u/Gold_Syrup8935 — reddlx

$Image 1 — Python fractal generator (or how to turn your computer into a campfire)$

$Image 2 — Python fractal generator (or how to turn your computer into a campfire)$

$Image 3 — Python fractal generator (or how to turn your computer into a campfire)$

▲ 25 r/fractals+1 crossposts

Python fractal generator (or how to turn your computer into a campfire)

Got bored and decided to try my hand at a terminal-native vortex generator that uses ASCII characters to render.

It is definitely a bit rough around the edges and I am no mathematician or anything but definitely a fun project and if you leave it running for long enough you can roast marshmallows over your cpu.

I tried to optimize the load as much as I could but if anyone has recommendations let me know.

u/Gold_Syrup8935 — 13 days ago

▲ 4 r/u_Gold_Syrup8935+4 crossposts

I've been thinking about what it takes for AI agents to actually become more capable over time.

Over the last few months I've noticed that most agent frameworks are really good at giving an LLM tools, memory, and prompts, but they're surprisingly weak at preserving actual capabilities.

An agent might solve a problem today, but tomorrow it's often starting from scratch. We talk a lot about memory, but memory alone doesn't create experience.

That led me down a rabbit hole.

I've started working on something I'm calling the **Agent Capability Formation Standard (ACFS)**.

The goal isn't to build another agent framework. It's an attempt to define a durable format for preserving *capabilities*—not just conversations or memories—so agents can develop reusable knowledge, refine behaviors over time, and share those capabilities across systems.

The questions I'm trying to answer are things like:

- What makes a learned behavior reusable?
- How should capabilities be versioned, updated, or retired?
- How do agents distinguish durable procedures from temporary context?
- What metadata is required to govern capabilities over time?
- Can capabilities become composable building blocks instead of isolated memories?

I'm still exploring these ideas, and the project is in its early stages, but I wanted to share it because I think capability formation is an underexplored area of agent architecture.

Repository:

https://github.com/ajbeaver/Agent-Capability-Formation-Standard

I'd genuinely appreciate feedback from people working on long-running agents, agent memory, MCP, orchestration frameworks, or knowledge management. I'm less interested in defending a "standard" and more interested in figuring out whether this is the right direction to solve a problem I've been seeing repeatedly.

u/Gold_Syrup8935 — 3 months ago

▲ 2 r/Agentic_Marketing+1 crossposts

Calling everything an agent learns “memory” was too vague for me

I needed a better way to handle what agents learn while doing work.

For an agent’s skills, knowledge, and standards to be useful, they need to be indexable and addressable. Otherwise, the agent ends up reading its entire AGENTS.md every time it needs to think instead of knowing where to look for the answer.
Calling all of that “memory” was too vague for me. I needed to track where something came from, how much it should be trusted, whether it had been superseded, and who or what should be able to use it.

So I started working on the Agent Capability Formation Standard (ACFS).
The goal was to build something that could complement MCP by giving durable agent knowledge and capabilities a defined lifecycle without trying to replace how tools and context are exposed.

I published the current specification, schemas, examples, and security model (see comment below).

It is still early, and I would really like feedback from people building agent systems. Is this a problem you have run into too?

reddit.com

u/Gold_Syrup8935 — 3 months ago

▲ 1 r/codex+1 crossposts

Codex stopped guessing repo context after discovering a FlowState runtime over MCP

Been experimenting with a project called FlowState.

The idea is pretty simple:

Instead of stuffing repo standards into giant prompts, duplicated AGENTS.md files, or re-explaining architecture every session, projects attach to a runtime source of truth over MCP.

The agent discovers the runtime, authenticates, retrieves the active Runtime Stack, and then interprets the repository through that operational context.

What surprised me most was watching Codex naturally decide to use the runtime instead of inferring standards from local files alone:

“I can pull the active Runtime Stack rather than guessing from the local Python files.”

The interesting part is that multiple agents/projects can attach to the same FlowState runtime and inherit the same operational standards, review rules, deployment expectations, and architecture direction from one source of truth.

Still early, but this feels much closer to infrastructure than prompt engineering.

https://github.com/offband/FlowState

u/Gold_Syrup8935 — 5 months ago

▲ 21 r/farmingsimulator

Brush clearing and stump removal on a new rural jobsite build

First phase of site prep is underway. Cleared trees and brush, burned debris piles, ground stumps, and rough graded the area. Planning to start rock removal and lay down the gravel entrance next.

u/Gold_Syrup8935 — 5 months ago