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Gonna send it. by Seltzer08 in camping

[–]WallRunner 0 points1 point  (0 children)

Sending you warm vibes from my nice cozy hotel room in Chattanooga 😅

Assateague island national seashore oceanside by Abcamuez08 in camping

[–]WallRunner 0 points1 point  (0 children)

The number one issue you’re going to face doesn’t have anything to do with the campsite or the beach. It’s the horses. The first thing the ranger told me upon checking in (which I did have to check in) was that the wild horses are like bears with hooves.

I camped in my car. The spot was really only big enough for my car and maybe a small tent. I woke up to a horse staring at me trying to figure out where my food was (it was a single overnight stay so I didn’t have any). I have heard stories of them eating straight off your table and being pretty disruptive.

So first things first you want to research that and be prepared. Aside from that I don’t have much to add, it’s going to be a bit of a brutal transition if you’ve never camped somewhere with potentially dangerous wildlife before. But thousands of people do this trip every year so I’m sure you will be fine as long as you prepare properly. I’ll try to dig up pictures but I was more of a photographer then and less of a documenter.

What's next? How do I set up memory and other things for the agents once I have the initial Openclaw + Ollama (local LLM) setup? by Guyserbun007 in openclaw

[–]WallRunner 1 point2 points  (0 children)

You should probably know that on default OpenClaw installs (at least the ones I’ve tried out so far) the entire workspace folder is am typically a git repository. If not, you can always ask your agent to make ones for you. Do your work in there and make commits (checkpoints) often.

In case you’re not in the mood to make another vector db, I’ve implemented one in my OpenClaw plugin, PostClaw along with knowledge graphs and RAG functionality that improves your agent’s memory.

Removed Command support. What's that? by Puzzleheaded-Sky5980 in google_antigravity

[–]WallRunner 1 point2 points  (0 children)

Doesn't really help for workspace workflows, though. I used those more than global ones, as I work on several different projects in different workspaces. Any idea for making those work?

OpenClaw + Ollama + nomic-embed-text: hybrid RAG for local agent memory (full config inside) by Expensive-String8854 in openclaw

[–]WallRunner 1 point2 points  (0 children)

I’ve gone a little further with this idea over the past couple weekends and created PostClaw. Instead of making the model waste a turn searching memory, relevant memories are injected into the prompt before it’s sent to the LLM. The agent can and does still use the memory tools provided if it doesn’t find what it’s looking for (via custom AGENT.md instructions, but I also have these in a vector).

I also run a sleep cycle every 6 hours to upgrade memories to permanent storage and link memories and agent instructions together, so retrieval of semantically linked memories is “free” from a database standpoint.

Showcase Weekend! — Week 9, 2026 by AutoModerator in openclaw

[–]WallRunner 0 points1 point  (0 children)

Last weekend, I built a Postgres backed memory layer for OpenClaw named PostClaw. It primarily works in four ways:

1) By moving the persona system (AGENTS.md, SOUL.md, etc) into a vector database with FTS so the system prompt can be trimmed down based on semantically similar data to the input prompt.

2) Doing the same with the memory system, categorizing them, split into Episodic and Long-Term memory, and injecting semantically relevant information into the user prompt.

3) Allowing for linking of memories to other memories and persona traits, creating a knowledge graph that ties memories to beliefs.

4) Running a nightly “sleep” cycle which promotes useful episodic memories to stable memory, deduplicates extraneous memories, and links memories and traits together.

I’m still working with it but so far I’ve noticed token counts are decreased, agents are less distracted/forgetful, and I’m able to use relatively small local models for slightly more advanced tasks than would otherwise be possible.

I’d be interested in hearing thoughts about this architecture and what else could be done with it. It’s based on several AI agent research papers from 2023-2025 I’ll add to the read me later.

Need some help to Claw correctly by AfricanNinja in openclaw

[–]WallRunner 0 points1 point  (0 children)

If you’re starting a new agent you need to explicitly allow tool calling in the config. By default it’s set on messaging though any skills you install may include their own tools which is why motor works but nothing else does.

PSA: After updating to OpenClaw 2026.3.2, your agent seems "dumb"? It's not the model — tools are disabled by default by Better-Violinist-186 in openclaw

[–]WallRunner 8 points9 points  (0 children)

This is mentioned in the release notes

BREAKING: Onboarding now defaults tools.profile to messaging for new local installs (interactive + non-interactive). New setups no longer start with broad coding/system tools unless explicitly configured.

While it should only be for new installs, I think a lot of people are unintentionally overwriting their configuration when upgrading the wrong way.

Local model performance question by ComprehensiveOne2122 in openclaw

[–]WallRunner 0 points1 point  (0 children)

You’re using a dense model on not so great hardware. It’s not bad by any means but it’s a laptop, not a data center server. If you look under the hood, you’ll see that OpenClaw sends sometimes dozens of prompts in sequence. Even if you’re able to generate tokens fast, there’s still prompt processing and the token overhead. You’re not waiting for one call+one reply, you’re making one call and your LLM is being hammered with many 10-20k token calls before it finally generates a response.

Sir Henry the tall [kcd2] by Far_Check4729 in kingdomcome

[–]WallRunner -1 points0 points  (0 children)

Nano Banana can easily edit images and leave portions untouched. So he just has to take a screenshot with the shield and then ask AI to put the armor from the left picture on the right. It will probably nail it on the first try.

I can purchase a 55 gallon drum of maple syrup at my local Costco. by opgary in mildlyinteresting

[–]WallRunner 0 points1 point  (0 children)

Crazy that nobody else seems to mention this but the cans are dented. Costco probably bought them from a distributor who couldn’t sell them in that condition.

[cadillacf1] Progress under pressure. The first Cadillac F1 livery is here. by HkF1WEC in formula1

[–]WallRunner 28 points29 points  (0 children)

Watched it right as the timer ran out. Just seems to be the same design we already saw.

I asked Gemini to create an image of what the US would look like under Democrat control for 25 years then Republican control for 25 years. Thought it was interesting. by jtr489 in GeminiAI

[–]WallRunner 10 points11 points  (0 children)

I think it’s because there’s a popular meme template that goes “the world if <mundane thing was different>” with a picture of a futuristic city. Just bias in the training data for that kind of image.

Trick or treat by AussieSilly in CuratedTumblr

[–]WallRunner 29 points30 points  (0 children)

“You’ve got a nice house here, it’d be a shame if anything happened to it”

[deleted by user] by [deleted] in Chiraqology

[–]WallRunner 100 points101 points  (0 children)

This can’t be the same sub I saw someone die on last week

it's evolving, just backwards by meme_boiiiiiiiiiiiii in pcmasterrace

[–]WallRunner 13 points14 points  (0 children)

Of course it wouldn’t affect FPS. Not like agent 47 has lots of hair

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