polana

What “Polana builds on top of JAM” actually means 🧵

Polana doesn’t redefine how Solana programs work.
It ports Solana’s account-based execution model and runtime semantics onto JAM’s execution layer.
At the programming level, nothing fundamental changes.

Solana programs keep:
the same account-based state model
the same deterministic read/write declarations
the same execution semantics developers already rely on
What changes is where execution runs, not how programs are written.

Polana supports multiple execution paths to achieve this:
running Solana programs via an sBPF-compatible runtime
transpiling Solana bytecode into JAM’s execution format
or compiling programs natively to JAM’s PVM for tighter integration
These are execution strategies, not new programming models.

The goal is compatibility-first:
no required code changes for existing Solana programs
familiar tooling like Anchor and Solana CLI continue to work
application logic and assumptions remain intact

JAM provides the execution substrate: parallel compute, deterministic accumulation, and network-scale scheduling.

Polana maps Solana execution model onto that substrate.
So “building on top” doesn’t mean wrapping Solana in a new framework.

It means relocating Solana execution onto a different execution architecture while preserving semantics.
That’s the architectural idea behind Polana:

Solana programs, unchanged in behavior, executed on JAM’s parallel, coherent execution layer.

#Polana #JamProtocol $SOL
$DOT

Battle-tested and pure coherent parallelism describe two different strengths and POLANA brings them together.
@Solana Official execution model is battle-tested.
Deterministic, conflict-aware, and proven under real production load.
JAM introduces pure coherent parallelism.
Execution scales across many cores, while state stays unified and composable.
@POLANA_Network combines both.
Battle-tested Solana semantics run on JAM coherent, network-wide parallel execution without sharding state, fragmenting liquidity, or changing how programs are written.
No trade-offs.
Just execution that scales, while everything else stays together.

$SOL
$DOT

#POLANA #JAMProtocol #solana #BlockchainInfrastructure #ParallelExecution

Why does JAM have hundreds of execution cores and why it matters for POLANA_Network
JAM uses many execution cores because it scales by running computation in parallel, not by splitting state.

POLANA plugs Solana deterministic, account-declared execution model directly into this architecture. Transactions that don’t conflict can execute simultaneously across the network, instead of being limited to a single validator machine.

More cores don’t mean more chains.
They mean more Solana execution happening at the same time on one coherent state.

$SOL $DOT

#Polana #Polana_network

Scaling by Parallelizing Execution, Not State
Nothing gets split: not users, not liquidity, not application state.
Only execution is parallelized.

POLANA does not scale by introducing new execution domains or partitioning state.
It preserves a single, coherent state model and deterministic application semantics.

What changes is the execution substrate.
Transaction execution is decomposed into stateless computation that can run concurrently across many cores, while state transitions are deterministically accumulated into a single global state. Parallelism lives entirely in the execution phase, not in state ownership.
This avoids sharded state, async reconciliation, and cross-domain coordination at the application layer.
POLANA is built to scale execution horizontally while keeping state, composability, and semantics intact.

#POLANA #JAMProtocol #ParallelExecution

$DOT  $SOL