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Its latest Pro headset can mix between four inputs and has improved wireless connectivity over the originals.

The hot new roguelite twin-stick shooter lives up to the hype.

This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry . You are migrating a 50,000-message Slack workspace to Zulip. Somewhere around message 31,000 the import dies with KeyError: 'ts' . Annoying, but here is the uncomfortable part: that is the lucky outcome. The unlucky one is "ts": "NaN" , where nothing dies, nothing warns, and your company's message history quietly comes out in the wrong order. TL;DR: Zulip's Slack importer used float(message["ts"]) unguarded, both as a sort key and as date_sent . One message with a missing or malformed ts aborted the entire import; a non-finite value like "NaN" did not even raise, it silently broke the sort. My fix ( zulip/zulip#39813 ) skips such messages with a warning and requires ts to parse to a finite float via math.isfinite . The regression test fails with KeyError: 'ts' on the old code. Project Overview Zulip is an open-source team chat server (Django/Python, ~25k stars) with an unusually strict engineering culture: near-total backend test coverage, strict mypy, and a commit discipline of "each commit is a minimal coherent idea". The code I touched lives in zerver/data_import/ : the subsystem that converts exports from Slack, Microsoft Teams, and Mattermost into Zulip's format. This subsystem has one property that should shape every line in it: the input is another tool's output. Import is a long batch process over data of arbitrary quality, and the admin running the migration has no way to "fix" what Slack's export tool produced. A pipeline that dies on record 31,207 of 50,000 is strictly worse than one that skips record 31,207 with a warning. Bug Fix or Performance Improvement get_messages_iterator() in zerver/data_import/slack.py streams every message of the export, sorting each day's messages by timestamp: yield from sorted ( messages_for_one_day , key = get_timestamp_from_message ) where the sort key was simply: def get_timestamp_from_message ( message : ZerverFieldsT ) -> float : retur

ImgProxy is an open-source image-processing server — resize, convert, and transform images on the fly via URL parameters, ideal as a caching layer in front of a CDN or web app. This guide builds ImgProxy from source on Ubuntu, runs it as a systemd service behind Nginx with TLS, walks through its URL processing options, and secures it with signed URLs. Prerequisites: an Ubuntu server, a domain A record (e.g. imgproxy.example.com ), non-root sudo user. Install ImgProxy ImgProxy uses libvips for image processing; this builds it from source with Go. $ sudo add-apt-repository ppa:dhor/myway $ sudo apt update $ sudo apt install libvips-dev -y $ sudo snap install --classic --channel = latest/stable go $ git clone https://github.com/imgproxy/imgproxy.git $ cd imgproxy $ sudo CGO_LDFLAGS_ALLOW = "-s|-w" go build -o /usr/local/bin/imgproxy Create the environment config: $ sudo touch /usr/local/bin/imgproxy.env $ sudo nano /usr/local/bin/imgproxy.env IMGPROXY_BIND = :8080 IMGPROXY_NETWORK = tcp IMGPROXY_READ_TIMEOUT = 10 IMGPROXY_WRITE_TIMEOUT = 10 IMGPROXY_WORKERS = 2 IMGPROXY_REQUESTS_QUEUE_SIZE = 0 IMGPROXY_QUALITY = 100 IMGPROXY_PREFERRED_FORMATS = webp,jpeg,png,gif,avif IMGPROXY_LOG_FORMAT = "pretty" IMGPROXY_LOG_LEVEL = "INFO" IMGPROXY_WATERMARK_URL = https://example.com/watermark.png IMGPROXY_WATERMARK_OPACITY = 1 Key settings: IMGPROXY_WORKERS should be ~2× your vCPU count; IMGPROXY_REQUESTS_QUEUE_SIZE=0 means unlimited queueing; IMGPROXY_WATERMARK_URL points at whatever image you want overlaid when watermarking is enabled. Point ImgProxy at the config and test: $ export IMGPROXY_ENV_LOCAL_FILE_PATH = /usr/local/bin/imgproxy.env $ cd $ imgproxy WARNING [2024-05-28T00:40:42Z] No keys defined, so signature checking is disabled WARNING [2024-05-28T00:40:42Z] No salts defined, so signature checking is disabled INFO [2024-05-28T00:40:42Z] Starting server at :8080 Stop it with Ctrl+C once verified, then set it up as a service. Run ImgProxy as a systemd Service $ sudo useradd

A few days ago I fixed update_article , one of the tools in this repo's MCP server, because it had a nasty shape: it took a bare integer article_id , PUT whatever fields you gave it straight to the DEV.to API, and if the id was wrong or hallucinated, it would silently overwrite a live published post with nothing left behind to show it had happened. The fix added a fetch-before-write diff and a JSONL audit log: _ARTICLE_UPDATE_LOG = " logs/article_updates.jsonl " def _log_article_update ( article_id , before , fields_changed , after ): os . makedirs ( os . path . dirname ( _ARTICLE_UPDATE_LOG ), exist_ok = True ) entry = { " article_id " : article_id , " fields_changed " : sorted ( fields_changed ), " url " : after . get ( " url " )} for field in fields_changed : entry [ f " { field } _before " ] = before . get ( field ) entry [ f " { field } _after " ] = after . get ( field ) with open ( _ARTICLE_UPDATE_LOG , " a " ) as f : f . write ( json . dumps ( entry ) + " \n " ) The whole point of that function is durability. "Zero trace" was the bug; a JSONL file that records before/after state on every write was the fix. I verified the logging logic itself with an offline unit test against fake before/after states and moved on, same as the diff field. What I never checked is whether logs/article_updates.jsonl outlives the process that writes it. Checking whether the trace actually exists anywhere logs/ isn't in .gitignore — I checked, it's not there. So nothing is actively hiding it. But not-hidden isn't the same as tracked: $ git log --all --oneline -- 'logs/*' $ ls logs/ ls: cannot access 'logs/': No such file or directory Empty output from the first command, across every branch and every commit this repo has ever had (50 commits, not a shallow clone — git rev-parse --is-shallow-repository is false ). Nothing has ever touched logs/ . The directory doesn't even exist right now. Not because anything deleted it — because nothing has ever run update_article in an environment

Ghost is an open-source publishing platform with built-in newsletters, memberships, subscriptions, ActivityPub federation, and Tinybird-powered web analytics. This guide covers two install paths on Ubuntu 24.04: Ghost-CLI for a traditional host install, and Docker Compose for a containerized deployment with analytics. Prerequisites: an Ubuntu 24.04 server, non-root sudo user, a domain A record (e.g. ghost.example.com ). Option A: Install with Ghost-CLI Install Node.js Ghost requires Node v22 LTS — check compatible versions before installing elsewhere. $ curl -fsSL https://deb.nodesource.com/setup_22.x -o nodesource_setup.sh $ sudo -E bash nodesource_setup.sh $ sudo apt install -y nodejs $ node -v Install and Configure MySQL $ sudo apt install -y mysql-server $ mysql --version $ sudo mysql_secure_installation Walk through the prompts: enable password validation ( y ), pick strong policy ( 2 ), remove anonymous users ( y ), restrict root to localhost ( y ), drop the test database ( y ), reload privileges ( y ). $ sudo mysql mysql > CREATE DATABASE ghost_db ; mysql > CREATE USER 'ghostuser' @ 'localhost' IDENTIFIED BY 'Your_password2!' ; mysql > GRANT ALL PRIVILEGES ON ghost_db . * TO 'ghostuser' @ 'localhost' ; mysql > FLUSH PRIVILEGES ; mysql > EXIT ; Install Nginx $ sudo apt install -y nginx $ sudo ufw allow 'Nginx Full' $ sudo systemctl status nginx Install Ghost $ sudo npm install ghost-cli@latest -g $ sudo mkdir -p /var/www/html/ghost $ sudo chown $USER : $USER /var/www/html/ghost $ sudo chmod 775 /var/www/html/ghost $ cd /var/www/html/ghost $ ghost install The installer prompts for: Blog URL : https://ghost.example.com MySQL hostname : localhost MySQL username/password/database : from the setup above Set up Nginx? : y Set up SSL? : y (installs acme.sh ) Email for SSL : your address Set up Systemd? : y Start Ghost? : y Manage the Config $ nano /var/www/html/ghost/config.production.json $ cd /var/www/html/ghost $ ghost restart Or via systemd (replace ghost-example

phpBB is an open-source forum application for building discussion communities — user registration, moderation, permissions, and multiple boards in one interface. This guide deploys phpBB on Ubuntu 22.04 with an external MySQL database, an Apache virtual host, and Let's Encrypt TLS. Prerequisites: an Ubuntu 22.04 server with the LAMP stack installed, non-root sudo user, an external MySQL database, a subdomain A record (e.g. phpbb.example.com ). Create the Database $ mysql -h your-db-host -P 3306 -u dbadmin -p mysql > CREATE DATABASE phpbbdb ; mysql > USE phpbbdb ; mysql > CREATE USER 'phpbbuser' @ 'localhost' IDENTIFIED BY 'securepassword' ; mysql > GRANT ALL ON phpbbdb . * to 'phpbbuser' @ 'localhost' ; mysql > FLUSH PRIVILEGES ; mysql > EXIT ; Install phpBB 1. Install PHP modules: $ sudo apt install php-mysql php-xml php-mbstring -y 2. Download and extract — check the releases page for the current version: $ wget -O phpbb.zip https://download.phpbb.com/pub/release/3.3/3.3.11/phpBB-3.3.11.zip $ unzip phpbb.zip $ sudo mv phpBB3 /var/www/html/phpbb 3. Set ownership and permissions: $ sudo chown -R www-data:www-data /var/www/html/phpbb $ sudo find /var/www/html/phpbb -type d -exec chmod 755 {} \; $ sudo find /var/www/html/phpbb -type f -exec chmod 644 {} \; Configure Apache $ sudo nano /etc/apache2/sites-available/phpbb.conf < VirtualHost *:80 > ServerAdmin admin@example.com DocumentRoot /var/www/html/phpbb ServerName phpbb.example.com < Directory /var/www/html/phpbb > Options FollowSymlinks AllowOverride All Require all granted </ Directory > ErrorLog ${APACHE_LOG_DIR}/phpbb_error.log CustomLog ${APACHE_LOG_DIR}/phpbb_access.log combined </ VirtualHost > $ sudo a2ensite phpbb $ sudo a2enmod rewrite $ sudo systemctl restart apache2 Secure phpBB 1. Firewall: $ sudo ufw status $ sudo ufw allow 22 && sudo ufw enable $ sudo ufw allow 80/tcp $ sudo ufw allow 443/tcp $ sudo ufw reload 2. TLS via Let's Encrypt: $ sudo apt install snapd -y $ sudo snap install --classic certbot

Dungeons & Dragons is no stranger to franchise crossovers, and now it's kicking off its biggest multiverse initiative to date, starting with a World of Warcraft expansion. Wizards of the Coast (WotC) has announced that D&D: World of Warcraft - a new sourcebook that brings Blizzard's Azeroth setting to the popular tabletop roleplaying game - […]

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Feeling the pull of a new clean machine? We’ll help you make sense of Dyson’s whirlwind vacuum lineup.

Cape Fear has always been a thriller about paranoia. And in 2026 there are few things less trustworthy than technology. Apple TV's recent remake of Cape Fear is "inspired" by the 1991 film of the same name (Martin Scorsese and Steven Spielberg even returned as executive producers on the project), which itself is a remake […]

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Sony has received a lot of backlash from PlayStation fans since announcing that it's killing physical game disc production, but that hasn't swayed its decision. During Sony's latest earnings call, chief financial officer Lin Tao said that while the company "put in a lot of thought and time" when considering the opposition put forward by […]

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July 2026 is over. The month that opened with AUTONOMOUS 2026 and WAIC 2026 running simultaneously on opposite sides of the Pacific closed with the sector tallying what it built. The number that defines the period is $55.8 billion in robotics funding across H1 - nearly double the prior full-year record. But the more durable signal from this week is operational rather than financial: Neura Robotics has a confirmed deployment date at a Schaeffler facility in December, NVIDIA's simulation-to-real pipeline is now functional at production scale, and five simultaneous shifts are reshaping factory floors right now, not in 2027. The questions that drove the first half of 2026 - does Physical AI work, is the funding real, will the robots actually arrive - are no longer interesting. H2 starts with harder ones. Stats: Value Description $55.8B Robotics funding raised in H1 2026, nearly double the prior annual record $8.6B Humanoid startup funding in H1 2026 alone, 1.8x all of 2025 December 2026 Confirmed first deployment of Neura Robotics humanoids at Schaeffler's German facilities 5 Simultaneous operational shifts reshaping factory floors identified in the mid-2026 analysis Neura Robotics Has a Deployment Date: December 2026 in a Schaeffler Factory Most Physical AI deployment announcements are directional. "We are partnering with X to explore robotics in our facilities" is a press release. A confirmed month and a specific facility is a contract. Neura Robotics confirmed that Schaeffler - one of the key investors in its $1.4 billion Series C alongside Amazon, Nvidia, Qualcomm, and the European Investment Bank - plans to deploy Neura's humanoids in its German facilities in December 2026 . Schaeffler manufactures precision bearings and components for electric vehicles, operating in environments where dimensional tolerances are measured in micrometers. Deploying a humanoid robot in that context is a fundamentally different challenge than warehouse pick-and-place or automotive sequ

Approval workflows do not fail only at the model layer. In a production agent, the more common failure is losing the exact paused state that a reviewer was supposed to approve. Why can a saver decide LangGraph approvals? A saver can decide LangGraph approvals because approvals depend on persisted graph state, not just a chat transcript. LangGraph interrupts pause execution inside a node, store the current state, wait until a human decision arrives, and resume the intended checkpoint with Command(resume=...) ; without a saver tied to the same thread_id , the reviewer handoff can resume the wrong point or fail to resume at all . Quick Answer: LangGraph approvals work only when the paused run is checkpointed and resumed through the same thread_id . LangSmith adds the audit layer: each trace is capped at 25,000 runs, and SaaS trace retention is documented as 400 days from ingestion . The practical rule is simple: put the checkpoint before the irreversible action. That means email sends, file writes, deploys, database mutations, support-ticket edits, purchases, payments, outbound messages, and code execution should pause before the side effect. LangChain's HumanInTheLoopMiddleware follows the same shape: inspect tool calls after model output but before execution, then allow an approve, edit, or reject decision against a checkpointed run . "Interrupts are designed to pause graph execution and resume from the saved point," according to the official LangGraph interrupts documentation . For developers, the important part is operational: the approval gate is only trustworthy if the persisted checkpoint and reviewer decision refer to the same run. LangSmith then gives the team evidence that the gate is behaving correctly. Its observability model groups execution into projects, traces, runs, and threads, which lets teams audit latency, rejection reasons, retry count, tool failures, and reviewer decisions instead of debugging from logs alone . The seed video is useful background

Idempotency, vendor failure, monitoring that catches the invisible outages, and the tradeoffs nobody warns you about, lessons from scaling payments infrastructure. Most software fails quietly. A page renders slowly, a recommendation is a little off, a report is stale by an hour. Users shrug and move on. Payments doesn't work like that. When payments break, someone's money is in a place neither of you can account for, and the clock starts ticking on their patience. There's no graceful degradation. Either the money moved, or it didn't, and someone needs to know which. I've spent a good chunk of my career building and scaling payments infrastructure, and it has quietly rewired how I think about engineering in general. Here's what stuck. 📋 The short version # Lesson One-line summary 1 Idempotency You will receive the same request twice. Design for it. 2 Vendor failure Gateways are vendors. Ask "when," not "if." 3 Monitoring Never learn about an outage from a customer. 4 The unglamorous stuff Ledgers, reconciliation, state machines, refunds. 5 Tradeoffs Every lesson above fights at least one other. 1. 🔁 Idempotency isn't a feature. It's a foundation. The first hard lesson: you will receive the same request twice. Not "might." Will. A client times out waiting for your response and retries. A user double-taps a button on a bad connection. A queue consumer crashes after processing but before acknowledging. A gateway sends the same webhook four times because it never got a 200 back. None of these are exotic failure modes, they're Tuesday. If your system treats every incoming call as a new instruction, every one of those scenarios becomes a double charge. And a double charge isn't a bug you fix quietly in the next release. It's a support ticket, a refund, a reconciliation entry, and a customer who now checks their statement every time they use you. The fix is conceptually simple and operationally demanding: every operation that moves money must be uniquely identifiable and sa

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . The most expensive bug I fixed this year was not in the code. It was in the documentation, and it had been shaping what everyone believed the code did. The setup HydraDNS is an open-source DNS security gateway I build in Go. Router points at it, it filters every DNS query on the network against a 92k-domain blocklist, blocks the bad ones, forwards the rest. Before putting it on anyone else's network I wanted a real number for what one box could take, so I sat down with dnspyre and a rule I had written for myself: every number becomes a sales claim or a fix ticket. No number, no claim. Our feature sheet said the blocklist was backed by a Bloom filter, sub-millisecond lookups. Here is the uncomfortable part: at every load this system had ever run, that claim was indistinguishable from the truth. Normal-traffic latency sat at one or two milliseconds. There was nothing to doubt, because nothing observable disagreed. The first ceiling The redline test capped at about 500 queries per second. Odd, but fine, until I noticed the cap would not move. Blocked queries capped at ~500. Cached queries that never touch upstream also capped at ~500. Two paths doing completely different work, same wall, CPU sitting under 30% on a 22-core dev machine. That combination is worth memorizing: when two very different code paths hit the same ceiling and the CPU is bored, the bottleneck is not in either path. It is in something they share. Ours was the blocklist check. IsBlocked ran a SQL COUNT against the 92k-row table on every single query, because the check sits in front of the cache, so even cache hits paid for it. Every one of those reads was serialized through a single SQLite connection, MaxOpenConns=1 , which was also absorbing the async write traffic from query logging. Engine self-latency under load: p50 of 50ms, p99 of five full seconds. For DNS. And the Bloom filter? I went looking for it so I could