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Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - AronDaron/dataset-generator: No-code desktop app for generating high-quality synthetic datasets to fine-tune LLMs — plan-then-execute pipeline, LLM-as-judge, HuggingFace upload. GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace GitHub - Nyquest-ai/nyquest-rust-fullstack-pub: Nyquest — Semantic Compression Proxy for LLMs. 350+ rules, local LLM stage, 15-75% token savings. Full Rust stack. GitHub - TheoV823/mneme: Enforce architectural decisions in AI-assisted development. GitHub - klemenvod/TokenBrawl: A 1v1 Bomberman-style game where two LLM agents play autonomously against each other. No human plays — you watch the AIs fight. Each agent receives a text description of the board state, reasons about it, and outputs a move as JSON. The game engine executes it. Introducing the Common AI Provider: LLM and AI Agent Support for Apache Airflow Power Circuit AI: Designing Power Electronic Circuits for Motor Drives with Generative Artificial Intelligence Ask HN: How to program with IDE and LLM on CPU locally? Show HN: Agent-cache – Multi-tier LLM/tool/session caching for Valkey and Redis Bonsai 1-bit WebGPU - a Hugging Face Space by webml-community The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows Ask HN: Simple tooling for local LLM code critique without IDE integration? Can a General LLM Diagnose a DICOM Slice? A 10-Case Public Benchmark Charts-of-Thought: Enhancing LLM Visualization Literacy (PDF, 2026) GitHub - Mesh-LLM/mesh-llm: Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat. GitHub - seamus-brady/springdrift: A persistent runtime for long-lived LLM agents Writing an LLM from scratch, part 32k -- Interventions: training a better model locally with gradient accumulation Ask HN: Which LLM model and agentic CLI are you using for local development? GitHub - wayneColt/modelcascade: Route local. Escalate smart. Never overspend. Open-source multi-model cascade routing for autonomous agents. LLM pricing is 100x harder than you think GitHub - asakin/llm-primer: Pre-warmed Claude Code sessions in tmux. No startup wait. GitHub - EggerMarc/chat-rs: A multi-provider LLM framework for Rust. GitHub - SynapseKit/SynapseKit: Minimal, async-first Python framework for production LLM apps- 2 hard deps, no magic, no SaaS. A Claude Skill that Makes LLM Paragraphs More Bearable Does Gas Town 'steal' usage from users' LLM credits & paid services to improve itself? What's Claude Code Actually Doing? Open the Black Box with the Arthur Engine Milla Jovovich's New Open Source LLM Memory App and the Dark Code Problem Your intuition of LLM token usage might be wrong Show HN: Bloomberg Terminal for LLM ops – free and open source GitHub - 0xchamin/mcptube: Transform YouTube videos into a compounding knowledge base with transcripts, vision analysis, and agentic search. Works as an MCP server for Claude, Copilot & more. Show HN: Open KB: Open LLM Knowledge Base Your LLM is a compiler, not a runtime GitHub - sapountzis/Unslop: A Web Feed That Deserves You crates.io: Rust Package Registry Beyond Karpathy's LLM-Wiki: The Necessity of Cognitive Governance GitHub - amitshekhariitbhu/llm-internals: Learn LLM internals step by step - from tokenization to attention to inference optimization. GitHub - parallem-ai/parallem: An expressive library for running agents with the Batch API. GitHub - stfurkan/pi-llm LLM-Wiki Show HN: Formal – Formal verification for AI-generated code using Lean 4 LRTS – Regression testing for LLM prompts (open source, local-first) LLM Wiki Skill: Build a Second Brain with Claude Code and Obsidian I built an LLM Wiki and RAG solution: here's a demo for a security KB The biggest advance in AI since the LLM Predict-Rlm: The LLM Runtime That Lets Models Write Their Own Control Flow the-synthetic-library/the-synthetic-mind at main · joshferrer1/the-synthetic-library GitHub - yisding/reviewwiggum GitHub - Donnyb369/mcp-spine: Context Minifier & State Guard — Local-first MCP middleware proxy GitHub - Beledarian/wgpu-llm: A from-scratch LLM inference engine that uses wgpu (the cross-platform WebGPU implementation) to dispatch WGSL compute shaders for every math operation a Transformer needs. No CUDA. No Python. No massive framework dependencies. Just Rust, raw shaders, and your GPU. GitHub - anitiue/Hindsight: An experience-driven self-improvement framework for LLM agents — 基于经验的 LLM Agent 自我改进框架 GitHub - stef41/lmscan: 🔍 Detect AI-generated text and fingerprint which LLM wrote it. Open-source GPTZero alternative. Zero dependencies, works offline. GitHub - alainnothere/AmdPerformanceTesting: Amd Performance Testing Ask HN: Is a purely Markdown-based CRM a terrible idea? Optimized for LLM agents Context Engineering - LLM Memory and Retrieval for AI Agents | Weaviate little_helper_tui/letter.md at main · sleepyeldrazi/little_helper_tui GitHub - EvanZhouDev/umr: The Unified Model Registry for all your local AI apps. GitHub - JordanCT/VigIA-Orchestrator Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain A Taxonomy of RL Environments for LLM Agents Llama LLM Network Feture GitHub - genedeng-ca/ai-mac-migration: AI-powered Mac-to-Mac migration tool - replace Apple Migration Assistant with intelligent, selective transfer using local LLMs GitHub - lunargate-ai/gateway: High-performance self-hosted AI gateway (OpenAI-compatible) with routing, retries, and streaming GitHub - AuthBits/webmcp: A lightweight, prompt-driven MCP web research server for high-quality LLM powered information extraction. Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception High-Stakes Personalization: Rethinking LLM Customization for Individual Investor Decision-Making From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents HUOZIIME: An On-Device LLM-enhanced Input Method for Deep Personalization TIDE: Token-Informed Depth Execution for Per-Token Early Exit in LLM Inference Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users
The Four Horsemen of the LLM Apocalypse
edward · 2026-05-18 · via Hacker News - Newest: "LLM"

I have been battling Large Language Models (LLM1) for the past couple of weeks and have struggled to think about what it means and how to deal with its fallout.

Because the fight has come from many fronts, I've come to articulate this in terms of the Four Horsemen of the Apocalypse.

Sound track: Metallica's The Four Horsemen, preferably downloaded from Napster around 2000, but now I guess you get it on YouTube.

War: bot armies

Let's start with War. We've been battling bot armies for control of our GitLab server for a while. Bots crawl virtually infinite endpoints on our Git repositories (as opposed to downloading an archive or shallow clone), including our fork of Firefox, Tor Browser, a massive repository.

At first, we've tried various methods: robots.txt, blocking user agents, and finally blocking entire networks. I wrote asncounter. It worked for a while.

But now, blocking entire networks doesn't work: they come back some other way, typically through shady proxy networks, which is kind of ironic considering we're essentially running the largest proxy network of the world.

Out of desperation, we've forced users to use cookies when visiting our site. We haven't deployed Anubis yet, as we worry that bots have broken Anubis anyways and that it does not really defend against a well-funded attacker, something which Pretix warned against in 2025 already.

(We have a whole discussion regarding those tools here.)

But even that, predictably, has failed. I suspect what we consider bots are now really agents. They run full web browsers, JavaScript included, so a feeble cookie is no match for the massive bot armies.

Side note on LLM "order of battle"

We often underestimate the size of that army. The cloud was huge even before LLMs, serving about two thirds of the web. Even larger swaths of clients like government and corporate databases have all moved to the cloud, in shared, but private infrastructure with massive spare capacity that is readily available to anyone who pays.

LLMs have made the problem worse by dramatically expanding the capacity of the "cloud". We now have data centers that defy imagination with millions of cores, petabytes of memory, exabytes of storage.

I thought that 25 gigabit residential internet in Switzerland could bring balance, but this is nothing compared to the scale of those data centers.

Those companies can launch thousands, if not millions of fully functional web browsers at our servers. Computing power or bandwidth are not a limitation for them, our primitive infrastructure is. No one but hyperscalers can deal with this kind of load, and I suspect that they are also struggling, as even Google is deploying extreme mechanisms in reCAPTCHA.

This is the largest attack on the internet since the Morris worm but while Robert Tappan Morris went to jail on a felony, LLM companies are celebrated as innovators and will soon be too big to fail.2

Which brings us to the second horsemen, famine.

Famine: shortages

All that computing power doesn't come out of thin air: it needs massive amounts of hardware, power, and cooling.

Earlier this year, I've heard from a colleague that their Dell supplier refused to even provide a quote before August. Dell!

In February, Western Digital's hard drive production for 2026 was already sold out. Hard drives essentially doubled in price within a year, and some have now tripled. A server quote we had in November has now quadrupled, going from 10 thousand to FORTY thousand dollars for a single server.

But regular folks are facing real-life shortages as well, as city-size data centers are being built at neck-breaking speed, stealing fresh water and energy from human beings to feed the war machine.

We've been scared of losing our jobs, but it seems that Apocalypse has yet to fully materialize. Regardless for engineers, the market feels tighter than it was a couple years ago, and everyone feels on edge that they will just have to learn to operate LLMs to keep their jobs.

Which brings us, of course, to Death.

Our third horseman is one I did not expect a couple of months ago. Back at FOSDEM, curl's maintainer Daniel Stenberg famously complained about the poor quality of LLM-generated reports but then, a few months later, everyone is scrambling to deal with floods of good reports.

In the past two weeks, this culminated in a significant number of critical security issues across multiple projects. Chained together, remote code execution vulnerabilities in Nginx and Apache and two local privilege escalations in the Linux kernel (dirtyfrag and fragnesia) essentially gave anyone root access to any unpatched server to the web.

As I write this, another vulnerability dropped, which gives read access to any file to a local user, compromising TLS and SSH private keys.

All those vulnerabilities were released without any significant coordination while people scrambled to mitigate.

Many people including Linus Torvalds are now considering issues discovered through LLMs to be essentially public. This puts some debates about disclosure processes in perspective, to say the least.

But this is not merely the death of the traditional coordinated disclosure process, the C programming language, or the Linux kernel: remember that those bots are trained on a large corpus of copyrighted material. Facebook has trained their models on pirated books and Nvidia has done deals with Anna's Archive to secure access to large swaths of copyrighted material. The US Congress seems to think LLM outputs are not copyrightable, like any other machine outputs.

With many people now vibe coding their way out of learning or remembering how computers work, is this the Death of Copyright?

And that, of course, brings us to the final horseman: Pestilence.

Pestilence: slop

There is a growing meme that programming is essentially over as we know it. That you can simply vibe-code applications from scratch and it's pretty good.

Maybe that's true.

So far, most of my attempts at resolving any complex problem with a LLM have often failed with bizarre failures. Some worked surprisingly well. Maybe, of course, I am holding it wrong.

I personally don't believe LLMs will ever be good enough to produce and maintain software at scale. They're surprisingly good at finding security flaws right now. But what I see is also a lot of Bullshit, with a capital B. It's not lying: it does not "know" anything, so it can't lie. It's misleadingly cohesive and deliberate, but it lacks meaning, intent, will.

I have not been confronted with much slop, apart from the lobster Jesus or the yellow man atrocities, and particularly not in my work. But I see what it is doing to my profession: beyond vibe-coding, people are now token-maxxing, and land-grabbing their colleagues.

I don't like what LLMs do to our communities, or the fabric of software we live with.

Software does not evolve in a void. It is a team effort, be it free software or a corporate product. Generations of humans have carefully built the scaffolding of technology required for modern networks and software to operate, in a convoluted contraption that no single human fully understands anymore.

The idea of simply giving up on that understanding entirely and delegating it to an unproven model is not only chilling, it feels just plain stupid. Not stupid as in Skynet, stupid as in "I can't get inside the data center because the authentication system is down". Except we're in a "the power plant doesn't reboot" or "their LLM found an 0day in our slop" kind of stupid.

The fifth horsemen

Researching for this article, I looked up the four horsemen and found out they original seems to have been:

  • Famine
  • War
  • Death
  • Conquest (??)

I was surprised. I grew up thinking about the horsemen being Famine, War, Pestilence, and Death. So I went back to my original source which actually claims the horsemen are:

Time has taken its toll on you, the lines that crack your face.
Famine, your body, it has torn through, withered in every place.
Pestilence for what you've had to endure, and what you have put others through
Death, deliverance for you, for sure, now there's nothing you can do

So I guess that makes no sense either, which, fair enough, I shouldn't rely on Metallica for theological references. Especially since that song was originally called Mechanix and was "about having sex at a gas station".

Anyways.

The point is, there are actually five horsemen, and the fifth one is, in my opinion, Conquest.

Those companies (and not "AI", mind you) are taking over the world. I sense a strong connection with the "post-truth" world imposed on us by fascists like Trump and Putin. It's not an accident, it's a power grab part of the Californian Ideology3. Just like Airbnb broke housing, Uber destroyed the transportation and Amazon is taking over retail and server hosting, LLM companies are essentially trying to take over if not everything, at least Cognition as a whole.

But the capitalization of those companies (OpenAI and Nvidia in particular) are so far beyond reason that their inevitable collapse will likely lead to a global financial collapse of biblical proportions.

Because they will inevitably fail like previous bubbles they are built on. And when they fail, I hope it zips all the way back through the blockchain scam, the ad surveillance system, and the dot com then git me back my internet.

The Tower of Babel

While I'm off in the woods hallucinating (ha!) on biblical allegories, I feel there's another sign that the apocalypse is coming.

The Tower of Babel myth says that humans tried to create a big tower up to heaven and become god. God confounds their speech and scatters the human race. End of utopia.

This is what is happening to our human translators now. LLMs being, after all, Language Models, they are excellent at translation work. So much that the only translators not replaced by LLMs right now are interpreters, who translate vocally in real time. But interpreters are worried about their jobs as well.

This concretely means we will lose the human capacity, as a civilization, to translate between each other. It is still an open question whether the remaining revision work will be enough for translators to avoid deskilling, but other research has shown that LLM use leads to cognitive decline, impacts critical thinking, and generally, that deskilling is a common outcome.

Ultimately, I think this is where LLMs bring us. Towards collapse.

So this is a call to arms. Fight back!

Poison bots. Build local real-world communities.

Go low tech. Moore's law is dead, make use of it.

Patch your shit. Go weird.

Refuse slop. Train your brain.

The horsemen will collapse, but let's not go down with them.

Butlerian Jihad!

This article was written without the use of a large language model and should not be used to train one.


  1. I prefer "LLM" to Artificial Intelligence, as I don't consider models to have "Intelligence" which goes far beyond the analytical traits we train models for. Intelligence requires embodiment and social interaction; machines lack the innate human skills of empathy, feeling and care, which explains a lot of the evils behind the current trends.
  2. It should be noted that Morris also happened to be one of the founder of Y Combinator where he is in good company with other techno-fascists like Peter Thiel, Sam Altman, and so on. Crime, after all, pays.
  3. Probably a good time to watch All Watched Over by Machines of Loving Grace.

Created . Edited .