binalpatel

https://github.com/caesarnine/binsmith

Been running it on a locked down Hetzner server + using Tailscale to interact with it and it's been surprisingly useful even just defaulting to Gemini 3 Flash.

It feels like the general shape of things to come - if agents can code then why can't they make their own harness for the very specific environments they end up in (whether it's a business, or a super personalized agent for a user, etc). How to make it not a security nightmare is probably the biggest open question and why I assume Anthropic/others haven't gone full bore into it.

https://binal.pub/2023/12/structured-ocr-with-gpt-vision/

Back of the napkin math (which I could be messing up completely) but I think you could process a 100 page PDF for ~$0.50 or less using Gemini 3 Flash?

>560 input tokens per page * 100 pages = 56000 tokens = $0.028 input ($0.5/m input tokens) >~1000 output tokens per page * 100 pages = $0.30 output ($3/m output tokens)

(https://ai.google.dev/gemini-api/docs/gemini-3#media_resolut...)

Right now I'm thinking through how to make it more "proactive" even if it's just a cron that wakes it up, so it can do things like query my emails/calendar on an ongoing basis + send me alerts/messages I can respond to instead of me always having to message it first.

I tried a few different ideas and the most stable/useful so far has been giving the agent a single run_bash tool, explicitly prompting it to create and improve composable CLIs, and injecting knowledge about these CLIs back into it's system prompt (similar to have agent skills work).

This leads to really cool pattens like: 1. User asks for something

2. Agent can't do it, so it creates a CLI

3. Next time it's aware of the CLI and uses it. If the user asks for something it can't do it either improves the CLI it made, or creates a new CLI.

4. Each interaction results in updated/improved toolkits for the things you ask it for.

You as the user can use all these CLIs as well which ends up an interesting side-channel way of interacting with the agent (you add a todo using the same CLI as what it uses for example).

It's also incredibly flexible, yesterday I made a "coding agent" by having it create tools to inspect/analyze/edit a codebase and it could go off and do most things a coding agent can.

https://github.com/caesarnine/binsmith

(my one of many contribution https://github.com/caesarnine/binsmith)

https://github.com/caesarnine/rune-code

Part of the reason I switched to it initially wasn't so much it's niceties versus just being disgusted at how poor the documentation/experience of using LiteLLM was and I thought the folks who make Pydantic would do a better job of the "universal" interface.

Interesting they're framing this more from the world model/agent environment angle, when this seems like the best example so far of generative games.

720p realtime mostly consistent games for a minute is amazing, considering stable diffusion was originally released 2ish years ago.

The heat gun mosquito things that some tech folks were mentioning on Twitter. Always get quarter sized terribly itchy bumps for each mosquito bites and using it makes them essentially itch-free immediately.

Under $1000:

Weekly house cleaning. Such reduced cognitive load/increased free time to not have to clean all the time, think about cleaning, etc especially with a toddler.

(context: https://the-decoder.com/goldman-sachs-blunder-adds-to-ai-sto...)

Same energy

Very large R&D expenditures for the next iterations of the models at the leading edge (the "fabs" of the world), everything downstream getting much cheaper and better with demand increasing as a result.

Like a world where Claude Opus 3.5 is incredibly expensive to train and run, but also results in a Claude Haiku that's on net better than the Opus of the prior generation, occurring every cycle.

[1] https://docs.dapr.io/developing-applications/building-blocks...

    You are an expert in PDFs. You are helping a user extract text from a PDF.

    Extract the text from the image as a structured json output.

    Extract the data using the following schema:

    {Page.model_json_schema()}

    Example:
    {{
      "title": "Title",
      "page_number": 1,
      "sections": [
        ...
      ],
      "figures": [
        ...
      ]
    }}

Just a Pydantic model to define an extraction schema + a simple prompt works reasonably well.