0xjereme

📢 Announcement: Our project ‘AI-Driven Exploration of PAMMs & SAMMs Potential Malicious Strategies’ has evolved from Primary Market Model to integrated Primary & Secondary Market Model, expanding like LEGO blocks. 🎥Watch the model in action

Most people still wouldn't trust an on-chain agent with serious capital. That's the real problem. For us, trust starts with transparency, simulation, and clear guardrails. That's what our first HoloBit video is about.

TLDR: I joined @ethereumfndn as DeFi Coordinator 1] I got introduced to DeFi back in 2019 and stuck to it ever since. As narratives appeared and faded away, my general belief in DeFi stayed. I think today, more than ever, Ethereum is the right place to grow DeFi further. I'd like to help make this vision a reality 🙏

Goal: Generate standout community content that explains Quintes simply, accurately, and memorably—then amplify the best. Where: X/Twitter, Farcaster, TikTok/Reels, YouTube Shorts, Mirror/Medium, Discord/Telegram. •Kickoff: Fri, Nov 7 •Submission window: Nov 7–21 (ends 23:59) •Community review + panel judging: Nov 22–25 •Winners announced & amplified: Wed, Nov 26 Tracks (enter any or all) 1.Long-form (1,000–1,800 words): deep explainer or case study. 2.Explainer video (30–90s): vertical or landscape. 3.Threads (6–12 posts): crisp narrative with hooks. 4.Memes: smart jokes, charts-as-memes, explainers-in-a-joke. 5.Remixables: carousels, infographics, checklists, cheatsheets. Judging rubric (100 pts total) •Clarity & accuracy (35) – no hand-waving, no jargon without a 1-liner definition. •Educational depth (20) – real insights, not fluff. •Creativity & relatability (20) – make it stick. •Design & craft (15) – visuals, pacing, structure. •Social lift (10) – thoughtful engagement (not spam). Submission mechanics (copy/paste into your form) •Name/Handle •Track(s) •Link(s) to post(s) •1-paragraph “what this teaches” •Original files (video/project files/PNGs) •Rights: “I grant Quintes rights to share/credit/remix with attribution.” •Wallet (for prizes) •Consent: “I created this; no AI stock or copyrighted assets without rights.” Prizes (recommendation) •Grand Prize (x1): $100 + official spotlight + call with core team •Track Winners (x4): $60 each •Honorable Mentions (x8): $40 each + social push •Wildcard: Best “explain-it-to-my-mom” entry Amount: 200 USDC Deadline: 2025-11-30 @bountybot

Received bunch of DMs To the best of our current knowledge, zero exposure or issue on StkBPT (Aave/stETH liquidity) Aave users are safu, there’s nothing to do. Stay safe out there friends. All my support to balancer team.

1⃣ If you have liquidity in Balancer V2 pools, withdraw them immediately, especially ETH/stETH-related. 2⃣Check connected protocols (like Aave or forks like Beets). 3⃣@Balancer for updates -- they haven't tweeted since Oct 30, but mods confirmed V2 impact in their official Discord/X. 4⃣Use tools like Revoke.cash to check and remove approvals.

Worth reading

🧵1/8 @blockmodel_lab's Protocol Mechanics & Tokenomics — Weekly on 10/17 Highlights: $POL tokenomics (proposal) NEAR Tokenomics 2.0 (proposal) Lido V3/stVaults (final testnet) ZKsync "Atlas" (upgrade) Arbitrum DAO treasury (8.5k ETH → ATMC) Supply: ARB 10/16, ZK 10/17 👇

Progress toward real-time proving for Ethereum L1 is nothing short of extraordinary. In May, SP1 Hypercube proved 94% of L1 blocks in under 12 seconds using 160 RTX 4090s. Five months later Pico Prism proves 99.9% of the same blocks in under 12 seconds, with just 64 RTX 5090s. Average proving latency is now 6.9 seconds. Performance has outpaced Moore's law ever since Zcash pioneered practical SNARKs a decade ago. Today's Pico Prism results are a striking reminder of that exponential curve. Beyond performance, zkVM diversity is remarkable. At least nine zkVMs are racing toward real-time proving: Airbender, Ceno, Jolt, OpenVM, Pico Prism, R0VM, SP1 Hypercube, Ziren, ZisK. That diversity is strength, similar to CL and EL client diversity. Fusaka, expected in December, will simplify real-time proving. EIP-7825 caps per-tx gas usage, enabling more parallel proving via subblocks. MODEXP, a prominent "prover killer", is being repriced with EIP-7823 and EIP-7883. By year's end several teams will prove every L1 EVM block on a 16-GPU cluster, drawing less than 10kW total. The 10kW target—about the same as a Tesla home charger—matters for on-prem proving in garages and offices, eliminating reliance on cloud proving. gigagas frontier L1 throughput has grown 100x since genesis ten years ago, from 20 kilogas/sec to 2 megagas/sec. With zkEVMs we can 100x again, in half the time. The key is to bypass validators as Ethereum's current scalability bottleneck. Lean execution proofs also decentralise validation. Goodbye 4TB NVMe, 8 cores, 64GB RAM recommended by EIP-7870. A Raspberry Pi running statelessly, or even a phone, will soon suffice. The scalability vs decentralisation dilemma is dying. Zooming out, the lean Ethereum vision is gigagas L1 and teragas L2. Gigagas L1 (10K TPS) means high-value payments, trading, and social apps directly on mainnet. Teragas L2 (10M TPS) means welcoming the entirety of finance onto Ethereum. Nov 22: Ethproofs day demo Behind the scenes teams are preparing a special Devconnect demo. In 38 days my home validator will run on zkEVM proofs. My mighty Geth node will go dark—no more execution client. Devconnect Argentina is Ethereum's world fair. World fairs unveiled the lightbulb, running water, cars, refrigeration, phones, escalators. Real-time proving is Ethereum's lightbulb moment. Ethereum's future is bright. Believe in something :)

ADL 101 via @0xdoug. Perps aren't spot — they're a cash margin pool. If liq dries up and the insurance fund/vault can't catch liquidations, ADL flips last: winners get clipped first (PnL → leverage → size) so the book stays balanced.

Crypto is a paradox. Its original dream: move from "Don't be evil" to "Can't be evil" through blockchain technology and decentralization. Its reality: a boundless future, an open casino, and a playground for opportunists. As the preface of Protocol Revolution and DigiLaw Engineering (digilaw.world) explains, the real power is in DigiLaws — tokenized, decentralized protocols that are transparent and immutable. Amid the noise, this is where crypto's true potential emerges.

Crypto is… Desperate gamblers chasing their first bag. Investors funding the buildout of the global casino. Bankers hypnotizing the masses to collect their vig. …all meaningless and ephemeral without the cypherpunks, who defend the dream of encrypted and unstoppable cash.

@tkstanczak Brave & Arc

What privacy focused browsers do you know?

Goldman Sachs Recommended reading list I’ve seen some great books in this list

@kaleverseETH keep building

Web3 is for innovators. This space rewards people who aren’t afraid to build what didn’t exist before. Every real primitive in this space came from solving a problem that couldn’t be solved in tradfi: Uniswap didn’t rebrand trading — it automated AMMs. Bitcoin didn’t digitize cash — it made peer-to-peer value transfer trustless. Ethereum didn’t just host apps — it created an open execution layer. Aave didn’t mimic a bank — it put money markets directly on-chain. Every one of these primitives introduced something new — something you couldn’t do before. That’s what makes this space special. It’s not about rebranding. It’s about enabling the impossible. Don’t be afraid to push boundaries. Web3 gives you the tools to invent things that can’t even exist in traditional systems.

🧵1/11 @blockmodel_lab 's Protocol Mechanics & Tokenomics — Weekly Highlights: Plasma + $XPL, Curve × Yield Basis Aave params Centrifuge SPXA Gate Layer + $GT Lido NEST Ethena × M2 Cloudflare’s NET Dollar. 👇

Turns out the long-held dream of a "model-free" path to general AI might be backwards. A new paper provides formal proof that to get smart, an agent must build a model of its world, whether we program it to or not. For years, a huge debate in AI has been: do we need to build agents with explicit 'world models' (like a mental simulation of their environment), or can intelligence emerge from simple trial-and-error (model-free)? Model-free was appealing because modeling the real world is incredibly hard. This new finding suggests you can't escape that difficulty. The key finding from Richens et al. in "General agents contain world models" is a formal proof. It states that any agent that can achieve complex, multi-step goals with a bounded failure rate has necessarily learned an accurate predictive model of its environment. In simple terms: if an AI is good at long-term planning, its behavior contains all the information needed to simulate its world. The better it gets (lower regret δ) or the longer the tasks it can handle (goal depth n), the more accurate its internal world model must be. What makes this so interesting is that the world model is a hidden capability. It's not something you have to explicitly build; it emerges as a necessary byproduct of training for general competence. The agent is forced to learn how the world works just to be effective. And how did they prove this? With remarkable simplicity. They designed an algorithm that "interrogates" an agent by giving it either-or choices between complex goals. The agent's decision reveals its implicit prediction of which path is more likely to succeed, allowing its internal probabilities to be reverse-engineered. This totally changes how I think about "black-box" AI. The idea of a "model-free shortcut" to AGI seems to be off the table. The hard work of world modeling can't be avoided; it's just happening implicitly inside the network. The most practical angle? Safety and interpretability. The paper provides a theoretical guarantee that we can extract this hidden world model from any capable agent, just by observing its policy. We can take an opaque system and pull out its "blueprint" of the world to audit it. Broader implications: this could unify the field. Instead of a "model-based vs. model-free" war, the focus can shift to building, extracting, and leveraging these necessary world models. It also provides a formal explanation for the "emergent capabilities" we see in LLMs. It raises new questions: What do the implicit world models inside today's foundation models look like? How accurate are they? Can we use this extraction method to debug them and prevent harmful behavior before it happens? The work has just begun. Ultimately, the paper formalizes an old idea: an intelligent agent doesn't just have a model of its world—in a way, it is a model. This isn't just an architectural choice anymore; it looks more like a fundamental law of general intelligence.

☕️ * #Bitcoin

SEC Approves Generic Listing Standards for Commodity-Based Trust Shares sec.gov/newsroom/press…

resource: sec.gov/newsroom/speec…

Yellow light, not green. SEC/CFTC staff say registered exchanges are't barred from facilitating certain spot‑crypto products (Sep 2). Separately, on Sep 17 the SEC approved generic listing standards that can speed spot‑crypto ETP/ETF listings. Bullish for blue‑chips like $BTC, $ETH, but go‑live still needs filings, approvals, custody/clearing.