That being said the real danger is not coming from AI today, it's more C-suites believing AI can just zero shot any problem you throw at it.

--- I said it because I did the analysis, and realized that if I implement the original version, which basically is a crazy way to iteratively solve the MIP problem, it's much harder to reason with internally, and much harder to code correctly. But obviously it keep the broker happy (the developer is doing exactly what I said)

E.g. once I was tasked to build a new matching algorithm for a trading platform, and upon fully understanding of the specs I realized it can be interpreted as a mixed integer programming problem; the idea got shot down right away because PM don't understand it. There're all kinds of limiting factors once you get into the details.

Thought process: The user is asking if 2026 is next year. According to the context, today's date is Tuesday, December 02, 2025. So the current year is 2025. That means next year would be 2026. So yes, 2026 is next year.Yes, 2026 is next year. Actual resp Since we're currently in December 2025, 2026 is just about a month away.

IMHO another good thing of using JB IDE git UI (especially in a corporate setting) instead of using another software is that everyone has the IDE so it's easier to collaborate. Imagine if you're helping a junior member debug their local branch and they don't have lazy git installed.

Things that I use (and I like): 1. quick checkout to another branch and automatically stash and unstash your local changes; when I just need to inspect code elsewhere I find it really useful. My changes are small so I can always remember to stash them later; 2. compare branch/commit etc via UI; again I know you can do that in git diff, but then you would need to know the command and the commit SHA to compare; in UI it comes in really handy, just select the branch or commits you want to compare and that's it. I've seen my coworkers trying to come up with the command and I just say: use IDE and a couple of clicks they got it working. 3. filter commits by user and by folder.

> OpenBrain focuses on AIs that can speed up AI research. They want to win the twin arms races against China (whose leading company we’ll call “DeepCent”)16 and their US competitors. The more of their research and development (R&D) cycle they can automate, the faster they can go. So when OpenBrain finishes training Agent-1, a new model under internal development, it’s good at many things but great at helping with AI research.

> It’s good at this due to a combination of explicit focus to prioritize these skills, their own extensive codebases they can draw on as particularly relevant and high-quality training data, and coding being an easy domain for procedural feedback.

> OpenBrain continues to deploy the iteratively improving Agent-1 internally for AI R&D. Overall, they are making algorithmic progress 50% faster than they would without AI assistants—and more importantly, faster than their competitors.

> what do we mean by 50% faster algorithmic progress? We mean that OpenBrain makes as much AI research progress in 1 week with AI as they would in 1.5 weeks without AI usage.

To me, claiming today's AI IS capable of such thing is too hand-wavy. And I think that's the crux of the article.

To quote the original article,

> OpenBrain focuses on AIs that can speed up AI research. They want to win the twin arms races against China (whose leading company we’ll call “DeepCent”)16 and their US competitors. The more of their research and development (R&D) cycle they can automate, the faster they can go. So when OpenBrain finishes training Agent-1, a new model under internal development, it’s good at many things but great at helping with AI research. (footnote: It’s good at this due to a combination of explicit focus to prioritize these skills, their own extensive codebases they can draw on as particularly relevant and high-quality training data, and coding being an easy domain for procedural feedback.)

> OpenBrain continues to deploy the iteratively improving Agent-1 internally for AI R&D. Overall, they are making algorithmic progress 50% faster than they would without AI assistants—and more importantly, faster than their competitors.

> what do we mean by 50% faster algorithmic progress? We mean that OpenBrain makes as much AI research progress in 1 week with AI as they would in 1.5 weeks without AI usage.

> AI progress can be broken down into 2 components:

> Increasing compute: More computational power is used to train or run an AI. This produces more powerful AIs, but they cost more.

> Improved algorithms: Better training methods are used to translate compute into performance. This produces more capable AIs without a corresponding increase in cost, or the same capabilities with decreased costs.

> This includes being able to achieve qualitatively and quantitatively new results. “Paradigm shifts” such as the switch from game-playing RL agents to large language models count as examples of algorithmic progress.

> Here we are only referring to (2), improved algorithms, which makes up about half of current AI progress.

---

Given that the article chose a pretty aggressive timeline (the algo needs to contribute late this year so that its research result can be contributed to the next gen LLM coming out early next year), the AI that can contribute significantly to research has to be a current SOTA LLM.

Now, using LLM in day-to-day engineering task is no secret in major AI labs, but we're talking about something different, something that gives you 2 extra days of output per week. I have no evidence to either acknowledge or deny whether such AI exists, and it would be outright ignorant to think no one ever came up with such an idea or is trying such an idea. So I think it goes down into two possibilities:

1. This claim is made by a top-down approach, that is, if AI reaches superhuman in 2027, what would be the most likely starting condition to that? And the author picks this as the most likely starting point, since the authors don't work in major AI lab (even if they do they can't just leak such trade secret), the authors just assume it's likely to happen anyway (and you can't dismiss that). 2. This claim is made by a bottom-up approach, that is the author did witness such AI exists to a certain extent and start to extrapolate from there.

Of course you can get a lot of mileage via synthetically generated CoT but does that lead to LLM speed up developing LLM is a big IF.

Also, coming up with good problems is an art in its own right; the Soviets was famous for institutionalizing anti-Semitism via special math puzzles for Jews in Moscow Univerisity entrance exams. The questions are constructed as such that are hard to solve but have some elementary solutions to divert criticism.