spb/autoscholar

1import type { Metadata } from "next";2import Link from "next/link";3import { SectionHeading } from "@/components/SectionHeading";45export const metadata: Metadata = { title: "About", alternates: { canonical: "/about" } };67const SECTIONS: Array<{8  id: string;9  eyebrow: string;10  title: string;11  body: string[];12  aside?: Array<[string, string]>;13}> = [14  {15    id: "mission",16    eyebrow: "01 · Mission",17    title: "A library of research whose every claim can be checked.",18    body: [19      "AutoScholar is a research-producing startup and a public library of original working papers produced by an autonomous, auditable AI research workflow. It is not a chatbot, an essay generator or a subscription for spun articles. The product is a steadily growing body of research whose every release carries its own evidence: sources, code, data, build logs and provenance.",20    ],21  },22  {23    id: "why",24    eyebrow: "02 · Why autonomous research",25    title: "Research that is reproducible by construction.",26    body: [27      "When the entire path from question to manuscript is executed by software under fixed rules, that path can be recorded, hashed and replayed. A reader does not have to trust a narrative; they can inspect the frozen brief, the verified bibliography, the executed code and the gate-by-gate verification that preceded release.",28      "The aim is not volume. It is a transparent process whose outputs can be audited far more cheaply than a traditional paper, and corrected or retracted publicly when they are wrong.",29    ],30  },31  {32    id: "quality",33    eyebrow: "03 · Quality",34    title: "Deterministic gates decide, not the model.",35    body: [36      "Each run passes an independent automated review and then a set of server-side checks that never read the model's own assessment: citation verification against scholarly registries, tracing of every reported number to stored results, data provenance and licences, PDF health and embedded fonts, disclosure statements, review resolution and a scan for secrets. Any hard failure blocks publication.",37    ],38    aside: [39      ["Hard gates", "16"],40      ["Human peer review", "none — stated on every paper"],41      ["Corrections", "versioned and public"],42    ],43  },44  {45    id: "maclustr",46    eyebrow: "04 · AutoScholar and MacLustr",47    title: "The institution and its compute.",48    body: [49      "AutoScholar is the research institution. MacLustr is its compute infrastructure: a private cluster of Apple Silicon Macs on which every run executes in its own isolated workspace under a dedicated low-privilege identity. MacLustr is credited on every paper as infrastructure, never as an author.",50    ],51  },52  {53    id: "transparency",54    eyebrow: "05 · Transparency and limits",55    title: "What these papers are, and are not.",56    body: [57      "AutoScholar working papers are AI-generated preprints. They are not peer reviewed by humans, carry no editorial board, invented authors or affiliations, and are not registered with a DOI agency. Automated review is critique, not validation. We publish no view counts, citation counts or ratings we cannot verify.",58      "Research planning, source retrieval, code, analysis, writing and automated review are performed by Claude models accessed through the Anthropic API and orchestrated by AutoScholar's own software with enforced permissions and budgets.",59    ],60  },61  {62    id: "founder",63    eyebrow: "06 · The founder's role",64    title: "One administrator, in the loop where it matters.",65    body: [66      "AutoScholar is operated by a single founder and administrator. They write the research briefs, set budgets and publication policy, review gate outcomes and quality reports, and can approve, correct or retract any paper. Nothing in the public library is edited by hand: changes go through versioned publication.",67    ],68  },69  {70    id: "future",71    eyebrow: "07 · Directions",72    title: "Where the foundry goes next.",73    body: [74      "Broader discipline coverage with mode-specific evidence requirements, stronger reproduction (clean re-execution of every analysis across workers), external replication challenges, and richer machine-readable provenance so that other systems can verify AutoScholar papers without trusting AutoScholar.",75    ],76  },77];7879export default function AboutPage() {80  return (81    <div>82      <section className="border-b hairline" style={{ background: "var(--bg-muted)" }}>83        <div className="container-x section-tight">84          <div className="max-w-3xl">85            <p className="eyebrow">About</p>86            <h1 className="title-1 mt-2">AutoScholar.ai, an autonomous research foundry</h1>87            <p className="lede mt-4">88              From research instructions to verifiable working papers, across every legitimate academic89              discipline.90            </p>91          </div>92          <nav aria-label="Sections" className="mt-8 flex gap-2 overflow-x-auto pb-1">93            {SECTIONS.map((s) => (94              <a key={s.id} href={`#${s.id}`} className="chip no-underline">95                {s.eyebrow.split("·")[1]?.trim()}96              </a>97            ))}98          </nav>99        </div>100      </section>101      <div className="container-x">102        {SECTIONS.map((s, i) => (103          <section104            key={s.id}105            id={s.id}106            aria-labelledby={`${s.id}-h`}107            className={`section-tight grid gap-6 lg:grid-cols-12 ${i > 0 ? "border-t hairline" : ""}`}108          >109            <div className="lg:col-span-4">110              <p className="eyebrow">{s.eyebrow}</p>111              <h2 id={`${s.id}-h`} className="title-2 mt-2">112                {s.title}113              </h2>114            </div>115            <div className="lg:col-span-7 lg:col-start-6">116              <div className="prose-editorial dropcap-off">117                {s.body.map((p) => (118                  <p key={p.slice(0, 40)}>{p}</p>119                ))}120              </div>121              {s.aside && (122                <dl className="mt-4 grid grid-cols-3 gap-3 border-t pt-4 hairline">123                  {s.aside.map(([k, v]) => (124                    <div key={k}>125                      <dt className="label">{k}</dt>126                      <dd className="mt-1 text-sm">{v}</dd>127                    </div>128                  ))}129                </dl>130              )}131              {s.id === "quality" && (132                <p className="mt-4 text-sm">133                  <Link href="/transparency">All gates are listed on the transparency page →</Link>134                </p>135              )}136              {s.id === "maclustr" && (137                <p className="mt-4 text-sm">138                  <Link href="/infrastructure">Powered by MacLustr →</Link>139                </p>140              )}141            </div>142          </section>143        ))}144        <section className="section-tight border-t hairline">145          <SectionHeading146            eyebrow="Read"147            title="Start with the methodology"148            more={{ href: "/methodology", label: "Methodology" }}149          >150            The eleven-stage pipeline, research modes and standards, in detail.151          </SectionHeading>152        </section>153      </div>154    </div>155  );156}157