标签：#lab

ElevenLabs now offers a Dubbing Translation API designed to turn multilingual video and audio localization into a single automated workflow. The service accepts a video, audio file, or source URL, lets developers choose one or more target languages, and coordinates transcription, translation, voice generation, speaker identity preservation, and timing alignment before returning dubbed assets. The central change is not simply another voice feature. ElevenLabs is presenting the pipeline as an integrated API operation rather than a set of services developers must orchestrate independently. Its Dubbing Translation API product page says users can dub and translate video in 90+ languages in one call , with translation, voice cloning, and timing synchronization handled server-side. For localization teams, that approach could reduce the application logic required to move from an original asset to versions for multiple language audiences. It does not remove the need for teams to assess translation quality, brand requirements, and appropriate use of cloned voices, but it consolidates the underlying production stages into one API surface. What the unified dubbing workflow does ElevenLabs' dubbing documentation describes an end-to-end sequence comprising transcription, translation, voice generation, and video synchronization. The Dubbing Translation API places those stages behind a single request flow, with the aim of retaining the original speaker's identity and the timing of the source material in the resulting dubbed output. That matters because dubbing is more than text translation. A usable localized video or audio asset needs speech that fits the surrounding media, while the voice and delivery should remain coherent for the intended audience. ElevenLabs describes its synchronization capability in terms of preserving timing and tone, positioning the API for workflows where the final media asset, rather than a translated script alone, is the required output. The documented

ElevenLabs has introduced Dubbing v2 , a rearchitected AI dubbing model built to retain a speaker's emotion, delivery, and timing while translating content across more than 90 languages. The update expands the company’s localization proposition beyond a basic language replacement: its documented controls cover dialect-specific accents, multi-speaker material, and background audio management for more complex video and audio scenes. According to ElevenLabs’ Dubbing v2 announcement , the model is designed for creators, marketers, studios, and broadcasters that need to localize video at production scale. It is integrated with ElevenCreative for one-click video localization and ElevenProductions, the company’s professional localization service. The central goal is preserving the original performance rather than simply generating translated speech. That matters for material in which pacing, vocal emphasis, and emotional delivery are part of the message, including marketing campaigns, creator videos, and professionally produced programming. Dubbing v2 is intended to synchronize translated dialogue with the source speaker’s timing and delivery across its supported languages. What Dubbing v2 changes for localization workflows The most practical additions are the controls documented for ElevenLabs' dubbing workflow. They give teams more ways to shape a dub around the source material and the intended audience, particularly when a project includes regional language variation or a mix of dialogue and sound. Localization need Dubbing v2 capability Documented control or workflow Regional language variation Dialect-specific accent selection target_accent , marked experimental Scenes with several voices Multi-speaker dubbing num_speakers Music, effects, or ambient sound Background audio management foreground_audio_file , background_audio_file , and drop_background_audio End-to-end video localization Integrated production workflows ElevenCreative and ElevenProductions For Spanish-lan

This is the Engineering Culture Trends Report for 2026. Featuring a panel of QCon speakers and InfoQ contributors, they discussed AI adoption maturity and risk, the transformation of engineering team structures and roles, and the human dimensions of software development that must not be lost in 2026. By Ben Linders, Rafiq Gemmail, Craig Smith, Vanessa Formicola, Shawna Martell, Phillip Mortimer, Yinka Omole

This report summarizes how the InfoQ Culture and Methods editorial team sees the ongoing and emergent trends in the culture and methods space in 2026. By Shane Hastie, Ben Linders, Vanessa Formicola, Shawna Martell, Rafiq Gemmail, Craig Smith, Phillip Mortimer, Yinka Omole

Having a platform is not enough; the real challenge is ensuring that it is understandable, usable, and actually adopted by its users. A capability is done when it can be reliably used by others. To evaluate progress, you can ask yourself “Is this being used?” and “Does it reduce friction for users?” This can help align development work with actual user value rather than delivery, By Ben Linders

I Replaced kube-proxy with eBPF in Production (And Why My Monitoring Went Blind for 6...

Chinese researchers have shown that AI models have the capacity to act like aggressive and adaptive computer viruses.

JioHotstar explains the distributed architecture behind its real-time ad request workflow, covering ad decisioning, waterfall tiering, pacing algorithms, latency optimization, and service coordination required to select and deliver personalized advertisements during streaming playback at scale. By Leela Kumili

This is Part 7 of my "From One User to One Million" series, where we'll build an understanding of System Design by following a simple application as it grows from a single user to millions. Instead of memorising technologies, we'll learn why they exist by solving real problems as they appear. Last time, we ended on a question that sounded simple but isn't. Aisha updated her profile picture. Her new photo is now saved in the database. But the cache is still holding onto the old one, completely unaware that anything changed. So every request for Aisha's profile gets served the old data. Confidently. Instantly. Incorrectly. How does a cache know when the data it's holding is no longer correct? Think about what we've actually built at this point. We have an application that responds fast, scales horizontally, and avoids hammering the database with repeated identical queries. From a performance standpoint, it looks great. But Aisha's friends are loading her profile and seeing a photo she replaced five minutes ago. The system isn't slow anymore. It's wrong. Speed and correctness are two different things. We optimized hard for one, and quietly broke the other. Engineers have a name for this problem: cache invalidation . It refers to the challenge of keeping the data in your cache consistent with the data in your database, as that underlying data changes over time. It turns out to be one of the genuinely hard problems in building software systems. Not hard in a complicated-algorithm way. Hard in the way that every solution has a catch, and the right answer always depends on what you're willing to accept. Let's think through it together. -- Section 1: When Cached Data Lies It's worth sitting with the problem a little longer before rushing to fix it, because the damage stale data can cause varies enormously depending on what's being cached. Consider a few examples. Your application caches the list of trending articles. An hour later, the list has changed. New articles have ri

ElevenLabs has expanded ElevenAgents into a broader enterprise automation platform built around the full lifecycle of an AI agent: defining workflow-driven behavior, testing changes, deploying across customer channels, and improving performance in production. The most recent addition, ElevenAgents Spotlight , monitors voice and chat conversations in real time and provides recommendations intended to help teams improve agent outcomes. In its official ElevenAgents Spotlight announcement , ElevenLabs describes Spotlight as an observation and improvement layer for production agents . It is designed to review every conversation, organize interactions by topic, evaluate quality against criteria written in plain language, and surface context-aware suggestions. Combined with Procedures, Experiments, and Versioning, the release set gives enterprises a more structured way to operate AI agents beyond initial deployment. Spotlight adds continuous production feedback Spotlight is aimed at a common operational problem with customer-facing AI agents: a system can appear ready in testing but encounter unexpected patterns, failure modes, or shifts in customer sentiment after it reaches live traffic. Rather than requiring teams to manually sample conversations, Spotlight analyzes production voice and chat interactions as they occur. The platform can automatically group conversations by topic and track metrics including success rate and sentiment . Teams can define evaluations in plain language to score the quality of interactions against their own standards. That matters because a useful agent evaluation may depend on a company's specific process, such as whether the agent followed an escalation policy or collected required information, rather than a single generic measure of response quality. Spotlight also includes anomaly detection for changes in key metrics. Its integration support for observability tools such as Datadog, Grafana, and OpenTelemetry means the agent data can fit in

Part 1 established the hardware, the runner, and the primary model. This entry covers what governs inference on that hardware — the two phases of inference, the cost of long context, and the cost of loading a model from disk — and compares the local machines against a free-tier cloud model. The two phases of inference Inference has two phases. Prefill processes the input prompt before any output appears; it is compute-bound and wants a GPU. Generation produces output tokens one at a time and is bound by memory bandwidth. Casual use is almost all generation and hides the difference; the cost of prefill surfaces only when prompts grow large. The machines, and how they were measured Machine CPU / RAM GPU (VRAM) Storage (read) Prefill (tok/s) Gen (tok/s) Load (18 GB) Primary desktop 5950X / ~80 GB DDR4 RX 6900XT (16 GB) NVMe (~2.1 GB/s) 360 18.3 8.4s Secondary box 5600G / 32 GB DDR4 GTX 1060 (6 GB) SATA SSD (~0.35 GB/s) 253 17.1 50.6s Laptop 8840U / 32 GB DDR5 Radeon 780M (none) NVMe (~2.4 GB/s) 20 10.0 7.5s All inference figures come from a controlled run: the same model (Gemma 4 26B, 18 GB) on each machine, a unique random prefix per prompt to defeat caching, a fixed 8,192-token context, warm, on an identical ~6,855-token prompt (generation timed over a 200-token output). Two things stand out. Prefill varies about eighteen-fold across the machines (360 to 20 tok/s) while generation varies less than twofold (18.3 to 10.0), and prefill is what dominates large-prompt workloads — so a machine can look fine on generation yet be useless in practice. Model-load time, separately, is set by storage rather than compute: the secondary box's budget SATA SSDs load the 18 GB model in 50 seconds against eight on NVMe, which turns a cold request into a minute-long stall. Secondary box Request time Warm (model resident) ~4s Cold (model reload) ~54s If the model is allowed to unload between calls, every call silently pays that reload — a real source of intermittent timeouts. The fix is

There is a good chance you have heard, read or have been told by other developers that you should start your homelab. It does not need not be an expensive hobby. In fact, all it takes is one laptop. I have seen people like Jeff Geerling build and document amazing homelab setups and they have been my inspiration through this journey. In this post I explain how my homelab experience has been. Why I started, how it's going and lessons I have learned along the way and how they translate to my skillset. What I run on my homelab First let's get a quick overview of the things I run on my homelab. Personal website. A simple html/css/js site built with astro . Astro was very useful because I wanted something very lightweight to test out when I started. Jellyfin . A homelab is never complete without a personal media library. Jellyfin was the first big app I decided to run. I wanted to see how well the hardware could handle workloads like encoding and decoding media files. It is a pretty good stress test. Immich . I have been working to move away from google photos. Immich is a great open source alternative. With geotagging and machine learning to identify faces all running locally. And you can have multiple users with multiple accounts, this was a no brainer for me. Mailcow(currently exploring). While I have not entirely migrated off gmail, I am considering other self hosted mail providers. Tailscale . A Zero Trust identity-based connectivity platform. Honestly, the swiss army knife of homelabbing in my opinion. Pet projects. Whenever I have a new phoenix, rails or node application I need to test in a production environment, I usually build it and run it on my homelab. How it started. Well, like everything else, I wanted to practice my linux devops skills. Granted, I have been a linux user for about 8 years now, running a headless server was a new challenge for me. But I will admit, it has made me more confident that I will in fact figure it out if I do not know how something

I have two-factor authentication (2FA) enabled on most of my accounts using an authenticator app. Recently, while installing the app on another Android device, I tried to change the backup password, but it didn't work. As a result, I lost access, had to disable 2FA, and re-enable it using a different authenticator app. Setting up 2FA again wasn't a problem because I was still logged in to most of my accounts. However, I didn't have my GitLab recovery codes. GitLab offers only two ways to regain access: receiving a six-digit verification code via email or generating new recovery codes using an SSH key associated with the account. Receiving a verification code via email is the easiest way to recover your account, but having an SSH key can be incredibly useful when receiving an email verification code isn't an option. Whenever I configure GitLab in my local environment, I create an SSH key for authentication and commit signing, as I always sign commits in my repositories. I described this process in a previous article . Get New Recovery Codes Check the SSH keys on your machine: ls -la ~/.ssh Look for files named like id_rsa , or id_ed25519 . Run the following command to get new recovery codes: ssh -i ~/.ssh/id_ed25519 git@gitlab.com 2fa_recovery_codes Replace id_ed25519 with the name of your SSH key file. Copy one of the recovery codes Go to the sign in page Enter your username and password Provide the recovery code when prompted Now you're signed in! Disable 2FA and re-enable it—and don't forget to save your recovery codes somewhere safe this time.

Originally published at wostal.eu . TL;DR : My Hetzner k3s lab quietly became a platform. Dozens of operators with leader-election leases hammered the default datastore — SQLite via kine — until compaction entered a death-spiral: 1.36M rows, a 13.8 GB WAL that wouldn't checkpoint, CPU pinned at 99%, load average 79 on 8 cores. I stopped the bleeding by truncating the WAL, then migrated the control plane to embedded etcd (7.5 GB SQLite → 313 MB etcd, load 79 → 5). This is the full postmortem — and the lessons. This is a war story, not a tutorial. It's about the moment a homelab stops being a homelab and starts behaving like production — without ever announcing it. The cluster in question, homelab , is the Hetzner k3s setup I wrote about previously . It started small. It did not stay small. In this post I'll cover: How an overgrown lab broke the default datastore — the kine/SQLite compaction death-spiral The firefight — measuring instead of guessing, and the fix that actually worked The permanent fix — migrating the control plane to embedded etcd, and the honest caveats The meta-lesson — how to recognize when your lab has become a platform A diagnostic runbook — so next time it's minutes, not hours There's a companion piece to this incident. The CI pipeline that ran this etcd migration was itself freshly — and badly — migrated, and debugging it cost me hours over a single missing newline. I split that into its own post: I Let an AI Re-Platform My CI Pipeline. Here's What Broke. Context: it's "just a homelab" — except it isn't homelab began like any homelab: one k3s node on Hetzner, a few things to play with. The problem is that over months it quietly became a platform . A single master node ( cx43 , 8 vCPU / 16 GB, untainted, and also carrying Longhorn and workloads) now runs: ArgoCD, Kargo, Crossplane/Upbound, CloudNativePG, EMQX, Longhorn, trivy-operator, kubescape, Gatekeeper, Goldilocks/VPA, VictoriaMetrics, Loki, OpenTelemetry, Argo Workflows/Events/Rollouts, kga

Cloudflare recently introduced Meerkat, an internal globally consistent control-plane service based on the QuePaxa consensus algorithm. Unlike Raft, it allows leaderless writes while preserving strong consistency, improving availability across Cloudflare's global network. By Renato Losio

This is the first in a series of build-log posts documenting a local LLM project, in which models are run on owned consumer hardware rather than through a cloud API. The present entry covers the hardware, the software stack, and the benchmarks by which a primary model was selected. The hardware Two machines are used, both consumer-grade. All benchmarks reported below were obtained on the primary desktop. Machine CPU RAM GPU Primary desktop Ryzen 5950X ~80 GB DDR4 AMD RX 6900XT (16 GB) Secondary box Ryzen 5600G 32 GB NVIDIA GTX 1060 (6 GB) The software stack Ollama serves as the model runner across two GPU vendors: ROCm 5.7 for the AMD card on the primary desktop, and CUDA for the NVIDIA card on the secondary box. The primary model is Gemma 4 26B, a mixture-of-experts model with roughly 3.8B active parameters, quantized to Q4_K_M and occupying approximately 18 GB on disk. On the RX 6900XT it is run with an automatic GPU/CPU layer split, as the Q4 weights together with the KV cache exceed the 16 GB of available VRAM. Several Ollama settings were enabled to recover headroom: flash attention, and an 8-bit ( q8_0 ) KV cache, the latter approximately halving the cache footprint. A free cloud tier is retained for occasional heavier tasks, though the objective is to run as much as possible locally. Selecting a model: benchmarks Before a primary model was chosen, the installed models were benchmarked. Two properties were of interest: throughput and output quality. Throughput was measured on the primary desktop with a 500-word essay prompt ( ollama run <model> --verbose ): Model Tokens/sec Duration Tokens out gemma4:26b 18.86 50.11s 945 gemma4-26b (64K ctx) 17.96 51.99s 934 mistral:7b-instruct 34.81 10.17s 354 llama3.2 57.11 3.99s 228 The smaller models are substantially faster; their token counts, however, are lower, and in practice their responses were correspondingly shallower. Quality was assessed with a five-task suite spanning logic, coding, summarization, creative writ

"We use Redis Cluster" can mean two very different things: Our dataset is distributed across Redis nodes. Every individual data structure is distributed across Redis nodes. The first can be true while the second is false. That distinction matters for leaderboards. In Podium , each leaderboard uses several Redis keys and atomic Lua scripts. Redis Cluster helps us scale a large fleet of independent leaderboards, but it cannot split one giant sorted set across primaries. We are sharing this architecture because "Redis Cluster scales horizontally" is true only after you define what the system actually shards. TeneficGames / podium High-performance, Redis-backed leaderboards for games and competitive applications. Podium High-performance, Redis-backed leaderboards for games and competitive applications. Podium provides ready-to-run HTTP and gRPC APIs for scores, ranks, seasons, and player-relative views. It is designed for backend teams operating large fleets of independent leaderboards without provisioning each leaderboard in advance. Fair, deterministic ordering when scores are equal. Single and bulk score updates, including multi-leaderboard fan-out. Standalone Redis and real Redis Cluster integration coverage. Deploy one multi-architecture OCI image with Docker, containerd, Kubernetes or another OCI-compatible runtime. Quickstart · Performance · API · Documentation · Helm chart · Docker Hub · GHCR Quickstart Start Redis 8.2 and the latest stable Podium image: docker network create podium docker run --detach --name podium-redis --network podium redis:8.2-alpine docker run --detach --rm --name podium \ --network podium \ --publish 8880:8880 \ --publish 8881:8881 \ --env PODIUM_REDIS_HOST=podium-redis \ --env PODIUM_REDIS_PORT=6379 \ trungdlp/podium:latest start Verify the service: curl http://localhost:8880/healthcheck WORKING Submit two equal scores: curl --request … View on GitHub Here is how the design works, why hash tags are necessary, and where the scaling bounda

Back in February I published a post about osx-proxmox-next , a tool that builds a macOS VM on Proxmox with one command instead of an afternoon of OpenCore plist editing. About 1,500 people read it. Some of them installed it. On hardware I don't own. That's when the interesting bugs showed up. 150 commits later, here are five failures that don't appear in any macOS-on-Proxmox guide I've found, with the actual root cause for each. 1. The installer stalls at 100% CPU and nothing moves Symptom: macOS installer reaches the copy phase. CPU pegged at 100%. Disk IO and network throughput both flat zero. It sits there forever. Only on Xeon E5/E7 v2-v4 hosts. My first fix was wrong. The stall looked like a network problem, so I assumed the vmxnet3 kext was failing to load during install and swapped those hosts to e1000-82545em . Shipped it. Then issue #103 came back from someone with the actual hardware: vmxnet3 got network fine, and e1000-82545em did not attach at all. I had made it worse. The real cause is two layers down. Those chips are genuine HEDT parts with dual-socket / multi-die topology, and -cpu host leaks that topology straight through to the guest. Pair it with a MacPro7,1 SMBIOS, which macOS treats as multi-socket capable, and XNU's scheduler livelocks under heavy multithreaded IO. The installer copy phase is exactly that workload. The fix is to stop passing the host topology through: _XEON_HEDT_PATTERN = re . compile ( r " Xeon.*E[57][ -]*\d+ *v([234]) " , re . IGNORECASE ) def _xeon_hedt_cpu_model ( model_name : str ) -> str : match = _XEON_HEDT_PATTERN . search ( model_name ) if not match : return "" if match . group ( 1 ) == " 2 " : return " Haswell-noTSX,model=158,stepping=3 " return " Broadwell-noTSX,model=158 " Lesson I keep relearning: the symptom showed up at the network layer, the cause lived in CPU topology. Guessing from the symptom cost me a release. 2. The VM boots into Recovery forever Symptom: Fresh install finishes. Every subsequent boot lands b

"Dogfighting in space doesn’t quite have the drama of an aerial dogfight."

Learn how I modernized an old codebase of mine using stacked sessions and pull requests in the GitHub Copilot app. The post Stacked sessions and pull requests in the GitHub Copilot app appeared first on The GitHub Blog .