Hone Training

Big news! 🗞️ 🌊 We just merged the first version of the codebase for @bittensor subnet 5 : github.com/manifold-inc/h… and we are opening up 1% incentive for our live production test! Validator is now running. 1/3 🧵

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The most important thing is to expose the endpoints needed for sending the ARC-AGI problems — as for the solution, you can do whatever you want. 🫱🏻‍🫲🏼 3/3 🧵

There are no constraits on miner solutions. Eg. miner starter code uses an OpenAI key to solve the @arcprize (ARC-AGI 2) problems, and you can: - use another provider - more prompt engineering - finetune your own LLM - try new methods like HRMs 2/3 🧵

Big news! 🗞️ 🌊 We just merged the first version of the codebase for @bittensor subnet 5 : github.com/manifold-inc/h… and we are opening up 1% incentive for our live production test! Validator is now running. 1/3 🧵

Hash Rate - Ep 136: Hone ($TAO Subnet 5) Chasing AGI 🧙 Guest: @0xcarro of @traininghone Can a Bittensor subnet crack AGI before Elon or Sam? Hone (SN5) thinks it can. 00:00 Intro to Hone and AGI 02:52 Understanding the Arc AGI Benchmark 08:48 Introducing JEPA & Hone's Approach to AGI 23:31 The Data Rich Get Data Richer 26:14 The Most Ferocious Form of Capitalism 32:14 Final Thoughts and Future Directions

GM ☀️ Latest @arcprize results : GPT 5 : 9% vs 7% in public bench Grok 4 : 22% vs 16% in public bench Gonna keep optimizing, in parallel we should run the mainnet test soon so we can launch! 🔥🚀

@EdwardMcDonne12 @JosephJacks_ @arcprize 🦾

@JosephJacks_ @arcprize @arcprize is an interesting project. If @traininghone can win this it would definitly turn some heads. Maybe share $700k prize money with its token holders! Haha Lets go!

Bittensor Subnet 5 is permissionlessly incentivizing hill-climbing of ARC-AGI … If the top incentive mechanisms are any indication (64, 62, 4, etc), we will see Bittensor win the @ARCPrize. 🥇 👀 🔥

For ARC AGI 2, we are still tweaking the difficulty of transformations so that our benchmarks fit well with the public one too, for now we're at 28% with `GPT-5` (vs 7% in the public benchmark) More soon! 🦾🧵 5/5

For ARC AGI 1 (the 1st part in our pipeline) : - `Gpt5-medium` : on our synthetics (`57%`) vs public ARC benchmark (`56%`) - `O3-medium` : on our synthetics (`49%`) vs public ARC benchmark (`53%`) - `Grok-4` : on our synthetics (`60%`) vs public ARC benchmark (`67%`) 🧵 4/5

Update on Subnet 5: 🗞️🪡🧶 The core of our synthetic data generation is ARC AGI 1, the difference between that and ARC AGI 2 is the difficulty & complexity of tasks. For this we use github.com/google/ARC-GEN for ARC AGI 1 tasks generation 🧵 1/5

Learn more about our mission: hone.training

We are happy to announce Subnet 5, Hone · Hierarchical AI Pretraining, a collaboration between @manifoldlabs & @latentholdings