This is perfect for my Chrome Extension for recording sessions and capturing screenshots, audio narration and videos. The output is a zip file with everything so if user wants to share they can use this
I built above chrome extension because anything in this area has been trying to monetize the solution. I wanted a free and open source version of this to exist.
I have a philosophical problem with adaptive thinking. It’s a dumb guess for how much thinking budget to allocate ahead of thinking. At least in the context of LLMs there is probably no way of knowing how much thinking (token generation) is needed. The problem space is infinity vast, similarly of two prompts is not going to help any LLM decide how much thinning is needed. Models already stop thinking before hitting the thinking budget.
Why there is so much effort in making adaptive thinking happen and don’t we train models to produce the end of thinning token better?
Feels like a bandaid. We need models to be trained to do a reasonable amount of reasoning (no pub intended):
reason
estimate remaining uncertainty
continue?
reason more
repeat
This seems to be a new trend. Noticed it with GPT "ultra" in their announcement[1]. I'm with you, a large language model and a system of many language models working together are not the same thing
It seems like we forget that LLMs are next token prediction systems. Using raw models without instruction following and chat completion bells and whistles will give you a better feeling of what LLMs are.
The current interface to LLMs are heavily biased towards "predict the next token in the context of a user with a helpful assistant" but LLMs are capable of other modes of next token prediction too.
Before the ChatGPT release people often measured LLM performance by how well they could produce a coherent story or a poem. that's where Anthropic model names are originating from I am guessing.
Nice thing about the microvm is that you can snapshot it and restore it. Keeping the fs minimal is my goal. Snapshots restore much faster if they do not include 5GB of source code
I’ve been working with AgentCore that uses the same MicroVMs. They are capable in many ways but for coding agents that load a big got repo they get bloated quickly with the git repo.
I’m building this google3 style mounting to address this.
> Additionally, we’re introducing a new `ultra` mode that goes beyond the capabilities of a single agent by leveraging subagents to accelerate complex work.
I'm curious about how does this work? Do the subagents also get to use the same tools? Will the client be flooded with tool calls? Why extra pricing for a new "model" when the same thing can happen in the client with more controls?
And if it's an army of subagents, why do they compare it to Fable and Mythos? Those models with similar harness would probably bench better I'm guessing
- if this port is happening on a single machine, you are better off with work trees. You can reuse worktrees if creating a new one is expensive. https://github.com/satococoa/git-worktreeinclude can help you initing work trees smooth
- If you want the system scale on the cloud with many many VMs doing work on the repo and repo is large enough that lazy-mount gives you meaningful perf benefits, yes definitely. It can be useful. Note that if the repo is private, you will need to configure sgrep's code search backend to be reachable from the microVMs
The lazy mount is very interesting. This is similar to how google3 works at Google that I have not seen any similar implementation in open source so far.
Git sparse checkout is helpful but checking files out as they are needed is much more flexible and intuitive.
Microsoft VFS for Git / GVFS is the closest that I can think of.
There is room for this lazy mount idea to be built on top of Git