Launch copy and outreach drafts

Canonical stable installation: pip install implicit-ai. Stable release: https://github.com/zeitcow/implicit-core/releases/tag/v1.0.0.

Repository and package

Public repository: zeitcow/implicit-core. Description: The experience layer for AI agents - versioned environment addressing and selective state materialization. Topics: ai-agents, agent-environments, experience-virtualization, lazy-materialization, python, mcp, provenance. Package: implicit-ai; import implicit. See RELEASE_PLAN.md for availability/fallbacks.

Landing page / README hero

Implicit is the experience layer for AI agents. Virtualize large agent environments. Materialize only the state each experience actually needs. Keep your agent, learner and evaluator; connect a Python adapter. Run an offline benchmark, inspect provenance, and use bounded local MCP tools. Calls to action: Install, Run the demo, Create an adapter, Read the benchmark.

Documentation site structure

Home, install, quickstart, architecture, adapters, benchmark, FAQ, security/privacy, troubleshooting, MCP and agent integration each have stable static pages and mirrored Markdown. BENCHMARKS.md is the authoritative result/limitation page. No login or JavaScript is needed for facts. The canonical URL is explicit in RELEASE_PLAN.md.

Announcement / GitHub Release

Introducing Implicit Core: the experience layer for AI agents. It represents large environments with versioned addresses and loads pages only when needed, preserving your native execution/evaluation stack. RC1's measured 155/155 state/tool/reward equivalence came with 93.88% aggregate retained serialized/materialized-state reduction and approximately +0.554 seconds/case mean full-pipeline latency overhead. These are not RAM measurements. RC2 adds bounded local MCP and agent integration assets; its validation is separate. Try the public toy and compare your workload before adopting it.

Hacker News draft

Show HN: Implicit - virtualize agent environments and materialize only needed state

We built a Python experience layer with versioned addressing, progressive paging and durable provenance. You keep your agent/framework/evaluator. The offline toy demonstrates eager versus selective state. The rc1 native population preserved 155/155 systems cases with 93.88% aggregate retained serialized/materialized-state reduction, adding approximately +0.554 seconds/case mean full-pipeline latency. No RAM or learning claim; native replay assets are not in the public bundle. We are interested in adapters for environments with large unused state.

X draft

Implicit: the experience layer for AI agents. Versioned environments, selective state materialization, local journals and MCP. Keep your stack; benchmark your own workload. Offline demo and adapter examples in the approved release. https://github.com/zeitcow/implicit-core

LinkedIn draft

Agent environments often contain much more state than one interaction needs. Implicit supplies versioned experience addressing, progressive loading and durable provenance while leaving native semantics in your adapter. It includes Python protocols, an offline demonstration and bounded local MCP. We are preparing examples for teams evaluating large, separable environments. https://github.com/zeitcow/implicit-core

Technical lab outreach draft

We would like to compare environment construction and selective materialization on a representative workload using your existing agent/evaluator. Implicit offers versioned addresses, provenance and offline Python adapters. We would measure state/tool/reward equivalence, all storage categories and complete latency, and preserve your data privately. Are you interested in reviewing an adapter example? [Owner selects recipient and authorizes sending.]

Design-partner outreach draft

If environment state construction or recovery makes your agent workflow difficult to operate, we can evaluate a small adapter without changing your learner/framework. The first goal is measured equivalence and a transparent byte/latency comparison, not a guaranteed speedup. [Owner approves recipient, terms and sending.]

Investor technical summary draft

Implicit Core supplies experience virtualization: a versioned address plane, selective state loading, lifecycle evidence and explicit recovery. RC1's 155-case systems comparison showed 155/155 equivalence, 93.88% aggregate/99.55% median retained serialized/materialized-state reduction and approximately +0.554 seconds/case mean full-pipeline latency overhead. No RAM, learning, allocator or universal advantage is established. Adoption hypotheses concern large separable environments; product evidence includes clean installation, independent adapter shapes and local MCP rehearsal. No commercial traction, revenue or private research result is asserted.

Repository/release copy is approved for launch. Social and outreach drafts remain unsent; sending requires explicit account/channel and recipient authorization. No paid assets are used.