Now you can have a quake style console in your operating system. Except with an infinitely patient agent to help you customize it instead of esoteric variable names and slash commands like in the 90s
@kaffurtado The only certain thing is that no Peruvian pro player will ever make a team with br again. Even Scofield who never talk bad against past team mates, said that he didn't feel confortable with br being envious.
@astinidota Worst part, per TaiLung's own statement: LGD told him resign quietly and skip TI, or 'the matter gets fully investigated.' Wait, if they truly believed he fixed matches, why offer NOT to investigate? He refused, and got banned the next day. Guilty or not, that process stinks.
@KaelThas91@astinidota 'Valve and ESIC are checking it', but you already handed down the sentence. Fun fact: even if he did 322, coercing a minor into a phone search at 4am without his guardian is still illegal. Criminals investigating a crime doesn't make it justice.
@MarianoDOTA Tema cerrado luego de que te pones a revisar sus partidas en búsqueda de 322 y hacer tus bromas cojudas? Que fácil lavarse las manos así, este país es muy generoso con los extranjeros...
Leonardo de Moura (@Leonard41111588) is the creator of Lean and the Z3 theorem prover. I talked with him about how Lean works and why LLMs plus Lean will fundamentally change how we write software and do math.
In this episode:
• How formal verification and proof assistants work
• How Lean will impact handwritten math & software
• Lean’s role in recent math breakthroughs
• When software is worth formalizing
Where to watch:
• YouTube - https://t.co/RVC8PbzQQe
• Spotify - https://t.co/moW2HQq9xE
• Apple Podcasts - https://t.co/jOYDGtGVnt
• Transcript - https://t.co/rYFlh7Z9Dc
Thank you to the sponsor of this episode for supporting my work:
• WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at https://t.co/y8noBzFEem
Chapters:
00:00 Intro
00:28 How formal verification works
05:21 A new way of writing software
13:15 Proof assistants vs programming languages
21:06 How Lean has assisted in mathematical breakthroughs
32:03 When is it worth formalizing software
33:29 How Lean will impact handwritten math
38:55 The Z3 theorem prover project he started
45:44 The most technically challenging work of his career
51:10 Lean vs its competitors
01:00:37 The future of Lean
01:04:10 Technical book recommendations
01:06:15 Advice for his younger self
01:07:10 Outro
@zmbnski the real problem is using claude over there, I had some success using it with a linux machine and a 12vpx subscription, just use deepseek to setup the thing and try new configs until it works.
This is why spidey is relatable, having to force yourself to let go of someone you love is a different kind of pain.
But you still gotta go about your day like you’re not brokenhearted on the inside.
@justopancho Bien Kj ptmre, que daño le ha hecho gente como benjaz y smash al dota peruano. Ya de por si la sociedad peruana aplaude la criollada, perdona lo imperdonable y enaltece a quien la supo "hacer", hasta se ve en los políticos que elegimos.
@MarianoDOTA desde que entro 1xbet a la escena pasan estas cosas, una casa de apuestas rusas y sus grandes streaming que promueven las apuestas sin responsabilidad. La escena dotera de sudamerica ya murio.
when you ask an LLM to audit your code, it is essentially answering: "does this code have some pattern that *looks like* a bug?" this is important because many things can "look like" a bug, while still being correct. and "fixing" something that is correct will make it incorrect.
I've ran an experiment where I set up a loop:
- GPT 5.6 Pro audits an algorithm
- Fable 5 to fixes the bugs GPT found
- repeat
with a catch: the algorithm was *already correct* (literally, proven so). the result is complete degradation: in the first iterations, the correct algorithm becomes incorrect. in the next iterations, the (now incorrect) algorithm gets patched. but the patches introduce new cases and bugs. this keeps going until the code is destroyed.
so, a naive loop where a model audits its own code ("adversarially") and then fixes it will lead to failure. of course, as LLMs get smarter, *looks like* a bug will converge to being equivalent, in practically 100% of the cases, to finding a real bug. I think we're very close to that, and it will be interesting when the error rate is so low that these loops start working. but, as of 2026, we're not quite there yet