binalpatel

The agent making it's own harness idea is really powerful, I gave it a try here with some opinionated choices:

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.

This is admittedly dated but even back in December 2023 GPT-4 with it's Vision preview was able to very reliably do structured extraction, and I'd imagine Gemini 3 Flash is much better than back then.

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...)

It reuses previously generated code, so tools it creates persists from session to session. It also lets the LLM avoid actually “seeing” the tokens in some cases since it can pipe directly between tools/write to disk instead of getting returned into the LLMs context window.

I've been using it daily, so far it's built CLIs for hackernews, BBC news, weather, a todo manager, fetching/parsing webpages etc. I asked it to make a daily briefing one that just composes some of them. So the first thing it runs when I message it in the morning is the daily briefing which gives me a summary of top tech news/non-tech news, the weather, my open tasks between work/personal. I can ask for follow ups like "summarize the top 5 stories on HN" and it can fetch the content and show it to me in full or give me a bullet list of the key points.

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 went down (continue to do down) this rabbit hole and agree with the author.

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

100% - sharing CLIs with the agent has felt like another channel to interact with them once I’ve done it enough, like a task manager the agent and I can both use using the same interface

Cool to see lots of people independently come to "CLIs are all you need". I'm still not sure if it's a short-term bandaid because agents are so good at terminal use or if it's part of a longer term trend but it's definitely felt much more seamless to me then MCPs.

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

Pydantic-AI is lovely - I've been working on a forever, fun project to build a coding agent CLI for a year plus now. IMO it does make constructing any given agent very easy, though the lower level APIs are a little painful to use but they seem to be aware of that.

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.

This is super impressive.

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.

Under $100:

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.

This is going to wreck Goldman's next analysis.

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

https://newsletter.pessimistsarchive.org/p/slide-rule-fast-a...

Same energy

My personal (overly biased view after reading Chip War recently) take is pretty much there, seems like a lot of the same early dynamics of semiconductors are playing out here.

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.

Agreed with both - #1 especially: https://www.propublica.org/article/philips-kept-warnings-abo...

One fun detail - I don't know any Typescript nor have I ever coded a VSCode extension before, so this was all made going back and forth with Sonnet 3.5 + digging through the VSCode repo to figure out how their new proposed shell integration API works.

Virtual actors are so nice, Dapr[1] uses them as well. You can get hack together something like a virtual actor with Ray too but I'm hoping their actors eventually evolve into something like virtual actors eventually.

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

You can do some really cool things now with these models, like ask them to extract not just the text but figures/graphs as nodes/edges and it works very well. Back when GPT-4 with vision came out I tried this with a simple prompt + dumping in a pydantic schema of what I wanted and it was spot on, pretty much this (before json mode was a supported):

    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": [
        ...
      ]
    }}

Gemini 1.5 Pro is really good at long context in my experience.

Here's a direct link to the code: https://github.com/caesarnine/llm-experiments/blob/main/3_st...

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

I'd recommend checking out https://whisk.com too.

Some of it sounds surprisingly creepy:

  look around
  YOU CAN'T SEE ANYTHING
  IT'S TOO WIDE TO CROSS
  YOU CAN'T HEAR THE SONGBIRD NOW
  IT CAN'T BE FOLLOWED

This is awesome, thanks for sharing. It feels like this could be useful for detecting data quality issues in things like daily ETL, I'll have to give it a try.

Discussion of this on /r/machinelearning:

https://www.reddit.com/r/MachineLearning/comments/dj5psh/n_n...

Inflation adjustment is weird across time and geographies. I'm not saying the article is wrong, but just something to keep in mind. In 1973 the internet was nascent, we didn't have smartphones, insulin was still hard to produce, and so on. Even making the same amount "inflation adjusted" as 1973, our lives are much richer in part to all these advances.

As an example - it's likely in adjusted terms someone in the top 1% a 100 years ago would be close to the top percentiles today, and yet it's pretty easy to argue that the average person today is still doing much better in almost any measure than a 1%-er from a century ago.

You certainly read a lot into that statement. Who said the data isn't anomymized and deidentifed? It's not a matter of just stripping identifiers off data and sending it off left and right, even then it's better to control access in a central, secure area. Even if you've deidentifed to an individual level there's inherent risk, that's why accessing even "public" datasets like Mimic III is still process heavy.

100% right - part of what I wanted to experiment with was being able to share not just code but everything related to the environment (including IDE), and be able to check that into version control as well. So there's little to no friction in mundane setting up tasks like linters/installing packages/messing with config and so on.

That being said it's still an idea, but here's hoping it works!

Glad it worked for you! Hadn't even thought about Docker commit, but the latter was one of the ideas I had in mind for this (not just share code with teammates, but also a fully working environment, IDE included, that they can jump right into).

Very long article - but a good read. It's incredible to me how incentives misaligned in such a way that essentially every medical practitioner in the US has to use software they hate.

Anecdotally via the physicians I interact with its' terrible how much time it takes away from actual patient care, when the promise was the opposite.

Prior discussion: https://news.ycombinator.com/item?id=18037613

This is a terrible, incendiary headline. They're using AWS to host things - big whoop.

"All of that data and the algorithms powering ICM are now being migrated to Amazon Web Services (AWS) in their entirety; Palantir pays Amazon approximately $600,000 a month for the use of its servers, according to the report’s authors."

If it was on-prem would they then be targeting the Cisco, or IBM, or whatever servers used to host the same content?