AI 资讯

AWS released Managed Knowledge Bases for Amazon Bedrock on 17 June 2026. The feature significantly reduces the operational complexity of building Retrieval-Augmented Generation (RAG) solutions by allowing Bedrock to manage the vector storage, indexing, embeddings, and retrieval infrastructure on your behalf. For teams looking to deliver an Agent Core proof of concept or their first production RAG workload quickly, this can be a compelling option. However, there are some important trade-offs to understand before committing to the managed approach. Traditionally, a Bedrock Knowledge Base required a customer-managed vector store such as: OpenSearch Serverless OpenSearch Managed Clusters Aurora PostgreSQL with pgvector Pinecone DocumentDB Other supported vector databases With a Managed Knowledge Base, Bedrock handles the underlying vector infrastructure and embedding model selection for you. Creating one from the AWS CLI is straightforward: aws bedrock-agent create-knowledge-base \ --name "my-managed-kb" \ --role-arn "arn:aws:iam:: ${ AWS_ACCOUNT_ID } :role/service-role/AmazonBedrockExecutionRoleForKnowledgeBase_ihv1p" \ --knowledge-base-configuration '{ "type": "MANAGED", "managedKnowledgeBaseConfiguration": { "embeddingModelType": "MANAGED" } }' Advantages Lower operational overhead There is no need to provision, secure, monitor, patch, or scale a separate vector database. Lower costs S3 storage is cheaper than database storage. Pay only for each ingestion and retrieval operation. Indexing and searching compute is free. No 24/7 server costs. Faster time-to-value Managed Knowledge Bases make it possible to stand up a RAG solution in minutes rather than days. Automatic embedding management Bedrock manages embedding selection and indexing, reducing the number of architectural decisions required from development teams. Cost-effective for smaller workloads The managed model can be attractive for: Proofs of Concept Departmental knowledge bases Agent Core pilots Workloads wi

A few weeks ago I was in Copenhagen for my first Codegarden, and one quiet thought has stuck with me since. It didn't come from a keynote. It came from the bit the keynote leaves out. I've worked with Umbraco for years, but I'd never been to Codegarden, and I turned up without much of a fixed idea of what the two days would be. I kept that open on purpose. I wanted to take it in rather than measure it against something I'd decided in advance. What struck me most was that the value came from two places at once. The sessions were a fantastic source of inspiration; everything from keynotes to guest speakers all seemed to resonate in some way or another. The conversations in between the sessions - drifting around the event space and finding common ground with anyone and everyone - proved just as valuable. I came home more energised than I've been in a while, with a notebook full of half-formed ideas and a better feel for the community I'm part of. But the thing I kept turning over afterwards was that bit the keynote leaves out. That's what I want to write about. The easy half and the hard half Every major Umbraco release gets the same treatment. A polished keynote, a clean demo, a feature that looks effortless on stage. There's plenty in 18, and which part matters most depends on what you're building. For me it's Elements: a new Library section where you manage reusable content and reference it through a new element picker. Create once, use everywhere. It's a genuinely good direction. Reusable content has lived awkwardly in the content tree for years, and Library finally gives it a proper home. What the demos don't show you is the part I've been playing around with for the past few weeks. Taking a real Umbraco 17 site, with content pickers threaded through block lists, block grids, rich text blocks and base document properties, and getting all of it to point at the new Library without an editor ever noticing anything moved underneath them. The feature is the easy half.

I know the title was way bold or offensive but listen to me. I study in a convent school where some teachers are nuns and there's one nun teacher that seriously pisses me off cause she changed my main subject from computer to commerce without telling me and when I asked she said "It is for your well-being!" and I was literally feeling I should crash out right there about why does she cares about my options and well fare and I was so targeted by her that I had to involve the principal and other teachers. She did a frickin poll that we should let me have the computer or not which was her way to embarrass me still everyone voted for me. After I got my main subject as computer science back she tried to embarrass me in class saying "Some people play some random only games on PC/Laptops and think they are now coders and devs" but my classmates were at my side and said at her own face how many lines of code do u know? how many languages of computer do u know? We all have saw u from months targeting him this isn't good he takes the subject he likes and its his own option if he fails in the commerce in future what will you do? will u compensate him for not letting him be at his option like everyone crashed out and she dismissed the class 15 minutes before the bell and She is a woman who thinks that what she is doing is good and will always be if anything goes wrong it is the front guy's blunder not hers she got transferred here because of this same behavior You might be thinking Why everyone voted for me: This is Because before she joined I was in my prime in my school life I was a almost celeb(just kidding that didn't happened, unlucky me :C ) I was at my prime I was competing with 9th to 12th graders while being a 7th Grader I made a solar system model using threejs while everyone out there was using micro: bit and I was so good at that time that i became the spike of some ppl eyes, even principal was one of them. Conclusion This post contains my frustration about some ppl

Hey DevHunt community! 👋 I'm incredibly excited to launch Scankii! As developers, we are building more and more AI Agents using frameworks like LangChain, OpenHands, and AutoGen. The standard paradigm is giving these agents "skills" or "tools" — which are basically just Python functions combined with Natural Language instructions (prompts or docstrings). But here is the problem: Standard secret scanners (like GitLeaks or TruffleHog) are blind to AI-specific vulnerabilities. They only scan source code for hardcoded secrets. But what if your Python code securely loads an API key, and your English instructions accidentally trick the agent into printing that key to stdout? The agent framework captures that output, injects it into the LLM context window, and your secret is suddenly exposed. We call this Cross-Modal Leakage. Enter Scankii. 🛡️ Scankii solves this by analyzing the intersection of your Natural Language and your code. It uses a dual-engine pipeline (NL Semantic Analyzer + AST Syntax Analyzer) to track variable flows between your prompts and your code sinks. ✨ Core Features: Dual-Engine Scanning: Correlates English instructions with Python ASTs. Local-First & Fast: Your proprietary agent tools and code never leave your machine. CI/CD Ready: Outputs standard SARIF reports. Drop it into GitHub Actions or use it as a pre-commit hook. Framework Agnostic: Works with LangChain, AutoGen, CrewAI, MCP, or any custom python agent framework. I built Scankii to give developers peace of mind when scaling their agent toolchains. Security shouldn't be an afterthought when building autonomous systems. I would love for you to try it out on your agent repos, star the project, and leave any feedback or questions below! I'll be here all day answering them. 👇 GitHub Repository: https://github.com/ashp15205/scankii Installation: pip install scankii

In the open source world, we often say that "your code is your resume." However, what many developers forget is that every time they run a git push , they're handing over much more than just lines of code. They're delivering a permanent digital trail that can be tracked, analyzed, and used against them years later. This article is an educational guide about privacy in Git and how the metadata you generate every second can compromise your security and your professional future. The command that reveals your "naked identity" If you want to see exactly what you're sharing with the world, open your terminal in any repository and run the following command: git log --format = fuller Unlike the standard log, the fuller format breaks down the complete anatomy of your contributions. This is where most developers are shocked to see what they're actually leaking: 1. Name and Email (Direct Exposure) Git stores your user.name and user.email locally in every commit. This data: Is harvested by spam bots for marketing databases. Allows recruiters to map all your historical activity, even on projects you no longer represent. Exposes you to doxxing attacks if you contribute to controversial projects. 2. Dates and Timestamps (Time Analysis) Git not only saves the day, but also the exact second and time zone of the authorship and commit. This allows you to create an activity pattern : What time do you usually code? Are you working on personal projects during office hours? What geographical area are you actually in? 3. File Patterns and Metadata In addition to text, if you upload binaries (PDFs or images), these usually contain EXIF ​​metadata (GPS coordinates, camera model, etc.) that Git doesn't clean up by default. The Problem with "Permanent Logs" GitHub is, by design, an accountability platform, not a privacy one. Once a commit enters the public history, deleting it is extremely difficult and often pointless if the repository has already been cloned or indexed by third-party service

Exam Guide: Developer - Associate 🏗️ Domain 3: Deployment 📘 Task 1: Prepare Application Artifacts To Be Deployed To AWS Before you can deploy anything to AWS, you need to package it properly. This task covers Lambda deployment packaging (zip vs container), managing dependencies, structuring projects for multi-environment deployment, and using AWS AppConfig for runtime configuration. 📘Concepts Lambda Deployment Packaging Options Option Max Size Build Complexity Cold Start Best For Zip Package (inline editor) 3 MB (editor limit) None Fastest Simple functions, no dependencies Zip Package (upload) 50 MB compressed / 250 MB uncompressed Low Fast Most Lambda functions Zip + Lambda Layers 250 MB total (function + all layers) Medium Fast Shared dependencies across functions Container Image 10 GB Higher Slower (first invoke) ML libraries, large dependencies, custom runtimes 💡 If a scenario is about a deployment package exceeding 250 MB, the answer is container images. If it mentions sharing dependencies across multiple functions, the answer is Lambda Layers. Zip is the default for most workloads. Lambda Layers Aspect Detail What They Are Zip archives containing libraries, custom runtimes, or other dependencies Max Layers Per Function 5 Size Limit 250 MB total (function code + all layers uncompressed) Versioning Each publish creates an immutable version Sharing Can be shared across functions, accounts, or made public Path Contents extracted to /opt in the execution environment Dependency Management Strategies Strategy How It Works Pros Cons Bundle In Zip Install deps into package directory, zip together Simple, self-contained Larger package, duplicated across functions Lambda Layers Package deps as a layer, attach to functions Shared across functions, smaller deploys Layer version management, 5-layer limit Container Image Install deps in Dockerfile Full control, large deps supported Slower cold starts, ECR management sam build SAM resolves deps from requirements.txt automatic

TL;DR: The idea is to use a set of ordinary JPEG images as a distributed medium for an encrypted container. The data is split into redundant fragments and distributed across the images, allowing the container to be recovered even if some of the JPEG files are lost. We will not examine the code; instead, we will focus only on the general principle behind the approach. Where the idea came from Most steganographic systems follow a "one image — one file" model. If the image is lost or damaged, the embedded data is lost with it. The idea was to distribute the data container across multiple JPEG images while preserving the ability to recover the data even if some of those images are lost. I should point out right away that this is not steganography in the traditional sense. The additional data is written to JPEG images after the EOI ( End Of Image ) marker, where it can be easily detected by even the simplest analysis. The goal is not to completely hide the presence of the data, but to use ordinary JPEG images as a distributed medium for an encrypted container. At the same time, in everyday use the JPEG images remain fully functional, look like ordinary photographs, and do not attract attention. Even if the additional data is detected, it does not reveal whether it is an encrypted container, write artifacts, or simply an arbitrary sequence of bytes. Why JPEG First, JPEG is the most widely used image format. Photographs in this format naturally accumulate on computers and phones and are shared between people. Second, this format has one important property: a JPEG file ends with a special EOI marker. Everything written after it is ignored during image decoding, so the additional data does not affect how the image is displayed. This is the area we will use: JPEG JPEG Additional data EOI data │ │ │ ...░░░░░░░░░░░░░▓▒▒▒▒▒▒▒▒▒▒▒▒▒▒ At first glance, the solution looks extremely simple: just split the encrypted container into fragments and write one fragment to the end of each JP

Here is a question that sounds like a trick: can you build an accurate classifier out of models that are barely better than flipping a coin? Surprisingly, yes. That is the whole idea behind boosting, and AdaBoost is the algorithm that made it famous. I built it from scratch and dropped it into an interactive demo — here's how it actually works, real math, no hand-waving. Play with the live version: https://dev48v.infy.uk/ml/day21-adaboost.html The weak learner: a decision stump AdaBoost's building block is the simplest classifier you can imagine: a decision stump . It is a decision tree with exactly one split. Look at one feature, compare it to one threshold, and call everything on one side "+1" and everything on the other side "−1". That's it. One line, one cut. def stump_predict ( X , dim , thresh , polarity ): pred = np . ones ( len ( X )) if polarity == 1 : pred [ X [:, dim ] <= thresh ] = - 1 else : pred [ X [:, dim ] > thresh ] = - 1 return pred On anything that isn't trivially separable, a single stump is hopeless — on a checkerboard layout it barely passes 55-60%. That is exactly why it's a "weak learner": a model that only beats random guessing by a hair. The magic is in how we combine hundreds of them. Sample weights: a moving spotlight The engine of AdaBoost is a weight on every training point that says "how much does getting this one right matter?" Everything starts equal: n = len ( X ) w = np . full ( n , 1.0 / n ) # uniform: every point weighs 1/n These weights are a probability distribution — they sum to 1. After each round they change: points we got right get lighter, points we missed get heavier. Since we always pick the next stump to minimise weighted error, the heavy points end up dominating the search. The next stump is effectively forced to stare at whatever the committee keeps blowing. Weighted error, not a plain count When we hunt for the best stump each round, we don't count mistakes — we add up the weight of the mistakes: def weighted_error

For years I copied cron expressions off Stack Overflow, pasted them into a config file, crossed my fingers, and moved on. 0 9 * * 1-5 ? Sure, that "looks like weekday morning." */15 * * * * ? "Every 15 minutes, probably." I never actually read them. So I did the thing that always cures this for me: I built a tool that parses a cron expression, explains it in plain English, and shows the next five times it will fire. No library. About 50 lines of real logic. Here's everything I learned. The five fields (and the order that trips everyone up) A standard cron expression is exactly five fields separated by spaces: ┌──────── minute 0 - 59 │ ┌────── hour 0 - 23 │ │ ┌──── day - of - month 1 - 31 │ │ │ ┌── month 1 - 12 │ │ │ │ ┌ day - of - week 0 - 6 ( 0 = Sunday ) * * * * * The order never changes, and the number-one beginner mistake is swapping the first two. Minute comes first. If you write 9 30 * * * thinking "9:30am," you actually get "minute 9, hour 30" — which is invalid, because hours only go to 23. Say it out loud every time: minute, hour, day-of-month, month, day-of-week. Each field answers one question: which values of this unit does the job run on? An * means "every value." Most real schedules pin down a couple of fields and leave the rest as * . Daily at 9am is 0 9 * * * — minute and hour fixed, everything else "every." Lists, ranges, and steps Beyond single numbers, each field understands three operators, and they combine: Comma makes a list: 1,15 in the day field means the 1st and the 15th. Hyphen makes an inclusive range: 1-5 in the day-of-week field means Monday through Friday. Slash makes a step, taking every n-th value: */15 in the minute field means 0, 15, 30, 45 . Steps can apply to a range too, so 0-30/10 means 0, 10, 20, 30 . That's the whole grammar. Number, list, range, step. Once you can expand a field into the concrete set of numbers it matches, you understand cron. Here's the expansion function, which is the heart of the parser: function expandFie

Tree of Thoughts was a genuine leap. Instead of reasoning in one straight line, it branches into several lines, scores them, prunes the dead ends, and searches for the best path — so a puzzle that would sink a single chain of thought becomes solvable. But a tree has one restriction baked right into its shape, and once you see it you can't unsee it: every node has exactly one parent. A branch can be extended or abandoned. It can never be combined with another branch. That matters more than it sounds. Real problems decompose, and when they do, different branches each get part of the answer right. Branch A nails the first half; branch B nails the second half; neither is fully correct on its own. A tree is forced to pick one and throw the other's good half away. Graph of Thoughts (GoT) removes exactly that restriction. 🕸️ Interactive demo (a real merge-sort that branches, merges, and refines — with live-verified scores): https://dev48v.infy.uk/prompt/day21-graph-of-thoughts.html The core idea: thoughts are nodes in a graph GoT reframes reasoning as building a graph . Each vertex is a thought — a partial solution or intermediate result. Each edge is an operation that produced one thought from one or more others. Because it's a general graph and not a tree, a thought is allowed to have several parents, and edges can even loop back on themselves. That single change in the data structure is the entire conceptual leap. Everything else is just the operations you're now free to run on that network. The four operations generate (branch) — the familiar move, straight from Tree of Thoughts. From one thought, produce several different next thoughts. This can also be a split : break the input into independent sub-problems solved on separate branches. Diversity matters here — near-duplicates waste budget. score / rank — turn each thought into a number so the controller can compare them. Objective scorers win: a validator, a test, a metric. In the demo, the scorer is deliberately con

NBCUniversal executives are about to find out whether Peacock will sink or swim in the streaming industry. Now that Comcast is planning to split NBCUniversal, Peacock, and Sky from its broadband and wireless businesses, Peacock will be forced to stand on its own - without the backing of a combined company that pulled in more […]

We're a team of three friends who have been working with forms and Open Source for a decade, and we joined forces together to create something where we can apply all of our experience. We recently released GolemUI, an Open Source library to generate forms dynamically from JSON definitions, with a typed layer to simplify authoring. This library has a lot to offer. These are the main characteristics: 1. A JSON engine. The form is governed by a JSON definition that you can store in a DB, version, d

Please state the location and include REMOTE for remote work, REMOTE (US) or similar if the country is restricted, and ONSITE when remote work is not an option. Please only post if you personally are part of the hiring company—no recruiting firms or job boards. One post per company. If it isn't a household name, explain what your company does. Please only post if you are actively filling a position and are committed to replying to applicants. Commenters: please don't reply to job posts to compla

Share your information if you are looking for work. Please use this format: Location: Remote: Willing to relocate: Technologies: Résumé/CV: Email: Please only post if you are personally looking for work. Agencies, recruiters, job boards, and so on, are off topic here. Readers: please only email these addresses to discuss work opportunities. Searchers: try https://nthesis.ai/public/hn-wants-to-be-hired , https://www.wantstobehired.com .

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [留言]

Dbrand apparently knows how to really whiff a product launch, as we recently saw with the Steam Machine Companion Cube shell. But it also makes the best case out there for the Nintendo Switch 2, which happens to be discounted right now. The Killswitch 2 is versatile, with better build quality than other similar models, […]

Let’s start with a game. Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1 and 10.” You’re going to get 7. Almost always. Now type “Another” and you’ll get 3 or 4. Type “Another” again and you’ll get 8 or 9. That won’t work every time—but if it…