Alex Sojib Hasan

Somnia — The Blockchain Built for Games & Virtual Worlds
Imagine a blockchain designed not just for finance, but for living worlds — games, social spaces, avatars, virtual economies — things where people expect real-time responses and smooth interaction. That’s Somnia: a new, EVM-compatible Layer-1, built so developers can build mass-consumer experiences on-chain without sacrificing speed or usability.
Why Build Something Like This?
Existing blockchains are great for DeFi, tokens, and finance, but they struggle when dozens or hundreds of thousands of users interact in real time (e.g. moving avatars, trading inventories, updating shared world state). The lag, throughput limits, and gas bottlenecks get in the way.
Somnia’s goal is to let creators build those kinds of interactive experiences directly on-chain. No off-chain servers doing the heavy lifting. You get the security, composability, and decentralization of a blockchain — but with performance more like a game engine.
Who’s Behind It & When It Launched
The team & backers: Somnia is being built in part by Improbable (a company known for virtual world tech) along with support from the Somnia Foundation and other ecosystem partners.
Launch date: Mainnet went live on September 2, 2025.
Testnet hype: Before launch, the project ran big stress tests. They claimed billions of testnet transactions, hundreds of millions of unique addresses, and strong performance. Real testnets, of course, are more forgiving than handling a million real users — but it was promising.
How Somnia Works — At a High Level
I’ll avoid the heavy formalism and break it down in plain terms:
Somnia uses a design called MultiStream, where each validator can process their own “stream” of data/transactions. Later those streams are anchored into a single global chain view. The idea is: parallelize as much work as possible, then synchronize.
It also uses a custom storage engine and execution tweaks so that common smart contracts (especially ones used in games/worlds) run faster.
In lab/testing settings, Somnia claims extremely high throughput (hundreds of thousands to potentially over a million transactions per second) and very fast finality. But in real usage, it still depends on validator networks, network latency, gas economics, and how smart contracts are built.
Money, Tokens & Gas (SOMI)
Token name & supply: The native token is SOMI, with a fixed supply of 1,000,000,000 (one billion).
Fees and burning: When people pay transaction/gas fees, a big chunk of that gets burned, i.e. destroyed. The rest goes to validators. This gives the token a deflationary pressure if usage is strong.
Validators & staking: To run a validator node, you need to stake 5,000,000 SOMI. For normal token holders, you can delegate your SOMI to validators and earn a share of the fees. There are rules about lockups, unbonding, etc.
Tools & Ecosystem — Build with Less Friction
One of Somnia’s big bets is that creators (especially from Web2, gaming, 3D, creative backgrounds) shouldn’t have to learn all the intricacies of blockchain to build.
Playground & Dream Builder: These are visual, web-based tools where you can upload 3D assets, build worlds, place objects, and mint creations as NFTs — all with minimal developer overhead.
SDKs & integrations: Somnia works with common tooling like thirdweb (for smart contract handling), Sequence (for account abstraction), messaging bridges, and infrastructure supports. Helps reduce the boilerplate.
Ecosystem support: Somnia has run grant and accelerator programs. They also partner with custody providers, infrastructure players, and game studios to help grow the network.
Safety, Audits & Risks
Somnia is not untested — they’ve engaged third-party auditors (e.g. Hacken) and run bug bounty programs to catch issues before and after mainnet. But like any new blockchain, there are risks:
Novel architecture means unknown edge cases (stream coordination, validator misbehavior, syncing issues).
Real-world performance may fall short of idealized lab numbers.
Token allocation, governance centralization, and initial validator distribution all matter a lot.
Always best practice: use audited contracts, limit how much value you put in new systems, and monitor updates and security bulletins closely.
Why It Matters & What to Watch
For game studios, virtual-world creators, and metaverse builders, Somnia could be a breakthrough — enabling fully on-chain experiences at scale.
For infrastructure providers, wallets, and tooling companies, there’s room to build services around validator nodes, custody, monitoring, and bridging.
For investors or token holders, this is still early-stage technology — the upside is large, but so is the risk.
Getting Started — Your First Moves
1. Read the official docs — get familiar with the RPC endpoints, contract tooling, and protocol conventions.
2. Play in the Playground — try building a small world or object to see the flow from design → asset upload → minting.
3. Deploy a simple contract — use thirdweb or Somnia’s SDK in testnet to experiment with gas, state changes, events.
4. Track audits & security updates — before building anything serious, verify security reports and monitor the protocol’s updates.
@Somnia Official
$SOMI

In DeFi, the price is not just 'there', but directly participates in liquidation, pricing, and leverage logic.
@Pyth Network The value lies in: it can inject high-precision, low-latency data into these key modules, making many high-risk, high-latency logic controllable. Let me break it down for you.How to use Pyth in on-chain finance? Look at its key role in liquidation, pricing, and leverage.
In DeFi, the price is not just 'there', but directly participates in liquidation, pricing, and leverage logic.
@Pyth Network The value lies in: it can inject high-precision, low-latency data into these key modules, making many high-risk, high-latency logic controllable. Let me break it down for you.
1️⃣ Liquidation system: who suffers first depends on who has the accurate price.
When talking about liquidation, one must say: in lending agreements, if the price delay and deviation are too large, it is easy to have erroneous liquidation—users being liquidated early, agreements being attacked. Many times, liquidation losses are not due to a market crash, but because the oracle did not keep up.
Pyth's low latency + high-frequency updates + confidence interval mechanism provide a more reliable reference for liquidation logic. The protocol can set a 'safety buffer zone' based on the current price plus the confidence interval; liquidation is only triggered when the price touches the outer limits of this zone, making it safer than using a rigid single price.
Many projects using Pyth, lending or leverage protocols, will label the price source as 'Powered by Pyth' before and after liquidation to emphasize the credibility of the data source in the liquidation logic.
2️⃣ DEX Pricing: Slippage Control + Market Depth Compensation
Two pain points in DEX: large slippage + inconsistent prices. Large slippage is due to insufficient market depth and the impact of trading volume; inconsistent prices arise from pricing delay differences across different chains and pools.
The role of Pyth here is to provide a reliable benchmark price for DEX to perform 'price correction'. For instance, if the price in the liquidity pool deviates too far from Pyth's benchmark price, it can trigger arbitrage or compensation distribution. This way, when you trade, you won’t be pulled away drastically due to minimal trades in the pool.
Additionally, during cross-chain DEX or cross-pool trading, Pyth can be used to unify the price benchmark, reducing the unfairness of cross-pool price differences for users.
3️⃣ Leverage Strategies / Perpetual Contracts: A favorite of high-frequency traders
Leverage / perpetual contracts are most afraid of price delays and volatility; even a 0.1-second error could lead to forced liquidation. Pyth's high-frequency update capability allows contracts to obtain prices at speeds close to off-chain markets, reducing slippage or liquidation risk caused by delays.
What's even more impressive is that it can work with the Express Relay module to provide a 'priority reading channel' for strategies that require extremely high speed, further reducing losses in block sorting and front-running.
I also saw in the materials that Pyth provides high-frequency price data for hundreds of assets (including crypto, forex, commodities, ETFs, etc.), which is very helpful for leveraging strategies to expand asset targets and arbitrage across markets.
4️⃣ Structured Products / Asset Management: Seller Pricing & Benchmark Calculation
In structured products (such as options combinations, yield certificate products), pricing requires not only real-time prices but also historical volatility, time window prices, index benchmarks, etc. Pyth's Benchmark module precisely fills this gap: it can provide 'historical price snapshots' and 'interval average prices'. This data is often used by asset management contracts or strategy funds for valuation, settlement, or backtesting.
As a result, structured yield products can directly reference on-chain data without relying on centralized servers or external APIs, thereby reducing trust costs.
5️⃣ Risk Hedging & Insurance: Incident triggers have evidence to rely on
In insurance, hedging, and risk protection products, it is common to set trigger conditions such as 'if a certain asset drops below a certain amount, or if a certain time sees a surge'. This trigger must be more trustworthy than the users or participants. The high-frequency, confidence interval data provided by Pyth serves as a natural basis for trust. Insurance contracts can state 'trigger payouts when falling below the lower limit of the Pyth price band', making such contracts more credible and less contentious.
Moreover, hedging strategies can dynamically adjust in real-time based on the multi-asset prices provided by Pyth without waiting for block confirmations or excessive delays.
6️⃣ Overall Ecosystem Interaction: Pyth is the 'public data layer' for many DeFi modules
Putting together the above use cases forms a picture: liquidation, pricing, leverage, asset management, insurance... Most DeFi products need 'reliable data'. If each project fetches prices, aggregates, and prevents manipulation on its own, the costs are too high and the error probability is significant. Pyth acts like a public infrastructure, allowing these products to avoid reinventing the wheel at the data layer.
From the materials, many well-known protocols (Synthetix, Vela, Solend, Alpaca, etc.) have already integrated or referenced Pyth prices. Thus, whenever projects create new assets, implement cross-chain features, or expand contracts, they can directly call Pyth data, avoiding the risks and costs of building price logic themselves.#PythRoadmap $PYTH
Conclusion:
In DeFi applications, data is not an 'optional' auxiliary function; it directly enters core processes like liquidation, pricing, leverage, and insurance. The existence of Pyth makes the underlying 'price dependence' of these processes more reliable, auditable, and unified. Understanding these use cases helps you see Pyth's 'indispensable' position in the future of DeFi.