Vivek Nair

1.1K posts

Vivek Nair

Vivek Nair

@virtuallyvivek

MTS @AnthropicAI

Brooklyn, NYC Beigetreten Ocak 2012
674 Folgt646 Follower
Claude
Claude@claudeai·
Claude for Excel and Claude for PowerPoint now sync together seamlessly. When you’ve got more than one file open, Claude shares the full context of your conversation between them. Pull data from spreadsheets, build out tables, and update a deck — without re-explaining a step.
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Claude
Claude@claudeai·
This is Claude Sonnet 4.6: our most capable Sonnet model yet. It’s a full upgrade across coding, computer use, long-context reasoning, agent planning, knowledge work, and design. It also features a 1M token context window in beta.
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Mihika Kapoor
Mihika Kapoor@mihikapoor·
Simile is out of stealth! At Simile, we have built the first AI simulation of society, populated by agents based on real humans. We are building a foundation model that predicts human behavior in any situation, and a product that deploys it at scale. Thrilled to be on this mission.
Simile@simile_ai

x.com/i/article/2021…

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Vivek Nair@virtuallyvivek·
@elmd_ Wow so fast, using Jax js?
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Lance Martin
Lance Martin@RLanceMartin·
excited to share that i've joined Anthropic! ive been a user of Claude / Claude Code for a while and have seen how fast the frontier moves. i want to help people get the most out of Claude's rapidly expanding capabilities.
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Vivek Nair
Vivek Nair@virtuallyvivek·
Truly privileged to have worked on this! We're bringing Claude to millions of knowledge workers - beyond just coding. If you're heavy in finance (or just an Excel nerd), let us know what you think.
Claude@claudeai

Claude in Excel is now available on Pro plans. Claude now accepts multiple files via drag and drop, avoids overwriting your existing cells, and handles longer sessions with auto compaction. Get started: claude.com/claude-in-excel

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Anvisha
Anvisha@anvisha·
Finally, AI designs you can edit. Moda - create brand assets on a fully editable canvas. This is not another ChatGPT or nano banana wrapper. We actually taught an agent layout, typography, and color. We gave it taste. Use it for slides, social, infographics, e-books …
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Vivek Nair
Vivek Nair@virtuallyvivek·
@arpit_bhayani Pretty good for a high-level description! Causal masking is a pretty important omission though. I would focus on that to improve this further. Each token should only be able to attend to prior tokens during auto regression.
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Arpit Bhayani
Arpit Bhayani@arpit_bhayani·
Wrote an article on how LLM inference works. I spent about a week trying to build a clear understanding, and this is my distilled version of it. It covers the full inference journey: embeddings, attention, KV caching, quantization, and more. I am no expert, but if you are looking for a starting point, this might help. Give it a read.
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Akshat Bubna
Akshat Bubna@akshat_b·
The best part about working on @modal is watching our customers literally build the future. Today, they’re using @modal for everything from generating AI music to powering real-world robots to curing cancer. And yet, this is just the beginning. We have so much more to build. If you like making computers do impossible things to solve user problems, please reach out. Especially if you are interested in low-level or large-scale systems, applied AI, GPU performance, delightful user experiences, security, or any combination of these.
Erik Bernhardsson@bernhardsson

It's true – @modal has raised a $87M Series B at a $1.1B valuation to advance the future of AI infrastructure.  Thank you to @Lux_Capital, @Redpoint, @AmplifyPartners, and others. Now more than ever, AI demands a complete reinvention of traditional compute infrastructure

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Vivek Nair
Vivek Nair@virtuallyvivek·
We spent the first year at @GentraceAI helping customers run traditional LLM evaluations. But we realized late last year that agents changed everything. Agents don't just generate text - they take actions, make decisions, and interact across multiple steps. Traditional "hallucination" checks can't evaluate whether your agent booked the right flight or resolved the customer’s support ticket correctly. Old LLM observability tools were built for simple input/output prompts. They break in an agentic world. For the last 9 months, we rebuilt Gentrace from first principles 🔥 Here's our new approach: - Chat with your AI trace data using our agent to discover what's actually breaking - Describe problems in plain English - Our agent creates custom AI-powered monitoring columns that catch these failures across all future traces Example: Instead of checking "factual accuracy," you can now evaluate complex behaviors: - "Figure out if a user is frustrated with my AI agent in the customer support chat" - "Detect when my agent takes more than 5 steps to answer simple queries." We stopped measuring outputs and started measuring outcomes. Watch @dougsafreno break down how to solve agent observability with Gentrace. 👇
Doug Safreno@dougsafreno

Agents are significantly more powerful than standalone LLM calls. But, debugging them is a nightmare. You can trace their reasoning and tool use, but traces get huge and are impossible to parse. To solve this, we spent the lasts several months building Gentrace for Agents, which puts our own agents to work on yours. In Gentrace for Agents, you can: • Chat with AI to debug agent traces • Create smart monitoring columns • Build out tailored evaluations It’s like a giant AI powered spreadsheet over your trace data, with a Cursor-style chat sidecar. If it sounds a little meta, it is, but it is very powerful in practice. We recorded this video to show you how it works. Take a look, and let me know what you think:

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daniel
daniel@DanielEdrisian·
Just released a 20 minute beginner tutorial making a flight tracker app with @alexcodes_ai Check it out if you're just getting started!
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Vivek Nair@virtuallyvivek·
@DanielEdrisian His setup isn’t ergonomic at all. Really makes you wonder how he got so cracked
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daniel
daniel@DanielEdrisian·
This is Alex
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Vivek Nair@virtuallyvivek·
@wadefoster It's far easier to polish software when the friction is so damn low. Animations, responsiveness, consistency in colors and padding are much more managed. More tactical developer example: custom ESLint rules are much simpler to make.
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Wade Foster
Wade Foster@wadefoster·
This is what we see at Zapier. While some use cases replace human tasks. Far more are doing things humans couldn't or wouldn't do because of cost, tediousness, or time constraints. I'm bullish on innovation in a whole host of areas that would have been considered "niche" in the past.
Aaron Levie@levie

The biggest mistake when thinking about AI Agents is to narrowly see them as replacing work that already gets done. The vast majority of AI Agents will be used to automate tasks that humans never got around to doing before because it was too expensive or time consuming.

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Vivek Nair
Vivek Nair@virtuallyvivek·
Press ⌘ + C twice to copy to clipboard. This mimics Superhuman's behavior with copying an email thread. If you're selecting text elsewhere on the page, the extension does not activate.
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Vivek Nair
Vivek Nair@virtuallyvivek·
I made a simple Chrome Extension to copy a Github PR diff into your clipboard. This makes it dead simple to understand what's changed.
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