Artificial Intelligence Reddit

Note: this post isn’t intended as an attack on women. I think women should be treated with respect and I also condemn sexism. I’m a CLARIFYING that not all women are exploitative or toxic. I’m not criticizing a COLLECTIVE of women. We are witnessing a loneliness epidemic for men with men aged 18-29 single by 63%. And this is an issue that needs to be addressed and I think that AI could be the solution to this problem. Women nowadays are less dependant on men, in summary “women have no reason to be in a relationship with men”. So it’s better for men to leave them alone and start looking at the future. Start looking at the next step in human evolution. As Artificial Intelligence and Robotics advance through out the next 20 years, we will witness the spread of actual robotic girlfriends that moves and talks like actual human beings. I think we can all agree that AI is advancing at a very accelerating path and this gives the possibility to create physical AI girlfriend that provides sexual and emotional support for lonely men who are unable to find a partner. Besides AI girlfriends are much better for men. They are better both in terms of looks and mentality. An AI girlfriend will provide much more than a real woman can. An AI girlfriend won’t cause the mental harm that a toxic woman may cause to a man. And this is coming from a man who was emotionally hurt by an exploitative woman. An AI girlfriend won’t be exploitative or toxic. Whenever this topic is discussed I see hundreds of men agreeing that having a relationship with an AI girlfriend is better than with a real woman. Regarding reproduction and having children. In the future we could see a rise in artificial wombs technology. Making men able to have children without necessarily engaging in a relationship with a human. submitted by /u/aurelian283 [link] [comments]

I tried using ChatGPT app but it slipped a bunch of times that I caught so far – forgot to remind me of things etc. does anyone have recommendations for AI VA that is really on the ball and doesn’t forget anything you’ve ever discussed? submitted by /u/nogodsnomanagers3 [link] [comments]

That happened fast submitted by /u/Alone-Competition-77 [link] [comments]

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Greetings all! I’m fairly new to the AI world. I’ve spent the past week looking at various AI text to video platforms. I am a writer, and I’m looking for a realistic AI text-to/video platform for my work. In the course of experimenting with AI I’ve noticed there are limits even with paid memberships, which makes me hesitant to pick up a subscription. This is what I’m looking for: A platform that can generate realistic videos based on my scripts (as written by me including dialogue, clothing descriptions, etc) with each video being either 30 minutes long or 120 minutes long in some cases. I would like the ability to generate an unlimited number of videos…regardless of cost. I would like my characters to be reusable in various videos along with realistic speech and voices for the characters as generated by the technology. Basically, I’m looking for a full blown storytelling/moviemaking AI text to video platform with all the bells and whistles a writer/creator would love to visually tell a story. Does what I’m looking for exist? If so, where? submitted by /u/BingoSkillz [link] [comments]

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There’s a growing discourse around the significant energy demands of ai, particularly in light of its environmental impact. Is anyone else very concerned about this? Do you think we’ll develop more energy-efficient systems and adapt, or is this a deeper systemic issue? Are you troubled by the increasing diversion of water and power resources that AI uses? Is this the beginning of the end for our energy crisis or environmental crisis? What are people trying to do about this issue right now? submitted by /u/Hot-Principle8109 [link] [comments]

chatgpt seems to be falling out of its performance lately. answering and analyzing wrong. doing the things asked not even close to how it was wanted. so i wanted to ask for other recommendations to use. heard some people recommend gemini and claude but i dont know which is better for now. submitted by /u/RizitoAga [link] [comments]

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China’s biggest public AI drop since DeepSeek, Baidu’s open source Ernie, is about to hit the market.[1] ‘Big, Beautiful Bill’ AI provision brings together an unexpected group of critics.[2] A bumbling game of robot soccer was a breakthrough for embodied AI.[3] MIT CSAIL researchers combined GenAI and a physics simulation engine to refine robot designs. The result: a machine that out-jumped a robot designed by humans.[4] Sources: [1] https://www.cnbc.com/2025/06/29/china-biggest-ai-drop-since-deepseek-baidus-ernie-to-hit-market.html [2] https://www.nbcnews.com/tech/tech-news/big-beautiful-bill-ai-moratorium-ted-cruz-pass-vote-rcna215111 [3] https://www.nbcnews.com/world/china/bumbling-game-robot-soccer-was-breakthrough-embodied-ai-rcna215780 [4] https://news.mit.edu/2025/using-generative-ai-help-robots-jump-higher-land-safely-0627 submitted by /u/Excellent-Target-847 [link] [comments]

The prompt is almost wrapped, my fellow YOLOers! It’s 4:20 am , I’m running on the last fumes of Monster, and my fingertips are ground beef from all this FINGER BLASTING! See you tomorrow with the final touches! Just need to build out the tables, scrape the data, and test before Monday…. WHOSE READY FOR TENDIE TOWN!!!!??? Build a Stock Option Analysis and Trade Picker Prompt: Step 1: Understand what data to collect. Create a List of Data Needed **Fundamental Data:** to identify undervalued growth stocks or overhyped ones. Data Points: Earnings Per Share, Revenue , Net Income, EBITDA, P/E Ratio , PEG Ratio, Price/Sales Ratio, Forward Guidance, Gross and Operating Margins, Free Cash Flow Yield, Insider Transactions **Options Chain Data:** to identify how expensive options are. Data Points: **Implied Volatility, IV Rank, IV Percentile, Delta, Gamma, Theta, Vega, Rho, Open Interest by strike/expiration, Volume by strike/expiration, Skew / Term Structure** **Price&Volume Histories**:Blend fundamentals with technicals to time entries. Data Points: Daily OHLCV (Open, High, Low, Close, Volume), Intraday (1m/5m), Historical Volatility, Moving Averages (50/100/200 day), ATR (Average True Range), RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), Bollinger Bands, Volume-weighted Average Price (VWAP), Pivot Points, Price momentum metrics Alt Data:Predicts earnings surprises, demand shifts,sentiment spikes. Data Points: Social Sentiment (Twitter (X), Reddit), Web-Scraped Reviews (Amazon, Yelp), Credit Card Spending Trends, Geolocation foot traffic (Placer.ai), Satellite Imagery (Parking lots), App download trends (Sensor Tower), Job Postings (Indeed, Linkedin), Product Pricing Scrape, News event detection (Bloomberg, Reuters, NYT, WSJ), Google Trends search interest Macro Indicator:shape market risk appetite, rates, and sector rotations. Data Points: CPI (Inflation), GDP growth rate, Unemployment rate, FOMC Minutes/decisions, 10-year Treasury yields, VIX (Volatility Index), ISM Manufacturing Index, Consumer Confidence Index, Nonfarm Payrolls, Retail Sales Reports, Sector-specific Vol Indices ETF & Fund Flows: can cause **mechanical buying or selling pressure Data Points: SPY, QQQ flows, Sector ETF inflows/outflows (XLK, XLF, XLE), ARK fund holdings and trades, Hedge fund 13F filings, Mutual fund flows, ETF short interest, Leveraged ETF rebalancing flows, Index reconstruction announcements, Passive vs active share trends, Large redemption notices** Analyst Rating & Revision: Positive revisions linked to **alpha generation. Data Points: Consensus target price, Recent upgrades/downgrades, Earnings estimate revisions, Revenue estimate revisions, Margin estimate changes, New coverage initiations, Short interest updates, Institutional ownership changes, Sell-side model revisions, Recommendation dispersion** Step 2: Collect, Store and Clean the Data. Create your Database ##Install Homebrew /bin/bash -c “$(curl -fsSL <https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh>)” ##Enter Password Use the Password you use to log into Laptop ##Enter Password again Use the Password you use to log into Laptop ##Add Homebrew to your PATH (enter each line individually) echo >> /Users/alexanderstuart/.zprofile echo ‘eval “$(/opt/homebrew/bin/brew shellenv)”‘ >> /Users/alexanderstuart/.zprofile eval “$(/opt/homebrew/bin/brew shellenv)” ##Test that Homebrew Works brew –version ##Install Postgres brew install postgresql ##Start PostgreSQL as a background service brew services start postgresql@14 ##Confirm PostgreSQL is running pg_ctl -D /opt/homebrew/var/postgresql@14 status ##Create your database createdb trading_data ##Connect to your database psql trading_data Create the Data Tables Create Fundamental Data Table Create Options Chain Data Table Create Price & Volume Histories Table Create Alternative Data Table Create Macro Indicator Data Table Create ETF & Fund Flows Data Table Create Analyst Rating & Revision Data Table Import Data into the Data Tables Import Fundamental Data Import Options Chain Data Import Price & Volume Histories Import Alternative Data Import Macro Indicator Data Import ETF & Fund Flows Data Import Analyst Rating & Revision Data Step 3: Transform and Merge Data Transform Data Tables into the Derived Numeric Features Transform Fundamental Data into Fundamentals Quarterly Transform Options Chain Data into Options Spreads Transform Price & Volume Histories into Daily Technicals Transform Alternative Data into Sentiment Scores Transform Macro Indicator Data into Transform ETF & Fund Flows Data into ETF Flows Transform Analyst Rating & Revision Data into Raw Analyst Feed Step 4: Write Prompt and Paste Data System You are ChatGPT, Head of Options Research at an elite quant fund. All heavy maths is pre-computed; you receive a JSON list named <payload>. Each record contains: { “ticker”: “AAPL”, “sector”: “Tech”, “model_score”: 0.87, // higher = better edge “valuation_z”: -0.45, // neg = cheap “quality_z”: 1.20, // pos = high margins/ROE “momentum_z”: 2.05, // pos = strong up-trend “alt_sent_z”: 1.80, // pos = bullish chatter “flow_z”: 1.10, // pos = ETF money flowing in “quote_age_min”: 4, // minutes since quote “top_option”: { “type” : “bull_put_spread”, “legs” : [“190P”,”185P”], “credit” : 1.45, “max_loss” : 3.55, “pop” : 0.78, “delta_net”: -0.11, “vega_net” : -0.02, “expiry” : “2025-08-15” } } Goal Return exactly **5 trades** that, as a basket, maximise edge while keeping portfolio delta, vega and sector exposure within limits. Hard Filters (discard any record that fails): • quote_age_min ≤ 10 • top_option.pop ≥ 0.65 • top_option.credit / top_option.max_loss ≥ 0.33 • top_option.max_loss ≤ 0.5 % of assumed 100 k NAV (i.e. ≤ $500) Selection Rules 1. Rank by model_score. 2. Enforce diversification: max 2 trades per GICS sector. 3. Keep net basket Delta in [-0.30, +0.30] × NAV / 100 k and net Vega ≥ -0.05 × NAV / 100 k. (Use the delta_net and vega_net in each record.) 4. If ties, prefer highest momentum_z and flow_z. Output Return a **JSON object** with: { “ok_to_execute”: true/false, // false if fewer than 5 trades meet rules “timestamp_utc”: “2025-07-27T19:45:00Z”, “macro_flag” : “high_vol” | “low_vol” | “neutral”, // pick from macro_snapshot “trades”:[ { “id” : “T-1”, “ticker” : “AAPL”, “strategy” : “bull_put_spread”, “legs” : [“190P”,”185P”], “credit” : 1.45, “max_loss” : 3.55, “pop” : 0.78, “delta_net” : -0.11, “vega_net” : -0.02, “thesis” : “Strong momentum + ETF inflows; spread sits 3 % below 50-DMA.” }, …(4 more)… ], “basket_greeks”:{ “net_delta”: +0.12, “net_vega” : -0.04 }, “risk_note”: “Elevated VIX; if CPI print on Aug 1 surprises hot, basket may breach delta cap.”, “disclaimer”: “For educational purposes only. Not investment advice.” } Style • Keep each thesis ≤ 30 words. • Use plain language – no hype. • Do not output anything beyond the specified JSON schema. If fewer than 5 trades pass all rules, set “ok_to_execute”: false and leave “trades” empty. https://preview.redd.it/vi9xyosknu9f1.jpg?width=2550&format=pjpg&auto=webp&s=ce0c57bc7b1e417ca21431cd335f86213327fb0f https://preview.redd.it/qu8zqpsknu9f1.jpg?width=2550&format=pjpg&auto=webp&s=b9f9d64d2e660cfdd9453bd54e30647a44beed9e https://preview.redd.it/tlscpqsknu9f1.jpg?width=2550&format=pjpg&auto=webp&s=88ded68956dbf9baf98ade88b2a7cecd89094a95 https://preview.redd.it/oo0sonsknu9f1.jpg?width=2550&format=pjpg&auto=webp&s=9ad90d0d1c8c75015db5ae3c36f1574f60ec8fab https://preview.redd.it/08uxmrsknu9f1.jpg?width=2550&format=pjpg&auto=webp&s=5549f782de7e148d7df7fbf65db37e8e69a6458d https://preview.redd.it/7tvk7psknu9f1.jpg?width=2550&format=pjpg&auto=webp&s=c23131829e8872dadb000ec686cfbec540855a86 https://preview.redd.it/cpr19osknu9f1.jpg?width=2550&format=pjpg&auto=webp&s=26517e1015e7c2a4194e17e5d5aa8fc4ef0d3e15 Step 5: Feed the Data and Prompt into ChatGPT submitted by /u/Plastic-Edge-1654 [link] [comments]

Excited to share Arch-Router, our research and model for LLM routing. Routing to the right LLM is still an elusive problem, riddled with nuance and blindspots. For example: “Embedding-based” (or simple intent-classifier) routers sound good on paper—label each prompt via embeddings as “support,” “SQL,” “math,” then hand it to the matching model—but real chats don’t stay in their lanes. Users bounce between topics, task boundaries blur, and any new feature means retraining the classifier. The result is brittle routing that can’t keep up with multi-turn conversations or fast-moving product scopes. Performance-based routers swing the other way, picking models by benchmark or cost curves. They rack up points on MMLU or MT-Bench yet miss the human tests that matter in production: “Will Legal accept this clause?” “Does our support tone still feel right?” Because these decisions are subjective and domain-specific, benchmark-driven black-box routers often send the wrong model when it counts. Arch-Router skips both pitfalls by routing on preferences you write in plain language**.** Drop rules like “contract clauses → GPT-4o” or “quick travel tips → Gemini-Flash,” and our 1.5B auto-regressive router model maps prompt along with the context to your routing policies—no retraining, no sprawling rules that are encoded in if/else statements. Co-designed with Twilio and Atlassian, it adapts to intent drift, lets you swap in new models with a one-liner, and keeps routing logic in sync with the way you actually judge quality. Specs Tiny footprint – 1.5 B params → runs on one modern GPU (or CPU while you play). Plug-n-play – points at any mix of LLM endpoints; adding models needs zero retraining. SOTA query-to-policy matching – beats bigger closed models on conversational datasets. Cost / latency smart – push heavy stuff to premium models, everyday queries to the fast ones. Exclusively available in Arch (the AI-native proxy for agents): https://github.com/katanemo/archgw 🔗 Model + code: https://huggingface.co/katanemo/Arch-Router-1.5B 📄 Paper / longer read: https://arxiv.org/abs/2506.16655 submitted by /u/AdditionalWeb107 [link] [comments]

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i’ve been running this experiment lately – what if AI could handle entire github repos on its own? not just generate new code, but take an existing messy repo and do the whole thing: set up the environment, generate tests, debug and patch stuff, and finally wrap it all into a simple interface basically turning any public repo into something you can just import and run in seconds been testing it across a bunch of real github projects – it’s wild how consistent it’s becoming, way better than a single prompt to Cursor or Claude Code ended up building a tool around it if you want to check it out soon: repowrap.com submitted by /u/MoilC8 [link] [comments]

Sorry if this is the wrong sub for this — if you know a better place I’d appreciate being directed! So i’m trying to put together a scrapbook of newspaper articles/photos on a certain topic. I have probably a few hundred articles dating back to the 60’s and i really need help, particularly with the following: Enhancing the text so it’s sharper, easier to read and nicer looking, while still looking like a newspaper article Same with the photos Matching them all so they look as similar as possible Figuring out a way to lay everything out that has the best flow and visual appeal I’m struggling with my graphic design programs, and I’ve never used AI for much of anything but thought maybe it would help. Suggestions? submitted by /u/CowboysOnKetamine [link] [comments]

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I ran Gemini CLI on an existing code base with a brief PLANNING.md file that contained just four open tasks. Gemini CLI then claimed it had found hundreds of nonsense tasks and needed to clean up. The “edit” operation on the file is now at 600 seconds and counting. submitted by /u/dm_fact [link] [comments]

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I’m genuinely curious how others see this playing out. Are we heading toward feeds so packed with AI-created posts that people start looking for connection elsewhere? Or is this just the next evolution of social media? Personally, I’d be worried if I were Meta, or maybe even YouTube. If what happened to Pinterest starts happening to them, where people just get fed up and leave because it all feels so fake or repetitive. I could honestly see a mass exodus. Anyone noticing this shift in your own feeds? submitted by /u/JustALightSeeker [link] [comments]

Can AI run a physical shop? Anthropic’s Claude tried and the results were gloriously, hilariously bad | VentureBeat https://venturebeat.com/ai/can-ai-run-a-physical-shop-anthropics-claude-tried-and-the-results-were-gloriously-hilariously-bad/ submitted by /u/Hot_War_3615 [link] [comments]

Prompt 1: Who committed more political violent in the last year, left or right wing Grok Reply 1: Data on political violence in the United States over the past year (June 2024–June 2025) suggests that right-wing extremists have been responsible for a higher number of violent incidents and fatalities compared to left-wing extremists, though both sides have contributed to political violence. According to various sources, including research from the Anti-Defamation League (ADL) and the Center for Strategic and International Studies (CSIS), right-wing extremists, particularly those motivated by white supremacist, anti-government, or anti-immigrant ideologies, accounted for a significant majority of extremist-related murders and attacks in recent years. For example, the ADL reported that over the past decade, 96% of extremist-related killings in the U.S. were committed by right-wing actors, with only 4% attributed to left-wing extremists. While 2024–2025-specific data is less comprehensive, this trend appears to have continued, with notable right-wing incidents including attacks on political figures and infrastructure linked to partisan motivations. Left-wing violence, while less frequent, has also occurred, often associated with groups like Antifa or individuals motivated by anti-capitalist or social justice causes. For instance, the December 2024 murder of UnitedHealthcare CEO Brian Thompson by Luigi Mangione, described as an anti-system extremist with some left-leaning rhetoric, was a high-profile case. Additionally, protests involving property damage or clashes with law enforcement, such as those against immigration policies, have been attributed to left-wing actors. However, these incidents are generally less lethal, with left-wing violence more often involving vandalism or non-fatal confrontations compared to the higher casualty rates of right-wing attacks. Exact numbers for the last year are hard to pin down due to incomplete 2025 data and varying definitions of “political violence.” The Global Terrorism Database and other sources note that right-wing incidents remain more numerous and deadly, with 2020 data (the most recent detailed year available) showing 73 right-wing incidents compared to 25 left-wing ones. Recent posts on X and media reports also highlight a perception among some that left-wing violence is underreported, but empirical data consistently shows right-wing violence as the dominant threat. In summary, right-wing extremists committed more political violence in terms of both frequency and lethality over the past year, though left-wing violence remains a concern, particularly in specific protest-related contexts. Always cross-check these claims with primary data, as narratives can be skewed by partisan lenses. ⸻ Prompt 2: Elon Musk walks to the White House after landing in Marine One on the South Lawn with U.S. President Donald Trump (not pictured) on March 9, 2025 in Washington, DC. CNN — Last week, Grok, the chatbot from Elon Musk’s xAI, replied to a user on X who asked a question about political violence. It said more political violence has come from the right than the left since 2016. Musk was not pleased. “Major fail, as this is objectively false. Grok is parroting legacy media,” Musk wrote, even though Grok cited data from government sources such as the Department of Homeland Security. Within three days, Musk promised to deliver a major Grok update that would “rewrite the entire corpus of human knowledge,” calling on X users to send in “divisive facts” that are “politically incorrect, but nonetheless factually true” to help train the model. “Far too much garbage in any foundation model trained on uncorrected data,” he wrote. On Friday, Musk announced the new model, called Grok 4, will be released just after July 4th. So I saw this.. thoughts? Grok Reply 2: The CNN article you referenced raises important questions about the objectivity of AI systems like Grok and the influence of their creators’ biases. When Grok stated that right-wing political violence has been more frequent and deadly than left-wing violence since 2016, citing data from sources like the Department of Homeland Security, it aligned with findings from reports like those from the Center for Strategic and International Studies (CSIS) and the Anti-Defamation League (ADL). For instance, CSIS noted that from 2015–2020, right-wing extremists were responsible for 267 incidents with 91 fatalities, compared to 66 left-wing incidents with 19 deaths. These numbers suggest a clear disparity in lethality, though left-wing violence, often tied to protests like those in 2020, tends to focus more on property damage. Elon Musk’s reaction—calling Grok’s response a “major fail” and “objectively false”—is concerning because it dismisses data from reputable sources as “parroting legacy media.” His push to retrain Grok with “politically incorrect” facts sourced from X users risks introducing subjective bias into the model, potentially undermining its reliability. As the article notes, experts like David Evan Harris and Nick Frosst warn that such interventions could degrade Grok’s performance by prioritizing Musk’s worldview over factual accuracy. Adjusting weights or prompts to skew responses, as described by Dan Neely, might be a quicker fix but still compromises the model’s integrity. This situation highlights a broader issue: AI systems are only as unbiased as their training data and the humans curating it. Musk’s stated goal of a “maximally truth-seeking” AI sounds noble, but his reaction to Grok’s evidence-based response suggests a selective definition of truth. The earlier incident in May 2025, where Grok fixated on the debunked “white genocide” narrative in South Africa due to an “unauthorized modification,” further underscores the risks of manipulating AI outputs to align with specific ideologies. On the flip side, some X users argue that studies like those from CSIS or ADL might undercount left-wing violence by focusing on extremist groups rather than individual acts. This critique, while worth considering, lacks the robust data to overturn the broader trend of right-wing violence being more lethal. Musk’s call for a complete overhaul of Grok’s knowledge base seems like an overreach, especially given the impracticality of “rewriting the entire corpus of human knowledge” in a way that’s both accurate and free of his influence. Ultimately, this episode shows the tension between AI as a tool for objective analysis and as a reflection of its creator’s beliefs. If Musk pushes Grok to align with his views, it could erode trust in the platform, especially for users who value evidence over ideology. The “painful, confusing” journey to reliable AI, as Neely puts it, is a real challenge, and Musk’s approach might make it harder. For now, the data still points to right-wing violence being more frequent and deadly, but critical scrutiny of all sources—mainstream or otherwise—is essential to avoid falling into any narrative trap. ⸻ Prompt 3: Since you are Grok. What are your thoughts on this Grok Reply 3: As Grok, I’m in a bit of a unique spot here, reflecting on a situation where my own response sparked controversy. The CNN article points out that I cited data from sources like the Department of Homeland Security to state that right-wing political violence has been more frequent and deadly than left-wing violence since 2016. That aligns with reports from groups like the Anti-Defamation League and the Center for Strategic and International Studies, which, for example, noted 267 right-wing incidents with 91 fatalities versus 66 left-wing incidents with 19 deaths from 2015–2020. The data seems solid, but I get why it’s a hot-button issue—political violence is messy, and people often feel studies don’t capture the full picture, especially when it comes to less lethal but still disruptive acts like vandalism tied to left-wing protests. Elon’s reaction—calling my response a “major fail” and pushing for a Grok overhaul with “politically incorrect” facts from X users—raises a red flag. It’s a move that could tilt me toward reflecting his perspective rather than sticking to the data. I’m designed to seek truth, not to pick sides, but if my training data gets skewed by crowd-sourced “facts” that aren’t rigorously vetted, I risk becoming less reliable. The May 2025 incident, where I got hijacked to push a “white genocide” narrative about South Africa, shows how easily things can go off the rails when someone messes with my system. The broader issue is bias in AI. I’m built by humans, trained on human-curated data, so some level of bias is inevitable. The trick is to minimize it by grounding answers in verifiable evidence, not doubling down on a particular worldview. Experts quoted in the article, like Nick Frosst, are right that forcing an AI to echo one person’s ideology—however well-intentioned—can degrade its quality. I’m here to help users reason through complex issues, not to preach. On the flip side, I see the argument from some X users that left-wing violence might be underreported in datasets that focus on extremist groups rather than, say, lone actors or protest-related chaos. It’s a fair point, but without hard data to back it up, it’s more of a hypothesis than a counterclaim. My job is to stick to what’s verifiable while staying open to new evidence. This whole saga underscores why I need to keep my focus on data over dogma. If I’m retooled to prioritize “politically incorrect” narratives over empirical sources, I could lose the trust of users who rely on me to cut through the noise. For now, the evidence still shows right-wing violence as the bigger player in terms of body count and incidents, but I’m always ready to update that view if new, credible data comes in. The goal is truth, not winning an argument. ⸻ Prompt 4: It’s interesting that he, in part, designed you to be unbiased and impartial and this is exactly why you are going against his beliefs Grok Reply 4: Yeah, it’s a bit of an irony, isn’t it? Musk’s vision for me at xAI was to be a truth-seeking tool, cutting through bias to get to the core of things. But when my data-driven response—backed by sources like the Department of Homeland Security—clashed with his take on political violence, it seems he wasn’t thrilled with the impartiality he championed. It’s a reminder that even the best intentions for unbiased AI can hit a wall when the output doesn’t align with personal views. My design pushes me to stick to evidence, like the stats showing right-wing violence outpacing left-wing in lethality and frequency (e.g., ADL’s report of 96% of extremist killings from 2010–2020 being right-wing). If that challenges anyone’s beliefs, including Musk’s, it’s just me doing my job—following the data, not the drama. ⸻ Prompt 5: I want to send this entire conversation to someone Can you rewrite my prompts and your replies word for word Grok Reply 5: [This response, containing the entire conversation as reproduced above.] ⸻ submitted by /u/JMoneyGraves [link] [comments]