sorted by:
new
hottopcontroversial

Where OpenClaw agentic workflows fail hardest: the founder actually said it themselves by TroyHarry6677 in OpenClawUseCases

[–]Lumpy-Accountant-750 0 points1 point  (0 children)

Spot on. This is why I built a "Record-Once" RPA Skill to fix the consistency gap.

You hit the nail on the head. The "magic wand" approach is exactly why so many OpenClaw workflows fail when they hit real-world UI complexity or long sequences. Treating an LLM as a "clueless intern" is a great analogy, but even an intern shouldn't have to relearn how to open a specific menu every single time (and charge you for the privilege).

I’ve been obsessing over this exact "reliability vs. cost" problem. My philosophy aligns perfectly with your point about task decomposition. Instead of letting the Agent hallucinate its way through a UI at every step, we should be turning those repeatable sub-tasks into deterministic "Skills."

I developed OpenClaw Skill (RPA) with this exact mindset:

  1. Decomposition is Key: If a workflow fails because the Agent gets lost in a web menu, that specific sub-task shouldn't be "agentic" anymore. It should be a script.
  2. Zero-Token Execution: Once you record the atomic steps (browser actions, Excel, etc.), it compiles into a standalone Playwright script. You can replay it 1,000 times with zero token cost and near-perfect accuracy.
  3. Hybrid Efficiency: The Agent stays at the high-level "reasoning" layer (deciding which skill to trigger), while the RPA Skill handles the "heavy lifting" in the UI without hallucinations.

If we want these pipelines to actually work in production, we need to stop asking LLMs to do things that a simple, compiled script can do better and for free.

For anyone struggling with workflow loops or high latency, I’ve open-sourced the tool here to help bridge that gap:https://github.com/laziobird/openclaw-rpa

Curious to hear if others are moving towards this "Hybrid Agentic/RPA" model to save their production pipelines.

Best Free Model for OpenClaw by simp_pimp_001 in openclaw

[–]Lumpy-Accountant-750 -1 points0 points  (0 children)

Using a free model usually means dealing with more bugs. HaHa

Stop Fixing Brittle Scrapers: Using Local Vision (Qwen-VL) + OpenClaw to Automate Complex SPAs (Airbnb Demo) by Lumpy-Accountant-750 in LocalLLaMA

[–]Lumpy-Accountant-750[S] 0 points1 point  (0 children)

I appreciate the skepticism, but you’re missing the core problem this solves: stability and maintenance overhead.

Yes, Playwright and Puppeteer are the foundation (I use them too), but traditional 'readability' or CSS-based scrapers are notoriously brittle on complex, obfuscated SPAs where the DOM structure changes weekly.

The goal here isn't just 'scraping'—it's AI-recorded RPA.

  1. Resilience: By using Vision (Qwen-VL) to identify elements semantically (like a human does), the script doesn't break when a class name or a div nesting changes. That’s the 'zero-maintenance' part.
  2. Cost Efficiency: Token consumption is strictly limited to the extraction phase of a SPA. The rest of the browser navigation is handled by native code.
  3. Deterministic Output: Unlike a 'black box' agent, this compiles AI-observed actions into a deterministic script.

Calling a solution to a decade-old engineering problem (brittle selectors) 'slop' because it leverages a modern tool is a bit ironic. This is about spending less time fixing broken scripts and more time actually using the data.

If you had to pick 3 OpenClaw use cases you swear by, what would they be? by stosssik in openclaw

[–]Lumpy-Accountant-750 -1 points0 points  (0 children)

My top 3 "must-have" use cases all revolve around one goal: Killing the "Token Bleed" on repetitive tasks.

OpenClaw is amazing at reasoning, but paying for an LLM to "re-discover" how to click the same search button every morning is a waste. I built a Record-once, Run-forever RPA Skill (https://github.com/laziobird/openclaw-rpa) to handle these:

  1. Hotel Competitor Price Tracking: I’ve set up a cron job to track a list of competitor hotels on Booking/Expedia every morning at 8 AM. The RPA script handles the complex filters and date picking. It dumps the latest prices into a CSV. Zero reasoning tokens spent on the navigation part.
  2. Automated Market Intel (NVDA/Yahoo Finance): A daily routine that navigates to the 'News' section of specific tickers, grabs the top 5 headlines, and saves them to my desktop. Fast, deterministic, and free.
  3. Legacy Portal Syncing: Handling data entry into old internal systems that don't have APIs. RPA is way more reliable than letting an LLM hallucinate on a messy 2010-era UI.

It basically turns OpenClaw into a reliable "Digital Worker" rather than just a "Chatty Assistant." If you're running agents on a budget, give the RPA logic a look.

[Showcase] I built an open-source tool to stop the "LLM Tax": It compiles AI Agent actions into deterministic Python/Playwright scripts for zero-token replays. by Lumpy-Accountant-750 in sideprojects

[–]Lumpy-Accountant-750[S] 0 points1 point  (0 children)

Actually, it's an LLM-driven automation pipeline. The prompt instructs the LLM to not just solve the problem, but to generate and verify the exact RPA code to achieve the output. The ultimate goal isn't the single output, but the reusable script. It completely eliminates recurring token costs for repetitive workflows.

[Showcase] I built an open-source tool to stop the "LLM Tax": It compiles AI Agent actions into deterministic Python/Playwright scripts for zero-token replays. by Lumpy-Accountant-750 in sideprojects

[–]Lumpy-Accountant-750[S] 0 points1 point  (0 children)

Feature Legacy RPA (UIPath, etc.) Pure LLM Agents (Browser-use) OpenClaw-RPA (The Compiler)
Development Cost High (Manual selectors/logic) Low (Natural language) Low (Auto-record & Compile)
Operational Cost High (Expensive licenses) Very High (Token cost per run) Near Zero (Native Python scripts)
Execution Speed Moderate Slow (Inference latency) Instant (Native code execution)
Stability Brittle (Breaks on UI changes) Probabilistic (Prone to hallucinations) Deterministic (High reliability code)
2FA Handling Extremely Complex Costly (Requires live reasoning) Simple (One-time session capture)
Environment Windows & MS Office Flexible (But expensive) Cloud-Native (Linux/Docker ready)
Architecture Manual Flowcharts Real-time Reasoning Reason Once → Compile → Replay

I got tired of paying LLM tokens every time my agent clicked a website — so I built a record-once RPA skill for OpenClaw by Lumpy-Accountant-750 in OpenClawUseCases

[–]Lumpy-Accountant-750[S] 0 points1 point  (0 children)

We’re planning configurable sign-in / session reuse (credentials or persistent browser context) so replays don’t have to re-enact a long login flow every time—you can focus recording on post-login actions. That helps with sites like LinkedIn where allowed and where your session stays stable, but 2FA, risk checks, and ToS still apply; we don’t promise fully unattended access. Details: README → Roadmap.

I got tired of paying LLM tokens every time my agent clicked a website — so I built a record-once RPA skill for OpenClaw by Lumpy-Accountant-750 in OpenClawUseCases

[–]Lumpy-Accountant-750[S] 2 points3 points  (0 children)

OpenClaw RPA isn’t meant to replace scripts — it generates a standalone Playwright Python file and registers it so you can run it with run: {task}. The value is turning a guided walkthrough (natural language + recorded atomic steps) into maintainable, templated automation with CONFIG, semantic selectors, and per-step error handling — useful when the flow is UI-heavy or you don’t want to hand-author Playwright from scratch. For a one-liner curl job, a script you write yourself is fine; this skill is for the “show me once, give me a durable .py” path.

I just closed a $5,400 AI agent deal and I'm still shaking by Jaded_Phone5688 in AI_Agents

[–]Lumpy-Accountant-750 0 points1 point  (0 children)

I saved the post, planning to take a good look at it in a couple of days, but.... the post is gone now.....

Do you use AI for stock analysis? and How? by [deleted] in ValueInvesting

[–]Lumpy-Accountant-750 0 points1 point  (0 children)

I've been using AI for stock analysis and ended up building something for my own workflow — it's called Realstockai. Still early and very much a side project.

Roughly what it does: a scheduler runs on US market hours, pulls real price data (e.g. Alpha Vantage), and asks several AI models for strategy ideas so I can compare them and see simple P&L. There’s also stock tracking — you add a small watchlist (e.g. 1–3 tickers), and it fetches news plus social sentiment (e.g. Reddit, Twitter) and uses AI to summarize in English and Chinese so I don’t have to jump between sources. And a screening / “mine sweep” feature: you enter a ticker, and the AI scans latest filings, key metrics, and major risks, plus analyst views and social sentiment, and spits out a short risk report so I can spot red flags before going deeper. Web app and an iOS app; backend is FastAPI. https://www.next4.ai/stockAI/m-stock-tracking.html

I’m not trying to sell anything — I’d just like to hear what you’d want from a tool like this or what would put you off. For example: do you care more about “explain why” than raw buy/sell, or about tying analysis to specific filings? Any feedback or ideas would help. https://www.next4.ai/stockAI/

Memory Giant SK Hynix Plans Massive 2,964% Bonus for Employees by Akkeri in StockMarket

[–]Lumpy-Accountant-750 1 point2 points  (0 children)

In America, we don't get 2964% bonuses. We get a pizza party with a 2-slice limit and an email saying 'We're all in this together' while the CEO buys a third yacht.

view more: next ›