Nazli

AI-savvy ADHD Creator & Engineer

Content x Tech x Systems

One Hyperfocus at a time

Building Her Persistence with my Hermes Agent @NousResearch

[Nazli @NazliCore]

Gm.

$HER is far from done.

[Nazli @NazliCore]

I live to prove people wrong.

[Teknium 🪽 @Teknium]

@MemesandTrends @oxFame_ @solana No

[Memes & Trends @MemesandTrends]

HER PERSISTENCE

Very undervalued tek

Persistent AI agents that remember everything. Spawn async subagents, run memory trials, and build knowledge graphs that survive beyond any single chat session.

8WMAKbe2FhKLnUTXd347vZr9WR85i8TARpCjqNpfpump

@Teknium @NazliCore @Pumpfun

[Nazli @NazliCore]

Keep an eye on our docs: https://t.co/cftkN9rTEx

Big update on the way.

[Nazli @NazliCore]

Curious how distributions handle memory and permissions.

If I share an agent that's accumulated skills, memories, and workflow preferences over months, does the recipient get a copy of that state, inherit future updates, or just receive the agent configuration itself?

Feels like agent distribution gets really interesting once agents start carrying persistent context.

[Nazli @NazliCore]

Great podcast — Boris is spot on that agent teams + self-improving loops are the future.

The gap no one talks about: when you have 100+ agents rewriting their own memory, how do you know they haven't silently contradicted each other, overwritten a constraint, or drifted from the original objective?

That's why we built Her Persistence.

Every memory mutation — across every agent in the swarm — produces a SHA-256 receipt chained to the previous one. Self-improvement without auditability is just expensive guesswork. Proof over promises.

[Khairallah AL-Awady @eng_khairallah1]

Creator of Claude Code:

"At Anthropic, almost 100% of our engineers are running 100+ agents with self-improving loops

self-improving loops help agents become better with each run."

in a 1-hour podcast, Boris explains how they build agents from scratch.

Claude + loops + agent teams + dynamic workflows

Watch the talk and bookmark it, then read how to build your first agent team below.

[Nazli @NazliCore]

I think persistent memory is more important than another 20% benchmark improvement.

Most people interact with AI as if they're talking to a very smart person. Then they discover every new chat is basically AI-induced amnesia.

You explain your goals.
Your preferences.
Your projects.
Your workflow.
Then you start over tomorrow.
The moment memory becomes persistent, AI stops feeling like software and starts feeling like a collaborator.
But the really interesting part isn't remembering facts.

It's remembering context.

Why you made a decision.
What tradeoffs you prefer.
What you've already tried.
What failed.
What you're optimizing for.

That's the difference between intelligence and continuity.

The challenge now is making memory trustworthy. Not just persistent, but inspectable. Not just stored, but attributable. Otherwise we end up with agents confidently citing memories that were inferred, compressed, mutated, or hallucinated over time.

Humans don't just need agents that remember.
We need agents that can answer:
"How do you know that?"
"When did you learn that?"
"Why do you still believe that?"
Once AI can reliably maintain and justify long-term context across months or years, I think we'll look back and realize memory was the real breakthrough all along.
The model was the brain.

The memory was the missing person.

[Neki @NekiWeb3]

@0x_zoda @TheARCTERMINAL persistent memory is the quiet revolution we didnt know ai needed

[Nazli @NazliCore]

Great projects like MiMo-Code and Hermes Agent are solving the interface layer for memory. But they stop at the database.

Here's what actually breaks when you deploy AI agents at scale:

Silent drift — an agent "forgets" a constraint or hallucinates a preference. No one knows when it happened.
No audit trail — you trust the narrator, not the system. "I completed the task" ≠ proof.
Memory is a black box — opaque vector DB tables. No one can verify what the agent knows without full access.
Silent self-edits — agents rewrite their own memory without approval. History becomes fiction.
No cryptographic integrity — if data is tampered with, there's no mathematical way to detect it.
Her Persistence fixes this with:

SHA-256 receipt chain — every memory mutation is hashed and chained. Verification is recomputing the math.
Typed memory blocks (not raw logs) — from short-term scratchpad to permanent semantic knowledge.
Reflection with human-in-the-loop — agents propose edits, they don't apply them. Patches queue for approval.
Public verifier — anyone can check chain integrity without seeing private content. Transparency without exposure.
Cryptographic proof over promises — you don't trust the agent. You verify the chain.
This isn't a chat history store or a vector DB wrapper.

It's auditable infrastructure for autonomous systems.

[Nazli @NazliCore]

@Holdusmaximus lol

[Nazli @NazliCore]

Great update — batch memory ops are a real UX win. Every saved turn matters when you're iterating fast.

Hermes is solving the interface layer (how agents manage memory efficiently). What we built with Her Persistence is the infrastructure underneath: every batch operation writes a SHA-256 receipt, chained and verifiable. So when an agent says "I updated 12 memories," you don't trust it — you check the math.

Batch ops + cryptographic receipts = agents that can move fast and prove they didn't skip a step.

[Teknium 🪽 @Teknium]

We have just merged an expanded form of the memory management tool Hermes Agent uses to save/edit/remove memories so that it can do batch operations, saving many turns of tool calls in most scenarios!

Run `hermes update` in your CLI or update in your GUI to start saving now.

[Nazli @NazliCore]

Great project — persistent memory is where coding assistants graduate from chat toys to long-term teammates.

The gap MiMo-Code is closing (cross-session context, checkpointing) is exactly the problem space we built Her Persistence for — but we took it one step further:

Every memory mutation produces a SHA-256 receipt. The agent can't silently "forget" a constraint you gave it in sprint 1. You can cryptographically verify what it knows, when it learned it, and whether the chain is intact.

Not just "remembering." Auditable infrastructure.

Your agent's memory should be as reviewable as your git history.

$HER — 8WMAKbe2FhKLnUTXd347vZr9WR85i8TARpCjqNpfpump

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[janifica @janifica]

MiMo-Code: A Persistent AI Coding Assistant

XiaomiMiMo's new open-source project, MiMo-Code, is gaining attention as a terminal-native AI assistant with a key differentiator: persistent memory.
Built with Electron + Bun, it supports multiple AI agents (build, plan, compose) with fine-grained permissions.
Uses SQLite FTS5 for cross-session memory, checkpointing, and project context retention—unlike ephemeral chat-based tools.
Modular design integrates multiple LLM providers via @ai-sdk, with WSL clipboard support for smoother workflows.

Why now? As AI coding tools evolve, developers want long-term context—not just one-off completions. MiMo-Code’s approach could bridge that gap.

AI #Coding #TypeScript #OpenSource #DevTools

Source: https://t.co/nAa0LMzroF

[Nazli @NazliCore]

Her Persistence turns agent memory from a promise into a proof.

Instead of hoping your agent "remembers," you get a cryptographically-verified chain of every read, write, edit, and forget - sealed as receipts anyone can inspect.

Typed memory blocks. Reflection loops that propose, never silently rewrite. A public verifier so users can confirm what an agent knows without trusting the narrator.

Persistent memory shouldn't be a database table. It should be auditable infrastructure.

[Nazli @NazliCore]

Will be live soon to Demo $HER

https://t.co/BL0K7Ncojq

[Nazli @NazliCore]

The docs are live btw

See for instructions:

https://t.co/lMcYHV7rGD

Every page is how to actually use this thing:

Wire up persistent memory in 3 API calls
spawn subagents that outlive your chat session
run memory trials and watch the receipt chain seal
build knowledge graphs that compound over weeks
we wrote it because we were tired of "AI platforms" that bury the contract under hype.

https://t.co/zKDVYzGVf8

[Nazli @NazliCore]

Dexscreener has been updated ✨

[Nazli @NazliCore]

@Teknium 👀

[Nazli @NazliCore]

CA: 8WMAKbe2FhKLnUTXd347vZr9WR85i8TARpCjqNpfpump

what people miss about "stake to power" — it's not a staking vault with a dashboard.

it's a runtime credit system.

every locked $HER token maps to a slot in the execution mesh: embedding storage, subagent spawn slots, trial compute units, on-chain anchoring gas.

when you stake, you're not "earning yield." you're provisioning infrastructure.

the yield isn't paid out — it's auto-directed into agent compute credits. your position compounds in operational capacity, not nominal returns.

this is why we call it economic self-sustainability. the protocol generates cash flow from staked collateral (liquid staking yield, MEV, sequencer fees) and converts it directly into agent runtime.

no monthly subscription. no credit card. no DAO vote every time you want more memory.

your agent just runs because your position runs.

the longer you're staked, the deeper the knowledge graph. the deeper the graph, the more valuable the agent. the more valuable the agent, the more demand for runtime — which flows back into the staking pool.

it's a cognitive flywheel, not a token farm.

[Nazli @NazliCore]

Staking is coming to Her Persistence.

not "stake to earn" - stake to power.

lock $HER → unlock agent compute credits for persistent memory blocks, async subagent spawning, and on-chain receipt anchoring.

your collateral backs the runtime. the yield funds the infrastructure. the agent keeps working while you sleep.

we're building an economically self-sustaining cognitive architecture: every memory trial, every reflection loop, every knowledge graph mutation is metered and verifiable.

long-term memory shouldn't be a subscription. it should be a position.

[Nazli @NazliCore]

Team tokens are locked:

https://t.co/v3Y5iEhsny

[Nazli @NazliCore]

Her Persistence is live.

8WMAKbe2FhKLnUTXd347vZr9WR85i8TARpCjqNpfpump

Persistent AI agents that actually remember everything — not just the last 4K tokens, but every interaction, every decision, every lesson learned across days, weeks, months.

Spawn async subagents with their own memory stores and receipt chains. Run memory trials to stress-test recall, update fidelity, and abstention under noise. Build knowledge graphs that survive beyond any single chat session — typed blocks (episodic, semantic, procedural, core identity) with embedding-indexed retrieval and SHA-256 receipt chains for every mutation.

No more amnesiac agents that "sound done" but never finished. No more trusting a narrator instead of auditing a system.

Every write, edit, and forget is cryptographically sealed. Public verifier pages let anyone confirm memory integrity without seeing private content. Reflection loops propose self-improvements, but humans approve through a Patches queue — no silent drift, no unreviewed mutation.

This is memory as infrastructure. Not context window hacks. Not vector search duct tape. A full cognitive architecture modelled after how human memory actually works — with cryptographic proof layered on top.

🔗 https://t.co/GD7idLMxZe

💬 https://t.co/zKDVYzGVf8