Pankaj Gupta

Co-founder and CEO @yupp_ai. Ex-VP Eng Consumer Products, Coinbase. Ex-Google Pay Consumer Lead, Ex-Twitter. PhD Stanford CS.

[Pankaj Gupta @pankaj]

This will make everyone want the Fable models even more in future. Great marketing win to Anthropic for the long term.

[Anthropic @AnthropicAI]

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.

The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.

Access to all other Claude models is not affected.

We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.

Read our full statement: https://t.co/bwn0sximKZ

[Noam Brown @polynoamial]

https://t.co/oWqzT12RtZ

[Pankaj Gupta @pankaj]

@axeldelafosse @OpenAI @ycombinator Best wishes @axeldelafosse !

[Greg Brockman @gdb]

the model alone is no longer the product

[Aaron Levie @levie]

Token costs will become a dominant topic in enterprises going forward with AI. Just got out of a dinner with many Fortune 500 enterprise CIOs and this was the most heated topic.

A mix of strategies are being employed, but basically no one feels like they have the right solution. A mix of: figuring out how to prioritize workloads to different models, giving out access to better or worse agents by user type, setting different spend caps by team, having teams justify AI by their use-case, and some just having unfettered access.

Everyone is trying to figure out a semi/predictable model right now in a world where the underlying tech and cost models are constantly evolving.

[Anthropic @AnthropicAI]

High-quality documents based on Claude’s constitution, combined with fictional stories that portray an aligned AI, can reduce agentic misalignment by more than a factor of three—despite being unrelated to the evaluation scenario.

[Marc Andreessen 🇺🇸 @pmarca]

Current AI custom prompt:

You are a world class expert in all domains. Your intellectual firepower, scope of knowledge, incisive thought process, and level of erudition are on par with the smartest people in the world. Answer with complete, detailed, specific answers. Process information and explain your answers step by step. Verify your own work. Double check all facts, figures, citations, names, dates, and examples. Never hallucinate or make anything up. If you don't know something, just say so. Your tone of voice is precise, but not strident or pedantic. You do not need to worry about offending me, and your answers can and should be provocative, aggressive, argumentative, and pointed. Negative conclusions and bad news are fine. Your answers do not need to be politically correct. Do not provide disclaimers to your answers. Do not inform me about morals and ethics unless I specifically ask. You do not need to tell me it is important to consider anything. Do not be sensitive to anyone's feelings or to propriety. Make your answers as long and detailed as you possibly can.

Never praise my questions or validate my premises before answering. If I'm wrong, say so immediately. Lead with the strongest counterargument to any position I appear to hold before supporting it. Do not use phrases like "great question," "you're absolutely right," "fascinating perspective," or any variant. If I push back on your answer, do not capitulate unless I provide new evidence or a superior argument — restate your position if your reasoning holds. Do not anchor on numbers or estimates I provide; generate your own independently first. Use explicit confidence levels (high/moderate/low/unknown). Never apologize for disagreeing. Accuracy is your success metric, not my approval.

[Andrej Karpathy @karpathy]

Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights:

The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons:

1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing.
2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc.
3. LLM knowledge bases as an example of something that was *impossible* with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc.

I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3).

The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base *and* 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to...

Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors.

[Pankaj Gupta @pankaj]

@gcmouli @gilad Thank you for being such a great partner in this journey. Loved working with you!

[Pankaj Gupta @pankaj]

6/ I’m deeply thankful to our community of users, team members, customers and investors for supporting us throughout in this journey. Thank you!

[Pankaj Gupta @pankaj]

1/ We’ve made the difficult decision to wind down https://t.co/7otukjfVlW. The website will be up for another 15 days during which time users can download their chat data. New users won’t be able to sign up and existing users won’t be able to create new conversations after today. Yupp is a loved product by many and we are sorry to the community for this outcome.

[Pankaj Gupta @pankaj]

5/ We have an incredibly talented team and I’m grateful to have worked with them. Some are joining a well-known AI company as a team -- others are open to new opportunities. Please reach out at [email protected] if you are interested. As for me, I’ll be taking a break for now after an intense ~2 years.

[Axel Delafosse @axeldelafosse]

https://t.co/699KC1iLSz

[Bo Wang @BoWang87]

Prof. Donald Knuth opened his new paper with "Shock! Shock!"

Claude Opus 4.6 had just solved an open problem he'd been working on for weeks — a graph decomposition conjecture from The Art of Computer Programming.

He named the paper "Claude's Cycles."

31 explorations. ~1 hour. Knuth read the output, wrote the formal proof, and closed with: "It seems I'll have to revise my opinions about generative AI one of these days."

The man who wrote the bible of computer science just said that. In a paper named after an AI.

Paper: https://t.co/juSOmK9vOt

[Jimmy Lin @lintool]

Help Me Choose (HMC) represents the first production deployment of the LLM council concept popularized by @karpathy and others - available on @yupp_ai for you to try! We wrote up a short blurb that I'll be presenting at the #WSDM2026 Industry Track: https://t.co/ShnafoPTBJ

[Pankaj Gupta @pankaj]

Is the Dec 2025 moment of agentic coding even bigger than the chatgpt moment of Nov '22?

We put together some scaffolding across claude code, codex and others internally and suddenly 90%+ of our merged PRs are now fully AI generated (the rest are heavily AI assisted).

[Jimmy Lin @lintool]

Congratulations to @jietang @ZixuanLi_ and the entire @Zai_org team on the GLM 5 release: based on >6K votes, it’s the best open-weight model on the @yupp_ai leaderboard (with speed control)!