Mirrored from filpgf.io — ProPGF Batch 3 (Karma program 1479, application 6a31c6b9d747b346a0b8a343, status: pending). Contact details redacted; canonical application lives on filpgf.io. 1.1 Project Name Filecoin Docs 1.2 Project Github https://github.com/orgs/FIL-Builders 1.3 Project Website https://fil.builders/ 1.4 Team Lead/Point of Contact Sarah Thiam, CEO, Telegram 1.5 Category [ "RFP 3 - AI infrastructure products on Filecoin" ] 1.6 Open Source Status Fully Open Source 2.1 Project Summary Filecoin's developer documentation at docs.filecoin.io is a critical but underserved piece of the network's growth infrastructure — it is the first place developers, AI agents, and enterprise evaluators land when assessing whether to build on Filecoin. This project delivers the 6-month continuation of a refresh of the Filecoin documentation ecosystem, addressing direct feedback from the recent Filecoin Dev Summit 8 and aligned to the demands of an AI-native developer landscape. Concretely, we will: - implement deeper analytics instrumentation (referral tracking, search query visibility, MCP/CLI click-throughs) to replace assumptions with signal - build a dedicated "Filecoin with AI" section optimized for both human developers and AI agent consumption (AEO, structured scraping, machine-readable formats) - resolve the spec.filecoin.io alignment gap so the docs and spec are coherent. - keep the docs updated alongside evolving priority products like Filecoin Onchain Cloud (FOC) and Fil One. The primary beneficiaries are developers building AI infrastructure and onchain applications on Filecoin, and the network operators and ecosystem teams who depend on developer inflow to sustain paid onchain deal growth. The gap this addresses: today, Filecoin lacks the documentation quality, discoverability, and analytics feedback loop needed to convert developer interest into active builders — a direct bottleneck on flagship client adoption and onchain deal volume. *Note: This application represents a new, discrete scope of work within the ongoing Filecoin documentation ecosystem. It does not overlap with or duplicate existing documentation work being carried out under FIL-B. The milestones, deliverables, and team are additive — addressing gaps and new requirements surfaced at the Filecoin Developer Summit that fall outside current funded scope.* 2.2 Who does this work support? [ "Pods", "Onramps", "Storage Providers", "Application Builders", "Application Users", "Network Infrastructure" ] 2.3 Total Funding Requested (USD) 200000 2.4 Milestones & Budget [ { "title": "M1: Audit & Instrumentation", "description": "Full docs audit against FDS feedback; analytics instrumentation (referral tracking, search query capture, MCP/CLI click-throughs); spec.filecoin.io gap map; AI-readiness architecture plan.", "dueDate": "2026-08-30", "fundingRequested": "70,000", "completionCriteria": "- Analytics instrumentation live on docs.filecoin.io: referral tracking, search query capture, MCP/CLI click-throughs visible in dashboard\n- Full audit report published: gap analysis against FDS feedback, spec.filecoin.io misalignment mapped, AI-readiness baseline scored\n- Architecture plan approved and implementation-ready for M2\n- Filecoin's priority products are updated and visible on the site." }, { "title": "M2: Production Build", "description": "- Add \"Filecoin with AI\" section (≥5 pages)\n- spec.filecoin.io update\n- MCP/CLI docs overhaul\n- AEO implementation (llms.txt, structured metadata, machine-readable formats)", "dueDate": "2026-10-30", "fundingRequested": "90000", "completionCriteria": "- \"Filecoin with AI\" section live with ≥5 pages covering agent use cases and user journeys\n- spec.filecoin.io updated and cross-linked with docs.filecoin.io\n- MCP/CLI documentation overhauled based on FDS feedback\n- llms.txt deployed; structured metadata implemented across ≥90% of docs pages\n- Filecoin's priority products are updated and visible on the site." }, { "title": "M3: Iteration & Handoff", "description": "- Analytics-driven content iteration\n- Maintenance cadence\n- Final AI-readiness audit\n- Contribution guide and handoff documentation", "dueDate": "2026-12-18", "fundingRequested": "35000", "completionCriteria": "- Analytics-driven content iteration complete: top zero-result searches addressed, high-exit pages improved\n- Contribution guide and handoff documentation published\n- Final AI-readiness audit report delivered with before/after comparison against M1 baseline\n- Docs maintainable by community post-engagement with no single-team dependency\n- Filecoin's priority products are updated and visible on the site." } ] 3.1 Impact pathway Output: Instrumented, AI-optimized documentation with a "Filecoin with AI" section, analytics visibility layer, and spec alignment. Outcome: Developers — particularly those building AI infrastructure — can find, understand, and evaluate Filecoin faster and with less friction. AI coding agents can discover and consume Filecoin's docs autonomously, expanding the effective reach of the documentation beyond human readers. Ecosystem teams gain a real analytics feedback loop (what people search, where they drop off, which MCP/CLI paths are used) to continuously improve the developer experience. Impact: More developers reach the point of building and deploying onchain — directly supporting paid deal growth and flagship client adoption. The network's documentation becomes a compounding asset rather than a static liability: each iteration informed by real usage data improves conversion further. For the AI-native developer cohort specifically, Filecoin becomes the default documented, agent-readable infrastructure layer for verifiable storage. 3.2 Verification metrics - Docs traffic and referral conversion: Plausible dashboard shows measurable increase in docs → product/GitHub referrals; new referrer source tracking per page - Search query coverage: Reduction in zero-result searches in Filecoin docs search; new content created for top-searched terms with no current result - AI agent discoverability: llms.txt present and indexed; structured metadata implemented across ≥90% of docs pages (verifiable via site crawl) - spec.filecoin.io coherence: Public changelog of spec updates made; cross-link coverage between docs and spec (measurable via crawl) - "Filecoin with AI" section: New section live with ≥5 substantive pages covering AI use cases and user journeys; page-level engagement tracked via analytics 3.3 References Clara Tsao, Molly Mackinlay, Danny O'Brien 4.1 Monthly Operating Burn [ "$10-$100K (small team)" ] 4.2 What % of total team monthly burn depends on this grant? 41% 4.3 If this grant is not awarded, what happens? If not awarded, FIL-B will not be able to take on this scope of work. 4.4 Core Team - Sarah Thiam, CEO: 3 years in Filecoin devrel, managing builder experience through key Filecoin feature/product launches like FVM and FOC.. - Michael Seiler, Technical Lead: 3 years in Filecoin as a senior engineer on Ecosystem, Lotus, IPC and now FIL-B's technical lead with a deep technical understanding on the Filecoin stack. - Nick Lionis, Developer Relations Engineer: Engineer on FIL-B for the past 1.5 years and has owned the FOC docs, starter kits, MCP and CLI, and was a core contributor to the recent docs revamp in H1 2026. - Katie Wawro, Lead Technical Program Manager: PM for FOC builder programs and customer integrations. Previously owned and directed Protocol Labs Launchpad program for technical onboarding. 4.5 Has your team received a ProPGF grant or funding from PLFIF before? [ "Yes" ] 5.1 Key risks & dependencies External dependencies: - Access to Filecoin docs repo and deployment pipeline (Protocol Labs infra team). Mitigation: early alignment and contributor access secured before milestone 1. - spec.filecoin.io ownership — unclear who controls updates. Mitigation: identify spec maintainer in month 1 audit; scope accordingly. - Analytics tooling decisions may require Filecoin infra approval. Mitigation: flag early; fallback options scoped. Technical risks: - AEO/machine-readable optimization may conflict with existing docs framework constraints. Mitigation: architecture assessment in month 1 before committing to implementation approach. - AI agent discoverability standards (llms.txt, structured schemas) are still evolving. Mitigation: follow most widely adopted standards; document approach for easy revision. Team risks: - Scope creep from FDS feedback — stakeholders have many opinions. Mitigation: prioritize against the three core tracks; use analytics data in months 5–6 to justify what's in vs. out. Any feedback you have on the application process? Better! Karma Profile 0x9483c18b09cf4535768f6aaf6d3e10def7d51ad64c20658939dba76d7a4f036a Objective 1 Indirect Objective 2 Indirect Objective 3 Direct