LinkedIn video ad examples

Introducing AssemblyAI-TTS-1, our first text-to-speech model. Natural speech for voice agents, streaming in 90 ms, at $15 per million characters. On accuracy, naturalness, and cost together, it’s the best TTS model you can run at scale. It’s served on the infrastructure that already moves 3M+ hours of audio a day for 1M+ speech-to-text developers, so concurrency is unlimited on pay as you go. Ten calls or ten thousand, no commitments required. Live today: - Leading accuracy on independent benchmarks - 90 ms time to first audio - Trained on millions of hours of audio - Instant voice cloning - 4,000+ voices, with new ones added daily Every voice, language, and region included at $15 per 1M characters Available today. Watch a kid use it to order a pizza. Full announcement: https://lnkd.in/eE_H7xj9

AI labs chase benchmarks. Your board chases revenue. WRITER delivers agents built for marketing and revenue teams. The only AI metric that counts has a dollar sign.

Ogni lettera d’incarico nuova parte dal copia-incolla di quella vecchia? Fai il conto in 30 secondi: scegli il tuo ruolo, inserisci persone e ore, e scarica gratis il calcolo dettagliato in PDF, con il confronto oggi / con l’Assistente Studio di Brainyware. Scegli il cliente dall’anagrafica e il tuo modello si compila in un colpo. I dati dei clienti restano sul server dello studio.

Il vostro team usa già l'AI? Allora contratti, bilanci e dati dei clienti stanno già finendo su server di qualcun altro. Quanto è esposta la vostra azienda? Quattro domande, due minuti: ricevete un report riservato con l'indice di esposizione e le priorità per il vostro ruolo, da portare in consiglio. Gratuito e senza impegno.

We just shipped a small update that carries a lot of weight in making conversations with Anam avatars feel more natural. When we think about conversations that just 𝘤𝘭𝘪𝘤𝘬, it’s not one big thing. It’s a collection of small behaviors that you barely notice until they’re missing. One of those happens when someone needs a moment to think. Ask someone something they don’t know off the top of their head, and they won’t just go silent, they'll ask you to give them a second or will follow with “let me check that” while they figure it out. Those tiny responses keep you in the conversation. Now Anam avatars can do the same while they’re waiting on a tool call, knowledge base, or API - responding naturally in the moment rather than leaving a gap of silence, staring into the abyss. And you can decide what those responses sound like for your avatar. Check out the demo video, and find a link to all our latest product updates in the comments.

I added two of our avatars to a video call today. They joined as teammates, listened to the conversation, responded in real time, and had access to everything about Anam through Claude Code. Why does this matter? Think about every business that runs on live conversation - sales calls, customer onboarding, language tutoring, coaching, employee training. Right now those all need a human on the other end. That is expensive, hard to scale, and restricted by time zones. Not to mention limited by what one person can know versus an agent. An AI avatar that can join a video call changes the math. - Coaching at 3am without burning out staff - A technical expert in every sales demo - 1-on-1 onboarding for every customer And the user on the other end isn't staring at a chatbot window. They're in a video call with a face that looks at them, reacts to what they say, and speaks back naturally. A face that represents your brand. Localised to each user. True personalisation at machine scale. That's a very different experience from a chatbot or a voice agent. Our customers report that adding a face to their agents has increased engagement by 44%. We're building this at Anam. The avatar engine handles speech recognition, reasoning, voice synthesis, and face generation in real time. Recall.ai handles getting the avatar into the call. It works with Google Workspace Meet, Zoom, and Teams today. Once Claude Code had access to the Anam MCP server, it took one line and 10 seconds to have both avatars join the call.

Answer a few quick questions to check NVIDIA B300 availability and get a USD quote Our team replies within 1 business day NVIDIA B300 VMs match bare metal on the same 8-GPU node: 834.21 GB/s on VMs and 833.70 GB/s on bare metal (NCCL all_reduce busbw, 8 GiB messages, median of 3 runs) VM overhead is up to 0.5% at message sizes of 8 MiB and above, and up to 1.4% on GPU-to-host copies On-demand list price is $7.79* per GPU-hour Prepaid reserved terms of 3 to 5 years are priced lower *List price as of October 2026, billed per second, excluding taxes

From asset maintenance to regulatory analysis, not every AI task needs the most expensive AI model. Haystack Enterprise Platform gives you visibility into AI spend and the ability to optimize cost and performance as adoption grows.

Picking the right LLM for a Voice AI pipeline is hard. Today we're launching Voice Standard Bench to rank 50+ of the best LLMs on real voice tasks, and making all of them available in AssemblyAI's LLM Gateway. Most Voice AI products run an LLM on messy, multi-speaker transcripts: - Voice Agents responding to human speech - Summarizing a meeting, sales call, or support call - Pulling names, dates and numbers out of a multi-speaker conversation - Fixing transcription errors - Cleaning up dictation Voice Standard Bench tests every model on the input these products actually send an LLM: lengthy conversational transcripts with multiple speakers, raw speech-to-text output with filler words, false starts and the occasional misheard word. Each model is scored on transcripts from a 30-second voicemail to a 60-minute meeting. Here's a glimpse of what we found: - The top-scoring model costs 55-65x more per hour of audio than a model within two quality points of it. - That gap narrows to about 5x on hour-long transcripts, where bigger models start to pull ahead on quality. - Open models we host on our own infrastructure are the fastest on short audio. On a 30-second transcript, gemma-4-31b answers in under a second at a quality score of 93. Learn more here: https://lnkd.in/gS_FsV3W Try 50+ models in our Playground: https://lnkd.in/g-kG2diJ

The model people liked most for a science publication finished last when we asked it for an Italian soda pitch deck. Same model, same evaluators, same rubric. Only the brief changed, and a single benchmark score would never show it. See all 100 designs and how every model ranked.

We gave ten AI models the same website brief and asked people to pick the better design. Then we changed the brief. The model that won the boutique hotel brief finished dead last on a science publication. One leaderboard score would have hidden that, which is why we run evals with people. All 100 designs and the full rankings are on the page.

Art direction usually starts with references. The harder part is seeing what that style actually does to the product. Style Match lets you start with the product image, add 1–4 art references, and restyle it while keeping the product, framing and composition in place. Useful for testing a direction before final illustration or production, or taking the result straight into social. Try Art Style Match in Luma.

We asked Imran Nawaz what he’s working on. This is what his tabs said. Keeping Tabs EP 1. Today’s tabs: - What goes behind our validation pipeline? - Why egocentric footage gets rejected? - Why quality comes first in model-ready data and how do we protect it?

Capitol is AI for consulting, and we think it should produce more than answers. It should produce the work behind them: research you can audit, repeat and put in front of a client. Here’s a public example. Five companies will spend the equivalent of 2.5% of US GDP building AI this year, America’s biggest bet since the Louisiana Purchase. So we asked Capitol how America’s big bets have turned out, from the Erie Canal to the Interstates, and published what it found. Some paid off fast. Some took decades. Some overshot. Every number traces to its source. The method is the same for every bet. And it’s designed to be read. That’s what Capitol builds for consulting. See the numbers: https://lnkd.in/eSGwmGf2

AI agents don’t announce their mistakes. Most enterprises can’t see which decisions are safe until one becomes a business risk. The Trusted AI at Scale paper shows how leading organizations score every AI decision, route uncertain ones to human experts and keep a full audit trail from data to deployment.

Dubai is turning AI ambition into action, and we're excited to be part of the conversations shaping what's next! C5i is heading to the Dubai AI Festival 2026, with Anees Merchant (Executive Vice President) and Shreyas Sundaresh (Head of AI Transformation & Client Success) representing us at the event. As organizations across the UAE accelerate their AI initiatives, an important question is emerging: What does it really take to make AI work at enterprise scale? At C5i, we bring experience from working with leading global enterprises and financial institutions, helping them navigate complex business challenges through AI, analytics, and intelligent automation. From modernizing enterprise data and transforming banking operations to identifying high-value use cases for agentic AI, we've seen how the right combination of technology, business context, and execution can make a meaningful difference. We're looking forward to exchanging perspectives with the region's business and technology leaders on their AI priorities, the challenges they're navigating, and the opportunities ahead. Hear from Anees Merchant on why the Dubai AI Festival is an exciting opportunity and what we're looking forward to. Whether you're exploring new AI opportunities or looking to scale existing initiatives, connect with Anees and Shreyas. We'd love to meet you in Dubai! Hemant Julka | Parag Penker | Samer Hamadneh #C5iHumanAIImpact #MiddleEastBanking #UAE #BankingAI #ResponsibleAI #DataAndAI #AgenticAI

Deploy anywhere your data lives (on-prem or in the cloud) with secure multi-agent workflows and enterprise guardrails.

The countdown is on for the Dataiku Summit London! Here's what I'm looking forward to most: Vodafone's AI Opportunity: Paul Blair reveals how rapid prototyping uncovered a single IT testing use case projected to save £8 million. Scaling GenAI at Aviva: Shantanu Deo and Chris Butler explore how AI agents are transforming internal auditors into data analysts. Platform Migration at L&G: Sara Budd shares practical lessons on driving user adoption and managing change when moving to Dataiku. Beyond the tech, it’s going to be a great space for high-level industry conversations and sharing market insights on how companies are truly scaling AI today. If you're in London, it would be great to see you there! Register via link here: https://lnkd.in/egMYjTF2

My keynote at Dataiku Succeed came down to one point: Vibe coding is becoming the next generation of Excel workbooks. Fast. Useful. Hard to inspect. The answer isn’t stopping business teams from building. It’s giving them a governed way to build under IT oversight. At Succeed, we expanded Cobuild to support that: • Dataiku Headless: Build and manage Dataiku projects from tools like Claude Code, Codex, and Cursor - coming soon. • Agent Management: Discover, monitor, and improve agents across the enterprise, wherever they were built - available now. More people building is inevitable. The real question is whether they’re building in a system you can trust. More on what we shared at Succeed: https://lnkd.in/eprXtXXN #DataikuSucceed26

Excited to see our work featured in Jensen Huang’s keynote. As he put it: “Now you can turn your proprietary data into sovereign intelligence.” That’s exactly the vision we’re building at AI21 Labs. We believe every company will become an AI company - and that means owning and customizing its own AI, not just consuming it. Proud of the team’s tremendous work, and excited to partner with NVIDIA and industry leaders like Gong, Deel, and Alice (Formerly ActiveFence) to turn this into reality.

🗣️ "Most operating models were built for a world of scarce intelligence, sequential workflows and predominantly human execution. AI has changed all three." Tom Oliver, Head of Product, and Myles Kirby, Vice President, join forces with Accenture colleagues Kent McMillan and team to set out the five shifts reshaping how companies operate - starting with designing around decisions, rather than tasks. Read the full article here: https://lnkd.in/eQZDubwH

For thirty years, it didn't matter who sat in front of the software. Now it does. I've been in software testing for about three decades. Traditionally, it didn't matter who was using it, male or female, any location, everyone got the exact same experience. Once you move to an AI-based experience, that stops being true. Who you are, your context, your history, all of it shapes the experience you get. Male or female. Europe, the Middle East, the US. The language you speak, your tone of voice, your history. One bank insisted we test dyslexic users specifically, because they get a completely different experience once AI is involved. Most people still don't realize this. The only way to actually know your AI holds up across that many contexts, behaviors, languages, religions, and intents, is to simulate thousands of real user interactions and see how the system actually handles the variety.

It's official: the Arkane Cloud extension for VS Code is live! Millions of developers open VS Code every morning. Starting today, they can use the best open-source AI models right inside their editor, with inference running on our GPUs. No complicated setup. Install the extension, pick your model, start coding. This launch is first and foremost the work of a team. A small team that gave everything over the past few weeks, across infrastructure, product and testing. Arkane Cloud, thank you. I'm proud of what we've built together. And this is only the beginning. Several announcements are coming in the next few weeks, and some of them will make some noise. Stay tuned. The install link is in the comments. Try it, push it to its limits, and tell us what you think: every piece of feedback counts.

Your RAG pilot worked. ✅ Now the agent needs access to tools, internal data, and business systems. 🔐 That is where the architecture gets harder: 1. Who can the agent act for? 2. What can it access? 3. How do you trace its decisions? 4. And what evidence will risk and compliance teams need before approving production deployment? This session explains how to design governed agentic systems - from MCP and access controls to auditability, reliability, and production readiness. Watch the full session: 👉 https://lnkd.in/ekZj-e3S/?utm_campaign=%255Bvideo%255DLLM&utm_source=linkedin&utm_medium=paid&utm_id=776239064&hsa_acc=508184568&hsa_cam=776239064&hsa_grp=416564784&hsa_ad=1468807454&hsa_net=linkedin&hsa_ver=3