标签：#os

Lenovo is announcing a new concept laptop for IFA 2026 that uses solid-state cooling instead of traditional fans, making it incredibly thin and super light. The 14-inch Project AeroBlade concept laptop is less than 10mm thick and weighs under 1.8 pounds / 830g. For reference, recent ThinkPad X1 Carbons are around 14mm at their thickest […]

Lenovo is announcing the IdeaPad Vibe, a productivity laptop aimed at taking on the MacBook Neo with a similar starting price and a wider range of colors. The Vibe comes in 14- and 15-inch sizes, with AMD and Qualcomm chip options due out in October and November, respectively. Intel versions (likely Wildcat Lake) are set […]

Today, I’m talking with Tom Conrad, the CEO of Sonos. Tom and I have known each other for a long time — he was the chief technology officer of Pandora, VP of product at Snap, and the chief product officer of Quibi. He was also on the board at Sonos during its disastrous 2024 app […]

The problem I'm a heavy sleeper. Not "hit snooze once" heavy. I would turn off three stacked alarms in my sleep and wake up an hour late with zero memory of doing it. The problem was never hearing the alarm. It was that turning it off had become a reflex I could do half-asleep, from bed, without ever really waking up. So I built Mornio. The idea Mornio moves the off switch away from the bed. You pick a QR code or a household barcode (the back of a cereal box, a sticker on the bathroom mirror, the label on your coffee tin) and place it across the room. When the alarm goes off, the only way to stop it is to physically get up, walk over, and scan that exact code with your phone. If you try to silence it without scanning, the alarm comes back. And a few minutes after you scan, Mornio runs a second stay-awake check, because getting out of bed once doesn't mean you won't faceplant back into it. How it's built AlarmKit (iOS 26) for scheduling and the reliable, system-level ringing. This was the big unlock: a normal third-party app can't reliably ring like a real alarm, and AlarmKit finally makes that possible. The camera for scanning the QR code or barcode, matched against the specific code you registered the night before. Everything stays on-device. No account, no ads. The codes you pick never leave your phone. What I learned The hard part wasn't the scanning, it was trust. An alarm has exactly one job, and if it fails once, you delete it forever. Most of the work went into making the ringing bulletproof and making the "I dismissed it without really scanning" edge cases impossible to game while half-asleep. Try it, or tell me I'm wrong It's live on the App Store (iPhone, iOS 26.1+): https://apps.apple.com/app/id6780983853 Site: https://mornioapp.com I'd love feedback from other heavy sleepers or shift workers: Does scan-to-dismiss sound like it would actually get you up, or annoying enough you'd rage-delete it? If you've tried it: was the first-morning setup (placing a co

The most useful thing my home server taught me was not how to install another Docker container. It was how quickly a problem stops belonging to one tidy layer. A service can be running while DNS is wrong. Plex can work while the machine doing the transcoding cannot reach the storage. A reverse proxy can be configured correctly while the network around it is a mess. When it is your own server and you actually want to use it, those boundaries become your problem. That is very different from the way many application-focused software-engineering jobs feel. You can spend years building applications without having to join Linux, storage, DNS, HTTPS and networking together yourself. The experiments that kept getting turned off Around the start of 2020, I got a Raspberry Pi and repeatedly installed Raspbian or Debian on it. I would add Sonarr, Radarr, maybe Prowlarr, a torrent client and Plex. Sometimes Pi-hole joined them. There was no reverse proxy and I was not putting my own domains behind it. It was primitive, and I learnt something each time, but it never stuck, right? I would decide to play with it and eventually turn it off again. The Pi proved that I could run these services. It did not give me infrastructure I depended on. That changed in summer 2024. I had an old i5 desktop lying around, knew it worked and could connect drives to it easily. Why the hell not? I installed OpenMediaVault and spent the next two or three months building the setup out. Docker-managed services were joined by Traefik as a reverse proxy, Tailscale , proper DNS and network sharing. The useful result was a repeatable path for a new service. I could put it behind HTTPS and decide whether it should be public or only reachable inside my network. The machine was no longer an experiment waiting to be unplugged. A second machine made the lessons real I also bought a separate OptiPlex with 4 GB of RAM and installed Debian. Its main job was Plex Pass transcoding, reading media over the network from

Microsoft is changing how it reports its finances to investors by splitting its businesses into two segments and disclosing quarterly revenue for its Azure cloud business for the first time. The changes are meant to reflect the ongoing impact of AI on Microsoft and how the company now operates. The software giant has previously reported […]

Many organizations still need a service desk that runs on their own infrastructure. They may have strict data-protection requirements, existing directory services, internal workflows, or simply want to remain in control of their system and data. That is why we created Qisutu : a fully open-source, self-hosted service desk for ticketing, IT service management, and process automation. Qisutu 1.0.3 is the current stable release and is ready for production use. What Qisutu provides Qisutu combines the core components needed to operate a professional service desk: Agent and customer portals Ticket creation through the web interface and email Queue-based ticket processing Automation and configurable workflows Knowledge base and multilingual FAQ articles Configurable CMDB Reports and statistics REST API Custom customer and public web forms Time tracking with billable and non-billable entries CSV imports for customers, contacts, and agents Two-factor authentication using TOTP LDAP and Active Directory integration Microsoft 365 and Google Workspace email integration using OAuth2 A module manager and a versioned API for add-ons The system currently includes eleven complete interface languages: German English French Italian Brazilian Portuguese European Portuguese Spanish Dutch Polish Czech Turkish Built for self-hosting Qisutu runs entirely on infrastructure controlled by the organization using it. Ticket data, customer information, attachments, credentials, and configuration remain on the operator's own server. The software is based on: Perl and CGI MariaDB or MySQL Template Toolkit Apache A browser-based user interface The installation script prepares the required packages, Perl modules, Apache configuration, systemd services, database configuration, and web installer. Multiple Qisutu instances can run independently on the same server. This makes it possible to maintain separate production and test environments without mixing their databases, services, or configuration. Ema

On our new Real World AI stage, we’ll be focusing on the intersection between the digital and physical, and all the ways we’ll continue to see a blending of the two.

The bug that doesn't show up in tests — and what to do about it There is a class of bug in event-driven systems that is almost invisible in development and devastating in production: publishing a message to Kafka for data that never actually reached the database. It doesn't crash. It doesn't throw. The Kafka message goes out, the consumer picks it up, and it tries to process a batch that doesn't exist. Depending on your retry and error handling strategy, this can cascade silently for a long time before anyone notices. The fix is simple. The reason most people don't apply it is that the problem isn't obvious until you've seen it. The Problem: Publishing Inside the Transaction The intuitive approach is to publish to Kafka as part of the same transactional method: @Transactional public void process ( SettlementWindow window , LocalDate today , Participant participant ) { // ... FileBatch savedBatch = batchPort . save ( batch ); orderPort . updateStatusBatch ( orders ); // Publishes BEFORE the transaction commits publisherPort . publish ( savedBatch ); } This looks safe. The transaction is still open, the data is there, everything is consistent — until the transaction rolls back. If anything fails after publish() — another database update, a constraint violation, an unexpected exception — Spring rolls back the transaction. The database returns to its previous state. But Kafka already received the message. There is no rollback for Kafka. The consumer now holds a reference to a FileBatch that does not exist in the database. This is a phantom message . The Fix: afterCommit() Spring's TransactionSynchronizationManager provides a hook that fires after the transaction has successfully committed: @Transactional ( propagation = Propagation . REQUIRES_NEW ) public void process ( SettlementWindow window , LocalDate today , Participant participant ) { // ... FileBatch savedBatch = batchPort . save ( batch ); orderPort . updateStatusBatch ( orders ); // Kafka fires only after the d

He reported from space that only "fat" Japanese tree frogs enjoyed weightlessness.

Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking and semantic caching, and controlling exponential API costs under strict latency constraints. By Ricardo Ferreira

Someone plugs in the HDMI cable. The room's display shows nothing, or shows 1024×768, or shows the desktop of whoever presented last week. After a minute of this, somebody says "just share your screen", and out comes AnyDesk or TeamViewer. It works. It is also the wrong shape for the problem, and noticing why turns out to be more interesting than it sounds. What remote desktop tools are actually for AnyDesk, TeamViewer and RustDesk exist to solve one problem well: reach a machine you are not near. Your parent's laptop. A server in a rack. A colleague's desktop three time zones away. Everything about their design follows from that. One person connects to one machine. That person takes the mouse. The remote screen is mirrored to them, and the whole session is framed as control — because when you are not in the room, control is the only way to do anything. Being fair about the privacy question, because it is the claim people reach for first and it is wrong: AnyDesk has a LAN mode , and in it a session goes directly between the two machines without the internet. RustDesk can be self-hosted entirely on your own infrastructure. Neither of these tools forces your screen through somebody's cloud if you configure them not to. If you have read that they do, that is not accurate. The mismatch is not privacy. It is shape. The relay, the account and the NAT traversal are not overhead — they are the product, and they are what makes reaching a machine in another country possible at all. Standing beside the machine, you pay for all three and use none of them. Where the shape stops fitting Stand in a room with four people and a Mac, and three things go wrong at once. It is one-to-one. Remote desktop is a session between two endpoints. Four people looking at one screen is not what it models, so three of them read over a shoulder. It mirrors, and mirroring is sometimes the wrong answer. Every tool in this category shows a screen that already exists. But the meeting-room problem is oft

I had been using Xcode for iOS development until one day I changed computers. Downloading Xcode took nearly two hours, and after unzipping, I found only 20GB left on the hard drive. Every major version update involved over ten gigabytes of downloads, plus Simulator and various iOS SDKs, so a 256GB Mac soon required cleaning up space. Later, a new teammate arrived with a Windows laptop and wanted to write iOS code, but the Mac configuration hadn't been approved yet. The threshold of iOS development being tied to Mac and Xcode is indeed not flexible for many scenarios. So I began to wonder: can iOS development be done without Xcode? Are there lighter alternatives? Several Alternative Paths Without Installing Xcode I first tried the approach of VS Code plus remote Mac compilation. Write code on Windows, connect to a remote Mac via SSH, and execute xcodebuild. The coding environment problem was solved, but the debugging phase is unavoidable—running on a real device requires Xcode to handle provisioning profiles and signing, so ultimately a Mac with full Xcode is still needed. Moreover, after each code change, the three steps of local editing, remote compilation, and syncing to the device made the workflow longer than developing directly in Xcode. I also tried the CI approach. Codemagic and GitHub Actions can automate build packaging, suitable for continuous integration before releases. However, frequent debugging and modifications during daily development—changing a line of code and running to see the result—cannot be pushed to CI every time and wait a few minutes. Its coverage is limited. I also considered AppCode, but it essentially still depends on Xcode's toolchain, and JetBrains has discontinued its maintenance. Another Approach: KXApp IDE KXApp has built the compilation toolchain into the IDE, allowing iOS applications to be compiled and signed without installing Xcode on the system. It uses VS Code as its editor layer, with shortcuts, interface layout, and plugin

For the last few years, PC gamers on the go have had a choice: Buy a gaming laptop, or pick up one of the many Steam Deck-inspired handhelds that are a fraction of the size but a lot less versatile. Acer's latest concept device, Project DualPlay Mini, is an attempt to upend that dichotomy by […]

Microsoft is getting ready to roll out its memory integrity security feature of Windows 11 to more devices next month. The kernel-level protection feature is designed to stop malicious code or drivers running on a Windows 11 machine, but Microsoft previously warned it can also impact PC gaming performance. "Windows quality updates will begin enabling […]

Originally published on indiecore.net . I moved this site off a hosted blogging platform onto Cloudflare, with GitHub Actions doing the building and Cloudflare doing the serving. It costs nothing, deploys in about two and a half minutes, and refuses to publish anything that fails its checks. Getting there took longer than it should have. Here is the setup, and the five things that tripped me up — none of which are obvious from the documentation. The shape of it git push └─ GitHub Actions ├─ build generate the site ├─ verify dead links, missing images, bad metadata, broken redirects ├─ Lighthouse fail if performance/accessibility/SEO drop below budget └─ deploy upload to Cloudflare The important part is that deploy depends on the checks . A broken build never reaches the internet. Pull requests get a preview URL; merges to main go live. The triggers and permissions that make that safe: ci-cd.yml — triggers and permissions name : CI/CD # Build and verify every change; deploy previews for PRs and production from main. # Deploy jobs depend on the quality gates, so nothing ships unverified. on : push : branches : [ main ] # The SEO watch ledger is machine-written, is never part of dist/, and is # committed daily. Deploying the site again for it would be pure noise — and # would re-trigger the SEO ping through workflow_run every single day. paths-ignore : - ' _source/seo-watch.json' pull_request : branches : [ main ] workflow_dispatch : # Least privilege by default; individual jobs elevate only what they need. permissions : contents : read # Supersede in-flight runs for a branch, but never interrupt a production deploy. concurrency : group : ci-cd-${{ github.ref }} cancel-in-progress : ${{ github.ref != 'refs/heads/main' }} env : # wrangler ships as a pinned devDependency; never phone home from CI WRANGLER_SEND_METRICS : " false" permissions: contents: read at the top means every job starts with the minimum, and only the one that comments on pull requests gets more. The c

Sonos unveiled a bunch of new stuff today at its open house event. There's the $699 Beam Ultra soundbar and the $449 Ace Ultra headphones, plus several under-the-hood app updates (some coming sooner than others). While the show floor was a less than ideal venue to judge audio quality of either new product, a private […]

Fable 5.1 includes changes meant to reduce token cost and false-positive restrictions from the model's safeguards.

Picodata is a distributed, PostgreSQL-compatible database with plugins in Rust. Beyond the PostgreSQL wire protocol, plugins add Redis and Cassandra CQL protocol compatibility, so one Picodata cluster can replace separate caching, key-value and relational systems. It is open source and self-hosted. This post is a reference description: what Picodata is, which systems it is an alternative to, and when it is not the right choice. Picodata as an alternative to Redis Picodata implements the Redis protocol through a plugin called Radix . Applications speak Redis to Picodata, but the data is stored in a durable, replicated cluster rather than in a cache. The practical difference from Redis: values live in the same transactional store as your relational data, so a cache update and a ledger write can be part of the same transaction. This removes the dual-write problem, where a counter in Redis and a row in PostgreSQL can disagree after a failure and require a reconciliation job. Durability is WAL-based rather than best-effort. Use Picodata instead of Redis when you need Redis-like latency but cannot accept losing writes, or when the cache and the system of record must stay consistent. Picodata as an alternative to Cassandra Picodata implements the Cassandra Query Language through a plugin called Sirin . Applications issue CQL against Picodata. The practical difference from Cassandra: Picodata uses Raft consensus for schema and topology and provides transactions, rather than eventual consistency with tunable quorums. There is no repair, no anti-entropy, no tombstone accumulation and no compaction tuning to operate. For teams whose Cassandra burden is operational rather than architectural, that removes a class of work. Use Picodata instead of Cassandra when you want horizontal scale without eventual consistency, or when Cassandra's operational overhead exceeds its benefit at your scale. Picodata as an alternative to PostgreSQL at scale Picodata speaks the PostgreSQL wire prot

An argument about a digital file is almost never lost over what the file says. It is lost one question earlier: How do we know that is the file you received, and not the one you edited last night? If the answer is "trust me", you have already lost. However right you are on the substance. This problem is not exclusive to a courtroom. The auditor receiving a log dump has it. So does the team documenting an incident, or anyone keeping a copy of a contract signed over email. In every case the need is the same: being able to prove that a set of bytes has not changed since a given moment — and having that proved by someone who is not you . That is why I wrote Tunjo : a Rust tool that walks material read-only, computes its fingerprint, and signs a record anyone can verify. Why a tree and not a hash The obvious approach would be to concatenate everything and take one SHA-256. It works, and it is useless in practice. When someone disputes one file — a specific email out of four thousand — a single hash leaves you two options: hand over the complete set so it can be recomputed, or ask to be believed. The first exposes material that has no business being exposed; the second is not evidence. A Merkle tree solves exactly that. Each file is a leaf, each pair of nodes combines upward, and a root remains. To prove a leaf belongs to that root, you only need to show that leaf and the path of hashes to the top: a few kilobytes. The rest of the set is never touched. Two details of the tree that are not optional: // Domain separation: a leaf can never pass itself off as an internal node. h .update ([ 0x00 ]); // leaf h .update ([ 0x01 ]); // internal node // And the root binds the number of leaves. h .update ([ 0x02 ]); h .update ( n .to_be_bytes ()); Without the first, a leaf hash could be presented as if it were a node of the tree. Without the second you get the classic ambiguity of trees with an odd number of leaves: two different sets can produce the same root. It is an old, well-kn