dimitry12

9 ms·

1.
by dimitry12 5mo ago
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
2.
by dimitry12 5mo ago
https://github.com/earendil-works/gondolin is another project addressing a similar use-case.
3.
Self-hosted disposable sandboxes with exe.dev-like UX(github.com)
2 points by dimitry12 5mo ago|1 comments
4.
by dimitry12 5mo ago
(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
5.
by dimitry12 1y ago
Now I want open-source self-hosted BYOM version of this
6.
Paradagm – cool spreadsheet UX for interacting with LLMs(paradigmai.com)
1 points by dimitry12 1y ago|2 comments
7.
by dimitry12 1y ago
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 i
8.
by dimitry12 1y ago
Thank you!
9.
by dimitry12 1y ago
Thank you! Not my states but seems spot on and I can extract keywords from there.
10.
by dimitry12 1y ago
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
11.
by dimitry12 1y ago
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") traj
12.
by dimitry12 1y ago
https://github.com/TheRobotStudio/SO-ARM100/tree/main/Simula... I hope applies to this first gen of the product.
13.
by dimitry12 1y ago
Thank you for confirming! Love how simple yet magical your demos look, the elegance of bridging LLM-driven long-horizon planning with the arm.
14.
by dimitry12 1y ago
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
15.
by dimitry12 1y ago
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,
16.
by dimitry12 1y ago
> 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!
17.
by dimitry12 1y ago
I can't find the "Coming from Hackernews?" button. Where should I look for it?
18.
by dimitry12 2y ago
No content, no code. "Roadmap" and "Training pipeline" in README are summaries of Section 2.3 of "DeepSeek-R1"-paper. Sad.
19.
by dimitry12 2y ago
"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
20.
by dimitry12 2y ago
"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-a
21.
by dimitry12 2y ago
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 70
22.
by dimitry12 2y ago
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 fo
23.
by dimitry12 2y ago
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 solut
24.
by dimitry12 2y ago
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 (be
25.
by dimitry12 2y ago
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 &quot
26.
by dimitry12 2y ago
Curious about that too. There are plenty of forks left, for example: https://github.com/plastic-labs/llama3_interpretability_sae (no affiliation)
27.
by dimitry12 2y ago
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.
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Bash script implementing LLM-powered TDD-loop(codeinthehole.com)
2 points by dimitry12 2y ago|0 comments
29.
by dimitry12 2y ago
Awesome!
30.
by dimitry12 2y ago
Looks great as a self-host alternative if/when you make self-hosting feasible.

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