Nass.js 🚀 retweeted
Vídeo 1: uma excelente explicação sobre o por que a Bolsa caiu: a aposta dos investidores foi em Flávio e deu errado. Vídeo 2: um exemplo prático do que o vídeo 1 explicou. Vejam um investidor desesperado pela derrota da aposta que ele fez.
Nass.js 🚀 retweeted
Holy sh*t! Opus 5.5 (Launch) vs Opus 5.5 (Now)
Same prompt, same effort.
The usage: They nerfed the usage limits. Hard limits are draining MUCH faster !!!
The quality: It feels similar no big change but sometimes you feel a little laziness but other than that, it's the same
Anthropic, why do you nerf the limits?
Nass.js 🚀 retweeted
IBOVESPA during Lula’s previous terms:
Term 1: 9,000 -> 65,000
Term 2 (pre-2008 crash): 65,000 -> 73,000
*crashed from 73,000 down to 30,000 during 2008
Term 2 (post-crash): 30,000 -> 70,000
Term 2 (entire term): 65,000 -> 70,000
Term 3: 114,000 -> 180,000
~23% per year (1100% cumulative) for the 12 years in office, including 2008 crash
~4% per year (63% total) for the 12 years he wasn’t in office from 2010-2022
index currently down ~25% overnight
Nass.js 🚀 retweeted
RIP em dash
We analyzed how @claudeai’s Opus 5.5 writes compared with Opus 5 across high-reasoning Text Arena outputs.
10 of 12 writing measures moved in a better direction.
Opus 5.5 should be easier to read:
- Long content words fall from 41.7% to 38.6%, the lowest share of any Claude model we analyzed.
- Sentences are 17% shorter on average, dropping from 12.14 to 10.03 words.
The tradeoff is length. Answers get 6% wordier, rising from 453 to 481 words on average, making Opus 5.5 give the longest answers across the Opus family.
It also sounds less recognizably AI on two familiar tells:
- 95% fewer em dashes
- 73% fewer semicolons
But a new giveaway may be emerging. Hedges and caveats such as “perhaps” and “arguably” rise 97%, from 0.39 to 0.77 per 1,000 words, the highest rate of any Claude model we analyzed.
What do you think: does Opus 5.5 read more naturally?
Nass.js 🚀 retweeted
We can finally talk about it:
We found a way to extract hidden reasoning of frontier models using a vulnerability in the APIs of every frontier AI company.
We verified that our reasoning token count matches billed API thinking tokens 1:1 for most of the prompts we queried.
Thread. I thought I was immune from ever feeling hollowed out by AI as a programmer, because I've always gotten far more enjoyment from shipping, getting users, and solving problems than indulging in the art of coding.
As the LLMs have eaten deeper and deeper into our field, I've empathized with my peers who've expressed a sense of loss and disillusionment as the art of programming has become more and more automated. But, I've always seen myself as someone who saw coding as a means to an end to solve problems. Not something whose craftsmanship, culture, methodologies, and fads were worth getting too hung up on, beyond what was needed to adeptly deliver value to others and not fall behind the (frankly, rare) genuine advancements over the years.
This all changed for me over the last week. The frontier probably shifted a bit earlier than today, but I didn't see it until now. The change has come about for me because GPT-5.5 was able to build complex software I needed built autonomously for 2-3 days at a clip. Work that would have taken me months, or even years if you include learning the requisite languages, libraries, and tooling, being completed over a weekend.
This isn't something I think anyone who has been programming as long as I have can really be prepared for, this kind of velocity jump is just mindboggling. This is truly superhuman performance - it's not perfect, and there certainly is a level of simplicity and clarity that would come in the hands of the world's best programmers, but that margin is so small so as to be unnoticable when contrasted with the sheer volume of working software that it can produce per unit time.
So, why has this caused a shift in the way I feel about these technologies, after all this time not having felt it as each subsequent model advanced closer to what we see now? There are two reasons.
First, it's clear that the age of humans understanding how software works is over. Yes, humans will need to understand things, at least for a few more years, but we are now at a kind of escape velocity where the % of lines of code that are created every year that are even read, nevermind understood, by humans, is now permanently declining.
But the real shift, is I am no longer a programmer, I am a manager. Good managers do not take credit for the work of their team - they see themselves in service of their team. Up until now, claiming "I built this" still felt true when talking about things I had created with the help of LLMs.
But now, when the LLMs are writing thousands of lines of code, and I am simply providing guidance, direction setting, and oversight to catching the bigger errors, I found myself in the bizarre situation (that many will be in soon, I presume) of no longer feeling entitled to take credit for the work being done.
Not being able to say "I built this" when sharing something whose basic conception came from my own mind, but under the tireless effort of these insane machines to actually reason through and materialize into a working solution, is devastating. Not because of the fact it doesn't feel truthful now, but because I know it will never be truthful again for myself and soon for all of the rest of us.









































