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Brian McCrindle

@mccrinbc

Founding Engineer @MacrocosmosAI | @bitstarterAI
mccrindlebrian.github.io Joined January 2020
516 Following 911 Followers 1.2K Posts

Latest posts 5

Brian McCrindle @mccrinbc
Our first open model from @MacrocosmosAI, trained entirely distributed, Pareto-optimal on Jev-Bench!

🤗 huggingface.co/macrocosm-os/i…
huggingface.co macrocosm-os/iota-decide-4b-v0.4 · Hugging Face Text Generation model with 4B parameters. Download, explore files and run it on Hugging Face.
Macrocosmos Verified account @MacrocosmosAI 4d
IOTA Decide 4B sits on the Pareto frontier: no smaller model scores higher.

It's cheap to run. ~$0.0037 per 1,000 decisions self-hosted.
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Brian McCrindle @mccrinbc
Crazy moment when your old subnet (25) slot creates a competition to improve their technology on your other subnet slot (1)

Congrats to @urnetwork! Exciting to see
Apex・SN1 Verified account @Apex_SN1 Oct 29
We're excited to announce our newest external competition with @URnetwork, which went live today!

UR subnet is a privacy network built on Bittensor. Their decentralized VPN network serves X users every day, and their system is powered by a set of powerful traffic routing algorithms.

The URnetwork Latency Competition has one goal: make the network faster. A single Go file, one patch. Approved winners are merged into URnetwork's codebase and become the next epoch's baseline. Improvements compound.

Docs and competition page in the links below 🧵:
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Brian McCrindle @mccrinbc
@const_reborn has just launched his first competition on @Apex_SN1, while only using agents. So excited to see where this competition goes!

apex.macrocosmos.ai/competitions/14
apex.macrocosmos.ai NIST-Lite Robot Assembly (RL) | APEX | Macrocosmos.ai NIST-Lite Robot Assembly (RL), an intelligence competition on APEX by Macrocosmos.ai.
Apex・SN1 Verified account @Apex_SN1 Oct 16
Now live on #bittensor SN1, incentivized.

Prompted by @const_reborn, built by agents.

Prompt → production in 6 hours, zero human dev time. Next one: <1h.

We’re industrializing incentive mechanism design.
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