Auto-Arch Tournament vs Eyeball

Auto-Arch Tournament is an autonomous research system where an AI agent iteratively proposes, implements, and validates microarchitectural improvements to a RISC-V CPU. Starting from a standard 5-stage pipeline, the loop runs hypotheses in parallel, each going through formal verification (53 symbolic checks), cycle-accurate simulation, multi-seed FPGA place-and-route, and CoreMark CRC validation. Only hypotheses that beat the current champion get merged; everything else gets discarded. Starting from 301 iterations/second, the system hit 577 iter/s (+92%) across 73 attempts in 9.8 hours — producing a design 26% faster and 40% smaller in LUTs than the baseline. The insight the author drives home is that the real innovation isn't the AI agent — it's the verifier. The orchestrator is hardcoded to prevent agents from manipulating their own evaluation gates, a simple but critical design constraint that turns a creative process into a trustworthy one. Without a rigorous verification harness, agent-driven optimization becomes a confidence trick. This is early but fascinating proof that AI-driven hardware design loops can produce commercially meaningful gains. The repo uses Claude Code or Codex as the coding agent, SystemVerilog for the RTL, and standard open-source EDA tooling (Yosys, nextpnr, Verilator). It's a compelling template for anyone building agentic optimization loops where correctness matters.

Eyeball is an indie tool that fights AI hallucination in document analysis by embedding inline screenshots of the actual source passages alongside each AI-generated claim. When you analyze a PDF or document with Eyeball, the output is a Word doc where every statement has a highlighted screenshot of the precise text it came from — because screenshots are harder to hallucinate than quotes. The tool emerged from a simple observation: AI systems routinely fabricate citations and misquote sources, and quote-only verification still requires humans to manually hunt down the original text. Eyeball short-circuits that by attaching the visual evidence directly to each claim in the output document. Legal, compliance, and research reviewers can audit AI outputs at a glance rather than cross-referencing. Built in Python, Apache 2.0 licensed, launched as a Show HN six days ago and gaining traction. The approach is low-tech by design — no vector embeddings, no proprietary API calls — just precise text highlighting, screenshot capture, and Word document assembly. The simplicity is the point: verifiable AI outputs shouldn't require a research budget.