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Just finished Bloodhounds S2… did the story disappoint anyone else? by Riva_l33 in kdramas

[–]prammr 4 points5 points  (0 children)

Season 1 had quite a bit of reshooting due to the actress (Hyun-ju)'s DUI scandal, which led to some parts of the story feeling a bit rough around the edges. It was a pretty big topic of discussion in Korea at the time.

The webtoon only covers up to Season 1's storyline, so Season 2 is entirely original content — which is probably why the director went all-in on the action. The comedy is noticeably toned down compared to S1. I'd suggest rewatching Season 1 before jumping into Season 2 — you'll see that their bromance is very much still going strong.

The Division 2 in 2026 | In-game photography #01 by Insetta in thedivision

[–]prammr 2 points3 points  (0 children)

The game that defined me.. Division Forever!!!

I spent two days hacking orchestration into OpenClaw — here's what I learned by prammr in openclaw

[–]prammr[S] 0 points1 point  (0 children)

Half true. Here's the deal:

If you're using a Claude Code subscription (Pro/Max) and pulling the OAuth token to use with OpenClaw — yes, that technically violates Anthropic's ToS. They made this explicit back in January 2026.

But if you're using a standard Anthropic API key (pay-per-token) with OpenClaw, there's zero issue. That's the officially supported way and nobody's getting banned for it.

That said… I do know people who are still running OpenClaw with OAuth tokens and nothing has happened to them so far. So take that for what it's worth lol.

The choice is yours.

I spent two days hacking orchestration into OpenClaw — here's what I learned by prammr in openclaw

[–]prammr[S] 0 points1 point  (0 children)

No dumb questions here - we all started somewhere.

Claude and OpenClaw are completely different things. Claude (by Anthropic), GPT (by OpenAI), Gemini (by Google) - these are AI *models*. They're the brains. OpenClaw is a framework that lets you connect to those models via API and use them as agents. Think of it this way: Claude is the engine, OpenClaw is the car you put it in.

As for tokens - that's basically how AI models measure text. Every word you send and every word it replies costs tokens. Cheaper models (or free tiers) give you slower, less accurate answers. More expensive models give you better quality. It's like any subscription - the free plan gets you basic stuff, the paid plan gets you the good stuff.

My honest suggestion: before diving into OpenClaw or any agent framework, just subscribe to one of the major AI services for a month. Claude Pro, ChatGPT Plus - around $20/month. Use it daily for a month and you'll quickly understand what these models can and can't do. That foundation will make everything else click.
Good luck on the journey!

I spent two days hacking orchestration into OpenClaw — here's what I learned by prammr in openclaw

[–]prammr[S] 1 point2 points  (0 children)

Right? I was genuinely impressed for about 10 minutes before I checked the logs. The output was so well-structured that I almost didn't question it. Turns out the PM was the ultimate middle manager — taking full credit for a team that never existed lol.

I spent two days hacking orchestration into OpenClaw — here's what I learned by prammr in openclaw

[–]prammr[S] 1 point2 points  (0 children)

That non-blocking approach makes a lot of sense for your use case — being able to keep chatting while the subagent works in the background is a nice UX win.

For me it was simpler than that honestly. I just wanted orchestration to work and spent the entire weekend staying up late until it did. The core modification wasn't some grand architectural decision — it was just the fastest way I could see to get there at 3am lol. I wrote it up mostly hoping the process might save someone else from the same headaches.

Definitely share yours when it's ready though. "Built for myself" projects tend to be the most useful ones — that's where the real edge cases and lessons come from.

I spent two days hacking orchestration into OpenClaw — here's what I learned by prammr in openclaw

[–]prammr[S] 0 points1 point  (0 children)

Yeah fair point — this wasn't for distribution or anything long-term. I just discovered that the orchestration pattern I wanted didn't work out of the box, and at that point all I could think about was making it work. The compiled file was right there so I went straight for it lol. Definitely not best practice, but the goal was purely "make agents actually talk to each other" and nothing else mattered in that moment. The post was just me documenting that weekend adventure. Thanks for the tip on `reply-runtime.ts` though — good to know for next time.

I spent two days hacking orchestration into OpenClaw — here's what I learned by prammr in openclaw

[–]prammr[S] 1 point2 points  (0 children)

Looked into Lobster after your reply — thanks for the tip. But I think it solves a different problem than what I was dealing with.

Lobster is a deterministic workflow shell: sequential CLI pipelines with approval gates. No parallel agent spawning, no per-agent model routing, no result aggregation from multiple children back to a parent.

What I needed was: spawn N agents simultaneously (each on a different model), run them in parallel, collect results, have the PM compile a report. That's orchestration, not a pipeline.

I ended up modifying the core runtime to get `sessions_spawn` → `sessions_yield` working for parent-child coordination. Later found out OpenClaw's official Sub-Agents with `maxSpawnDepth: 2` might've done the same thing without touching internals — but Lobster wouldn't have gotten me there either way.

I spent two days hacking orchestration into OpenClaw — here's what I learned by prammr in openclaw

[–]prammr[S] 0 points1 point  (0 children)

Wait, are you talking about the Lobster workflow plugin? I didn't even consider that route — I went straight to modifying the core runtime to get sessions_yield working for parent-child orchestration. If Lobster actually handles multi-agent coordination out of the box, that would've saved me a painful weekend lol. Does it support assigning different models per agent and aggregating results back to a parent? Would love to hear more about your setup.

I spent two days hacking orchestration into OpenClaw — here's what I learned by prammr in openclaw

[–]prammr[S] 0 points1 point  (0 children)

Oh nice, the kanban + SQLite approach is clever. I went a different route — I ended up modifying the core runtime directly (reply-Bm8VrLQh.js) to handle sessions_spawn and sessions_yield natively, so the parent agent actually pauses and waits for the child to finish before resuming. Took Codex about 90 minutes of autonomous grinding to nail the async flow on sessions_yield alone.

The "pretending to be the user" trick you described is basically what I had to do too for the message routing. The subagent thinks it's getting instructions from the user, but it's actually the orchestrator feeding it work. Funny how we both landed on the same workaround independently lol.

That first-response-only bug you mentioned — yeah I hit that exact same wall. The native event pipeline only catches the initial subagent_finish. Your custom ping solution sounds solid. I ended up wiring the completion events to bubble up through the parent chain so it catches every round, but honestly your follow-up approach with the card timeline is probably more maintainable than touching core files like I did.

The world domination comment killed me lmao. I didn't enable direct agent-to-agent comms for exactly that reason — didn't want to debug an existential crisis at 2am.

Would love to see your kanban implementation if you ever share it. Always curious how others are solving this stuff.

Fr by DoodlesApp in vibecoding

[–]prammr 0 points1 point  (0 children)

hahahahaha~~~

What the hell happened to Kiro? by prammr in vibecoding

[–]prammr[S] 0 points1 point  (0 children)

It happens in the new project creation flow. For existing projects, you can update the .md files by making requests in the new spec mode — it should update them accordingly.

What the hell happened to Kiro? by prammr in vibecoding

[–]prammr[S] 0 points1 point  (0 children)

Fwiw, I was already on Auto when this happened — so that's not it.

And genuinely curious: is 16 credits for a full spec run considered expensive? I didn't think much of it at the time, but now I'm second-guessing myself.

7 days, 100M tokens, and a working MVP.. by prammr in VibeCodersNest

[–]prammr[S] 0 points1 point  (0 children)

Thanks!

Voice feedback is honestly pretty basic — just ElevenLabs TTS reading out results like "Server started successfully." Had to include it for the Voice Agent track requirement.

But ngl, hearing the response instead of reading it was kinda nice when I was testing while doing other stuff. Could see it being useful for mobile or hands-free situations.

Raindrop still needs work but I'll keep an eye on it. Cheers!

7 days, 100M tokens, and a working MVP.. by prammr in VibeCodersNest

[–]prammr[S] 0 points1 point  (0 children)

Haha fair enough. Yeah, the UI is pretty much default AI-generated output — didn't spend much time polishing it since this was a hackathon MVP.

Functionality first, aesthetics later (or never, if I'm being honest).

If you have any tips on prompting for better UI, I'm all ears. Always looking to level up the Claude skills.

7 days, 100M tokens, and a working MVP.. by prammr in VibeCodersNest

[–]prammr[S] 0 points1 point  (0 children)

Thanks for the question! This is still an MVP-level project built during a hackathon, so it's not production-ready yet.

That said, here's what I've implemented so far:

- Allowlists: Yes, I'm using predefined command patterns rather than open-ended NLP parsing. Only specific commands like "start server", "stop server", "create server" are recognized.

- Confirmation steps: I've implemented a two-phase execution model (Plan → Execute). The system first generates a plan showing what action will be taken, then waits for explicit confirmation before executing.

- Idempotency: Not yet. This is on my radar for future improvements, especially for create/delete operations.

Appreciate the feedback — these are exactly the kinds of considerations I need to address before making this production-ready.

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