Search: “applications”

Bulk import is now free up to 1 TB. Reads directly from S3, GCS, or Azure Blob into the index builder. Standard and Enterprise get a 250 credit applied automatically. After 1 TB: 0.25/GB, down 75%. ht

Enterprise AI usage leaderboards are BAD and lead to the wrong behaviors. Employees feel pressure to hit higher token usage numbers without any of the positive work transformation. I’ve heard directly

I actually read this & it is super weird, it appears to be an argument that prior machine learning systems (not generative AI) did not generate savings due to data issues so that will lead to a lack o

I think it is really worth reading this piece on RSI at Anthropic. There is a bit of navel-gazing, some marketing, and a lot of very sincere beliefs about what Anthropic thinks is likely in the near f

Ethan Mollick observes that both OpenAI and Anthropic's official documentation frequently falls behind their actual product releases by several months, resulting in outdated and conflicting informatio

reiterating: 'We're using the more expensive models to explore. Once we scale some of these experiences, we'll look to bring in more efficient models that are more efficient on a token basis or are op

SpaceX’s literally destroyed the cost to orbit so much even you can afford the ticket to ride on it in the future - For decades, launch cost to low Earth orbit was roughly 18,500 per kg - Falcon 9 bro

There are lots of issues with GenAI implementations but surveys seem to suggest broad positive ROI. Data & stories about AI failures are real and useful but need to be clearer about what they are meas

Day 2 at #MSBuild is about what it takes to move beyond generic foundation models. Think customization, inference performance, and getting production-ready AI deployed at scale. @chahvivi will lead a

Fine-tuning to production inference is the gap where teams get stuck. At #MSBuild today, our own Rob Ferguson, @danielhanchen (@UnslothAI) and @marksaroufim (@coreautoai) discuss: model customization

Harvey just published a study showing a hybrid setup, open source GLM 5.1 as primary worker, routing to Opus 4.7 only when needed beats pure Opus 4.7 on quality and costs less. This is the multi-model

Hey, its our paper! One of the most-viewed PNAS articles in the last week is “Persuading large language models to comply with objectionable requests.” Explore the article here: https://ow.ly/wOxl50Z6f

This appears to be a GitHub repository shared by Ethan Mollick containing code or a project called 'snake-awakening' that is available for public download and modification. Based on the repository str

Perplexity is inviting users to join a waitlist for a Windows version of its Personal Computer product, which appears to be an AI-powered tool or application designed to run on Windows operating syste

Most people, including really accomplished people, don't have an accurate mental model of how LLMs operate (and why would they?) You see this in wide beliefs that AI is just copying from known sources

HIPAA and FERPA compliant AI systems reduce institutional and legal risks when used by students and researchers by ensuring sensitive health and educational data are properly protected. Compliance wit

Personal Computer is coming to Windows. Personal Computer for Windows runs on your machine and orchestrates across the apps and files you use every day. We'll roll out first to paying Max and Enterpri

Routing and post-training open-source models won't only give you more accurate systems but also meaningfully faster and cheaper systems as most companies are currently learning (in addition to giving

Ethan Mollick expresses skepticism about online posts that explicitly use words like 'genuine' or 'honest,' suggesting such language may indicate the opposite of what's being claimed. This observation

There are good things and bad things about AI in education & research just like everywhere else, but it’s also true that it’s not going away and that AI increasingly is becoming part of the tool set a

Today at Microsoft Build: Pinecone Nexus now integrates with Microsoft OneLake. Your enterprise data, turned into task-scoped, governed, cited knowledge your agents can use directly. 95%+ token reduct

Try the most natural TTS and cost-effective STT APIs in @Vapi_AI Grok STT and Grok TTS from @xai are now live on Vapi, the platform for enterprise voice AI. Build on Vapi to create custom voice agents

Two bits here I think we're going to see a lot more: 1. custom harnesses / finetunes on smaller open source models to beat frontier models at specific skill or task 2. using frontier models as critics

We just announced the integration of our knowledge engine, Pinecone Nexus, with @Microsoft OneLake at #MSBuild. Want to build a reliable, production-grade knowledge layer directly over your structured

AI will become our interface to the world. It will sit higher in the stack than the OS. It will collapse current SaaS layers, chat, communications, apps, app creation, into a single new kind of interf

Amazing deep dive from the @togethercompute team on serving MiniMax M3 in production. M3 with its 1M context, native multimodality and MiniMax Sparse Attention requires real work across paged decode,

Another thing about AI writing is that while a single instance of AI writing on a topic may be fine, any situation where lots of people use AI to respond to a particular prompt (comments sections, hom

Building apps has never been easier. With Sites, Codex can turn your work, ideas, and plans into an interactive website or app your team can explore, use, and share with a URL. Rolling out to Business

Computer-use agents are moving from the cloud to your local machine. Fast. When we launched Holo3 two months ago, the production feedback was clear: digital agents need to be blazing fast, cost-effect

For organizations already using Azure and Fabric, this means a much faster path from governed data to internal software. • Build apps securely on top of governed Fabric data • Use existing authenticat

Here's the paper, which confirms that big changes are happening very quickly. Some really interesting approaches to teasing out what is happening in the software development cycle as well: https://pap

I have seen senior managers outpace ICs, and I have seen ICs run circles around the org chart. Titles tell you almost nothing about who is actually going to operate well in the next few years (and mig

It is difficult to know how good MAI-Thinking-1 is from the scores alone (like weirdly low GPQA & Terminal Bench 2.0) But Microsoft makes it really hard to try its models upon release (a general issue

Move from test to production by running high-performance inference directly on Foundry. At #MSBuild, we demoed an end-to-end workflow showing how unified infrastructure improves latency, reduces cost,

Since a lot of comments ask about it, they cannot really say anything about code quality using their methods. In part, the reason humans are the bottleneck is because humans are doing some sort of qua

The everything apps still look a lot like hybrids between chatbots and IDEs, rather than something built for general knowledge work. Too much assuming linearity & that final outputs are the only goal,

The execution across Elon Musk’s companies right now is operating at an unprecedented scale • SpaceX — The largest IPO in history expected around June 12 • xAI — Grok V9 (1.5T parameters) launching mi

We’re making Codex more useful for your work by expanding plugins beyond individual tools. These plugins turn Codex into a specialist for a specific role with a single install, no coding required. Cod

For those who wants stats, rather than anecdotes https://x.com/emollick/status/1983217724714066310?s=20 👀New data on the corporate ROI from generative AI from a large-scale tracking survey by my colle

Production AI systems require fundamentally different infrastructure approaches as workloads scale, with demands that differ significantly from development or testing environments. Fireworks AI discus

The gains are most obvious in coding and operational areas. Even conservative organizations are adopting coding agents fast. That doesn't mean AI adoption doesn't come with huge challenges and cost co

/goal and other fully automated AI agents are cool, but not a great model for the future of work with people. Instead you want your AI to know when to ask you GOOD questions, maybe because it is stuck

Timeline of the Iran War: 🔸2015: JCPOA agreed to. Iran gives up most of its enriched uranium, leaving it with a limited amount enriched to under 4%. 🔸2016-2018: Iran is complying with the agreement. 🔸

I think Epoch does a great job benchmarking, but I continue to believe that open weights models are much more fragile, especially out-of-distribution, than their benchmarks indicate. Vibe-wise, I don’

It does seem like meaningfully better AI releases are accelerating, especially from OpenAI & Anthropic. To illustrate, I caused this timeline to be created. It only lists new models that scored 3 poin

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