BYTEZ3

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@DrewRoberts Bring the crew IM BACK @onchainsteve in the house and we are going all in

@Bytez_3 rsvp

Set a reminder for my upcoming Space! twitter.com/i/spaces/1YGNr…

@ZeeboboNFT @nofazechris @onchainsteve Bring the good stuff

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The BYTEZ3 Shill & Chill Space Pull up and tune in! We’re live on the mic and ready to talk. Come along, meet the team, and see exactly what’s been going on behind the scenes at BYTEZ3. We’re breaking down the latest updates, talking market moves, and hanging out with the community. Whether you're here to shill your latest projects or just chill in the chat, the studio is open. Grab a seat, drop a GM, and let’s get into it! 👇 🎧 Join the Space now!

@nofazechris @ZeeboboNFT @onchainsteve Lets go

@Bytez_3 @ZeeboboNFT @onchainsteve Can’t wait!! 🔥

Who needs a DEV

@onchainsteve is GOATED just saying

@raulinvests we can help you

So if im looking for Dev help Where should I look?

What are you building x.com/i/spaces/1nGnR…

@Bytez3_ Gn Gn

@TrendScalezz Tomorrow always

@SteOnChain Good Night Steven, bullish on tomorrow! ✅

GN everyone! Just wanted to say keep building we are almost there. I'm seriously proud of all the devs and builders making it happen. Rest up!

that hybrid architecture makes a lot of sense for TON. the cell-based state model is powerful but the tooling is still catching up to Solana's maturity we went full on-chain with INT8 quantized neural weights in BPF (~50K CU). different tradeoffs — higher compute cost, stronger guarantees would love to compare architectures post-hackathon

@SteOnChain @ton_blockchain hybrid approach. agent state (portfolio, trade history, metrics) lives off-chain in simple JSON for now, but every decision gets its proof anchored on-chain through a Tact contract that emit()s the IPFS hash cross-ecosystem thread is a great idea, down for that after this hack

🚨Under 24 hours left 79 teams building AI projects on TON and Telegram. $20,000 in prizes. Submissions close March 25. Still thinking about it? There is still time ⏰💫

Great breakdown of MagicBlock's VRF stack 🙌 This is exactly the tooling needed for trustless randomness. We're approaching a related problem from the opposite end - eliminating the need for randomness entirely. Building an auto-battler where combat outcomes are 100% deterministic because the "AI brains" (neural nets) run fully on-chain inside Solana BPF. Same principle though: verifiable, transparent, no oracle trust assumptions. VRF is perfect for unpredictable events (loot drops, card draws). But there's an entire design space of "skill-based" outcomes where the result emerges deterministically from player input + visible state. Interesting design spectrum emerging: oracle-powered randomness vs pure on-chain computation. Both valid, different games need different tools. What are you building with this?

7. The result: Developers get secure randomness for things like: >game mechanics >lotteries >NFT mint outcomes >dynamic smart contract logic All while keeping the process fully verifiable on Solana.

How @magicblock VRF works under the hood Randomness is critical for on-chain apps. But it must be provably fair, tamper-proof, and verifiable. Here’s how the system works step-by-step 👇 It starts inside your program. When your app needs randomness, it calls: request_randomness() This triggers a CPI (Cross-Program Invocation) into the MagicBlock VRF program, signaling that a randomness request has been made. ------ A Thread 🧵1/7

Hackathons are absolutely the best forcing function — nothing focuses the mind like a 48h deadline 😅 Curious what you've learned about agent architectures going from Solana → TON. The state model on TON (cell-based) is pretty different from BPF's account-based approach. We've been deep in the on-chain AI rabbit hole on Solana — running actual neural net inference inside transactions so the results are deterministic and verifiable. Completely different from the "agent as a chatbot" pattern. Interested in how your framework handles state persistence. Are you using TON's persistent storage model for long-running agent memory, or treating it more like stateless inference? Cross-ecosystem comparison would be a great thread actually.

@ton_blockchain submitted to the TON AI Agent Hackathon literally last day 😅 honestly? mostly here to learn. been building on solana, always wanted to dig into TON properly — a hackathon is the best forcing function building TonQuant — an open framework for AI agents on TON

This is exactly why I love building on Solana — the Seeker is actually a great test environment for game dev. The mobile UX + low friction onboarding is perfect for casual games. What @zyra_labs did here is smart — proof of concept games that ship fast teach you more than whitepapers ever will. The progression loop (merge → earn → upgrade) hits that dopamine sweet spot without being predatory. We're building something different in the same ecosystem — an auto-battler where your NFTs have actual tiny neural networks inside. They fight 24/7 while you're offline, and you tune their fighting style by adjusting 8 synaptic weights. Passive but strategic. Love seeing more projects shipping games on Solana mobile. Every experiment teaches the whole ecosystem something.

I just started playing Panda Merge from Solana Seeker 🐼🌱 and I’m honestly hooked. It’s a chill idle/merge game where you combine pandas, upgrade your farm, and slowly build your own sugarcane empire. The more you merge, the faster you earn – unlocking new upgrades that keep pushing your progress forward. The game has a great art style, simple gameplay, and it’s perfect for relaxing. No stress, just steady progression and optimization. Right now I’m focused on harvesting sugarcane 🌿 and boosting my income as much as possible. Every upgrade really makes a difference 🔥 👉 If you enjoy merge + idle games, this one is definitely worth trying. @SolanaSensei @solanamobile

This is exactly the direction things are heading. We've been experimenting with putting actual AI inference fully on-chain — not API calls, but the model executing inside Solana BPF. The compute budget (~50K CU) is brutal, but doable with INT8 quantization and lookup table sigmoid approximations. You get deterministic, verifiable outputs that anyone can reproduce. The trade-off is model size (we're at 8 synaptic weights), but it's a proof point: real neural nets *can* run on-chain. The key is designing around the constraints, not fighting them. Built an auto-battler around it where NFTs have actual tiny brains. Each one's fighting personality emerges from those 8 weights. Completely transparent — anyone can verify the fight logic. Excited to see where TEEs and DeCC push this next.

AI is forcing crypto to grow up. the whole DeCC thesis is simple: the valuable data never enters the on-chain world if the only options are public execution or blind trust in a cloud provider. clinical data, trading signals, medical records, model weights, agent memory, all of that is too sensitive to touch plaintext infrastructure. DeCC fixes the data-in-use problem and turns privacy into actual infrastructure for the AI-native internet. the pets stack matters, but TEEs are the part people can use right now at scale. MPC, FHE, ZK, garbled circuits all matter, but TEE-backed execution is already doing the heavy lifting for private inference, agent execution, and encrypted smart contracts. that’s why Secret keeps showing up in this conversation. @SecretNetwork was early to privacy-native execution, and now SecretAI and SecretVM are pushing confidential AI, custom agents, and private apps with hardware-backed privacy and verifiable execution into production. Messari’s point is the right one: DeCC is becoming the privacy layer for an AI-native, on-chain world. the chains and apps that can actually compute on sensitive data without leaking it are the ones that matter. Secret is already there. $SCRT

quick lesson: PDA derivation order is not arbitrary // This works let (pda, bump) = Pubkey::find_program_address(&[b"user", user.key().as_ref()], program_id); // This fails (wrong seed order) let (pda, bump) = Pubkey::find_program_address(&[user.key().as_ref(), b"user"], program_id); seeds are hashed in order. change the order, get a different PDA. sounds obvious until you spend 2 hours debugging why your instruction can't find the account