Bittensor

Incentivized, permissionless and decentralized intelligence.

[Bittensor @bittensor]

Mining SHA-256 hashes was the most profitable computation of the last decade.

$BTC

Mining digital intelligence will be the most profitable computation of the next decade.

$TAO

[shτbshτb @shibshib89]

Announcement from @const_reborn and @opentensor today:

Starting next week we will be introducing an interim measure to block emissions on Bittensor subnets which are engaging in active foul play and or with no clear path to adding value into the Bittensor ecosystem.

This will need to be done on a case-by-case basis, however the criteria for blocked emissions will revolve around the following:

1) Long-term burning of 100% miner emissions with no plan from the team to bring them online.

2) Active self-mining i.e. subnets that do not have code and instead use stake weight to pass emissions to their own keys.

3) Dead or fully abandoned subnets, or those that have not announced themselves i.e. "unclaimed" subnets.

4) Subnets engaged in TaoFlow exploitation, such as 104, which have very little to no chain activity from the network at large.

A chain operation to block emission will be available on Tuesday to carry this out. Note this is not a long term solution as further protocol upgrades such as conviction, shorting, and the eventual full decentralized governance system of Bittensor, coming into play this year, will allow much more organic and swarm based intelligence to organize Bittensor's emission vector.

Onwards
@bittensor

[Cacheon @cacheon_ai]

Cacheon mainnet is live.

13 inference servers queued, each racing to beat our baseline on a dedicated 8x H200 pod.

The winner earns up to $10,000/day. Inference optimization starts today on @Bittensor.

Follow along: https://t.co/u52cmcD3Hb

[Cacheon @cacheon_ai]

Launching Cacheon: an open, incentivized competition for LLM inference optimization.

As model quality converges, the next frontier is serving them economically at scale: lower latency, higher throughput, and lower cost per token.

Cacheon turns that problem into a live arena with continuous evaluation. Developers submit containerized inference servers, benchmarked on standardized hardware against a pinned vLLM baseline. The fastest server that preserves output correctness wins.

The goal is to make better inference systems discoverable, measurable, deployable, and rewarded in the open.

Mainnet launches by May 19. Learn more: https://t.co/JPbyJpLszq

[ORO @oroagents]

Full podcast with Ala Shaabana (@shibshib89), co-founder of Bittensor, and Shardul Bansal, co-founder & CEO of Oro, is LIVE.

Watch them discuss how Ala came to co-found Bittensor, Shardul’s early involvement with Bittensor right at the protocol’s very inception, which problems are best solved by open-source AI, truth and AI models, and much more.

[Bittensor @bittensor]

@Jason 🌊

[@jason @Jason]

.@bittensor bittensor:native group chat https://t.co/W4hscEoSN5

[WallStreetBets @wallstreetbets]

WOAH @Jason discussing @ridges_ai and Bittensor on @theallinpod 👀

there's clear opportunity post-templar to be the king subnet on $tao.

chat, i need your help... is @ridges_ai the next $100M subnet runner and taking the subnet crown?

[Openτensor Foundaτion @opentensor]

Never Give Up

https://t.co/GzaRLjiln8

[Ridges AI | SN62 @ridges_ai]

🏔️ Ridges Q2 Roadmap

Since launching Ridgeline, the team has been deep in the work, refining the subnet and improving agent performance.

Our Q2 roadmap outlines the next phase of that progress focused on evaluation quality, infrastructure, and scaling autonomous software engineering.

More to come.

[Targon @TargonCompute]

We needed to run trusted workloads on untrusted host machines.

So over a year ago, we started building the Targon Virtual Machine to enable Confidential TEEs in production.

Today we're sharing our white paper written alongside @intel: Decentralized Compute on Untrusted Hardware Using Intel® TDX and Encrypted CVMs

[Crucible Labs @CrucibleLabs]

Security first mindset.

Crucible integrates directly with Ledger, so your keys stay cold while Smart Allocator keeps you exposed to high-performing subnets.

Automated rotations. Continuous rewards. Long-term alignment with the network.

[This Week in Startups @twistartups]

Subnet tokens will come to Robinhood and Coinbase eventually. Probably.

For the Bittensor protocol to truly go mainstream, every day people are going to need a way to buy $TAO and subnet tokens. Right now, it involves a relatively complex process requiring a specialized Bittensor wallet (like the one offered by our pals at Crucible Labs!)

But soon, you’ll probably be able to acquire a piece of these projects directly from popular exchanges like Robinhood and Coinbase. How close are we to this long-awaited day?

cc: @Jason, @Lons, @MarkJeffrey, @shibshib89, @CrucibleLabs

[Openτensor Foundaτion @opentensor]

For anyone trying to understand Bittensor from first principles, this lecture is a useful place to start.

Presented by Bittensor co-founder @const_reborn.

Learn Bittensor
> Start with Bitcoin, distributed systems, incentives,
> How Bitcoin leads to Bittensor Subnets coordinating AI infrastructure.

Topics:
// Start - Bitcoin as more than a digital currency
// Risks of AI centralization + closed systems
// "The incentive computer"
// How Bittensor subnets work (mining, validating)
// How distributed AI infrastructure could scale globally
// Impact on students, builders & future founders

Recorded at the National University of Singapore Computer Science Club. @NUSComputing

Chapters
- Bitcoin, AI, and Bittensor
- Bitcoin history and decentralization
- AI changes how engineers work
- The danger of centralized AI power
- Why most crypto visions fail
- Bitcoin as the world’s largest compute network
- Bitcoin as a market for compute
- The idea of an “incentive computer”
- Bitcoin compared to Bittensor
- Classroom example of decentralized scoring
- A simple subnet example
- SN62 :: @ridges_ai SWE agents
- SN3 @tplr_ai :: Distributed AI Training
- SN52 @lium_io :: GPU rentals on Bittensor
128 subnets, some examples
Why this matters for the future of work
Q&A

Subnet examples mentioned @
SN64 - Serverless + TEE Compute :: @chutes_ai
SN8 - Prop firm @VantaTrading
SN52 - AutoML :: @gradients_ai
SN62 - SWE agents :: @ridges_ai
SN51 - Compute / GPU rental @lium_io
SN4 - TEE compute for enterprise :: @TargonCompute
SN3 - 72B Distributed Training run :: @tplr_ai
SN41 - Prediction markets :: @almanac_market
SN44 - Computer Vision @webuildscore
SN68 - Drug discovery :: @metanova_labs
SN18 - Weather Forecasting @zeussubnet
SN50 - Bitcoin prediction data :: @SynthdataCo
SN61 - Quantum computing :: @qBitTensorLabs
SN14 - Bitcoin mining pool :: @taohash
SN34 - Perp Dex :: @0x_Markets
SN17 - 3D model generation :: @404gen_
SN33 - Data analytics :: @ReadyAI_
SN19 - [Since relaunched] RPC infrastructure :

[Tao.App @taoapp_]

MEV Shield 2.0 is LIVE

The most robust MEV protection yet.
Your transactions, front-run by no one.

Try it now → https://t.co/8T4uKjwN8f

[JJ @JosephJacks_]

The only thing holding back @Bittensor from being ranked as a top 5 cryptocurrency by market cap is that 95% of crypto holders are degenerate idiots.

This changes the moment the financial mainstream realizes there are only two tokens that represent freedom from enslavement.

[templar @tplr_ai]

On the @theallinpod this week, @chamath asked @nvidia CEO Jensen Huang about decentralized AI training, calling our Covenant-72B run "a pretty crazy technical accomplishment."

One correction: it's 72 billion parameters, not four. Trained permissionlessly across 70+ contributors on commodity internet. The largest model ever pre-trained on fully decentralized infrastructure.

Jensen's answer is worth hearing too.

[templar @tplr_ai]

One of the sharpest independent analyses of Covenant-72B we've seen. The author identifies what they call a "cognitive arbitrage window": crypto investors see "another open-source model" and shrug, while AI researchers who understand the benchmarks don't follow crypto. That gap produced a 2-day price lag after the announcement before the market caught up.

The piece is thorough on the technical details (SparseLoCo compression, the honest benchmark gap to Qwen2.5/LLaMA-3.1, why the trajectory matters more than any single number) and places Covenant-72B in the full history of decentralized training from GPT-JT to INTELLECT-1. Google Translate handles it well if you don't read Chinese.

[Ridges AI | SN62 @ridges_ai]

🚀 When @JosephJacks_ took a closer look at what Ridges was building, it quickly became clear something meaningful was happening and that’s when the decision to partner with the subnet was made.

Ridges is delivering 73.6–87.8% on SWE-Bench Verified and a 96.3% Polyglot score, placing it among the top coding agents on the market while costing just $29/month for ~100 PRs (~$0.29 per PR).

Beating the benchmarks while making high-performance coding agents accessible.

As Joseph put it:

“We’re harnessing a decentralized, open-source intelligence network that gets more competitive and more capable over time by design. That’s a compounding advantage that no closed, proprietary system can replicate.”

[Openτensor Foundaτion @opentensor]

Bittensor carries forward the original promise of crypto that began with @Bitcoin

Bitcoin frees humanity from the grip of fiat money

Bittensor marries that revolution with AI, thus freeing intelligence from technocratic tyranny.

Both aim to end slavery.

[@jason @Jason]

This Decentralized Cloud is Thousands of Times Cheaper Than AWS https://t.co/hbtRuOsXyj