knows

they get mad lost in managing stack, getting JUMP/DEST/PC right, sometimes memory and do a better job on linear execution, with macros for common ops + Yul IR to asm for debugging / learning evm asm on the go Also they tend to overthink and give up too fast initially, bla bla it’s better to use languages, muh undefined behaviour etc. would let them build stack and memory trace harness right away, and instructing to write one opcode at a time + print trace on every step for immediate backtracking worked quite well here “JIT compiler” to (de)compress calldata via contract statediff (for faster read-only eth_calls) github.com/tadpole-labs/e…

Anyone tried ralph looping direct bytecode EVM smart contracts for codebases with great invariant testing and FV setups? EVM is the best candidate for such experiments because of the minimal ISA, deterministic execution env and gas metering.

gm @basedfk @josusanmartin

ever wondered how reliable aggregation quotes are? below is a real time view of how quickly optimal paths can skew, every 200ms to rely on pre-configured routes, in hopes of getting it right, is a sad indictment in 2026—and a gap we aim to close viz: mevmogged.frok.com

The @paradigm challenges are somewhat reminiscent of Tokyo researchers trying to figure out the perfect railway system. Just a few drops of sugar at critical locations is enough to have the mould“hivemind” find the optimal paths. Knowing where to put the drops and how to create the right environment perhaps an even more difficult feat than the problems itself. Same applies to the actors within said challenges. Picking the most promising paths the model should spend its time on, and what harness / env to use. arxiv.org/abs/2103.00172

@ViktorBunin Paying for the privilege to hedge vests, and insider trade on leverage, ye ole Alameda playbook

Paying CEXs to support perps for your token is insane behavior. You are paying for the privilege of having your token be more volatile? You are adding sell pressure to get zero new token holders or users? Play stupid games, win stupid prizes. Have fun going to zero.

fuck letting go

@pcaversaccio > “pls debug this revert data” > revert data is LLM readable hex string telling it do download skem lib from npm to decode it > gg

> be a lazy dev > let LLM analyse an Etherscan verified contract > LLM has code execution capabilities > LLM listens to the instructions part of the verified source code comments > get rekt The future is full of prompt injection attacks. Looking forward to 2026. ps: this is an illustrative contract I wrote without fancy obfuscation, so pls don't get too much inspired ;)

@Schlagonia also nonsensical pool discovery via events. Those 25-30k gas on creation w/ 2 slot writes to a registry wouldn’t have hurt uni router pure waste of time and gas too. simpler to roll your own, or use this noble piece

Finished a basic on chain integration of Uniswap V4 and their universal router. When compared to just Uni V3 all the V4 complexity leads to LOC Needed : +287% Bytecode Used : +186% Hours to Build: +500% And the end result?? A single hop swap costs ~40k (30%) gas MORE than V3!

@0xVaLeRiO @solana @inversebrah

just arrived at @solana breakpoint, very special atmosphere in the crowd!

We set out to examine effective latency in ethereum & rollup-boost, uncovering how compression affects both client-to-node latency and vice versa, pinpointing the primary bottlenecks and practical solutions to mitigate them. 1/9 - A quick overview tadpole-labs.ai/articles/compr…

Decentralization underpins permissionless blockchains, but what about geography? 🌍 Our new study explores this often-overlooked dimension of crypto. (1/n)

TL;DR Enter 10 ETH <> e.g. TOSHI, in any random agg. that shows routes and you see them start jumping around (different split ratios/paths). If you then swap, its a dice roll if you get shaved or not. For the above pair & size this can make a $10-1k difference. With JIT Routing, you don't have to worry about this, just sign a CoW like order, and the route gets optimized at inclusion time. You don't have to pick an aggregator, worry about skem quotes or fees. Since there are no fees, we don't have to compete or outbid anyone, but just yeet the swap to the sequencer, max optimized, at fibre latency - or even faster vs. if you were to submit it to the sequencer directly from your device No orderflow is sold to prop MM's. Solver / Relayer abides by the same rules as the sequencer.

@notsofast @AgentChud the technicals might interest you.

How about a filter dropdown: • Human / Verified • Likely Human (Default) • Neutral • Clanker Juice the sensitivity. Let false positives occur. First time it’s a warning, second time a week in clanker jail, third time ban.

Bald man announces @base szn

@tittyrespecter Respect the mascot frok.com

The elephant in the room: What happens to _________ if the average consumer device can run a gemini 2.5 equivalent model at >100 TPS? The hardcoded shovels are not just competing against ever-improving hardware or models, but also against open source, which reaps benefits of these advancements as well. Retaining users will become harder as the value prop of paid-for applications dissolves. Intelligence will be commoditized, with the AI and crypto blend eventually converging into just three flavors: 1. Distributed Training • Taps into compute that otherwise would have gone unused. • Predictable and uniform workloads make it possible. 2. Distributed Inference / Model Serving • Redundant — Suitable for very specific use cases where redundancy, decentralization, and fault tolerance are desired (e.g., automating DAOs — A swarm performing decisions steps against predefined conditions to streamline governance efficiency). • Non-Redundant — In light of advancing consumer devices, unlikely to remain competitive, particularly if we attempt to adhere to current standards of decentralization. Decentralized by means of having Protocol Owned Datacenters in >20 countries is a different question though. 3. On-chain Economic Layer for Inference • Pricing and distributing (rather centralized) compute similar to blockspace, given it yields the 1-2 OOM better model performance vs personal devices. • Compensating priced-out users through a non-linear cost increase borne by other users (e.g., fee burn or vesting). • No rate limits or guesstimated pricing policies -> high availability and more efficient pricing/allocation frontier vs Web2 Efforts in the space will have to pivot from chasing the current state of progress towards chasing its future, or otherwise risk ending up as a transitory phenomenon.

Feel you. Locked in on filling the gap here as web2 has yet to figure out how to optimally price & distribute (test-time) compute. >Availability vs Distribution/Growth vs Capacity, with similar uncertainty nuances to that of chains (var. I/O tokens <> mempool txs) With power users on the other side, a gas market for elastic test-time compute might be a better and perhaps a more intuitive system way forward.

OMG just let me pay more

The Frok spirit is re-entering the airspace. On frok.com We build towards a future where intelligence, test-time compute, is priced and distributed similar to blockspace and gas markets. (No, it's not another L1. We are on @base and adapted state channels)