schipperai

5 ms·

1.
by schipperai 9d ago
I’m working on nah, a coding agent guard to block catastrophic tool calls. It’s easily extensible, has a nice simple TUI, and is resilient to an adversarial agent that tries to remove the guard. https://github.com/manuelschi
2.
by schipperai 20d ago
Point taken, I agree Ansible could have worked nicely for this tool. While I do think there's value in packaging the workflow into a ready-made tool. I do appreciate the input, thanks!
3.
by schipperai 20d ago
Ansible can of course handle the templating and deployment. Not arguing that. mdmanager packages runtime-specific discovery, reusable Sections and Profiles, an instruction-focused TUI, and bundled CLI docs into something you can install and
4.
by schipperai 20d ago
The tool is more than templating and automation. It adds a workflow for inspecting and composing the instructions. The TUI and the docs understand how each coding agent runtime loads instructions into context (they all have nuanced differen
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Mdmanager.ai – Manage your Claude.md and AGENTS.md across machines and runtimes(github.com)
3 points by schipperai 20d ago|7 comments
6.
by schipperai 20d ago
One agent standard that causes me pain: AGENTS.md / CLAUDE.md. I have multiple machines (personal laptop, work laptop, server) and runtimes (Claude Code, Codex, Pi) which share common instructions, but also have special cases for each
7.
by schipperai 1mo ago
Fair - some of the points in these examples will rot. What I think transfers are the failure modes: context rot, agents filling in decisions, whether a codebase is greppable. Those can turn into team principles, and you can only learn this
8.
by schipperai 1mo ago
Author here. Thanks for the comment. I did refer to some specific models, though a lot of these learnings are from experience over the past 6 months or so, and continue to generalize as frontier models improve. Curious if there’s a specific
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by schipperai 1mo ago
I’d agree Linus is an exceptional leader. And I don’t think most rules apply to him.
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Technical leaders should have the largest AI exhaust(schipper.ai)
10 points by schipperai 1mo ago|16 comments
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by schipperai 2mo ago
I’m working on “nah” [1] an agent hook guard that blocks catastrophic agent actions like filesystem destruction, secrets exfiltration, and git disasters. All the agent hook guards I’ve seen can be easily removed by agents (and they often do
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How to Spot AI Writing(economist.com)
2 points by schipperai 2mo ago|0 comments
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by schipperai 3mo ago
Do I understand correctly that you scope least-privilege creds/tokens and pass those to the sandbox? I'd be curious to learn more
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by schipperai 3mo ago
If an organization decides the engineering team should not be looking at code, that should be coupled with a mandate to figure out what good engineering looks like working that way - what constitutes a good contribution vs what's slop?
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by schipperai 3mo ago
Working in the era of 200k context window meant I had to narrowly scope tasks to fit in the context window, forcing me to think about how to reduce complexity and naturally resulting in atomic work. 1M context windows and the promise that t
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by schipperai 4mo ago
Let's hope not all frontier AI assimilates these guardrails. It would be a shame for independent researchers and students.
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by schipperai 4mo ago
I get a sense that I was click-baited by article's title with the classic trope of "X is all you need". This research is a solid contribution, but is far from all we need to understand grep vs semantic search in agent retriev
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by schipperai 4mo ago
Cognition did well in documenting their approach [1]. TL;DR - they worked with OSS project maintainers to build tasks. They score models based on whether a PR is mergeable. All tasks are graded by a human researcher. SoTA models have hill-c
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by schipperai 4mo ago
I trust AI to surface general information and best practices on established knowledge domains. For example: best practices for securing my VPS. For domains whete SoTA is constantly changing like AI, I use LLMs to aggregate and interact with
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by schipperai 4mo ago
You can dig deeper into problems with AI. For me, it supplements my knowledge in domains I don’t fully understand. It also helps me learn. So I can tackle problems I wouldn’t otherwise. I’m excited for ultrafast AI. It likely means less te
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by schipperai 4mo ago
Demis at YCombinator said that they think its best their edge models are open cause once they are put on device they are vulnerable anyways https://youtu.be/JNyuX1zoOgU?is=PdzCILyi8SP6cfDr
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by schipperai 5mo ago
Yes, you can define sensitive paths and assign 'ask' or 'block' policies to them. .env, .ssh, and others are treated as a sensitive filenames by default. Similarly, with hosts and network access - unknown hosts pause, tr
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by schipperai 5mo ago
This recent article from Semianalysis did a great job explaining part of it: https://newsletter.semianalysis.com/p/are-ai-datacenters-inc...
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by schipperai 5mo ago
Very cool. How do you classify negative signals?
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by schipperai 5mo ago
Which platform have you found is most hackable? I have Garmin atm and like it but there’s no easy way to pipe my data into my agent or server for offline analysis.
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by schipperai 5mo ago
I like the overall premise and would be curious to learn more. The Amazon overview reads like it was written with or by AI though.
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by schipperai 5mo ago
A better permissions layer for coding agents. The tool works like auto-mode for Claude Code, so you can stay in the flow and only get prompted to allow or deny tool calls when it truly matters, but it is fully deterministic. My benchmarks s
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by schipperai 5mo ago
Thanks, makes sense. I meant Blackwell is explicitly optimized for MoEs.
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by schipperai 5mo ago
With most OSS releases being MoEs, and modern GPUs optimized for MoEs, can somebody with knowledge of the topic explain or speculate why Mistral might have opted for a dense model?
30.
by schipperai 5mo ago
100%. The exclusivity of the network is the differentiator here.

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