praveen

Building warehouse automation

[praveen @PravKumar1988]

@Karmedge Aren't these the exhaustive set of options? what else is missing?

[Hux @huxlab]

Alexandr Wang 说了一句让技术流创始人集体沉默的话。

“做技术的人习惯了数据说话，觉得东西更好、数字更漂亮，客户总会识货”

他直接点破：大公司不是这么运转的，政府更不是。

在那些客户眼里，感知比现实更真实。

真实的业务现状太难看了，没几个人愿意直面。

大家要的不是真相，是一套能让自己在系统里安全生存的叙事。

所以更好的产品照样被踢出局。

买方根本没在评测产品，谁掌控了感知，单子给谁。

他甚至点名 Palantir：表面是软件公司，底色是个剧团。新员工入职，真的会发一本戏剧表演书，发了整整好多年。

做企业级市场，改写现实只是一半的工作；

塑造一套客户愿意相信的说法，是另一半。

技术人最容易栽在这道坎上：

你的东西确实碾压竞品，但坐在对面签字盖章的人，自始至终连看都没看屏幕一眼。

[Vishal Misra @vishalmisra]

1/ These slides are from AI in Context, a course I helped design and co-teach. My module (From NN to LLM: Rise of the Machines) starts with biological neurons and follows the abstraction all the way to modern LLMs. I’m a little allergic to “brains are literally neural networks.”

[Rohan Paul @rohanpaul_ai]

Ben Affleck (Hollywood star & Artists Equity CEO) talks about how he fine-tunes open video models by unfreezing weights and trained only the last cinematic layer so a film crew can hit real production standards.

for context, Ben Affleck founded InterPositive in 2022, a 16-person AI shop for film post and Netflix bought it in March 2026 for $587 mn in cash.

He needed that model because public video models were trained on his peers' films, and he did not think that was a real business. So InterPositive raised money, shot its own dataset for 8 months on a controlled stage, and used it only as late-stage training.

Each new film then trains a private model on its own dailies, so the production keeps the footage and the learning. That is the product Netflix paid $587 million for.

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From "Bloomberg Live" YouTube channel, (link in comment)

[praveen @PravKumar1988]

@waiting4_asi Finetuning and RL makes smaller models for many use cases significantly cheaper at comparible quality. Boris is incentivized to sell Claude and "general" models.

[Garry Tan @garrytan]

Karma isn’t a thing that happens, it’s a choice you can make now

[Wisdom Stoics @WisdomStoics]

This mentality is everythiiiiiiiing.

[praveen @PravKumar1988]

Love this but is this true for most people? : "Most people never say what they want"

[Alexandr Wang @alexandr_wang]

Here’s to the wanting. Here’s to the dreaming. Here’s to everyone. https://t.co/FHcmqU3fYf

[Possible | Video Editor @Possible_Ugwa]

Always come back to this video when you think you can't.

[Mayukh @mayukh_panja]

Any one who is privileged therefore owes it to the world to work on the most obnoxiously hard problems, not just build a life for themselves, but uplift people by building great companies and employing as many people as they can.

The privileged must risk it all, over and over again, until they are tortured to greatness.

This is my version of socialism, re- distribute risk. The rich have a moral imperative to embrace more risk.

[praveen @PravKumar1988]

Hurl yourself into the abyss and discover its a feather bed 👏👏

[maya @mayareminders]

nature loves courage

[praveen @PravKumar1988]

@VazeKshitij startup that aligns closely with your values. or your own startup. or public service.

[fahmeedx @fahmeedx]

druski genuinely saying some real shit here

[Chelsea Finn @chelseabfinn]

New blog post with @perryadong on what we need for robots to be broadly useful in the real world.
https://t.co/jxsNeMUhFg

Reliability is the one of the biggest open challenges in AI right now. Current models work out okay if a person will be reviewing the outputs (eg drafting code), but it will become more of a bottleneck as we want systems to act with more autonomy and more trust.

[praveen @PravKumar1988]

Great article!

[Sam Padilla @theSamPadilla]

After 2+ years in the robotics data space, we are shutting @Eidon_AI down.

The thesis was right. But the business is brutally hard.

We close this chapter by open-sourcing everything we built and sharing lessons for anyone venturing into the space. https://t.co/pyL9IkjQPT

[Slava Akhmechet @spakhm]

Long range career advice. (Won't help you get a job tomorrow, this is a ~10-20 year thing)

You want to run two processes. Your main loop:

1. Knock on doors, many outside your league, and get yourself in a room with the most competent people who will have you.
2. Work your ass off to be reliably useful to them. Like, really, _really_ hard. However hard you think you're working to be reliably useful, work ten times harder. Also, be cheerful.
3. Every once in a while pop your head out and go to step 1.

Your background loop: look around for weird asymmetric opportunities. A startup to found/join, a project to hack on, an angel investment into a friend's company, whatever. When your heart sings, jump on it. (You will fail a lot but that's fine so long as you handle failure well, I'll cover that in another post)

In exchange for working really hard you get two things. First, knowledge/experience-- you learn a ton. Second, relationships-- ppl will remember how great it was to work with you. Knowledge and relationships with competent ppl create crazy asymmetric opportunities. You then jump on those. (There is a lot of twitter slop about compounding, well this is what career compounding looks like.)

---

Now some failure modes. Do _not_ do this:

"I deserve much better than this company/role, I'm just going to begrudgingly collect a paycheck" --> first, you deserve what the market clears and that's that. Second, if you act this way you'll only hurt yourself. Your colleagues will remember you as a grouch and it will wreck the compounding loop. Third, it's a very unpleasant state to be in, and you should choose not to be in it.

"But I work at Home Depot, what's there to compound?" --> that's extremely shortsighted. Maybe you do a super good job helping a customer who then has an open spot and remembers you. Maybe your store manager becomes a regional manager and calls you. Maybe years down the line your quiet peer is looking for a partner to start their own store. Maybe none of this happens, but it doesn't matter because empirically ppl who try hard to be useful wherever they are tend to do great, and people who don't, don't. (Home Depot is a metaphor, obviously)

"I hate this place, I quit! And anyway they're a faceless corporation that will lay me off any time." --> don't do that. The company may be a faceless corporation, but your colleagues are not faceless. They bet on you, hopefully you did a great job, and now they depend on you. Leaving is never easy, but do it in a way that respects the trust ppl put in you.

"Everyone around me is an idiot, I hate this!" --> maybe they are, or maybe you're not ready and some day will discover you were wrong about this. In any case you can learn from everyone and everything. So do that and be useful, don't break the compounding loop because opportunities will surprise you and things are often not what they seem.

“_I_ should have been promoted not that other guy/why is my manager layering me, screw this!” --> every time that happened i eventually understood I deserved it, you probably do too. Don’t hold grudges, do your best to figure out how to learn from this and move on, it will make sense eventually.

P.S. it helps if you’re a little talented.

[praveen @PravKumar1988]

Started avoiding AI for any first draft (tech design, requirements and so on) and noticing a big boost in productivity and clarity of thought! Thanks to Deep work by Cal Newport for the suggestion. I'm trying to get back to pen and paper for the initial draft - highly recommend :)

[✒️ @Literariium]

Dostoevsky: