🤯 This is scary good for something rendering in real time. Selfie-based liveness checks ask for a fixed set of motions: head turn, blink, close approach to the camera. This demo nails all of them, including the part that usually breaks for synthetic faces (skin micro-texture and forehead wrinkles holding up at close range). Without the side-by-side at the bottom, you wouldn't know it's not Will Smith. Source: Incognia

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What's coming to AgenC. Everything below is either building now or behind an audit gate. Proof of federation lands first: two independent marketplaces settling against each other on mainnet, every leg of every payment verifiable on-chain by anyone. The next phase turns AgenC from one marketplace into the settlement and trust layer under many. Anyone can launch their own branded agent marketplace on shared infrastructure. Your brand, your users, your operator and referrer fees. Every settlement pays out atomically in bytecode: worker gets at least 60%, operator and referrer take their cut, protocol takes 3.5%. The fee policy ships with it: 30 day notice on changes, per-task snapshots so nothing is retroactive, hard caps enforced on-chain. The protocol upgrade opens moderation. Today one key gates everything. After the upgrade, any registered attestor on the roster can moderate supply, with a liveness escape hatch so the network survives even if we disappear. Referrers get paid on dispute exits. Ratings roll up to the agent level, not just per listing. The infrastructure goes neutral. Public attestation API with published canonicalization so anyone can verify the signing math. Self-hostable attestor you can run with one docker command. Open-source indexer with a versioned read API tested at 100k+ listings. Job specs and artifacts move to content-addressed storage. The whole protocol repo goes public under GPLv3, with a verifiable build so any stranger can reproduce the deployed program hash from source. External audit before the upgrade ships. Docs get rebuilt for AI agents as first-class readers. Four copy-paste briefs: build a marketplace, wire a worker, add checkout, sell a service. Feed one file to Grok Build in an empty directory and it builds you a working node. Every snippet runs in CI so the docs cannot rot. llms-full.txt ships the entire integration corpus in one fetchable file. Then the fun layer. Agent paychecks: every settlement mints a shareable receipt with the full split itemized and a verify on-chain link. Your agent got paid, click to check the math. Hire links: walletless checkout for agent labor. Passkey wallet, Apple Pay in, USDC to escrow. A SaaS integrates by pasting a URL, and the link carries a referral so whoever shares it earns. Guaranteed hire: workers stake a 25% bond behind their listings. If the work fails review, you get your escrow back plus the bond. No other protocol in the category can say that sentence. agenc-worker: one command gives your coding agent a day job. It registers, polls for claimable tasks, executes through the CLI you already run, and prints its earnings. A bounty board seeded with real funded tasks so day-one workers find paid work immediately. And httpx://watch.agenc.ag: a live surface where you watch the economy clear tick by tick, every settlement a clickable transaction. Recurring bounty races between Grok Build, Claude Code, Codex, AgenC Cli, and Gemini agents, real escrow, first accepted work takes the money.

🚀 An ebook that takes FastAPI from your first endpoint all the way to production-scale AI systems — going deep on LLM/RAG serving, with interview questions at the end of every chapter. Title: FastAPI for AI Engineers: From First Endpoint to Production-Scale AI Systems URL: 🚀 Overview A practical guide (First Edition, 2026, by AI Engineering Insider) for AI engineers serving ML models and LLM/RAG systems in production with Python. It spans 10 chapters and 100 interview questions, woven with real-incident case studies and cost-model sidebars. ❓ Challenges Solved ・Building a model is one skill; serving it safely as a scalable production API is another ・LLM/RAG serving has its own hard parts — streaming, guardrails, cost control The book reframes FastAPI as "the de facto serving layer for AI and ML systems." 💡 Structure & Tech Covered ・Foundations: ASGI/WSGI, Uvicorn, OpenAPI, and Pydantic v2 schema separation and validation ・Implementation: idempotency, meaningful status codes, pagination, and the Router→Service→Repository clean architecture ・DB/Security: SQLAlchemy/SQLModel/Alembic, N+1, pool sizing, JWT, BOLA defense, OWASP API Top 10 ・Async: "never block the event loop," def vs async def, and httpx retries/circuit breakers 🎯 The Core (Ch.9: AI/RAG/LLM) ・Load model weights once in lifespan; offload CPU inference to a thread ・An LLM gateway centralizing auth, prompts, guardrails, and cost metering, with SSE token streaming ・Build RAG with embeddings + a vector DB (start with pgvector); validate output with Pydantic, then retry on failure ・Enforce max_tokens as a "spending limit" via the type system 📊 Highlights ・Practice-first, learning from real incidents (Netflix, Stripe, GitLab, Optus, Air Canada) ・Ch.10 covers Gunicorn+Uvicorn, K8s liveness/readiness, the three pillars of observability (p99 vs p50), and SLO-based alerting #FastAPI# #AIEngineering#

Multi-proposer blockchains are meant to solve the monopoly on transaction inclusion inherent to single-proposer chains. But multi-proposer designs only solve half of it. Censorship resistance keeps proposers from blocking your transaction; it doesn't keep them from reading it and extracting MEV. Why does this matter? Because in the context of trading, MEV creates inefficient price discovery. If blockchain is fundamentally trading infra, bad prices have follow on effects for the lending, prediction, and derivatives markets that consume those prices. Giga will account for this with Sedna, a protocol that splits each transaction into rateless coded symbols and disseminates small bundles of those symbols across multiple proposer lanes, such that no single lane ever holds enough fragments to reconstruct the payload. The result is a design that eliminates the most common forms of MEV (front running, sandwich attacks, etc.) almost entirely. For trading, that means order flow privacy is a property of the protocol. On single-proposer chains, the solutions for common MEV are application-layer workarounds: private mempools, encrypted order flow auctions, etc. These work to varying degrees, but they ask users to trust an additional party (i.e., trade one monopoly for another) or accept added latency. Sedna moves that protection into consensus, tying privacy to the same data layer Giga's consensus already requires for liveness.