I was most excited about the "frontier tuning." Like, it will actually watch you do stuff and learn to do it for you? That would be actually interesting.
But no, it's just a data labelling interface: https://learn.microsoft.com/en-us/microsoft-365/copilot/copi.... You have to provide the instruction and give feedback and there is a whole UI with hour-lonf wait between steps. So basically they want you to do the labelling to train a model, or at least that's how it looks from the outside
Also the mission statement of Humanist AI is the most boring, but tries to sound way too grand. Like "all the cool labs have a mission statement, so we should also have one" vibes
I wouldn't call it a benchmark since it's just one sample. They do highlight a real problem, though. Computer use is immature right now and far behind language agents
Try playing fruit ninja via text and llm toolcalls though
Appreciate the effort, but this is very basic and nothing you need the source code to understand. I was expecting a deep dive into what specific decisions they made, but not how an loop of tool calls works
While I do not have much sympathy for Altman, the article is very low quality and contains zero analysis
Yeah, maybe on the surface chatbots turned out to be chatbots. But you have to be a poor journalist to stop your investigation of the issue at that and conclude AI is no big deal. Nuance, anyone?
It's 100% clear why his I initiative was buried. The guy is a reckless egomaniac jerk. He is very transparent that he would do illegal stuff on a whim, target civillians, escalate conflict with other nations without even consulting a superior, and then reveal his face to the whole world, spilling secrets left and right, for a minute of internet publicity. And do a reddit AMA. And put "I am single LOL" there, showing just how much he can be trusted with leading a government cyberwarfare task force
What I read is that guy did a clever DDoS, which is mildly impressive, and decided to become a twitter celebrity/NK assassination victim. I am no hacker, but I expect real hackers to be laughing their asses off.
Planet Farms | Onsite, Remote Hybrid | Portugal (Coimbra) | Full-time | ML Engineer, Data Engineer
We make vertical farms. We are looking for two people to join our data team.
For the Data Engineer we expect enough proficiency to help us build our DWH/Data Mesh infrastructure from scratch. We have a basic prototype for our data infra, but we are looking to make it more mature and be a good foundation for years to come. You will be supported by the rest of the data team, but we expect you to bring on board experience with DWH design and ETL processes. The stack is BigQuery, Google Cloud, Airflow, ScyllaDB, Postgres, Flask/FastAPI.
Apply here: https://planet-farms.factorial.it/job_posting/34108
For the ML Engineer we are looking for a mix of computer vision and software engineering experience. The tasks are about improving the efficiency of factories by building ML-based tools. We as the data team own our solutions end-to-end, so we expect you to train the model, deploy the model build the required APIs to ship it and monitor it. The good news is that most of the infrastructure is setup and you will be the third ML engineer in the team, so you will not be alone. The stack is Pytorch, ClearML, Flask/FastAPI, Airflow, ONNX.
Apply here: https://planet-farms.factorial.it/job_posting/machine-learni...
When applying please specify that you came from HN
Planet Farms | Data Engineer/Analyst | Office in Coimbra, Portugal
We make vertical farms.
Hybrid office job, Coimbra, Portugal. Relocation, assistance of lawyers at the expense of the company.
The task is to found the data team and grow into its lead. At first it will be just you, but during the year it is planned to hire more people to the team.
It is necessary to lay the foundation of Data Mesh (current leading idea, up to discussion) and data culture in the company, which will allow scaling to dozens of factories.
In the beginning, you need to work a lot with your hands on the backend and infrastructure: raise the database, write the API and everything else.
Requirements: Data Engineering experience, Data Analysis experience, Good people skills. You will have the support of our team, but we need you to be the driver of the project.
Planet Farms | Data Engineer/Analyst | Office in Coimbra, Portugal
We make vertical farms.
Hybrid office job, Coimbra, Portugal. Relocation, assistance of lawyers at the expense of the company.
The task is to found the data team and grow into its lead. At first it will be just you, but during the year it is planned to hire more people to the team.
It is necessary to lay the foundation of Data Mesh (current leading idea, up to discussion) and data culture in the company, which will allow scaling to dozens of factories.
In the beginning, you need to work a lot with your hands on the backend and infrastructure: raise the database, write the API and everything else.
Requirements:
Data Engineering experience, Data Analysis experience, Good people skills. You will have the support of our team, but we need you to be the driver of the project.
2. Flask. Pure server-rendered HTML, no fancy frontend. Avoid JS as long as I can, just use jinja templates. If my app is not going to work with good old HTML and CSS chances are it's not going to work at all.
4. Sqlite3 or Postgres. Prefer postgres because with docker it's 5 minutes to set up. I use SQLAlchemy from the start because it makes life easier. If you ever deploy and worry about real data you add alembic for migrations.
5. One docker compose for everything.
6. Cheap VPS for like $3 per month.
That's it. I would like to upgrade my MVP stack, but it's battle-tested and it works well. After the 9 to 5 I only have like 3 brain cells alive so I need to get stuff done as fast as possible, which means using what I know.
One thing I often reinvent and reimplement is OAuth. Definitely "Sign in with google" is the best way to add auth to your app, but it's such a pain to set up.
Very shallow article. Would like to see a list of mentioned "recent breakthroughs" about using hashes in ML besides the retrieval applications, because this is genuinely interesting
So many people comment about how Snowden can't say things like this because he is in Russia. I wonder: how does it invalidate the facts? He links to Wikipedia. Maybe you don't want to believe Snowden, him being a traitor. Don't you believe Wikipedia? I don't see how Snowdens identity is even relevant to the subject.
I am currently also playing around with this. The best part is that for storage you don't need to store the reconstructed image, just the latent representation and the VAE decoder (which can do the reconstructing later). So you can store the image as relatively few numbers in a database. In my experiment I was able to compress a (512, 384, 3) RGB image to (48, 64, 4) floats. In terms of memory it was a 8x reduction.
However, on some images the artefacts are terrible. It does not work as a general-purpose lossy compressor unless you don't care about details.
The main obstacle is compute. The model is quite large, but hdds are cheap. The real problem is that reconstruction requires a GPU with lots of VRAM. Even with a GPU it's 15 seconds to reconstruct an image in Google Collab. You could do it on CPU, but then it's extremely slow. This is only viable if compute costs go down a lot.
I was most excited about the "frontier tuning." Like, it will actually watch you do stuff and learn to do it for you? That would be actually interesting.
But no, it's just a data labelling interface: https://learn.microsoft.com/en-us/microsoft-365/copilot/copi.... You have to provide the instruction and give feedback and there is a whole UI with hour-lonf wait between steps. So basically they want you to do the labelling to train a model, or at least that's how it looks from the outside
Also the mission statement of Humanist AI is the most boring, but tries to sound way too grand. Like "all the cool labs have a mission statement, so we should also have one" vibes