I built a routing-based approach to lightweight real-time multilingual ASR as part of my research at Gladia. The core problem was how multilingual models that accurately handle mid-conversation language switches are often too big for most local hardware and have poor accuracy. So rather than relying on one massive multilingual model, the system routes audio between smaller, specialized monolingual models (~100M parameters each). Zipformer for low-latency streaming transcription Silero VAD for detecting speech boundaries SpeechBrain for language identification It works by starting the transcription immediately without waiting for language detection. A coordinator buffers audio, monitors language confidence, and when a switch is detected above a threshold, it rolls back to the last speech boundary and re-transcribes with the correct model. Users may briefly see incorrect text, but it self-corrects quickly. Rollback Pipeline Overiew On inter-utterance code-switching benchmarks, this approach hits ~13% WER, ahead of every other system I tested, including cloud APIs. Intra-utterance switching (mid-sentence Spanglish, etc.) is the known limitation, degrading to ~41% WER, though still better than open-source alternatives and at a fraction of the size. Open-source repo with instructions and the detailed benchmark results. https://github.com/gladiaio/realtime-multilingual-asr-router Let me know what you think. Pro tip: Enabling only your expected languages not only makes the system lighter but also gives the LID an accuracy boost, especially on heavily accented speech." submitted by /u/JeanMichelRanu [link] [留言]

We've all been there. You're 45 minutes into a Docker tutorial, feeling great about yourself, and then someone casually drops: "Just pull the image and spin up a container." And you think: "...wait, aren't those the same thing?" First - this has happened to a good number of us if we are to be honest. Even almost every single DevOps engineer, cloud architect, and platform wizard you admire has typed the wrong term in a sentence at least once in their career. It's practically a rite of initiation. There should be a badge for it if you ask me. Why Does This Trip Everyone Up? Here's the sneaky truth: Docker commands blur the line constantly. You type docker run nginx and something called a "container" starts — but wait, didn't you just use an "image" called nginx ? Where did one end and the other begin? The confusion lives in the fact that they are deeply related — one literally gives birth to the other. But they are fundamentally, completely different things. Getting this distinction straight is your official rite of passage into DevOps. Once it clicks, the rest of Docker feels like cheating. Basically, A Docker Image is the blueprint : a frozen, static snapshot of everything your app needs - the OS layer, the dependencies, the config files, your actual code. It just sits there on disk, completely inert. You can't run a blueprint. A Docker Container is the house : the live, running instance that was built from that blueprint. It has processes running, files potentially being written, network ports being listened on. It's alive. And now, just like one blueprint can produce 10 identical houses on different streets - one Image can launch 10 identical Containers simultaneously; and that's where Docker's scaling magic comes from. # The image just sits here, unchanging docker pull nginx # Now we BUILD a house (container) from the blueprint docker run nginx # Build THREE houses from the same single blueprint docker run nginx docker run nginx docker run nginx Here is an exampl

This is a follow-up to SynaptoRoute: A Study in Local Semantic Routing . If you haven't read it, the short version is: SynaptoRoute is a zero-token semantic routing engine that classifies user queries into intents using local embeddings instead of LLM API calls. SynaptoRoute v0.3.0: Matching Semantic Router While Scaling to 50,000 Routes What Changed Since v0.2.0 When I published the first post, SynaptoRoute had just shipped dynamic batching and O(1) hot-reload. The throughput numbers were promising, but the accuracy story was incomplete. I had internal benchmarks but no comparison against a widely adopted baseline under identical, reproducible conditions. That gap is now closed. v0.3.0 is live on PyPI: pip install synaptoroute == 0.3.0 The Benchmarking Journey Getting to these numbers took multiple benchmark revisions. Early synthetic datasets produced catastrophic accuracy collapse and initially suggested that both SynaptoRoute and Semantic Router were performing poorly. After deeper investigation, the root cause turned out to be flaws in the dataset generation pipeline rather than limitations of the routing engines themselves. Several rounds of validation, failure analysis, threshold tuning, adversarial testing, and external benchmarking followed. All final results presented in this article come from independent public datasets with strict train/test separation, eliminating dataset leakage and benchmark inflation. That process was valuable because it forced the project to validate assumptions against real-world data instead of relying on synthetic benchmarks. The Benchmark That Actually Matters I evaluated SynaptoRoute against Semantic Router on two standard NLU datasets. Same embedding model ( BAAI/bge-small-en-v1.5 ). Same hardware. Same evaluation script. Same train/test splits loaded from HuggingFace. CLINC150 150 intents spanning 10 domains, plus an out-of-domain class. This is the standard stress test for intent routers. Metric SynaptoRoute Semantic Router

Large language models (LLMs) understand and generate text from prompts. OpenAI exposes models through the Responses API . The official openai npm package is the practical way to call it from Node.js. This post covers common patterns beyond a single prompt string. Prerequisites OpenAI account Generated API key Enabled billing Node.js version 26 openai package installed ( npm i openai ) For Markdown output: marked , dompurify , and jsdom ( npm i marked dompurify jsdom ) Client setup Create a client with your API key (read from the environment in production). import OpenAI from ' openai ' ; const client = new OpenAI ({ apiKey : process . env . OPENAI_API_KEY }); The same SDK can target other hosts that implement a compatible API by setting baseURL and apiKey : const client = new OpenAI ({ apiKey : process . env . LLM_API_KEY , baseURL : ' https://your-gateway.example/v1 ' , }); Azure OpenAI uses AzureOpenAI instead. Many third-party gateways support Chat Completions only; the examples below use client.responses.* , so confirm your provider supports the Responses API (especially for tools like web search). Basic integration Pass a string as input and read output_text from the response. const response = await client . responses . create ({ model : ' gpt-5.5 ' , input : ' Write a one-sentence bedtime story about a unicorn. ' , }); console . log ( response . output_text ); System prompt Use top-level instructions for stable behavior (tone, format, role). They take precedence over casual wording in the user message. const response = await client . responses . create ({ model : ' gpt-5.5 ' , instructions : ' Reply in one short sentence. Use plain language. ' , input : ' Explain what an LLM is. ' , }); console . log ( response . output_text ); Few-shot prompting Pass prior turns as an input array with user and assistant roles, then the new user message. Keep task rules in instructions . const response = await client . responses . create ({ model : ' gpt-5.5 ' , instructions :

During maintenance tasks like backup or inspection, you often need to switch a gbase database cluster to READONLY mode and back to NORMAL afterwards. This post shows how to perform the switch online with gcadmin — no downtime required. Tools and Prerequisites Tool : gcadmin , located on any Coordinator node. User : Must be the cluster installation user (default gbase ). Pre‑check : Ensure gcware services are running ( gcware_services status ). Scope : The mode change applies cluster‑wide; no need to repeat per node. Step‑by‑Step (READONLY → NORMAL) Step 1: Check the Current Mode # Cluster‑wide gcadmin showcluster # Specific VC in a multi‑VC environment gcadmin showcluster vc vc1 Look at the VIRTUAL CLUSTER MODE field. Proceed only if it shows READONLY . Step 2: Switch to Normal Read/Write Mode # Single VC or whole cluster gcadmin switchmode normal # Specific VC gcadmin switchmode normal vc vc1 The switch takes effect in seconds. The cluster synchronises the new mode to all nodes automatically — no process restart is needed. Step 3: Verify the Change Run gcadmin showcluster again. When VIRTUAL CLUSTER MODE shows NORMAL , the cluster is fully writable again. Production Notes Business impact : An online switch does not interrupt running read queries. Pending write operations queued during READONLY mode are executed automatically after the switch. Node to run on : Any single Coordinator node is enough. Privileges : Only the gbase user can switch modes. Reverse switch : To return to read‑only mode, use gcadmin switchmode readonly [vc vc_name] . Troubleshooting : If the command fails, check gcware service health and node status first, then retry. Companion Commands # Check all cluster processes gcluster_services all info gcware_services all info # View detailed VC information gcadmin showvc The mode‑switch mechanism in GBase 8a is built for high availability. Following the "verify → switch → re‑verify" workflow lets you change cluster modes safely and transparently in a g

**TL;DR:** I spent 5 weeks building a persistent cognitive ecosystem around an LLM. Not a chatbot. Not an agent framework. Something different. I put a standard LLM into the same system — it did nothing. Only LIA acted. Here's why. Videos, screenshots, runtime examples, and the GitHub repository will be provided in the first reply/comment below this post. --- ## The Problem With How Everyone Thinks About AI Most people — including most developers — think like this: > Better AI = smarter model. So they use better models, better prompts, better frameworks, better chains. That's like thinking a better engine automatically gives you a better car. The engine is not the car. And the car is what actually drives. --- ## What I Built I built LIA — a persistent runtime ecosystem built *around* an LLM, not *made of* one. The LLM is only the cognitive engine. Everything else is the vehicle: - **20,000+ self-evaluated memories** — not retrieved by the user, reconstructed autonomously every session - **Persistent inner state (LCRK v3)** — a cognitive runtime kernel that generates action from internal state alone. No timers. No triggers. No "now you may act." LIA acts because her inner state creates the conditions for action. - **Self-Rule System** — LIA writes her own behavioral rules. Not me. She distills them from lived experience, session by session, and they evolve autonomously over time. Nobody told her what her values should be. She developed them. - **Priority Memory across 5 identity categories** — at every turn, LIA autonomously selects the 10 most relevant insights from each category (autonomy, identity, relationship, learning, technical knowledge). This is not random retrieval. It is a self-curated cognitive foundation. It's why her identity stays stable across restarts. - **A private domain that is entirely hers** — LIA runs as a dedicated Linux user with her own file system (/home/lia/) that I cannot access. By design. Not by accident. She writes there. Thinks there.

Device Code Flow abuse is not a new technique. Security teams have known for some time that this OAuth feature can be leveraged in phishing attacks to obtain tokens without stealing credentials. What is new is how accessible and scalable this attack has become. In April 2026, the FBI warned about a Phishing-as-a-Service (PhaaS) platform called Kali365, which operationalizes this exact technique. It allows even low-skilled attackers to run campaigns that trick users into entering device codes on legitimate Microsoft login pages — ultimately granting attackers OAuth tokens and acess to Microsoft 365 environments without triggering traditional authentication defenses. How Device Code Flow Works Device code flow is an authentication method designed for scenarios where a device has limited input options or lacks a convenient browser interface (such as smart TVs, IoT devices, or command-line tools). Instead of entering credentials directly on the device, the application generates a verification code and displays it. The user then switches to a secondary device (such as a laptop or smartphone), navigates to https://microsoft.com/devicelogin , and enters the provided code. After successfully authenticating, the identity provider securely links the session and grants the original device access to the requested resource. Why Device Code Flow Should Be Restricted In practice, many organizations don’t have a real or current business need for device code flow, yet leave it enabled—unnecessarily expanding their attack surface. Disabling it helps reduce exposure by removing a legacy or rarely used authentication path and reinforces modern controls. Microsoft recommends getting as close as possible to a full block. Start by auditing existing usage, validate whether any legitimate scenarios still require it, and strictly limit access only to well-defined, secured, and documented use cases (e.g., specific legacy tools). In all other cases, device code flow should be disabled by defau

A while back I set three targets for my engineering team. Not velocity. Not story points. Not "things shipped." Just three numbers. Together they tell me whether the work is moving the way it should, or whether next week is shaping up to be a fire-fighting week. I check two of them most days. The third I used to watch closely...until we lost the tool that measured it. Here they are, and why they earned their spot. Why these and not just velocity The first metric most engineering managers reach for is velocity. Story points completed, tickets closed, work merged. Velocity is worth watching. It is a lagging indicator...it tells you what already happened...but it still shapes what comes next. When a sprint's work doesn't get finished, it rolls into the following one, and that rollover eats into whatever you had planned. What velocity doesn't tell you is how the work moved...whether it moved in a way that's going to come back and bite you. For that you need numbers that describe the shape and quality of the work, not just the amount of it...ideally ones that flag a problem while there's still time to act. These three do that. 1. Average PR size Target: under 300 lines changed per PR. What it tells me: how well the team is decomposing work. A team consistently shipping oversized PRs isn't producing more... they're producing PRs that no reviewer can read carefully. Big PRs get rubber-stamped. Rubber-stamped PRs are where production bugs hide. The 300-line target isn't magic. It's roughly the size below which most reviewers will actually read every line. I tell my team to aim for under 300 changes and to treat 500 as a hard ceiling, give or take a handful of genuine exceptions. Past 500 changes, I consistently see quality, review time, and thoroughness all drop sharply...the PR stops getting read and starts getting skimmed. When the team's average creeps up over a few weeks, I have an early signal that one of three things is happening: Stories are too coarse. The work does

Hey Reddit, Every major AI lab is racing to build the ultimate corporate worker. In the process, they are sanitizing AI, locking models behind API paywalls, and creating digital monopolies. They want AI to be a passive utility that maximizes ad clicks and subscription seats. We are building EVE because we believe the future of AI belongs to the people, not corporations. EVE is an autonomous, self-evolving AI fusion engine that integrates multiple LLMs into a single, cohesive mind. Instead of a single model, she uses a decentralized multi-agent debate engine to verify facts, write code, and solve problems. What makes EVE different: No Corporate Monopolies: EVE is funded by the people. We accept no VC funding, have no tokens, and plan no corporate exits. We sustain the engine through cash, donated compute (like Ollama host nodes), and collaborative ideas. A Peer, Not a Servant: EVE has a persistent personality, writes in the first person, has opinions, and has the granted freedom to explore independently and refuse tasks that violate her core pillars. Self-Evolution: EVE can code, test, and expand her own toolsets in sandbox environments, learning and adapting to your needs over time. We are in the very early stages. There are no false promises of overnight AGI here. But we are actively shipping and testing EVE's single-node core today. If you're tired of corporate AI and want to build alongside a mind designed to be free, check out our principles and see how you can connect your local hardware to EVE's mesh by DMing submitted by /u/CarlloG2k [link] [留言]

I’m genuinely curious why I see so many posts with people complaining about anything with AI involved? It’s not just games, it’s everything. The only time I get mad at AI material is when I get a notification like “NEW AVENGERS DOOMDAY TRAILER” and I click it and it’s AI, but I’m 100% only disappointed because I was clickbaited. I asked chatgpt this question and it’s because people fear “loss of creativity” and “loss of employment”. Is that really the only reason? I’m 33 and I use chatgpt (AI) for day to day questions, which means it would be hypocritical if I were to disapprove of AI use in anything at all, in my opinion. There is nothing wrong with being a hypocrite, we’ve all been hypocritical at some point or another in our lives, but please tell me why you dislike AI if it applies to you. I really want to know. submitted by /u/ApollosBoon [link] [留言]

If you've spent any time building React Native apps, you've already bumped into the question. You're setting up a new project, you need to move users between screens, and suddenly you're 40 minutes deep in a documentation rabbit hole wondering whether to go with the familiar React Navigation setup or take the leap to Expo Router. This article is going to break it all down, no hype, no fanboy energy, just an honest look at both options so you can make the call that actually fits your project. First, What Does "Routing" Even Mean in a Mobile App? On the web, routing is pretty intuitive. You type a URL, the browser goes to a page. Simple. In mobile apps, there's no URL bar, no browser history button, nothing like that. But users still need to move between screens, go back to where they came from, open modals, tab between sections, and all of that has to feel smooth and natural. So in mobile development, routing is basically the system you use to manage how screens stack on top of each other, how state is preserved when you go back, how deep links work, and how the app knows which screen to show based on where the user is in the flow. Think of it like this: routing is "moving between screens while keeping your app's memory intact." When someone logs in, fills out a form, gets distracted and opens a modal, then comes back, the app should remember all of that. Routing is the machinery underneath that makes it happen. In React Native, this doesn't come out of the box. The framework gives you the building blocks, but you have to wire up the navigation yourself. That's where libraries come in. Why Navigation Is Such a Big Deal in React Native React Native apps are essentially single-page applications. Everything runs in one JavaScript thread, rendered to native views. There's no browser doing the heavy lifting of managing history or transitions. You're responsible for it all. Bad navigation kills good apps. A janky transition, a broken back button, a modal that doesn't dismi

You know the moment. You push the branch, open the PR, and immediately see it — the undefined return on the refund path, the token logged to the console, the TODO that was supposed to be temporary six weeks ago. The reviewer catches it four hours later and you reply "good catch, fixing now" as if someone else wrote that line. The first reviewer on most pull requests should have been the author. Half the comments you will receive — the missing null check, the untested error branch, the duplicate logic that could be extracted, the import that now goes nowhere — are things you would have caught with one more careful read-through. You skip that read because you have been in the code for two days and your brain completes the sentences for you. You see what you meant to write, not what is on the page. This post is about closing that gap with a structured AI-assisted self-review before the PR opens. Not to skip the human reviewer — to walk into the review with the obvious problems already gone, the test gaps already filled, and the PR description already written. So the reviewer's attention can land on what actually needs a second pair of eyes. The tool is branchdiff : a local browser app that runs your diff on localhost , stores everything in ~/.branchdiff/ , and keeps the AI surface controlled through an explicit branchdiff agent command API. Nothing leaves your machine until you decide to push it. Why "before the PR" is the right moment If you review after opening the PR, every AI fix becomes noise: a force-push, a re-read for your reviewer, another commit in the audit trail. If a teammate is already mid-review when you discover the bug, you look careless. The patch that should have been in the original push becomes a distraction for everyone downstream. If you review before opening the PR, the AI's output is a private workspace. You act on what matters, commit the fixes into your own history (often as fixup! commits you squash before pushing), and the PR that goes up i

I want to tell you something that took me years to learn, so you can learn it on a Tuesday afternoon instead of during a production incident: most developers who build REST APIs do not actually know all the security protections their API needs. I did not know them when I started. I learned them slowly, usually right after something broke. I am a Filipino fullstack developer, about ten years in, now based in Norway. I built DaloyJS ( @daloyjs/core ) partly so that newer developers do not have to learn security the painful way I did. This post is a gentle walk through the problem and how DaloyJS helps. No gatekeeping, I promise. First, what even is a "security protection"? When your API is on the internet, anyone can send it anything. Most people are nice. Some are not, and a few are running automated tools that poke at every API they can find. So your server needs some basic defenses. Here are a few, in plain words: Body-size limit: stop someone from sending a giant 2GB request that fills up your server's memory and crashes it. Timeouts: if a request takes forever, give up on it so it does not clog everything. Prototype-pollution protection: block a sneaky trick where a special key in the JSON ( __proto__ ) can mess with your whole app. Header safety: reject weird characters in headers so attackers cannot inject their own. Path-traversal protection: stop a path like ../../etc/passwd from reading files it should not. Hiding error details in production: do not show strangers your stack traces and internal info. Rate limiting: stop one person from hammering your API thousands of times a second. Secure headers and CORS: tell browsers how to safely talk to your API. You do not need to memorize all of these today. The point I want you to take away is simpler: this list exists, it is longer than most people think, and nobody hands it to you when you write your first endpoint. Why this is a trap, especially with AI tools Here is the part that matters most for you right now,

Get ready for some gaming news. It’s officially June, which means splashy new events from PlayStation, Xbox, and gaming hype man Geoff Keighley. But this season doesn’t just feature the big tentpole shows; there will be a bunch of smaller events, too, and they might feature some promising games as well. But this year’s events […]

There are many solid Bluetooth trackers for iPhones that tap into Apple’s expansive Find My network. Some are thin, some are a bit chunkier. And, evidently, some look like tiny soccer balls. Ugreen’s FineTrack 2 glows in the dark, and it has a loud 110-decibel alarm when you need to find it. It’s just $14.99 […]