Vibe coding is the creation of large quantities of complex AI-generated code. Executives push lay-offs claiming AI can handle the work. Managers pressure employees to meet quotas of how much of their code must be AI-generated... yet results are far from what was promised 1/
@JFPuget@_arohan_ You have an enterprise plan? I was wondering how people working for private companies were using Claude Code for instance and dealing with the related IP issues.
@fpedregosa@amuellerml@agramfort Code generation, or even worse agents tackling issues, is a major problem in @scikit_learn these days. The devs are drowned with contributions that don't solve the actual problem, because of poor quality or failure to account for broader context and discussion.
"Because solveit dialogs are fluid and editable, it’s much easier to go back and edit/remove mistakes, dead ends, and unrelated explorations. You can even edit past AI responses, to steer it into the kinds of behaviour you’d prefer" This is such a good idea!
It's a strange time to be a programmer—easier than ever to get started, but easier to let AI steer you into frustration. We've got an antidote that we've been using ourselves with 1000 preview users for the last year: "solveit"
Now you can join us.🧵
https://t.co/GLKm0woI8b
🚀 I'm happy to share that our latest paper has been accepted at #ICML2025 🌟
📌 "AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting"
👉 paper & code: https://t.co/nv9LDysyKP
See you in Vancouver! 🍁
Hello friends! 👋
🔥 We’re working on ecosystem recommendations called specs to foster collaboration and shared best practices across the scientific Python ecosystem. 🤝
⭐ Support this effort by starring:
🔗 https://t.co/MAMfFCpaJj
@GuillaumeRozier@france_identite Pourquoi est-il plus facile d’utiliser l’identité numérique qui m’envoie un code sur mon téléphone (pour me connecter via France Connect) alors que France Identité me demande d’aller scanner ma carte d’identité, que je n’ai pas toujours si je suis dans mon fauteuil par exemple ?
🚀 I'm happy to share that our latest paper has been accepted at #ICLR2025 🌟
📌 "Zero-shot Model-based Reinforcement Learning using Large Language Models"
See you in Singapore! 🇸🇬
@sh_reya I understand the concern and can agree with it. But I would tend to try to rely on one of them before starting from scratch and having to maintain everything. Who would you recommend implementing from scratch for someone that just wants to try something and play with LLM agents?
@HamelHusain Don’t you have to take care (and be good at) many more areas when doing independent consulting than when being employed at a FAANG company : administrative work, marketing, communication, finding clients, …?
I am excited to present Agent K as the first end-to-end agent (i.e., autonomous from Kaggle URL to submissions that win competitions) to achieve an equivalent of Kaggle grandmaster level. Our agent codes the whole data science pipeline from a natural language description of the competition and raw data!
It does at least the following:
1. Cleans and pre-processing the data automatically;
2. Do feature engineering if needed automatically;
3. Write machine learning models that it thinks can solve the tasks automatically;
4. Trains the models and optimises their hyperparameters with HEBO automatically;
5. Write Kaggle submission files and decide to upload them to Kaggle to get the score automatically;
It uses this score to improve its pipeline and submission automatically.
Regarding results, we win six gold, three silver, and seven bronze medals. We also score in the top 38% against Kagglers.
Since we win medals in all competition types, we make a fair comparison to human participants by awarding them extra medals if needed. Here, we also see that our Agent K is more likely to earn more medals than humans. The difference is particularly significant for bronze medals, where Agent K outperforms in 42% of match-ups and underperforms in only 23%. Similarly, for gold medals, the agent's winning rate of 14% is over twice its losing rate of 6%.
How's that for LLMs that can't reason ;) Whoop whoop!
#AI #machine_learning #MachineLearning #DataDriven #DataScientist #DataScientist
https://t.co/nvkg4qi5R5
uv and mamba make package management much faster. I already use mamba and am considering moving to uv instead of pip. One thing I will miss from pip and conda: they are implemented in python so it was easier for me to read/debug their code if needed.