Containerization only helps with the second one, not the first, but that still matters. If you’re going to run random third-party MCP servers, isolating them from your host and any sensitive local data is still an obvious improvement over no isolation.

If capitalism requires constant vigilant government intervention to prevent monopolistic practices, anti-competitive markets, and asymmetric bargaining power, then it seems to me that this is absolutely a complaint about capitalism. If anything, your comment just makes the indictment more damning.

This happens because most people just paste a draft and say "make this better" with zero style direction. The model defaults to its own median register, and that register gets very recognizable after you've seen it a hundred times.

But this is a usage problem, not a fundamental one. I actually ran an experiment on this — fed Claude Code a massive export of my own Reddit comments, thousands of them across different subreddits, and had it build a style guide based on how I actually write and argue. The output was genuinely good. It sounded like me, not like Claude. The typical Claude-isms were just about gone.

I wouldn't expect most people to do that. But even a small prompt adjustment makes a real difference. Compare "improve this email" to something like:

    Your job is to proofread and edit the following email draft. 
    Don't make it longer, more formal, or more "polished" than it needs to be. 
    Fix anything that's actually wrong (grammar that changes meaning, tone misreads). 
    Leave stylistic roughness alone if it fits the voice. 
    If the draft is already fine, say so.

But if we're being honest, most people don't care about preserving their voice. They need to email their professor or write a letter to their bank, and they don't want to be misunderstood or feel stupid.

That, and hindsight bias. People know the second version came from an LLM, so it's automatically "flat." But if that edited comment had just been posted, nobody would've blinked. It reads fine.

IMO, there's a distinction worth drawing here: "AI edited" and "AI generated" are not the same thing. If you write something to express your own thinking, then use an LLM to tighten the phrasing or catch grammar issues, that's just editing. You're still the one with the ideas and the intent. The LLM is a tool, not an author.

The real failure mode is obvious enough: people who dump raw model prose into threads without critical review. The only one who "delved into things" was the model - not the human pressing send. That does flatten everything. But that’s a different case from a non-native speaker using a tool to express their own point more clearly.

The "preserve your voice" argument also smuggles in a premise I don't necessarily share - that everyone should care about preserving their voice. I'm neurodivergent. Being misunderstood when I know I've been clear is one of the most frustrating experiences there is. For some of us, being understood sometimes matters more than sounding like ourselves.

Not repeating the prompt got a mix of walk and drive answers.

I love how prompt engineering is basically techno-alchemy

1: https://arxiv.org/pdf/2512.14982

It’s a bit like arguing that a Porsche GT3 RS is an 'affordable' car because the monthly payment is low, provided you finance it over 30 years. The sticker price hasn't changed, you've just engaged in extreme creative accounting to make it fit a monthly budget.

If health insurance companies would be able to cover these drugs, there'd have to be negotiations between Eli Lilly and the insurance companies, and insurance companies have a bigger lever than individual patients who pay out of pocket. Self-payers are just price-takers. We pay whatever Eli Lilly wants us to pay.

I'd suspect if the effects on non-weight indications check out in studies, we might see drugs that could specifically target those effects without also slowing down your digestive tract. Addictions like nicotine and alcoholism and their consequences cost health insurance companies (and us as a society) billions of Euros/Dollars each year, so there'd be a strong incentive to pursue this.

The part of Mounjaro that regulates the craving side of the weight loss equation (like reducing 'food noise' and the desire for sugary and fatty foods) seems to also affect other behaviors due to Mounjaro's effect on the brain's reward circuitry. I believe there are also early preliminary studies that indicate it can help with addictions like alcoholism.

Those drugs really are quite something. Shame they're so damn expensive. Insurance here in Germany is unfortunately legally prohibited from covering GLP-1 class drugs for weight loss unless you have a diabetes diagnosis.

My point was specifically about the news filtering part, which was something I had tried in the past but never managed to solve to my satisfaction.

The agent's job in the end for a morning briefing would be:

  - grab weather, calendar, Todoist data using APIs or MCP  
  - grab news from select sources via RSS or similar, then filter relevant news based on my interests and things it has learned about me  
  - synthesize the information above

Yeah, absolutely. And that was going to be my approach for a personal AI assistant side project. No need to reinvent the wheel writing a Todoist integration when MCPs exist.

The difference is where it runs. ChatGPT Tasks and MCP through the Claude/OpenAI web interfaces run on their infrastructure, which means no access to your local network — your Home Assistant instance, your NAS, your printer. A self-hosted agent on a mac mini or your old laptop can talk to all of that.

But I think the big value-add here might be "disposable automation". You could set up a Home Assistant automation to check the weather and notify you when rain is coming because you're drying clothes on the clothesline outside. That's 5 minutes of config for something you might need once. Telling your AI assistant "hey, I've got laundry on the line. Let me know if rain's coming and remind me to grab the clothes before it gets dark" takes 10 seconds and you never think about it again. The agent has access to weather forecasts, maybe even your smart home weather station in Home Assistant, and it can create a sub-agent, which polls those once every x minutes and pings your phone when it needs to.

An embedding-based memory system (letta, mem0, or a self-built PostgreSQL + pgvector setup) lets you retrieve selectively and only grab what's relevant to the current query. Much better fit for anything beyond a narrow use case. Your assistant doesn't need to know your location and address when you're asking it to look up whether sharks are indeed older than trees, but it probably should know where you live when you ask it about the weather, or good Thai restaurants near you.

What I've always wanted: a morning briefing that pulls in my calendar (CalDAV), open Todoist items, weather, and relevant news. The first three are trivial API work. The news part is where it gets interesting and more difficult - RSS feeds and news APIs are firehoses. But an LLM that knows your interests could actually filter effectively. E.g., I want tech news but don't care about Android (iPhone user) or MacOS (Linux user). That kind of nuanced filtering is hard to express as traditional rules but trivial for an LLM.

We've seen some absolutely glaring security issues with vibe-coded apps / websites that did use Claude (most recently Moltbook).

No matter whether you're vibe coding with frontier models or local ones, you simply cannot rely on the model knowing what it is doing. Frankly, if you rely on the model's alignment training for writing secure authentication flows, you are doing it wrong. Claude Opus or Qwen3 Coder Next isn't responsible if you ship insecure code - you are.

Another value add is that theoretically agents should trigger skills automatically based on context and their current task. In practice, at least in my experience, that is not happening reliably.

> Since 31 August 2013 companies which operate public petrol stations or have the power to set their prices are required to report price changes for the most commonly used types of fuel, i.e. Super E5, Super E10 and Diesel “in real time” to the Market Transparency Unit for Fuels. This then passes on the incoming price data to consumer information service providers, which in turn pass it on to the consumer.

As a consumer, there is no direct API by the MTS-K that you can use, but there are some services like Tankerkoenig which pass this data on to you. I have used their API in Home Assistant before I switched to an EV.

https://www.bundeskartellamt.de/EN/Tasks/markettransparencyu...

When I investigated the issue, I found a bunch of hardcoded developer paths and a handful of other issues and decided I'm good, actually.

    sre@cypress:~$ grep -r "/Users/steipete" ~/.nvm/versions/node/v24.13.0/lib/node_modules/openclaw/ | wc -l
    144

    sre@cypress:~$ grep -Fr "workspace:*" ~/.nvm/versions/node/v24.13.0/lib/node_modules/openclaw/ | wc -l
    41

I really don't understand how anyone just hands this vibe coded mess API keys and access to personal files and accounts.

But when I use Claude code, I also supervise it somewhat closely. I don't let it go wild, and if it starts to make changes to existing tests it better have a damn good reason or it gets the hose again.

The failure mode here is letting the AI manage both the implementation and the testing. May as well ask high schoolers to grade their own exams. Everyone got an A+, how surprising!

The petition explicitly highlights maintainer burnout and the "unausgewogene Verantwortungslast" (unbalanced responsibility burden) as core problems. Excluding project owners/maintainers from recognition would exclude precisely the people carrying the heaviest load – the ones triaging issues at 2am, reviewing PRs, making architectural decisions, and bearing the psychological weight of knowing critical infrastructure depends on their continued engagement.

The XZ Utils incident is instructive here: the attack vector was specifically a burned-out solo maintainer who was socially engineered because he was overwhelmed and desperate for help. If anything, recognition and support structures should prioritize these individuals, not exclude them. Your concern about "pet projects with no impact" is valid, but the solution isn't to exclude owners categorically – it's to define impact criteria. A threshold based on adoption metrics, dependency chains, or inclusion in public infrastructure would filter out portfolio projects without penalizing the people doing the most critical work.

Point c) also seems problematic for similar reasons: much of maintainer work isn't "merged contributions" – it's code review, issue triage, documentation, community management, security response. Under your criteria, the person who reviews and merges 500 PRs per year while writing none themselves would receive no recognition.

The petition is trying to address a structural problem where society extracts massive value from unpaid labor while providing no support structures. Excluding the most burdened participants seems like it would perpetuate rather than solve that problem.

Bariatric surgery shows 25-65% significant regain rates depending on definition and timeframe. And regular dieting is even worse. Nobody would frame that as a safety issue. That's... just how weight loss works, not a unique GLP-1 problem.

Calling a return of symptoms (obesity) a "safety issue" is like saying insulin has "no safe off-ramp" because diabetics get hyperglycemic when they stop taking it.

Fear gets clicks, I guess.