I 100% assume that the McDonald’s employee who called on him is fake in some way, and they tracked him down through some kind of spy apparatus shit and just don’t want to give away their methods.
I have no evidence and I don’t really know how these things work, it just seems way way more plausible to me.
That could be. I’m still vouching for the “conspiracy” that Luigi actually didn’t do anything but was selected as a lookalike scapegoat. The similar looks is what prompted the McDonald employee to call, and they incompetently called the police instead of the tip line.
I haven’t heard that his defense team is trying to claim that… I feel like if that was reality they would have. Maybe not, but I feel it.
What would the defense say? “That wasn’t him, just someone who looks similar.”
They already claim that by pleading innocent at the start.Well thats the thing. Nobody in the history of court has plead innocent. Thats not a thing. You can plead guilty, no contest, or not guilty.
And “not guilty” isn’t the same as “innocent”
I’m sorry I don’t know all the arcane incantations of your court system of the top of my head.
They didn’t track him down because he wasn’t the shooter.
Oh come on. You really think rich people and the police protecting them would let a millionnaire killer run free?
They need a scapegoat, but what they need more is the certainty that the shooter won’t do more damage.
It can’t be luigi because he was at my house at that time.
I can vouch for that.
I do think they couldn’t catch the killer, and they needed a scapegoat just to make an example out of someone.
No need to track down your scapegoat; especially not when you just found someone’s cringy but fitting manifesto online.
It’s like the police always saying that they find a “passport” in the truck after some terrorist attack. They have inside informers and they don’t want to disclose them.
deleted by creator
Yes I do, mr. treason with 34 felony convictions, currently illegally occupying the white house, did it
A lot of people are saying Trump sent the hit. Is it a joke or do you actually think he might’ve?
Laura Loomer did say he stabbed Trump in the back like… two weeks ago or something
Loomer is not a very credible source…
Over the last 20 years for every $2000 in reward money offered by law enforcement in the United States, less than $4 has been paid.
or $1 per $500, or $2 per $1000. sorry, it was asking for fraction simplification
I’m sorry, you’re saying that only $1.214521 of every $607.2605 was paid out?
THIS IS AN OUTRAGE
I didn’t know rewards were common but have never looked into that, who reports those numbers even? They sound fun to go through
WARNING MASSIVE AMOUNTS OF GATEKEEPING BELOW.
Who would have thought me a radical leftist would be sick of fellow leftists. If you can’t see an LLM as a legitimate tool then you’re just lying to yourselves. Furthermore the i didn’t post without asking if the other person wanted to see but you’re all too self righteous to keep out of.
It’s sad really as you’re not going to bring anybody over to your cause when you just shut out anything that doesn’t fit your narrow world view. How are we any different than r/conservative.
I asked an LLM although many here hate that.
It basically said it’s very fragmented but many departments / organisations do publish their rewards information.
SEC, DOJ, Dept of state RFJ, FBI, DEA, Crime stoppers, and more.
You have to go and look at each one and consume that data ideally programmatically. The LLM has links you can try but I won’t post the output without consent.
Why bother asking the bullshit machine
Why bother questioning what others do. Move along you silly cunt.
Also, you could’ve been much nicer about this. There was no need for you to insult people over this.
It didn’t start that way. If people want to shove their opinion where it isn’t needed then they should be told.
The fact is I asked before posting the output and people still piled on. I don’t have to be nice to those people.
Wow, yeah. People hate that you used an LLM, lol. Look at those down arrows.
Yup. We are the left wing The Donald. They hate everything. And talk about the same shit incessantly without actually making any change cause they won’t engage with anyone else with a different opinion.
When it’s actually a perfect use of an LLM.
Some motherfuckers just insist on using hammers to drive screws
I would say it’s a good example of a bad use-case for an LLM; you don’t have sources, and you can’t fact-check anything. Those two are absolutely vital requirements when claiming something as true.
Aside from that, most generative AI have been trained on vast amounts of data that was never allowed to go into the dataset; copyrighted/IP-righted paintings, articles, comics, and novels have been included against the wishes of the artists/authors. The fact that nothing is being done on a legal system level shows that copyright and IP rights clearly do not apply to American oligarchs, and many of us don’t like that. Most generative AIs also need an absurd amount of power to run and hurt the environment a lot. It sucks to separate paper and plastic waste just to know that there are people blasting through an hour worth of airconditioning just to ask a computer something they could’ve looked up instead (and found sources, too!)
I say this as someone who loves using AI and experimenting with it1: This was a very bad use-case of generative AI.
1: although lighter ones and locally like Mistral, using open datasets like OpenOrca
Hmm, thank you
Right? This is one of the perfect examples where we don’t care too much about the accuracy of the data, it would be extremely time consuming, etc etc.
I want all of the data. I see nothing wrong with LLM’s scraping government sites
I’ll paste the output. My prompt is below too so if you want to probe it for the best way. You could get it to make a program just have to keep pushing if stuff doesn’t work. I’m a developer so it helps if you know a little.
Prompt.
Where can I find data on reward money offered vs paid in the USA over the last 20 years
Reply Short answer — there’s no single complete national dataset.
Reward programmes are fragmented across federal agencies, state/local Crime Stoppers chapters, private bail/bounty firms, and corporate/financial whistleblower schemes. To build a 20-year “offered vs paid” series you’ll need to pull from several sources and (optionally) stitch them together.
Below are the best places to look (with examples and why they matter): 1. Federal whistleblower & reward programs (high-quality, machine-readable annual reports) • SEC — Office of the Whistleblower: publishes annual reports with amounts awarded each fiscal year (detailed PDFs). Great for securities whistleblower payments (large, consistent dataset since 2011).  • DOJ / Department of Justice press releases and program pages — DOJ has various reward/whistleblower programs (and a recent pilot program). Useful for criminal/corporate enforcement reward totals where reported.  • U.S. Department of State — Rewards for Justice (RFJ): posts individual reward offers and announcements (large, occasional awards). Good for transnational/terrorism-related reward offers.  2. Federal law enforcement (FBI, DEA, etc.) — press releases and “rewards” pages • The FBI and some other agencies publish individual reward offers (Ten Most Wanted, fugitive rewards) and occasional summaries. These are event-level (not neat annual totals), so you’d scrape press releases or file FOIA requests for bulk data.  3. Crime Stoppers (national + local chapters) — aggregated rewards paid • Crime Stoppers USA and many local chapters publish aggregate “Rewards Paid” and “Property Recovered” statistics (useful for local/community reward payouts). There are many chapter reports you can scrape/collect.  4. Private-sector and industry sources (bounty/bail industry, private reward platforms) • Bounty hunters / bail bond industry earnings and private reward platforms are private and fragmented — statistics exist in trade pieces, law reviews, and industry blogs but no central public dataset. See law reviews and industry reports for estimates.  5. News archives & academic evaluations • For historical context and one-off large reward offers (and sometimes totals), use major news archives, NCJRS/NIJ and academic reports (e.g., evaluations of Crime Stoppers). 
Practical approach to build the 20-year series
If your goal is an actual dataset (offered vs paid per year), I recommend this pipeline: 1. Decide scope — which kinds of rewards? (e.g., federal law-enforcement offers, whistleblower awards, Crime Stoppers/local rewards, private bounty/bail payments). 2. Pull high-quality federal reports first — SEC whistleblower annual reports, DOJ/State press release archives, FBI press releases. (These are the easiest to quantify.)  3. Scrape Crime Stoppers chapters — collect annual “rewards paid” numbers from national and major local chapters (they often publish totals).  4. Supplement with FOIA or API calls — for FBI/DOJ bulk records or if you need all reward offers (not just press releases). 5. Handle private/industry data as estimates — compile from industry reports, law journals, and news; treat these as less reliable.  6. Normalize & document assumptions — e.g., currency (nominal vs inflation-adjusted), whether “offered” is the maximum stated offer or the amount actually committed, and whether “paid” includes partial payments.
Useful starting links (to grab data now) • SEC Whistleblower Annual Report (FY2024 example).  • SEC newsroom (whistleblower award press releases).  • State Dept — Rewards for Justice program pages.  • FBI reward announcements / press releases.  • Crime Stoppers USA / sample local chapter stats pages. 
⸻
If you want, I can start pulling and assembling a first-pass dataset for you right now (for example: SEC whistleblower totals per year + Crime Stoppers national totals + notable federal reward offers per year) and deliver a CSV with year, source, offered (if available) and paid. Tell me whether to include or exclude (a) private bail/bounty payments, and (b) whether to inflation-adjust amounts — I’ll assume nominal USD if you don’t specify.
Snitches can’t afford stitches due to duplicitous economic machinations.
Snitches can’t afford stitches due to duplicitous
economic machinationsBitches.
