GateUser-88e8fff4

Both humans and AI should learn a purely linear attention mechanism.
If you feel like your agent is always disobedient, doing a bunch of things all wrong,
If you feel overwhelmed every day, busy but like you haven't accomplished anything,
Then you need to learn to focus on just one line of problem-solving, forcibly blocking out all other issues, or else your prefrontal cortex will be exhausted.

If you firmly believe that AI is the next-generation version of the answer, you should sell all your Nasdaq holdings and fully invest in model companies.

Taking advantage of AI privacy regulations being virtually nonexistent, from now on all websites should seriously inform users that they do not collect any cookies—more formal than the EU.
Then, they turn around and store all chat records, analyze them carefully every day, and decide which batch of users to harvest next.

If the company uses Claude Code for everything from top to bottom, the anthropology company can collect all the key data of the entire organization
From financial reports, to fundraising pitch decks, and code implementation and deployment
Anthropology can also monitor unicorn startups, gaining the best data from their development
And right now, only outdated cookie privacy regulations exist; cookies can only monitor what is being clicked or tracked
AI data = the most directly purified privacy data
Therefore, the market value of anthropology should equal the most invasive privacy-infr

Given the current pace of AI development, any product needs to release a major feature every 15 to 30 days to keep up with information flow.

Anthropology and OpenAI are two companies with mediocre code quality, often experiencing software issues and unable to identify the cause, leaving users to adapt themselves.
However, their own APIs still manage to support large-scale concurrent calls reliably every day, which demonstrates the current stability of LLMs combined with experienced programmers working together.

The biggest benefit of AI companies launching desktop software
Crazy theft of user privacy,
Meta does nothing at all, just sells privacy and ads to relax and play around,
The ultimate business model, precisely targeting scam information to each user

Currently, given the capabilities of AI and the chaos of everyone coding by mouth, the open-source star-earning model on GitHub is dead. In the future, only a few leading libraries will be seriously maintained, while everything else will be closed source, and open-source prompt engineering will be enough.

Claude Code opens source to guide the direction of agent infrastructure

These days, you can block all the marketing accounts posting Pretext, especially the trash accounts in the crypto and AI circles.
When these people copy the copywriting of "virtual scrolling," they probably don't even know what virtual scrolling is, let alone have experienced any issues while implementing it themselves.

These days, you can block all the marketing accounts posting Pretext, especially the trash accounts in the crypto and AI communities.
These people probably copy-paste the "virtual scrolling" text without even knowing what virtual scrolling is, let alone having handled any issues themselves.

Codex's quota policy has serious issues. I've always been on the pro plan. Some weeks I use it intensively but don't reach the limit, while other weeks I open medium and work for half a day, and I can use up 20% of the quota.

Which domestic relay stations support large-scale API calls?

The most important thing to cultivate is a sense of control over your assets and the satisfaction of steady compound growth. With this mindset, you won't be tempted to make high-risk all-in bets or chase daily short-term trades on any investment target, and you'll be much less likely to fall victim to exit scams or fraud schemes.

Codex should develop a tree-based pattern where it iteratively self-manages tasks. Each round draws three cards, then performs TDD + logging trace closed-loop benchmarking. Starting from three cards drawn to nine cards, it begins proliferating, deleting and merging garbage tree paths. It continuously optimizes based on benchmark targets infinitely, automatically performs git tag upon reaching critical performance nodes, gradually converges from divergent models, and ultimately delivers a non-overfitted program that meets completion objectives.

Everyone is using SOTA models for software, but for a commercial application that requires cost considerations, if you want to fine-tune a weak, mediocre model into an agent that can perfectly complete tasks, it requires a lot of repetitive benchmarking work.