Joined November 2018
An AI might find loopholes in the rules we set for it. But those loopholes might not apply in the world running its simulation.
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1/ In TIG, writing an algorithm is only the beginning. Its reward design also asks whether other participants actually use it to produce qualifying results. That creates a feedback loop between the people improving software and the people running it.
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3/ The public code calls this metric adoption. It feeds into rewards for eligible code submissions. When multiple implementations reference the same registered algorithmic advance, their adoption also contributes to that advance's reward calculation.
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4/ Here, adoption means use inside TIG's competition. The useful idea is to reward research as others put it to work, rather than treating submission as the finish line. That could help useful methods keep attracting contributors as implementations improve.
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Thinking out loud here.. I've always assumed a #Bittensor subnet would struggle to replicate what $TIG is building But the more I think about it.. the more I'm not sure $TIG has its own Optimisable Proof of Work system.. where algorithms compete, benchmarkers test them, and then innovators get rewarded based on adoption $TAO obviously works differently.. with miners, validators and Yuma Consensus deciding how rewards get distributed But.. Given how flexible #Bittensor subnets can be.. whats actually stopping someone from building a subnet with its own version of $TIG's benchmarking, verification and reward system? Could someone essentially recreate something similar to OPoW inside #Bittensor? Someone like @Apex_SN1, for example.. Obviously.. just because something is technically possible doesn't mean it would economically work Still questions around incentives.. verification.. licensing.. how rewards are distributed.. and whether it would all work properly at scale And maybe $TIG does have some very real advantages from being built specifically for algorithmic discovery But.. does that mean theres something fundamental about $TIG's architecture that would make it incredibly difficult for a #Bittensor subnet to replicate? Or.. does it basically come down to who designs the better system and executes it properly? I can already see the replies coming.. lol I'm not trying to FUD anything here.. I'm obviously heavily interested in both $TAO and $TIG I just think its a genuinely interesting question worth exploring.. Because the more I think about this stuff.. The less convinced I am that its as simple as $TIG = algorithms and $TAO = intelligence My dumb monkey brain does not know the actual answer.. So happy to be educated 👍
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I’m converging toward a final design on the V2 AI PoW work, which is the main remaining ingredient for accomplishing the next big roadmap item, the Useful Inference Routing Protocol. I’m looking forward to showing it off once it’s ready.
$NOCK may build a usage based burn into its chain in some form, if this happens and orchestration/useful routing comes to fruition it will be arguably the strongest fundamental tokenomic play ever, honestly struggling to see how it could get much stronger on paper. Capped supply. Halving tightening. Mechanical revenue capture. Supply burns. Supply sinks (compute markets, programmable eco on top). as I’ve mentioned imagining a dream tokenomic coin from scratch will look possibly like $NOCK a few upgrades from now.
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Nockchain has successfully entered its fourth EON, ~18 months after genesis. At block #170,501, emissions decreased by 25%, from 2,048 to 1,536 NOCK per block.
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1/ NOCK's fee design asks a useful question. What does a payment leave behind? Its public code charges approval data at a lower rate than the data creating new spendable balances. The difference is about what nodes must keep track of next.
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3/ Nockchain's Bythos rules price witness data, which includes spending proofs, at a quarter of the output-data rate per measured unit. There's also a minimum fee. This is a pricing rule in the code, not a promise that every payment costs 75% less.
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4/ Bitcoin's SegWit also discounts witness data. The shared idea is to distinguish the information used to approve a payment from the state it creates. For NOCK, it makes the cost of leaving new records part of the transaction design.
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1/ QTC's team is tackling a cost hidden inside future quantum computers. Some operations need their own 'factory' inside the machine. Better software could reduce how many of those operations a program needs, before anyone builds more hardware.
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3/ Superquantum's public RMSynth tool rewrites small circuits to use fewer T gates while checking that their behavior stays the same. Its four-qubit example goes from 15 T gates to zero. This is a tiny demonstration, not a large application benchmark.
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4/ The public optimizer supports up to five qubits. The broader opportunity is to improve the software before larger machines arrive, so useful programs need less supporting hardware. This research sits alongside QTC, rather than being its current mining task.
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1/ TIG has to check that useful computing work really happened. Repeating every calculation would create another large workload. Its protocol instead commits a batch of results to a digital fingerprint, then asks for evidence from selected runs.
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3/ TIG's public code selects samples from qualifying batches and checks that the returned proofs match both those samples and the original fingerprint. Its docs also describe checking the program's execution fingerprint, not just accepting a claimed answer.
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4/ This is one of the less visible parts of rewarding useful computation. Finding a better algorithm is only half the job. The network also needs a practical way to check reported work, so more of its resources can go toward the search itself.
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