标签：#m

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

What is MCP (Model Context Protocol)? Complete 2026 Guide TL;DR — Model Context Protocol (MCP) is an open JSON-RPC 2.0 specification, introduced by Anthropic in late 2024, that lets AI agents talk to external systems — file systems, databases, APIs, custom services — through a single standardized interface. An MCP server exposes capabilities (tools, resources, and prompts); an MCP client (Claude Desktop, Cursor, Zed, Sourcegraph Cody, your own agent) consumes them. Write the server once, and every MCP-compatible client can use it — no per-app integration work. If you have built agent tooling before, think of MCP as "LSP for AI tools" : the same idea that unified language servers across editors, now applied to the plumbing between agents and the systems they need to act on. Why MCP Exists Before MCP, every agent framework defined its own tool format. A tool written for LangChain didn’t work in Claude Desktop, which didn’t work in your custom agent, which didn’t work in Cursor. Each integration was bespoke, every prompt-engineered "function description" was framework-specific, and every team rebuilt the same wheel. The pain points MCP solves: Fragmentation. Five frameworks, five tool formats. Five times the work. No discovery. Clients couldn’t enumerate what a tool server offered without a hard-coded manifest. No portability. A debugging assistant you wrote for one agent wouldn’t move to another. Auth was ad hoc. Every integration invented its own way to handle API keys and OAuth. MCP makes the contract uniform: a server declares its tools, resources, and prompts; a client speaks the same JSON-RPC dialect to discover and call them. The same MCP server that ships with Anthropic's TypeScript SDK today will work with any future client that implements the spec, regardless of which LLM the client uses underneath. The Wire Protocol in One Page MCP rides on JSON-RPC 2.0 , which means every message is a JSON object with a jsonrpc: "2.0" envelope, a method , optional params ,

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

I remember the day I hit my breaking point. I had spent the entire morning — five hours — wrestling with a React component that an AI assistant had generated for me in about four minutes. The code looked flawless at first glance. Proper hooks, clean JSX, even decent comments. But it didn't work. And worse, I couldn't figure out why. Everyone talks about how AI speeds up coding. And it's true — when it works, it's magical. I've personally seen my feature delivery time drop by maybe 40-50% on good days. But what nobody talks about — what I certainly never saw in the breathless LinkedIn posts — is the debugging nightmare that follows when the AI gets it wrong. That day, I realised I had spent ten times longer debugging AI-written code than I would have spent writing it myself from scratch. I started tracking it. Over three months, I logged every AI-assisted task. The numbers were sobering: on average, each AI-generated snippet took me 3.2 times longer to verify and fix than to write myself. And for complex tasks — anything involving state management, async flows, or edge cases — the ratio jumped to 8-12x. The AI was giving me confidence, not correctness. And confidence, as any seasoned developer knows, is the enemy of debugging. The Hallucination That Cost Me a Sprint One incident stands out. I was building a data pipeline in Python that needed to batch-process JSON files from an S3 bucket and push transformed records into a PostgreSQL database. I gave the AI a detailed prompt: "Write a function that reads all JSON files from a given prefix, validates each record against a schema, and inserts them in batches of 500. Use threading for I/O." The AI returned a beautiful 60-line function. It used concurrent.futures.ThreadPoolExecutor , had proper error handling, even logged progress. I was impressed. I dropped it into the codebase, ran the tests — they passed. Deployed to staging. Worked like a charm. Then production hit. Three hours later, the database had 30,000 duplicat

Hello. No idea if anyone's going to read this, but writing it feels like I've done something, so here we go. And maybe it helps someone. For the past few years, I've been building a piece of software in Unity. It has actual users, somehow. My role was everything: founder, product owner, and whatever else needed doing. Development, UI, the website, the content. That's startup life. I'm good at learning fast and shipping, so it worked.(of course not all of it... I'm not trying to take all the credit for others' work Im just saying what I did) But I never came into this as a leading developer, so updating the product became kinda frustrating. Moreover, graphics are central to this product, and even with HDRP, Unity wasn't getting me where I wanted. I know my way around C#. C++, not so much. With Unreal, I've learned the basic UI and not much else. BuT~ You study, you keep going, and things tend to work out. So wish me luck I'll reveal what the product is once the switch to Unreal succeeds I'll take some courses. I don't care if it's in Korean or English. I'll make it work. Time passes either way, we get older, we all die anyway. So let me just learn and build what I want to build. I'm writing this to leave a record of what I learn and what I try. Let's go 헬로 누가 이걸 보기나 할 지 모르지만 이런 글이라도 쓰면 성취감이 드니까 걍 씀 그리고 누군가에게는 도움이 될 수도 있으니까 킬킬 난 지난 몇년간 유니티로 소프트웨어를 하나 만들었음. 나름 유저도 있는 상황 ㅋㅋ 나의 역할은 대표이자 기획자이자 뭐 올라운더로 참여했음. 개발도 하고... 화면도 만들고 뭐 웹사이트도 만들고 콘텐츠도 만들고 뭐 다 그랬음. 스타트업이 다 그런 거지 뭐. 뭐든 빨리 배우고 결과물을 만들어내는 걸 잘하는 편이라 나름 잘 했음 다만 내가 개발자로 참여한 건 아니라서 이 프로덕트를 업데이트하는 과정이 좀 아쉽기도 하고 그래픽이 중요한 프로덕트인데 unity는 hdrp라 하더라도 아쉬웠음 c#에 대한 이해도는 있는 편인데 c++은 잘 모름 unreal도 기본적인 ui 익힌 거 빼고는 모름 공부해서 하다보면 뭐든 되지 않겠음? 위시 미 럭 프로덕트가 뭔지는 unreal로 업그레이드 하는데 성공하면 공개하겠음. 한국어 강의나 영어 강의 닥치는대로 다 볼 거고 뭐 어떻게든 해 보겠음 어차피 시간은 흐르고 나이는 들고 죽을텐데 이렇게 하고싶은 거 어떻게든 해보면서 뭐라도 만드는 게 남는 거인듯 내가 공부하고 실행해본 걸 흔적으로 남기려고 이 포스트 쓰는 걸 시작해본다 아자뵤

The ROI Black Hole in Social Marketing Consider a mid-market B2B software company whose social team manages campaigns across X, LinkedIn, Instagram, and TikTok from a single shared workspace. Each week the managers review platform-native dashboards that display rising follower counts, solid engagement rates on short-form video, and respectable click-throughs from carousel posts. They export weekly performance reports, paste the numbers into shared spreadsheets, and celebrate the month-over-month lift in impressions. Yet when the sales operations team asks which campaigns contributed to qualified pipeline, the social group cannot produce a single account-level match. Campaign links carry UTM strings, but many prospects arrive through mobile apps or shared links that strip those parameters, leaving the CRM with only anonymous referral domains and no usable journey data. The team attempts manual reconciliation by cross-referencing campaign dates with opportunity creation timestamps, but the exercise quickly collapses under volume. One campaign on LinkedIn might drive 400 clicks while another on TikTok drives 1,200, yet both appear in the CRM as undifferentiated social traffic. Without a consistent identifier that survives across platforms and into the marketing automation system, the social team cannot isolate which creative or audience segment produced the meetings that closed. Budget conversations therefore remain anchored to vanity metrics rather than incremental revenue, and executives grow increasingly skeptical of further platform spend. Medallion Architecture and the Absent Silver Layer Modern data platforms often organize information according to a medallion architecture that progresses through successive stages of refinement. The initial bronze layer captures raw event logs exactly as they arrive from each social API, preserving original timestamps, platform-specific identifiers, and unprocessed metadata. A subsequent silver layer then standardizes those recor

For decades, Java's object model has powered one of the world's most successful programming languages. But the hardware we run on today looks very different from the world Java was born into in 1995. Modern CPUs are incredibly fast, but memory access, cache locality, and object allocation have become some of the biggest performance challenges in software. Project Valhalla is one of the most ambitious changes in Java's history. It isn't just about making Java faster... it's about rethinking what a Java object actually is. submitted by /u/OSBY_Glabay [link] [留言]

Introduction Artificial Intelligence has moved from being an experimental technology to becoming a core component of modern software systems. Companies today are integrating AI into customer support, analytics, automation, healthcare, finance, education, and enterprise applications. However, as organizations start building AI-powered solutions, one major question appears: “How do we make an AI model work specifically for our business needs?” Many teams immediately assume they need to train their own AI model. Others believe a well-written prompt is enough. Some organizations invest heavily in fine-tuning without understanding whether it is the right approach. The reality is that there is no single solution. Modern AI development usually revolves around three major strategies: Prompt Engineering Retrieval-Augmented Generation (RAG) Fine-Tuning Choosing the wrong approach can lead to higher costs, poor AI performance, security issues, and unnecessary complexity. This article explains the differences between these approaches and how businesses can select the right AI strategy. The Problem: Making General AI Models Business-Specific Large Language Models (LLMs) such as GPT, Claude, Gemini, and Llama are trained on massive amounts of publicly available data. They are excellent at: Understanding language Generating content Writing code Answering general questions Summarizing information ** However, businesses usually need AI systems that understand:** Internal company documents Customer information Product knowledge Industry-specific terminology Private databases Business processes For example: A hotel company wants an AI assistant that can answer: “What is our cancellation policy for premium customers?” A general AI model does not know this information because it was never trained on the company’s private policies. So the challenge becomes: How do we customize AI without rebuilding an entire model from scratch? This is where Prompt Engineering, RAG, and Fine-Tuning come

I built a free ATS resume checker after seeing too many friends get ghosted on Easy Apply. Most people think ATS means “add more keywords.” In practice, a lot of resumes fail earlier — on parsing. The 5 blockers I see most Columns / two-column layouts Nice for humans. Bad for many parsers. Contact info or skills in a sidebar often get read in the wrong order (or skipped). Fix: one-column layout. Standard section titles: Experience, Education, Skills. Icons instead of text Phone / email / LinkedIn as icons only = empty fields for the ATS. Fix: real text next to icons (or drop the icons). Text in images / fancy graphics If you can’t Ctrl+A → copy readable text from your PDF, neither can the ATS. Fix: real text PDF/DOCX, not a designed image export. Tables for experience Tables often scramble dates and bullets. Fix: simple headings + bullet points. Keyword stuffing Repeating “Python Python Python” can look spammy and still miss role-specific terms from the job post. Fix: mirror the job’s real skills in your bullets with proof (tools + outcomes). Quick self-test Open your PDF Select all → paste into a blank doc If the order is messy or text is missing, fix the format before you apply again Free checker I use for this I shipped a free diagnosis that flags these blockers first (full score/keywords optional): https://myatscheck.com/free-ats-resume-checker If you’re job hunting through Workday / Greenhouse / Easy Apply, fix parsing before you rewrite every bullet.

This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry. ...

Two minutes and thirty seconds into the title track of Rachika Nayar's Heaven Come Crashing, an absolutely massive drum and bass beat drops. As a fan of Nayar's debut record Our Hands Against The Dusk, it caught me off guard. That album has no percussion on it at all. Heaven Comes Crashing is almost entirely […]

submitted by /u/Aires_id [link] [留言]

The global memory chip shortage appears to be affecting the availability of Apple’s most popular Mac.

This was first published on the Vibsync blog . Reposting for the DEV community. The short version: AI reliably makes each developer faster. Whether it makes the team faster is a separate question — and the gap between the two is where a lot of quiet cost hides. Below: the five coordination costs that eat the difference, a ten-question diagnostic, and five operating principles. Picture three developers, three AI coding agents, and one repository. Each developer can now produce candidate code, tests, and refactors faster than before. Yet releases move at the same pace, review queues grow, and the same facts keep getting rediscovered. That's not a paradox, and it isn't a reason to slow anyone down. It's a reminder that individual speed and team speed are different quantities , and AI coding agents scale the first far more easily than the second. Give everyone a faster typewriter and you get more pages — not necessarily a better book, written faster, by a group. Individual output is not team throughput It's worth separating two things we tend to blur: Individual output — how much finished work one developer (plus their agent) produces. Team throughput — how much shippable, coherent work the group produces together, after review, rework, waiting, and reconciling everyone's changes. AI agents lift individual output directly. Team throughput is what's left after the coordination overhead is paid, and that overhead doesn't shrink just because each person got faster. A useful way to hold it in your head — not as a formula to compute, just as a shape: team throughput ≈ the sum of local speed-ups − rework − waiting − reconciliation When you add agents, the first term grows. If nothing else changes, the last three grow too — because there's now more work in flight, produced faster, by people who can't all see what the others are doing. The interesting question for a team lead isn't "how do I make everyone faster?" It's "which of those subtraction terms is my real ceiling?" Ther

Most conversations about CRA, DORA, and NIS2 compliance for IoT hardware boil down to one uncomfortable binary: redesign the board around newer, security-capable silicon, or accept that your existing product line falls out of compliance on a fixed deadline. For a product with years left in its lifecycle and a BOM that took months to qualify, "just redesign it" is rarely a real answer. There's a third option that gets far less attention than it deserves: pair the legacy chip with a modern security co-chip that absorbs the cryptographic boundary, while the legacy part keeps doing exactly what it already does well - application logic, peripherals, display, sensor polling. Call it a soft fade-out. The old silicon stays in service until its natural end-of-life; the compliance gap gets closed by a second, much cheaper part sitting next to it, not by replacing it. The Three Gaps a Legacy Chip Has - and Why a Co-Chip Fixes Them The regulatory pressure driving all of this isn't abstract. NIST finalized its post-quantum cryptography standards in 2024, and IR 8547 sets real dates: ECDSA and RSA are deprecated after 2030, disallowed after 2035. Germany's BSI has gone further - TR-02102-1 (2026 edition) sets a stricter 2030 deadline for high-protection-need data, and treats the migration as "alternativlos" (without alternative) rather than a recommendation. Older embedded silicon typically lacks three things simultaneously: a hardware-isolated key store (TEE/APM), side-channel countermeasures (DPA protection) strong enough for physical-access threat models, and enough RAM/compute headroom to run lattice-based PQC algorithms in software without starving the rest of the firmware. Redesigning the whole board to fix all three at once is expensive and slow. But none of those three gaps require touching the part that's already doing its job - they're all boundary problems. A second, purpose-built chip can own the boundary. Three concrete pairings Using the ESP32 family as a worked exa

It happened during a medical appointment. I was sitting in the quiet waiting room, my thoughts occupied by the upcoming consultation, when my phone erupted with a loud, aggressive ringtone. The entire room turned to look at me, and I fumbled to silence it, accidentally hitting the volume up button instead of the mute toggle in my panic. I felt that specific, burning embarrassment that comes from being the person who disrupts a quiet space. I realized then that I had spent years writing code for others, yet I couldn't solve my own basic problem of managing my phone's profile. We live in a world of constant notifications and persistent demands on our attention. The real friction isn't just that phones ring; it's that we are expected to remember to manually toggle settings in a dozen different contexts every single day. Whether it is a classroom, a house of worship, or a professional meeting, the human element of remembering to flip a switch is the point of failure. I wanted an app that handled this silently, without me having to open an interface or even think about the current state of my device. I needed a system that functioned as an extension of my environment rather than an additional task. Building Muffle required me to confront the reality of modern Android background execution. Initially, I thought a simple BroadcastReceiver listening for time changes or geofence triggers would suffice. I was wrong. As soon as the phone entered Doze mode—the power-saving state introduced in Android 6.0—my triggers would either be delayed significantly or killed entirely by the system’s restrictive task scheduler. I had to architect a solution that could survive these aggressive optimizations while remaining battery-efficient. The core of the application resides in a ForegroundService that maintains a persistent notification. While many developers avoid these because of the UI footprint, it is the only way to signal to the OS that your process is performing an essential, user-v

On the latest episode of Equity, we discuss why Sam Altman has calling on the industry to "pace the rate of AI development."

When engineers discuss quantum computing, the conversation usually focuses on future supercomputers cracking traditional encryption passwords in a matter of seconds. As a systems architect who spends my days building distributed platforms, which are networks of independent cloud servers working together as a single application, I see a different, highly practical challenge taking shape. The transition to quantum-resistant security is not simply a theoretical math problem. It is an infrastructure challenge that will directly impact network throughput, memory usage, and messaging efficiency across global cloud environments. To protect sensitive enterprise records and business platforms against future quantum threats, security organizations are transitioning to Post-Quantum Cryptography. This field involves building new mathematical algorithms that quantum computers cannot easily solve. However, these stronger defense mechanisms come with a major trade-off in size. Traditional cryptographic signatures, which are digital verification stamps used to prove that a data message comes from an authentic sender and was not altered, are remarkably small. An older, standard signature might only take up sixty bytes of memory. By comparison, a quantum-safe signature can easily require several thousand bytes. In a simple website, adding a few extra kilobytes to a security header goes unnoticed. But modern cloud infrastructure relies heavily on event-driven architecture, a design strategy where dozens of microservices communicate by constantly publishing tiny, real-time updates to shared message queues. In these systems, the actual business payload might only be a small status change containing twenty bytes of text. If the quantum security stamp attached to that message is three thousand bytes, the overhead of the security layer completely outweighs the actual data being sent. When security footprints expand by orders of magnitude, the physical realities of computer networking take

Fender CEO Edward "Bud" Cole gave an interview to T3 in May celebrating the 75th anniversary of the Telecaster with comments on AI and music that initially flew under the radar. But it has started making the rounds recently, pouring more fuel on an already raging fire of bad PR following the company pissing off […]

Full title: [Advanced Rust] 2.3. API Design Principles of Unsurprising Pt.3 - Implementing serde Serialize and Deserialize Traits, and Why Copy Is Not Recommended 2.3.1. It Is Recommended to Implement Serialize and Deserialize in serde Serde is the core Rust library for serialization and deserialization : Serialization : converts a Rust struct or enum into a string or binary representation such as JSON or YAML Deserialization : parses a string or binary representation such as JSON or YAML back into a Rust struct or enum Serialize and Deserialize are both traits from the serde crate. Serialize Trait The Serialize trait allows a type to be converted into a serializable data format such as JSON, YAML, or TOML. Its main methods include: serialize_bool serialize_i32 serialize_str serialize_struct These are methods on the Serializer (and related) traits that a Serialize implementation calls; the Serialize trait itself only requires serialize . Its definition is: pub trait Serialize { fn serialize < S > ( & self , serializer : S ) -> Result < S :: Ok , S :: Error > where S : Serializer ; } Here is an example showing how to implement Serialize manually: use serde :: ser ::{ Serialize , SerializeStruct , Serializer }; struct Point { x : i32 , y : i32 , } impl Serialize for Point { fn serialize < S > ( & self , serializer : S ) -> Result < S :: Ok , S :: Error > where S : Serializer , { let mut state = serializer .serialize_struct ( "Point" , 2 ) ? ; state .serialize_field ( "x" , & self .x ) ? ; state .serialize_field ( "y" , & self .y ) ? ; state .end () } } serializer.serialize_struct("Point", 2)? creates a struct serializer state, and 2 is the number of fields state.serialize_field("x", &self.x)? serializes the struct fields one by one state.end() finishes serialization Deserialize Trait The Deserialize trait allows Rust types to be parsed from various data formats. Its main methods include: deserialize_bool deserialize_i32 deserialize_string deserialize_struct These are