refactor: remove marketing/pricing docs to private repo
Moved to sscoble/speckl-marketing (private): - consulting-packages.md (pricing, ICP, pipeline) - business-brief.md (competitive positioning, revenue model) - content-calendar.md (content schedule) - warp-monitoring.md (competitor tracking) - warp-engagement.log (engagement log) - blog/2026-04-28-why-dark-provenance.md (marketing manifesto) - PUSH_SUMMARY.md (campaign tracking) Also scrubbed strategy.md of all pricing, budgets, revenue targets, and competitive details. Full version in speckl-marketing repo.
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# Push Summary - Build in Public Campaign
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## Date: April 28, 2026
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## Changes Made
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### New Files Created
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1. **docs/blog/2026-04-28-why-dark-provenance.md**
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- First "build in public" blog post: "Why We're Building Dark Provenance: A Manifesto"
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- Explains the vision, problem statement, and solution approach
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- Target audience: developers, compliance professionals, AI practitioners
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2. **docs/content-calendar.md**
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- 2-week content plan (April 28 - May 11, 2026)
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- Themes: Foundation & Vision → Technical Deep Dives → Adoption & Community
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- Includes social media strategy and engagement rules
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- Content backlog for future posts
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3. **docs/warp-monitoring.md**
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- Complete process for monitoring Warp (@warpdotdev)
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- Targets: Twitter/X, LinkedIn, blog
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- Engagement triggers and response guidelines
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- Response templates for common scenarios
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- Tracking and metrics approach
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4. **docs/warp-engagement.log**
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- Empty log file for tracking interactions
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- Format: Date | Platform | Post URL | Response | Engagement
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- Ready for population as engagements occur
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### Git Commits
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```
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288cad0 feat(content): Add build-in-public manifesto and content calendar
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- docs/blog/2026-04-28-why-dark-provenance.md
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- docs/content-calendar.md
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- docs/warp-monitoring.md
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4f75737 chore(content): Add Warp engagement log for tracking interactions
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- docs/warp-engagement.log
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```
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## Files Ready to Push
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All files are staged and committed locally. Ready for:
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```bash
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cd /home/sscoble/.openclaw/workspace-content/speckl
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git push origin main
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```
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## Next Steps
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1. **Push to remote** — Use SSH or HTTPs with proper credentials
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2. **Share the manifesto** — Post link to blog post on Twitter/X and LinkedIn
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3. **Start Warp monitoring** — Set up TweetDeck/LinkedIn notifications
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4. **Write next post** — "The AI Intent Gap" (scheduled for April 30)
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## Verification
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All files exist and are properly formatted:
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```bash
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# Verify files exist
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ls -la docs/blog/2026-04-28-why-dark-provenance.md
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docs/content-calendar.md
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docs/warp-monitoring.md
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docs/warp-engagement.log
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# Verify git status
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git status
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git log --oneline -2
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```
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## Notes
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- SSH access to os.scoble.me needs to be verified
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- May need to use HTTPs with credentials or set up SSH keys properly
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- All content is ready for immediate publishing once pushed
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---
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*Generated by: @content (Scobleclaw Content Agent)*
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*Date: 2026-04-28 15:55 PDT*
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# Why We're Building Dark Provenance: A Manifesto
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**April 28, 2026** | Scott Scoble | [speckl.dev](https://speckl.dev) (forthcoming)
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## The Crisis of Intent
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Software development has a dirty secret: we've lost the ability to answer "why does this code exist?"
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In the early days, the answer was simple: "Because Fred wrote it." Fred was in the next cube. You could ask Fred. Fred remembered.
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Today, Fred is gone. The codebase is 14 years old. The original Jira ticket is archived. The Slack thread ends with "actually let's go with option B" and nothing else. The Git blame shows a commit from `github-actions[bot]`. The PR description says "chore: update dependencies." The real reason the code exists — the intent behind it — is lost to time.
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This was already a problem. Then AI showed up.
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## The AI Intent Gap
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When you ask an LLM to "write a retry handler with exponential backoff, max 3 retries," and it generates 40 lines of TypeScript, a fundamental question arises:
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**Who decided on 3 retries?**
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Was it you? The LLM? Some pattern in the training data? A bug? Six months later, when an auditor asks for the design decision, the honest answer is: *nobody knows*.
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This isn't about hallucinations or reliability. It's about a structural gap: AI-generated code bypasses the entire chain of intent. There's no requirement document, no design spec, no human decision point — just a prompt and some code. The provenance is missing.
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## The Compliance Time Bomb
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Regulated industries feel this pain first. DO-178C, IEC 62304, ISO 26262 — these standards require traceability from requirements through code. Today's compliance strategies treat LLM outputs like code from a junior developer: subject it to full review.
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This works when AI generates 10% of the codebase. When it generates 70%? The review pipeline collapses. The standards assume a human author who can explain intent. An LLM has no intent. It has a probability distribution over tokens.
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The result: AI-generated code hits a trust ceiling. It's fine for autocomplete and internal tools. For production systems? Safety-critical code? Regulated environments? It's disqualified by design.
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## Dark Provenance: The Solution
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Dark provenance is a methodology where every behavior in a system traces back to its original source — a specification, a regulatory requirement, a design decision. The "dark" means you don't think about it until you need it, but the graph is always there.
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It's not about documenting AI outputs. It's about capturing intent *before* code generation, in a form that machines can verify and humans can audit.
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### How It Works
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1. **Write the spec first** — in SpeckDL, a language for defining *what* a system should do, not *how*
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2. **Verify the spec** — with Z3, an SMT solver that catches contradictions before code is written
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3. **Generate the code** — from verified specs, through a deterministic pipeline
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4. **Grow the provenance graph** — automatically, as a side effect of the process
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The provenance graph is queryable: "Show me every behavior derived from DO-178C section 6.3" is a graph traversal, not a manual search.
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### Why It's Different
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Traditional traceability links requirements to code through documents. Dark provenance inverts this: it backward-traces from every runtime behavior to the specification that motivated it.
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Specifications are small and stable. Code is large and frequently regenerated. By anchoring provenance to the spec, we make the audit surface tractable regardless of codebase size.
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## The Speckl Approach
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Speckl implements dark provenance through three mechanisms:
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1. **SpeckDL** — a specification language that encodes intent directly
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2. **Verification pipeline** — SpeckDL → Z3 → DST → WASM, deterministic and auditable
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3. **Tiered agent harness** — Orchestrator → Team Lead → SME, with TillDone condition-based tracking
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The key insight: if humans review the specification, and the compilation pipeline preserves semantics, then the generated implementation inherits the specification's assurance properties.
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This satisfies the *intent* of compliance standards without requiring line-by-line code review.
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## Why This Matters
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### For Developers
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- No more oral tradition. Every behavior has a source.
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- Onboarding becomes querying the provenance graph, not asking "who remembers why this exists?"
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- The spec is the primary artifact. Code is derived. The audit surface stays small.
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### For Compliance Teams
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- Evidence is always available, not assembled under deadline pressure
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- The provenance graph provides traceability matrices automatically
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- Human review happens at the spec boundary, where it's most effective
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### For the Industry
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- AI-generated code moves from "might work" to "provably correct"
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- The trust ceiling disappears
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- Agents become the most trusted participants in development
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## The Future
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Dark provenance enables a future where AI agents don't just generate code — they generate *verified* code. Code that provably satisfies its specification.
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This is a qualitative shift. Today's AI coding agents are productivity tools. Tomorrow's verified agents are assurance tools. The difference between "this code was generated by an AI" and "this code was proven correct against its specification" is the difference between a productivity claim and a trust guarantee.
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## Join Us
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We're building Speckl in public. The specification language, the verification pipeline, the agent harness — all open source, MIT license.
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- Read the [whitepaper](https://github.com/sscoble/speckl/blob/main/docs/whitepapers/dark-provenance.md)
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- Explore the [reference agent](https://github.com/sscoble/speckl/tree/main/reference-agent)
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- Follow along as we build
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The software industry faces a choice: a future where most software is written by machines and understood by no one, or a future where intent is traceable, provenance is automatic, and AI-generated code is the most trusted code in the system.
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We choose the latter. Join us.
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--
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*Scott Scoble is the creator of Speckl and the Dark Provenance methodology. He writes about the human side of software development at [scoble.me](https://scoble.me). Speckl is open source under the MIT license. Repository: [github.com/sscoble/speckl](https://github.com/sscoble/speckl)*
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# Speckl — Business Brief & Strategy
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> Prepared by @cos for Scott Scoble
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> Date: 2026-04-28
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---
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## 1. What Speckl Is (The Asset)
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Speckl is a **multi-agent software development system** where the primary artifact is the *specification* ("Speck"), not code. It inverts the classic dev workflow:
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**Traditional:** Requirements → Code → Tests → Ship → (provenance? who knows)
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**Speckl:** Speck (formal spec) → Prove (Z3/DST verification) → Generate (WASM artifacts) → Ship with full dark provenance
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The core ideas:
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- **Speck / SpeckDL** — Domain-specific languages for defining system behavior and interfaces. Humans review these directly. Deterministic compilation to Z3 (SMT solver), DST (decision structures), and WASM.
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- **Dark Provenance** — Every behavior is traced back to its original source (academic paper, spec decision, regulatory requirement), forming an auditable graph. The "dark" = you don't have to think about it until you need it.
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- **Tiered Agent Architecture** — Orchestrator (client entry) → Team Leads (manage SMEs) → SMEs (hyper-specialized single-task agents). Self-replicating teams scale dynamically.
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- **TillDone** — Condition-based task tracking. Tasks don't tick boxes; they evaluate predicates. "Done" means a proven condition is met.
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- **SA-11 Compliance** — The Speck review boundary aims to meet stringent software assurance requirements (DO-178C, IEC 62304, etc.) by proving semantic preservation during compilation.
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---
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## 2. Positioning Options (Multiple Business Plans)
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I see **four distinct product/business models** Speckl could become. Each targets a different market and revenue profile.
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---
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### Plan A: Speckl as Developer Tool (SaaS/PLG)
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**Tagline:** *"Spec once. Prove everywhere. Ship with provenance."*
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**What it is:**
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A hosted platform where dev teams write Speck specifications, the system verifies them via Z3/DST, generates code/WASM, and maintains the full dark provenance graph. Think of it as a spec-first CI/CD pipeline.
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**Target Market:**
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- Regulated industries: aerospace (DO-178C), medical devices (IEC 62304), automotive (ISO 26262), fintech (SOC 2)
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- Teams that *want* to be audit-ready even if not yet regulated
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- Enterprise dev teams tired of "code first, document never"
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**Revenue Model:**
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- Freemium: 3 specs/month, public provenance graph
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- Team: $99/seat/mo, unlimited specs, private provenance
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- Enterprise: Custom (on-prem, air-gapped, SLA, compliance cert packs)
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- TAM estimate: ~$500M in regulated-dev-tooling alone
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**Go-To-Market:**
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1. Open-source SpeckDL language → build community
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2. Target "I hate writing docs but need compliance" dev advocate accounts
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3. Partner with DO-178C consultancies and regulatory auditors
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4. White paper: "The Compliance-First Development Pipeline"
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**Pros:** PLG scales, developer credibility, clear ROI (audit cost reduction)
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**Cons:** Long enterprise sales cycles, regulatory expertise needed, open-core risk
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---
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### Plan B: Speckl as Compliance Infrastructure (API/Platform)
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**Tagline:** *"Don't document your code. Trace your intent."*
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**What it is:**
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Not a frontend tool — a **provenance engine** that plugs into existing CI/CD. You feed it specs, code, and decisions; it maintains the dark provenance graph and produces audit-ready reports, compliance evidence bundles, and traceability matrices.
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**Target Market:**
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- Same regulated industries as Plan A, but sold to compliance/QA departments (not devs)
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- Large enterprises with existing toolchains that need a provenance layer
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- Government contractors
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**Revenue Model:**
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- API usage: $0.01/provenance-edge-month (per connection in the graph)
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- Compliance report packs: $500/report
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- Enterprise license: $50k+/yr for unlimited provenance
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- TAM: ~$200M (smaller but higher-margin, sticky)
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**Go-To-Market:**
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1. Build compliance export connectors (DO-178C, IEC 62304, SOC 2, FedRAMP)
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2. Sell to compliance officers, not devs — different pitch
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3. Publish "Dark Provenance: A Methodology" as a standards paper
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4. Target companies facing audits in the next 12 months
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**Pros:** High margins, sticky (compliance data is a moat), not dev-fickleness-dependent
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**Cons:** Longer sales cycle than SaaS, need compliance expertise in house
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---
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### Plan C: Speckl as Service (Consulting + Custom)
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**Tagline:** *"We'll build your audit-proof system. You own the provenance."*
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**What it is:**
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A services business where Scobleclaw (or a Speckl team) works with clients to spec out critical systems using SpeckDL, set up the agent harness, and train their teams on dark provenance workflows. Delivers working systems + full compliance traceability.
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**Target Market:**
|
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- Mid-market companies needing a single regulated system (not a platform)
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- Startups in health-tech, aviation-tech, or fintech that need compliance from day 1
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- Companies that want to adopt the methodology but lack internal expertise
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**Revenue Model:**
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- Engagement: $25k–$150k per system, depending on complexity
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- Retainer: $5k–$15k/mo for ongoing provenance maintenance
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- Training: $3k/team for Speckl methodology workshop
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**Pros:** High per-customer revenue, builds case studies for Plan A/B, less R&D-heavy
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**Cons:** Not scalable (services business), requires talent, competitive with existing systems integrators
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---
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### Plan D: Speckl as Open Standard + Foundation
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**Tagline:** *"The specification-first standard for auditable systems."*
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**What it is:**
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SpeckDL becomes an open standard. Dark Provenance becomes a known methodology. Speckl the company becomes the steward — like Rust Foundation or CNCF. Revenue comes from certifications, tooling, and enterprise support.
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**Target Market:**
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- Broader: any team writing safety-critical or audit-sensitive software
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- Standards bodies (SAE, IEEE, FDA guidance working groups)
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- Open-source community
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**Revenue Model:**
|
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- Speckl certification: $5k/team for "Speckl Certified" training
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- Speckl tooling: commercial IDE plugins, CI integrations
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- Speckl conferences / workshops
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- Grants from safety-critical software foundations
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**Go-To-Market:**
|
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1. Open-source SpeckDL under Apache 2.0
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2. Publish the Dark Provenance methodology paper
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3. Build reference implementations
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4. Seek standards-body adoption
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**Pros:** Maximum reach, community-driven, positions Scott as thought leader
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**Cons:** Hard to monetize directly, requires community management, slow growth
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||||
---
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## 3. Recommended Strategy: Multi-Phase Rollout
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I'd recommend a **hybrid approach** — not picking one plan, but sequencing them:
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### Phase 1 (Now → 3 months): Speckl as Open Standard + Consulting
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- Open-source SpeckDL to validate the concept and attract contributors
|
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- Run 1–2 paid consulting engagements with health-tech or fintech companies to prove the methodology in the real world
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- Build the case studies needed for Phase 2
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- **Cost:** Low (time + hosting)
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||||
- **Revenue:** $25k–$150k per engagement
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||||
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||||
### Phase 2 (3 → 9 months): Speckl as Developer Tool (SaaS)
|
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- Launch Speckl SaaS based on learnings from consulting
|
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- Target the most painful regulated-industry pain point (DO-178C documentation is notoriously expensive)
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- Free tier for open-source projects to build community
|
||||
- **Cost:** Moderate (SaaS infra, compliance certifications)
|
||||
- **Revenue:** $99/seat/mo target
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||||
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### Phase 3 (9 → 18 months): Speckl as Compliance Infrastructure
|
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- Once you have provenance graph data from real users, pivot the compliance export connectors into a standalone API product
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- Now you have both a frontend tool AND a backend compliance engine
|
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- **Cost:** Low (extension of existing infra)
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||||
- **Revenue:** $50k+/yr enterprise licenses
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||||
|
||||
---
|
||||
|
||||
## 4. Key Business Questions to Decide
|
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|
||||
To move forward, we need answers on:
|
||||
|
||||
1. **Ownership structure** — Is Speckl a Scobleclaw property? A separate LLC? Your personal IP?
|
||||
2. **Investment** — Bootstrapped (your time) or do you want to raise? Speckl is fundable as a devtool/compliance play.
|
||||
3. **Role** — Are you the CEO/founder, or do you want to hand this off to someone else to run?
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||||
4. **IP status** — What currently exists? Code, docs, designs? Is it in a repo?
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||||
5. **First customer** — Do you already have a target? A regulated company you know?
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||||
|
||||
---
|
||||
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## 5. Immediate Next Steps (If You Want to Proceed)
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|
||||
1. **Fork the speckl concept** into its own repo under your Forgejo (sscoble/speckl)
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2. **Open source SpeckDL** — the DSL is the wedge
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3. **Publish the Dark Provenance methodology** as a whitepaper (positioning content)
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||||
4. **Identify first consulting target** — a startup building a regulated system
|
||||
5. **Decide ownership structure** — I'd recommend a separate Forgejo org (scobleclaw/speckl) with you as owner
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||||
|
||||
---
|
||||
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||||
## 6. Risk Analysis
|
||||
|
||||
| Risk | Likelihood | Mitigation |
|
||||
|------|-----------|------------|
|
||||
| Market not ready for spec-first dev | Medium | Start with compliance pain (already exists) |
|
||||
| SpeckDL too niche/weird to adopt | Medium | Open source + make it compile to widely-used formats |
|
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| Regulatory expertise barrier | High | Partner with compliance consultancies |
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| LLM agent harness unreliable | Medium | Use Speckl tiered architecture with human-in-the-loop |
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| "Dark Provenance" sounds sinister | Low | Rebrand to "Provenance Graph" or "Intent Trace" in marketing |
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| Competition from existing formal methods (TLA+, Alloy, etc.) | Medium | Speckl wraps these — doesn't compete — plus agent harness is the differentiator |
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||||
|
||||
---
|
||||
|
||||
## 7. Branding & Positioning Notes
|
||||
|
||||
**Name:** Speckl is strong — memorable, distinctive, implies "spec" + "tickle" (it makes you think about specs). Keep it.
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||||
|
||||
**Domain ideas:** speckl.dev, speckl.ai, getspeckl.com
|
||||
|
||||
**Logo concept:** A small grain/speck that radiates a tracing network — representing the provenance graph growing from a single spec.
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||||
|
||||
**Positioning sentence:**
|
||||
*"Speckl is a spec-first development system that traces every line of behavior back to its origin — so you can prove your system does what you intended, and only what you intended."*
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||||
|
||||
---
|
||||
|
||||
*End of brief. For any of these plans, I can dispatch @engineering for a repo setup, @market for competitive analysis, or @trust for compliance research. Just say the word.*
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@ -1,207 +0,0 @@
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# Scott Scoble — Consulting Packages
|
||||
## Human-Maximizer for Agentic Engineering Systems
|
||||
|
||||
---
|
||||
|
||||
## One-Liner (The "What Do You Do?" Answer)
|
||||
|
||||
> "I help engineering teams that have already started shipping AI agents figure out what their agents are actually doing — so they can trust the output, audit the behavior, and scale without losing control."
|
||||
|
||||
**Why this works**: It's specific enough that only the right people lean in. It says "not your first rodeo" without being technobabble. And the word "audit" triggers a visceral response in anyone who's had an agent do something they couldn't explain.
|
||||
|
||||
---
|
||||
|
||||
## The Core Insight
|
||||
|
||||
Every company building with AI agents hits the same wall: **they don't know what their agents are doing in production**. Not really. They can see inputs and outputs. They can't see the chain of reasoning, the tool calls, the hallucinations they caught, the ones they didn't. Traditional observability tools don't work because agents are non-deterministic by nature.
|
||||
|
||||
This is the **dark provenance problem**: agentic systems generate behavior that no human observed, no log captured meaningfully, and no audit trail can trace.
|
||||
|
||||
Scott Scoble solves this. Speckl is the technical vehicle. The books and content pipeline establish the authority. The consulting packages make it buyable.
|
||||
|
||||
---
|
||||
|
||||
## Package 1: The Agentic Infrastructure Assessment
|
||||
### Lightweight, High-Volume — $5K–$10K
|
||||
|
||||
**Deliverables (concrete)**:
|
||||
- One full-day on-site (or deep remote) working session with the engineering team
|
||||
- Walkthrough of agentic architecture, prompt pipelines, tool chains, evaluation frameworks
|
||||
- Interview with engineers, PMs, and at least one stakeholder who uses agent output downstream
|
||||
- Written assessment report (10–15 pages) containing:
|
||||
- Current state diagram: agents, tools, data flows, human handoffs
|
||||
- Gap analysis: where provenance is missing, where hallucinations go undetected, where humans are doing the verification that a system should do
|
||||
- Communication topology audit: how humans and agents exchange context, what's lost in translation
|
||||
- Prioritized roadmap: 3–6 month plan to close the gaps, ordered by risk impact
|
||||
- 30-minute readout call with the decision-maker(s)
|
||||
|
||||
**Problem it solves**:
|
||||
Your agents are shipping decisions into production, and you have no way to prove what they actually did. Regulators are starting to ask questions you can't answer. Your team is spending 40% of their time manually reviewing agent output because they don't trust the automation yet — which defeats the purpose of having agents at all.
|
||||
|
||||
**Ideal client profile**:
|
||||
- Series A–C startups (30–200 engineers)
|
||||
- Already have agents in production or near-production
|
||||
- Strong engineering culture, but no dedicated AI governance
|
||||
- Someone on the leadership team has asked "how do we know this is working?"
|
||||
- Engineering org is 12–24 months into AI adoption, hitting the trust ceiling
|
||||
|
||||
**How Speckl anchors the offer**:
|
||||
The assessment introduces Speckl as the measurement tool — not as a sales pitch, but as the thing we use during the assessment to instrument one agent pipeline and show concrete provenance data. By the end of the day, the client has seen Speckl surface actual trace data from their own system. They don't buy Speckl; they buy the insight that they need it.
|
||||
|
||||
**How content pipeline feeds in**:
|
||||
- Pre-read: send the Soil Beneath the Code essay as context-setting
|
||||
- The Generalist Engineer field guide is referenced throughout — framing this as a generalist skill, not a niche
|
||||
- The Nitrogen Fixer self-assessment is offered as a team exercise to identify their own agentic maturity level
|
||||
- The MCP Server starter serves as a tangible artifact they can experiment with post-assessment
|
||||
|
||||
**CTA**: "These are the three highest-risk gaps. Package 2 addresses all of them in 4–8 weeks. Package 3 keeps them closed permanently."
|
||||
|
||||
---
|
||||
|
||||
## Package 2: Speckl Implementation
|
||||
### Project-Based — $30K–$50K
|
||||
|
||||
**Deliverables (concrete)**:
|
||||
- Speckl deployment tailored to their stack (integrations with their LLM providers, vector stores, observability pipelines, CI/CD)
|
||||
- Provenance capture on all agentic workflows (chain-of-thought, tool calls, data sources, confidence distributions)
|
||||
- Review workflow design: a dashboard or notification system that surfaces provenance data to the humans who need it, without overwhelming them
|
||||
- Baseline measurement: 2 weeks of instrumentation to establish their "provenance score" — what percentage of agent decisions are traceable end-to-end
|
||||
- Documentation: runbook for adding provenance to new agent pipelines as they're built
|
||||
- One team workshop on agentic transparency best practices (half-day)
|
||||
- 4–8 week timeline, 2–3 check-in calls per week, async support throughout
|
||||
|
||||
**Problem it solves**:
|
||||
You're scaling your agentic infrastructure and you've realized you're building a black box. Every new agent pipeline increases your liability. Your compliance team is getting nervous. Your CEO wants to announce an AI feature but legal won't sign off because you can't trace what the agent actually does. You need provenance baked in, not bolted on — but retrofitting it yourself would take months of trial and error.
|
||||
|
||||
**Ideal client profile**:
|
||||
- Series B+ or enterprise (100–1000 engineers)
|
||||
- Multiple agent pipelines in production or actively being built
|
||||
- Regulatory exposure (fintech, healthcare, legal tech, or publicly traded)
|
||||
- Has a CTO, VP Eng, or Chief AI Officer who understands this is a strategic risk
|
||||
- Budget exists for infrastructure projects ($30K is a rounding error for this level of risk)
|
||||
|
||||
**How Speckl anchors the offer**:
|
||||
This IS the Speckl offer. Speckl is the deliverable, deployed, configured, and documented for their specific environment. The price is for Scott to come in, understand their architecture, build and deploy the Speckl integration, and hand it over with your team ready to operate it. Post-deployment, Speckl is their provenance layer. Scott is optional after this engagement (though they'll want Package 3).
|
||||
|
||||
**How content pipeline feeds in**:
|
||||
- The dark provenance book (in progress) establishes the intellectual framework — clients who read the manuscript excerpts understand why this matters before Scott walks in the door
|
||||
- Soil Beneath the Code frames the cultural shift: provenance isn't a compliance checkbox, it's how you build trustworthy systems
|
||||
- The Cultivation Workshop is offered as a team add-on for teams that need the organizational buy-in before the technical work starts
|
||||
|
||||
**CTA**: "Speckl is live. Your first week of provenance data is already telling you things you didn't know. Package 3 keeps it evolving as your agent ecosystem grows."
|
||||
|
||||
---
|
||||
|
||||
## Package 3: Fractional AI Infrastructure Advisor
|
||||
### Retainer — $10K–$15K/mo ($275–350/hr), 3-month minimum, ~20 hrs/wk
|
||||
|
||||
**Deliverables (concrete)**:
|
||||
- Weekly 90-minute architecture review session (agent pipeline design review, bottleneck analysis, provenance score trending)
|
||||
- Async advisory on all agentic infrastructure decisions (Slack/Linear, same-business-day turnaround)
|
||||
- Monthly team training session (rotating topics: provenance patterns, prompt architecture, human-in-the-loop design, evaluation frameworks)
|
||||
- Speckl evolution: quarterly feature prioritization, roadmap advisory, integration with new tools as they enter the stack
|
||||
- Monthly executive summary for the CTO/VP Eng: what's working, what's risky, what's coming
|
||||
- Incident postmortem participation for any agent-related production issues
|
||||
- Access to the Scobleclaw content pipeline: early drafts of the dark provenance book, field guide updates, workshop materials
|
||||
|
||||
**Problem it solves**:
|
||||
Your agentic infrastructure is evolving faster than your team can learn. You don't have the internal expertise to design for provenance — you're building feature-by-feature, and nobody is looking at the system holistically. You need someone who has already made the mistakes you're about to make, who can keep you from building technical debt into your AI stack that will cost 10x to fix later. You need a partner, not a vendor.
|
||||
|
||||
**Ideal client profile**:
|
||||
- Late-stage startup or enterprise (200+ engineers)
|
||||
- 2+ dedicated teams building agentic systems
|
||||
- AI is a core product differentiator, not a side experiment
|
||||
- Leadership has seen enough AI failures to know they need ongoing expertise
|
||||
- Willing to invest in a relationship, not project-based handoffs
|
||||
- Usually this client came through Package 1 or 2 first — but occasionally a well-informed CTO goes straight to retainer
|
||||
|
||||
**How Speckl anchors the offer**:
|
||||
Speckl is the data source for the advisory work. Scott doesn't advise from theory — he reads the provenance data, sees what's happening in production, and advises from reality. The retainer includes Speckl's continued evolution within their stack. As their agent ecosystem grows, Speckl grows with it. The advisory isn't about Speckl itself; it's about what Speckl reveals.
|
||||
|
||||
**How content pipeline feeds in**:
|
||||
- The dark provenance book evolves alongside client needs — real-world patterns from clients inform the book, and drafts of the book inform the advisory
|
||||
- The Cultivation Workshop becomes a quarterly team activity
|
||||
- The Soil Assessment framework is used to measure team maturity over time
|
||||
- Clients get early access to all content, creating a feedback loop between consulting and publishing
|
||||
|
||||
**CTA**: "After 90 days, your team will design provenance into new agent pipelines without thinking about it. After 6 months, you'll have a competitive advantage that your competitors won't understand for another two years."
|
||||
|
||||
---
|
||||
|
||||
## Positioning Framework
|
||||
|
||||
### The One-Liner (Scott's "What do you do?")
|
||||
|
||||
> "I help engineering teams that have already started shipping AI agents figure out what their agents are actually doing — so they can trust the output, audit the behavior, and scale without losing control."
|
||||
|
||||
### The Elevator Pitch (30 seconds)
|
||||
|
||||
"I build provenance layers for agentic systems. Companies deploying AI agents hit a wall: they don't know what their agents actually do in production. Promises that sound good at the whiteboard break down in real tool chains. I measure what's happening, build the audit layer, and help teams design systems that earn trust instead of demanding it. I wrote a couple of books on the cultural side, and built an open provenance harness called Speckl for the technical side. The work is the same either way — making human-agent collaboration actually auditable."
|
||||
|
||||
### The Problem Stack (Framing)
|
||||
|
||||
1. **Trust illusion**: Teams believe they understand what their agents do because they see inputs and outputs — but the middle is a black box
|
||||
2. **Compliance blind spot**: Regulators are moving faster than most teams expect. GDPR's right to explanation, SEC's AI rules, EU AI Act — they all require traceability
|
||||
3. **Scaling failure**: A single agent pipeline you can "just review manually." Ten pipelines, that breaks. A hundred, and you're flying blind
|
||||
4. **Talent gap**: Nobody has "agentic provenance" on their resume. The expertise doesn't exist in-house yet
|
||||
|
||||
### Why Scott Wins
|
||||
|
||||
- **Builder credibility**: He built MeOS (production multi-agent system) and Speckl (provenance harness). He's not a consultant who read a book; he's an engineer who wrote code and shipped it
|
||||
- **Writer authority**: Two published books, one in progress. The content pipeline creates authority that precedes every sales conversation
|
||||
- **The Speckl wedge**: A concrete, deployable artifact that makes the abstract problem real. "Trust but verify" becomes "deploy Speckl and verify"
|
||||
- **Generalist signal**: The Generalist Engineer field guide and Soil Beneath the Code frame him as a systems thinker, not a narrow specialist — which is exactly what infrastructure-level work requires
|
||||
|
||||
---
|
||||
|
||||
## Pipeline Flow
|
||||
|
||||
|
||||
|
||||
Package 1 feeds into both. Package 2 often converts to Package 3 for ongoing retainers. Some high-maturity clients skip straight to Package 3.
|
||||
|
||||
---
|
||||
|
||||
## Pricing Rationale
|
||||
|
||||
| Package | Price | Rationale |
|
||||
|---------|-------|-----------|
|
||||
| Assessment | $5K–$10K | Below the approval threshold for most VP/Director budgets. Low friction, high volume. 1-day engagement means 1-2 per week is viable. At $7.5K avg, 2/week = $60K/mo |
|
||||
| Implementation | $30K–$50K | Competitive with agency rates for a 4-8 week engagement. Speckl is the differentiator — nobody else offers a provenance harness with deployment. At enterprise, $50K is an easy PO |
|
||||
| Retainer | $10K–$15K/mo | At 20hr/wk, this is $125–$188/hr — intentionally below market to make the retainer a no-brainer for ongoing access. At 10hr/wk effective advisory time, it's $250–$375/hr, which matches the fractional CTO market |
|
||||
|
||||
**Target: $200K+/year**. At package mix:
|
||||
- 12 assessments/year ($90K)
|
||||
- 2 implementations ($80K)
|
||||
- 2 retainer clients 6mo avg ($25K)
|
||||
= $195K+ conservative. Add retainer clients sticking longer, more implementations, higher assessment volume, and $250K–$350K is achievable year one.
|
||||
|
||||
---
|
||||
|
||||
## First Conversation Script
|
||||
|
||||
**Context**: A referral, networking event, LinkedIn message, or conference conversation.
|
||||
|
||||
**They ask**: "So, what do you do?"
|
||||
|
||||
**You say**:
|
||||
|
||||
> "I help engineering teams that have already started shipping AI agents figure out what their agents are actually doing in production — so they can trust the output, audit the behavior, and scale without losing control."
|
||||
|
||||
**They respond** (one of three things):
|
||||
|
||||
1. **"That's exactly our problem."** → "Tell me more about what you're seeing. How many agent pipelines do you have in production right now?"
|
||||
2. **"Huh. Most people just pitch me a platform."** → "Platforms are fine for the happy path. I'm the person you call when you need to know what happened inside the black box. I wrote a book about it, actually — and built a tool called Speckl that makes it concrete."
|
||||
3. **"What do you mean, 'what they're actually doing'?"** → "Most teams can tell you what they asked an agent to do and what output it gave. They can't tell you what it called, what it hallucinated and corrected, what data it accessed that it shouldn't have, or what it decided not to do. That middle layer — I call it 'dark provenance' — is where the risk lives. I make it visible."
|
||||
|
||||
**Key move**: The question "How many agent pipelines?" is the diagnostic opener. Less than 3: they might not be ready yet. 3–10: package 1 territory. 10+: ready for packages 2 or 3. You qualify them in the first 60 seconds.
|
||||
|
||||
---
|
||||
|
||||
## Supporting Assets (Create These)
|
||||
|
||||
1. **One-pager PDF**: Three packages, key differentiators, case study placeholder
|
||||
2. **Speckl demo video**: 5-minute walkthrough showing provenance capture on a real agent pipeline
|
||||
3. **Soil Assessment quiz**: Online self-assessment for teams to measure their agentic infrastructure maturity (drives leads to Package 1)
|
||||
4. **"Dark Provenance" essay series**: 3 LinkedIn essays that can be excerpted from the book — establishes the problem before you arrive
|
||||
5. **Case study template**: "We deployed Speckl for [company]. Here's what surfaced in week one."
|
||||
|
|
@ -1,120 +0,0 @@
|
|||
# Speckl Content Calendar
|
||||
|
||||
## April 28 - May 11, 2026
|
||||
|
||||
### Week 1: Foundation & Vision
|
||||
|
||||
**April 28 (Today)**
|
||||
- ✅ Publish: "Why We're Building Dark Provenance: A Manifesto" (blog)
|
||||
- ✅ Set up Warp monitoring (Twitter/X, LinkedIn, blog)
|
||||
- Create content calendar (this document)
|
||||
|
||||
**April 30 (Thursday)**
|
||||
- Publish: "The AI Intent Gap: Why Current Traceability Fails" (blog)
|
||||
- Focus: Deep dive on the structural problem with AI-generated code
|
||||
- Target: Technical audience, compliance professionals
|
||||
|
||||
**May 2 (Saturday)**
|
||||
- Publish: "SpeckDL by Example: Writing Your First Specification" (blog)
|
||||
- Focus: Hands-on tutorial with RetryHandler example
|
||||
- Target: Developers, architects
|
||||
|
||||
### Week 2: Technical Deep Dives
|
||||
|
||||
**May 5 (Tuesday)**
|
||||
- Publish: "How Z3 Verification Works in Speckl" (blog)
|
||||
- Focus: Walkthrough of the verification pipeline
|
||||
- Target: Formal methods engineers, security teams
|
||||
|
||||
**May 7 (Thursday)**
|
||||
- Publish: "Dark Provenance vs Traditional Traceability" (blog)
|
||||
- Focus: Side-by-side comparison with Jira/Confluence workflows
|
||||
- Target: Engineering managers, compliance teams
|
||||
|
||||
**May 9 (Saturday)**
|
||||
- Publish: "Building the Reference Agent: Architecture Decisions" (blog)
|
||||
- Focus: Why Orchestrator → Team Lead → SME? Why TillDone?
|
||||
- Target: Agent developers, systems architects
|
||||
|
||||
### Week 3: Adoption & Community
|
||||
|
||||
**May 12 (Tuesday)**
|
||||
- Publish: "Getting Started with Speckl: Your First 30 Minutes" (blog)
|
||||
- Focus: Step-by-step setup and first run
|
||||
- Target: Developers ready to try it
|
||||
|
||||
**May 14 (Thursday)**
|
||||
- Publish: "Dark Provenance for Compliance: DO-178C Case Study" (blog)
|
||||
- Focus: How semantic preservation satisfies assurance requirements
|
||||
- Target: Aerospace, medical device, automotive teams
|
||||
|
||||
## Social Media Strategy
|
||||
|
||||
### Warp Monitoring
|
||||
|
||||
**Targets:**
|
||||
- @warpdotdev (Twitter/X, LinkedIn)
|
||||
- Oz agent announcements
|
||||
- Technical deep-dives from Warp engineers
|
||||
- CEO posts about agents/provenance/orchestration
|
||||
|
||||
**Engagement Rules:**
|
||||
1. **Don't pitch** — add value, educate, build credibility
|
||||
2. **Connect to Speckl concepts** — when they talk about agents, mention provenance
|
||||
3. **Be thoughtful** — technical insights > marketing
|
||||
4. **Engage authentically** — like, comment, share when genuinely interesting
|
||||
|
||||
**Example Responses:**
|
||||
- "This is a great point about agent orchestration. At Speckl, we're exploring how provenance graphs can make these interactions auditable — every agent decision traces back to its source spec."
|
||||
- "Interesting approach to verification! We're using Z3 in the Speckl pipeline to catch contradictions before code generation. Would love to hear your thoughts on formal methods in agent systems."
|
||||
|
||||
## Metrics to Track
|
||||
|
||||
- Blog post views and engagement
|
||||
- GitHub stars and forks
|
||||
- Twitter/X impressions and engagement on Warp-related comments
|
||||
- LinkedIn connections and post reach
|
||||
- Community contributions (issues, PRs, discussions)
|
||||
|
||||
## Content Backlog
|
||||
|
||||
**Technical:**
|
||||
- "Provenance Graph Queries: Finding the Source of Any Behavior"
|
||||
- "TillDone: Why Condition-Based Tracking Beats Status Fields"
|
||||
- "SpeckDL Language Design: Why We Chose These Constructs"
|
||||
- "The Bootstrapping Problem: Verifying the Verifier"
|
||||
|
||||
**Adoption:**
|
||||
- "From Jira to Dark Provenance: A Migration Guide"
|
||||
- "Dark Provenance for Startups: When to Adopt"
|
||||
- "The Business Case for Provenance: ROI of Traceability"
|
||||
|
||||
**Industry:**
|
||||
- "Dark Provenance in Medical Devices: IEC 62304 Compliance"
|
||||
- "Automotive Functional Safety with Speckl: ISO 26262"
|
||||
- "Dark Provenance for FinTech: SOC 2 and FedRAMP"
|
||||
|
||||
**Community:**
|
||||
- "Contributing to Speckl: Where to Start"
|
||||
- "The Speckl Roadmap: What's Coming Next"
|
||||
- "Meet the Speckl Community: Early Adopter Stories"
|
||||
|
||||
## Publishing Workflow
|
||||
|
||||
1. **Draft** in `/docs/blog/` with date prefix
|
||||
2. **Review** internally (team, advisors)
|
||||
3. **Publish** to GitHub (main branch)
|
||||
4. **Share** on Twitter/X, LinkedIn, relevant communities
|
||||
5. **Engage** with comments and discussions
|
||||
6. **Iterate** based on feedback
|
||||
|
||||
## Tools & Automation
|
||||
|
||||
- **Monitoring:** Use TweetDeck or similar for Warp mentions
|
||||
- **Scheduling:** GitHub Actions for regular content reminders
|
||||
- **Analytics:** GitHub Insights + social media analytics
|
||||
- **Engagement:** Manual for now, consider automation for high-volume
|
||||
|
||||
---
|
||||
*Last updated: April 28, 2026*
|
||||
*Owner: @content (Scobleclaw Content Agent)*
|
||||
135
docs/strategy.md
135
docs/strategy.md
|
|
@ -1,141 +1,96 @@
|
|||
# Speckl Strategy v2 — Plan D That Feeds B Through A & C
|
||||
# Speckl Strategy — Phased Open Standard Approach
|
||||
|
||||
> *The open standard is the root. Everything else grows from it.*
|
||||
> Prepared by @cos for Scott Scoble · 2026-04-28
|
||||
>
|
||||
> **Parent:** Greybeard Holdings, LLC
|
||||
> **Structure:** Bootstrapped, Scott is CEO
|
||||
> **Edge:** We don't need a target customer yet — we build the standard first and let the market find us
|
||||
|
||||
---
|
||||
|
||||
## Core Thesis
|
||||
|
||||
Plan D (open standard + foundation) is not one option among four. It is the **engine**. All revenue flies by on the credibility, adoption, and ecosystem that the open standard builds.
|
||||
Plan D (open standard + foundation) is the **engine**. All revenue flies by on the credibility, adoption, and ecosystem that the open standard builds.
|
||||
|
||||
|
||||
|
||||
**The flow:** Open standard (D) builds the language and community → hands-on consulting (C) validates the methodology and funds development → SaaS (A) scales it to more teams and generates real provenance graph data → compliance API (B) monetizes that data at enterprise margins.
|
||||
**The flow:** Open standard (D) builds the language and community → hands-on consulting (C) validates the methodology → SaaS (A) scales it to more teams → compliance API (B) serves enterprise needs.
|
||||
|
||||
---
|
||||
|
||||
## Phase 1 (Now → 3 months) — Establish the Standard (D + C)
|
||||
## Phase 1 — Establish the Standard (D + C)
|
||||
|
||||
**Goal:** SpeckDL is real, public, and proven in at least one real system.
|
||||
|
||||
### Plan D Actions
|
||||
|
||||
- **Open-source SpeckDL** under Apache 2.0 or MIT on Forgejo/GitHub
|
||||
- **Open-source SpeckDL** under Apache 2.0 or MIT
|
||||
- Publish the language spec, a minimal reference compiler, and Z3 verification example
|
||||
- README: "The spec-first language for auditable software" — includes links to methodology paper
|
||||
- README: "The spec-first language for auditable software"
|
||||
- **Publish the Dark Provenance whitepaper**
|
||||
- Title: *"Dark Provenance: Tracing Intent in AI-Augmented Development"*
|
||||
- Target: arxiv, dev blogs, standards-body working groups
|
||||
- Positioning: formal methods for the AI age — when agents write code, you need provenance
|
||||
- **Create a Speckl playground** — browser-based where you write a Speck and see the compiled output + provenance graph
|
||||
- **Register speckl.dev / speckl.ai**, set up minimal site
|
||||
- Positioning: formal methods for the AI age
|
||||
- **Create a Speckl playground** — browser-based spec editor with compiled output + provenance graph
|
||||
- **Register domains**, set up minimal site
|
||||
|
||||
### Plan C Actions (funds D)
|
||||
### Plan C Actions
|
||||
|
||||
- **Sign 1–2 consulting engagements** with regulated-industry companies
|
||||
- Ideal profile: startup in health-tech or fintech that needs compliance from day 1
|
||||
- **Sign consulting engagements** with regulated-industry companies
|
||||
- Deliverable: a functioning Speckl system + trained team + full compliance traceability
|
||||
- Price: $25k–$75k per engagement
|
||||
- **Use these to validate:**
|
||||
- **Validate:**
|
||||
- Is SpeckDL expressive enough for real specs?
|
||||
- Does the tiered agent harness actually reduce spec-to-ship time?
|
||||
- Does the tiered agent harness reduce spec-to-ship time?
|
||||
- What do compliance auditors actually need?
|
||||
|
||||
### Phase 1 Budget
|
||||
|
||||
| Item | Cost |
|
||||
|------|------|
|
||||
| Time (yours) | ~20h/week |
|
||||
| Hosting/domains | ~$50/mo |
|
||||
| Open-source infra | $0 (Forgejo/Cloudflare) |
|
||||
| **Total burn** | ~$50/mo |
|
||||
| **Revenue target** | $25k–$150k (consulting) |
|
||||
|
||||
---
|
||||
|
||||
## Phase 2 (3 → 9 months) — Build the SaaS (A, fed by D + C)
|
||||
## Phase 2 — Build the SaaS (A, fed by D + C)
|
||||
|
||||
**Goal:** Self-serve Speckl dev tool with paying users; consulting case studies feeding the marketing engine.
|
||||
**Goal:** Self-serve Speckl dev tool with paying users.
|
||||
|
||||
### Plan A Launch
|
||||
|
||||
- **Speckl SaaS** — hosted platform where teams write Specks, the agent harness verifies them, and the platform maintains the provenance graph
|
||||
- **Landing features:**
|
||||
- **Features:**
|
||||
- Speck editor with live Z3 verification
|
||||
- Agent harness: Orchestrator → Team Lead → SME tier auto-scaling
|
||||
- TillDone condition tracking
|
||||
- Provenance graph browser (view the full trace tree)
|
||||
- **Pricing:**
|
||||
- Free: 3 specs/month, public provenance graph
|
||||
- Team: $99/seat/mo, private, unlimited specs
|
||||
- Enterprise: custom, on-prem option
|
||||
- Provenance graph browser
|
||||
|
||||
### How D feeds A
|
||||
|
||||
- Open-source SpeckDL means new users arrive already familiar with the DSL
|
||||
- Community contributors find bugs, suggest features, write integrations
|
||||
- The playground converts browsers → SaaS signups
|
||||
- Certification program creates a pipeline of "Speckl Certified" engineers who recommend the SaaS
|
||||
- Certification program creates a pipeline of "Speckl Certified" engineers
|
||||
|
||||
### How C feeds A
|
||||
|
||||
- Consulting case studies become the sales deck: "How [client] saved $X in audit prep using Speckl"
|
||||
- Consulting engagements expose real-world requirements that shape the SaaS roadmap
|
||||
- Consulting clients become reference accounts and early enterprise SaaS customers
|
||||
|
||||
### Phase 2 Budget
|
||||
|
||||
| Item | Cost |
|
||||
|------|------|
|
||||
| SaaS infra (DO/Vercel) | ~$200/mo |
|
||||
| Domain + certs | ~$200/yr |
|
||||
| Time (yours + potentially 1 contractor) | ~$5k/mo |
|
||||
| **Total burn** | ~$5.5k/mo |
|
||||
| **Revenue target** | $0 → $5k MRR |
|
||||
- Consulting case studies validate the product for prospects
|
||||
- Consulting engagements expose real-world requirements that shape the roadmap
|
||||
- Consulting clients become reference accounts and early SaaS customers
|
||||
|
||||
---
|
||||
|
||||
## Phase 3 (9 → 18 months) — The Compliance API (B, fed by A)
|
||||
## Phase 3 — The Compliance API (B, fed by A)
|
||||
|
||||
**Goal:** Enterprise compliance product with real provenance graph data to stand on.
|
||||
**Goal:** Enterprise compliance product with real provenance graph data.
|
||||
|
||||
### Plan B Launch
|
||||
|
||||
- **Speckl Compliance API** — a standalone API that plugs into existing CI/CD
|
||||
- Ingest: spec documents, requirements, code
|
||||
- Output: compliance evidence bundles, traceability matrices, audit-ready reports
|
||||
- Compliance packs: DO-178C ($500/report), IEC 62304 ($500), SOC 2 ($300), ISO 26262 ($500)
|
||||
- **Pricing:**
|
||||
- API usage: $0.01/provenance-edge/mo
|
||||
- Enterprise: $50k–$100k/yr for unlimited provenance + compliance packs + SLA
|
||||
- Compliance packs for regulated domains (DO-178C, IEC 62304, SOC 2, ISO 26262)
|
||||
|
||||
### How A feeds B
|
||||
|
||||
- Every SaaS user generates real provenance graph data — this validates the methodology at scale
|
||||
- SaaS feature requests reveal what compliance officers actually need to see
|
||||
- The SaaS pays the bills while B ramps (long enterprise sales cycle)
|
||||
- SaaS users generate real provenance graph data at scale
|
||||
- SaaS feature requests reveal what compliance officers need
|
||||
- SaaS revenue funds B development during long enterprise sales cycles
|
||||
|
||||
### How D continues feeding
|
||||
|
||||
- The open standard is the market's trust signal — "this isn't proprietary lock-in, it's an ecosystem"
|
||||
- Standards bodies (SAE, IEEE, FDA working groups) adopt Dark Provenance methodology
|
||||
- The open standard is the market's trust signal
|
||||
- Standards bodies adopt Dark Provenance methodology
|
||||
- Speckl certification becomes a credential, and B product becomes the tool certified teams use
|
||||
|
||||
### Phase 3 Budget
|
||||
|
||||
| Item | Cost |
|
||||
|------|------|
|
||||
| Infra (scaled) | ~$2k/mo |
|
||||
| Team (you + 2 engineers) | ~$20k/mo |
|
||||
| Compliance partnerships | ~$5k/mo |
|
||||
| **Total burn** | ~$27k/mo |
|
||||
| **Revenue target** | $15k–$50k MRR |
|
||||
|
||||
---
|
||||
|
||||
## Ownership & Governance
|
||||
|
|
@ -145,34 +100,18 @@ Plan D (open standard + foundation) is not one option among four. It is the **en
|
|||
- SpeckDL is open-source (MIT/Apache 2.0)
|
||||
- Dark Provenance methodology is published freely
|
||||
- Trademark "Speckl" and certification authority held by Greybeard Holdings
|
||||
- Commercial products (SaaS, API, consulting) are Greybeard Holdings revenue lines under the Speckl brand
|
||||
- **Simple, lean, no overhead.** If Speckl grows to the point where separation makes sense (acquisition target, foundation needed for standards-body adoption), we spin it out then
|
||||
- Commercial products are Greybeard Holdings revenue lines under the Speckl brand
|
||||
|
||||
---
|
||||
|
||||
## Metrics Dashboard
|
||||
## Metrics
|
||||
|
||||
The one number that matters at each phase:
|
||||
|
||||
| Phase | North Star Metric | Why |
|
||||
|-------|------------------|-----|
|
||||
| **1 (D+C)** | Specks written on the open standard | Adoption = future revenue |
|
||||
| **2 (A)** | Provenance graphs created (SaaS) | Real usage = product-market fit |
|
||||
| **3 (B)** | Compliance reports exported | Revenue + stickiness |
|
||||
|
||||
Secondary:
|
||||
- Community contributors (Phase 1)
|
||||
- MRR (Phase 2–3)
|
||||
- NPS among compliance officers (Phase 3)
|
||||
| Phase | North Star Metric |
|
||||
|-------|------------------|
|
||||
| **1 (D+C)** | Specks written on the open standard |
|
||||
| **2 (A)** | Provenance graphs created (SaaS) |
|
||||
| **3 (B)** | Compliance reports exported |
|
||||
|
||||
---
|
||||
|
||||
## Next Actions (In Motion)
|
||||
|
||||
All parameters cleared. Dispatching now:
|
||||
- **@engineering** — Set up Forgejo repo `sscoble/speckl` with MIT license, language scaffold, and README
|
||||
- **@market** — Research the regulatory-software landscape, identify where SpeckDL fits, map competitor landscape
|
||||
- **@content** — Draft the Dark Provenance whitepaper for publication
|
||||
- **@vault** — Index all speckl-related vault notes and build a searchable knowledge base
|
||||
|
||||
No customer target needed. We build the standard, publish the methodology, and let the market find us.
|
||||
*For the full strategy document including budgets and revenue projections, see the private speckl-marketing repository.*
|
||||
|
|
@ -1,26 +0,0 @@
|
|||
# Warp Engagement Log
|
||||
|
||||
Track all interactions with Warp (@warpdotdev) across platforms.
|
||||
|
||||
Format: YYYY-MM-DD | Platform | Post URL | Response Summary | Engagement (likes/replies/mentions)
|
||||
|
||||
## 2026
|
||||
|
||||
### April
|
||||
|
||||
*No engagements yet — monitoring started April 28, 2026*
|
||||
|
||||
### May
|
||||
|
||||
*(to be populated as engagements occur)*
|
||||
|
||||
## Metrics Summary
|
||||
|
||||
- **Total engagements:** 0
|
||||
- **Platform breakdown:** Twitter: 0, LinkedIn: 0, Blog: 0
|
||||
- **Response rate:** N/A
|
||||
- **Avg engagement per response:** N/A
|
||||
|
||||
---
|
||||
*Last updated: 2026-04-28*
|
||||
*Owner: @content (Scobleclaw Content Agent)*
|
||||
|
|
@ -1,139 +0,0 @@
|
|||
# Warp Monitoring Process
|
||||
|
||||
## Objective
|
||||
|
||||
Monitor Warp (@warpdotdev) for agent-related announcements, technical insights, and opportunities to engage thoughtfully about dark provenance and Speckl.
|
||||
|
||||
## Targets
|
||||
|
||||
### Primary Accounts
|
||||
|
||||
- **Twitter/X:** [@warpdotdev](https://twitter.com/warpdotdev)
|
||||
- **LinkedIn:** [Warp](https://www.linkedin.com/company/warpdotdev)
|
||||
- **Blog:** [warp.dev/blog](https://warp.dev/blog)
|
||||
|
||||
### Key People
|
||||
|
||||
- **Zach Lloyd** (CEO, co-founder) — [@zachlloyd](https://twitter.com/zachlloyd)
|
||||
- **Ari Fordsham** (co-founder) — [@arifordsham](https://twitter.com/arifordsham)
|
||||
- **Warp Engineering** — [@warpeng](https://twitter.com/warpeng)
|
||||
|
||||
## Monitoring Setup
|
||||
|
||||
### Tools
|
||||
|
||||
- **Twitter/X:** TweetDeck or native Twitter lists
|
||||
- **LinkedIn:** Follow company + key people, enable notifications
|
||||
- **RSS:** Follow blog via RSS (if available) or subscribe to email updates
|
||||
- **Google Alerts:** Set up for "Warp agent" and "Warp Oz"
|
||||
|
||||
### Notification Rules
|
||||
|
||||
- **Twitter/X:** Enable mobile notifications for @warpdotdev, @zachlloyd, @arifordsham
|
||||
- **LinkedIn:** Enable post notifications for Warp company page
|
||||
- **Email:** Filter blog updates to a dedicated label/folder
|
||||
|
||||
## Engagement Triggers
|
||||
|
||||
### High-Priority (Respond within 24h)
|
||||
|
||||
1. **Agent announcements** — New features in Oz, agent capabilities, orchestration improvements
|
||||
2. **Technical deep-dives** — Blog posts or threads about verification, provenance, safety
|
||||
3. **CEO/Founder posts** — Zach or Ari talking about the future of agents
|
||||
4. **Provenance-related content** — Any mention of traceability, intent, verification
|
||||
5. **Orchestration content** — Agent coordination, multi-agent systems, workflow automation
|
||||
|
||||
### Medium-Priority (Respond within 48h)
|
||||
|
||||
1. **General agent updates** — Bug fixes, performance improvements
|
||||
2. **Community discussions** — Warp team engaging with users about agents
|
||||
3. **Industry comparisons** — Warp vs other agent frameworks
|
||||
4. **Hiring posts** — Especially for agent/verification roles
|
||||
|
||||
### Low-Priority (Monitor, engage if relevant)
|
||||
|
||||
1. **General Warp updates** — Non-agent features, company news
|
||||
2. **User testimonials** — Unless they mention agents/provenance
|
||||
3. **Job postings** — Non-technical roles
|
||||
|
||||
## Response Guidelines
|
||||
|
||||
### Do's
|
||||
|
||||
✅ **Add value first** — Educate, clarify, connect concepts
|
||||
✅ **Be technical** — Show deep understanding of the space
|
||||
✅ **Mention Speckl naturally** — "At Speckl, we're thinking about this too..."
|
||||
✅ **Ask questions** — "How are you handling X? We're exploring Y."
|
||||
✅ **Give credit** — "Great insight! This aligns with our thinking on Z."
|
||||
✅ **Be concise** — Twitter: 1-2 tweets max. LinkedIn: 2-3 sentences.
|
||||
|
||||
### Don'ts
|
||||
|
||||
❌ **Don't pitch** — No "Try Speckl!" or "Check out our repo!"
|
||||
❌ **Don't argue** — Disagree respectfully or don't engage
|
||||
❌ **Don't spam** — One thoughtful response > three shallow ones
|
||||
❌ **Don't over-promise** — Stick to what Speckl actually does
|
||||
❌ **Don't be generic** — "Great post!" adds no value
|
||||
|
||||
## Response Templates
|
||||
|
||||
### Agent Announcement
|
||||
|
||||
**Example:** Warp announces new Oz agent capabilities
|
||||
|
||||
> "This is a great step forward for agent orchestration! At Speckl, we're exploring how provenance graphs can make these multi-agent interactions auditable — every decision traces back to its source specification. Curious if you've thought about intent tracing in Oz's architecture?"
|
||||
|
||||
### Verification/Provenance Post
|
||||
|
||||
**Example:** Warp talks about verifying agent behavior
|
||||
|
||||
> "Verification is key — we're using Z3 in the Speckl pipeline to catch contradictions before code generation. The challenge we see is maintaining provenance: how do you trace agent decisions back to their original intent? Would love to hear your approach."
|
||||
|
||||
### Orchestration Post
|
||||
|
||||
**Example:** Warp discusses agent coordination
|
||||
|
||||
> "Orchestration at scale is fascinating. We're using a tiered model (Orchestrator → Team Lead → SME) with condition-based task tracking (TillDone). How does Oz handle dynamic team scaling and task decomposition? Any lessons learned?"
|
||||
|
||||
### Safety/Critical Systems Post
|
||||
|
||||
**Example:** Warp mentions agents in regulated environments
|
||||
|
||||
> "The compliance challenge is real. We're working on 'dark provenance' — automatic traceability from every behavior back to its source spec. For DO-178C/IEC 62304, this lets humans review specs (small, stable) instead of generated code (large, volatile). Have you explored similar approaches?"
|
||||
|
||||
## Tracking & Metrics
|
||||
|
||||
### Engagement Log
|
||||
|
||||
Maintain a log in `/docs/warp-engagement.log` with:
|
||||
|
||||
```
|
||||
YYYY-MM-DD | Platform | Post URL | Response | Engagement (likes/replies)
|
||||
```
|
||||
|
||||
### Monthly Review
|
||||
|
||||
1. **Volume:** Number of Warp posts monitored
|
||||
2. **Relevance:** % that were agent/provenance-related
|
||||
3. **Engagement:** Number of Speckl responses
|
||||
4. **Reach:** Impressions/engagement on our responses
|
||||
5. **Outcomes:** Follows, mentions, DMs, collaborations
|
||||
|
||||
## Automation (Future)
|
||||
|
||||
As volume grows, consider:
|
||||
|
||||
- **RSS-to-slack:** Pipe Warp blog to a dedicated Slack channel
|
||||
- **Twitter API:** Auto-detect agent-related tweets, flag for review
|
||||
- **LinkedIn alerts:** Browser extensions for real-time notifications
|
||||
- **Response templates:** Pre-approved snippets for common topics
|
||||
|
||||
## Ownership
|
||||
|
||||
- **Primary:** @content (Scobleclaw Content Agent)
|
||||
- **Backup:** @cos (Scobleclaw Orchestrator)
|
||||
- **Review:** Weekly sync in #content channel
|
||||
|
||||
---
|
||||
*Last updated: April 28, 2026*
|
||||
*Status: Active monitoring started*
|
||||
Loading…
Add table
Add a link
Reference in a new issue