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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
Stop Paying Analysts to Search the Internet | Quoin.ai
quoinai · 2026-06-25 · via Hacker News - Newest: "LLM"

Research Engine · Not a Chatbot

Decision-grade research. In 20 minutes.

Quoin delivers sourced, cited, primary-document research in 20 minutes. The kind of output you can defend in a partner meeting, a regulatory review, or a board room. Not a summary. Not a chatbot. Research.

What 20 minutes buys you

Your analysts didn't go to school to search the internet.

Quoin handles the searching, verifying, and structuring, fully cited, primary-source-backed, and ready in 20 minutes. What's left for your team is the judgment you actually hired them for.

200 companies. One portfolio. Still just 20 minutes.

Portfolio reviews shouldn't take weeks. With Quoin, upload your full list of portfolio companies and get decision-grade research on every single one, simultaneously. Whether you're covering 1 company or 200, Quoin delivers the same depth in the same 20 minutes.

When the research is complete, ask Claude to synthesize everything into a single executive summary. Upload additional context, such as your own notes, prior reports, or internal data, and Quoin folds it all in. Your quarterly review just got a lot shorter.

User story

Who
A fund manager, venture investor, family office principal, or portfolio analyst responsible for quarterly reporting.

The challenge
A quarterly portfolio review requires fresh, structured research on every company in the portfolio. For a 20-company portfolio, that's 80 to 160 hours of manual work, every single quarter, before a single slide is written.

With Quoin
Upload your portfolio company list to Claude and instruct it to run your custom Quoin agent against each company. In 20 minutes, you have a fresh stack of decision-grade research on every portfolio company, simultaneously. Ask Claude to synthesize the research into a board report or LP update, and enhance it with your own financials, board decks, or prior transcripts for full context.

The result
What used to take weeks now takes about 30 minutes, start to finish. Every insight is sourced. Every quarter.

200 companies. One portfolio. Still just 20 minutes.

Portfolio reviews shouldn't take weeks. With Quoin, upload your full list of portfolio companies and get decision-grade research on every single one, simultaneously. Whether you're covering 1 company or 200, Quoin delivers the same depth in the same 20 minutes.

When the research is complete, ask Claude to synthesize everything into a single executive summary. Upload additional context, such as your own notes, prior reports, or internal data, and Quoin folds it all in. Your quarterly review just got a lot shorter.

User story

Who
A fund manager, venture investor, family office principal, or portfolio analyst responsible for quarterly reporting.

The challenge
A quarterly portfolio review requires fresh, structured research on every company in the portfolio. For a 20-company portfolio, that's 80 to 160 hours of manual work, every single quarter, before a single slide is written.

With Quoin
Upload your portfolio company list to Claude and instruct it to run your custom Quoin agent against each company. In 20 minutes, you have a fresh stack of decision-grade research on every portfolio company, simultaneously. Ask Claude to synthesize the research into a board report or LP update, and enhance it with your own financials, board decks, or prior transcripts for full context.

The result
What used to take weeks now takes about 30 minutes, start to finish. Every insight is sourced. Every quarter.

AI tools generate. Quoin investigates.

Every claim sourced

Every statement in a Quoin report links back to a primary document: filings, press releases, databases, court records.

No hallucinations

Quoin doesn't generate plausible-sounding facts. It finds real ones and shows its work.

Hundreds of pages in 20 minutes

The output isn't a paragraph. It's a full research document, the kind that used to take a team of analysts days to produce.

Why Quoin

Research built for decisions

Most tools generate text. Quoin generates evidence. Here's how the approaches compare.

Quoin

Agentic AI

Decision-grade intelligence. Every claim atomized, sourced, and mechanism-anchored. Defensible in any room where being wrong has consequences.

  • Reads thousands of live primary sources in minutes
  • Every claim traced to a primary document (no hallucinations)
  • Structured output: claim, source, mechanism, timestamp, confidence, bounds
  • Always current, verified against live web and documents
  • Scales across any business, topic, or industry

Time to complete~20 minutes

Primary sources read1,000s per brief

Hallucination riskNone

Manual

What serious analysts have produced for a century: credentialed and sourced, but constrained by human bandwidth.

  • Hours or days to produce a single research brief
  • Limited to what one analyst can read and retain
  • Cognitive bias and fatigue affect quality and consistency
  • Expensive to produce at the depth decisions require
  • Does not scale; every new question starts from scratch

Time to complete5 to 20 hours

Sources read5 to 20 sources

Output defensibilityVaries by analyst

Frontier AI

Consumer-grade grounding. Fast at generating text. Unable to produce output that survives a serious decision.

  • Produces plausible claims with no primary source behind them
  • Cannot trace a claim to an actual filing, contract, or document
  • Knowledge frozen at the training cutoff, not refreshed from the live web
  • No mechanism, no confidence grade, no stated bounds
  • Will not survive a partner meeting or a regulator's review

Time to complete~1 min

Primary sources readNone

Hallucination riskHigh

That's not a research problem. That's a tax on every decision your organization makes.

Whether you're running investment diligence, competitive research, or sales preparation, Quoin returns a defensible, fully cited document in the time it takes to grab lunch. Hours spent hunting for information you're not sure you can trust, stitching together sources you'll have to verify anyway, and producing output that would be twice as good if you just had twice as much time. Quoin exists for anyone whose work demands a defensible answer, whether you're in a partner meeting, a discovery call, a briefing room, or a university library. If you need to know things, and being wrong has consequences, you've been doing this the hard way. You don't have to anymore.

Guardrails

No agent ever checks its own work.

Quoin is built on MARCH - a verification architecture where the agents that generate research and the agents that check it are completely separate systems. They don't share context. They can't influence each other's conclusions. Every claim is found, then independently verified, then traced to a primary source before it appears in your report. That's not a feature. It's the architecture.

1

Research scoping

Structured intake translates your question into a research plan, defining scope, source requirements, and the guardrails each agent will be held to.

2

Specialized generator agents

Domain-specific agents research filings, extract narratives, benchmark peers, and surface anomalies, each optimized for a specific task, none responsible for checking what another produced.

3

Isolated verification layer

Independent verifier agents check every output against primary sources, without access to the generator's reasoning chain. No shared context means no confirmation bias. What gets flagged is what a single model would have missed.

4

Reconciled, citation-backed delivery

Contradictions are resolved before anything reaches you. Outputs are structured as research briefs, credit memos, or analyst reports, with every claim traceable to the source that verified it.

Get started

Ready to transform your research?

Join organizations using Quoin to streamline research and make data-driven decisions with confidence.