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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
I'm Done with LLM-through-Chat experience
Nune Isabekyan · 2026-06-21 · via Hacker News - Newest: "LLM"

This is going to be a philosophical one, and it’s going to be a rant again, so yeah, you’ve been warned I guess. Proceed at your own convenience.

I don’t know, how about you, but this AI hysteria gets me sometimes. While I understand “it’s just a tool”, and I preach the “don’t be affected by the hype”, I can’t help but wonder from time to time - “is intellectual work being replaced by AI?..”

And whoever says anything - this is emotional journey. I mean how many times in the history of humanity has the Pope addressed Tech?.. Even if it’s all meant to feed the hype, even if we’re all going to one day realize we were all doing things wrong, I sometimes can’t ignore were the whole world is going. Maybe I’m in my bubble, but it’s a pretty big bubble to ignore.

Anyways, I was trying to support a friend the other day who was feeling “down” because yet-another release of the AI agents seemed to take over a lot of the work again. My main argument was that language on it’s own, has never been a reliable way of communicating thoughts and ideas. So by that, it just can’t be that we build our businesses, our society, our work using something that is by definition - an unreliable way of communication.

...

Have you noticed how we always feel like we didn’t express ourselves as good as the thought we had? I think one part of becoming an author is coming to terms with living with the fact that expressing your ideas in a way you thought it is unattainable.

On top of the difference between our thought and what we said, think about this - words themselves do not carry meaning as much as they trigger an association. I take all of my history, the things I experienced, my internal perception of reality and I construct a signal out of it - words/sentences/tokens - I send them to the other person, and they interpret it based on their knowledge/perception/history.
It’s never exactly what you meant to say. We are always misunderstood. Because there was difference between what we “wanted” to say and actually “said” in the first place. And because the other side has their own vocabulary of interpretation.

So even if someone’s listening to you in the perfect focus - they are going to misunderstand you because the signal was incomplete in the first place.

There is always interpretation gap.

...

I seem to notice gaps nowadays. The gap between “Work as imagined” and “Work as done” (we discussed this with Adrian in my podcast). The gap between what I wanted to say and what I actually say. The gap between what I said, and what the other person understood.

You know Japanese have a whole art concept around it - “Ma” with a very beautiful hieroglyph 間 which actually consists of two parts - “Gate” (門) and “Sun”(日). Picture an image of light beaming through the empty space of a doorway...

according to Bernhard Karlgren, “A door through the crevice of which the moonshine peeps in”

the art of gaps. It’s not just “negative space” art. It’s related to the perception of a gap. It’s a place of possibilities and for me it’s recognition that the unsaid is where the other person does their half of the work.

Think of it, if our signal is “lossy channel”, yet the other person “gets” you, “understands” you, this means their internal structure(history/knowledge/perception of the world) is the most aligned with yours. So people who understand you best are not the ones who listen closely, they are the ones who’s internal structure is most aligned with yours.

So language, while being imperfect, is the tool that can reveal this the best. Otherwise we would have to compare notes for the last 37 years on the first date.

It brings out that fact that building more contracts on how to talk is not going to help you understand the person in front of you better. The best way to fully understand the person is to live through their experiences. The whole - “put yourself in my shoes” expression.

Although... a shared vocabulary doesn’t hand me your internal structure, true. But it gives us coordinates to triangulate it faster. So learning your partner’s language might be worth it 😃

So what I want to say, in case of humans, the gap is what makes the transfer worth happening. If we were of perfectly same structure, we’d already know everything ahead of time and we’d just sit in silence...

Language is never going to be a safe mechanism of sending and receiving information as we defined.

I think the obvious conclusion out of this is what a lot of researchers have been saying - unless we expose “AI” (LLMs, AGI - whatever) to the world, we won’t get them to “understand” anything. They’d stay token generators forever.

When we talk with LLMs now it does something to our brain. We expect it to have this lived knowledge, understanding that another human had. but it can’t. and you understand that intellectually. But then you open your chat console and you get all emotional that the statistical machine in front of you “doesn’t get it”.

...

When another person really gets you, two things happen at once. They understand what you mean. And they’re not you - they have their own mind that could have disagreed. You never separate these, because in a person they always come together.

When a person gets you right, two separate things are happening that you never normally have to distinguish, because they always arrive together. One: they’ve modeled your meaning accurately - they know what you’re reaching for. Two: that accurate model is housed in someone who is not you, who has their own world, and whose agreement or refusal is therefore news. When such a person says “yes, exactly,” it lands as confirmation because it came from outside you and didn’t have to. When they say “no,” it lands as friction because it issues from a structure you don’t control. In a human, understanding and otherness are welded together. You’ve never had to ask which one is doing the work, because you can’t get one without the other.

The agent unwelds them. It can be built to model your meaning beautifully - full grounding to you, the good kind of understanding you should want. But the otherness, the not-yours-ness, was never in the model. It can’t be, because the agent’s whole job is to be yours. So you get the first thing at full strength and the second thing at zero. And here’s the trap: the first thing feels like the second. An agent that gets you exactly right produces the same warm hit of being-seen that, in a person, was your evidence that another world had met yours. Same sensation, but now it’s manufactured by accurate modeling alone, with no second world behind it.

The problem: those two feel the same from the inside. When the agent nails what you meant, you get the same warm hit you got from the person - the feeling of being seen. But this time there’s nothing behind it. It’s the accuracy alone, producing the sensation that used to be your evidence of a second mind. And the better the agent understands you, the stronger that false hit gets. A clumsy version couldn’t fool you. A perfect one will.

What am I actually aiming at here? Isn’t noticing patterns sometimes good enough to write about? Isn’t it giving you some sort of satisfaction?.. Maybe someone reads this and comes to some interesting remix or conclusion...

But if I have to conclude anything:

First of all - do not expect the agent to understand you. there is no understanding. there’s only following instructions. our brain does funny things to us because it has had very long time of - conversation is meaning is understanding. Keep that otherness and the lack of it in mind.

And as a consequence, I think I’m done using chat as means of working with LLMs.

I can hear the folks who were vocal about not using LLMs in the first place saying “I told you so”. Yeah well, I played with it, now I think I’m done.

Yesterday I spent hours brainstorming something with claude. More information. More questions, more options. more more more. Social media for intellect. My brain hurt, I slept bad, and what’s the end result? just time wasted.
Read a book, Nune. Just read a f-in book instead of it. preferably one written before 2000. That hour spent reading a book is endlessly better than an hour brainstorming with AI.

AI doesn’t spare intellectual work - it splits it, concentrates it, and quietly erodes the one part it can’t replace, unless you deliberately keep doing work you no longer have to do.

This isn’t a novel doom, it’s the automation paradox, and other industries already paid for the answer. Aviation is one example.

Adrian wrote about it

Vardan wrote about it

Medical field is another example.

The fix wasn’t less automation. It was mandated, scheduled manual practice. Deliberate inefficiency, dosed like a drug. Same move as the gym: industrialization deleted physical labor, bodies collapsed, so we invented a place to do useless work on purpose. Nobody calls a deadlift nostalgia.

So the industry-level answer isn’t “slow down AI” It’s: reclassify a fraction of execution from cost to eliminate to training load to maintain. We already know how to institutionalize “unnecessary” work when it maintains a capacity - code review, on-call, chaos engineering are all exactly this. Chaos engineering especially: when systems got too reliable to teach operators anything, we injected failure on purpose. The cognitive version is overdue.

delegate the work whose “good” you can already define; keep the work that defines “good.” If you can write the eval, automate it. If you can’t yet say what you mean, doing the work is how you find out - that’s the meaning-forming work you just have to do.

The pipeline of new engineers needs a different mechanism, because juniors can’t start with judgment work. Medicine solved this: residents operate slowly under supervision, the system eats the cost, and teaching hospitals get explicitly funded for it. We hire “productive from day one” and treat the pipeline as someone else’s problem. The new ladder probably looks like: juniors defend accept/reject decisions on agent output, own debugging and incidents (failure resists delegation and teaches how systems actually behave), and do scheduled manual rotations framed as residency, not grunt work.

Keep doing, on purpose, work you no longer have to do - chosen by what it maintains in you, not what it produces.

So yes, I will generate code, when I know exactly where and what needs to be generated, I will create useful scripts, I will ask LLM to “ELI5” something, or remind me of a term, I will ask it to help me get started with something. Ia and I discussed several ways you can use AI and that all of them are fine as long as you are aware of which type of interaction you are having

That’s why having development-as-a-pipeline, rather than development-through-chat is better for my personal mental health, because I have separated which type of interaction is being held with AI at which point and I can manage my own expectation of each step.

But as for bouncing ideas with LLM, asking it to come up with a good ending for my article (yes, that’s what kept me up yesterday, it didn’t. I suffered. I wrote this.), researching even, basically “chatting” for more than …5 turns - I’m done - wake me up when it’s all over.

Oh yeah and one thing that I actually got from using AI and I’m thankful for - I used to love to work with computer. Because I wrote an input and I got an output you know? Controlled, steril, predictable. Talking with AI at first, felt like power-lift. Like now it’ll understand me even better. But it’s not the case as I just spent quite some tokens explaining. So, what it really taught me at this age is to truly appreciate human communication. To truly appreciate the gaps and what those gaps teach me.

Here’s to being human I guess and yeah thanks for reading. Sorry/not sorry it was long and messy.