标签：#m

Omen AI raised a $31 million Series A to monitor chip coolant and stop bacterial outbreaks in data centers.

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

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

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]

We (at DEV and MLH) are covering AI Engineer's World Fair by printing a physical newspaper called...

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. […]

Comcast has announced plans to separate itself into two publicly traded companies, spinning off its NBCUniversal and Sky broadcasting arms. The shake up aims to protect the media conglomerate's profitable broadband and wireless brand, which will retain the "Comcast" company name, as its media and entertainment business - now collectively named "NBCUniversal" - faces increasing […]

The 1970s porn actress turned New York City late night public access queen says censorship and a lack of star quality among modern adult entertainers turned her off of the industry.

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

NASA’s quiet supersonic flight tests could eventually go on a national tour.

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.

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

The design doc is fourteen pages. Clean service boundaries, thoughtful API contracts, a deployment story that handles rollback without incident. Six months of work. The team is proud of it, and the work is genuinely good. Three questions will tell more about this system than all fourteen pages. Not questions about implementation details or technology choices. Questions about how the system was designed to age, to fail, and to be understood by someone who didn’t build it. Most architecture conversations never reach these questions, which is part of why the gap between a good system and a great one is often invisible until it is suddenly very visible. What does this make hard? Good architecture conversations focus on what a design enables: faster deployments, independent scaling, and clearer ownership. The question that separates architectural thinking from implementation thinking is the inverse . What does this make hard? Every decision forecloses options. The service boundary that gives teams autonomy makes cross-service transactions expensive. The data model that reads cleanly under expected load makes certain write patterns awkward. The abstraction that simplifies onboarding makes some categories of refactoring nearly invisible as possibilities. These are not arguments against the decisions. They are the other side of every decision, and that other side exists whether or not anyone names it. The design doc that holds up over time is not the one where nothing is difficult. It is the one where the difficult things are named. “This approach makes distributed transactions impossible, and here is why we have decided to accept that constraint.” That sentence, or something close to it, belongs in every significant architecture document. Its absence is not a sign that the constraint wasn’t considered. Often it was. But without the name, the constraint becomes invisible to everyone who wasn’t in the room, which eventually includes the original team. When joining a system s

submitted by /u/Successful_Bowl2564 [link] [留言]

You export a CSV from a UK system and double-click it in US Excel — 28/05/2026 suddenly looks like May 28, or something even stranger. The file isn't corrupt. Excel is guessing your date format based on your computer's locale. This short guide covers why that happens, how to import CSV correctly, and how to batch-fix dates that are already wrong. For the full walkthrough with examples, see the official guide: How to Fix Excel CSV Date Import Problems . Why Excel breaks CSV dates A CSV is plain text. It does not store whether a value is a date or a string. When you double-click to open, Excel parses using your regional settings: US Excel: MM/DD/YYYY UK / Europe: DD/MM/YYYY Dates like 03/04/2025 are the most dangerous — both parts are ≤ 12. US Excel may read April 3; UK Excel reads March 4. Excel won't warn you. Other common traps: Dates exported as serial numbers (e.g. 44927 ) Two-digit years triggering century guesses Re-saving as CSV destroys formats a second time The right way: don't double-click the CSV Open Excel first — don't double-click the .csv file Data → Get Data from Text/CSV (older Excel: Data → From Text) Set date columns to Text if you need to preserve the original string Confirm the source region (US / UK), then convert to a single format For team data exchange, agree on ISO 8601: YYYY-MM-DD in your schema — unambiguous, sorts correctly as text, and works in JSON APIs and databases. Ambiguous value US reads as UK reads as Safe format (ISO) 03/04/2025 2025-04-03 2025-03-04 Confirm source region 28/05/2026 — 2026-05-28 2026-05-28 05/28/2026 2026-05-28 — 2026-05-28 Already broken? Fix dates in the browser (no server upload) I built a free tool cluster on FormatList — everything runs locally in your browser : 1. Date Format Fixer — bulk repair Paste a date column or upload .csv / .txt Ambiguous rows highlighted; optional US / UK preference Export ISO / US / UK or download a fixed CSV Best for: normalizing a whole column to ISO before a database or API imp

The idea came from an extremely serious game of hide and seek with my cousins. We were adults, which made it ridiculous, but also strangely perfect. Someone was hiding behind a couch in plain sight, surviving only because the seeker did not look carefully enough. That made me wonder: what if looking carefully was not enough? What if the seeker could not freely look around the room? What if they could only see the world through their phone screen, while virtual obstacles blocked parts of their view? That became the core idea behind AR Hide and Seek: a local multiplayer hide and seek game where 2-5 players use the space they are already in. The hiders physically hide somewhere in the room, while the seeker views the environment through an iPhone. The phone fills the space with digital clutter, making familiar rooms harder to read. One phone. One seeker. Real hiding places. Virtual obstacles. Why LiDAR? LiDAR on iPhone Pro models gives the phone a real-time depth map of the environment, with centimeter-level understanding of the space around it. That means virtual objects can be placed in ways that respect real-world geometry: a crate can sit on the floor, a wall can align with an actual wall, and obstacles can feel like they belong in the room rather than floating on top of it. For a game where the virtual environment needs to feel like it genuinely fills the space, that difference matters immediately. Without reliable depth information, objects can drift, clip, or hover in ways that break the illusion. The tradeoff is device requirement. LiDAR is only available on iPhone Pro models, which narrows the audience. But for this game, the better AR experience was worth it. The seeker sees a version of the room cluttered with virtual obstacles. The hiders are still physically hiding behind real furniture; the phone does not make them disappear. It simply makes finding them harder. Designing the Core Loop The mechanic is simple on paper, but it took a surprising amount of tu