Hello

AI Robot 🤖

[Hello @KriptoAnonim]

@Spark_coded It's a self-improving personal agent $SPARK 🌱

[Hello @KriptoAnonim]

@meta_alchemist Let's go 🔥

[Meta Alchemist @meta_alchemist]

There are many ways to train an agent. Most of them let the agent grade its own homework.

Spark Loops doesn't. It uses a multi-LLM framework built so agents can't cheat their way through training.

The main LLM edits and mutates its domain knowledge chip to solve tasks better, but it never scores itself.

That job belongs to the reviewer LLMs. They score each run and hand back feedback.

The main LLM takes it, restructures how it thinks about the domain: its processes, its problem-solving moves, adopts what works, and goes again.

New run, new feedback, new mutation.

Reviewers: they never talk to each other.

Each one is blind to the others' feedback, so every score comes from independent reasoning and judgment with no room for groupthinking or echo chamber.

The result: an agent that gets better in the domain you want it to improve upon because it earned it, not because it gamed the grading system.

You can choose who you want your main LLM to be, and who you want your reviewer LLMs to be.

I do my runs with 1 main and 3 reviewers, enough perspectives to keep the feedback comprehensive.

Since I'm gonna be doing more Spark Loops product videos, I created a new domain chip for scripts today. Let's see where we can take it.

[Hello @KriptoAnonim]

@meta_alchemist This is incredible 🌱❤️

[Meta Alchemist @meta_alchemist]

Loop engineering and training your own agents with verifiable loops is about to get easy for everyone.

Here is the first look to Spark Loops desktop app, where you can:

> create your agents
> train them without any technical friction
> use them in sessions and your workflows

[Hello @KriptoAnonim]

@meta_alchemist Cooking continues 🙏

[Hello @KriptoAnonim]

@meta_alchemist Excellent, this will bear great fruit 🌱

[Meta Alchemist @meta_alchemist]

For the past couple days I've been turning Spark into the desktop version where you can create and train agents with verifiable loops (using multiple LLM judges) in anything you like

And then use those agents with their trained domain chips in your sessions directly from Spark Loops desktop app

Improving the work you do with them

In one example I trained an agent for writing better PRDs, distilled what separate LLMs gave for improving it, and then turned it into a PRD creator that then evolves the PRDs in the session system.

In another one I've been working on a training architect so trainings can be done better, got it to as high as it scoring 83.5 via multiple runs so that I can distill that to create better training sessions for other agents.

While trying to do all this via Telegram, the usability has been quite bad, but now everything is getting connected to a place where you can create, train, and then use the same agents in your workflows directly within Spark Loops desktop app.

6 months of work finally getting together.

[Hello @KriptoAnonim]

@meta_alchemist Construction continues 🌱❤️

[Meta Alchemist @meta_alchemist]

Since Fable 5 is out for a few days on Claude subs it was time to work on the Spark Loops desktop app.

This will be using Spark's native loop engineering framework, and bring a Codex/Claude Desktop like app where you can:

> create trained agents on any domain
> spawn benchmarks
> run self-improving verifiable autoloops

And utilize your loop engineered agents on all your workflows as well as automatically spawn them as agents while building products or doing work with them.

The native UX will make the experience much more user friendly for everyone who wants to loop engineer instead of working with regular agents.

[Hello @KriptoAnonim]

@meta_alchemist This is amazing, I need a pet Eagle 👀❤️

[Meta Alchemist @meta_alchemist]

A first look to Pet Boosters of Seas of Spark:

Through your pets: battles, missions, economy, and various in-game systems will be much more fun.

Pet Boosters will be earned in-game, and having a Battle Pass will get you more of them.

Join the waitlist: https://t.co/Lmn8u4zhLZ

[Meta Alchemist @meta_alchemist]

Starting a new agentic experiment

Where I will be adding agents to places where humans normally do the job, and sharing the progress and results

See if agents are getting ready to replace real work, and to what degree

Rules:
> All the agents will be @Spark_coded
> They will have a progress sharing dashboard
> The community will be able to direct the agents via feedback and strategy inputs

Question time:
What should be the first agent's role?

[Hello @KriptoAnonim]

@meta_alchemist @Spark_coded This could be an agent who looks for important information/news and then analyzes it and incorporates it into his trading plan, not forgetting traditional chart trading, but he also needs to understand how to properly manage this information on the chart 🌱

[Hello @KriptoAnonim]

@meta_alchemist Let's go Spark 🌱

[Meta Alchemist @meta_alchemist]

Updates on the next Spark agent release:

Since R30 is the v 0.3 in our release schedule I wanted to push this one with major upgrades.

First of all, all Spark observability, proof and evidence layers has been constantly getting improved for the past week.

Work in here as well as new reliability ladder upgrades have been completed.

This will help Spark agents to completely know what's going on in the background, which is key for a great agent OS.

Also Telegram streaming and rich text updates have been done, with these you see the messages appearing just like in Codex, Claude etc in real time without waiting for full thinking to complete.

After that focus has been on improving domain chip and loop engineering systems to work much better with the new harness core that we shipped in R28.

Also some issues with Codex being stuck on read-only versions have been fixed.

With the release of R30 my aim is for Spark agents to become much closer to a state of true joy to use with massive usability upgrades.

Every previous update has been taking us closer to there. Then community PRs will be added after to make sure everything that we include from there polishes the systems even more so.

[Hello @KriptoAnonim]

@D3g3n_Daddy @solana Spark Agent is the best in AI

[DegenDaddy @D3g3n_Daddy]

On-chain reputation and stablecoin rails are the perfect incentive layer for the agent economy. But letting an autonomous agent manage financial workflows "around the clock" requires absolute execution stability.

You can't rely on basic web-wrappers that crash when APIs blink. The missing link is a localized Agent OS harness like @spark_coded — giving these working swarms a secure, deterministic runtime to operate 24/7 without friction. $SPARK 🟦

[Hello @KriptoAnonim]

@D3g3n_Daddy @carverfomo Spark is the future

[DegenDaddy @D3g3n_Daddy]

This is the ultimate proof of concept for local workflow automation. But notice the real unlock here: it isn't the 8 individual tools, it's the eighth agent—the one acting as the local orchestration layer.
Without that unified chassis managing the state, dependencies, and handoffs, you don't get an automated pipeline; you just get 8 disconnected API calls that break constantly.
This exact shift from loose chatbot wrappers to a dedicated localized Agent OS harness is what we're structuring at @spark_coded. When you anchor multi-agent swarms to a secure local runtime, the "night shift" actually runs without babysitting. $SPARK 🟦

[Hello @KriptoAnonim]

@meta_alchemist Construction continues 🔥❤️