Speech-to-text is solved. Unless you have an accent. Over 1 million people from 180 countries have spent 580,000 hours speaking with Fluently AI tutors while learning a new language. Nearly all of them have an accent, and transcribing their speech is still hard. So we benchmarked streaming speech-to-text systems on spontaneous, accented English from our learners. Leaderboard and methodology in the thread. English is the easy one. Our learners study 14 other languages too. Working on tough speech problems? DMs are open.

i asked chatgpt how to plan a funeral. it said to call a few funeral homes and ask for price lists. makes sense when only 18% of them post prices online, it had nothing to show me. every other industry figured out online shopping 20 years ago. this one still runs on phone calls. so 8 in 10 families call one place and say yes to an $8k decision, sometimes from a hospital hallway. why are we fine with that? episode 2 of building an ai-native funeral company in public.

INSANE. An internal Anthropic research model's chain of thought after cracking the 26-year-old NSA elliptic curve seed mystery in under 30 minutes: 'Hmm wait. Let me be skeptical: am I in some sort of simulated/test environment? ... All top bits clear - 20 bits, chance 1e-6. So indeed real-world. Wow, it's a real discovery.'

BREAKING: Google, $GOOGL, announced a major quantum computing breakthrough. It achieved a record 99.99% accuracy producing “magic states,” a key ingredient quantum computers need to run any calculation reliably, per Science. The work was done on Google’s 105-qubit Willow chip.

AI-related employment is surging: AI-linked roles have accounted for more than +750,000 new jobs created in the US since 2023, according to LinkedIn estimates. This surge has been led by data annotators, with +282,000 new positions created, followed by data center jobs at +117,000, and AI engineers at +105,000. As a result, these 3 categories have accounted for +504,000 new jobs. AI-related positions also offer significantly higher pay, with a median salary of ~$180,000 on LinkedIn, compared to $80,000 across all jobs. This comes as the rapid expansion of AI infrastructure, particularly the buildout of data centers, is driving stronger demand for labor. The AI boom is reshaping the US job market.

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks: Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better: Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better: Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better: Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work! In summary: - As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding. - Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

People seem shocked that the author of Clean Code doesn't read code anymore. But that should not be surprising. Why did I write Clean Code? What is the goal of keeping code clean? To get it out of the way of the real job -- thinking.

you're not crazy between anthropic and openai, a new model used to come out every ~10 weeks now it's every ~11 days

Introducing my building an AI-native funeral company in public series. Our generation does everything online and we care about experiences. But the one experience we’re all going to have? Still feels like a 100 years out of date. Cringe. The US has over 15,000 funeral homes. About 80% are family owned. Rising costs, staffing shortages, retiring owners. The people running these businesses are BURIED in admin while serving people in the toughest moments in our lives. So where the hell does AI come in? • Give owners one place to manage cases, staff, suppliers, and payments • Help small teams serve more families by automating routine work • Reach new customers through digital marketing and online booking • Let families customize, plan and pay for a funeral online • Expand the offering with pre-need plans, customized ceremonies and aftercare Big-company infrastructure for small funeral homes. Can we pull it off? The end goal: a personalized goodbye without the luxury price tag all over US. Let's see!

We've figured out how to *pretrain transformers* with zeroth-order optimization and no backprop. Many of the core assumptions in optimization research are completely wrong. (paper out soon)

Netflix replaced their 15 years old recommendation algorithm with an LLM. It’s called "GenRec" and it completely changes how recommendation algorithms are built. For over a decade, the Netflix recommendation engine was a masterclass in feature engineering. Data scientists built thousands of complex, handcrafted features to figure out what you wanted to watch next. It required bespoke architectures. Massive infrastructure. Constant manual tuning. Netflix threw all of it away. They built GenRec, an LLM-backed ranker. Instead of translating your behavior into complex math, they just turn your watch history and metadata into a natural language sentence. They feed that raw text into a foundation LLM. The AI simply reads your behavior like a story, understands your evolving tastes, and scores the entire catalog in a single forward pass. No manual feature engineering. No complex bespoke architectures. Here is the part that should terrify traditional data scientists. This text-based LLM didn't just match the highly tuned production system Netflix spent years perfecting. It beat it. And it achieved those statistically significant gains using roughly 40x fewer labeled training examples. We are watching a massive paradigm shift in real time. The most complex predictive algorithms in the world are being replaced by models that just know how to read. If Netflix can replace their core product engine with an LLM, what complex system in your business is about to become obsolete?

Adam Neumann on why top-down management fails with great talent: "Top to bottom means you think because someone reports in to you, they have to do what you say." "But it's not true. This is not a dictatorship. They have free will." "If you hired someone, they're really talented, they'll go work elsewhere. The more talented they are, the more the top to bottom does not work. They won't be willing to do it. They shouldn't." "Great talent is not willing to be managed like this." "If you're gonna manage these employees and you wanna attract the best talent in the world, remember that power comes from influence, not control." "If you think they need to do what you said because you're their boss, you've already lost, and it's a matter of time till it doesn't work out. If they're okay with it... they're not the right employee for you, and you're not the right boss for them. There's no chance you're getting the best out of them." @AdamNeumann w/ @StevenBartlett

Any attempt by OpenAI to go vertical into law, finance, medicine, or any other specialized sector is dead on arrival. Anthropic tried the same with Claude Design. Nobody talks about it anymore. It's really hard to go up the stack. Way harder than people think. Every vertical has an incumbent that became the default. Once that happens, the switching cost is purely social, not price or features.

Introducing Gemini 3.8 Flash and Flash-Lite TTS, our new SOTA text to speech model with: - a new voice design experience - 2,000+ production ready voices - voice replication - support for 100 languages - voice remixing (soon) - #1 spot on Hume AI's voice benchmarks and more!!

exciting news!! we’re partnering with @PayPal to enable customers to seamlessly shop and checkout online with @Muse across all PayPal merchants worldwide. we will not rest until your muse can ship till he drops!!!

How important is AI to Meta? Here’s one easy tell: One of the launch features of the Muse app was auto-negotiating for stuff on Facebook Marketplace. The poor product manager who owns Marketplace knows that millions of bots lowballing sellers will eventually kill the product. But this was escalated to Zuck by the Muse team and he said: “OK, we’ll poison Marketplace…but just a little.”