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What if a system didn't just investigate its failures, but actually learned from them?
Most systems record what went wrong. The problem is that the next decision often starts from zero.
I built AFTERMATH.
Every Failure Should Leave a Safeguard.
🧵
It's time to remember.
Total Recall is open for building.
tatum.io/hackathon/total-rec…
You have three weeks to ship during the #hackathon:
🧠 Walrus Memory on Mainnet
⚡ Tatum to help you build and ship
💰 $4,500 in prizes
An AI agent can make the right decision, verify it, and still make a dangerous mistake later.
Why?
Because the assumption behind its decision may have changed and the agent may never notice.
I built something to address that problem.
Introducing Hermes Assumption Ledger.
Plugin: github.com/itzebe/hermes-ass…
Hermes catalog PR: github.com/NousResearch/herm…
Built for the Hermes Agent ecosystem.
@NousResearch
..restarts
- No external LLM calls in its verification core
The goal is bigger than remembering what an agent did.
It's remembering what its decisions depended on and refusing to treat a known false assumption as true again.
..agent starts a new session.
This is not just another prompt or instruction telling an agent to be careful.
It's a persistent plugin with:
- SQLite-backed state
- Deterministic evidence verification
- Provenance tracking
- Dependency-aware blocking
- Persistence across...
..contradiction.
It also understands dependencies.
If one assumption becomes invalid, only decisions that depend on it are blocked.
Unrelated decisions can continue.
And the ledger persists across restarts, so previously discovered contradictions don't disappear when the...
But here's the important distinction:
A contradiction is NOT the same as uncertainty.
If evidence proves an assumption false, it becomes INVALIDATED.
If a server returns HTTP 500, DNS fails, or a request times out, the result is UNVERIFIED.
An outage must never erase a known
Assumption Ledger gives operational assumptions a lifecycle:
🟢 VALID
🔴 INVALIDATED
🟡 UNVERIFIED
⏳ STALE
Every assumption carries evidence and provenance.
The system uses deterministic verification rather than asking another AI model whether something is true.
The problem is simple:
Agents operate on assumptions.
A file exists.
A transaction is authorized.
A service is available.
A condition has been satisfied.
But assumptions are not permanent facts.
When evidence changes, decisions built on those assumptions should change too.
What if the next big idea doesn't fail because it's impossible to build, but because the person who imagined it never believes they can build it?
That's a thought I've been sitting with lately.
🧵
Every builder needs a base.
Your idea doesn't have to remain a thought, a sketch, or a someday project.
Give it a starting point.
Build your idea with Base44:
base44.com/
Because some of the most useful things in the world probably started as ideas that someone else would have dismissed as too complicated.
And the person who finally builds them might not be the person anyone expected.
What I find most interesting isn't just the technology.
It's the shift in perspective.
Instead of immediately asking, "Do I have the technical skills to build this?"
You can start by asking, "What problem do I want to solve?"
That's where @Base44 caught my attention.
You can describe what you want to create, use AI to help build it, refine the result, and turn your idea into a real, usable product.
It's a different way to approach the journey from imagination to execution.
I wanted to explore a different possibility: what happens when you stop treating building as something reserved for people with all the technical answers?
What if you could start with the idea itself and develop it into something useful?
For many people, that gap looks like technical knowledge, complicated development processes, time, or simply not knowing where to begin.
Eventually, a promising idea becomes another "maybe someday" project.
And that's the frustrating part.
We live in a world full of ideas.
People see problems every day. They imagine better ways to do things. They picture products that could make someone's life easier.
But there's often a huge gap between imagining something and actually bringing it to life.
A failure should change what a system knows and considers the next time.
AFTERMATH — Every Failure Should Leave a Safeguard.
Built for the Total Recall: @tatum_io × @WalrusProtocol hackathon.
Track: Smart Agents for Real Life.
Each component has a distinct responsibility.
The goal is bigger than transaction verification.
Payments, agent actions, automated workflows and operational incidents all create lessons worth preserving.
AFTERMATH is built around one principle: