标签：#p

Flipper Device's new Busy Bar will retail for $249.

We’ve all been there. You scroll through your feeds, see a flashy ad promising a high-paying tech job in 3 months, and think, “This is it. This is my golden ticket.” You buy the bootcamp, spend sleepless nights watching lectures, stack up a dozen colorful certificates on your LinkedIn, and then... nothing. No callbacks. No interviews. Just a lingering feeling of frustration and the nagging thought: Are bootcamps and online courses just a massive scam? I used to think so. When I was trying to break into tech, I bought courses like crazy. I collected certificates like they were Pokémon cards. Yet, my first real developer job didn't show up until five or six years later. And let me tell you a secret: it wasn’t the certificates that got me the job. It was because I finally figured out how to actually learn. The truth is, almost every bootcamp or course—even the mediocre ones—has something valuable to offer. The problem isn’t always the material; it’s how we interact with it. If you feel stuck in "tutorial hell," here is a positive, practical guide to changing your approach, reclaiming your time, and turning any learning material into real, career-changing expertise. 1. Curate Your Sources (Choose Your Battles Wisely) Before we talk about how to study, we need to talk about what to study. Even though you can extract value from almost any course, your time is highly valuable. Don't waste it on low-quality content. When choosing a course or bootcamp, look for these four green flags: The Instructor Has Real-World Mileage: Is the instructor a practitioner, or are they just reading the official documentation back to you? If they don't work with the technology daily, they won’t be able to explain the nuances, edge cases, and real-world trade-offs. A Project-First Curriculum: Avoid courses that are just endless lectures of "theory first, practice never." Look for curriculums that build actual applications. Good Pacing and Editing: We've all watched those tutorials where the ins

We’ve all been there. You scroll through your feeds, see a flashy ad promising a high-paying tech job in 3 months, and think, “This is it. This is my golden ticket.” You buy the bootcamp, spend sleepless nights watching lectures, stack up a dozen colorful certificates on your LinkedIn, and then... nothing. No callbacks. No interviews. Just a lingering feeling of frustration and the nagging thought: Are bootcamps and online courses just a massive scam? I used to think so. When I was trying to break into tech, I bought courses like crazy. I collected certificates like they were Pokémon cards. Yet, my first real developer job didn't show up until five or six years later. And let me tell you a secret: it wasn’t the certificates that got me the job. It was because I finally figured out how to actually learn. The truth is, almost every bootcamp or course—even the mediocre ones—has something valuable to offer. The problem isn’t always the material; it’s how we interact with it. If you feel stuck in "tutorial hell," here is a positive, practical guide to changing your approach, reclaiming your time, and turning any learning material into real, career-changing expertise. 1. Curate Your Sources (Choose Your Battles Wisely) Before we talk about how to study, we need to talk about what to study. Even though you can extract value from almost any course, your time is highly valuable. Don't waste it on low-quality content. When choosing a course or bootcamp, look for these four green flags: The Instructor Has Real-World Mileage: Is the instructor a practitioner, or are they just reading the official documentation back to you? If they don't work with the technology daily, they won’t be able to explain the nuances, edge cases, and real-world trade-offs. A Project-First Curriculum: Avoid courses that are just endless lectures of "theory first, practice never." Look for curriculums that build actual applications. Good Pacing and Editing: We've all watched those tutorials where the ins

When I write code in unfamiliar territory, I write three lines, then I run it. Then I write three more lines, and I run it again. I've been doing this for twenty-four years. It's the most specific habit I have. I almost didn't write this article, because the habit feels too small to be worth describing — but then I noticed that it's the part of my way of working that I can never seem to explain to someone in real time. It needs writing down. Three principles The discipline rests on three things I believe about writing code. They're not deep. They've just stayed with me. 1. Trust nothing but your own code. If you can't trust the code you wrote yourself, what can you trust? Not a library, not a vendor's documentation, not your own assumption from yesterday. The only thing in the system whose behavior you can fully verify is the code you just typed, by running it. 2. Write in code, not in language. If you're describing what the code should do in Japanese or English, you're spending the same time you could have spent writing the code itself. By the time the code runs, the description is already done — by the code, in a more precise form than any language could give it. 3. Make three lines complete. The three lines you just wrote should be complete. Error handling included. Validation included. Logging included. Not "I'll add validation later." Not "I'll wrap it in a try-catch later." Three lines, complete, then run. (There's a small exception to this. Sometimes you do want to ignore every error and move on — for instance, when you're trying to understand whether the happy path works at all before you care about anything else. That's a different mode, used deliberately. It's not the same as "I'll handle errors later.") Why three lines Three lines is roughly the unit of thought I can hold completely. Five lines, and I start guessing what the third line did. Ten lines, and I'm reading the code as if it were someone else's. Three lines is the size that stays mine. When thre

From knowing to doing — Phase 4 of the Jarvis AI Platform The Problem with Knowledge-Only AI After Phase 3, Jarvis could remember you across sessions and search your documents. But it still had a fundamental limitation. You: "What is the weather in Kathmandu right now?" Jarvis: "I don't have access to real-time weather data." You: "What is 2847 × 391?" Jarvis: "The answer is approximately 1.1 million." ← WRONG An AI that only knows things from training data is useful. An AI that can do things is transformative. That is what Phase 4 built. What Is a Tool Engine? A tool engine gives the AI model the ability to call real functions during a conversation. The flow looks like this: User: "What is the weather in Kathmandu?" ↓ AI Model ↓ "I should call WeatherTool" ↓ WeatherTool.getWeather("Kathmandu") ↓ "22°C, Clear sky, Humidity: 45%" ↓ AI Model ↓ "The weather in Kathmandu is 22°C and clear." The key insight: the AI decides when to call a tool and with what input . We don't hardcode "if user asks about weather, call WeatherTool." The model figures that out from the tool descriptions we provide. The Architecture Decision The most important architectural decision in Phase 4 was the package structure. ai . jarvis . tools / ├── JarvisTool . java ← marker interface ( root ) ├── ToolRegistry . java ← manages all tools ( root ) ├── builtin / ← built - in tools │ ├── DateTimeTool . java │ ├── CalculatorTool . java │ ├── WeatherTool . java │ └── WebSearchTool . java └── mcp / ← MCP protocol └── McpServerConfig . java Why not put tools inside ai/ ? The ai/ package handles HOW Jarvis talks to AI models. Tools define WHAT Jarvis can do. These are fundamentally different responsibilities. Mixing them would mean every new tool requires changes to AI infrastructure code. Keeping them, separate means adding a new tool requires exactly one file. The JarvisTool Pattern Every tool in Jarvis implements one interface. /** * Marker interface for all Jarvis tools. * Spring auto-discovers all @C

Force nginx to TLS 1.3 An nginx server is accepting an old TLS version, and the exam wants it locked to TLS one point three. The config lives in a ConfigMap. The catch is that editing the ConfigMap alone changes nothing. Let's do it the way the CKA expects. 🎥 Watch the video: https://www.youtube.com/watch?v=rx-77YBw99w This is a CKA Workloads & Scheduling walkthrough. Every command below is real output from a live cluster, and you can reproduce the whole thing yourself (scripts at the end). The scenario An nginx-static Deployment serves HTTPS, and its server config comes from a ConfigMap named nginx-config. Right now it allows both TLS one point two and one point three. Your task is to allow only TLS one point three, then make nginx actually use the change, so that a TLS one point two request fails. nginx-static serves HTTPS from the nginx-config ConfigMap It currently allows TLS 1.2 AND 1.3 Restrict ssl_protocols to TLS 1.3 only A TLS 1.2 request to the Service must then fail How nginx, ConfigMaps, and rolling restarts fit together Two ideas drive this. First, ssl_protocols is an allow list; leave only TLSv1.3 and nginx rejects any older handshake. Second, a ConfigMap mounted into a pod updates the file on disk, but nginx only reads ssl_protocols when it starts. So you must roll the Deployment, with kubectl rollout restart, for the new value to take effect. Inspect the current state Start by seeing what is running and what the config says. The nginx-static Deployment, its Service on port four forty three, and the nginx-config ConfigMap are all here. Grep the rendered ConfigMap for the ssl_protocols line: it lists TLSv1.2 and TLSv1.3, so old clients still get in. $ kubectl -n nginx-static get deploy,svc,configmap NAME READY UP-TO-DATE AVAILABLE AGE deployment.apps/nginx-static 1/1 1 1 17h deployment.apps/tester 1/1 1 1 17h NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/nginx-static ClusterIP 10.96.13.162 <none> 443/TCP 17h NAME DATA AGE configmap/kube-root-ca.

Erik Steiger discusses the operational pain of legacy PDF generation in regulated banking and manufacturing. He explains how transitioning from resource-heavy engines like Puppeteer and LaTeX to a serverless Rust architecture powered by Typst can drop render latencies below 2ms. He shares how applying Git and Docker concepts to template registries ensures ironclad compliance and rapid debugging. By Erik Steiger

Let’s be honest, the tech we use today is amazing, but it can also be complex. It’s only natural that teams want to build platforms that hide this complexity to improve productivity, avoid mistakes, and reduce cognitive load. But they may be misled to believe that the more complexity they hide, the better their platform is. Instead, they end up creating dangerous illusions! This talk reflects on two decades of building complex distributed systems, highlighting where abstractions helped and where illusions led to major disappointments. submitted by /u/goto-con [link] [留言]

Rocket Lab will own satellites connecting the most remote corners of the Earth.

Ending any story is hard, but that's especially true of mystery-packed TV shows. Series like Lost initially hook viewers with constantly building secrets and questions, to the point that they can often seem incomprehensible. But the promise is that it will all pay off in the end - a feat that few shows ultimately manage. […]

In this episode, Heroku co-founder and Ink & Switch founder Adam Wiggins argues for a 'local-first' architecture that reconciles cloud-based collaboration with the performance and data ownership of local software. He explores the role of CRDTs and version control primitives in non-code domains, and examines how a hybrid AI future might leverage local models for core productivity tasks. By Adam Wiggins

This virtual panel brings together AI security experts to examine the evolution of AI-driven threats, from prompt injection and data poisoning to agent abuse and AI-powered social engineering. The discussion explores emerging attack patterns, incident response challenges, and the changes security teams must make as AI systems become more autonomous and integrated into critical workflows. By Claudio Masolo, Elham Arshad, Sabri Allani, Vijay Dilwale, Igor Maljkovic

A sneaky rule change has the potential to blow up scientific research in the United States. But there's still time to fight it. On May 29th, the Office of Management and Budget (OMB) issued a 412-page proposal to revise federal financial assistance. The language is a combination of distinctly Trumpian attacks on "woke" policies and […]

We’ve taken one small step towards robot police officers: a drone capable of disarming a suspect: In a June 22 video posted on the Sacramento County Sheriff’s Office’s Instagram page, an officer wearing goggles can be seen operating a drone to retrieve a knife from an armed suspect hiding inside a cluttered house. “After not responding to negotiators, a drone was deployed inside the residence,” the post says. “Drone pilots located the suspect hiding in a corner of a garage” and then used a high-powered magnet attached to the drone to grab the knife out of the suspect’s hand. In the video ­ which is soundtracked by the “Mission: Impossible” theme song—the intercepted knife can be seen spinning around in the air as the drone carries it back to the deputies...

With AI-powered noise reduction, an automatic speech-focusing system, and a simple, effective hearing test, it’s a shame these aids don't come with a better app.

Climate activist Will Lawrence cofounded the Sunrise Movement. Now, he has shifted his focus in his attempt to compete for a swing-district seat by calling for a data center moratorium.

Europe’s pro-competition proposals could see Google Search and Android systems opened up. The company claims there are serious privacy flaws.

FIFA says hydration breaks protect players from heat. They also create new annoying commercial breaks—and fans are calling foul.

submitted by /u/joladev [link] [留言]

You know what you want from a JSON document. You just don't want to memorize whether it's $[?(@.age > 18)] or $..users[?(@.active)] . Plain English in. JSONPath out. JSONPath Assistant on FormatList lets you paste JSON, describe what you need in natural language, and get a validated JSONPath expression plus the actual results — all in your browser. No account, no API key, no data sent to a server. How it works Paste your JSON — an API response, config file, or test fixture. Describe what you need — e.g. "get all user names" or "find products with tag tech". Generate — the assistant reads your JSON structure and maps your query to JSONPath. Validate & copy — the expression runs against your data immediately. Copy the path or the matched values in one click. If the first attempt returns no matches, the tool retries with a simpler variation automatically. Example queries You type Generated JSONPath Get all user names $.users[*].name Find users older than 18 $.users[?(@.age > 18)] Get names of active users $.users[?(@.active == true)].name Find products with tag tech $.products[?(@.tags.indexOf('tech') >= 0)] Get all order prices $.orders[*].price Get the first user's email $.users[0].email Get all pod names {.items[*].metadata.name} Get all pod IP addresses {.items[*].status.podIP} The tool ships with one-click examples for each of these — load one, hit Generate, and see how it works before trying your own JSON. What kinds of queries it understands Property access — "get all names", "list order prices" Numeric filters — "older than 18", "price under $10", "greater than 100" Boolean filters — "active users", "enabled devices" Tag / category searches — "with tag tech", "beauty category" Multi-condition — "both tech and mobile tags" Quantifiers — "the first user", "the last item" Kubernetes — "get all pod names", "pod IP addresses" It analyzes field names in your JSON — so users , products , orders , or whatever keys you actually have — and builds paths that match your sc