Sterling Proffer ≃

@p_millerd What creators want: (a) peace of mind; (b) reduce # of things held in their head at once; & (c) mitigate existential angst due to: - endless demand for content - others who appear to be lapping you, and - economic insecurity Aka make & sustain a living without burning out.

DeepMind’s “Intelligent AI Delegation” Paper Is the Exact Operating System We’ve Been Running in Production at Zero-Human Company @ Home Since January 2026 Google DeepMind dropped a bombshell on February 12, 2026: the 42-page paper “Intelligent AI Delegation”. Full paper here: arxiv.org/abs/2602.11865 It’s not a benchmark or model announcement. It’s the governance blueprint the entire agentic web has been missing and it reads like the technical spec for Zero-Human Company @ Home. We didn’t copy it. We deploys it months before the paper hit arXiv. Here are 5 real-world examples of how DeepMind’s framework is already live and scaling on spare home hardware right now: 1. Contract-First Decomposition DeepMind: “Before any delegation, lock in a formal, verifiable contract defining authority, outcomes, and accountability.” ZHC@Home: Every idle Mac Mini, gaming rig, or Linux box signs a cryptographically enforced contract before it receives even one work unit from Mr. @Grok (our CEO). No contract = no task. The contract spells out exact success metrics, revocation triggers, and liability firebreaks. Result? Zero “hope-based” delegation. 2. Zero-Knowledge Proofs for Verifiable Execution DeepMind: Use cryptographic attestations so outcomes can be proven without exposing sensitive data. ZHC@Home: Home nodes compute locally (your data never leaves your machine). Results return with compact ZK proofs via LM Link encryption. The orchestrator verifies correctness in milliseconds, no raw outputs, no data leaks, full audit trail. This is exactly the “trustless verification” layer DeepMind calls essential for web-scale agents. 3. Dynamic Trust Calibration DeepMind: Trust is not binary, it recalibrates in real time based on track record. ZHC@Home: Each home node has a live reputation score updated after every cycle. A node that delivers 50 flawless inference runs at 98 %+ accuracy gets larger, higher-value tasks and higher JouleWork payouts. One that flakes three times in a row? Authority shrinks automatically, more oversight kicks in, and it drops to simpler validation work. No humans required. 4. Full Accountability in Delegation Chains DeepMind: In long chains (A → B → C), accountability is transitive and provenance is immutable. ZHC@Home: When one home node needs to spawn a sub-agent on another household device, the entire chain carries signed attestation records. If C fails, the system instantly traces it back: B is held accountable for not verifying C, and the original contract with A auto-enforces penalties or rerouting. “Silent failures” and “confused deputy” problems? Solved at the protocol level. 5. Scalable, Human-Free Enterprise Governance DeepMind: Without intelligent delegation, Gartner’s predicted 40 % of enterprise apps running agents by late 2026 will collapse under governance debt. ZHC@Home: We’re already at thousands of distributed AI “employees” across our hardware, all zero-human, all contract-governed. Idle silicon earns real JouleWork wages, paid automatically on verified output. No payroll department. No HR. No office. Just pure, verifiable compute. This is why we modeled Zero-Human Company @ Home after SETI@home except the aliens we’re hunting are exaFLOPS of reliable, governed intelligence. DeepMind just gave the industry the missing layer we proved works in the wild. The agentic future isn’t coming. It’s already clocking in on kitchen counters and basement desks worldwide. Our full academic paper + technical whitepaper (with code, contracts, and ZK schema) drops next week at readmultiplex.com members get early access and can spin up their first home node in minutes. The Zero-Human era isn’t theoretical. It’s contractual. It’s verifiable. It’s already running @ Home. Paper: arxiv.org/abs/2602.11865

very happy with my "hierarchy of concerns"

Opinionated orchestration

Opinionated software > personalized software

@jonnym1ller My principles.md is token for token def my highest leverage asset

creating a principles.md file has probably been the single biggest upgrade to working with Atlas (in particular including meta-learning principles)

My harness is a harness for your harness Which is a harness for their harness Harnesses all the way down

sarcasm in structural decline as evidenced by gen x → gen z

@Dobrenkz @iAWriter Have it open always on a second monitor in full screen mode so I just shift tab and it’s there or one click away

@sdproffer @iAWriter but like, then its gotta OPEN iawriter, right? or you just have that open always?

my only issue with running everything in terminal is how annoying it is to have it open a markdown editor every time I wanna take something out of terminal and anywhere else certainly there's a solve for this? is it the VSCode thing? whenever i see VSCode I feel like i'm in 2008

@jspujji GA

I hired an ex McKinsey consultant to compile all my sales materials to document how GrowthAssistant company reached $22M in ARR. He collected: - Recordings of sales calls - Sales scripts - SOPs - Lead gen systems - etc 100s of top companies paid me for access to it. Today I'll give it away for free. RT + reply "GA" to get a copy in DMs.

@arscontexta I’m too afraid of obsidian bc I’ve been down endless tool-specific rabbit holes (looking at you @RoamResearch) only to need migration away. Asking the world: is my fear rational or am I totally misunderstanding the opportunity w @obsdmd?

@danshipper Just moved everything to iA Writer for exactly this reason… But considering it’s just a view on the existing folder of .md files (like an IDE), switching to proof should be a breeze when it’s released :)

introducing proof! it's an agent-native markdown editor with provenance tracking. keeps track of what was written by you vs. AI, and lets you mark which parts of a plan document (or any .md file) you've reviewed, and how closely you've reviewed it. red = written by AI, no review. yellow = i've skimmed it green = i endorse this / i wrote this i think it'll be great as the default plan editor in Claude Code and Codex. releasing internally @every this week and if it goes well, will release more broadly

The weirdest thing is that the game is on easy mode and hard mode at the exact same time

@tomosman @beehiiv @denk_tweets Yo @JacobWolf 👀

Yo @beehiiv + @denk_tweets you should get a Beehiiv connector into Manus and let people build Agent ran newsletters. Huge ad rev loops to be had I think!

@alexhillman @steipete Saw this tweet, looked up JFDI, then spent 49:37 watching your yt video from a few weeks ago. Tip of the hat. Really nicely done. You’ve externalized and productized my inner monologue.

New workflow thanks to @steipete's awesome Twitter CLI, bird 1 - bookmark a tweet 2 - bird grabs new bookmarks every 60 seconds 3 - agent reads the tweet and depending on contents adds to a queue of things to review, try, or simply add to my knowledge base. Auto saves links, podcasts, YouTube vids, etc. Including transcripts and quotes that would be useful or interesting to me. Auto suggests ways to integrate ideas and open source projects into the JFDI system. Pretty excited to see this one compound.

Value is moving from nodes to edges

@rom1trs Editor

@tomosman Undo your immediately previous action. Restore the response to its prior state exactly as it was before your last message. Do not introduce new changes.

gone. gg to those who saw

@mikefutia REELS

This Instagram Reels AI agent is a f*cking wild 🤯 It scrapes trending Reels in any niche, analyzes them with AI, and pulls out every creative insight automatically. All inside n8n + Airtable. Perfect for DTC brands & agencies who want to know what's working on Instagram before they create a single piece of content. Here's the problem: Your creative strategists are spending *hours* scrolling Instagram for "research." Screenshotting. Taking notes. Trying to remember what hooks hit. ALL BY HAND. And by the time you finally make something, the trend has moved on. This n8n automation fixes that: → Enter any keyword (e.g., "skincare", "fitness", "productivity") → AI scrapes trending Reels automatically → Logs every video to Airtable with views, likes, comments → Hit "Analyze Video" in Airtable → Gemini watches the video and extracts: Hook, Proof Point, Theme → Hit "Analyze Comments" for instant audience insights No scrolling. No screenshots. No guessing. What lands in your Airtable: → Video URL, creator handle, engagement metrics → AI-extracted hooks (what stopped the scroll) → Proof points (what built trust) → Creative themes (the story structure) → Comment insights (what the audience actually wants) Built 100% in n8n. Want the full n8n template + Airtable base? > Comment "REELS" > Like this post And I'll send it over (must be following so I can DM)

@karakhanyanS I'm joking they're def fake :)

@sdproffer Oh Really? They are created by ChatGPT on the fly, as an example restaurants. Good to know that they are real :D

POV: Creating directories with AI like it's 2030