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Launch playbook — ECHOFORM Ghost Memory

Pre-drafted posts you can fire off yourself. Nothing here is posted automatically; the goal is to remove the blank-page friction so launch day is "copy / paste / hit send" instead of "what do I write?"

1. Hacker News (Show HN)

Title (80 char max — HN truncates harder than you think)

Show HN: ECHOFORM – unlimited LLM memory via a single 64 KB hypervector

Body (HN rewards short, no marketing voice)

ECHOFORM is a memory substrate for LLM agents. Instead of retrieving chunks back
into the prompt every turn (Mem0 / Zep / Letta style), it stores the entire agent
history as ONE 64 KB FHRR hypervector and uses it to pre-bias the model's
residual stream at inference time. The bias call is O(1) regardless of how many
episodes you've stored — context-token cost is exactly zero.

Every recall ships with an Ed25519-signed "forgetting certificate" attesting
(D, β, episode_count, archive_count, theoretical_recall_curve), canonicalized
with RFC 8785 JCS. A 100-line standalone verifier reproduces the math offline.
Side effect: GDPR Article 17 erasure ships with a cryptographic proof, not a
promise — DELETE /v1/forget purges the live superposition AND the S3 cold tier
(Object Lock GOVERNANCE + STS BypassGovernanceRetention) and signs an
ErasureReceipt.

Stack: Python 3.11+, FastAPI, Postgres 16 + pg_partman, Redis, S3/MinIO,
OpenTelemetry, mamba-ssm worker on the GPU path. Apache 2.0. CI is green on
ubuntu/macos/windows × py3.11/3.12.

One-click install (`curl … | bash` brings up the full prod stack and opens a
working web UI):

  https://github.com/OpenAgentic-Labs/echoform-ghost-memory

Architecture deep-dive: docs/architecture.md
Provenance (built by a 30-agent dispatch under the mind-build@v2 workflow):
BUILT_FROM.md

Looking for: people poking holes in the FHRR capacity math, the JWS spec, and
the governance-bypass flow. v0.2 is the auth-bypass fixture + Locust load test.

Timing: Tuesday or Wednesday, 8:00 AM Pacific. Avoid Mondays and weekends.

2. X / Twitter (thread)

Tweet 1 (hook)

We just shipped open-source unlimited memory for LLM agents.

Zero context tokens.
Zero weight updates.
A single 64 KB hypervector holds the whole agent history.

And every recall comes with a cryptographic forgetting certificate.

A thread.

https://github.com/OpenAgentic-Labs/echoform-ghost-memory

Tweet 2

The trick: instead of retrieving chunks back into the prompt (Mem0 / Zep /
Letta), ECHOFORM stores history as one FHRR hypervector at D=8192 and uses it
to pre-bias the model's residual stream at inference time.

The bias call is O(1). Storage is O(1). Tokens used per turn: 0.

Tweet 3

Every recall ships an Ed25519-signed JWS attesting:

(D, β, episode_count, archive_count, theoretical_recall_curve)

…canonicalized with RFC 8785 JCS. A 100-line standalone verifier reproduces the
math offline. You can prove what the system retains AND what it has provably
let go.

Tweet 4

Side effect we didn't expect: this is the only memory layer that answers GDPR
Article 17 with a proof, not a promise.

DELETE /v1/forget purges the live superposition + the S3 cold tier (Object
Lock GOVERNANCE + STS BypassGovernanceRetention) and signs an ErasureReceipt.

Tweet 5 (CTA)

Apache 2.0. CI green on ubuntu/macos/windows × py3.11/3.12.

One-click install brings up the full prod stack (Postgres + Redis + S3 +
worker) and opens a working web UI.

If you build agents, give it a spin. Star the repo if it's useful.

https://github.com/OpenAgentic-Labs/echoform-ghost-memory

3. LinkedIn

Less snark, more "why this matters to enterprises."

Today we open-sourced ECHOFORM — a memory substrate for production LLM agents.

Most agent memory systems on the market today (Mem0, Zep, Letta) solve memory
as a retrieval problem: fetch relevant chunks, stuff them back into the prompt,
watch the token bill scale with episode count.

ECHOFORM inverts the direction. Agent history is encoded as a single 64 KB
hypervector (FHRR — Fourier Holographic Reduced Representations) and used to
pre-bias the model's residual stream at inference. The bias call is O(1)
regardless of how many episodes were stored. Context-token cost is zero.

The wedge for enterprise: every recall ships with an Ed25519-signed
"forgetting certificate" — a closed-form, cryptographically verifiable
attestation of what the system retains and what it has provably let go.

That property is, as far as we can tell, the only audit-grade answer to
GDPR Article 17 ("right to be forgotten") in the LLM memory space. Most
existing solutions can prove deletion of *records*; ECHOFORM proves
non-recoverability of *content*.

Apache 2.0. One-click install. Postgres + Redis + S3 + GPU worker stack.
CI green across Linux/macOS/Windows.

Repo: https://github.com/OpenAgentic-Labs/echoform-ghost-memory
Architecture: https://github.com/OpenAgentic-Labs/echoform-ghost-memory/blob/main/docs/architecture.md

If your team is shipping agents and wrestling with the cost-of-memory or
data-retention questions, we'd love a critical read.

#LLM #AIAgents #OpenSource #GDPR #MLOps

4. r/LocalLLaMA + r/MachineLearning

r/LocalLLaMA rewards practical demos. Lead with the UI screenshot and the "zero context tokens" line.

r/MachineLearning rewards the FHRR math. Lead with the capacity ceiling + forgetting curve.

For r/MachineLearning, post under the [P] (project) flair. Don't promise benchmarks unless you have them — the sub punishes that.

5. Communities to ping (one-to-one, not spray)

  • LangChain Discord — #showcase channel. Frame as "memory adapter."
  • LlamaIndex Discord — same.
  • Mamba Discord — the mamba-ssm worker is a nice hook here.
  • Cryptography mailing list — the RFC 8785 JCS + Ed25519 certificate story is interesting independent of the LLM angle.
  • EU GDPR / privacy-engineering communities — the Article 17 proof is novel and worth a write-up.

6. Press / writeups (cold email template)

Subject: Open-sourced today — cryptographic "forgetting certificate" for LLM memory

Hi <name>,

We just shipped ECHOFORM, an open-source memory layer for LLM agents that
answers GDPR Article 17 with a cryptographic proof of non-recoverability —
not a promise of deletion.

The repo is here: https://github.com/OpenAgentic-Labs/echoform-ghost-memory

If the privacy-engineering / LLM-memory angle is interesting for <publication>,
I'd be happy to walk through the math, share the dossier (full provenance is
in BUILT_FROM.md), or hand off any artifacts you need.

— <your name>

Targets worth a try: The Register, TechCrunch (Connie Loizos covers infra), Hacker Noon, The New Stack, InfoQ.

7. Things NOT to do

  • Don't buy stars. GitHub flags them and they don't convert.
  • Don't post the same wording to 10 subreddits in one hour — that's how accounts get shadowbanned.
  • Don't claim benchmarks you haven't run. The forgetting-curve math is defensible; the latency p99 is currently in-process only (v0.2 ships the Locust cross-process numbers).
  • Don't lead with the AI buzzwords. Lead with the concrete property — "one 64 KB vector, zero context tokens, signed forgetting" — and let readers draw the conclusion.

8. Measure what matters

After each launch beat, check:

  • Stars added: gh api repos/OpenAgentic-Labs/echoform-ghost-memory/stargazers --paginate | jq length
  • Clones: gh api repos/OpenAgentic-Labs/echoform-ghost-memory/traffic/clones
  • Top referrers: gh api repos/OpenAgentic-Labs/echoform-ghost-memory/traffic/popular/referrers
  • Issues opened — real engagement signal: readers ask questions

If a channel produces traffic but no stars, the README hook isn't landing. If it produces stars but no issues, the project looks shiny but nobody's actually using it.