Web3.com Ventures retweeted
Tomasz Stańczak: Ethereum, Robotics & the Coordination Layer for AI
Ethereum’s next frontier? @tkstanczak explains why robotics feels like Ethereum in 2017, and why crypto could become key infrastructure for AI agents and machines.
👉Full episode: youtu.be/lALJv6t9wsM
Great founder story from the one and only - @retrimentum.
Meet @retrimentum from @upshot_cards
From selling World of Warcraft Gold, to building prediction markets on Base in the form of digital trading cards
This is his story ↓
BaseLayer S2 - Episode 03: Wayne Sun (Upshot)
Check out @derpetual founder & latest Thiel Fellow @antoni_kiszka on the @tbpn podcast.
youtube.com/live/yCLMzS-daWQ…
Watch out for @printr 👀
BREAKING: Printr CEO drops alpha a $PRINT community presale and airdrop will be happening
"We're about to make some huge announcements in the coming days, so I guess I could share some alpha here. We will be planning to do a community sale so that all of our supporters and believers can be part of the journey. We're going to be raising some money and letting the community participate before we TGE so people can invest in the future PRINT token."
"And yes, we will definitely be doing an airdrop. I know nobody believes anyone anymore because everyone said there's an airdrop and nobody did it. So I won't promise you guys anything. Just watch our actions."
We had to invest in this cracked dev.
Check out parse.bot.
Every website is now an API that your agent can easily interact with.
The first time @retrimentum explained @upshot_cards, it was obvious.
We’re heavy users of prediction markets. We check odds constantly.
But actually placing a bet? Still friction. Still a decision.
Upshot removes the decision.
You draw a card. Now you care.
It’s a small shift with big implications:
- Less cognitive load.
- More emotional alignment.
- Wider participation.
Speculation becomes engagement. Engagement becomes distribution.
That’s why we invested.
UPSHOT MAINNET IS HERE.
A whole new way to experience predictions has arrived.
Welcome to Prediction Play.
Sign up now & starting ripping packs 👇🏼
Upshot.cards
Web3.com Ventures retweeted
AI agents aren’t failing because they’re dumb.
They’re failing because the web isn’t programmable.
Parse.bot turns websites into APIs, and that helps agents operate unencumbered.
Check out what @alexscraping built and why we invested in Parse @Web3com_VC.
👇👇👇👇
DeFi exploits aren’t magic — they’re optimization problems.
At Devcon Argentina, @mamori_xyz broke down how modern smart contract fuzzing actually works, why it breaks at scale, and how a 3-layer framework (LLMs + state-based CFG fuzzing + GPU-accelerated EVM) changes the game.
If you care about AI × security × DeFi infra, this one’s worth your time 👇
youtube.com/watch?v=DidSdyN1…
Great time speaking at @summit_defi during @EFDevcon in Argentina🇦🇷! Some comments said the talk went at a rapid pace with overwhelming information on fuzzing technology.
Here's the a recap of my talk:
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Part A: Understanding of Exploits & Challenges of Smart Contract Fuzzing
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Key Insights and Core Concepts:
Value Extraction Exploit: At its core, exploits in DeFi are characterized by two-step optimization process:
1. Sequence generation: Deciding which state-changing smart contract functions to invoke and in what order.
2. Parameter mutation: Continuously optimizing input parameters for these functions to trigger vulnerabilities.
- Fuzzing and State Change: The goal is to modify the state via sequences and parameters that lead to an exploit.
- Feasibility of fuzzing: Dimensionality Reduction. Real-world examples of loop-based exploits and various types of reducible actions in practice.
Challenges of Smart Contracts Fuzzing in Testcase generations:
- State Explosion: The combinatorial explosion of possible function sequences and states.
- Multi-contract dependencies: Protocols often span multiple smart contracts interacting with one another.
- Proxy contracts and storage separation: Logic and data stored across different contracts complicate state tracking.
- External Calls: Protocols may invoke external contracts, adding layers of complexity and uncertainty.
Current Approach for Testcase generations:
- Sequence-based approach: Pseudo-random sequence mutation with Read-After-Write (RAW) relationship construction by leveraging SLOAD and SSTORE opcode.
- Custom Invariants/Property-based testing/specifications: Auditors with expert knowledge can specify testcases with deep understanding of the program under test (PUT).
- Snapshot-based approach: Exploring interesting states and mutating based on chosen corpus
Challenges of Smart Contracts Fuzzing in Input Parameters generations:
- Common specific ABI-specific input types: String and address types are uniquely defined.
- Dynamic input types: dynamic array type and dynamic tuple...etc.
- Complex input types: Dynamic tuples, arrays, and compressed calldata increase fuzzing difficulty.
Current Approach for Input parameter generations:
- LibAFL with Havoc Strategy assisted with abi-type mutation: (bitflip, RandMutator, …etc)
- Coverage-based feedback mechanism: Code-coverage metrics, distance-metrics.
- Optimization algorithms: Leveraging algorithms such as Particle Swarm Optimization, Stochastic gradient descent, Genetic algorithms and learning-based methods.
Fuzzing Jargon and Framework:
- Argument Initialization: Setting initial input values for fuzz testing.
- Sequence Generation: Creating sequences of contract calls to test.
- Mutation: Modifying input parameters for subsequent fuzzing iterations.
- Feedback Mechanism: Metrics like code coverage or distance to branch conditions that guide mutations.
- Oracle: In the fuzzing context, defines what constitutes a failure or exploit (not to be confused with price oracles).
- Scheduling: The energy allocation in the fuzzing process.
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Part B: Proposed Solution - Three-Layer Fuzzing Framework
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1. Language Model (LM)-Guided Fuzzing
- Use LLMs for static and dynamic analysis to guide fuzzing intelligently. Four key components:
- Taint Analysis: Tracking data flow to identify relevant inputs.
- External Call Trace Analysis: Understanding call hierarchies and dependencies.
- Compressed Data Generation: Generating complex calldata inputs.
- Dynamic Runtime Information: Observing runtime behavior to guide mutation.
Example: Using an LLM to identify vulnerable code lines and map them to control flow graph (CFG) basic blocks to target fuzzing efforts.
- LLM aids in linking caller and callee functions, understanding which input parameters affect nested calls—crucial for mutating the correct parameters in complex functions like batchSwap.
2. State-Based Fuzzing Approach - CFG-guided fuzzing with three phases:
--- Identify the basic block corresponding to a vulnerable branch. --- Analyze opcode-level conditions (e.g., JUMPI, comparison opcodes) to discover which storage or arguments influence branch decisions. --- Use distance metrics on storage and arguments to guide input mutation.
- Maintain a state pool: A repository of interesting blockchain states encountered during fuzzing, enabling reuse and combination to increase coverage.
- Introduce state diversity: Combine states from different execution paths to explore more scenarios.
3. GPU-Accelerated EVM Execution
- Transform smart contract bytecode into GPU-parallelizable code to massively speed up fuzzing.
- Enables running multiple fuzzing instances concurrently, enhancing exploration of the state space.
---------------------------------------------------
This recap is intentionally concise — the full talk goes much deeper. Watch it here if you want the unfiltered version: youtube.com/watch?v=DidSdyN1….
I'm genuinely curious:
→ Which of the three layers (LLM-guided, state-pool CFG, or GPU acceleration) excites you most?
→ Have you already hit one of the fuzzing pain points I described in production?
→ Which of the open-ended questions the talk implicitly raises do you believe will shape the next 1-3 years of smart contract fuzzing?
→ Or any topics in AI / Security :]!
Drop it in the replies or DM me!
Web3.com Ventures retweeted
.@BitfuryGroup just made its largest decentralized AI compute bet to date: a $50M phase-one commitment into next-generation GPU infrastructure.
This is why I believe the architecture behind GONKA represents a ‘Bitcoin moment’ for AI 👇
medium.com/@bitfurygeorge/go…
Imagine if you could trade traders, instead of getting farmed by copy-trading them...
Now you can on @derp_trade.
An important milestone with tons of momentum to make it to the next one.
Merging worlds, and building bridges.
Congrats @build_on_bob!
Congratulations on this win, @upshot_cards! We're proud to be part of this journey.
Big thanks to @Base for the recognition and platform for what @retrimentum and his team are building.
Onwards and upwards!
50 teams took the stage at @efdevcon to pitch what they're building to peers and investors. We narrowed the list to 12 standouts, though every team that pitched brought real strength to the stage. Here are the winners of the Base Batches Startup Track:
WINNER: @upshot_cards
FINALISTS:
@firmjeff
@rovadotxyz
@nedapay_xyz
@bitmor_btc
@paycrest
@fractalizedio
@glider__
@bpdotfun
@Khugaverse
@hypersurfaceX
@rapixchange
The Lombard team continues to execute and expand at an exceptional level and pace.
We're proud backers.
Lombard has acquired BTC.b—the leading Bitcoin asset on Avalanche with ~$538M in circulation.
BTC.b will operate exclusively on Lombard's protocol infrastructure as a neutral public good, and introduce a permissionless, decentralized, next-generation BTC asset to the masses.
Sapien has been pioneering a new standard for AI data with their distributed model and today’s TGE marks the start of unlocking their full potential.
With 2M+ Human-AI trainers (and growing) and customers like Baidu, Alibaba, Midjourney, Toyota & more, the next chapter is set to be huge.
Congrats on this milestone 👏
Web3.com Ventures retweeted
Introducing Questflow - The Orchestration Layer for the Multi-agent Economy onchain.
To build the AI Agent Economy for Every Workflow.
We are excited to announce our seed round $6.5M raise led by @cyberfund, with participation from @delphi_labs, @Systemic_VC, @edenblockvc, @WhiteStarCap, @Web3com_VC, @HashKey_Capital, @animocabrands, @wagmi_vc, @ElizaEcoFund, @tezos, @CatcherVC, and with grant from @Aptos, @CoinbaseDev, @virtuals_io.
The beginning of DeFi on Bitcoin is upon us.
The first trustless AMM on Bitcoin is live.
Contracts deployed. Pools running. Built on Alkanes.
What’s live now
• Headless Mainnet contracts live
• Signet UI app for testing → app.oyl.io
• Docs for builders → docs.oyl.io
Coming soon
• XP checker + $DIESEL claim
• Stablecoin flow (from ETH stables <> bUSD Alkane)
• Partner integrations
• Full Mainnet UI launch
The foundation is here. The rest is yours to create.
see the full announcement → oyl.io/announcement/


























