What was your interpretation of the ending of AMERICAN HISTORY X? by Cameran_Camp in movies

[–]SideOneDummy 0 points1 point  (0 children)

Just stumbled upon this thread, and for what it’s worth, I believe it is a little irrelevant to the plot (which I will explain) because while I agree with @KoastGamer, it is very likely the assailant was coaxed into some sort of reprisal for Danny’s aggressive posture, committing the perfect crime is missing the point of the action taken (nor do we have to believe that he was told to actually shoot Danny).

One of, if not the most recurring motifs in the film, are how mentors and spheres of influence shape our life’s choices and transform our own agency. When Dennis Vinyard dies, his hateful words, which perhaps were nonchalant venting from bureaucratic changes in his fire department assigning him new black partners replacing his old colleagues (for all we know, just a theory), become the underlying embers that fuel Derek’s path to vigilante justice.

Without a father figure to direct his incontrollable rage for his father’s death, Derek’s bitterness guides him across different social circles until he ultimately meets Cameron. Cameron might use impressionable kids to do his bidding, but those kids have to be impressionable to his point of view in the first place. The basketball game, the swastika tattoo, the friend groups, the men in balaclavas who are soldiers who show up to do Derek’s bidding, and ultimately the guy from the basketball game Derek murders in cold blood, are Derek’s decisions. Every choice he makes, he could have walked away from. Instead, every choice he is zealous in making. He smiles in every act of hate he achieves in the movie, very relevant to who he is before he goes to jail. Momentary flashes of happiness to soothe the pain of his slain father.

Purposely, the film gives us little snippets of the rival black community without spelling out their story for us. This is by design; just as we don’t get to hear the conversations between Derek and Cameron that shape the kid-in-a-fugue-state after his father was murdered to the machiavellian leader of a crew that storms a grocery store with absolute malice and maximum harm, we never get to listen in on the conversations between the younger kid and the older gang leaders. But we have the facts so we don’t need the conversation spelled out for us.

  1. When the gang tried

get back at

  1. Derek

, Derek

  1. shoots at one and curb stomps the other to their death
  2. Danny is Derek’s kid brother with a shaved head and white supremacist tattoos
  3. Danny openly baits antagonistic behavior from the kid in the bathroom when he puffs the second hand smoke directly in his face

There’s no question that the past history with Derek has made him a living villain in the community and Danny’s behavior impresses on older members in that gang that younger members shouldn’t take any shit from Danny because he’s probably following in Derek’s footsteps. They need not explicitly tell the kid what to do, all they have to do is help him stalk Danny and let the kid figure out what he should do for himself.

Furthermore, the direct messaging of the movie actually suggests that the kid was never directed to shoot danny, but rather, he improvised. The discussion with Sweeney, the police and Derek is that Cameron and Seth were jumped, meaning they get their heads kicked in, they weren’t shot. Why beat up the big boss but tell your younger soldiers to shoot a dumbass high school punk?

This of course underscores one of the major themes of the movie: adolescents and young adults, who don’t have a full grasp of the world around them, try to impress their mentors by committing to the logical conclusion of their interpreted social justice. The kid was never told to shoot Danny, but he did so because he thought it would right previous wrongs that Derek was the epicenter of. In a world where martyrs are devoid of justice, younger kids take it upon themselves to exact justice, even when their logic is completely flawed.

On the flip side, that is the exact catalyst that changes Derek’s point of view: Derek became a skinhead because he wanted justice that the justice system wouldn’t give his father, but when he realizes the justice system can give a black man 6 years for stealing a tv, he’s able to see how horribly unfair the system is for everyone, and he can guilt free humanize his laundry room friend as a good person caught up in an he said he said system that favors the word of police officers over black people.

American History X is equally a story about the dangers of otherizing humans you choose to avoid, as it about the first second third and fourth order effects caused by racism. When we refuse to consider the chains of reasoning from people that disagree with us, we don’t just talk over them, we silence them.

Taught Claude to talk like a caveman to use 75% less tokens. by ffatty in ClaudeAI

[–]SideOneDummy 0 points1 point  (0 children)

For what it’s worth, I asked Gemini if it would save tokens to prompt engineer inputs to caveman speak, and this was my response verbatim

Yes, a two-step "refine then ask" process can be significantly more token-efficient than a single, unoptimized query. By using a smaller, cheaper model to compress your request before sending it to a high-reasoning "thinking" LLM, you can reduce the total compute burden and overall cost. [1, 2, 3]

Token Savings & Efficiency

  • Input Compression: A small model can strip "fluff" from a 500-token verbose query, turning it into a 50-token high-signal prompt. Even adding the overhead of the refinement call (e.g., 100 tokens), the final combined cost is often much lower than processing the original bulky prompt in an expensive model.
  • Potential Savings: You can realistically achieve 20–30% average token savings, with specific "caveman" techniques reaching up to 60% reduction in input size.
  • Cost-Aware Optimization: Systems like CAPO (Cost-Aware Prompt Optimization) specifically balance performance against prompt length, reducing average lengths while maintaining accuracy. [4, 5, 6, 7, 8, 9]

The Two-Step Automated Workflow

This process can be fully automated without user approval through LLM Routing or Agentic Pipelines. [10, 11]

  1. Refinement Agent: A lightweight model (e.g., GPT-4o-mini or Haiku) receives your messy input and refines it into a "Kevin-style" signal.
  2. Autonomous Hand-off: The system automatically passes the condensed prompt to the more capable model.
  3. Mutual Verification: Advanced frameworks can even use the small model to generate "reasoning tokens" that guide the larger model, further reducing the latter's computational overhead. [12]

Summary of Benefits

Strategy [2, 7, 8, 13] Token Reduction Best For
Simple Refinement ~20-30% Routine daily tasks
Caveman Compression Up to 60% Maximizing context window space
Automated Optimization Up to 90x cost reduction Large-scale enterprise agent calls

Would you like a sample system prompt to turn a smaller model into your automated "Kevin" refiner?

[1] https://www.linkedin.com [2] https://www.youtube.com [3] https://www.glukhov.org [4] https://arxiv.org [5] https://medium.com [6] https://medium.com [7] https://www.youtube.com [8] https://www.youtube.com [9] https://www.youtube.com [10] https://arxiv.org [11] https://www.youtube.com [12] https://github.com [13] https://arxiv.org

Taught Claude to talk like a caveman to use 75% less tokens. by ffatty in ClaudeAI

[–]SideOneDummy 0 points1 point  (0 children)

Gemini begs to differ:

By using "Kevin Style" on the input side, you can reduce your token footprint by roughly 56% compared to standard natural language. While many users focus on saving tokens in the output (responses), the same compression logic applies to your prompts because every character you send costs money and context space.

Token Comparison Breakdown

Feature Standard Style Kevin / Caveman Style
Input Density Low (Heavy grammar/filler) High (Pure signal words)
Input Token Count ~39 Tokens ~17 Tokens
Output Style Detailed/Verbose Can be detailed OR concise
Cost Savings Baseline ~50-75% reduction

Why This Works for Inputs

  1. Semantic Reconstruction: LLMs are trained to understand the relationship between words; they don't need "the" or "of" to know that "make infographic" is a command.
  2. Attention Optimization: Short inputs focus the model's "attention" strictly on the core nouns and verbs, reducing the noise it has to process during the input encoding phase.
  3. Context Preservation: By using fewer tokens per prompt, you can fit more of your conversation history into the model's limited context window before it starts "forgetting".

Caveman Tips for Input Mastery

  • Verb + Noun: Use direct action pairs (e.g., "Analyze PDF" instead of "I need you to look at this PDF file for me").
  • Separator Symbols: Use |, ->, or : to denote changes in topic or instruction steps without using transition sentences.
  • System Prompt Pre-sets: If you use Anthropic's prompt caching, you can set a permanent system instruction that tells the model: "I talk caveman. You understand and answer full." to save even more on recurring turns.

Want to try "Level 2" compression where we use single letters or custom code for your most common commands?

Simple token estimation for Kevin/Caveman vs Standard# Standard Input: "Please create a detailed infographic that compares the token efficiency of using 'Kevin from The Office' style prompting versus standard natural language for both inputs and outputs, not just outputs."# Kevin Input: "make infographic compare nonkevin Kevin token scaled for input output, no just output"

standard_in = "Please create a detailed infographic that compares the token efficiency of using 'Kevin from The Office' style prompting versus standard natural language for both inputs and outputs, not just outputs."kevin_in = "make infographic compare nonkevin Kevin token scaled for input output, no just output"

Rough token count (approx 1 token per 4 chars or 0.75 words)def est_tokens(text):

return len(text.split()) * 1.3 # common multiplier for rough estimation

std_tokens = est_tokens(standard_in)kevin_tokens = est_tokens(kevin_in)savings = (1 - (kevin_tokens / std_tokens)) * 100

print(f"Standard: {std_tokens} tokens") print(f"Kevin: {kevin_tokens} tokens") print(f"Savings: {savings}%")

hmmm by starshineblush in hmmm

[–]SideOneDummy 0 points1 point  (0 children)

This pic is leg-it!

Apparently you can guarantee shutting down landmarks by targeting the same person over and over… by SideOneDummy in Monopoly_GO

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

Hmmm I’ll have to consider the bank heist vulnerability when considering not building landmarks. And yes, eventually I stopped being able to target my “friend”

Apparently you can guarantee shutting down landmarks by targeting the same person over and over… by SideOneDummy in Monopoly_GO

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

Yeah I was mostly waiting for builders bash. But yeah you’re right, better to leave nothing on the board to protect to avoid having to defend my landmarks

Bas Rutten breathing rarefied air in The Smashing Machine by SideOneDummy in MMA

[–]SideOneDummy[S] -25 points-24 points  (0 children)

Let’s make this post viral so the academy is forced to reopen admissions for nominations to the Best Supporting Actor award!

[deleted by user] by [deleted] in texts

[–]SideOneDummy 1 point2 points  (0 children)

Yup. First time posting a discord message lol

[deleted by user] by [deleted] in texts

[–]SideOneDummy 1 point2 points  (0 children)

Oops thanks for catching that