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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 - 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 - achaljhawar/1rok: Multi-LLM trading harness.
satoshiclad · 2026-05-14 · via Hacker News - Newest: "LLM"

1rok leaderboard

1rok is a standalone harness for running portfolio-construction agents across OpenAI, Anthropic, Gemini, xAI, DeepSeek, GLM, and OpenRouter against the same financial tool surface. Agents query Alpaca, Yahoo Finance, FRED, and Tavily through an inline, in-process tool registry defined in this repo.

Live leaderboard tracking how each model's portfolio performs in paper trading on Alpaca (started 2026-01-20): investingbench.vercel.app.

Note

run produces an artifact. execute places orders. They are always separate commands. run never touches a broker; execute --live is the only path to real order placement.

Features

  • Inline tool registrylistTools / callTool over local handlers; one registry per pipeline run.
  • Eight tool groups — market, stock, research, technicals, options, earnings, portfolio, Tavily web search.
  • Seven LLM providers — OpenAI (GPT-5.2/5.4/5.5), Anthropic (Claude Opus 4.7 / Sonnet 4.6 / Haiku 4.5), Gemini, xAI, DeepSeek, GLM, OpenRouter — behind a single tool-calling loop.
  • Specialist agents — orchestrator, screener, fundamental, valuation, technical, sentiment, catalyst, macro, risk, constructor.
  • Two-stage pipelinerun emits a portfolio-construction JSON artifact; execute reads it and places orders via Alpaca (paper by default).
  • Provider-agnostic schemas — Zod definitions converted to each provider's tool-call format with shared retry/loop logic.

Agent Pipeline

Four stages, ten agents, one weekly run. Macro reads regime; Screener surfaces 25–30 candidates; six analysts score in parallel; Orchestrator composites; Constructor sizes trades; Alpaca executes (paper by default).

flowchart TD
    Macro["Macro Agent<br/><i>The Economist</i>"]:::entry
    Screener["Screener Agent<br/><i>The Scout</i>"]:::entry

    Sentiment["Sentiment<br/><i>Mood Reader</i>"]:::analysis
    Fundamental["Fundamental<br/><i>Accountant</i>"]:::analysis
    Valuation["Valuation<br/><i>Appraiser</i>"]:::analysis
    Catalyst["Catalyst<br/><i>Event Watcher</i>"]:::analysis
    Risk["Risk<br/><i>Risk Manager</i>"]:::analysis
    Technical["Technical<br/><i>Chart Reader</i>"]:::analysis

    Orchestrator["Orchestrator Agent<br/><i>The CIO</i>"]:::synthesis
    Constructor["Portfolio Constructor<br/><i>The Trader</i>"]:::execution
    Execute["Order Execution<br/><i>Alpaca API</i>"]:::execution

    Macro --> Screener
    Screener --> Sentiment
    Screener --> Fundamental
    Screener --> Valuation
    Screener --> Catalyst
    Screener --> Risk
    Screener --> Technical

    Sentiment --> Orchestrator
    Fundamental --> Orchestrator
    Valuation --> Orchestrator
    Catalyst --> Orchestrator
    Risk --> Orchestrator
    Technical --> Orchestrator

    Orchestrator --> Constructor
    Constructor --> Execute

    classDef entry stroke:#ff8c00,stroke-width:2px
    classDef analysis stroke:#888,stroke-width:1px
    classDef synthesis stroke:#22c55e,stroke-width:2px
    classDef execution stroke:#aaa,stroke-width:1px
Loading

Composite scoring weights: fundamental 20%, valuation 20%, risk 15% (inverted), technical 15%, catalyst 15%, sentiment 10%, macro gate 5%. Constructor caps at 8 positions, ≥85% invested, ≤40% per name.

Architecture

CLI (run | execute)
   │
   ▼
Provider ── TradingPipeline ── InlineToolRegistry (per run)
                │                       │
                ▼                       ▼
        Specialist agents ───── Tool handlers
                                        │
                                        ▼
                              src/data/services
                                        │
                                        ▼
                Alpaca · Yahoo Finance · FRED · Tavily
  1. Runner builds provider + TradingPipeline from model id.
  2. Pipeline instantiates one InlineToolRegistry per run.
  3. Agents execute through provider's tool-calling loop.
  4. Tool handlers call typed services in src/data.
  5. Services hit external APIs.

Layout

Path Role
src/data Provider clients, domain services, types
src/tools Inline tool definitions + registry (listTools / callTool)
src/harness/agents Specialist agents (orchestrator, constructor, …)
src/harness/providers Provider adapters + tool-loop
src/harness/pipeline Run orchestration
src/cli/1rok.ts CLI entrypoint (run, execute, help)

Requirements

  • Bun >= 1.1.0 — supported on macOS (x64/arm64), Linux (x64/arm64, glibc or musl), and Windows (x64/arm64).
  • API keys for the providers you intend to exercise (see Environment)

Quick Start

macOS / Linux (bash/zsh):

bun install
cp .env.example .env   # fill in keys you actually need
bun run typecheck

Windows (PowerShell):

bun install
Copy-Item .env.example .env   # fill in keys you actually need
bun run typecheck

Windows (cmd.exe):

bun install
copy .env.example .env
bun run typecheck

Run a portfolio-construction pipeline:

bun run 1rok -- run --model gpt-5.2-medium

Execute the resulting orders file (paper by default). Path separators differ per OS:

# macOS / Linux
bun run 1rok -- execute ./results/openai/gpt-5.2-medium/portfolio-construction-2026-04-16T07-00-00.json
# Windows PowerShell
bun run 1rok -- execute .\results\openai\gpt-5.2-medium\portfolio-construction-2026-04-16T07-00-00.json

Warning

--live places real orders. Without it, execution targets paper-api.alpaca.markets.

bun run 1rok -- execute ./results/<...>.json --live
bun run 1rok -- execute ./results/<...>.json --live --force

Install the CLI globally on your shell:

bun link
1rok run --model gpt-5.2-medium
1rok execute ./results/<...>.json

On Windows, bun link creates a 1rok.cmd shim on PATH; the commands above work unchanged from PowerShell or cmd.

Environment

Copy .env.example to .env. Nothing is required unless you exercise that integration.

Data providers

Var Purpose
ALPACA_API_KEY / ALPACA_SECRET_KEY Bars, quotes, positions, news, order execution
FRED_API_KEY Macro indicators, interest rates
TAVILY_API_KEY Web search, page extract, site crawl

Yahoo Finance needs no key.

LLM providers — set at least one:

OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, XAI_API_KEY, DEEPSEEK_API_KEY, GLM_API_KEY, OPENROUTER_API_KEY

Anthropic models

Model id Notes
claude-opus-4-7 Default Anthropic model
claude-opus-4-7-high Reasoning effort high
claude-opus-4-7-max Reasoning effort max
claude-sonnet-4-6
claude-haiku-4-5

The Anthropic adapter runs through @anthropic-ai/claude-agent-sdk, which ships a native claude binary as an optional dependency. The provider resolves that binary automatically for macOS (darwin-arm64, darwin-x64), Linux (linux-x64/arm64, glibc + musl), and Windows (win32-x64, win32-arm64). Override the resolved path with CLAUDE_CODE_EXECUTABLE if needed (e.g. pointing at an existing claude install, or claude.exe on Windows).

Optional

  • IROK_MODEL — default model id when --model is omitted.
  • ALPACA_API_KEY_<PROVIDER> / ALPACA_SECRET_KEY_<PROVIDER> — per-model paper credentials for execute. Falls back to the global ALPACA_* keys.
  • CLAUDE_CODE_EXECUTABLE — absolute path to the claude binary the Anthropic provider should use. Only needed if auto-resolution from the SDK's optional deps fails.

Scripts

bun run typecheck   # tsc --noEmit
bun run build       # tsc
bun run test        # bun test
bun run 1rok -- ... # CLI passthrough

Tool Groups

market, stock, research, technicals, options, earnings, portfolio, tavily. Each group registers Zod-typed definitions in src/tools/definitions/* and is aggregated through ALL_TOOLS in src/tools/definitions/index.ts.

Programmatic Use

import { createProviderFromModel } from "1rok/harness";
import { TradingPipeline } from "1rok/pipeline";

const provider = createProviderFromModel("gpt-5.2-medium");
const pipeline = new TradingPipeline({ provider });
const result = await pipeline.run();

Subpath exports: 1rok/tools, 1rok/data, 1rok/harness, 1rok/providers, 1rok/agents, 1rok/pipeline.