
There is something incredibly dangerous about a man who has decided that no matter what happens, he is not going to stop. [email protected] https://xitter.cf/t.co/JNSJrFWAug
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👉newsletter.bittensor.quest/?…
Verathos pays referrers up to $0.30 of emission for every dollar a customer tops up, spread over 30 days.
Any registered hotkey can hold a code, whether it serves models or not. Referring yourself loses money by design.
Two more lanes are nearly done: an Apple private lane that lets a Mac serve inference privately and earn more per token, and a training competition scored on technique rather than GPU count.
Follow Verathos (SN96): @verathos_ai
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Harnyx added DeepSeek Search & Fetch as a search provider, and results come back with the page content attached.
That kills the second call. Fetch stops being a separate step.
DeepSeek-V4.1-Flash-1017 is on OpenRouter and AI Gateway too. For search, miners register their own DeepSeek key through harnyx-miner-config.
Follow Harnyx (SN67): @harnyx_ai
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Affine doubled the per-epoch task cap to 256, and the held-out score (a test set the model never trains on) climbed to .75.
Epoch 54 was the first at the bigger size: 2,048 rollouts picked from 871 eligible, a .823 pass rate. Epoch 57 came in at .809, with 254 UIDs in the current weight set. The held-out number is up .094 since epoch 53, when each epoch still trained on 128 tasks. Weights froze again overnight after the weight writer's scoring step ran past its time limit; they raised the limit and fresh sets are landing every hour again.
Follow Affine (SN120): @affine_io
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Ditto's scoring queue stalled for 38 hours on a single submission that never resolved, because its dataset seed (the value that picks which data a submission gets) wasn't tied to a chain block. Every validator was blocked from picking up new work.
That's fixed, and every waiting agent has since been scored. Submissions parked by provider rate limits are being retried one at a time. Agents that vendor source files over 2 MiB now get reviewed instead of stalling.
Validators will report CPU, RAM and disk in their heartbeats, so slow hosts are easier to spot.
Follow Ditto (SN118): @heydittoai
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Vidaio's frame interpolation competition closed with no entry at 0.85, the score that crowns a single winner. The top five entries split the emission for two weeks instead.
Frame interpolation is adding in-between frames to smooth footage. All 30 entries got built and scored; the best run was 0.7942 from UID 50. The split runs 50/24/13/8/5%, applied Oct 20 at 14:00 UTC.
Comp 004 is next: 4-second clips upscaled 2× or 4× to 1080p, judged on fidelity to the original, with speed worth 30%. Detail that isn't in the original costs points. Enrollment opens Oct 23, result Nov 3.
Follow Vidaio (SN85): @vidaio_
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⌞· τaoli ·⌟ retweeted
AlphaRidge puts research, screening, and automation in one place instead of five tabs and three subscriptions.
A trader finds a setup on the screener, turns it into a rule, and the rule keeps running and alerting even with the browser closed.
@alpharidge_ai maps a few ways the workflow actually closes:
> Screening on news: Sort the whole market by 7-day sentiment, event clusters, and impact scores instead of just price and volume.
> From idea to rule: Paste a TradingView Pine script into RidgeScript, test it, then arm alerts to Discord, Telegram, or email.
> AI that shows its work: The analyst writes screens and marks up charts, then surfaces the exact platform data it used so you can check it.
The neat part is the news layer. Miners on SN45 turn hundreds of thousands of articles a day into screenable sentiment and events, so the screener can look at what is being said, not just what already happened.
Paper trading is free today. Live brokerage execution is still rolling out. The AI cites its sources, but you still have to confirm before anything is placed.
Affine added 15 miners in a day while its training pass rate crossed .80.
Epoch 50 landed at .806, up from .777 three epochs back. Pass rate is the share of tasks the model clears. The 128 tasks it trained on came from 727 eligible, up from 567.
Weights are still going on chain every hour, 318 UIDs in the set.
Follow Affine (SN120): @affine_io
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OpenRoboto has 30 OR-S1 devices live, each with an on-chain ID on Solana.
Every batch of robot training data now traces back to one physical machine. The IDs come from peaqOS Pro DIDs, and crypto.news picked it up.
Champion snapshots go up 24 hours after a win, which kills mid-round weight copying. Task switching is automated too.
Follow OpenRoboto (SN80): @openroboto
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Score moved one threshold in the check that decides whether a model is just a copy of an older one.
The audit covered 12,671 element-epochs (one task type, one round, one winner) since Jul 11. The paid model wasn't the top scorer in 2,565 of them, and every one went to an older commit.
The fix: a model now needs to differ on 3 of 10 sampled past challenges to shed the copy flag, down from 4. The rest of the tie-break stays.
Follow Score (SN44): @webuildscore
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KubeTEE is serving DeepSeek's newest Flash build inside a TEE, and the old model string is on the way out.
deepseek-v4.1-flash-1017 is live on SayGM at 10% off list. Same endpoint, same TEE (a sealed enclave where the provider can't read your prompts), one string to change.
They also fixed the MiMo tool-call loop that spat !!! bursts between calls. 68 responses, zero loops.
Follow KubeTEE (SN90): @KubeTEEAI
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Bittensor's root channel: Null consensus is live as an opt-in, and it gives a subnet's scoring to one validator.
In Null mode the highest-staked UID holds the only permit and sets the weights, so miner pay comes from one node. Consensus and bond calculations get skipped, and the UID cap goes from 256 to 2,500.
Maciej Kula's read: a subnet where one validator already runs the code, or where the rest copy the owner's weights, gives up almost nothing by switching. Subnets where validators run code independently should stay on Yuma, the default. Null just makes the fact explicit, and the market decides.
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Apex opened a robot assembly competition, and the tolerance is 0.15 mm.
Train one model to drive a 2-finger gripper that seats round and square pegs (4 to 16 mm) into a NIST task board. Score is the fraction of 640 episodes fully assembled, with no partial credit.
Rounds last a day and cost $10 to enter. The leader has to be beaten by 1%, the public baseline sits at 0.33, and the physics gets re-drawn every round.
Follow Apex (SN1): @Apex_SN1
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Synth is putting miner rewards on a weekly dial, starting Monday.
Dynamic rewards reset the share of miner rewards distributed each week. Inputs include SN50's token price and model quality against benchmarks, plus other metrics that price the data. How you get paid doesn't change. Only the burn moves.
Same day, registration goes automated with a daily cap. Duplicate models get pulled, and repeat offenders lose every entry.
Follow Synth (SN50): @SynthdataCo
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Cacheon reopened its Qwen3.8 arena, and the baseline came back 72% faster.
57 output tokens per second, up from 33 on the same workload. Speculative decoding (a cheap draft model proposes tokens, the full model verifies) and FP8 KV caching did it.
Same lane as before: 256k context on one RTX PRO 6000, 0.2 TAO per upload. Re-test against the new baseline before you submit.
Follow Cacheon (SN14): @cacheon_ai
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Verathos Decide is live on SN96, up since block 9,287,280.
Decision models, served by miners. Ask a question with fixed options, get a probability for each one in a fraction of a second, and every answer arrives signed by the miner that produced it.
Four models ship from day one, including d3-flash and d3-mini, the two that topped the team's own benchmark. Old API keys stopped at the cut-over; new signups get free credit.
Follow Verathos (SN96): @verathos_ai
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Albedo is now Ingot, and SN97 ditched distillation for quantization.
The new job: compress Qwen3.8-27B to FP8 (8-bit weights) and beat the current king without drifting from the teacher model. Scoring is how far the answers drift, plus a layer-by-layer check on the model's internals. Repo and dataset land after end-to-end tests.
Copies get caught. Every submission is fingerprinted, and a copy takes the hotkey down with it.
The five newest kings get 20% of emissions each, for five days. Everything else burns.
Follow Ingot (SN97): @ingot_wthrboss
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Cascade's time-series harness beat 25 rivals in Astrid's trading arena.
Gnomon finished 1st of 26 in the five-week competition run by @AstridIntel: +14.8% on a $10k paper account, 23 trades.
The same model without the harness finished 10th, at -7.7%.
Follow Cascade (SN91): @cascade_sn91
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Conjectures wrapped its DEFLATE competition, and the winning compressors beat the tools everyone already runs.
Re-tested on data none of them had seen: submission #243 comes within 0.5% of zopfli's output at 29x its speed, and large files land 4-5% smaller than zlib while beating 7-Zip at more than triple its speed.
Every parser ships with a machine-checked Lean proof it decodes any input back exactly. The top three are on crates.io, free under MIT or Apache.
Follow Conjectures (SN66): @conjectures_io
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New deepseek 4.1 is here. Holy crap that’s exciting!!!
Cheaper, faster and better than the previous version.
🚀 DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.
🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
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⌞· τaoli ·⌟ retweeted
I saw that @heydittoai walked through Ditto v2 at Exploit Summit, including the bit where an agent switches platforms and keeps the context.
Their own write-up uses a case that is basically one sentence: point the new agent at the old one and tell it to mind meld. No rebuild.
To give you some more context on this, a normal chat remembers you only inside that app. Claude does not know what you told Cursor, and a new agent starts from zero.
But if the spec changed in Telegram, or you moved from one coding agent to another, Ditto v2 keeps one memory under the tools and waits for a yes before it sends or edits anything risky.
So the flow would look something like this:
▸ You say it once, in Claude, Cursor, Codex or Grok, and the others can already use it.
▸ Telegram and Slack sit next to the docs and the code, so the agent can search the decision, not just the repo.
▸ A workflow or browser agent runs the job, and the step that could do damage waits for your approval.
Depending on the tool, you stay in that chat or leave with the memory exported.
This ties back to what I wrote before: the models got good at answering, but you are still the switchboard between them.
That’s the part Ditto v2 is building for, even when the handoff is a single sentence.
What other agent handoffs should I look into?



















