llm/122b8d72-a8a3-4fcf-8eca-6a52786d1a8b/batch-8-75513ec3-5db9-4b9f-9c22-b777d62dbad0-input.json
The following is content for you to classify. Do not respond to the comments—classify them.
<topics>
1. Lack of Concrete Evidence
Related: Commenters repeatedly criticize the article for providing no examples, code, projects, costs, or specifics about what was actually built, calling it empty hype and platitudes without substance or proof of claims
2. Author Credibility Concerns
Related: Multiple commenters point to the author's previous blog post praising the Rabbit R1 as evidence of poor technical judgment and tendency toward unfounded enthusiasm for new technology
3. AI Coding Tool Limitations
Related: Discussion of how AI tools work well for simple, repetitive, or locally-scoped tasks but fail with complex systems, large codebases, and non-trivial problems requiring significant human guidance
4. Greenfield vs Legacy Projects
Related: Observations that AI coding excels at new projects under 10,000 lines of code but struggles maintaining consistency and avoiding regressions in larger, established codebases
5. Astroturfing Suspicions
Related: Multiple commenters suspect pro-AI posts are marketing campaigns or astroturfing given the billions invested in AI, with some noting suspicious voting patterns and repetitive promotional content
6. AI-Generated Content Detection
Related: Many suspect the blog post itself was written by AI, citing lack of specifics, excessive em-dashes, and generic promotional language characteristic of LLM-generated slop
7. Manager Fantasy Critique
Related: Skepticism about the desire to become a 'super manager' rather than hands-on developer, with some viewing it as CEO cosplay or escapism from actual technical work
8. Productivity Illusion
Related: Discussion of whether AI tools create actual productivity gains or merely the feeling of productivity, with some noting impressive-looking output that lacks substance or quality
9. Security Concerns
Related: Significant worry about OpenClaw's security vulnerabilities, prompt injection risks, and the danger of giving AI agents access to production systems, emails, and sensitive data
10. Skills and Learning Curve
Related: Debate over whether effective AI tool usage requires significant skill development, with some arguing poor results indicate user skill issues while others see fundamental tool limitations
11. Real World Use Cases
Related: Commenters share legitimate use cases including utility scripts, exploring unfamiliar codebases, setup automation, and learning new tools, distinguishing these from transformative claims
12. Cost and Accessibility
Related: Discussion of the financial barriers including expensive subscriptions, Mac Mini hardware, and token costs that contradict claims of democratizing technology
13. AI Hype Cycle
Related: Observations that we're at the apex of AI hype, with predictions the bubble will pop and more realistic assessments will emerge over time
14. Context Window Problems
Related: Technical discussion of how AI agents lose coherence as context grows, with compaction causing confusion and requiring human redirection
15. Testing and Verification
Related: Emphasis on the need for humans to verify AI output, run tests, and maintain quality control since AI cannot reliably check its own work
16. Language-Specific Performance
Related: Observations that AI performs better with some programming languages like Python and JavaScript compared to Java, Scala, or enterprise frameworks
17. Engineering vs Management
Related: Philosophical debate about why engineers want to become managers, whether it's about power, career progression, avoiding obsolescence, or building bigger things
18. Model Selection Matters
Related: Discussion of significant quality differences between AI models, with frontier models like Opus and GPT-5.2 performing notably better than cheaper alternatives
19. Workflow Integration Tips
Related: Practical advice including using AGENTS.md files, breaking tasks into smaller chunks, brainstorming with agents, and having separate contexts for review and implementation
20. Vibe Coding Skepticism
Related: Criticism of fully autonomous AI coding without understanding the output, with warnings about technical debt, logical errors, and unmaintainable code accumulation
0. Does not fit well in any category
</topics>
<comments_to_classify>
[
{
"id": "46935833",
"text": "if 90% is good enough, you are a winner to try your idea and fail fast. if you want to reach 91 or more, AI is a slop and hype to burn our pensions and contribute to vastly to global warming and cognitive decline consumerism evolution"
}
,
{
"id": "46932280",
"text": "If you use Cursor or Claude, you have to oversee it and steer it so it gets very close to what you want to achieve.\n\nIf you delegate these tasks to OpenClaw, I am not really sure the result is exactly what you want to achieve and it works like you want it to."
}
,
{
"id": "46932093",
"text": "I think everyone cheering for AI will become its archenemy later. I’m very happy that companies like Salesforce and Duolingo, which fired so many people, are now tanking badly."
}
,
{
"id": "46932064",
"text": "This euphoria quickly turns into disappointment once you finish scaffolding and actually start the development/refinement phase and claude/codex starts shitting all over the code and you have to babysit it 100% of the time."
}
,
{
"id": "46932200",
"text": "That's a different problem and not really relevant to OpenClaw. Also, your issue is primarily a skills issue (your skills) if you're using one of the latest models on Claude Code or Codex."
}
,
{
"id": "46932356",
"text": "You have to be joking. I tried Codex for several hours and it has to be one of the worst models I’ve seen. It was extremely fast at spitting out the worst broken code possible. Claude is fine, but what they said is completely correct. At a certain point, no matter what model you use, llms cannot write good working code. This usually occurs after they’ve written thousands of lines of relatively decent code. Then the project gets large enough that if they touch one thing they break ten others."
}
,
{
"id": "46932819",
"text": "I beg to differ, and so do a lot of other people. But if you're locked into this mindset, I can't help you.\n\nAlso, Codex isn't a model, so you don't even understand the basics.\n\nAnd you spent \"several hours\" on it? I wish I could pick up useful skills by flailing around for a few hours. You'll need to put more effort into learning how to use CLI agents effectively.\n\nStart with understanding what Codex is, what models it has available, and which one is the most recent and most capable for your usage."
}
,
{
"id": "46932521",
"text": "Well, I will not be berated by an ostrich!"
}
,
{
"id": "46938104",
"text": "This sort of post is useless without examples. What projects have you built? How did you go about it? What challenges did you face? What did you learn? Just saying “this is amazing now I am a super manager turning out projects left and right” is not convincing."
}
,
{
"id": "46934378",
"text": "I get the impression LLM agents are a bit like tamagochi but for tech bros."
}
,
{
"id": "46938533",
"text": "This reads like a peacocking LinkedIn post where someone desperately shows they are not just with it, they are ahead of it. The space is absolutely filled with this sort of noise, primarily people who dismissed AI as something only the nubs like, so now their cope is to do the \"now it's useful and I have catapulted ahead of all the others bit\"."
}
,
{
"id": "46932128",
"text": "Press [X] to doubt\n\nPress [Space] to skip"
}
,
{
"id": "46936188",
"text": "another slop post - show costs, show what you have built, or at least a tiny snippet of code? (or even just direct links to git repo or projects IN post please?)\n\ngetting sick of this fluff stuff"
}
,
{
"id": "46932182",
"text": "okay dumbo"
}
,
{
"id": "46936598",
"text": "Ads Pff.."
}
,
{
"id": "46932162",
"text": "this feels like the only thing you've probably done with open claw"
}
,
{
"id": "46935697",
"text": "been writing code for 15 years now , agree with the author about this one , open-claw like agents are going to be the future. Already automated away a bunch of routine stuff like checkin FB marketplace if l’m looking to but something , daily stock position brief , calendar management , grocery planning and buying , workout and calorie tracking . Stopped using a bunch of app directly overnight . The “mid-wits” are the one with their head still stuck under that sand"
}
,
{
"id": "46936242",
"text": "and the \"hype-wits\" don't realize openclaw is just claude with good mcp. there is nothing new under the sun. its just the first time someone was benevolent enough to open source the codebase to the public or it went viral enough to matter... and yet what people focus on is its \"emergence\" or \"agi\" - neither of which are remotely true. but good luck \"crushing\" those \"mid-wits\""
}
,
{
"id": "46937255",
"text": "Yes claude + scripts without any big corp restrictions / bloat , if i want to connect to a website or api i can just do it. If you expose it to me as a human it is fair game for my assistant to read data the same way i do. Its like the old days of internet . I build harnesses for a living these days , i see why enterprises are slow to even to see what is possible"
}
,
{
"id": "46934529",
"text": "Since many posts mention lack of substance, providing a link to the All-In Podcast from last week in which they discuss Clawdbot (prior to re-brand).\nhttps://www.youtube.com/watch?v=gXY1kx7zlkk&t=2754s\n\nFor the impatient, here's a transcript summary (from Gemini):\n\nThe speaker describes creating a \"virtual employee\" (dubbed a \"replicant\") running on a local server with unrestricted, authenticated access to a real productivity stack—including Gmail, Notion, Slack, and WhatsApp. Tasked with podcast production, the agent autonomously researched guests, \"vibe coded\" its own custom CRM to manage data, sent email invitations, and maintained a work log on a shared calendar. The experiment highlights the agent's ability to build its own internal tools to solve problems and interact with humans via email and LinkedIn without being detected as AI.\n\nHe ultimately concludes that for some roles, OpenClaw can do 90%+ of the work autonomously. Jason controversially mentions buying Macs to run Kimi 2.5 locally so they can save on costs. Others argue that hosting an open model on inference optimized hardware in the cloud is a better option, but doing so requires sharing potentially sensitive data."
}
,
{
"id": "46940138",
"text": "I mean... If Jason Calacanis told me the sky was blue, I would be _checking_.\n\n(At some point he seems to have gone from professionally-wrong-about-everything blogger to magical-podcast-thought-leader. I have no idea how this happened.)"
}
,
{
"id": "46934586",
"text": "There is a reason I stopped listening to All-In Podcast."
}
]
</comments_to_classify>
Based on the comments above, assign each to up to 3 relevant topics.
Return ONLY a JSON array with this exact structure (no other text):
[
{
"id": "comment_id_1",
"topics": [
1,
3,
5
]
}
,
{
"id": "comment_id_2",
"topics": [
2
]
}
,
{
"id": "comment_id_3",
"topics": [
0
]
}
,
...
]
Rules:
- Each comment can have 0 to 3 topics
- Use 1-based topic indices for matches
- Use index 0 if the comment does not fit well in any category
- Only assign topics that are genuinely relevant to the comment
Remember: Output ONLY the JSON array, no other text.
22