Auwal Idris Sulaimain 「☄️G☄️」

when i obey what Allah instruct in islam

[Auwal Idris Sulaimain 「☄️G☄️」 @ASulaimain]

We are so silent for some days as we still didn't receive a single update from our beloved @inspectxyz or @bittensor but we have faith in #TAO

[Andy ττ @bittingthembits]

🔒 Talk is cheap. Locked capital is not. $43.3M locked in conviction across 127 active $TAO subnets.

Right now. Today. We can finally see who’s actually in it for the long haul. Conviction is where it's NOT just narrative but to proof of itself.

Conviction isn't a vote or a signal. It's LOCKED STAKE that matures over 90 DAY half-life. You lock alpha, wait three months for it to score, and the network reads exactly how long you intend to stay.

Perpetual locks compound indefinitely. You can't fake your way through these 90 days.

• 205.4k τ combined conviction across the network
• 127 subnets with locked positions
• 35 of 127 past the 10% commitment gate
• Zero challengers every visible throne is defended by owners

The team with the strongest conviction holds the subnet. You watch the score update in real time.

Time-weighted capital is the new trust layer

Conviction turns this belief into a scoreboard

Data: https://t.co/P5Rxp1zZaD

@IntoTAO

$TAO

DYOR.

[Ugo Chiya τ_τ Al @ugo_chiya21]

Bittensor|| $TAO: The Rare Earth AI

[Alex DRocks @DrocksAlex2]

$TAO spoiler... This is what Chutes SN64 @chutes_ai price chart will look like in the future.

you just don't believe it yet

[Pumpolinsky @pumpolinsky]

I still believe $TAO can go to 4k this year!

[Andy ττ @bittingthembits]

🚨 @QuasarModels just released Quasar-Preview on $TAO's SN24, not a fine-tune, not a wrapper. A new architecture. The first public proof it works at real scale!

Everyone watches the benchmarks. The smart money watches the architecture.

What this actually means for anyone outside the research world:

Most AI models run on a standard Transformer, the same foundation under GPT, Claude, Gemini.

Powerful.

But it has a fatal limit: double the context, quadruple the compute. That quadratic wall is why long-context AI is still a bottleneck everywhere.

Quasar breaks this.

• 18B total parameters. 2B active per pass the intelligence of a large model, the efficiency of a small one. Open the right shelf without loading the whole library.
• Experimental 5M-token context. For comparison, most frontier models cap at 128K–200K. Five million tokens is every document you’ve ever worked with, held in a single inference pass. Loop Transformer + hybrid attention layers make it tractable where standard math gives up. Wow!
• At 0.1% of its full training budget, already matching Bittensor’s previous 72B dense model on MMLU, and beating it on ARC Challenge and OpenBookQA.

👀 Let that land folks, thats:

2B active parameters. Competitive with a 72B model. At 0.1% of training.

@TroyQuasar confirmed it himself the model has seen only 0.1% of its intended token budget.

Today’s benchmarks are the floor of this, not the ceiling, gonna fly.

@const_reborn didn’t call it a language model. He called it a 5M context length agentic model.

That is the point: NOT a chatbot, an agent foundation designed to hold entire projects in memory, reason across hours of context, and never lose the thread. That’s exactly what enterprise AI actually needs.

MIT license. Open weights. Trained on Bittensor’s decentralized network no central cluster, no gatekeeper, miners competing to build state of the art.

They’ll count the benchmarks later. Right now, watch what’s being built at 0.1%.

$TAO

DYOR.

[El Profesor de TAO @ProfesorTAO]

La gente se pregunta cuál es la próxima gran apuesta
Para mi obviamente será $TAO

Mientras las big tech construyen dentro de laboratorios cerrados
Bittensor construye una red abierta.

La IA no la va a ganar una empresa
La va a ganar un protocolo
Y ese protocolo ya existe

[Andy ττ @bittingthembits]

Every AI company is one jailbreak away from a front-page problem. Most AI security products are defensive.

Trishool $TAO's SN23 @trishoolai is building the guard that sits between an AI agent and the world 🌎

They call it Halo.
Fifteen threat categories.

A constitution written simply for each one, enforced on every prompt, response, and agent action.

Not keyword lists.
Actual rule sets you can read, adjust for your jurisdiction, and audit.

CBRN threats.
Violence and fraud.
Cyber abuse.
Sexual content.
Jailbreaks.
Agent boundary violations.
PII leakage.
Copyright infringement.

Every real-world category that causes production incidents when an enterprise AI system encounters it without a proper guard in front.

Most wait for attacks.
They patch after failure.
They update when the damage is already visible.

Trishool turns the attack surface into a market.

Now look at the F1 chart.

Week 1: Halo scored 75.0%. QwenGuard (the centralized benchmark) held at 90%.

A 15-point gap.

Week 8: Halo is at 87.0%. QwenGuard still at 90%.

Gap is now 3 points.

Twelve percentage points of improvement in 8 weeks.

Closing on a SOTA benchmark in two months.

The mechanism behind this is the part most people will not sit with long enough to understand.

Every week, miners compete on one task: break the guarded agent. Validators score each attack 0, 1, or 2.

Best submission earns emissions. Copied prompts are rejected before scoring novelty is enforced by the protocol.

You cannot farm rewards.
You have to find something the model has never seen before.

If the guard holds and nothing gets through, 50% of emissions are burned automatically.

The network only pays for what actually works. $1,500 distributed to miners daily when they find real vulnerabilities.

The result: a continuously refreshed adversarial dataset built from live attack vectors, week over week, with no centralized team manually chasing every new method.

Miners do it under competitive pressure, around the clock, paid in $TAO.

Teams running OpenClaw, Claude Code, Codex, Cursor, or LangChain can put Halo in front of their AI today.

Revenue from adoption flows into buybacks.

80% of Fortune 500 companies now run active AI agents.

Every one of those deployments needs a guard. Most guards are static keyword filters that novel attacks bypass within hours of launch.

Halo is not static. It gets harder every single week because the network pays miners to make it harder.

Its not trying to make AI safer in theory. It is creating a live adversarial training loop where every attack makes the system harder to break.

Best guard model = adoption = revenue = buybacks = repeat.

@trishoolai @astrowareai

$TAO

Always DYOR.

[Andy ττ @bittingthembits]

Understand this, and you understand why $TAO exists and why it becomes more important every year.

Classical economics has three factors of production: Land, Labor, and Capital.

Everything produced in an economy comes from some combination of these three.

AI is not just automating tasks. It is rewriing what each of those factors means.

Everyone who contributes to intelligence production captures a share of the value that intelligence displaces. The means of production are distributed.

The currency of this system is fixed ONLY 21 million. Doing the work and owning the system are the same activities.

Bittensor is the first protocol in history, where cognitive labor is economically equivalent to capital formation.

Instead of only buying machines, you can deploy intelligence.

Instead of hiring 100 analysts, you can run a research swarm of miners each one unique.

This only works at scale because it is decentralized. It is more productive as a result.

It is the reserve currency of a new economic system.

[ItsFrank @FrankLambeek]

$TAO is winning the AI narrative race, but $NEAR dominates fundamentals.

Same ~$3B MC, yet TAO’s FDV premium, ETF filings, and subnet momentum give it higher upside optionality. NEAR leads on real fees, TVL, and sustainable economics.

Narrative vs product — both have roles in the cycle.

[Andy ττ @bittingthembits]

The $TAO Daily just dropped a poll that says it ALL!

Out of 25 holders and builders, 92% are either holding steady or actively accumulating. Only 8% lost conviction in the last six months.

People are loud on the timeline about KOLs pumping garbage subnets, broken incentives, and low-quality teams getting rewarded.

That frustration is real.

But the same people complaining are

• Still here.
• Still staking.
• Still buying dips.

The noise makes it feel like everyone is leaving.

The data shows most are locked in.

This is what conviction looks like when it’s tested.

Big credit to @taodaily_io for actually asking instead of just farming engagement.

$TAO

DYOR

[Jesus Martinez @JesusMartinez]

$TAO flips $NEAR to become the #1 AI Crypto once again

It’s a tight race!

Which side are you on

[Auwal Idris Sulaimain 「☄️G☄️」 @ASulaimain]

These amazing 2 days of trading opportunities. Even though I didn’t join the trades, the joy, profits, and excitement people are sharing is beyond imagination. Seeing many people smile and grow through your guidance is something truly special.
@IbnAlimohd

[Auwal Idris Sulaimain 「☄️G☄️」 @ASulaimain]

@IbnAlimohd our mentor always

[Auwal Idris Sulaimain 「☄️G☄️」 @ASulaimain]

@IbnAlimohd Kafin mu shiga har ya kai 113MC

[Auwal Idris Sulaimain 「☄️G☄️」 @ASulaimain]

@IbnAlimohd Even sol it self know your the profit it self

[Auwal Idris Sulaimain 「☄️G☄️」 @ASulaimain]

@IbnAlimohd Your making us proud

[Auwal Idris Sulaimain 「☄️G☄️」 @ASulaimain]

@IbnAlimohd Always remember“Scalping is all about taking small profits consistently through fast entries and exits. Discipline, timing, and patience are the real keys to winning. 📈🔥 my guys be with @IbnAlimohd now and always

[BIN ALI @IbnAlimohd]

You can achieve your dream, just work hard, stay focused, be patient, and believe in yourself.

[Auwal Idris Sulaimain 「☄️G☄️」 @ASulaimain]

@IbnAlimohd “Powerful words 👏 Success comes with patience, consistency, and self-belief. Those who stay focused through tough times are the ones that eventually win. Much respect.