maverick

90’s kid, muslim, engineer, founder, stoic, athlete — now @buildingalchemy

[maverick @heyusman]

we have entered the age of parabolic growth

[skyte @ihateskyte]

Get your dopamine from what you create not what you consume

[maverick @heyusman]

bro deserves an oscar for the aura farming

[maverick @heyusman]

the odyssey is a 10/10 cinematic adaptation even if it swings far from the original text (which it surely does and often times quite far) but it is hands down one of the finest works of cinema

[Paul Graham @paulg]

Founder mode means running a company the way a founder can and a hired CEO can't. We don't yet know all the things only a founder can do. But it's not an imprecise term. It's like the definition of a Mersenne prime. We know exactly what one is, but we only know 52 so far.

[maverick @heyusman]

we live in a compute-constrained world

[Kimi.ai @Kimi_Moonshot]

Kimi K3 has received far more love than we expected, and our GPUs are feeling it.

Over the past 48 hours, demand has pushed close to the limits of our current capacity. To protect the experience of existing subscribers, we're temporarily pausing new subscriptions and prioritizing compute for current members. Existing subscribed users are not affected.

We're adding capacity as fast as we can and will reopen new subscription spots in batches.

Going forward, we'll also split membership into two more focused plans: Kimi Membership for Kimi Web, App, and Work; and Kimi Code Membership for coding workflows. This will help us match compute more precisely and keep the experience stable.

Thank you for your patience and understanding!

[Paul Graham @paulg]

If you want to start a startup, don't learn "entrepreneurship." Learn how to build things. The hard part of startups is not "entrepreneurship" but product: to know what to build, and to be able to build it.

[gabriel @gabriel1]

this took so long for me to understand: the bottleneck to more innovation is not more high intelligence people, but more people having an interest in hard problems

it's impossible to create new useful things if you don't get immense happiness from making that thing

[lyxe @cryptolyxe]

“if Elon lost 1 trillion dollars he would still be the richest person in the world”

probably the craziest shit i’ve ever read 😭

[Daily Lindy Effect @LindyEffect]

Euripides

[Aaron Levie @levie]

Take whatever number of people you thought might be in jobs related to AI deployment in the enterprise and multiply it by 10. Then probably 10 again.

A major topic that keeps coming up in talking to CIOs across enterprises of all sizes and industries is the implementation gap for getting agents to work at scale and organizations on mission critical work.

As the task goes from implementing a chat system that’s basically an LLM plus search, to connecting to real production systems that both can deliver meaningfully better productivity gains but also introduces meaningfully more risk, a whole new set of work has to be done.

You have to ensure the right level of protection of data, updates to access control controls, migration of legacy systems to common modern platforms, create observability across what agents are doing, implement new workflows, figure out the human in the loop moments, drive the change management of the new workflows, and more.

Then, all of a sudden the model capabilities get updated and you have to do a set of the above steps over again. Half of what you’ve done is obsolete, and the other half needs to be upgraded to take advantage of new capabilities. Or, token budgets run hot and you have to peel off some of the workloads to lower cost models that will be more cost effective. But then you have to go through those same steps.

Enterprise are trying to figure out what is the right set of roles to go and implement the systems in their organization to ensure that the workflows are actually being executed properly, ensure it’s not just slop being produced, and to make sure their organization remains safe and secure.

Many companies are starting by repositioning existing IT talent in these functions, but there’s also a growing need for the equivalent of internal FDEs to go take on these tasks in an enterprise. The looks incrementally closer to software engineering than it does traditional IT implementation.

Next, almost all AI vendors (labs and the software players) will have some form of next-gen FDE or Applied AI architecture functions to help support these use-cases. The benefit here will be these companies have an incentive to make their capabilities work well so they can bring best practices from a range of customers they’re seeing and directly from the product innovation.

And finally, we’re seeing the rise of all new AI services firms or major parts of existing services firms move into AI implementation. Companies will often want to bring in ostensibly neutral players that can work across their tech stack but also have seen best practices across their vertical. There are going to be tons of new service providers that get launched to do this, and many will eventually go and disrupt (or get acquired) by the larger player.

Either way, all told, we’re in for years of AI diffusion, and along with it tons of new roles and areas of work to be done to deploy AI at scale.

[maverick @heyusman]

i run into this fun little ai productivity multiplier case study every day, claude estimates a human timeline, then we both get it done in code in tenth of that time. this is because claude is not yet trained on claude it’s trained on human data

[atulit @atulit_gaur]

when claude gives you a timeline of 1-2 weeks, is it aware it's gonna generate all the code in the next 5 minutes?

[n @ctrlarchive]

artemis space mission photos are so beautiful

[Reid Wiseman @astro_reid]

There are no words.

[BSF 🇬🇧🐀 @BSF42069]

We’ve come a long way

[Thariq @trq212]

Eid Mubarak!

a day of celebration after a month of reflection

[zerohedge @zerohedge]

It got lost in the news firehose, but this is the craziest chart today, and maybe this decade

[maverick @heyusman]

rule of thumb: to get the most out of language models you must have a firm understanding of the language yourself

[andrew gao @itsandrewgao]

you can instantly 10x your vibecoded frontends by just learning what different ui components are called

ofc opus is creating generic slop, the only words you know are menu and button.

[maverick @heyusman]

lol

[Marc Andreessen 🇺🇸 @pmarca]

Wiser words were never barked.