标签：#t

How RAGScope Knows Which Chunks Your LLM Actually Used Your retriever fetched 10 chunks. Your LLM only used 3. RAGScope shows a precision score of 30 out of 100. The question every new user asks: how does it know? There is no OpenTelemetry attribute that says "this chunk was in the context window." RAGScope infers it — and the way it does this is the most consequential piece of engineering in the whole tool. There Is No "In Context" Attribute in OTel The OpenTelemetry semantic conventions for generative AI ( gen_ai.* ) define attributes for model, input/output tokens, and retrieved documents. They do not define anything like gen_ai.chunk.reached_llm or gen_ai.retrieval.used_document_ids . When your RETRIEVER span fires, you get a list of documents. When your LLM span fires, you get a prompt and a completion. The two spans are connected by a parent-child trace relationship — but there is no attribute that maps which retrieved documents appear in which prompt. This gap matters. A reranker might drop 7 of your 10 chunks. Your application code might apply a token budget and truncate 4 more. From the trace alone, you cannot tell. RAGScope needs this information to compute the precision sub-score — the highest-weighted metric at 40% of the overall score. Getting it wrong would make precision meaningless. The Substring Match — How assembleContext Works RAGScope's answer is in src/enrichment/pipeline.ts , in a function called assembleContext : function assembleContext ( chunks : RagChunk [], llmSpans : ParsedSpan []): RagChunk [] { const llmPrompts = llmSpans . map (( s ) => s . prompt ). filter (( p ): p is string => !! p ); if ( llmPrompts . length === 0 ) return chunks ; let position = 0 ; return chunks . map (( chunk ) => { if ( ! chunk . content ) return chunk ; const inContext = llmPrompts . some (( p ) => p . includes ( chunk . content ! )); if ( inContext ) { return { ... chunk , inContext : true , contextPosition : position ++ }; } return { ... chunk , inContext :

I used to deploy Node.js apps on EC2 and manage servers like it was my second job. Port configs. PM2 restarts. Nginx rewrites. SSL renewals. Then I ran my first AWS Lambda function. 80% of that work is gone. Here's what Lambda actually does that nobody explains clearly: → You write a function → AWS runs it ONLY when triggered → You pay for milliseconds of execution → It scales from 1 to 1,000,000 requests without you touching anything As a full-stack developer in Bahrain, preparing for my AWS Developer Associate exam, this is the shift that changes how you think about backend architecture. Not "how do I manage a server" but "what should happen when this event fires." That mental model switch took me a week to fully get. I'm documenting everything as I study. Drop a 🔥 if you want me to share my Lambda notes weekly.

A walkthrough of the architecture decisions behind Flacron Gamezone a production full-stack app built with Next.js, Express, PostgreSQL, and Redis. When a client approached me to build a live football match discovery platform, the requirements sounded straightforward on the surface: show live scores, let users subscribe, handle authentication. But the moment you start thinking about how those pieces connect in production, straightforward gets complicated fast. This is the story of how I designed the backend for Flacron Gamezone — what decisions I made, why I made them, and what broke along the way. Table of Contents The Problem With "Just Building It" The Architecture: Four Distinct Layers Why This Matters to a Client The Bug That Taught Me Something Real The Full Stack at a Glance What I'd Do Differently The Problem With "Just Building It" The easiest version of this app is a single Express file: one route handler that queries the database, formats the data, and sends a response. I've seen this pattern in tutorials everywhere. It works for demos. It falls apart in production. The problems are predictable: you can't test business logic without hitting the database, a change in one feature quietly breaks another, and the moment a second developer joins the codebase, nobody knows where anything lives. I wanted to build something I could actually be proud to show an employer or a client. That meant committing to a proper layered architecture from day one, even on a project this size. The Architecture: Four Distinct Layers The entire Express backend is organized into four layers. Each layer has one job and talks only to the layer directly below it. Route → Controller → Service → Repository Here's what each one actually does. Routes are just maps. They declare that POST /api/v1/subscriptions exists, attach the auth middleware, and hand off to the controller. No logic lives here. Controllers handle the HTTP boundary. They extract data from req.body or req.params , call th

I sifted through the fine print and figured out how to score the best deal right now on all the major carriers.

In the past, when people heard the word “token,” they usually thought about cryptocurrency, trading,...

From specialized motors to the use of machine learning algorithms, Turkey’s billion-dollar hair-transplant industry is the result of a constant process of innovation.

Git style branching for your backend Discussion | Link

submitted by /u/Early_Mail9268 [link] [留言]

I used these two prompts on all AI apps to figure out who to vote for in the CA primaries: If you were running for governor of California, what will your big policies be ⁠Out of the candidates that are running in June election, who aligns closest to those policies Gemini, claude, chatgpt all ranked Matt Mahan (Democrat) as #1 Grok chose Steve Hilton (Republican) thoughts on AI use for voting decisions? submitted by /u/No_Mall_7299 [link] [留言]

Agent Memory: Short-Term, Long-Term, and Episodic Main Thumbnail Image Prompt: A human brain cross-section illustration in neon tones on dark background. Three regions clearly demarcated and labeled. The hippocampus region glows blue, labeled "Episodic Memory: what happened." The prefrontal cortex glows orange, labeled "Working Memory: what I'm doing now." A network of distributed nodes glows green, labeled "Semantic Memory: what I know." Arrows show information flowing between regions. Scientific but accessible, the memory architecture made neural and visual. Memory Architecture Diagram Image Prompt: Four storage boxes arranged vertically on dark background. Top: "In-Context Window (Working Memory)" — fastest, smallest, temporary, shown as RAM chip icon. Second: "External Vector Store (Semantic Memory)" — fast retrieval, persistent, shown as cylinder with search icon. Third: "Key-Value Store (Episodic Memory)" — structured facts, shown as database icon. Bottom: "Fine-Tuned Weights (Procedural Memory)" — slowest to update, most permanent, shown as brain with lock. Arrows showing read/write speeds between boxes. Clean, technical, the hierarchy is the insight. Memory Retrieval Flow Image Prompt: A query arrives at an agent on the left. Four parallel arrows go right to four memory sources: conversation history (short chat bubbles), vector database (semantic search visualization), structured database (table icon), model weights (brain icon). Each source returns relevant items. A "Memory Fusion" box on the right combines the results. The agent sees an enriched context. The retrieval from multiple stores is the architecture. Every conversation with an LLM starts from zero. You explain your project. You explain your preferences. You explain your constraints. You spend five minutes providing context. You come back tomorrow. You do it all again. The model remembers nothing between sessions. The context window closes. The state is gone. Every interaction is the agent's first

Most robot foundation model demos are hard to interpret because the impressive number usually comes after task-specific fine tuning. Wall-OSS-0.5, a new open-source VLA release from X Square Robot, is interesting because the report tries to measure what the pretrained checkpoint can do before that extra adaptation step. The setup is a 4B vision-language-action model built around a 3B VLM backbone plus action-generation components. According to the report, the pretrained checkpoint was evaluated on a 17-task real-robot suite without task-specific fine tuning. Four tasks crossed 80 task progress: block sorting, fruit sorting, ring stacking, and a held-out deformable task, rope tightening. The part that seems more important than the raw score is the framing. In language models, nobody would accept only a fine-tuned downstream score as evidence that pretraining worked. With robots, that has been much harder because the evaluation is physical, slow, embodiment-dependent, and expensive. A real-robot zero-shot suite is a useful step toward asking the same question directly: does pretraining itself produce executable behavior, or is it mostly a better initialization? The method is also trying to solve a specific training problem. Continuous action losses are useful for execution, but the paper argues they do not send a strong enough learning signal into the VLM backbone by themselves. Their recipe combines action-token cross entropy, multimodal cross entropy, and flow matching in one stage, using the discrete action-token path as a gradient bridge into the backbone while flow matching handles continuous actions at deployment time. For reference, the code is at https://github.com/X-Square-Robot/wall-x , the paper is at https://x2robot.com/api/files/file/wall_oss_05.pdf , the project page is https://x2robot.com/oss#resources , and the Hugging Face org is https://huggingface.co/x-square-robot . The caveat is obvious but important. Zero-shot still does not solve the hardest man

174 million smart TVs, most of which run Linux. 3.9 billion Android phones. Zero marketing. Tonight, somewhere around the world, a person will press the power button on their Samsung TV. A proprietary Samsung logo will appear. A polished menu will load. They will open Netflix, scroll through recommendations, and pick a movie. They will never know that every frame they see is being scheduled, managed, and rendered by a Linux kernel, the invisible engine that sits between apps and hardware. They will then reach for their Android phone to check something on social media. Another Linux kernel. If they are sitting in a Tesla, the touchscreen showing their charging status is running yet another Linux kernel. The “year of the Linux desktop” debate has been running for two decades. Entire forums exist to argue about whether 2025, 2026, or 2027 will finally be the year Linux takes over the PC market.

The Principle of Least Privilege: Operational Speed's Security Cost While developing a production ERP, delayed shipment reports were always a headache. One of the main reasons behind incomplete reports was the complexity of privilege layers in the system and, often, excessive permissions granted. In this post, I will delve into the costs we pay when we stretch security boundaries in an effort to gain operational speed. The principle of least privilege is more than just a security concept; it's critically important for operational efficiency and system stability. In this article, I will explain the impact of the principle of least privilege on operational speed, the security risks it entails, and how I've tried to strike this balance with concrete examples from my practical experience. My goal is to move beyond superficial definitions and dive deep into this topic based on my real-world field experiences, providing actionable insights to readers. Why Does the Principle of Least Privilege Seem to Hinder Operational Speed? The general tendency is to provide instant access to all relevant tools and data to speed up a task. This can be appealing, especially in an emergency or before a critical delivery. However, the Principle of Least Privilege (PoLP) advocates the opposite: a user or system component should have the absolute minimum privileges required to perform its task. This might initially seem to slow down operational processes. For example, a development team having unlimited SELECT rights to a production database might facilitate running an urgent query. However, the same developer could accidentally run UPDATE or DELETE commands, causing serious damage to the system. Such an incident, instead of speeding up a query in the short term, could lead to hours of downtime and data loss. This is where the long-term risk posed by operational speed, which PoLP is thought to hinder, becomes apparent. Another example is a system administrator frequently using the sudo su co

You've seen this before. You ask your AI agent: "Find ∫ x·e^x dx" It confidently replies: e^x + C , complete with a plausible-looking derivation. You nod. Then you check — the correct answer is (x−1)·e^x + C . It was wrong by a mile, and you almost shipped it. This is the fundamental problem with AI math today: LLMs can talk, but they can't verify their own work. They sound convincing while being catastrophically wrong. And the more complex the problem, the better the hallucination. Math.skill changes that. It's an open-source mathematical reasoning skill for AI agents — install it, and your agent stops guessing and starts verifying. What Makes It Different Typical AI Math Plugin Math.skill Workflow Prompt → LLM → answer Prompt → 7-step pipeline → ≥2 verifications → answer Verification None Answer blocked if verification fails Open problems Might hallucinate a "solution" Honestly says "this is unsolved" Error recovery No mechanism Auto-backtrack, fix, recompute, re-verify The core differentiator: a verification engine that runs at least 2 of 11 independent checks on every answer. No answer leaves the pipeline unverified. Period. The 7-Step Pipeline Every problem flows through this: Step What Happens Why It Matters 1. Parse Extract conditions, goals, variables, implicit domain constraints Catches misread problems before they waste your time 2. Model Build formal representation: equation, function, matrix, probability space, etc. Prevents building the wrong mathematical structure 3. Select Choose the optimal method from 30+ strategies Avoids brute-forcing when elegance exists 4. Solve Step-by-step with mathematical justification at every transformation Full traceability — nothing hidden 5. Verify Apply ≥2 of 11 independent verification methods The differentiator — catches what LLMs miss 6. Correct If verification fails: backtrack to last known-good step, fix, recompute, re-verify No "doubling down" on wrong answers 7. Deliver Exact answer (not approximate), domain con

The Moment I Realized I Didn't Really Know JavaScript I was 2 months into learning JavaScript. I could use .map(), .filter(), .reduce() like any bootcamp grad. I felt confident. Then my instructor asked me one question: "How does .reverse() actually work?" I froze. I had used it hundreds of times. But I had no idea what was happening inside. I was a user, not a builder. That was the day everything changed. The 01EDU Difference: Build Tools, Not Just Use Them Most coding courses teach you to use built-in methods. 01EDU does something different. They disable the built-in methods. Then they say: "Now build it yourself." No .split(). No .join(). No .indexOf(). No .slice(). Just you, a text editor, and your brain. What I Built in 2 Weeks (Without Using Built-ins) Here are the JavaScript methods I re-created from scratch: Method What I Learned abs() Math is logic, not magic multiply(), divide(), modulo() Arithmetic is repeated addition/subtraction indexOf(), lastIndexOf(), includes() Searching is just looping and comparing slice() Negative indexes count from the end reverse() Arrays and strings are both indexed collections join() Building strings step by step split() Parsing is character-by-character inspection round(), floor(), ceil(), trunc() Decimals are just numbers between whole numbers Each function took hours of thinking, failing, debugging, and finally — understanding. The Most Painful Lesson: Loops The first time I tried to build repeat() without using .repeat(), I wrote an infinite loop. My computer froze. I had to force restart. That failure taught me more than any working code ever could. I learned to trace each iteration mentally. I learned to check my exit conditions. I learned to respect the loop. You don't truly understand loops until you've crashed your computer with one. What 01EDU Taught Me That No Bootcamp Could I learned how computers think, not just how to write code When you build .split() from scratch, you understand string parsing at a deep level.

AI applications are moving quickly from simple chatbots to systems that can search, reason, recommend, summarize, and act on live business data. For developers, that usually means wiring together databases, embedding models, vector search, rerankers, orchestration logic, and application code. For no-code AI builders, it often means waiting for those integrations to exist before an idea can become a working prototype. The MongoDB extensions for Dify help close that gap. With the new MongoDB Atlas and Voyage AI extensions, Dify builders can visually compose AI workflows and agents that connect directly to MongoDB data, perform semantic retrieval with Atlas Vector Search, improve result quality with Voyage AI embeddings and reranking, and optionally interact with operational documents through controlled database tools. The result is a practical path from idea to working AI application: less custom orchestration code, more reusable building blocks, and a smoother experience for both developers and no-code builders. Why Dify and MongoDB Belong Together Dify provides a visual environment for building AI apps, workflows, and agents. It makes it easy to connect user input, model calls, tools, prompts, and outputs into a working application. MongoDB Atlas provides the data foundation: flexible documents, operational queries, aggregation, full-text search, and vector search in one platform. Together, they create a powerful pattern: Dify orchestrates the AI experience — workflows, agents, prompts, tools, and user interactions. MongoDB Atlas stores and retrieves the data — documents, application records, knowledge sources, and vector embeddings. Voyage AI improves retrieval quality — embeddings for semantic search and reranking for precision. For a no-code builder, this means you can assemble a retrieval-augmented generation workflow visually. For a developer, it means the integration points are packaged as reusable Dify tools rather than one-off glue code. Meet the Extensions

CSS @function - DEV Community CSS just got its biggest quality-of-life upgrade since custom properties. For years, if you wanted... dev.to

You’re Overpaying Every Day — You Just Can’t See It Think about the last time you asked an AI to clean up your meeting notes. You probably opened a new chat, pasted in the transcript — maybe 1,500 words — then pasted your usual notes template on top of that, then said something like “format this, bold the action items.” It worked. Useful, even. But here’s what actually happened: the model just read ~3,000 words to produce ~300 words of output. Do that five times a week. Every week. And now think about what’s riding along in that context every single time — your template, your formatting preferences, all the background you’ve already explained before. The model doesn’t remember any of it. It reads it fresh on every call. Every repeat. Every charge. This isn’t a flaw in ChatGPT. It’s the fundamental nature of chat as a paradigm. 2. Chat Is Great — But It Has a Structural Bug Chat is the most natural way to start with AI. Unclear what you want? Talk it out. Need to change direction? Just say so. The feedback loop is instant, the barrier is zero. That’s why everyone starts there. But chat has a structural problem: every single turn carries the entire history. This is how context windows work — the “conversation history the model reads every single time.” Every API call packages up your full history and sends it to the model. You pay for every token the model reads. Ten rounds in, round ten doesn’t cost the price of one message. It costs the price of all ten, stacked. Here’s a concrete version of this. Say you use AI to write your weekly status update. You paste in your bullet points from the week, say “turn this into a proper update,” tweak the tone, go back and forth a couple times. Feels efficient. But those bullets, plus the AI’s draft, plus your follow-up messages, plus the format you’re implicitly re-explaining each time — the real token cost of one weekly update is probably 5 to 8x what you’d guess. You’re paying for repeated context. The bill just isn’t obvious e

🔮 Hermes Agent 🤖: A Practical Guide 🔥 — and How It Stacks Up Against OpenClaw & GoClaw 📊 Hermes Agent Challenge Submission Truong Phung Truong Phung Truong Phung Follow May 18 🔮 Hermes Agent 🤖: A Practical Guide 🔥 — and How It Stacks Up Against OpenClaw & GoClaw 📊 # hermesagentchallenge # devchallenge # agents 7 reactions Comments Add Comment 16 min read

How I Built a Bilingual AI-Powered Business Manager for Street Vendors in Jamshedpur The Problem Walk through any street in Jamshedpur, Jharkhand, and you'll find hundreds of small shop owners and hawkers running their entire business from memory — stock levels, daily sales, employee wages, profit margins. They have no tools. Notebooks get lost. Mental math fails. And at the end of the day, most don't even know if they made a profit. These vendors are not tech-illiterate. They have smartphones. They use WhatsApp. But every existing business app is either too complex, too expensive, or only available in English. I wanted to solve that. The Solution — Dukaan Manager Dukaan Manager is a free, offline-capable Progressive Web App (PWA) and Android app built for small shop owners and street vendors in India. It runs entirely in the browser — no server, no subscription, no data charges beyond the initial load. Built using HTML, CSS, JavaScript , and powered by the Gemini 2.0 Flash API , it gives every small vendor access to a smart business advisor in their pocket. What It Does 📦 Stock Management Vendors can add items by name or photo. Each item stores buying price, selling price, and quantity. The app automatically calculates profit margins and flags low-stock items with colour-coded alerts. 💰 Daily Sales Recording Sales are recorded item by item, linked to which employee made the sale. The app auto-fills prices and calculates the total. Unsaved drafts survive accidental page refreshes using sessionStorage. 👥 Employee Tracking Add employees with their role, salary, phone number, and joining date. Mark daily attendance (present/absent) with one tap. Track total sales made by each employee across all time. 📅 Sales History Every saved sale is stored permanently in the browser's localStorage. History is grouped by date, collapsible, and shows daily revenue and profit — clean and simple. 🤖 AI Business Advisor (Gemini-Powered) This is where Google AI comes in. Using the Gemini