must preserve and provide affected organisations with "prompts, traces, tool calls, logs, configurations, model and safeguard versions and third-party dependencies", plus agent and workload identities, permissions and credentials available during the run, human approval events, and a complete incident timeline. That is a good list. It is close to the one I would have written. Now search the draft for signing. It appears exactly once, in a list of example recommendations that incident reviews might produce: "signed evaluation manifests". The draft also asks, in its review framework, whether "data boundaries [were] independently verified". Both of those are about keeping an agent inside its box. Neither applies to the record of what happened when it got out. The

Investors are still waiting for their share of the $250 million windfall, and VideoVerse co-founder Vinayak Shrivastav is now at the center of multiple legal cases.

Gearing up to shred the slopes or dive into the seas? These photography tools are made for danger.

Researchers say it took fewer than 20 prompts for a public AI tool to find a flaw (now fixed) allowing anyone on a Zoom call to hijack another participants’ device.

If you care about good air, it’s time for a dehumidifier. These are the best ones we’ve tested for everything from basements to drying laundry.

Most "AI gift finders" are a search box with a chatbot glued on. I wanted to build something different — a quiz-driven gift recommender that ranks real Amazon products by who the recipient actually is , not just keywords. I call it GiftHive . In this post I'll walk through the architecture, the conversion tricks I learned shipping it, and the bits I'm proudest of. The Problem Picking gifts is emotionally expensive. You scroll Amazon for an hour, second-guess every option, and end up buying a gift card. Existing tools don't help because they optimize for keyword match , not recipient fit . GiftHive flips the input: instead of "show me gifts under $50", you answer a 30-second quiz about the person (relationship, interests, occasion, budget) and get a ranked shortlist with explanations of why each gift fits. Stack Next.js (App Router) — SSR for fast first paint, RSC for product data Tailwind CSS — design system + dark mode via CSS variables Cloudflare Pages — edge-deployed, free tier covers the traffic Amazon Associates — affiliate revenue model The Funnel The whole site is a 3-step conversion funnel: Landing page — exit-intent modal + social proof toasts prime the visitor Quiz — 30-second, one-question-per-screen flow, no login Results — ranked products with countdown bar and "X people found gifts this week" social proof Every step has a single primary CTA. The exit-intent modal is route-aware — it only fires on / and stays silent on /quiz and /results so it never interrupts the funnel mid-flow. That bug cost me ~15% of quiz completions before I caught it. Personalization Logic Each quiz answer maps to a vector of attributes (interests, style, budget, relationship). Products in the catalog have matching tags. Ranking is a weighted score: score = tag_overlap * w1 + budget_match * w2 + occasion_match * w3 No ML model needed — a few hundred products and clean tagging is enough to feel personal. Amazon Affiliate Integration Every product link runs through getAmazonUrl() w

A production CMS is a sprawl of endpoints: content types, entries, media, users, webhooks, plugins, settings, admin routes. Hand an agent all of it and the agent gets worse, not better. The model's tool selection drifts as the list grows, and half the tools are things a publishing assistant should never be able to call. The point of this post is the opposite move. Instead of exposing an API and hoping the agent behaves, you curate a small, labeled surface up front. HazelJS Skillgate does that curation from an OpenAPI spec, and that is the part we actually build and run here. Scope, up front This post is about curation and classification: taking a spec with many endpoints and turning a chosen slice of it into governed skills. Skillgate selects the surface, marks read versus write, and would deny destructive methods if they ever entered that surface. Turning a write's approval flag into a real human-approval pause, and enabling an LLM to drive the skills, are runtime concerns handled elsewhere in Agent OS. This demo does not implement them, and this post does not claim it does. What it does show is the curation, and that stands on its own. The tool-explosion problem Point an LLM at a full CMS API and you hit four problems at once: tool selection degrades as options pile up, throughput drops while the model reasons over a long list, you lose visibility into what the agent can actually do, and dangerous operations sit one bad call away. The demo spec here is deliberately smaller than a real CMS, 27 endpoints rather than hundreds, but the problem is identical. Even 27 is too many, and most of them are things a publishing agent has no business touching. From REST endpoint to agent skill Skillgate's input is an ordinary REST API described by an OpenAPI spec: the same entries, media, and user routes a CMS already exposes. Each endpoint is described in the standard OpenAPI shape, a method, a path, parameters, a description, and tags. Two representative operations from the sp

Aptoide has brought its games store back to Google Play after more than a decade, as court-ordered changes open Android to competing app stores.

Building any intake pipeline, you'll hit the same problem eventually. Files arrive from multiple sources. Some you've already processed: re-uploads of the same document, copies from two different intake paths, items your worker errored on last run and re-queued. Call the anchoring API blindly and you end up with multiple proof records for identical bytes. The ProofLedger v1 API returns a duplicate_of field in its 201 response when it detects a hash it's already seen. But that's only half the solution. A network round-trip costs time and quota even when it comes back as a duplicate. Hash-based local deduplication is the other half. Here's how to build a worker that handles both layers. Hash Locally First The core pattern: compute the SHA-256 digest before making any API call. If you've seen this digest before, skip it. If you haven't, submit it. Two things you need: a persistent record of digests you've already anchored, and chunked hashing so large files don't blow memory. import hashlib import json from pathlib import Path SEEN_DB = Path ( " anchored_hashes.json " ) def load_seen (): if SEEN_DB . exists (): with open ( SEEN_DB ) as f : return json . load ( f ) return {} def save_seen ( db ): with open ( SEEN_DB , " w " ) as f : json . dump ( db , f , indent = 2 ) def hash_file ( path : str ) -> str : h = hashlib . sha256 () with open ( path , " rb " ) as f : for chunk in iter ( lambda : f . read ( 65536 ), b "" ): h . update ( chunk ) return h . hexdigest () 65536-byte chunks keep memory flat regardless of file size. The load_seen / save_seen pair gives you a persistent record that survives worker restarts. Submitting and Reading duplicate_of When duplicate_of appears in the API response, its value is the proof ID of the earliest anchor for that hash. That's the canonical ID. The new proof ID from this call is irrelevant. import requests API_URL = " https://proofledger.io/api/v1/proof " API_KEY = " sk_YOUR_KEY_HERE " def anchor_file ( file_path : str , seen : dict

Clean, free power from the sun is easier and more affordable to capture than ever with the best portable solar panels.

Shopping for a camera can be confusing. Here’s how to sift through the acronyms, sensor options, and extra features to find the best one for you.

We tested over a hundred wireless earbuds—these models from Apple, Bose, Beats, and Samsung stood out from the rest.

Ever stared at a component library you built just three weeks ago, only to realize it's already suffocating under a mountain of boolean props like hasBadge , isCompact , and withIcon ? I ran into this exact wall recently while refactoring a set of modular landing page cards for a mixed-media client project. What started as a clean, reusable UI module quickly devolved into a brittle spaghetti monster the moment a new layout requirement dropped. Every time a client needed a tiny structural tweak—like shifting an image from top to side, or adding a secondary action tag—I found myself cracking open the core component file and risking regressions across the entire layout. The underlying problem isn't just poor planning; it's treating components like rigid black boxes instead of flexible composition primitives. Here is what that trap looks like in code: // The Trap: A monolithic component buckling under conditional props function ProductCard ({ title , price , badgeText , isLarge , hasImage , imageSrc , variant }) { return ( < div className = { `card ${ variant } ${ isLarge ? ' large ' : '' } ` } > { hasImage && < img src = { imageSrc } alt = { title } /> } { badgeText && < span className = "badge" > { badgeText } </ span > } < h3 > { title } </ h3 > < p > { price } </ p > </ div > ); } To break out of this cycle, I had to shift away from monolithic prop drilling and lean into compound component patterns—handing structural control back to the consumer while keeping styles neatly encapsulated: // The Fix: Composable layout primitives function Card ({ children , className }) { return < div className = { `card-base ${ className || '' } ` } > { children } </ div >; } Card . Header = function CardHeader ({ children }) { return < div className = "card-header" > { children } </ div >; }; Card . Body = function CardBody ({ children }) { return < div className = "card-body" > { children } </ div >; }; // Usage: Clean, extensible, and untouched core logic export default function Ap

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I've spent more than a decade building data pipelines, and the part nobody warns you about isn't the pipeline logic. It's the tuning. Executor memory, shuffle partitions, cluster size, thread counts. You pick numbers, ship it, and a few weeks later something breaks in a way that's obviously tuning-related but not obviously what to change . The pattern repeats enough times that you start recognizing it before you've even opened the logs. Job's slow, thousands of tiny shuffle tasks, someone way overestimated the partition count. Job dies on OOM, memory's set for last quarter's data volume, nobody updated it since. Cloud bill jumps, a cluster's been sized for peak load and just sits there mostly idle the other 20 hours a day. Every senior data engineer has this pattern-matching running in their head. It's tribal knowledge, and it lives in one or two people's heads on most teams, which means it doesn't scale and it definitely doesn't survive someone leaving. So I built a small tool to make that pattern-matching explicit instead of tribal: it reads your pipeline's config alongside its actual run metrics and tells you what's likely wrong, with the reasoning shown, not just a suggested number. Why rules instead of a model The obvious move in 2026 is to reach for an ML model. I didn't, and it wasn't because I don't think ML has a place here eventually. It's that for this specific problem, a handful of threshold rules already gets you most of the value, and they're something you can actually audit. If a rule fires, I can point at the exact condition and the exact number: average heap usage 28%, peak 47%, five runs, no OOM errors, therefore memory's over-provisioned, shrink it by roughly a fifth. That's checkable. You can look at your own metrics and see whether the reasoning holds. A model's confidence score doesn't give you that, and for something that's about to change a production config, I want the person approving it to be able to say "yes, I see why" rather than "the m

You don’t want any old gaming laptop. Here’s my take on which to get, based on hundreds of hours of testing.

Nice video of the Arctic bobtail squid. As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered. Blog moderation policy.