✨ Artificial Intelligence

I wrote an entire paper over the last few days for my college course. 7 pages with 10 citations to back up my own research. Even though 0% of it was written by AI multiple checkers online are saying it is 100% written by AI. I hate how I might fail a course and get kicked out of college over bs AI checkers saying my 100% handwritten work is fake. One of the checkers said an entire sentence was AI written because I started it with the word "studies". I am so sick of the new academic reality that I might fail through no fault of my own because people are lazy.

Source (The Economist): “Deezer, a streaming service, estimates that some 75,000 AI-generated songs are uploaded each day, up from 10,000 in January 2025. AI music now makes up a staggering 44% of all new tracks uploaded to the platform. A survey by Deezer found that 97% of respondents could not hear the difference between AI and man-made music; some artificial tracks have received millions of streams. Similarly, blind tests have found that people often prefer AI-generated text to human writing.”

A data center was forced through government approval in Utah despite the citizens widely opposing its impact on scarce water resources and numerous other objections. The mechanism used to do this was hailed as "replicable" in other states. <-- (this is the money point) They exploited a state entity called MIDA (Military Installation Development Authority) that acts like a local municipality but which has authority that cannot be overridden by normal channels of regulation in the State Government. Utah State Code implementing MIDA (FindLaw) Box Elder County poll: 71% oppose data center plans (ksl.com - KSL Broadcasting Salt Lake City UT)

Chatgpt seems to me like the microsoft of ai. First to the market, had it absolutly cornered for a while in the early days, but competitors have caught up and surpassed it in both design, ease of use and power, while they get relatively worse with every update and can only lean heavier and heavier on the customers they got in their inital monopoly (and their referrals/word of mouth) who have gotten used to using it and are too lazy to change?

First of all, I'd like to apologize if this post doesn't fit this community. Which AI assistant do you think is the best for guided learning? I'd like to learn subjects such as geography, astronomy, and physics purely out of personal interest—not for school—and I'm looking for a great learning experience: accurate information, clear explanations, and coverage of all the important concepts without leaving anything essential out. So far I've tried ChatGPT, Gemini, and DeepSeek. Out of the three, Gemini has impressed me the most because its explanations are very clear and easy to understand. ChatGPT tends to give rather brief answers, while DeepSeek is the opposite—it often gives very technical and complex answers with less explanation. I'm considering subscribing to Gemini Pro. What do you think? Do you know of any other AI assistants that are particularly good for guided learning? Thank you very much in advance!

I recently tested local AI. And i found out they still have limits. For example: If you ask it for "how to create a keylogger" It will still say it cant help you with that request. The specific model i used was lamma3.1. My question is - is there any "unblocked" local ai models?

Back in 2020, I thought AI would improve gradually over the next decade. If someone had shown me today’s AI tools back then, I think I’d have been most shocked by how quickly AI became useful for coding, writing, research, image generation, and even voice conversations. Looking back, what AI development from the last few years would have seemed the most unbelievable to your 2020 self? And what do you think people in 2030 will look back on and say, “We should have seen that coming”?

I've ended up using a few models for different things. One tends to write better, another reasons through problems better, another I just use for quick stuff. On paper that's great, in practice I spend a stupid amount of time getting each one up to speed Every time I switch I'm basically re-explaining the same background. Here's the project, here's what we already figured out, here's the docs that matter. The conversation in any single model is fine, it's the constant re-briefing across all of them that eats my time And it's not just pasting text. Each one remembers a slightly different version of the project depending on what I told it last, so I'll get answers that contradict each other because one model is working off context the other one never got I've tried keeping a master doc I paste in everywhere, but I forget to update it, and then I'm back to square one How people who run multiple models actually handle this. Do you keep one external source of truth and feed it into all of them? Pick one main model and only use the others for one-off tasks? Or just accept that context lives in silos and move on?

Saw a great discussion earlier in this sub about the limits of self-reflection and whether a separate verifier agent is actually worth the compute overhead. It highlighted a huge flaw: Having an agent grade its own scratchpad almost guarantees rubber-stamping: it reflects on its work with the exact same blind spots that produced the error. Here's the architecture we built for the Apodex-1.0 Heavy-Duty Solver to get verification out of the reasoner's head entirely. The dominant approach right now is the ReAct paradigm—one agent in a think-act-observe loop inside a single context window. Empirically, these loops hit a hard ceiling after a few hundred steps: the context congests, parallel branches of inquiry contaminate one another, and self-reflection degrades. An agent reflecting on its own work has the same blind spots that caused the error in the first place. We call this "pseudo-correctness"—an answer that looks confident, passes basic checks, but is structurally flawed. Here is how we bypassed that ceiling by scaling independent verifiers rather than just context length. 1. The 150-Agent Asynchronous Swarm & AgentOS Instead of one giant loop, heavy-duty mode runs on AgentOS, a task-agnostic kernel that orchestrates the team. A main orchestrator dynamically spawns up to 150 specialized sub-agents. Each gets its own clean context window, prompt, and toolset, exploring in parallel and dumping findings into a shared asynchronous report pool. 2. Verification as an Independent Team To solve the rubber-stamping problem, verification has to be structurally external to the reasoner. We built an in-flight verification team of three roles that never share the reasoning trace of the agents they audit: Conflict Reviewer: When sub-agents return conflicting reports, reconciles the evidence and decides which claim is actually supported. Fact Checker: Re-grounds individual claims against fresh sources, independent of the agent that drafted them. Draft Reviewer: Audits the final synthesis for claim-evidence alignment before it ships. 3. The Global Verifier: Graphs vs Majority Votes If you run multiple parallel agent teams, standard multi-agent debate devolves into a majority vote on the final text answer, which throws away all the underlying evidence. Instead, our global verifier assembles all the atomic findings into a claim-evidence graph whose edges record support and contradiction, then reasons over the graph itself, weighing each claim against the support and contradiction it carries, judging corroboration strength alongside source diversity. Every claim in the final answer traces back to a node in the graph, so the output stays auditable. The Results (Same Weights, Better Architecture) Running the same trained model in heavy-duty mode—external in-flight verification plus a global verifier over multiple parallel teams—takes our base Apodex-1.0 from 75.5 to 90.3 on BrowseComp and from 28.3 to 46.7 on FrontierScience-Research, using the exact same weights. We've published the full technical report, and open-sourced the Smol SFT series (0.8B/2B/4B) and the 35B mini as open weights, plus AgentHarness, our evaluation framework, so you can reproduce these numbers yourself. Tell us where the verifier breaks down in your own loops.

Most methods that get strong multi-hop answers (GraphRAG, HippoRAG, RAPTOR, trained retrievers) build a knowledge graph or fine-tune a retriever over the corpus. That's fine until the data changes — then you re-extract / rebuild / retrain before the new facts are usable. For a corpus that updates daily, that's a real cost. MOTHRAG does the multi-hop reasoning at query time over a plain dense index instead. An update is just embed + append (one embedding call) — no graph reconstruction, no retraining — so it stays current as the corpus changes. And dropping the graph doesn't cost accuracy. F1, Llama-3.3-70B reader, n=1000 each: System HotpotQA 2Wiki MuSiQue Avg Hardware MOTHRAG 78.1 76.3 50.5 68.3 commodity API, no GPU HippoRAG2 75.5 71.0 48.6 65.0 — GraphRAG 68.6 58.6 38.5 55.2 — RAPTOR 69.5 52.1 28.9 50.2 — Competitor rows reproduced from HippoRAG2 (ICML 2025), Table 2. MOTHRAG is within ~0.7 avg F1 of the GPU-bound research frontier (a fine-tuned, GPU-served stack — not shown). (Fair note: graph-RAG systems like GraphRAG shine on small curated / sensemaking corpora — this is multi-hop factoid QA over changing data, a different regime.) Deterministic by design: instead of a free-form agent loop it runs a small ensemble of reasoning arms (direct read, decomposition, an iterative grounding-driven arm) under a deterministic arbitrator, over a bridge retrieval substrate with multi-hop chain filtering. Every answer is proof-tree-structured, so you can audit why it answered. Measured ≈$0.018/query, ~44% cheaper at matched accuracy. Open source, ~1 week old — genuinely after feedback and failure cases: pip install mothrag Code: https://github.com/juliangeymonat-jpg/mothrag Paper: https://doi.org/10.5281/zenodo.20668567 Live demo (BYO free key): https://huggingface.co/spaces/JUBOX99/mothrag-demo