"a malicious package that was briefly distributed"

"investigation found no evidence that end user vault data was accessed or at risk"

"The issue affected the npm distribution mechanism for the CLI during that limited window, not the integrity of the legitimate Bitwarden CLI codebase or stored vault data."

"Users who did not download the package from npm during that window were not affected."

Downplaying so hard it's disgusting. Bitwarden failed and became a vector of attack. A vendor who is responsible for all my passwords. What a joke. All trust lost: by the incident and comms-style. Time to move before they make an even bigger mistake.

Would be pretty magical if when you need a sandbox, you just SSH into it and it's already there, right? exe.dev popularized this UX, but I self-host a lot of things already, so I wanted something similar, but on my own server, which has plenty of spare RAM and CPU cores.

I tasked Gemini DeepResearch with finding what I can use to glue together something like exe.dev. In a typical Gemini Deep Research fashion, it came back with a very obscure recommendation claiming everyone is using it. Repository had one fork and six stars at the time of search.

While obscure, Gemini IMO found a gem: https://github.com/kkovacs/vmtree

It's a collection of a few short bash scripts which automatically provision sandboxes using ssh as a trigger. It has other nice touches such as: controlling how sandboxes get cleaned up, and provisioning subdomains with or without HTTP authentication to expose services running inside the sandbox.

I love it. Mine is deployed entirely inside the VM (HTTPS and SSH DNAT'ed), and doesn't interfere with other VMs on my server.

Comments URL: https://news.ycombinator.com/item?id=47666456

Points: 2

# Comments: 1

I imagine it works by:

- treating non-enrich columns as inputs

- running a prompt contained in the "description" of the enrich-column against web-enabled LLM

- populate the answer

- repeat for every enrich-column (each column corresponds to a different prompts against the same "subject")

- repeat for each row/subject

I run flows like this in Python almost every day and they were able to capture it perfectly in their UI I think.

I see they realized that "cold-email marketing" is a killer use-case and built-in templated send-email feature as well.

Comments URL: https://news.ycombinator.com/item?id=44946350

Points: 1

# Comments: 2

Asking because it turned out nearly impossible to find a local lawyer to advise on a dispute couple months ago - with 9 out of 10 telling me they only do divorces or real estate or immigration. I was literally calling one by one from a list based on what I believe were relevant search criteria on State Bar website.

If you go beyond pre-recorded "blind" trajectories into more robust task-policies (which you would have to train from many demonstrations) then cameras become necessary to execute the sub-task.

If you fully control the environment: exact positions of arm-base and all objects which it interacts with - you can just replay the trajectory on the follower-arm. No ML necessary.

You can use LLM to decide which trajectories to replay and in which order based on long-horizon instruction.

- you recorded precise arm-movement using leader-arm - for each combination of source- and target- receptacles/board-positions (looking at the shim visible in the video, which I assume ensures the exact relative position of the arm and chess-board);

- the recorded trajectories are then exposed as MCP-based functions?

Bought the kit. Thank you for the great price! Are table-clamps included?

Where can I read more about using iMessage as a medium for generic multi-player collaboration? Or if you can just share the right keywords, I will appreciate that!

Sad.

"1B solver + 8B verifier + search" beating 1B-0-shot or 1B-majority as baselines isn't illustrative imo. In other words, by using larger verifier, HF's replication fails to establish a "fair" baseline. Still an awesome blog and release/repository from HF's group - I love it!

See https://github.com/huggingface/search-and-learn/blob/b3375f8... and https://github.com/huggingface/search-and-learn/blob/b3375f8...

In the original paper, they use PaLM 2-S* as "solver" and its fine-tune as "verifier".