标签：#AR

You've got a recording and you want text back. For years that meant one thing at AssemblyAI: submit the file, wait for the job to finish, get a transcript. Async. It's reliable, it's cheap, and for a huge range of workloads it's exactly right. But "wait for the job to finish" is doing a lot of work in that sentence. If your file is two minutes long and your user is staring at a spinner, waiting is the whole problem. That's the gap the Sync API fills — and it's why "which transcription path" is no longer a two-way question. This post is about the two ways to transcribe a recording : async and sync. (If you're deciding between recorded and live audio in the first place — streaming versus the rest — start with our guide to real-time vs batch transcription , then come back here to choose between the two non-streaming paths.) The one-sentence difference Async transcription hands you a job: you submit audio, the work happens in the background, and you collect the result later by polling or via a webhook. Sync transcription hands you an answer: you POST a short clip and the transcript comes back in the same HTTP response — no job to track, no callback to wait for. Everything else follows from that. Async is built for throughput and depth on files of any length. Sync is built for speed on short files, when a person or an agent is waiting on the other end. How fast can each actually go? This is the question that usually settles it, so let's be concrete. Async processes the whole file and returns a single complete transcript, typically in seconds to a few minutes depending on file length and load. Crucially, it bills on audio duration ($0.21/hr on Universal-3.5 Pro), so a 30-minute file costs the same whether it comes back in 20 seconds or two minutes. You're optimizing for cost and completeness, not for the clock. Sync is built to return a transcript for a short clip almost immediately — roughly 134ms p50 — in one request/response, with no polling and no webhooks. It's price

Can a handful of engineers really do the work of an army of consultants? That’s the bet behind Ode with Anthropic — the joint venture dedicated to embedding forward-deployed engineers in enterprise firms, backed by Anthropic, Blackstone, Hellman & Friedman, Goldman Sachs and others. On this episode of TechCrunch’s Equity podcast, Rebecca Bellan sits down with Ode’s leaders Chris Taylor and Eddie Siegel, who founded Fractional AI, […]

New Delhi announced a $6.5 billion smartphone manufacturing program and a $13.3 billion semiconductor push to deepen India's electronics supply chain.

Similar to last time, What’s !important #15 is pretty stacked — read all about boundary-aware CSS, making grid lanes accessible, creating time-based web designs, fixing full-bleed CSS, improving customizable select, new web platform features, and more. What’s !important #15: Boundary-aware CSS, Time-based CSS, Full-bleed CSS, and More originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Anthropic-backed Ode launches as AI labs bet that embedding forward-deployed engineers inside enterprises is the key to accelerating enterprise AI adoption.

The app is designed for people who want to create social content, but find traditional video editing tools too complex or time-consuming.

Gwen Shapira shares how teams are scaling AI features using PostgreSQL for mission-critical apps. She explains how to leverage Postgres's multi-modal capabilities - including JSONB parsing and high-recall HNSW vector indexing - to deliver deterministic and semantic context to LLMs. She also discusses vector quantization to speed up queries by 4x and strategies for managing agentic memory. By Gwen Shapira

How an unexpected regional constraint forced us to deeply understand Azure GPU VM families, naming conventions, and workload fit. Introduction As architects, we often assume that infrastructure decisions are straightforward: "The workload is already running successfully in Region A. Let's deploy the same Kubernetes workload in Region B." That's exactly what we thought. Our workload consisted of a Visual Element Detection (VED) service hosted on Kubernetes. The application uses a PyTorch model to analyze images and detect various visual elements in an image file. The service was already running successfully on a node pool backed by Azure's NVads_A10_v5 GPU VMs. Then we hit an unexpected challenge. The target region did not offer NVads_A10_v5 instances. What looked like a simple deployment exercise became a deep dive into Azure GPU virtual machine families, GPU architectures, VM naming conventions, and workload characteristics. This article shares what I learned in the hope that it helps others who find themselves evaluating Azure GPU SKUs for AI inference workloads. I am relatively new to the world of MLOps, Model deployments, GPU Workloads etc and equally interested and excited to learn more on this front. The Workload Before discussing VM selection, let's understand the workload characteristics: Model Type : PyTorch Model Size : less than 200 MB (.pth) Image Resolution : ~2000 x 2000 Expected Throughput : 5-7 requests/sec Platform : AKS (Kubernetes) Workload Type : Inference only This is important because GPU sizing should always start from the workload and not from the VM catalog. Step 1: Understanding Azure GPU VM Families Many engineers first encounter Azure GPU machines through names like: NV12s_v3 NV6ads_A10_v5 NC4as_T4_v3 ND96isr_H100_v5 The naming can be intimidating. The first breakthrough was understanding that Azure organizes GPU VMs into three primary families: N-Series ├── NV ├── NC └── ND NV Series – Visualization and Graphics NV-series VMs are designe

I stared at the GitHub page for what felt like forever. The repo had thousands of stars, hundreds of issues, and a long list of contributors who clearly knew what they were doing. Me? I had a few small personal projects, some half-finished tutorials, and a nagging feeling that I wasn’t “ready” to contribute to real open-source software. Especially not an AI project with fancy models, complex pipelines, and people publishing papers off the codebase. But I wanted in. I wanted to learn how real-world AI systems are built, to get feedback on my code, and to be part of something bigger than my local src/ folder. So I made a deal with myself: no more waiting until I feel “ready.” I’d go from zero to my first pull request (PR) in one focused push. Here’s exactly how I did it, what I learned, and what I’d tell anyone hesitant about contributing to an open-source AI or machine learning project for the first time. Step 1: Pick the Right Project (Not the Biggest One) The biggest mistake I almost made was aiming for the most famous AI repo I could find. Big projects are great, but they can be intimidating and slow for a first-timer. Instead, I looked for: Active maintenance : recent commits, issues being closed, maintainers responding. Clear contribution guidelines: a CONTRIBUTING.md or at least a solid README. Beginner-friendly issues: labels like good first issue, beginner, or help wanted. Scope I could understand: I didn’t need to grasp the entire codebase, just enough to fix one small thing. I ended up choosing a mid-sized open-source AI library : not unknown, not legendary. Perfect. If you’re searching now, try queries like: “awesome open source llm” “open source machine learning projects good first issue” “open source AI tools GitHub” Then scan their issues tab for beginner-friendly tasks. Step 2: Set Up the Project Locally (Without Panicking) Once I picked a project, the next hurdle was getting it to run on my machine. The repo had a typical structure: project/ README.md

Samsung says its next foldables will have less visible creases thanks to its new titanium display tech.

Gidi Littwin's new AI startup, Hemispheric, makes diagnostic brain scans for conditions like depression, PTSD, and Parkinson’s. He wants the technology to be as cheap and easy as a blood test.

The startup has reached a $120 million annualized revenue run rate and more than 200,000 paying customers.

Withings' new $600 bathroom scale promising clinical-grade monitoring is finally available to buy.

But with great power comes a great price.

Co-founded by serial entrepreneur Shai Morag, Israeli identity management startup Oak is emerging out of stealth with $60 million in seed funding.

Make some noise for safety with our favorite alarms, from keychain sirens to wearable bracelets.

There’s now only one worth buying—but you can still access new features on older models you might already own.

Someone has to bundle all the wires that go into rockets, missiles, and satellites.

Companies’ increasing reliance on AI chatbots isn’t making the customer service experience smarter. It’s just making it more infuriating.

There's a specific kind of marketer who should be nervous right now. Not the strategist. Not the writer with a point of view. Not the creative director who can look at forty options and know, instantly, which one is alive and which thirty-nine are furniture. The one who should be nervous is the marketer whose entire job was being a slow version of a machine. You know this person. Maybe you've been this person — most of us have, at some point, in some job. The one whose week was resizing banners, rewording the same caption in six formats, pulling a report nobody reads, and calling a meeting to discuss the meeting. Their output was never brilliant, but it was there, and for twenty years, "there" was enough. Volume looked like value. Busy looked like good. AI just ended that arrangement. Quietly, without a memo. The excuse economy is closing For most of modern marketing, mediocrity had excellent cover. A bad campaign could hide behind timelines. A weak idea could hide behind budget. "We didn't have the resources" was the most useful sentence in the industry, and everyone accepted it, because everyone was using it. Now a two-person studio in Amman or Manila or Medellín can produce, in an afternoon, what used to require a floor of people and a quarter of runway. The drafts are instant. The variations are infinite. The production bottleneck — the thing entire careers were built on managing — is basically gone. Which means the only thing left to judge is the thing that was always the actual point: is the idea any good? That question used to arrive at the end of a long process, softened by exhaustion and sunk cost. Now it arrives immediately, naked, on day one. There's nowhere for a bad idea to hide anymore, because there's no longer a six-week production schedule standing in front of it. What the machine actually can't do Here's what gets lost in the panic. AI can generate. It cannot choose. It can write you a hundred taglines. It cannot tell you which one will make a foun