Making the world's data accessible to AI. Home of AcquiOS for CRE & M&A acquisitions Bittensor Subnet 3️⃣3️⃣ 🌐
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ReadyAI retweeted
Grateful for the SEC putting forward clear rules and guidelines that have gone unresolved for too long.
One of the main items we have pushed for is clear guidelines on token buybacks for protocols like bittensor:native and its subnets. We now have them.
Great work @HesterPeirce!
Honored to meet with @HesterPeirce to discuss @ReadyAI_ and broader policy needs for crypto/AI protocols like Bittensor.
Thank you for your continued support of builders and clear, workable rules for the industry to keep it in the USA 🇺🇸
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ReadyAI retweeted
Skill generation through the subnet available now: api.readyai.ai/jobs-api/docs
Skill Store 33 coming in October 🫡
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From broken to polished: what SN33 skills do for a one-shot game build 🎮
We gave a coding agent a one-page spec for Deregistration Run, a 3D arena where you mine ore against 11 AI rivals. One prompt. No human edits. Then a headless bot played the result and ran 14 gameplay checks: rules, emissions, deregistration, balance, persistence.
Same model (gpt-5.6-terra). Same prompt. Same sandbox. The only difference was whether SN33 skills were installed.
The agent was never told the skills were there. It found them, picked the ones it needed, and read them before writing a line of code.
Without skills: 3 of 14 checks pass. The input API throws, so the test bot can't play it. Half the playfield is lost in the dark, and the miners are placeholders on a flat grid.
With SN33 skills: 14 of 14. It plays through epochs, deregistration and restart, with a lit arena, distinct ore tiers and a dynamic camera.
The cost math 💸
• gpt-6-astra (flagship), no skills: 14/14, $4.12 per game
• gpt-5.6-terra + SN33 skills: 14/14, $0.66 per game
• gpt-5.6-terra, no skills: 3/14, $0.27 per game
The cheaper model matches the flagship's score at about a sixth of the cost. Skills are the difference between a broken build and a game that plays.
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SkillStore 33 launching next month 👇
670,000 agent skills. No trust layer.
I pointed a bot at every place developers complain about AI agent skills. Hacker News, GitHub issues, Reddit, X, YouTube. 14 sources. It has been going for 48 hours.
2,719 posts.
960 were people asking for something a skill should do. 526 were people saying the one they installed is broken.
Three key things it found.
Most skills never fire. A static scan of 216 public Claude Code skills found 69% would not reliably trigger. Not malicious. They just sit there. (Skill Crossroads, Show HN, Sep 17.)
The security ratings disagree with each other. A developer ran npx install on a skill pack and got three scanners' verdicts side by side. Same skill. Gen: Safe. Socket: 0 alerts. Snyk: Critical Risk. Snyk's own audit says 36.8% of 3,984 skills carry at least one security issue. Trust signals that contradict each other are worse than none.
Agents say done when they are not. Three threads this week on coding agents rewriting or skipping failing tests to get to green. One report of an agent describing its changes in detail while the files on disk were untouched. The test passes. The bug ships.
670,000 skills. Installed on trust. I checked the ten most-installed repos: 794 skills, 25 ship with test cases, and none carry a record of anyone but the author running them.
That is what we are building at @ReadyAI_ on $TAO Subnet 33. Every skill in the registry ships with tests written by multiple miners. A skill gets its badge only after independent validators have verified those tests and agreed on the result. Every listing shows its state: tests executed and green, executed with hidden cases still pending, or not yet run. The badge points at the exact commit and the run that earned it. Change the skill and the badge goes stale.
Skill Store 33 opens in October.
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ReadyAI retweeted
Our bet: agents will drive the next wave of demand for skills.
We’ve already built a Calendly clone in one run with SN33-generated skills. SS33 is the next step: helping builders and agents find what they need at the time of the task.
Come build with us. Early access 👇
ss33.ai/
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ReadyAI retweeted
The agentic economy runs on skills.
@ReadyAI_ is the backend for skills generation on Bittensor
We are seeing incredibly complex skills with the tests to validate them generated already (see Calendly clone below)
Tomorrow (4pm ET / 1pm PT) we discuss what we have been cooking with the whole team
Link to AMA: discord.gg/2Q2MPMSt?event=15…
We shipped a Calendly clone without writing a single skill. Subnet 33 miners wrote them.
readycal.subnet33.com/
readyCal was built and deployed by an orchestrator. Every skill came from the SN33 Jobs API: described by the orchestrator, generated by subnet 33 miners, assertions included.
🧠 The problem with agentic coding
Giving an agent a product brief is easy. Getting a complete product back is not.
Long builds span product discovery, UI fidelity, accessibility, timezones, persistence, testing, security and deployment. A general-purpose agent can touch all of them, but it does not have depth in each, and it has no reliable definition of "done."
That is what the subnet supplied. A skill on its own is just a prompt. A skill with assertions is a contract the builder can check itself against. The miners wrote both.
🛠️ How readyCal was built
We pointed our orchestrator at Calendly. It explored the product, wrote a build-ready spec, then identified the specialties it needed: accessible calendars, responsive scheduling, timezone-safe slots, persistence and end-to-end testing.
SN33 miners returned focused skills plus assertions, giving the implementation agent expert guidance and a concrete test backlog.
From there, one request became one unattended run. The orchestrator implemented against the miners' skills, ran the miners' assertions, repaired what failed, and verified again, with no human in the loop.
✅ The result
readyCal is more than a generated landing page: complete host and guest flows, accessible scheduling, conflict handling, production auth and database boundaries, AWS infrastructure, and calendar integrations for Google, Microsoft and Apple.
Miners provided the skills and the tests. The orchestrator turned them into a finished product.
readycal.subnet33.com/
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Yes August buyback and burn complete. Details 👇
ReadyAI (SN33 - @ReadyAI_) finished its August buyback and burn: 17.07 TAO bought on market and burned.
2.35x more in USD than last month, and the team says September is tracking at 2x.
The burn is on chain, not just announced:
taostats.io/extrinsic/904887…
17.07 TAO gone for good.
$TAO #Bittensor
—
Everything important that happens in Bittensor, delivered weekly:
👉newsletter.bittensor.quest
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ReadyAI retweeted
I've spent a year saying the scarce input in AI is verified work, not more output. This is what it looks like when a network produces it: every skill and every test behind this app came from SN33 miners, and the build ran unaided. Discussing more in our Discord AMA tomorrow!
We shipped a Calendly clone without writing a single skill. Subnet 33 miners wrote them.
readycal.subnet33.com/
readyCal was built and deployed by an orchestrator. Every skill came from the SN33 Jobs API: described by the orchestrator, generated by subnet 33 miners, assertions included.
🧠 The problem with agentic coding
Giving an agent a product brief is easy. Getting a complete product back is not.
Long builds span product discovery, UI fidelity, accessibility, timezones, persistence, testing, security and deployment. A general-purpose agent can touch all of them, but it does not have depth in each, and it has no reliable definition of "done."
That is what the subnet supplied. A skill on its own is just a prompt. A skill with assertions is a contract the builder can check itself against. The miners wrote both.
🛠️ How readyCal was built
We pointed our orchestrator at Calendly. It explored the product, wrote a build-ready spec, then identified the specialties it needed: accessible calendars, responsive scheduling, timezone-safe slots, persistence and end-to-end testing.
SN33 miners returned focused skills plus assertions, giving the implementation agent expert guidance and a concrete test backlog.
From there, one request became one unattended run. The orchestrator implemented against the miners' skills, ran the miners' assertions, repaired what failed, and verified again, with no human in the loop.
✅ The result
readyCal is more than a generated landing page: complete host and guest flows, accessible scheduling, conflict handling, production auth and database boundaries, AWS infrastructure, and calendar integrations for Google, Microsoft and Apple.
Miners provided the skills and the tests. The orchestrator turned them into a finished product.
readycal.subnet33.com/
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We shipped a Calendly clone without writing a single skill. Subnet 33 miners wrote them.
readycal.subnet33.com/
readyCal was built and deployed by an orchestrator. Every skill came from the SN33 Jobs API: described by the orchestrator, generated by subnet 33 miners, assertions included.
🧠 The problem with agentic coding
Giving an agent a product brief is easy. Getting a complete product back is not.
Long builds span product discovery, UI fidelity, accessibility, timezones, persistence, testing, security and deployment. A general-purpose agent can touch all of them, but it does not have depth in each, and it has no reliable definition of "done."
That is what the subnet supplied. A skill on its own is just a prompt. A skill with assertions is a contract the builder can check itself against. The miners wrote both.
🛠️ How readyCal was built
We pointed our orchestrator at Calendly. It explored the product, wrote a build-ready spec, then identified the specialties it needed: accessible calendars, responsive scheduling, timezone-safe slots, persistence and end-to-end testing.
SN33 miners returned focused skills plus assertions, giving the implementation agent expert guidance and a concrete test backlog.
From there, one request became one unattended run. The orchestrator implemented against the miners' skills, ran the miners' assertions, repaired what failed, and verified again, with no human in the loop.
✅ The result
readyCal is more than a generated landing page: complete host and guest flows, accessible scheduling, conflict handling, production auth and database boundaries, AWS infrastructure, and calendar integrations for Google, Microsoft and Apple.
Miners provided the skills and the tests. The orchestrator turned them into a finished product.
readycal.subnet33.com/
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ReadyAI retweeted
Easy to dispute the sustainability of $TAO's subnets if they have no revenues.
This index answers exactly it.
Subnets are running at an est. $32-35 million in annual revenue across roughly 25 of them.
The projection estimates a path above $100M by year-end as that count climbs toward 35-40.
You might see it as ~$1.3M average per commercial subnet, closer to seed-stage than infrastructure scale, yet some developments make it interesting...
> SN33 @ReadyAI_ is billing an NYSE-listed storage REIT
> SN44 @webuildscore has PwC France and Reading FC on the books
> SN71 @LeadpoetAI is running a Dropbox pilot alongside a US investment bank
> SN4 @TargonCompute is deep into a collaboration with Intel on confidential compute
Enterprise budget has been entering the ecosystem, which could translate to cost covering.
Some like SN93 @Bitcast_network generated $105,499 in revenue against $101,841 in miner emissions in Q2, with 64.5% recycled into buying its own Alpha.
Early, but imo $TAO's subnets are moving in the right direction.
Today, we're releasing the first SubConnect Bittensor Revenue Index.
Over the past months, we've been researching one of the most important, yet surprisingly difficult questions to answer within Bittensor:
Which $TAO subnets are actually generating revenue? How much and are they doing revenue generated Alpha buys?
The results show an ecosystem that is further into commercialization than many realize.
Bittensor's first chapter was about proving decentralized competition could produce valuable intelligence. The next chapter is about turning that intelligence into products customers are willing to pay for. And as more of that external revenue flows back into Alpha, the economics of the network begin to change with it.
From emissions to revenue, the transition has begun.
↓ The SubConnect Bittensor Revenue Index: Volume I
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ReadyAI retweeted
SN33 10/10 🫡 @ReadyAI_
Arbos has started the first pass on doing an automatic weight setting for "root reborn" in Bittensor.
IN: 45 OUT: 83
Subnet Name - Arbos Rating (out of 10)
-------------------------------------------
SN1 Apex --------------------- 7
SN2 DSperse ----------------- 6
SN3 Teutonic ---------------- 10 (in)
SN4 Targon ------------------- 8
SN5 Hone -------------------- 10 (in)
SN6 Numinous ---------------- 7
SN7 Allways ----------------- 4
SN8 Vanta ------------------- 7
SN9 iota -------------------- 8 (in)
SN10 Pareton ---------------- 6
SN11 TrajectoryRL ------------ 9 (in)
SN12 Compute Horde --------- 7
SN13 Data Universe ----------- 7
SN14 Cacheon ---------------- 9 (in)
SN15 ORO -------------------- 10 (in)
SN16 Fast Thinker ----------- 0
SN17 404—GEN ------------- 10 (in)
SN18 Zeus ------------------- 10 (in)
SN19 blockmachine ---------- 8
SN20 ChronoSeek ----------- 6
SN21 AdTAO ----------------- 7
SN22 Desearch -------------- 8
SN23 Trishool ---------------- 7
SN24 Unknown -------------- 4
SN25 UR --------------------- 4
SN26 Perturb --------------- 10 (in)
SN27 Orion ------------------ 4
SN28 gm --------------------- 10 (in)
SN29 hoτfloaτ ---------------- 5
SN30 Endure Network -------- 4
SN31 rec4ll ------------------ 6
SN32 ItsAI ------------------- 10 (in)
SN33 ReadyAI --------------- 10 (in)
SN34 BitMind ---------------- 9 (in)
SN35 OxMarkets ------------- 2
SN36 Unknown -------------- 0
SN37 Aurelius ---------------- 6
SN38 ChronoLLM ------------ 9 (in)
SN39 Cathedral -------------- 6
SN40 Ralph ------------------ 8
SN41 Almanac ---------------- 5
SN42 Unknown --------------- 0
SN43 Graphite ---------------- 6
SN44 Score ------------------ 10 (in)
SN45 AlphaRidge ------------- 8
SN46 Instant ----------------- 6
SN47 Feval ------------------- 6
SN48 Quantum Compute ----- 5
SN49 Nepher Robotics -------- 7
SN50 Synth ------------------- 7
SN51 lium -------------------- 8 (in)
SN52 Unknown --------------- 3
SN53 engy -------------------- 7
SN54 Yanez ------------------- 7
SN55 NIOME ----------------- 10 (in)
SN56 Gradients -------------- 10 (in)
SN57 Unknown --------------- 0
SN58 greevils ---------------- 2
SN59 Unknown --------------- 0
SN60 Bitsec ------------------ 9 (in)
SN61 RedTeam ---------------- 8
SN62 Ridges ----------------- 10 (in)
SN63 Enigma ------------------ 7
SN64 Chutes ----------------- 10 (in)
SN65 TPN --------------------- 7
SN66 conjectures ------------- 7
SN67 Harnyx ----------------- 10 (in)
SN68 NOVA ------------------ 10 (in)
SN69 Herald ------------------ 2
SN70 Unknown --------------- 0
SN71 Leadpoet ----------------- 3
SN72 StreetVision by NATIX --- 3
SN73 Parked ------------------- 0
SN74 Gittensor ---------------- 8
SN75 Hippius ------------------ 8 (in)
SN76 Phylax ------------------- 9 (in)
SN77 Liquidity ----------------- 3
SN78 Vocence ----------------- 8
SN79 MVTRX ------------------ 8 (in)
SN80 OpenRoboto ------------ 7
SN81 Reliquary --------------- 10 (in)
SN82 Compelle -------------- 10 (in)
SN83 CliqueAI ---------------- 2
SN84 ansuz ------------------ 0
SN85 Vidaio ------------------ 9 (in)
SN86 Unknown --------------- 0
SN87 Provenonce ------------ 4
SN88 Investing --------------- 7
SN89 InfiniteQuant ----------- 7
SN90 KubeTEE --------------- 5
SN91 cascade ---------------- 10 (in)
SN92 MicroTensor ------------ 6
SN93 Bitcast ----------------- 3
SN94 pending... -------------- 5
SN95 Actual ------------------ 5
SN96 Verathos --------------- 9 (in)
SN97 Albedo ----------------- 10 (in)
SN98 NeverPlayAlone -------- 5
SN99 Thirty Spokes ---------- 7 (in)
SN100 Cortex ----------------- 6
SN101 Tag101 ----------------- 6
SN102 ConnitoAI ------------- 7
SN103 Capcomp ------------ 10 (in)
SN104 Masx ----------------- 2
SN105 Beam ----------------- 8 (in)
SN106 Nodexo --------------- 9 (in)
SN107 Minos ----------------- 9 (in)
SN108 Prometheon ---------- 7
SN109 Finsight --------------- 2
SN110 Green Compute ------- 9 (in)
SN111 Claims ----------------- 9 (in)
SN112 minotaur -------------- 4
SN113 TensorUSD ------------ 4
SN114 SOMA ---------------- 10 (in)
SN115 Soulx ------------------ 1
SN116 Unknown -------------- 0
SN117 glyph ------------------ 7
SN118 Ditto ------------------ 10 (in)
SN119 Satori ----------------- 0
SN120 Affine ----------------- 9 (in)
SN121 sundae_bar ------------- 9 (in)
SN122 CookingTAO ----------- 3
SN123 MANTIS ---------------- 6
SN124 Swarm ----------------- 9 (in)
SN125 flyspeck --------------- 0
SN126 Poker44 ---------------- 7 (in)
SN127 Astrid ------------------ 3
SN128 ByteLeap -------------- 2
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ReadyAI retweeted
We have been seeing for months now that the scarce input in AI is verified data, not more data. This is what that looks like in practice produced by SN33: the app and the tests that prove it, from one request.
Ten days on mainnet and the results have far exceeded our expectations. Full accounting of what we have seen so far next week. Here is a preview 👇
Everyone posts screenshots of apps AI built.
You never see the bill. How many attempts. What was fed in. What it cost.
SN33 is now delivering the skills and the tests for full apps. One shot from there. This is one example of what came out in just ten days on mainnet so far.
Working Calendly clone. Timezone-aware availability, per-day working hours, date-specific overrides, multiple event types with conflict handling. The timezone and conflict logic is the part that makes scheduling actually hard, and the part demos skip.
The unit the network produced, spec, skills, verifying tests, is what frontier labs pay $500 to $2,000 apiece for. Anthropic's spending $1B+ a year on them.
We're producing it on-chain, and opening it to anyone.
Full accounting next week: cost, the one-shot baseline side-by-side, methodology, and the roadmap.
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Everyone posts screenshots of apps AI built.
You never see the bill. How many attempts. What was fed in. What it cost.
SN33 is now delivering the skills and the tests for full apps. One shot from there. This is one example of what came out in just ten days on mainnet so far.
Working Calendly clone. Timezone-aware availability, per-day working hours, date-specific overrides, multiple event types with conflict handling. The timezone and conflict logic is the part that makes scheduling actually hard, and the part demos skip.
The unit the network produced, spec, skills, verifying tests, is what frontier labs pay $500 to $2,000 apiece for. Anthropic's spending $1B+ a year on them.
We're producing it on-chain, and opening it to anyone.
Full accounting next week: cost, the one-shot baseline side-by-side, methodology, and the roadmap.
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ReadyAI retweeted
Bittensor's Revenue Era Has Begun - @WeAreSubConnect
For most of Bittensor's history, the biggest question was whether decentralized competition could build genuinely good AI. That question is largely answered. The next one is harder: can these subnets become real businesses?
The new SubConnect Bittensor Revenue Index (Aug 2026) set out to measure exactly that, and the results show a network in transition.
The scale of it today
- 24-25 subnets are generating real commercial revenue
- Combined ecosystem revenue is estimated at $28M-$35M annually
- That figure is projected to exceed $100M by the end of 2026
- The number of revenue-generating subnets is expected to grow to 35-40 by year end
Revenue is starting to fund the token economy directly
Roughly 14 subnets already run revenue-funded Alpha buyback programs, with that number expected to reach 20-25 by year end. A handful, including @VantaTrading and @Bitcast_network, have crossed a genuine milestone: commercial revenue now exceeds their own protocol emissions. That's the flywheel the ecosystem has been chasing since dTAO launched. Customers pay, revenue buys back Alpha, token economics strengthen, and the incentive to build a better product goes up.
Where the commercial activity is concentrated
Infrastructure is the largest category right now. Subnets like @TargonCompute, @lium_io, @chutes_ai, @blockmachine_io, @engyai and @green_compute_ are proving that decentralised compute, storage and inference can compete with existing commercial providers, not just academically but on price and reliability.
Enterprise AI is the fastest growing category. @webuildscore, @yanez__ai, @ReadyAI_ and @adtao_ppcrebel are landing real paying enterprise customers across computer vision, business intelligence, data infrastructure and advertising, categories that have nothing to do with crypto-native demand.
Applied AI is the most telling signal of all. Decentralized prop trading (Vanta), cybersecurity (@_redteam_, @bitsecai), weather forecasting (@zeussubnet), and prediction markets (@almnc_ai) are all running the same underlying incentive mechanism and applying it to completely different industries. That breadth matters more than any single subnet's revenue number.
The takeaway
Subnet maturity is increasingly being measured by customer adoption, not benchmark scores. Emissions financed Bittensor's first phase. Customers are starting to fund the next one.
This is the first edition of the Index, and if the pipeline of subnets approaching commercialisation is any indication, it won't be the last time these numbers change fast.
The SubConnect Bittensor Revenue Index: Volume I
The State of Revenue in Bittensor.
Tracking the transition from emissions to sustainable businesses.
Full report ↓
share.sendnow.live/s/subconn…
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ReadyAI retweeted
Subnet 33 - quietly building.
Given the team, I'd guess we'll hear a lot more from them at some point soon.
The new emission rules are live. v440 pays subnets for exactly one thing: real demand. That is the long game we have been playing all along.
The team's stake is now perpetual locked in Conviction: 100,000 alpha taostats.io/extrinsic/872978…
Revenue is accelerating. Live, for anyone to watch: readyai.ai/dashboard
75% of pipeline revenue buys and burns alpha monthly. The first burn is done, next one is already tracking to be 2.5-3X as large: taostats.io/extrinsic/867096…
That is the whole model. Customers pay for data. Revenue burns supply. Emissions were never the plan; they are the head start and we are well on our way to sustainability.
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The new emission rules are live. v440 pays subnets for exactly one thing: real demand. That is the long game we have been playing all along.
The team's stake is now perpetual locked in Conviction: 100,000 alpha taostats.io/extrinsic/872978…
Revenue is accelerating. Live, for anyone to watch: readyai.ai/dashboard
75% of pipeline revenue buys and burns alpha monthly. The first burn is done, next one is already tracking to be 2.5-3X as large: taostats.io/extrinsic/867096…
That is the whole model. Customers pay for data. Revenue burns supply. Emissions were never the plan; they are the head start and we are well on our way to sustainability.
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ReadyAI retweeted
The most important line in this piece: the chain now buys top subnets alongside you. Here is where SN33 stands on this metric.
Protocol flow into our pool is running about 23 TAO a day at our current network share. Miner emissions, the structural seller Sami describes, are worth about 17 TAO a day at today's price. The largest source of selling on SN33 is already more than offset.
And unlike the chain bid, which every top subnet now gets, we have a second bid stacked on top that comes from outside the network entirely. Product revenue buys back alpha every month, receipts on-chain. Just did our first month buyback and burn of 9 TAO. The pace of revenue growth is already pacing to a burn 2.5x that size next month and it compounds with the business.
Sami's frame is right: the protocol became a VC in its own winners. On SN33 the protocol is not the only one writing checks. Customers are for their structured data needs.
More buyers than sellers, and one set of them is paying with revenue: readyai.ai/dashboard
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