dimitry12

From Bitwarden official statement: https://community.bitwarden.com/t/bitwarden-statement-on-che...

"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.

https://github.com/earendil-works/gondolin is another project addressing a similar use-case.

(I am not the author of vmtree)

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.

Now I want open-source self-hosted BYOM version of this

Saw this today and instantly liked the UX. This is not the first attempt to cross spreadsheets and LLMs, but I like the conceptual simplicity here and how clearly it packages "multi-chat + one extra dimension" pattern.

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.

Thank you!

Thank you! Not my states but seems spot on and I can extract keywords from there.

What are the keywords for finding a lawyer who can advise on non-competes?

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 bridge recorded trajectories with LVLM, then cameras are necessary visual input for LLM to decide which sub-tasks need to be performed to accomplish long-horizon task, and sub-tasks correspond to pre-recorded ("blind") trajectories which are replayed.

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.

https://github.com/TheRobotStudio/SO-ARM100/tree/main/Simula... I hope applies to this first gen of the product.

Thank you for confirming! Love how simple yet magical your demos look, the elegance of bridging LLM-driven long-horizon planning with the arm.

SO-ARM101 has a leader-arm, which is the arm with same exact dimensions and same servos - but used to read/record the trajectory. You move it with your own hand and teleoperate the follower-arm in real-time. Follower-arm is visible in the demo videos.

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.

Do I understand correctly that chess-moving demo decomposes into:

- 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?

> Whenish is an iMessage app

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!

I can't find the "Coming from Hackernews?" button. Where should I look for it?

No content, no code. "Roadmap" and "Training pipeline" in README are summaries of Section 2.3 of "DeepSeek-R1"-paper.

Sad.

"1B solver + 8B verifier + search" beating 0-shot 70B is nice, agree.

"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!

"Solver" is `meta-llama/Llama-3.2-1B-Instruct` (1B model, and they use 3B for another experiment), and verifier is `RLHFlow/Llama3.1-8B-PRM-Deepseek-Data`.

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".

From a practical standpoint, scaling test-time compute does enable datacenter-scale performance on the edge. I can not feasibly run 70B on my iphone, but I can run 3B even if takes a lot of time for it to produce a solution comparable to 70B's 0-shot.

I think it *is* an unlock.

To spend more compute at inference time, at least two simple approaches are readily available:

1) make model output a full solution, step-by-step, then induce it to revise the solution - repeat this as many times as you have token-budget for. You can do this via prompting alone (see Reflexion for example), or you can fine-tune the model to do that. The paper explores fine-tuning of the base model to turn it into self-revision model.

2) sample step-by-step (one "thought"-sentence per line) solutions from the model, and do it at non-zero temperature to be able to sample multiple next-steps. Then use verifier model to choose between next-step candidates and prefer to continue the rollout of the more promising branches of "thoughts". There are many many methods of exploring such tree when you can score intermediate nodes (beam search is an almost 50 years old algorithm!).

I believe this is a valid point: HF's replication indeed uses larger off-the-shelf model as a verifier.

In contrast, in the original paper, verifier is a fine-tune of the exact same base model which is used to sample step-by-step solutions (="solver").

In this paper and HF's replication the model used to produce solutions to MATH problems is off-the-shelf. It is induced to produce step-by-step CoT-style solutions by few-shot ICL prompts or by instructions.

Yes, the search process (beam-search of best-of-N) does produce verbose traces because there is branching involved when sampling "thoughts" from base model. These branched traces (including incomplete "abandoned" branches) can be shown to the user or hidden, if the approach is deployed as-is.

Verifier is trained with soft values of reward-to-go for each solution-prefix, obtained from monte-carlo rollouts of step-by-step solutions sampled from the "base" model.

In other words: 1) sample step-by-step solutions from "base" model; 2) do it at non-zero temperature so that you can get multiple continuation from each solution-prefix; 3) use MATH-labels to decide if full solution (leaf/terminal node in MC rolloout) has reward `1` or `0`; 4) roll up these rewards to calculate reward-to-go for each intermediate step.

Yes, verifier trained in this manner can be used to score solution-prefixes (as a process verifier) or a full-solution (as an outcome verifier).

In the original paper (https://arxiv.org/abs/2408.03314) they fine-tune a fresh verifier. HF's replication uses an off-the-shelf verifier based on another paper: https://arxiv.org/abs/2312.08935

Curious about that too. There are plenty of forks left, for example: https://github.com/plastic-labs/llama3_interpretability_sae (no affiliation)

Looking at https://github.com/modelcontextprotocol/python-sdk?tab=readm... it's clear that there must be a decision connecting, for example, `tools` returned by the MCP server and `call_tool` executed by the host.

In case of Claude Desktop App, I assume the decision which MCP-server's tool to use based on the end-user's query is done by Claude LLM using something like ReAct loop. Are the prompts and LLM-generated tokens involved inside "Protocol Handshake"-phase available for review?

Awesome!

Looks great as a self-host alternative if/when you make self-hosting feasible.

Can you please expand on the topic of "learn the marketing side if only by doing it semi-professionally for a client"?

I mean, one side of this spectrum is doing affiliate marketing or direct-sales/MLM. Other point on this spectrum might be for an engineer to go get hired as a social media "manager" (lots of "jobs" like this on Upwork).

What possibilities do you have in mind?

Can anyone who has the visible "Upgrade plan" option share a link to it? I wonder if it's only disabled in UI and we can still upgrade.

Lambda Labs has (slow, low IOPS) cloud filesystem to persist data between instances. Attached storage does not persist but is high bandwidth and high IOPS, which is a necessity if training small-medium sized models.