标签：#ice

There’s no better time to get a Kindle than during Amazon's own sale event.

Your face called, and it’s low-key offended you might trust TikTok more than WIRED.

Introduction This is the second part of sharing my journey to create a microservices-based backend system, in the previous part, I introduced the system and its services. In this part I am going to mention the main things that happened with me from then until now. Transitioning to online DBaaS platforms The first main thing was to transition my databases from my local device to accessible over the internet. I used Neon and MongoDB Atlas . Although performance-wise it is better to deploy the database on the same server that the whole system so all services can access the databases without network overhead , This solution is better from a point, that is avoiding consuming the free VM instance that I got to host my main services, as with DBaaS platforms I will not require storage for databases or additional overhead to handle the DBMS on a free small resources-constrained VM. Observability Observability is one of the main concerns in any system, it gives the ability for the system to expose what happens inside it without any need to guess and manually trace the code to determine where the error may happened. I utilized OpenTelemetry , as I found it is most used and accepted tool to log, trace and collect metrics about the system and the traffic. Also, I loved that it is not related to specific framework, that is .NET by the way, I loved the idea that it is standalone tool. But logging and tracing and metrics collection will not be beneficial if we do not see it actually and can achieve monitoring from those telemetry data, so I used Grafana Cloud to export the telemetry data to some place that is accessible online and a tool that can generate dashboards and visualizing for the metrics ( Prometheus ), and UI that can show all the telemetry data easily. Deployment Until this point, we have a system that is fully functional and have consistent docker configuration for its services via Docker Compose, and it database is already there and available. Now this is the point at

The boat-agent stack here runs on a prime directive: if there's something usable out there, improve it; build our own only as a last resort. So when we needed a SignalK MCP server, the honest first move wasn't to write one — it was to evaluate the one that already exists. VesselSense/signalk-mcp-server (TypeScript, MIT) is good work. It exposes SignalK to an agent through a single execute_code tool: the model writes JavaScript, the server runs it in a sandboxed V8 isolate ( isolated-vm ), and only the result comes back. Its README claims a 90–96% token reduction versus traditional named MCP tools — 2,000 tokens down to 120 for a vessel-state query, 13,000 down to 300 for a multi-call workflow. Those numbers are plausible, and they line up with the broader industry result that code execution beats tool-calling on token efficiency for complex multi-step work. We read it, ran the numbers against our own agent, and kept our discrete-named-tool signalk-mcp anyway — then harvested three of VesselSense's ideas into our roadmap. This post is that evaluation: the two philosophies, why the obvious-sounding win doesn't bind for a voice-first agent, and a decision framework you can reuse before you adopt-or-build your own MCP server. This is a design-reasoning post, not a debugging saga, but it maps to the same arc: a question, the dead-end that looks like an obvious yes, and the call that actually held. The question Two SignalK MCP servers, two genuinely different designs: VesselSense/signalk-mcp-server sailingnaturali/signalk-mcp ───────────────────────────── ─────────────────────────── one tool: execute_code discrete named tools: → agent writes JavaScript read_sensor(path) → runs in a V8 isolate battery_state(bank) → queries SignalK, returns depth_state() only the result get_route() get_local_time() TypeScript / Node + isolated-vm list_paths(prefix) claims 90–96% fewer tokens get_active_alarms() Python, end-to-end The adopt-vs-keep question: does the token-efficiency win bin

I was convinced these devices were just clutter. I was proven wrong.

AWS released Blocks in public preview, an open-source TypeScript framework where each Block bundles application code, local mocks, and AWS infrastructure. Designed for AI agents to write correct backends from the start, it runs locally without an AWS account and deploys the same code to Lambda, DynamoDB, Aurora, and Bedrock with zero changes. By Steef-Jan Wiggers

There’s no better time to get a Kindle than during Amazon's own sale event.

Power companies are pushing aggressive time-based use pricing. Here's how a regular consumer can benefit.

Stainless-steel pans may lack nonstick coatings, but they’re unfussy, they sear well, and they’re built for a lifetime of hard work.

Sure, anyone can use OpenAI’s chatbot. But with smart engineering, you can get way more interesting results.

After adding one to my home, here's why you might want a home battery, how they work, and what to look for, plus some installation tips.

Using Claude Fable 5 or Mythos 5 on Amazon Bedrock requires opting into provider_data_share, sending prompts and outputs to Anthropic for 30-day retention with human review. Previous Bedrock models kept inference data inside the AWS boundary. Three days after launch, Anthropic asked AWS to revoke access to both models citing US export control compliance. By Steef-Jan Wiggers

This is a submission for the June Solstice Game Jam ( https://dev.to/challenges/june-game-jam-2026-06-03 ) What I Built I built Solstice Assassin, a tactical stealth-action game set inside a collapsing digital mainframe. You play as Alex, a self-aware digital anomaly trapped inside the Solstice Grid, a secure virtual system originally created by Alan Turing. Alex awakens with almost no memory except one critical piece of information: «CREATOR: ALAN TURING» The catch? Today is the Summer Solstice, the longest day of the year, and at midnight the system will execute a complete purge of all dynamic data. Alex has one final day to recover lost memories, outsmart the system's security forces, and find a way to survive. Gameplay combines tactical movement, stealth, procedural level generation, enemy AI, and resource management. Players infiltrate security sectors, collect awareness data, avoid or eliminate hostile "Cleaners", and manage powerful abilities such as Dash, Cloak, Radar, and Time Warp. The Solstice theme isn't just part of the story—it directly affects gameplay. Throughout each mission, the system progresses through different Solar Phases: 🌅 Golden Dawn ☀️ High Noon 🌇 Crimson Sunset 🌑 Eclipse Each phase changes visibility, stealth effectiveness, enemy behavior, and the overall tactical landscape. High Noon makes players highly exposed, while Crimson Sunset rewards stealth and careful planning. By the time Eclipse arrives, the entire system begins breaking down. My goal was to create a game where the passing of the longest day isn't just a background theme but something the player constantly feels through the mechanics themselves. Video Demo https://youtu.be/48RM1iTZjOg?si=JYz86wVzNMjeZmmw In the video I demonstrate: Tactical movement and infiltration Dynamic Solar Phase transitions Enemy AI behavior and pathfinding Awareness recovery and progression systems Alex's abilities AI-generated mission briefings and voice interactions Procedural level generation End-o

Apple’s fall macOS release will let you build Shortcuts by typing what you want to happen. But Claude Code and Codex users don’t have to wait.

📝 Originally published in Japanese on Zenn. This is the English version. Canonical: https://zenn.dev/uya0526_design/articles/satellite3_metrics-rationale 📚 This is satellite article #3 in my "Read-Aloud Speed Meter dev log" series. For the whole picture, see the main article . Where This Sits The read-aloud speed meter converts speaking speed into an evaluation label like "slightly fast," and stagnation rate into one like "few." Those labels ultimately become the foundation for Claude Haiku's feedback. So — on what basis did I draw the thresholds (the dividing lines)? This article digs into that "basis." The short answer from my research: I couldn't find a paper that defines an academic threshold for "N characters/min = fast/slow." This is a record of how I drew the lines honestly once I'd learned there was no firm basis. More than the metric numbers themselves, I believe being transparent about why I chose those numbers is what makes an evaluation app trustworthy. 💡 I'm an ex-Java engineer learning TypeScript in public. This one is mostly about design decisions. Why Obsess Over the "Basis"? An evaluation app passes judgment on the user: "your reading is slightly fast." Once you're passing judgment, if you can't explain "why we can say that," it's just guesswork. This app in particular passes the labels straight to Claude Haiku to generate coaching. If the foundational label has an unclear basis, the feedback built on top of it is a castle on sand. So I decided to nail down the basis for the thresholds first. Two things to research: The judgment basis for speaking speed (characters/min) The judgment basis for stagnation rate (the proportion of silence) As it turned out, these two had completely different kinds of basis. Speed Thresholds: No Academic Threshold → Draw From General Rules of Thumb What I found For speaking speed, I first looked for academic backing. Here's what I found: Speaking speed has traditionally been measured against mora count, but prior researc

How we decide is at the core of architecture, and the architecture advice process is a way to decentralize architectural decisions. It needs to be supported by Architecture Decision Records because of the speed at which technology and systems move, and can be complemented by a weekly architecture advice forum. By Ben Linders

When organizations scale, a simple monolithic architecture often becomes difficult to maintain, deploy, and scale. This is where microservices come into the picture. However, moving to microservices introduces new challenges: How do we migrate from a monolith safely? How do we handle transactions across multiple services? How do we scale read-heavy applications efficiently? Three popular patterns solve these problems: Strangler Pattern – Monolith to Microservices Migration Saga Pattern – Distributed Transaction Management CQRS (Command Query Responsibility Segregation) – Read/Write Scalability 1. Strangler Pattern Why Do We Need It? Most companies cannot shut down a production monolith and rewrite everything from scratch. A complete rewrite is risky because: Development takes a long time. Existing customers are affected. Bugs can impact business operations. Rollback becomes difficult. The Strangler Pattern allows teams to migrate gradually with minimal risk. What Is It? The Strangler Pattern is a migration strategy where new microservices slowly replace parts of a monolithic application until the monolith is no longer needed. The name comes from the strangler fig tree, which gradually grows around another tree and eventually replaces it. How Does It Work? [Insert diagram here showing Client → API Gateway → Monolith + Microservices] Step 1 All requests go to the monolith. Client | v Monolith Step 2 Introduce an API Gateway (or Controller). Client | v API Gateway | v Monolith Step 3 Extract one module into a microservice. Client | v API Gateway |------> Order Service | v Monolith Step 4 Gradually move more modules. Client | v API Gateway |------> Order Service |------> Payment Service |------> Inventory Service | v Monolith Step 5 Eventually remove the monolith completely. Example Consider an e-commerce application. Initially, everything exists inside one application: Monolith ├── Orders ├── Payments ├── Inventory └── Users Over time: Orders become Order Service Payme

Every non-trivial business operation touches more than one system. An e-commerce order reserves inventory, charges a payment method, and schedules a shipment — three services, three databases. A bank transfer debits one account and credits another across two ledgers that may not even be in the same data center. A cloud VM provisioning workflow reserves a network port, allocates storage, starts the hypervisor, registers billing, and sends a notification — five services, five independent state stores. The question is: what happens when step four fails after steps one through three have already succeeded? In a monolith backed by a single database, the answer is simple: roll back the transaction. The database engine guarantees atomicity; either everything commits or nothing does. But when your workflow spans multiple services, each owning its own storage, there is no transaction boundary that wraps them all. There is no rollback button. Step one through three have already made durable changes to systems that do not know about each other, and step four's failure has left the system in an inconsistent state. This is not a pathological edge case. It is the default condition in any distributed architecture. And it gets worse: the failure might not be a hard error. The network might time out. The billing service might return a 503. You do not know whether step four applied its effect or not — you only know you did not receive a success response. Now what? This is the problem sagas were designed for. Client Inventory Svc Payment Svc Shipping Svc │ │ │ │ 1 │──reserve(item)──►│ │ │ │◄──── 200 OK ─────│ │ │ │ [reserved ✓] │ │ │ │ │ │ 2 │──────────── charge(card, $99) ────►│ │ │◄───────────────── 200 OK ──────────│ │ │ │ [charged ✓] │ │ │ │ │ 3 │─────────────────────── schedule(order) ─────────────►│ │◄─────────────────────────── 503 ──────────────────── │ │ │ │ [no record ✗] │ │ │ │ ╔══════════════════════════════════════════════════════╗ ║ ⚠ Inconsistent state ║ ║ Inventory: it

The Quest Begins (The “Why”) Picture this: I’m knee‑deep in a legacy codebase that feels like the Death Star’s trash compactor—every time I try to add a feature, the walls close in and I’m squashed by tight coupling. I’d just spent three hours tracking down a bug that only showed up when the payment gateway was mocked in a test. The culprit? A new PaymentGateway() buried deep inside an OrderService class. It was like trying to defeat Darth Vader with a butter knife—no matter how hard I swung, the Dark Force (aka hidden dependencies) kept pulling me back. I realized I was instantiating collaborators inside the very classes that should be oblivious to their implementation details . The result? Tests that needed a real database, a real Stripe account, and a sacrificial goat to run. Any change to a third‑party API meant hunting down every new scattered across the project. Onboarding a new teammate felt like handing them a map written in ancient Sumerian. Honestly, I was ready to quit coding and become a professional napper. Then, during a late‑night coffee‑fueled refactor session, I stumbled upon a tiny line of documentation that whispered: “Depend on abstractions, not concretions.” It sounded like Yoda giving me a pep talk. The Revelation (The Insight) The magic spell I uncovered is Dependency Injection (DI) —specifically, constructor injection . Instead of a class creating its own collaborators, we hand them in from the outside. Think of it as giving a Jedi their lightsaber rather than making them forge one in the middle of a battle. Why does this feel like discovering the Force? Testability explodes – you can swap in fakes, mocks, or stubs without touching production code. Flexibility skyrockets – swapping a payment provider becomes a one‑line config change, not a scavenger hunt. Clarity reigns – the constructor becomes an honest inventory of what a class needs to do its job. The moment I applied it, the codebase felt lighter, like Luke finally trusting the Force ins

Google is betting generative AI can breathe new life into the smart speaker. The company's new $99.99 Google Home Speaker replaces the rigid commands of the Google Assistant era with more conversational Gemini interactions.