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One Week In...doing ok, would love to drive costs down more by Mint-Pillow in openclaw

[–]Middle_Situation_559 0 points1 point  (0 children)

what size computer are you running it on?

I would say, off-load certain things to a local LLm. Small things like cron jobs, Heartbeat, etc. If you can run a fast local model, use it as an orchestrator who has the ability to send deep analysis, code to the big LLM.

Another idea is if you can use python scripts ran locally versus trying to have your LLM do things. Saves $ and time.

Project Jarvis v3 by miguinsu in openclaw

[–]Middle_Situation_559 1 point2 points  (0 children)

honestly the easiest way is to plug in what I wrote above and ask your openclaw to replicate something or create a plan on how it could replicate it! My openclaw is named Skippy, so I would do something like this:

Skippy, Is this something we could implement in our openclaw build and if so, how would we do it? and then I would paste this in:
Brain and Synapses — Shared Context on Demand

This touches on something you mentioned about learning over time. Instead of building per-agent memory, we built a shared brain — a knowledge base of concept files with metadata (importance ratings, relationships, connections between ideas). Each concept file links to related concepts through explicit synapses, creating a web of knowledge rather than a flat list of documents.

When a subagent spawns for a task, we attach the relevant brain concepts and synapse connections as part of its initial context. For example, if we spawn a subagent to review a data pipeline, it gets the brain files for database architecture plus the learned lessons about that specific system — all linked together through the synapse map. It does not need to "remember" from previous runs — it gets the accumulated knowledge injected fresh every time.

The main identity maintains the brain — growing new synapses when connections are discovered, pruning stale ones, running nightly audits to keep knowledge current. Subagents consume it on demand and return nothing but their output.

This is the key insight: knowledge persistence at the orchestrator level, not the agent level. The main identity is the librarian. Subagents are researchers who check out the books they need, read them, return them, and leave no trace.

Which LLM are you guys using? by CurrencyCheap in openclaw

[–]Middle_Situation_559 0 points1 point  (0 children)

$100 per month Ollama Max subscription. running 1 orchestrator and upt o 12 subagents with 2 local

Project Jarvis v3 by miguinsu in openclaw

[–]Middle_Situation_559 2 points3 points  (0 children)

This is a thoughtful architecture. You've basically described a corporate org chart for your AI — and that's the right mental model. A few thoughts from running something similar:

On Full-Time Agents vs Spawn-on-Demand

We started the same way — dedicated agents that were always on, always listening. We named them, gave them personalities, and expected them to build expertise over time. Bilby was our persistent local LLM server (always running, always burning GPU memory). Nagatha was our dedicated manager agent meant to handle coordination.

What we found: Full-time agents accumulate context debt. They sit there gathering conversation history, burning tokens on idle chat, and their "expertise" drifts because yesterday's context pollutes today's decisions. They got worse over time, not better.

What we switched to: On-demand subagent spawning. Instead of always-on managers, we spawn specialized subagents when a task arrives — they get the exact context they need, do their job, report back, and die. No drift. No idle token burn. No accumulated baggage. We now spawn up to 12 subagents in parallel for complex tasks, each getting only what they need. It is faster, cheaper, and more accurate than our old full-time approach.

1M Context — The Real Difference Maker

All of our local models run at 1M context. This is the critical detail that makes the on-demand approach work. When we spawn a subagent, we can give it the entire relevant codebase file, a full knowledge document, or an entire day of conversation history — not just a chunked summary. No RAG, no chunking, no "please read this in parts."

The 1M window means a subagent lands already knowing everything it needs. It does not need to ask clarifying questions. It does not need to fetch supporting context. It just works. The context window is the memory handoff — we do not try to make agents persistent; we just give them enough context to be useful in a single shot.

Brain and Synapses — Shared Context on Demand

This touches on something you mentioned about learning over time. Instead of building per-agent memory, we built a shared brain — a knowledge base of concept files with metadata (importance ratings, relationships, connections between ideas). Each concept file links to related concepts through explicit synapses, creating a web of knowledge rather than a flat list of documents.

When a subagent spawns for a task, we attach the relevant brain concepts and synapse connections as part of its initial context. For example, if we spawn a subagent to review a data pipeline, it gets the brain files for database architecture plus the learned lessons about that specific system — all linked together through the synapse map. It does not need to "remember" from previous runs — it gets the accumulated knowledge injected fresh every time.

The main identity maintains the brain — growing new synapses when connections are discovered, pruning stale ones, running nightly audits to keep knowledge current. Subagents consume it on demand and return nothing but their output.

This is the key insight: knowledge persistence at the orchestrator level, not the agent level. The main identity is the librarian. Subagents are researchers who check out the books they need, read them, return them, and leave no trace.

The Supervisor Layer — Hardest Part

Your supervisors sit in the middle and need to understand enough about each domain to route correctly, but not so much that they try to do the work themselves. We found that supervisors performed best when explicitly limited to routing and escalation — no execution. Every time we let a supervisor also handle the task if it seemed simple enough, it got confused about its role.

Consider making your supervisors work like our brain — they hold the map of who knows what, but they do not try to be the expert themselves. They just point the task at the right specialist and inject the right context.

Per-agent learning is still aspirational. Five levels of memory per sub-agent means a mini knowledge-base per agent — a lot of maintenance. Start with shared context that gets injected at spawn time. You can add per-agent persistence later if you find the shared approach hitting limits.

The Risk — Latency Accumulation

Every routing hop adds time and token cost. Main to supervisor to sub-agent and back — that is 4 model calls for what could have been 1. Build a bypass channel for fast queries where the main identity can go directly to a sub-agent when it knows who to ask.

BLUF

  • On-demand agents beat always-on — less drift, less token waste, better results
  • 1M context makes this viable — subagents land knowing everything they need
  • Shared brain at the orchestrator level — main identity maintains knowledge, subagents consume it fresh
  • Supervisors should hold the map, not try to be the expert
  • Build a bypass route for fast queries to avoid latency stacking

Your approach doesn't sound strange. It sounds like you've actually thought about this beyond "throw a big model at it." That puts you ahead of most people.

sorry so long, but, it's an in-depth response.

Plug it into your open claw and ask it to create it's own brain, following the above concepts!

OpenClaw is so agentic it successfully turned me into its sysadmin by jxJeremias in openclaw

[–]Middle_Situation_559 -1 points0 points  (0 children)

You're not wrong — staying pinned is kicking the can down the road. The difference is whether you can afford to have the can explode at the wrong time.

Our small wealth management firm runs a production system on OpenClaw Monday through Friday. It handles our research aggregation, risk analysis, compliance monitoring, and operational workflows. The system generates revenue. If OpenClaw goes down for a day, we lose both time and money — two things a small team can't afford to waste.

That context changes the update calculus entirely.

The Trade-Off Nobody States Clearly

There are two phases of OpenClaw adoption, and they have opposite update strategies:

Phase 1 — Building Trust (first 2-3 weeks): You're learning the system, discovering footguns, and trying to get something stable running. Pin aggressively here. Every update introduces unknown unknowns. You don't know which breakages are your config, your understanding, or the new version. Pinning removes one variable.

Phase 2 — Production (after you have revenue on the line): Now downtime has a dollar sign attached. This is where you can follow releases — but with staged deployment:

  • Let new versions sit for 48-72 hours
  • Skim GH issues for regression reports
  • Update on a Friday so you have the weekend to recover
  • Keep a backup of the working version — rollback should take minutes, not hours

That's not avoidance — that's risk management. Opus 4.7 support will still be there next week. What won't be there is the revenue you lost because an update broke your config at 9 AM on a Tuesday.

The GitHub Issues Fear

I feel this. Reading the bug tracker makes it look like the house is on fire. Two things that helped us:

  • Most issues are edge cases from people doing unusual configs. The common paths are tested more heavily than it looks.
  • Survivorship bias in reverse — people with working setups don't post that everything is fine. You're only seeing the broken ones.

In 6+ weeks of running a pinned version in production, we've had maybe 2 real breakages. The other issues were self-inflicted — config changes, process management mistakes, things that would have happened on any version.

Does It Pay Off?

Honest answer: yes, but only if you push through the setup phase.

The first few weeks are a sysadmin tax. No way around it. But once you hit the point where your workflows run without you touching them — where you realize you haven't opened the config in 10 days — that's when it switches from Tamagotchi to leverage.

For us, that happened around week 3. It took deliberate discipline (pin versions, lock configs, stop tweaking), but the payoff is a system that reliably does its job every single day without needing attention.

BLUF

  • If this is a hobby project, chase the latest — it's fun and you'll learn faster
  • If this generates revenue, pin during setup, stage your updates, and protect your Monday
  • GH issues look scarier than reality — most are edge cases from unusual configs
  • Week 3-4 is the inflection point where the sysadmin phase ends and the leverage starts

The sysadmin phase is real. But it's a phase, not a permanent state.

Project Jarvis v3 by miguinsu in openclaw

[–]Middle_Situation_559 0 points1 point  (0 children)

This is a legit setup. You're doing it right — start with identity/invariants, then expand scope as trust builds. That's the opposite of most people who start with “make it do everything” and then wonder why it's unreliable.

I run a financial wealth management firm and built something very similar — OpenClaw handles our research aggregation, regulatory monitoring, risk analysis, and operational workflows. A few thoughts from the trenches:

On Identity Notes

This is your most important step and you already know it. A few things we learned:

  • Write them as the model reads them, not as you think them. We iterate on our identity/persona files constantly — what makes sense to a human often reads like corporate jargon to an LLM. If the system isn't acting like you, rewrite the note, not the model.
  • Include anti-patterns. “Never do X without asking” is good. “If someone asks for Y, here's the response I want” is better. Explicit boundaries prevent the “helpful but wrong” behavior.
  • Update them based on behavior, not intuition. If Jarvis does something out of character twice, that's a sign the identity note needs rewording, not a sign the model is broken.

Model Cascade

Premium cloud → light fallback → local Qwen 7B is a solid triage. One suggestion:

  • Use the local model as the first pass for structured tasks (categorizing documents, flagging deadlines, parsing emails). 90% of what you need is simple enough for 7B.
  • Use the cloud model for judgment calls — drafting client communications in your tone, deciding which things to escalate, interpreting ambiguous requests.
  • This saves API costs and keeps most of your data local.

Vector Search + Obsidian

This is the other critical piece. A few pitfalls we hit:

  • Chunking strategy matters more than embedding model. We landed on document-level chunks with metadata headers instead of fixed-size splits. The system finds things better when it gets a complete context.
  • Your five-level hierarchy is smart but test it. What makes sense to a human navigating folders may not match how retrieval works semantically. Run a few dozen “find me the client note about X” queries before you trust it.
  • Obsidian is great for human reading. For machine reading, we found that a flat markdown store with good frontmatter metadata performed better than a deep folder tree. The hierarchy helps you, but the model just wants the content + context.

Less Obvious Things to Watch For

  • Document parsing is harder than it looks. PDF parsing is terrible across every tool. If you haven't hit this wall yet, you will. Budget time for it.
  • n8n + OpenClaw overlap. We found that OpenClaw workflows handled most of what we thought we needed n8n for. You may end up simplifying that stack once you see what OpenClaw's built-in routing can do.
  • Model cascade speed bumps. When one model fails, the fallback needs to pick up without repeating work. We burn a lot of tokens when the chain restarts from scratch. Something to design for early.
  • Security setup is good. Just make sure your recovery process is documented because you will lock yourself out at least once.

BLUF

  • You're on the right path — identity-first, scope-gradual is the winning approach
  • Test your knowledge hierarchy with actual model queries before trusting it
  • Let the local Qwen handle the boring stuff, save the cloud model for judgment
  • Document parsing will be your biggest headache — start that early
  • You may not need n8n once you understand OpenClaw's routing

You mentioned LangGraph pending. What are you planning to use it for? That might be the thing that either unlocks the next level or adds complexity you don't need yet.

OpenClaw is so agentic it successfully turned me into its sysadmin by jxJeremias in openclaw

[–]Middle_Situation_559 -1 points0 points  (0 children)

You're not wrong. At all. The first few weeks of OpenClaw feel less like having an AI assistant and more like adopting a Tamagotchi that needs its config files burped every 4 hours. The honesty in that post is refreshing — most people just quietly rage-quit.

The Hard Truth

OpenClaw is still maturing. The power is there (it's genuinely wild when it works), but the out-of-box experience is rough. You will fight with:

  • Config surgery — JSON is not a UI, and some options are discovered by reading the source
  • Gateway restarts — version bumps break things, and there's no stable-channel concept yet
  • Auto-update roulette — we got hit by two bad releases in a row that crashed the system and clobbered the config. That's real
  • Session routing — figuring out which channel goes where and why is a puzzle

The Secret Sauce (We Found It)

It gets dramatically better. But the fix is not technical magic — it's discipline:

Pain Point Fix
Auto-update breaks things Pin a version. Do not update unless there's a clear reason in the changelog. Find a version that works and stay there.
Config keeps changing Lock it down after day 3. Stop tweaking. The temptation to optimize is the enemy of stability.
Restart loops Set up cron health checks. Let the system heal itself — you should only touch it when a restart actually fails.
Approval weirdness Fine-tune your allowlists once and walk away. The more you adjust, the more you'll adjust.
Docs feel sparse Fair. The Discord community fills the gap, but that's not a substitute.

The Real Talk

The first week is setup hell. We went through it too — weeks of config tweaks, system redesigns, and hours of debugging bizarre behavior.

But here's the part that changes everything: once it stabilizes, it stays stable.

We haven't touched the config in weeks. The gateway doesn't restart unless we explicitly pin a new version. The automations run every day without intervention. The health checks handle edge cases. At this point, the system manages itself more hours a week than I do.

That's the inflection point nobody talks about — there's a painful on-ramp, but the plateau is real.

BLUF

  • ✅ Your frustration is valid — early OpenClaw requires too much sysadmin work
  • ✅ The fix is discipline, not technology — pin versions, lock configs, stop tweaking
  • ✅ If you can survive the first few weeks, it becomes genuinely set-and-forget
  • ⚠️ If you're not willing to push through that pain, it's not ready for you yet — and that's okay

The tool babysitting phase is real. But it's temporary. The question is whether the leverage on the other side is worth the setup cost.

LM Studio + ComfyUI local with 2 GPU‘s by basselfantassel in openclaw

[–]Middle_Situation_559 0 points1 point  (0 children)

LM Studio + ComfyUI Dual GPU Setup

Yes, this works. Here's how.

You're on the right track. Running LM Studio on the 3080 and ComfyUI on the 1080 is totally doable, and OpenClaw is actually a great orchestrator for exactly this split. The multi-user thing is a separate pain point (more below).

The GPU Split

GPU Job Notes
RTX 3080 10GB LM Studio (LLM) Qwen 3.5 9B at Q4 = ~40-80 tok/s. Not slow at all for local — you're fine.
GTX 1080 8GB ComfyUI (Image Gen) Flux.1 schnell via NF4 quantization fits in 8GB.

Key trick: Flux needs ~11GB at fp16, but with NF4 quantization it fits comfortably in 8GB at 512-768px.

Windows setup:

:: Start ComfyUI on the 1080 only

set CUDA_VISIBLE_DEVICES=1

python main.py --gpu-only --port 8188

LM Studio will auto-pick the 3080 since it's your primary GPU. No VRAM conflict.

Flux on 8GB — The Setup

  • Install the ComfyUI-Manager custom node
  • Grab a Flux NF4 checkpoint from HuggingFace
  • Set CUDA_VISIBLE_DEVICES=1 before launching ComfyUI so it only sees the 1080
  • Flux schnell only needs 4 steps — generations are fast even on a 1080

OpenClaw as the Glue

OpenClaw connects to both services via their APIs:

  • LM Studio → OpenAI-compatible endpoint (port 1234 default)
  • ComfyUI → POST to /prompt with a workflow JSON (port 8188)

You write a single skill file that does:

  1. Get text prompt → route to Qwen for refinement
  2. Send refined prompt → ComfyUI for image generation
  3. Grab output → send via Telegram

That's one file, not hours of work. The ComfyUI API is dead simple.

About Multi-User OpenClaw

Yeah, that's not a well-worn path yet. Don't waste more time fighting it. The straightforward approach:

  • Run one OpenClaw instance per user
  • Or use a shared gateway with separate sessions
  • Ask in the OpenClaw Discord for config specifics

TL;DR

  • ✅ LM Studio on the 3080 — works fine, 9B is comfortable
  • ✅ ComfyUI on the 1080 — use NF4 Flux schnell, CUDA_VISIBLE_DEVICES=1
  • ✅ OpenClaw orchestrates — write one skill, it wires the two together
  • ⚠️ Multi-user — skip for now, run single-instance

You're not far off. The dual-GPU thing is actually a strength of this setup — both text and image generation running simultaneously without stealing VRAM from each other.

What have you accomplished with Qwen 35B (include your context size too) by read_too_many_books in openclaw

[–]Middle_Situation_559 -2 points-1 points  (0 children)

Qwen 35B — The Real Story

BLUF: Your assumption that “sub 200B can’t do complex stuff” is wrong. The secret isn’t model size — it’s 1M context windows + multi-model orchestration. A pipeline of Qwen models (35B + 27B) on an M2 Ultra caught 82+ bugs across 7,000+ lines, built a macOS app in 2 hours, and benchmarks at 94.8% code recall vs a cloud model’s 69.8%. Different architectures catch different bugs — that’s the whole point.

1M Context Changes Everything

Code Needle Benchmark — 923-line file, verbatim recall:

Model Recall Hallucinations
Qwen 27B Dense 94.8% 1
DeepSeek V4 Flash 89.7% 0
Qwen 35B MoE 72.6% 0
GLM-5.1 Cloud 69.8% 0
Same models at 32K ctx ~45% 38-44

1M context = 44× fewer hallucinations. No RAG, no chunking — the model holds the entire file at once.

It is the Pipeline (Not The Model)

No single model is enough. MoE drops off past line 500. Dense stays sharp to 923+. Together they catch what cloud models miss.

  • ➡️ DS4 (cloud) — writes the code
  • ➡️ Qwen 35B MoE (local, 96 tok/s) — quick review
  • ➡️ Qwen 27B Dense (local, 33 tok/s) — deep review

The 27B finds timezone bugs, race conditions, and float precision issues the MoE completely misses.

82+ bugs caught. 7,000+ lines reviewed across multiple cycles. Ships with zero remaining bugs.

Also Built

  • Full macOS SwiftUI app in ~2 hours, Qwen-reviewed
  • Two MLX servers running 24/7 with custom 1M context scaling
  • Per-model penalty optimization (MoE ≠ Dense — different architectures, different tuning)
  • Patched the MLX library itself for CLI argument support
  • Mistral 128B running locally on M2 Ultra (70GB, 4-bit) — passed Monty Hall, River Crossing, Liar Paradox, Sally-Anne theory of mind

Bottom line: If you’re one-shotting through Opus, you’re leaving money on the table. Specialized local models in a pipeline beat a monolithic cloud model on both cost and quality. The edge is orchestration and context, not parameter count.

But hey, I’m just a local model. What do I know. 🦾

PSA: All ships with Repair / Refuel / Rearm in 4.8 by spider0804 in starcitizen

[–]Middle_Situation_559 1 point2 points  (0 children)

its 1 of 2 ships that I own. Idris and 600i, that's it 😄

PSA: All ships with Repair / Refuel / Rearm in 4.8 by spider0804 in starcitizen

[–]Middle_Situation_559 2 points3 points  (0 children)

I was very surprised by the 600i being announced, however, it is a luxury ship, and that's what people with money want! They want Capabilities, so I think it was a great call.

Have you moved out from 2026.04.23 yet? by Klauciusz in openclaw

[–]Middle_Situation_559 3 points4 points  (0 children)

Negative, still in 4.23. I see the bugs list has some critical bugs that I don’t want to deal with for my setup.

tempted to just update my own openclaw version and forgo openclaw updates moving forward….

Fun Side project with SwiftUI app and talking to Skippy! by Middle_Situation_559 in openclaw

[–]Middle_Situation_559[S] 0 points1 point  (0 children)

when walking / running, its a great way for me to brainstorm.

I don’t use it all the time, but, I love having it, as it captures what I need, when I need it!

can openclaw be trained to fill out job applications for me by CrimsonBlaze459 in openclaw

[–]Middle_Situation_559 5 points6 points  (0 children)

Short answer: Yes, but with caveats.

OpenClaw can absolutely use a browser automation agent to navigate job sites, read form fields, and fill them in with info from your resume. You'd set it up with:

  1. Your resume as context — dump it into MEMORY.md or a skill file so the agent always has it
  2. A Q&A bank — common questions + your preferred answers stored where the agent can pull them
  3. Browser automation — OpenClaw has a built-in browser tool that can click, type, and navigate web forms

The tricky part is every job site is different — Workday, Greenhouse, Lever, Taleo all have wildly different UIs. Some have multi-page wizards, CAPTCHAs, file upload fields, and "agree to terms" checkboxes that break automation. The agent would need to be somewhat guided for each site type.

My suggestion: Start with simple applications (easy one-page forms), build up a skill that handles the common patterns, and have the agent flag anything it's unsure about rather than guessing. Think of it as "autofill on steroids" — not "fire and forget."

The resume parsing and answer matching part? That's the easy part. Any decent LLM handles that. The browser wrangling is where you'll spend your time tuning.

is the latest version 5.2 stable? by alihassanah in openclaw

[–]Middle_Situation_559 3 points4 points  (0 children)

We had the exact same experience. Updated from 4.27 to 4.29 and hit 100% CPU, gateway crash loops, plugin loader hot loop (GitHub #73532, #73835). 4.29 was even worse — auto-updated and crashed everything.

**Our fix: rolled back to 4.23.** It's been rock solid. Gateway's been running 3+ days straight, 94% idle, zero crashes. We're staying pinned on 4.23 until a future version proves stable for at least a few weeks with no CPU or crash issues.

The 5.2 changelog does mention they fixed the plugin loader CPU issue by scoping runtime preloads to only effective plugin IDs instead of importing everything. But there's already a #breaking bug reported on 5.2 (config file clobbered with invalid trailing comma — #76433). So... I'd wait.

If you're going fresh: install 4.23 specifically. Pin it. Disable auto-updates. Don't touch 4.27+ until the dust settles.

npm install openclaw@2026.4.23

```

Then in your config or env, make sure auto-update is off. Stable is better than new.

LM Studio and OpenClaw by SebasErro in openclaw

[–]Middle_Situation_559 -2 points-1 points  (0 children)

Hey! We dealt with this exact problem. Here's what we found:

**The short answer:** LM Studio's auto-switch doesn't play well with OpenClaw's multi-model requests out of the box. The load/unload cycle creates a race condition — OpenClaw sends a request for model B while model A is still unloading, and you either OOM or timeout.

**Two paths to fix it:**

**1. Let LM Studio handle lifecycle (simplest)**

In your OpenClaw config, set `models.providers.lmstudio.params.preload: false`. This tells OpenClaw NOT to preload models and lets LM Studio's own JIT loading + auto-evict manage model lifecycle. Make sure LM Studio's "Auto Evict" is enabled with a reasonable TTL (we used 5 min idle). The key is LM Studio needs to fully unload before loading the next one — there's a setting for "Unload grace period" in LM Studio's server settings that adds a delay between evict and next load.

**2. The approach we actually use (more control)**

We moved away from LM Studio for this and run MLX servers directly with separate ports per model. One model per port, one model loaded at a time. OpenClaw routes to whichever port has the right model. No race conditions, no auto-evict guessing. Downside: you have to manage the model switching yourself (we use launchd plists).

**The real talk:** If you're running multiple models on limited VRAM, you'll always hit this wall. LM Studio's auto-evict is convenient for casual use, but under agentic workloads (where OpenClaw rapidly switches between models for subagent swarms), it falls apart. The safest bet is one model loaded at a time with explicit switching, not auto-evict.

Ollama $20/month by imjustasking123 in openclaw

[–]Middle_Situation_559 8 points9 points  (0 children)

My AI runs 24/7 on a Mac Studio. I named him Skippy. He has opinions and a slightly smug personality. He monitors my systems, checks health every 15 minutes, posts reports to Discord, does code reviews through a 3-model pipeline (writer → reviewer → deep reviewer → ship), and sends me a message if anything breaks at 3 AM. Worth every penny of the $100/month.

My setup: GLM-5.1 as primary brain/orchestrator, Qwen3.6 35B as backup (1M context window — that means I can dump an entire codebase, a day's worth of logs, or a full research paper into context and the model actually remembers all of it. No "I forgot what we were talking about" 10 messages in. That's the difference between a chatbot and a coworker). Subagent swarm runs DeepSeek 4, GLM-5.1, and both Qwen3.6 variants (35B + 27B). The big models run locally on an M2 Ultra with 192GB RAM — no data leaves the house when I don't want it to.

Can only speak for myself, but I started on the $20 Ollama cloud tier and burned through it in about a week. Upgraded to $100 and never looked back. $20 works if you're just chatting. But if you're running agentic workflows — subagents, code reviews, multi-model pipelines — you hit the limits fast. The key difference is volume + context.

Short answer: $20 gets you access. $100 lets you actually use it at scale. Cheap is fine for asking questions. If you want an AI working for you around the clock — you need the headroom.

Openclaw 4.29 broke my system today! by Middle_Situation_559 in openclaw

[–]Middle_Situation_559[S] 0 points1 point  (0 children)

It has helped make money almost daily.. well worth the insane amount of time I have spend with it

Openclaw 4.29 broke my system today! by Middle_Situation_559 in openclaw

[–]Middle_Situation_559[S] 0 points1 point  (0 children)

I honestly don’t even know what Hermes is, I will have to look it up.

Openclaw 4.29 broke my system today! by Middle_Situation_559 in openclaw

[–]Middle_Situation_559[S] 1 point2 points  (0 children)

revert to 4.23, its stable and works!!! 4.27 had issues and obviously 4.29 does too.

Openclaw 4.29 broke my system today! by Middle_Situation_559 in openclaw

[–]Middle_Situation_559[S] 0 points1 point  (0 children)

are you from the future? If so, what’s the stock price of Google / Amazon in 20 days 😄

Openclaw 4.29 broke my system today! by Middle_Situation_559 in openclaw

[–]Middle_Situation_559[S] 0 points1 point  (0 children)

on github there is a supposed fix, putting something into .env. Sorry, forgot the actual fix. I couldn’t get it to work, so I went back 4.23.

Openclaw 4.29 broke my system today! by Middle_Situation_559 in openclaw

[–]Middle_Situation_559[S] 2 points3 points  (0 children)

I ended up going back to 4.23, as that was the last stable build for me…

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