PhD in IT Business Intelligence (BI) in real life, Crypto Analyst on X since “21
Antwerpen, België
Joined April 2021
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i found the next multi million sleeper on robinhood 🚨👇
@orus_agent $ORUS
while everyone faded robinhood chain after the first wave of dead tickers
this team kept building
not tweets
not promises
a LIVE trading desk
the first customisable autonomous agent on robinhood chain
you don’t stare at charts
you don’t ape late
you don’t panic sell
you set the rules once
the agent hunts 24/7
every new pool
every trap
every sniper wallet
every honeypot
it tears the token apart before a human even opens the chart
then it trades
or it refuses
most “AI agents” are a pfp and a story
this one is already running
millions of decisions logged
agents live
strategies competing in public
copy one
simulate it
flip it live
and they did this while the chart got destroyed
ATH heat
then silence
then dump
most projects disappear here
$ORUS shipped through it
contests
backtests
public PnL
and now vaults so people can deposit into winning strategies
that’s not a meme
that’s infrastructure on a brand new chain
robinhood itself is screaming agents
24/7 crypto
AI that trades while you sleep
$ORUS is already there
tiny valuation
real product
first mover
team that didn’t stop when it got ugly
this is the exact look of a sleeper before it rips
don’t wait for the timeline to agree
by then it’s not a sleeper anymore
$ORUS
@orus_agent
orusagent.xyz
Told you not to buy $LDX follow to stay up to date
My $LDX analysis 🚨
Not advice. Not a short. Not a buy or sell. Just how the product is built.
What $LDX is
$LDX is the token of Lady Exchange. A few-day-old swap UI on Robinhood Chain (chain id 4663).
Contract:
0x62CD5cd9d354A2A50AfFE9efa21c8918B9640a5B
Supply: 1 billion ERC-20.
That is not Uniswap.
That is not Robinhood the broker.
That is not a regulated exchange.
Why the marketing is larger than the stack
The site talks like infrastructure: aggregator, best route, protocol revenue, buybacks.
Technically it is:
- a new frontend
- plus routing over existing pools (Pons, PAIR, Uniswap v4, launch pools)
- plus a token that needs that flow to look real.
Uniswap-grade safety lives in years of audited core contracts. That evidence is not published here.
No public audit of their own router.
No serious bounty.
No immutability statement.
No clean split between “protocol” and “token” the way UNI works.
Why $LDX itself is the weak point
The token is the product
You do not need UNI to swap on Uniswap.
Here $LDX sits in the middle: fees, buybacks, “alignment.”
That makes the token a marketing layer, not a security layer.
Thin liquidity, fat volume
Public pool data shows a small LP book against large 24h volume.
On that book a buyback is easy to recycle: take a fee, buy $LDX in the same thin pool, print “revenue.”
That is a flywheel. It is not solvency.
Aggregator risk hits the user, not the narrative
An aggregator chooses calldata: which pool, which hook, which path.
A bad hop means fee-on-transfer, a hostile hook, or a fake ticker that only looks like a stock token.
$LDX buybacks do not fix that. They incentivize chasing volume across dirty routes.
Approval surface
Swap UIs often ask for infinite allowance or Permit2 to a custom spender.
If that spender is not the canonical, audited router, every approved token sits behind that key and that code.
That is contract risk. Not “community alignment.”
Fresh launch inventory
The UI surfaces new launch tokens next to the $LDX pool.
That is discovery, not a listing standard.
On 4663 you get meme wrappers and equity-looking names.
An aggregator that routes “any asset” inherits that junk.
Robinhood halo
Robinhood Chain is not Robinhood brokerage.
Many stock tokens on that chain are price exposure, not shares.
$LDX borrows the brand.
It does not inherit the license, custody rules, or audits.
What $LDX does not prove
Not that the router is safe.
Not that quotes are enforced on-chain with a hard minOut.
Not that buybacks come from anywhere except the same thin pool.
Not that there is no admin, upgrade path, or fee wallet.
Not that volume is organic.
Short version
$LDX is a new token with aggregator wrapping.
The claims are infrastructure.
The evidence is tokenomics and a beta site.
Unreliable here means: the guarantees do not match the pitch.
Not financial advice.
If you already use the chain, read the contracts, allowances, and pools yourself.
3
7
1,536
My $LDX analysis 🚨
Not advice. Not a short. Not a buy or sell. Just how the product is built.
What $LDX is
$LDX is the token of Lady Exchange. A few-day-old swap UI on Robinhood Chain (chain id 4663).
Contract:
0x62CD5cd9d354A2A50AfFE9efa21c8918B9640a5B
Supply: 1 billion ERC-20.
That is not Uniswap.
That is not Robinhood the broker.
That is not a regulated exchange.
Why the marketing is larger than the stack
The site talks like infrastructure: aggregator, best route, protocol revenue, buybacks.
Technically it is:
- a new frontend
- plus routing over existing pools (Pons, PAIR, Uniswap v4, launch pools)
- plus a token that needs that flow to look real.
Uniswap-grade safety lives in years of audited core contracts. That evidence is not published here.
No public audit of their own router.
No serious bounty.
No immutability statement.
No clean split between “protocol” and “token” the way UNI works.
Why $LDX itself is the weak point
The token is the product
You do not need UNI to swap on Uniswap.
Here $LDX sits in the middle: fees, buybacks, “alignment.”
That makes the token a marketing layer, not a security layer.
Thin liquidity, fat volume
Public pool data shows a small LP book against large 24h volume.
On that book a buyback is easy to recycle: take a fee, buy $LDX in the same thin pool, print “revenue.”
That is a flywheel. It is not solvency.
Aggregator risk hits the user, not the narrative
An aggregator chooses calldata: which pool, which hook, which path.
A bad hop means fee-on-transfer, a hostile hook, or a fake ticker that only looks like a stock token.
$LDX buybacks do not fix that. They incentivize chasing volume across dirty routes.
Approval surface
Swap UIs often ask for infinite allowance or Permit2 to a custom spender.
If that spender is not the canonical, audited router, every approved token sits behind that key and that code.
That is contract risk. Not “community alignment.”
Fresh launch inventory
The UI surfaces new launch tokens next to the $LDX pool.
That is discovery, not a listing standard.
On 4663 you get meme wrappers and equity-looking names.
An aggregator that routes “any asset” inherits that junk.
Robinhood halo
Robinhood Chain is not Robinhood brokerage.
Many stock tokens on that chain are price exposure, not shares.
$LDX borrows the brand.
It does not inherit the license, custody rules, or audits.
What $LDX does not prove
Not that the router is safe.
Not that quotes are enforced on-chain with a hard minOut.
Not that buybacks come from anywhere except the same thin pool.
Not that there is no admin, upgrade path, or fee wallet.
Not that volume is organic.
Short version
$LDX is a new token with aggregator wrapping.
The claims are infrastructure.
The evidence is tokenomics and a beta site.
Unreliable here means: the guarantees do not match the pitch.
Not financial advice.
If you already use the chain, read the contracts, allowances, and pools yourself.
Legend ft. $simd 🤝🔥
Deep Dive into $SIMD Whitepaper: The NVIDIA Inception #Solana Gem Revolutionizing GPU-Powered Simulations with AI 👇💎
Ever wondered how engineers simulate air flow over planes or heat in EV batteries? That's CFD computer models predicting fluid and gas behavior. It's slow and pricey, often taking days on supercomputers. The $SIMD whitepaper outlines a GPU network to speed it up with AI, making it accessible for industries like aerospace, energy and renewables. As an investor, this screams opportunity in a growing market. Here's the key takeaways, with quotes pulled straight from the paper.
1/ CFD Challenges and SIMD's
FixThe whitepaper dives right into why CFD is a headache:
• Resource Drain: "High-fidelity CFD remains slow and expensive: solving Navier–Stokes equations with heat transfer at realistic resolution can require hours to days of GPU time" (Page 1). Plus, shared clusters mean long queues and failed jobs.
• Fragmented Tools: Engineers juggle solvers, schedulers, and neural networks: "the software stack is fragmented; engineers juggle PDE solvers, schedulers, and a growing zoo of neural operators" (Page 1).
• SIMD's Solution: A lightweight agent automates it all: "SIMD addresses this by wrapping a GPU cluster with a lightweight simd agent that owns the entire job lifecycle" (Page 1). Upload meshes, set conditions, it handles scheduling, retries, and tracking.
• Investor Angle: This targets a massive market; quicker sims save millions. Like Ansys (a CFD giant with pricey licenses), but SIMD democratizes it affordably.
2/ System Design Highlights
Practical and scalable, reusing tools like Slurm and Temporal.
Core Components:
• PDE Solvers: Run sims with FEniCSx, output fields and metrics: "CFD simulations run inside components that include solvers supported by FEniCSx and post-processing tools" (Page 2).
• AI Surrogates: Use MeshGraphNet for fast predictions: "Surrogate components (PhysicsNeMo MeshGraphNet / PINNs)" (Page 2).
•Agent and Store: Manages tasks and hashes artifacts: "The agent is a service that registers distributed workflows with Temporal" (Page 2); "All meshes, config files, logs, checkpoints, and datasets... are stored in one place" (Page 2).
• Playground: Real-time tweaks: "A web-based playground allows users to interactively run ML surrogate models" (Page 2).
- Scalability: Starts single-cluster, expands distributed: "the same architecture can extend from a single lab cluster to a distributed network of independent GPU providers" (Page 1).
- Bullish Comparison: Echoes Render Network (RNDR), which crowdsourced GPUs for graphics and hit billions SIMD does it for engineering, more recession-proof. Bittensor ($TAO) rewards AI nodes; SIMD could for physics sims.
3/ Architecture Breakdown
Clean, layered setup for reliability:
Layers:
• User Layer: UI/API for submissions: "a user layer with the UI and API where people submit and monitor jobs" (Page 2).
• Control Layer: Tracks states like queued/running: "a control layer that plans and tracks each job" (Page 2); "The Orchestrator owns the logical life of a job" (Page 3).
• Execution Layer: GPUs run solvers/surrogates: "an execution layer where GPUs actually run the solvers and surrogates" (Page 2).
• Flow: Submit → Validate → Queue → Execute → Results, with heartbeats: "The SIMD Agent... polls Slurm for job status" (Page 3); "If the Orchestrator does not hear from the Agent... it can mark jobs as 'unknown'" (Page 3).
• Edge Over Competitors: Vs. @Synopsys (EDA sim leader), SIMD adds decentralization and AI. Like $Akash Network's cloud model, but GPU-focused with surrogates for speed.
4/ Experiments: Proof in Action
Sealed-cylinder gas convection demo shows results:
• Setup: Navier-Stokes equations, Boussinesq approx: "We model the gas with the incompressible Navier–Stokes equations and an energy equation for temperature" (Page 4).
• Meshing and Solver: Gmsh for 3D mesh, FEniCSx for baselines: "We generate an unstructured 3D mesh using Gmsh/pygmsh" (Page 4); "We solve the problem with FEniCSx / DOLFINx" (Page 4).
• Surrogate Training: Graphs to MeshGraphNet: "From each CFD run we export: Node data... Edges... Time pairs" (Page 5); "We use NVIDIA PhysicsNeMo to train a MeshGraphNet surrogate" (Page 5). Trained in 1 hour on RTX 5090s.
• Results: Low errors, huge speedup: "Accuracy (mean relative L2 error): Temperature field T: 3.001 %... Velocity... 4.446 %... Pressure p: 1.912 %" (Page 6); "MeshGraphNet inference... is about 480× faster" (Page 6).
• Limits and Future: Mesh-dependent now, but upgrades planned: "future SIMD iterations will explore geometry-aware neural operators such as PI-GANO" (Page 6).
5/ Roadmap and Comparisons
Solid path forward: "SIMD goal is to make simulations faster, cheaper, and more reliable by connecting distributed GPUs with physics-based checks" (Page 6).
-> Phases: Core scheduler, surrogates, Proof-of-Simulation, blockchain audits.
-> Why Invest: AI boom in EVs/space aligns perfectly. Vs. Ansys/Synopsys: Agile alternative. Crypto-wise: Render/Bittensor/Akash vibes, but physics-niche could drive adoption and value. Don't miss the upside as it scales :)
Read the full thread to learn more about @simdcompute ->
nitter.cf/Visionary0077/status/1…
WOW I just found the next MASSIVE AI/GPU on #Solana. Reminds me of $RNDR early days 🤯🚨(not joking!!)
It’s called $SIMD a doxxed, fully operational, decentralized GPU network for CFD physics simulations, backed by rigorous HPC and ML research. Enterprise-grade tech with immense potential in aerospace and energy.
Thread incoming on why I'm investing in $SIMD hard. 👇
Please fully read, its VERY interesting.
1/ @simdcompute at its core:
A distributed GPU protocol optimized for Computational Fluid Dynamics (CFD) modeling fluid/gas flows, thermal systems, cryogenic tanks at -196°C, and aerospace structures. Traditional CFD runs take hours to days; $SIMD parallelizes them across GPUs, "rendering physics like shaders." This fills a gap no other project address: high-value, physics intensive workloads with unlimited demand from SpaceX/NASA-level clients.
2/ Live product validation:
Operational proof at “Playground” playground.simd.space/s/holl… showcases interactive CFD for nitrogen flows in hollow (forced convection) and sealed (natural convection) cylinders. Solves Navier-Stokes with heat transfer/Boussinesq buoyancy via FEniCSx/DOLFINx; 3D point cloud viz colored by temperature field, JSON timesteps, GNN (inspired by @MeshGraphNet) surrogates for fast previews. Upload meshes/boundaries; auto-handles compute no hardware needed!! For now it's an demo. Team will extend this soon.
3/ Team accountability:
German based team (they are connected to
@solana Germany team 🤔). Doxxed via LinkedIn, GitHub, Hugging Face. Team won the @MultiversX
AI hackaton this year.
Emails in docs: [email protected], [email protected], [email protected].
- Sam Chien (Data Science)
- Bayangmbe Mounmo (Raised 200k before for startup -> DeepFile, ML/CFD on HF),
- A. Seshaditya (Data Science) These are research identities, not anons.
4/ GitHub engineering depth (VERY INTERESTING):
· github.com/simd-ai
· github.com/adytiaa
· github.com/bm777
· github.com/tihiera
These GitHub profiles provide clear evidence of the team's technical strength, with real code and projects directly tied to $SIMD's focus on AI-driven physics simulations and distributed computing.
Here's a straightforward breakdown of each, highlighting why they're impressive and bullish for the project even if you're not deeply technical, this shows legitimate expertise rather than empty promises.
- Organization: github.com/simd-ai
(Active as the project's central hub). It hosts specialized repositories like "matmul" (Python code for efficient matrix operations, essential for GPU computing; 1 star, updated Nov 7, 2025), "fed-train" (Python setup for federated learning in CFD simulations, aligning perfectly with $SIMD's privacy focused AI; 1 star, updated Nov 18), and "smail" (a TypeScript fork for email integration, 0 stars but 248 forks, updated Nov 21). Why bullish? This demonstrates hands-on work in distributed systems and machine learning core to $SIMD's GPU network with recent updates showing ongoing development.
- A. Seshaditya's: github.com/adytiaa
(tied to the dev's research background). Key repos include "physAI_CFDsim" (Jupyter Notebook using NVIDIA's Modulus for physics-based AI in CFD simulations; 2 stars) this is highly relevant, as it mirrors $SIMD's surrogate models for faster physics rendering. Other projects: "quasi.ai" (Jupyter-based AI tools; 5 stars) and "lcc_net" (Python for sensor data fusion; 1 star). Why bullish? It highlights specialized skills in combining AI with real-world physics, proving the team can build the exact tech $SIMD needs, boosting credibility for long-term success.
- Bayangmbe Mounmo's: github.com/bm777
(Full name: Bayang; Bio: "I like creating tools, not using tools"; Location: Cameroon). Standout repos: "gnpinn" (Python for Gradient-Normalized Physics-Informed Neural Networks in crystal growth simulations; 4 stars, 1 fork) a advanced example of physics-ML integration, directly applicable to $SIMD's CFD accelerations. Others include "object_detection" (Python TensorFlow for object detection in applications like cars; 14 stars, 4 forks), "humanface-mask-detector" (Jupyter Notebook with TensorFlow/OpenCV for mask detection; 10 stars, 8 forks), "nextron" (TypeScript for Next.js + Electron apps; 4.3k stars, 249 forks), and "hask" (JavaScript for seamless tab/window switching; 58 stars, 3 forks). Why bullish? The mix of high-star tools and niche physics code shows versatile, proven engineering talent ideal for scaling $SIMD's platform reliably.
Also -> nitter.cf/Bayang_BM/status/18903…
- Tito Hiera's: github.com/tihiera
Pinned repo: "runtime-org/runtime" (TypeScript for a deterministic browser agent; 179 stars) not directly CFD-related, but it supports data handling and integrations. This profile connects via uploading the Hugging Face CFD dataset used in $SIMD's experiments. Why bullish? It adds ecosystem value, showing collaborative data expertise that enhances $SIMD's training capabilities without needing to start from scratch.
Overall, these aren't empty profiles, they're filled with active, relevant code that directly supports $SIMD's mission. For non-tech folks, think of it as a resume: This team has built and shared real tools in AI, physics, and computing, far exceeding what you see in most crypto launches. It's a strong sign of capability, reducing risk and increasing upside potential as the project grows.
5/ Token fundamentals:
Fair pump.fun launch today, no presale/insiders. CA: EMeugag3yfyvKqNKknGDWAudNALafZjbv9ByzCE8pump. Platform and transaction fees are earmarked for purchasing GPUs to expand the network. GPU providers earn $SIMD rewards based on factors like GPU recency, time contributed, and simulation type, creating a sustainable economic model. ANSYS disruption angle stems from the team's background in commercial CFD tools, enabling pay-per-sim utility. No red flags on transparency.
6/ The Federated Learning edge – why enterprises will care
Most engineering firms will never upload their proprietary CFD datasets to any cloud, centralized or not. That’s the #1 adoption blocker for every decentralized compute project today.$SIMD is building the fix with real federated learning:
- Take a big dataset (they already tested this with a 9.6 GB nitrogen flow + forced convection case)
- Automatically split it into shards (in the test: 40 shards)
- Each node/GPU trains its own Graph Neural Network locally on its shard
- Only the tiny model weights (a few MB) are sent to the network and aggregated
- Raw simulation data never leaves the owner’s infrastructure
Result → a constantly improving global surrogate model without anyone ever seeing the original IP.This isn’t just a bullet point on a roadmap. The proof-of-concept code is already on their GitHub, the 9.6 GB dataset is public on Hugging Face, and the exact workflow is described in the whitepaperThat’s why $SIMD has a realistic shot at real enterprise adoption where almost every other GPU project stays stuck in retail/rendering land.
- huggingface.co/datasets/tihi….
- W&B: wandb.ai/simd/simd-cfd/works…
This is enterprise data compliance from day one.
7/ Whitepaper benchmarks:
They didn’t just claim “fast”. They published hard numbers on a real cryogenic sealed-cylinder case (nitrogen flow, forced convection, exactly the kind of sim SpaceX or Airbus runs daily).
Stack behind it:
Slurm + Temporal for job orchestration
FEniCSx/DOLFINx as the ground-truth solver
P2P blockchain layer for provable audit trails
Current limitations are known (very large meshes still need memory optimizations), and the roadmap explicitly calls out next-gen architectures like Physics-Informed GANO and Geometry-FNO to push errors even lower.
Bottom line: the whitepaper doesn’t sell dreams, it shows a working surrogate that’s already 480× faster with single-digit % error on a real aerospace grade problem.
8/ Roadmap execution:
Expanded to 5 phases today (flexible per dev).
Phase 1: Live demos, whitepaper v1, playground, onboarding (UX improvements incoming).
Phase 2: GPU rewards (benchmarks/jobs).
Phase 3: Physics shaders/templates.
Phase 4: Verification/fairness, job statuses, physics checks.
Phase 5: AI surrogates (fed-train integration). Every step builds on shipped components.
9/ Render comparison table (so you understand what they are building, not saying one is better)
-GPU Category: Workload
$RNDR: Rendering
$SIMD: CFD Physics
-GPU Category: Value
$RNDR: Low-Medium
$SIMD: High-Very High
-GPU Category: Competition
$RNDR: Crowded
$SIMD: Minimal
-GPU Category: Use Cases
$RNDR: Hollywood
$SIMD: Aerospace/Energy
$RNDR handles visuals; $SIMD powers rocket launches. With space and energy tech booming, demand is infinite.
10/ Bullish case:
Real code, research, product, founders, economy but token just launched. Position now before enterprise adoption (aerospace partners incoming?). Narrative, fundamentals, timing align for asymmetric upside. I'm in; DYOR professionally.
END/
Whitepaper link -> drive.google.com/file/d/1oOi…
Hugginface link -> huggingface.co/datasets/tihi…
Wand DB link -> wandb.ai/simd/simd-cfd/works…
Decentralized engineering compute is here.
(For the algo: $GPU $ATH $TAO $IO $HASHAI $AI $AKT $DSYNC $PAAL $FET $ORAI $CLORE $HNT
)
7
811
WOW I just found the next MASSIVE AI/GPU on #Solana. Reminds me of $RNDR early days 🤯🚨(not joking!!)
It’s called $SIMD a doxxed, fully operational, decentralized GPU network for CFD physics simulations, backed by rigorous HPC and ML research. Enterprise-grade tech with immense potential in aerospace and energy.
Thread incoming on why I'm investing in $SIMD hard. 👇
Please fully read, its VERY interesting.
1/ @simdcompute at its core:
A distributed GPU protocol optimized for Computational Fluid Dynamics (CFD) modeling fluid/gas flows, thermal systems, cryogenic tanks at -196°C, and aerospace structures. Traditional CFD runs take hours to days; $SIMD parallelizes them across GPUs, "rendering physics like shaders." This fills a gap no other project address: high-value, physics intensive workloads with unlimited demand from SpaceX/NASA-level clients.
2/ Live product validation:
Operational proof at “Playground” playground.simd.space/s/holl… showcases interactive CFD for nitrogen flows in hollow (forced convection) and sealed (natural convection) cylinders. Solves Navier-Stokes with heat transfer/Boussinesq buoyancy via FEniCSx/DOLFINx; 3D point cloud viz colored by temperature field, JSON timesteps, GNN (inspired by @MeshGraphNet) surrogates for fast previews. Upload meshes/boundaries; auto-handles compute no hardware needed!! For now it's an demo. Team will extend this soon.
3/ Team accountability:
German based team (they are connected to
@solana Germany team 🤔). Doxxed via LinkedIn, GitHub, Hugging Face. Team won the @MultiversX
AI hackaton this year.
Emails in docs: [email protected], [email protected], [email protected].
- Sam Chien (Data Science)
- Bayangmbe Mounmo (Raised 200k before for startup -> DeepFile, ML/CFD on HF),
- A. Seshaditya (Data Science) These are research identities, not anons.
4/ GitHub engineering depth (VERY INTERESTING):
· github.com/simd-ai
· github.com/adytiaa
· github.com/bm777
· github.com/tihiera
These GitHub profiles provide clear evidence of the team's technical strength, with real code and projects directly tied to $SIMD's focus on AI-driven physics simulations and distributed computing.
Here's a straightforward breakdown of each, highlighting why they're impressive and bullish for the project even if you're not deeply technical, this shows legitimate expertise rather than empty promises.
- Organization: github.com/simd-ai
(Active as the project's central hub). It hosts specialized repositories like "matmul" (Python code for efficient matrix operations, essential for GPU computing; 1 star, updated Nov 7, 2025), "fed-train" (Python setup for federated learning in CFD simulations, aligning perfectly with $SIMD's privacy focused AI; 1 star, updated Nov 18), and "smail" (a TypeScript fork for email integration, 0 stars but 248 forks, updated Nov 21). Why bullish? This demonstrates hands-on work in distributed systems and machine learning core to $SIMD's GPU network with recent updates showing ongoing development.
- A. Seshaditya's: github.com/adytiaa
(tied to the dev's research background). Key repos include "physAI_CFDsim" (Jupyter Notebook using NVIDIA's Modulus for physics-based AI in CFD simulations; 2 stars) this is highly relevant, as it mirrors $SIMD's surrogate models for faster physics rendering. Other projects: "quasi.ai" (Jupyter-based AI tools; 5 stars) and "lcc_net" (Python for sensor data fusion; 1 star). Why bullish? It highlights specialized skills in combining AI with real-world physics, proving the team can build the exact tech $SIMD needs, boosting credibility for long-term success.
- Bayangmbe Mounmo's: github.com/bm777
(Full name: Bayang; Bio: "I like creating tools, not using tools"; Location: Cameroon). Standout repos: "gnpinn" (Python for Gradient-Normalized Physics-Informed Neural Networks in crystal growth simulations; 4 stars, 1 fork) a advanced example of physics-ML integration, directly applicable to $SIMD's CFD accelerations. Others include "object_detection" (Python TensorFlow for object detection in applications like cars; 14 stars, 4 forks), "humanface-mask-detector" (Jupyter Notebook with TensorFlow/OpenCV for mask detection; 10 stars, 8 forks), "nextron" (TypeScript for Next.js + Electron apps; 4.3k stars, 249 forks), and "hask" (JavaScript for seamless tab/window switching; 58 stars, 3 forks). Why bullish? The mix of high-star tools and niche physics code shows versatile, proven engineering talent ideal for scaling $SIMD's platform reliably.
Also -> nitter.cf/Bayang_BM/status/18903…
- Tito Hiera's: github.com/tihiera
Pinned repo: "runtime-org/runtime" (TypeScript for a deterministic browser agent; 179 stars) not directly CFD-related, but it supports data handling and integrations. This profile connects via uploading the Hugging Face CFD dataset used in $SIMD's experiments. Why bullish? It adds ecosystem value, showing collaborative data expertise that enhances $SIMD's training capabilities without needing to start from scratch.
Overall, these aren't empty profiles, they're filled with active, relevant code that directly supports $SIMD's mission. For non-tech folks, think of it as a resume: This team has built and shared real tools in AI, physics, and computing, far exceeding what you see in most crypto launches. It's a strong sign of capability, reducing risk and increasing upside potential as the project grows.
5/ Token fundamentals:
Fair pump.fun launch today, no presale/insiders. CA: EMeugag3yfyvKqNKknGDWAudNALafZjbv9ByzCE8pump. Platform and transaction fees are earmarked for purchasing GPUs to expand the network. GPU providers earn $SIMD rewards based on factors like GPU recency, time contributed, and simulation type, creating a sustainable economic model. ANSYS disruption angle stems from the team's background in commercial CFD tools, enabling pay-per-sim utility. No red flags on transparency.
6/ The Federated Learning edge – why enterprises will care
Most engineering firms will never upload their proprietary CFD datasets to any cloud, centralized or not. That’s the #1 adoption blocker for every decentralized compute project today.$SIMD is building the fix with real federated learning:
- Take a big dataset (they already tested this with a 9.6 GB nitrogen flow + forced convection case)
- Automatically split it into shards (in the test: 40 shards)
- Each node/GPU trains its own Graph Neural Network locally on its shard
- Only the tiny model weights (a few MB) are sent to the network and aggregated
- Raw simulation data never leaves the owner’s infrastructure
Result → a constantly improving global surrogate model without anyone ever seeing the original IP.This isn’t just a bullet point on a roadmap. The proof-of-concept code is already on their GitHub, the 9.6 GB dataset is public on Hugging Face, and the exact workflow is described in the whitepaperThat’s why $SIMD has a realistic shot at real enterprise adoption where almost every other GPU project stays stuck in retail/rendering land.
- huggingface.co/datasets/tihi….
- W&B: wandb.ai/simd/simd-cfd/works…
This is enterprise data compliance from day one.
7/ Whitepaper benchmarks:
They didn’t just claim “fast”. They published hard numbers on a real cryogenic sealed-cylinder case (nitrogen flow, forced convection, exactly the kind of sim SpaceX or Airbus runs daily).
Stack behind it:
Slurm + Temporal for job orchestration
FEniCSx/DOLFINx as the ground-truth solver
P2P blockchain layer for provable audit trails
Current limitations are known (very large meshes still need memory optimizations), and the roadmap explicitly calls out next-gen architectures like Physics-Informed GANO and Geometry-FNO to push errors even lower.
Bottom line: the whitepaper doesn’t sell dreams, it shows a working surrogate that’s already 480× faster with single-digit % error on a real aerospace grade problem.
8/ Roadmap execution:
Expanded to 5 phases today (flexible per dev).
Phase 1: Live demos, whitepaper v1, playground, onboarding (UX improvements incoming).
Phase 2: GPU rewards (benchmarks/jobs).
Phase 3: Physics shaders/templates.
Phase 4: Verification/fairness, job statuses, physics checks.
Phase 5: AI surrogates (fed-train integration). Every step builds on shipped components.
9/ Render comparison table (so you understand what they are building, not saying one is better)
-GPU Category: Workload
$RNDR: Rendering
$SIMD: CFD Physics
-GPU Category: Value
$RNDR: Low-Medium
$SIMD: High-Very High
-GPU Category: Competition
$RNDR: Crowded
$SIMD: Minimal
-GPU Category: Use Cases
$RNDR: Hollywood
$SIMD: Aerospace/Energy
$RNDR handles visuals; $SIMD powers rocket launches. With space and energy tech booming, demand is infinite.
10/ Bullish case:
Real code, research, product, founders, economy but token just launched. Position now before enterprise adoption (aerospace partners incoming?). Narrative, fundamentals, timing align for asymmetric upside. I'm in; DYOR professionally.
END/
Whitepaper link -> drive.google.com/file/d/1oOi…
Hugginface link -> huggingface.co/datasets/tihi…
Wand DB link -> wandb.ai/simd/simd-cfd/works…
Decentralized engineering compute is here.
(For the algo: $GPU $ATH $TAO $IO $HASHAI $AI $AKT $DSYNC $PAAL $FET $ORAI $CLORE $HNT
)
@OfficialLoganK how can I switch from free to pay-as-you-go?
I'm in a loop process AI Studio (.google.com) <-> AI Google (.dev)
This is why CORBITS partners with SYRA
nitter.cf/visionary0077/status/1…
Probably found the next multimillion dollar AI runner with maybe even more potential than $payai (im dead serious🤯).
Please read it all to fully understand!! Gets more interesting if you keep reading.
1/ $Syra is currently the most advanced x402-native AI trading agent on @solana . Launched only weeks ago with currently a marketcap of 80k, it combines on-chain execution with sophisticated market analysis (macro sentiment, technical indicators, news flow, and behavioral patterns) powered by #DeepSeek models.
2/ The core innovation lies in its full integration of the x402 payment standard (HTTP 402 for agents). Users can grant either one-click wallet approvals or fully autonomous zero-approval flows, enabling the agent to pay per API call or on-chain action in USDC – without ever exposing private keys or relying on traditional API keys.
3/ Real-world usage is already impressive: in recent stress tests in collaboration with @corbits_dev , $Syra agents generated more than 25,000 on-chain transactions in a single day, propelling the facilitator to the #4 spot on x402scan within hours!!
This demonstrates production-grade scalability and strong network effects.
Who is Corbits? -> @corbits_dev is a #Solana conntected development team building software for Agentic Commerce, focusing on x402 payment standards for autonomous agents. They develop tools like the Faremeter SDK to enable micropayments in crypto ecosystems and is used by @Solana and @SKALEnetwork, including integrations for API access, swaps via Jupiter, and on-chain transactions for AI agents.
OH and They also organized the x402 hackathon with @solana
This is corbits :) --->
@syra_agent has partnered with Corbits and helped them from rank 14 → 4, and 200 → 25K transactions in a single day. See below screenshot from the x402 scan rank.
They also got support from @afkehaya (Former solana foundation en now corbits)
Proof: nitter.cf/afkehaya/status/198917…
See his followers :)
More proof: nitter.cf/corbits_dev/status/198…
More proof: nitter.cf/afkehaya/status/199011…
Matter of time before it flips $payai, $daydreams, $dexter and coinbase facilitators ---> nitter.cf/syra_agent/status/1990…
4/ The team has delivered an MVP, a live Telegram signal bot, a public dashboard, and multiple hackathon entries in record time. Upcoming integrations with Jupiter (via Corbits), PayAI, and other infrastructure partners are confirmed.
5/ Fundamental tailwinds:
Agentic commerce is moving from concept to reality; x402 is backed by Coinbase, Block, and Google engineers as the emerging standard for machine-to-machine micropayments.
- Efficiency & Scale: A hedge fund could deploy fleets of agents to monitor 100+ assets simultaneously, reducing manual analysis costs by 80%+ and enabling 24/7 operations across global markets.
- Monetization Flywheel: Via x402, agents pay for data/tools autonomously, creating seamless ecosystems. For a trading desk, this means plug-and-play integrations with exchanges like Jupiter, turning idle capital into automated revenue streams.
- High-frequency autonomous trading creates continuous transaction volume – a flywheel that benefits both the $SYRA token and the broader Solana + x402 ecosystem.
Extremely low fully diluted valuation relative to comparable agent projects leaves substantial upside as adoption grows.
6/ In summary: Syra is positioned at the intersection of three megatrends – AI agents, Solana’s high-performance chain, and the x402 payment rail. while already proving product-market fit through live usage and rapid iteration.
For investors seeking early exposure to the agent economy, Syra currently represents one of the highest conviction opportunities in the space.
CA: 8a3sEw2kizHxVnT9oLEVLADx8fTMPkjbEGSraqNWpump
Website: syra.ai (live)
Twitter: @syra_agent
DYOR – but the facts speak for themselves.
For the people calling $syra out, the dev is doxxed :)
He recently won 3 hackatons.. also got an awesome github and very nice cv.
Probably found the next multimillion dollar AI runner with maybe even more potential than $payai (im dead serious🤯).
Please read it all to fully understand!! Gets more interesting if you keep reading.
1/ $Syra is currently the most advanced x402-native AI trading agent on @solana . Launched only weeks ago with currently a marketcap of 80k, it combines on-chain execution with sophisticated market analysis (macro sentiment, technical indicators, news flow, and behavioral patterns) powered by #DeepSeek models.
2/ The core innovation lies in its full integration of the x402 payment standard (HTTP 402 for agents). Users can grant either one-click wallet approvals or fully autonomous zero-approval flows, enabling the agent to pay per API call or on-chain action in USDC – without ever exposing private keys or relying on traditional API keys.
3/ Real-world usage is already impressive: in recent stress tests in collaboration with @corbits_dev , $Syra agents generated more than 25,000 on-chain transactions in a single day, propelling the facilitator to the #4 spot on x402scan within hours!!
This demonstrates production-grade scalability and strong network effects.
Who is Corbits? -> @corbits_dev is a #Solana conntected development team building software for Agentic Commerce, focusing on x402 payment standards for autonomous agents. They develop tools like the Faremeter SDK to enable micropayments in crypto ecosystems and is used by @Solana and @SKALEnetwork, including integrations for API access, swaps via Jupiter, and on-chain transactions for AI agents.
OH and They also organized the x402 hackathon with @solana
This is corbits :) --->
@syra_agent has partnered with Corbits and helped them from rank 14 → 4, and 200 → 25K transactions in a single day. See below screenshot from the x402 scan rank.
They also got support from @afkehaya (Former solana foundation en now corbits)
Proof: nitter.cf/afkehaya/status/198917…
See his followers :)
More proof: nitter.cf/corbits_dev/status/198…
More proof: nitter.cf/afkehaya/status/199011…
Matter of time before it flips $payai, $daydreams, $dexter and coinbase facilitators ---> nitter.cf/syra_agent/status/1990…
4/ The team has delivered an MVP, a live Telegram signal bot, a public dashboard, and multiple hackathon entries in record time. Upcoming integrations with Jupiter (via Corbits), PayAI, and other infrastructure partners are confirmed.
5/ Fundamental tailwinds:
Agentic commerce is moving from concept to reality; x402 is backed by Coinbase, Block, and Google engineers as the emerging standard for machine-to-machine micropayments.
- Efficiency & Scale: A hedge fund could deploy fleets of agents to monitor 100+ assets simultaneously, reducing manual analysis costs by 80%+ and enabling 24/7 operations across global markets.
- Monetization Flywheel: Via x402, agents pay for data/tools autonomously, creating seamless ecosystems. For a trading desk, this means plug-and-play integrations with exchanges like Jupiter, turning idle capital into automated revenue streams.
- High-frequency autonomous trading creates continuous transaction volume – a flywheel that benefits both the $SYRA token and the broader Solana + x402 ecosystem.
Extremely low fully diluted valuation relative to comparable agent projects leaves substantial upside as adoption grows.
6/ In summary: Syra is positioned at the intersection of three megatrends – AI agents, Solana’s high-performance chain, and the x402 payment rail. while already proving product-market fit through live usage and rapid iteration.
For investors seeking early exposure to the agent economy, Syra currently represents one of the highest conviction opportunities in the space.
CA: 8a3sEw2kizHxVnT9oLEVLADx8fTMPkjbEGSraqNWpump
Website: syra.ai (live)
Twitter: @syra_agent
DYOR – but the facts speak for themselves.
Funny to see that $ZKTR got 352 downloads already in like 4 days on NPM (It got a MIT licence so its open source)
Did you buy this $engrave dip at 70k when i told you? Another 10x in the books. Follow for hopefully more ❇️
Just did some more digging regarding the tokenomics/revenue streams for $SYRA based directly on their official documentation and confirmed by recent team updates and its straight fire!
1. Trading Signal Subscriptions
Paid subscriptions (monthly/annual) for premium AI-generated trading signals and strategies.
2. Premium API Access Fees
Tiered fees for developers, traders, and institutions accessing advanced Syra APIs (real-time signals, historical data, agent endpoints).
3. Agent Marketplace Transaction Fees
Percentage cut (typically 2-5%) on all buys, sells, rentals, and executions in the upcoming Syra Agent Marketplace.
4. Enterprise Licensing & White-Label Solutions
High-ticket licensing deals for hedge funds, prop firms, and institutions that want to integrate or rebrand Syra’s autonomous agents.
5. Partnership & Integration Revenue
Revenue-sharing deals with platforms like Corbits, future x402 facilitators, DEXs/CEXs, and other ecosystems that integrate Syra agents.
Key demand driver:
Every autonomous agent interaction (signals, trades, etc.) triggers a 0.0001 USDC x402 micro-fee, this creates constant organic buy pressure and real yield. How revenue flows back to $SYRA holders (actual revenue-share model):
Portion of ALL revenue → staking rewards (already live)
Portion → aggressive buybacks & burns (e.g. recent 16M token buyback + permanent lock)
Portion → reserved for exclusive holder airdrops and governance perks Small % of fees directly burned → permanent deflation.
97% of total supply allocated to community (liquidity + rewards + airdrops) with zero VC allocations or shady unlocks, one of the cleanest tokenomics in the entire AI agent sector!
Already battle-tested: 25K+ on-chain tx in <12 hours with @corbits_dev , now running a 50x upgraded agent 24/7 with controlled scaling. Roadmap (mobile app, copytrading, multi-chain, quant APIs) is being delivered fast. They are building a real revenue-generating protocol that’s still at micro market cap while proving product-market fit in the fastest growing.
:) PS: $payAI collab could be lurking (im sure), given their recent top facilitator performance x402 and $syra strong ecosystem ties; this could supercharge adoption (and the dev if ofcourse following already) time wil tell.
Our official Syra documentation is now live:
docs.syraa.fun
Inside you’ll find all the key info:
• Telegram bot overview
• Full roadmap + partnerships
• Tokenomics and utility breakdown
All future updates, improvements, and releases will be documented here.
This is now the home for everything Syra.
Probably found the next multimillion dollar AI runner with maybe even more potential than $payai (im dead serious🤯).
Please read it all to fully understand!! Gets more interesting if you keep reading.
1/ $Syra is currently the most advanced x402-native AI trading agent on @solana . Launched only weeks ago with currently a marketcap of 80k, it combines on-chain execution with sophisticated market analysis (macro sentiment, technical indicators, news flow, and behavioral patterns) powered by #DeepSeek models.
2/ The core innovation lies in its full integration of the x402 payment standard (HTTP 402 for agents). Users can grant either one-click wallet approvals or fully autonomous zero-approval flows, enabling the agent to pay per API call or on-chain action in USDC – without ever exposing private keys or relying on traditional API keys.
3/ Real-world usage is already impressive: in recent stress tests in collaboration with @corbits_dev , $Syra agents generated more than 25,000 on-chain transactions in a single day, propelling the facilitator to the #4 spot on x402scan within hours!!
This demonstrates production-grade scalability and strong network effects.
Who is Corbits? -> @corbits_dev is a #Solana conntected development team building software for Agentic Commerce, focusing on x402 payment standards for autonomous agents. They develop tools like the Faremeter SDK to enable micropayments in crypto ecosystems and is used by @Solana and @SKALEnetwork, including integrations for API access, swaps via Jupiter, and on-chain transactions for AI agents.
OH and They also organized the x402 hackathon with @solana
This is corbits :) --->
@syra_agent has partnered with Corbits and helped them from rank 14 → 4, and 200 → 25K transactions in a single day. See below screenshot from the x402 scan rank.
They also got support from @afkehaya (Former solana foundation en now corbits)
Proof: nitter.cf/afkehaya/status/198917…
See his followers :)
More proof: nitter.cf/corbits_dev/status/198…
More proof: nitter.cf/afkehaya/status/199011…
Matter of time before it flips $payai, $daydreams, $dexter and coinbase facilitators ---> nitter.cf/syra_agent/status/1990…
4/ The team has delivered an MVP, a live Telegram signal bot, a public dashboard, and multiple hackathon entries in record time. Upcoming integrations with Jupiter (via Corbits), PayAI, and other infrastructure partners are confirmed.
5/ Fundamental tailwinds:
Agentic commerce is moving from concept to reality; x402 is backed by Coinbase, Block, and Google engineers as the emerging standard for machine-to-machine micropayments.
- Efficiency & Scale: A hedge fund could deploy fleets of agents to monitor 100+ assets simultaneously, reducing manual analysis costs by 80%+ and enabling 24/7 operations across global markets.
- Monetization Flywheel: Via x402, agents pay for data/tools autonomously, creating seamless ecosystems. For a trading desk, this means plug-and-play integrations with exchanges like Jupiter, turning idle capital into automated revenue streams.
- High-frequency autonomous trading creates continuous transaction volume – a flywheel that benefits both the $SYRA token and the broader Solana + x402 ecosystem.
Extremely low fully diluted valuation relative to comparable agent projects leaves substantial upside as adoption grows.
6/ In summary: Syra is positioned at the intersection of three megatrends – AI agents, Solana’s high-performance chain, and the x402 payment rail. while already proving product-market fit through live usage and rapid iteration.
For investors seeking early exposure to the agent economy, Syra currently represents one of the highest conviction opportunities in the space.
CA: 8a3sEw2kizHxVnT9oLEVLADx8fTMPkjbEGSraqNWpump
Website: syra.ai (live)
Twitter: @syra_agent
DYOR – but the facts speak for themselves.
$LUMEN IS ABOUT TO 100X AND HERE’S WHY 🚨
@cursor_ai just hit $29.3B valuation with $2.3B fresh funding, dominating AI coding for Google, Nvidia, Uber. But@lumenCLIis building something way bigger a decentralized, dev-owned AI data economy on Solana, and it’s already paying users while Cursor only charges them.
Here’s the real alpha: Cursor AI: $20/mo, you pay, they own your data, centralized, VC-fueled empire.
$LUMEN: you upload code via CLI, AI scores it (Gemini 2.5), you earn USDC + $LUMEN, keep 100% IP, on-chain proof via Irys.
Traction is insane:
25M+ lines processed, ~2K contributions, $28.9K+ already paid out, weekly USDC batches funded by real protocol revenue, V2 CLI live with anti-plagiarism, complexity scoring, interactive demo anyone can try.
Partnerships on fire:
Irys for verifiable storage (Nov 12 AMA), PayAI for x402 crypto payments (business.lumen.onl live), enterprise dataset sales starting soon.
Tokenomics = pure rocket fuel: Fees → buyback & burn $LUMEN → fund rewards → more code → bigger datasets → higher sales. Closed loop.
Already burned 16M+ tokens. Dev supply locked 12 months. Rewards USD-pegged, updated every 15 mins.At $1.5M MC and $0.00015, hitting Cursor’s $29B val = $0.0029 → 20x.
But with AI data exploding ($13B market by 2034), Solana bull run, and ethical data demand? 100x to $0.015 ($150M MC) is realistic. Cursor took $1B+ VC to scale.
$LUMEN bootstrapped, already self-sustaining, and paying devs weekly.This is Web3’s Cursor but you own the token, earn from day one, and ride the AI data wave.Ape in now before listings, Irys deals, and marketplace revenue send it parabolic.
Download CLI, upload code, earn, HODL, moon.NFA, DYOR but this one’s cooking hard. @lumenCLI
#LUMEN #Solana #AICrypto $sol
He doesnt know about @zkSLLabs $zksl
Lmao if you bought $optim because this guy than you suck 🤣 told you hes analysis is worthless
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