Mo Patel @mopatel
Leadership: AI | Security | Distributed System. Experiencing the human condition on Jiu Jitsu mats. Austin, TX Joined December 2008-
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If there is one constant in management as you get older it is that the companies at peak interest (by any measure) are the ones where no matter what they are doing it is a best practice and that is the source of endless fawning. Later in a corporate arc, these same companies will do the inverse and if successful that becomes the "pivot" playbook of the day. And when not doing well whatever they were doing becomes the third rail of management. If what you're doing works then it works. It isn't always best or the "right" answer. Context is everything. Also blindly pivoting to the other thing almost never works. You can tell from the reorg or announcement mail since it leads with the implementation and not the problem being solved (eg first, here's our new "functional" org chart, or first, here's our new "pizza team" org chart.)
Anthropic's leadership team on how they built a culture with no internal politics: In this conversation, Anthropic's co-founders and executives explain why the company works the way it does. It comes down to trust, low ego, and a single shared mission. President @DanielaAmodei
General take, exceptions ignored: This is a trap because low margin businesses cannot invest in technology. Their financial model doesn’t support it. Their investors have programmed in steady state returns. They cannot re-invest their low margin profits. This is where new entrants without the financial constraints, providing steady state returns to investors, can disrupt their businesses. You can see VCs shifting their attention to vertical AI companies that are disrupting low margin businesses for this reason. The other path is PE taking over these businesses and using debt to reinvest, reshape and repackage the business.
3) Harnesses make a huge difference in cost-performance. The very simple Pi harness (@badlogicgames) got the same success rate as harnesses from the LLM vendors with Opus and GPT 5.5, but at 2x less cost! Seems to be mainly due to smaller inputs to the LLM.
Bullish on harness engineering as the product interface, for enterprise use cases the question remains general purpose or domain specific harness wins out.
new post on harness engineering for AI self-improvement: lilianweng.github.io/posts/2026-07-… It is hard to forecast how much the future of RSI will rely on harnesses. Likely harness engineering will evolve in the direction of self-improvement and enable auto-research, and, in turn, smarter
This is how AI forward companies are reshaping themselves
Since Quick launched in July 2025, we’ve collectively built more than 50,000 sites. Over 50% of employees have at least one Quick site. When you remove the barriers to build & share, it’s incredible what happens. More examples of how we’re using Quick : shopify.engineering/quick
Beware AI FUD, many such cases.
I'm suspicious of that that whole story about Uber blowing their AI budget and being disappointed in the results - I dug into it and it appears to have been built on very shaky foundations
We have seen in tech cycles where value accrues. Database companies have had the hardest time getting the combined valuations of the applications built on top of their product. As models become plug and play commodity with an optimizer sitting between the harness and model layer, value will move further up the stack.
i'm strongly against model companies focusing too much on harness, but i would love to hear if anyone has a strong argument for it my reason against it: if openai didn't build GPT 5.5, no one else can. this is their core competence if openai didn't build codex cli and app, we
@rasbt Wishing you peace in your recovery process. 🫶🙏❤️🩹
@ChaitaliSuraj @trentjhughes Thank you! I love helping companies scale. 🌱↗️🌳
@jaminball @Hammerspace_Inc Thank you! I read your blog post and was wondering if there was more. All good. Find your writing insightful.
@siicamore @patrickc Tripod Passing by Josef Chen, Routolotine by Kade Routolo are some examples. youtu.be/M0rY6gZ0ehQ?si… youtube.com/shorts/Hlg0vwt…
Prepare to train your mind even more than the body. I have been in many matches where I was in great metabolic condition but my mind just didn’t work for variety of reasons and I couldn’t hear my coaches or I heard them but couldn’t act even the most obvious moves that I know very well. Here is how I have been successful and still working to improve: I have performed very well in tournaments where I was much more relaxed and casual. Instead of going on some type of a zone, I have tried to banter with my teammates etc. My coach asked me to visualize success and precise opening plays and not to veer from my planned path to submission. I have written down the path and checks on my phone which I am reviewing while waiting for match to start. Sure you can go 1 or 2 moves off the path but make sure to return to your game you have planned, visualized and trained 100s of time as opposed to the path your opponent wants to take which you may or may not be familiar with. I drilled my plan many times with variety of resistance and with partners that knew and partners that didn’t know. To work on the unpredictable, I do positional defensive and offensive sparring during regular training. Knowing that my defense is top notch gives me confidence to put myself in vulnerable positions offensively.
@julien_c Answers: 20 tks Research: 10 tks Coding: 100 tks Agent: 200 tks
8 years since first @TensorFlow Dev Summit with @JeffDean kicking things off
I am at #TFdevsummit let's connect if you are in the Google Mountain View location
@alvations Yes certainly challenging and the number of new approaches is staggering that it is hard to keep up, need a continuous updating survey paper
@alvations Is this helpful, I came across a talk from the team in YT recommendations and found their approach interesting github.com/LMCache/LMCache
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