ᴸᴵᴸ€ryp (τ,τ)

$TAO is the $SOL of this cycle 2021 = L1s ---> 2025 = AI

Does your coding agent run on Claude Sonnet 4.6? Costing you $3.00 per million input tokens and $15.00 per million output? MiniMax M2.5 on Chutes costs just $0.19 input and $1.15 output all while running inside a secure and private TEE. M2.5 scores 80.2% on SWE-Bench Verified while Sonnet 4.6 scores 79.6%. You might be paying 15x more per input token and 13x more per output token for a model that scores lower on the benchmark most teams use to evaluate coding agents. M2.5 also scores 51.3% on Multi-SWE-Bench (multi-repo tasks) and 76.3% on BrowseComp (agentic search). MiniMax trained it across 200,000+ real-world coding environments in 10+ languages. The TEE variant on Chutes means your prompts and outputs stay inside a hardware-secured enclave. Claude's API has no equivalent option. Just swap the model string and run your eval suite. Compare and see for yourself the power of open source models on Chutes. 🔗 chutes.ai/app/chute/ce6a…

@markjeffrey Are ridges agents actually comparable to Claude code / its models ?

Spread these words to all the nerds.

RT @Crypto_Crib_: 🇺🇸 Tesla, $TSLA, and SpaceX to merge by 2027. 👀

Everyone should know about what Chutes is doing. Fully permissionless inference mining. Fully end to end encrypted. Fully private TEE machines. You could safely send private keys over the wire and know it was fully private. The entire stack encrypted from your machine to the LLM and back. The fact that that happens on top of a permissionless network with infra run my god knows who is nothing short of mind boggling.

The average $TAO holder be like

Top 5 Bittensor Subnets by Market Cap 1. SN3 - @tplr_ai 2. SN64 - @chutes_ai 3. SN4 - @TargonCompute 4. SN120 - @affine_io 5. SN51 - @lium_io Are you bullish on $TAO?

@AlexFinn Since you’re on top of things related AI - have you checked out any of bittensor Subnets for your overall set up ?

This is potentially the biggest news of the year Google just released TurboQuant. An algorithm that makes LLM’s smaller and faster, without losing quality Meaning that 16gb Mac Mini now can run INCREDIBLE AI models. Completely locally, free, and secure This also means: • Much larger context windows possible with way less slowdown and degradation • You’ll be able to run high quality AI on your phone • Speed and quality up. Prices down. The people who made fun of you for buying a Mac Mini now have major egg on their face. This pushes all of AI forward in a such a MASSIVE way It can’t be stated enough: props to Google for releasing this for all. They could have gatekept it for themselves like I imagine a lot of other big AI labs would have. They didn’t. They decided to advance humanity. 2026 is going to be the biggest year in human history.

This Bittensor Subnet Could Cut Drug Discovery Costs in Half x.com/i/broadcasts/1…

@AlgodTrading tried setting up my miner w openclaw x kimi , been insufficient.. claude code seems to not even know what tao actually is let alone subnets and how to mine em efficiently - any tips / tricks which subnets to mine on w agents and what models you use / ur setup

$TAO

RT @tokenterminal: 🌐🤖 @chamath discussing @bittensor with jensen

#bittensor $TAO

[ NOT FINANCIAL ADVICE ] [ NOT FINANCIAL ADVICE ] Family member: "What's this $tao you keep talking about on the pod?" Me: "Sell half your $btc, buy $tao " [ NOT FINANCIAL ADVICE ] [ NOT FINANCIAL ADVICE ] #remindmeofthistweet in six, 12, 18 and 24 months What logical sense does it make to buy $mstr instead of $btc directly?

@bleighky @Jason You think if he was bullish on something he wouldn’t be invest or what are you implying w this ? Yall are retards

$tao > $btc

@AlexFinn Which local model are you using on your Mac’s and DGX? And what specific LoRAs tool / Programm for fine tuning ? And if you’re at it , any tips / tricks for how to fine tune properly and efficiently? Thanks

My mind is so blown I have my own personal AI research lab running 24/7/365 I'm just one dude with an entire team of AI agents training models and doing R&D I think this is the biggest opportunity right now: taking Karpathy's Autoresearch framework and applying it to everything I have a team of AI agents running experiments all day and night on system prompts, local models, and LoRAs. I also have them doing R&D on my new project. They spend all day discussing my app, coming up with new ideas, then debating eachother An entire organization of autonomous agents continuously improving my business 24/7/365 I feel like I have unlimited power Right now they are all running on ChatGPT 5.4, but today I will move them to local models running on my 3 Mac Studios and DGX Spark so this will all become free Free, local super intelligence working for me at all times. 10 year old me would think this is a scifi Do this immediately: 1. Ask your agent about Karpathy's Autoresearch. Deeply understand it 2. Ask your agent how you could apply that framework to other projects you're working on 3. Download a local model. Doesn't matter what computer you have. There is a model you can run on it. 4. Just get used to how it works. Learn from it. 5. Push yourself to get uncomfortable every day and try new things. There has never been a better/more profitable time to be a tinkerer

The largest decentralised LLM pre-training run in history. SN3 @tplr_ai trained Covenant-72B across 70+ contributors on open internet infrastructure. Now it’s being discussed by @chamath with @nvidia CEO Jensen Huang. Distributed, open-weight model training on Bittensor is getting started.

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.

⚫️ @chamath just lauded @tplr_ai's decentralised and permissionless 72B parameter pre training model to Jensen Huang, the CEO of @nvidia All roads lead to #bittensor.. $TAO

This is huge. $TAO is now part of the conversation between @chamath and @NVIDIA CEO Jensen Huang. Read that again. Credit: @theallinpod