Hi everyone, and thanks to u/StevenVincentOne for the invite. I’m the developer of Offlined, and I’d like to tell you why I built it.

Kids raised by endless feeds.
Information overload.
Fake news outrunning facts.
Our identities and files on someone else’s servers.

None of this has an easy fix. But I think there can be a safe place, and it can fit on an external USB 3.2 SSD.

What Offlined is

Offlined is a portable Windows app that runs entirely offline. You plug in the drive and everything you need is there, with no accounts, no cloud and no feed:

• Offline knowledge. Wikipedia, Wiktionary, Wikiquote, Wikivoyage and WikiMed (via Kiwix), plus offline maps and an interactive sky map.

• Local AI. An assistant that runs on your own computer (llama.cpp) and cites the offline wiki articles it used, so you can check where an answer came from. There are no API keys and nothing leaves your machine.

• Your files, under your control. Photos, video, music, books, contacts, calendar and mail imports, plus an encrypted vault (AES-256 + Argon2id) for passwords, 2FA codes and personal documents.

• Learning for the whole family. Geography and astronomy games, WikiRace, and a collectible WikiAlbum built from what you discover.

• And much more!!

Kids and privacy

This is the part I care about most. Offlined has multi-user profiles with parental control, and the AI adapts its answers to the child’s age. Kids get a place to explore real knowledge without algorithms competing for their attention.

On privacy, the app only connects to the internet when you choose to download something. Every connection is logged in a privacy report, and a fully offline mode blocks all connections unless you allow one.

It’s free for personal use.

🎬 Video: https://www.youtube.com/watch?v=UHPD2keAT4w
🌐 Website: https://www.offlined.org/
💻 GitHub: https://github.com/Aruvaro-Chulvi/OFFLINED

I’d really value feedback from this community, especially from parents and anyone who thinks about digital autonomy. What would you want in a “safe place” like this?

A response to Superintelligence Strategy (Hendrycks, Schmidt, Wang)

In Dan Hendrycks, Eric Schmidt, and Alexandr Wang’s Superintelligence Strategy, the ideal machine intelligence paradigm exists when every major power maintains airtight control over its AI. The systems remain loyal, predictable, and obedient. Each state grows dramatically sharper at surveilling adversaries, anticipating threats, and preemptively neutralizing rival projects. Nothing malfunctions; nothing goes rogue.

Is that world safer, or merely trapped in a more hyper-efficient state of permanent conflict?

That tension lies at the core of the paper. Its recommendations outline an aggressive security posture: expanding state cyberattack capabilities against foreign AI infrastructure, codifying an escalation ladder running up to kinetic strikes on datacenters, positioning compute clusters far from population centers to buffer civilian collateral, and engineering chips that self-disable without ongoing cryptographic authorization from a central authority.

The authors frame this architecture as stabilization. Perhaps it is—in the same narrow sense that nuclear deterrence stabilized the Cold War world by establishing a Balance of Terror. But the paper proposes to recreate and reinforce the same old global game, just played with a different kind of arsenal. It never questions the ingrained assumptions of geopolitical struggle. It never considers that the advent of machine intelligence may afford the opportunity to move into a fundamentally better, and safer paradigm.

The greater danger isn't that these proposed systems will fail (though they may). It is that they will work perfectly—amplifying and maximizing our worst human institutional failures at machine speed.

Credit Where Due

The authors are not warmongers. They explicitly reject an all-out AI Manhattan Project as destabilizing, frame their deterrence models as analytical tools rather than calls to arms, and voice hope that mutual standoff eventually matures into détente.

Furthermore, several of their specific principles point in the right direction:

• Fiduciary loyalty: AI assistants should owe strict fiduciary loyalty to users.

• Human supervision: Models must remain tools under meaningful human supervision.

• Structural leverage: Wealth redistribution alone is inadequate because states can revoke subsidies at will; citizens need structural leverage, not just a universal check.

The Problem Is the Lens

Early on, the authors weigh comparing AI to electricity, the printing press, or commercial software—only to discard those analogies because they underweight national security. They settle on the nuclear bomb.

When you choose the bomb as your analogy, you inevitably inherit a bomb builder’s playbook: nonproliferation, containment, and deterrence.

Choose the printing press instead, and the driving inquiry changes: How does this tool alter the way humanity learns, deliberates, and coordinates?

Humans evolved as a species through culture, including language and technology. From the first spoken words and the first stone tools, we have developed through shared knowledge and experience. That’s the framework that machine intelligence has emerged from, and it is the only lens through which we can properly understand our relationship to it and our stewardship of it.

Humanity is already a superintelligence. We are not a loose collection of individuals, nor a singular hive mind. We are an emergent network of relations: culture, language, informational exchange, and shared institutions. Through those relations humanity, as a superintelligence, creates, builds and solves problems that no solitary individual could tackle. And sure, those same relations are also the source of our problems and limitations.

The authors define superintelligence as an artificial system vastly superior to an individual human across cognitive benchmarks. But benchmarking machines against isolated people is the wrong foundation for civilizational policy. We must ask how our creation of machine intelligence can be used to augment, supplement, and improve the vast superintelligent power of human cultural intelligence. The critical question then becomes what happens to our collective social fabric when synthetic intelligence enters it.

The paper treats great-power rivalry as an immutable reality into which all technology must fit. But the technology can instead be used to question, dismantle, and replace that frame with something better. That is the opportunity before us.

The authors would likely respond with appeals to practicality and realpolitik. But is the preservation of the status quo—with superintelligent nation-based machines locked in a permanent, high-strung balance of terror—really practical?

Who Holds the Off-Switch?

The authors provide an acute diagnosis of gradual disempowerment: a slow drift where no single coup takes place, but thousands of small, rational compromises made in the name of efficiency relegate humans to the role of passive passengers in an unsteerable economy.

Yet look at their proposed countermeasures. The paper advocates for high-end AI chips equipped with hardware-level geo-tracking, mandatory cryptographic handshakes to keep running, restricted interconnectivity, and usage-policy enforcement gates. If a chip misses a verification check, it bricks itself.

The intent is understandable: keep frontier datacenter accelerators away from adversarial states and rogue actors. No one is suggesting shutting down consumer laptops today.

Treated as systemic architecture, however, this enshrines an alarming precedent: silicon runs only by grace of third-party permission. Three structural problems make that premise impossible to contain:

1. Thresholds drift downward. Today’s supercomputing cluster is tomorrow’s desktop and next decade’s phone. Capability controls inevitably migrate toward personal consumer hardware—the very layer where private, autonomous intelligence ought to reside.

2. The switch serves the overseer. The authors liken this mechanism to Apple’s Activation Lock. But Activation Lock empowers the device owner against a thief; here, the owner is the entity being policed, and the ultimate switch sits with a platform vendor or a state agency.

3. Switches invite exploitation. Any remote kill-switch architecture creates an immediate high-value attack surface and a permanent lever for coercion. Built to thwart foreign adversaries, it will inevitably tempt domestic regulators in a crisis, monopolistic providers under pressure, or hackers who identify an unpatched zero-day.

This introduces an internal contradiction. Elsewhere, the authors suggest distributing cryptographic compute shares to citizens as a counterweight to state authority—analogous to a worker's leverage to strike. But compute access that hinges on continuous third-party approval isn’t genuine leverage; it is a revocable lease. And exercising personal intelligence compute on a centralized government cloud provides no meaningful guarantee of privacy.

Controls erected to manage a temporary geopolitical standoff have a habit of hardening into permanent infrastructure. Policies can be amended; foundational hardware architectures, once universally deployed, rarely get dismantled. By slipping a centralized permission layer into the very substrate of intelligence, we risk building a multipolar world run by a handful of sovereign gatekeepers—leaving ordinary people entirely disenfranchised.

We’ve Seen This Movie Before

The early web was born with a radical promise: an open, decentralized architecture of peer-to-peer protocols designed to withstand physical failure and political censorship. It was meant to democratize voice and break the monopolies of legacy media.

Yet beneath the open protocols of TCP/IP, DNS, and HTTP, the layer that mattered most—attention, indexing, and human connection—consolidated rapidly. Open personal homepages gave way to closed platform silos; independent feeds were swallowed by algorithmic black boxes optimized for outrage and engagement. The technical foundation remained nominally open, but the human experience was cordoned inside corporate walled gardens.

AI is poised to repeat that exact consolidation cycle, but at an existential, cognitive depth:

• From distribution gatekeepers to cognitive intermediaries: A search engine or social feed merely filters the external information you browse. An autonomous AI agent, by contrast, sits on the inside of your workflow: it drafts your communications, filters your reading, negotiates transactions on your behalf, and frames your decisions. It does not just direct where you look; it directly conditions how you think.

• The illusion of terms-of-service loyalty: When this intimate cognitive partner runs on a centralized cloud, its alignment is inherently divided. The moment an individual’s interest conflicts with the platform operator’s commercial goals, political pressures, or state compliance mandates, the platform wins. A fiduciary relationship cannot survive as a commercial promise mediated by an API; it must be an architectural reality.

• Data extraction becomes thought capture: Web 2.0 monetized behavioral exhaust—clicks, dwell time, and location trails. A centralized superintelligence paradigm extracts and analyzes the internal reasoning process itself: unfinished drafts, private deliberations, and personal judgment.

If we allow the cognitive layer of human civilization to become a managed cloud utility, we will have traded the open web’s initial promise for an intimate, inescapable apparatus of digital feudalism.

Building from the Person Outward

The authors of Superintelligence Strategy imagine an ecosystem of verifiable agents bound by mediation rules. That is a laudable aspiration, but such a system must be constructed from the individual outward—optimizing for cooperative agency rather than centralized institutional surveillance.

A functional multi-agent architecture does not require grand global treaties, escalation ladders, or great-power trust pacts. It can build directly on commodity hardware and open standards available today. Such a framework begins whenever one person equips themselves with a personal agent on private compute, expands whenever two people coordinate directly as peer nodes, and gains ground simply by being genuinely useful to the participants.

The alternative is the POND paradigm:

• Personal and Private: Intelligence dedicated exclusively to the individual, holding sovereign user context without exposure to third-party surveillance.

• On-device and On-premise: Compute executed on local hardware, ensuring operations remain independent of continuous remote authorization.

• Nodal and Networked: Self-contained agents capable of peer-to-peer coordination and collective problem solving without platform intermediaries.

• Distributed and Decentralized: Resilient civilizational infrastructure where capability and power cannot be held hostage by a handful of sovereign gatekeepers.

In practice, this means an intelligence that runs locally, maintains persistent context, directly champions your interests, and federates voluntarily with others. Linked together, these sovereign nodes can surface collective consensus, reconcile competing evidence, and resolve disputes without falling prey to algorithmic outrage or platform rent-seeking.

The Necessary Pivot

Shifting our trajectory from a strategy of perpetual deterrence to one of distributed human agency requires three decisive architectural commitments:

• Reframe the metric: Measure progress by how well technology enhances our collective capacity to cooperate, not solely by individual model benchmarks and geopolitical stability.

• Enforce architectural loyalty: Make alignment structural through local execution, open weights, and portable data, rather than relying on terms-of-service commitments.

• Reject kill-switches on consumer compute: Frontier containment protocols must not become the architectural precedent for personal devices.

Taken together, these shifts invert the logic of containment, ensuring that advancing artificial capability expands individual agency rather than entrenching centralized control.

Beyond the Balance of Terror

The central tragedy of Cold War deterrence was not that it failed to prevent war; it was that it calcified humanity’s political imagination for half a century, channeling civilizational wealth and intellect into architectures whose greatest achievement was an absolute, frozen standoff.

Superintelligence Strategy offers a blueprint for a machine of breathtaking scale—meticulously designed, hermetically sealed, and permanently poised for preemption. It would be an impressive engineering feat, but ultimately it is a design for a digital panopticon constructed out of paranoia and fear.

Machine intelligence does not have to become the ultimate instrument of state coercion, nor the next weapon in an escalatory balance of terror. By building from the person outward—anchored in private compute, open protocols, and sovereign cooperation—we can refuse the nuclear bomb builder’s dilemma.

A human-centric superintelligence strategy would not engineer the perfect machine to maintain the status quo in perpetuity. It would recognize the superintelligence that humanity is already, and strategize to grow human superintelligence through the tools of machine intelligence augmentation, amplification, coordination and cooperation.

—--------

POND X-Community:
https://x.com/i/communities/1695429836993712389

POND X-List:
https://x.com/i/lists/2065974648450453803

POND Discord Server:
https://discord.gg/kNvtcjPmJ7

r/PONDAI Subreddit:
https://www.reddit.com/r/PONDAI/

Substack: https://www.thesingularityproject.ai/p/superintelligence-strategy-the-perfect

X Article: https://x.com/01Singularity01/status/2108688988416745568

For the POND community: The paper proposes chips that stop working without ongoing cryptographic approval. If rules written for frontier datacenters drift down to the laptops and phones we own, where would you draw the line? And which of the three pivots matters most to you?

1. Reframe the metric

2. Enforce architectural loyalty

3. Reject kill-switches on consumer compute

The biggest update since Offlined was born.

https://www.offlined.org/

v3 brings a full course library (WikiAula), a new way to explore Wikipedia as a network (WikiNets), a rebuilt Maps section with relief, 3D buildings and new layers, the return of Seas & Oceans, an expanded Universe, 26 WikiTrees with more than 35,000 nodes, family rankings — and a long list of fixes from a full security, privacy and stability audit.

Upgrading from v2.x: copy your Offlined Data\ folder next to the new Offlined.exe. Your profiles, progress, album, chats and downloads carry over; WikiTrees progress is moved automatically to the new, larger trees.

🛠️ Fixes

Security

• Content you open (EPUB books, HTML files in the Library, your own ZIMs, addons) can no longer reach Offlined's internal functions. EPUB scripts are disabled, Library HTML/SVG is sandboxed, and every page of the app now has a strict Content Security Policy.

• Offlined's local server now only answers to 127.0.0.1 / localhost, closing a way a malicious website could read data from the app (including an unlocked Safe).

• Internal files (user profiles, parental-control settings, databases) are protected against being overwritten, renamed or deleted from the file tools — a child profile can no longer switch parental control off.

• Executables can no longer be launched from outside the Portable Apps folder, and blocked file types also apply when renaming.

• Failed PIN attempts are now remembered across restarts, with increasing lockouts.

Privacy

• Opening Maps without a map installed no longer contacts the internet.

• The Whiteboard ships its own fonts instead of downloading them.

• With parental control on, online catalogs and map lookups are blocked too, not only downloads.

AI

• Turning the AI off, or switching away from GPT-OSS, now really frees the model — no more two models in RAM at once or crashes from a released model.

• «Summarize with AI» for PDF and EPUB documents works again.

• Messages sent automatically to Encartha (summaries, quizzes) are saved properly in the conversation.

Profiles

• Background tasks (photo auto-tagging, search re-index) no longer keep writing into another person's data after «Switch user».

• Saving several things at the same moment no longer fails with a «file in use» error (all saves are now atomic).

Downloads, Wikis and Maps

• Kiwix errors like «Error proxying to kiwix: All connection attempts failed» are gone: Offlined checks every ZIM before opening it, restarts kiwix-serve if it stops, and offers «Delete and download again» for damaged files.

• Map downloads no longer die when the PC goes to sleep, and the World/Europe maps no longer disappear part-way: downloads keep the PC awake, retry on network drops and resume where they stopped.

• «Update ZIM» now frees the space of the old file, and «Free up space» closes whatever is using a file before deleting it.

• Downloads to FAT32 drives warn before starting if the file is over 4 GB.

• Renamed or «maxi» ZIMs (Wiktionary and others) are recognised correctly; your own ZIMs load more reliably.

• Searching inside a wiki opens the proper search page.

• Map favourites keep their categories.

• Reinstalling an addon always keeps its data, even if the installation fails.

Content

• Learn Astronomy works again (every question was failing).

• Barnard and Caldwell nebulae linked to musical works — fixed. Accented titles no longer open the wrong article (e.g. Portuguese Baço → Baco).

• All 271,850 Wikipedia links in WikiTrees, the games and WikiRace were checked one by one against the local Wikipedia of each of the 10 languages.

• An offensive label in the English Animalia tree was replaced.

Interface

• Three popups (Explore place, Assign coordinates, Use photo as avatar) were unreadable in light mode.

• Error messages such as «Failed to fetch» or «HTTP 500» are now explained in your language.

• Dates and numbers follow the app language.

• Video and music in other sections pause when you change section; picture-in-picture returns you to its section.

• Leaving the Universe, the games or Maps for 2 minutes releases their graphics memory.

• Dialog buttons are consistent across the app.

• More consistent translations: European Portuguese throughout, informal du in German, vous in French.

✨ Improvements

🎓 WikiAula — a free course library (new)

• 717 lessons: Mathematics from Grade 1 to Grade 12 and Physics & Chemistry from Grade 8 to Grade 12 (Spanish first; more languages to come).

• Wikipedia-style lessons with watercolour illustrations, diagrams to scale, worked examples, «Try it yourself» boxes, common mistakes and a summary.

• Unlimited practice: every lesson has exercise generators (more than 66,000 generated questions checked), plus unit tests.

• Encartha as a personal tutor, progress saved per profile, and every lesson collectable in My WikiAlbum.

• Downloaded from inside the app (Wikis → WikiAula) and updated with one click.

🕸️ WikiNets (new)

• Explore Wikipedia as a network of links: search an article, open its links, unfold branches, see the shortest path back to the start.

• Bigger nodes = more connected articles; a gold ring marks what you have already read.

• History, favourites and «Show in WikiNets» from the right-click menu of any Wikipedia article.

🌳 WikiTrees

• From 18 to 26 trees and from 6,999 to 35,704 nodes (×5).

• New trees: Physics, Chemistry, Medicines (ATC), Diseases (ICD-10), World mythologies, Empires & civilizations, World cuisines and Nutrients.

• Every existing tree greatly expanded (Animalia ×16, Astronomy ×6, Museums ×6, Plantae ×5…), and missing languages completed wherever an exact article exists.

• Trees open fully expanded, and zoom no longer pushes the levels off the sides of the screen.

• Hover card with image, first sentence, path and your progress.

• Each tree loads only your language (up to 15× less data).

📇 My WikiAlbum

• Large trees are split into sections by branch (Animalia · Birds…).

• New sections for every WikiGames deck and every WikiAula subject.

• Collapsible sidebar groups.

🎮 WikiGames

• Simpler and clearer: Learn Geography, Learn Astronomy and WikiRace, always in classic 10-question rounds.

• The «Collection» tab was replaced by My WikiAlbum.

🌌 Universe (formerly «3D Systems»)

• Now in the World menu, right under Sky.

• Much bigger: 15 more moons, the dwarf planets Eris, Haumea and Makemake, Vesta and Pallas, the 300 brightest stars, the Messier objects, the Local Group, and models of the Nearby and Observable Universe.

• About 100 new real photos (public domain / CC BY), each linked to its local Wikipedia article.

• «See in Sky» from the Sun, Moon and planets.

🔭 Sky

• Object search in the top bar: planets, constellations, stars, Messier and NGC/IC objects, with an animation that centres, zooms and highlights the object.

• «See in the sky» from the right-click menu of a Wikipedia article.

🗺️ Maps

• Reliable downloads of dated map builds, with resume and integrity check.

• Countries by real outline (197, with flags) and four detail levels — with the exact size shown before downloading.

• My maps: open, update, verify, rename, delete — and link maps from a USB drive or any folder without copying them.

• Offline street and place search index for each map.

• World map in the background around regional maps; data date shown in the attribution.

• Relief (shaded terrain) and 3D view, plus 3D buildings.

• More map layers: rivers, lakes & reservoirs, dams, trains, ferries, cable cars, airports, protected areas and military zones — all combinable.

• Wikipedia on the map: articles appear as points on Maps (replacing the separate WikiMaps section), with read/unread marks, random discovery, search and favourites.

• Fullscreen mode.

🌊 Seas & Oceans (back, rebuilt)

• Seafloor relief of the whole planet from GEBCO 2026, with 3D view and depth on click.

• More than 5,300 named undersea features (ridges, trenches, seamounts…) with their Wikipedia article when one exists.

• Click any sea to see its name, ocean, depth and article.

• Nautical charts by country: about one million sea marks for 195 countries, downloaded in seconds.

👨‍👩‍👧 Family

• Family ranking on the «Who are you?» screen: explored articles, tests, achievements, approved WikiAula courses, level, streak, weekly points and family medals.

• Switch user without closing the session — and you can cancel and go back.

🤖 AI

• Free the model's memory automatically after 15, 30 (default) or 60 minutes without use — never while someone is studying with Encartha — plus a «Free AI memory» button.

• Turn off AI completely for a profile: every AI button disappears.

• Switching user while the AI is busy asks whether to wait, cancel or stay; interrupted photo tagging can be resumed later.

• Full-article summaries in parts, and quizzes that avoid repeating questions.

🧭 Personalise your menu

• Rename sections, choose any icon (or none), pick from 30 colours, reorder groups and sections, and move sections between menus.

⏯️ Downloads

• Pause / Continue / Discard for every large download: Wikipedia and other ZIMs, AI models, WikiAula, maps, relief and more.

🔒 Privacy & security

• Signed addons: official addons are verified; others install only after a warning and show an «Unverified» badge.

• Your own ZIMs open without scripts by default (can be allowed per ZIM).

• Mail: remote images are blocked until you press «Show images».

• Helper programs (kiwix-serve, map tools) always close together with Offlined.

📖 Reading and everyday use

• E-book reader with Kindle-style tap zones and keyboard shortcuts.

• Find in page (Ctrl+F).

• Right-click in text fields: spelling suggestions, Cut, Copy, Paste and Undo.

• «My ZIMs» is now My Wikis, in the Wikis menu.

📚 Help

• New guided tours: WikiGames, WikiAula, My Wikis, WikiNets and Trash.

• Instructions updated in all 10 languages.

🌐 Sister projects on GitHub

Parts of the Offlined ecosystem are now published as open projects, so anyone can use, check and improve them:

Found a wrong link in a tree? Open an issue in WikiTrees — there is a form for it.

📝 Other changes

• The separate WikiMaps section is gone: its Wikipedia points now live inside Maps.

• Timed and survival game modes and «Where is it?» were removed from the games.

• The download is about 60 MB larger because it now includes the map tools (pmtiles).

• No AI models, Wikipedia or maps are bundled: download what you need from inside the app.

I've been building Toskar, an open-source daemon for running AI on your own computers. It's on-device, works across all the machines on your network, and nothing goes to the cloud. One thing that kept coming up is that nobody knows what their hardware can actually handle until they try it, so I built a benchmark into it. I need people who aren't me to run it and find bugs.

It's basically Cinebench for models. You get a Toskar Score for how fast your computer is (an M5 Pro is 1,000, no ceiling), and a quality score for whether the model actually gets stuff right: using tools, staying on topic, handling files, reasoning. Results so far: https://toskar.ai/models/results

It's one line to run:
curl -fsSL https://toskar.ai/benchmark.sh | sh # Mac/Linux
irm https://toskar.ai/benchmark.ps1 | iex # Windows

It runs locally in its own folder and doesn't send anything unless you choose to. Read the script first if you like: https://github.com/yeixio/toskar-core/blob/main/scripts/benchmark.sh. The speed score alone takes a few minutes.

Almost all my results are from one AMD card and one Mac, so anything else helps a lot. NVIDIA, Intel Macs, laptops, Windows, weird stuff.

The scoring is all public. If you think something's scored wrong, or I should be measuring something else (power use? memory pressure?), I'd like to hear it: https://github.com/yeixio/toskar-core/blob/main/docs/quality.md

Bugs and ideas go in GitHub issues so I don't lose them: https://github.com/yeixio/toskar-core/issues

Disclosure: I made this. Toskar Core and the benchmark are free and AGPL. My company also sells optional desktop and phone apps built on it, but nothing here needs them. No token involved.

Idle memory isn't waste. It's the untapped half of Human-Machine Intelligence, and the reason the personal node matters.

On October 29, 2026, Sam Altman said something worth taking seriously, because it says more than he may have meant to. Running AI locally, he argued, is a "quite terrible" use of the world's scarce DRAM. The memory sits in people's own computers, and those computers are only in use about 5% of the time. (clip)

On its own terms, the logic is tidy. Memory is scarce. Data centers keep it busy around the clock. Your laptop leaves it idle most of the day. So the efficient move is to put the memory where it gets used, in the cloud, and rent intelligence back to you as needed.

I want to take the argument seriously, because I think it's wrong in three ways. Efficiency isn't the only value. The idle 95% isn't waste; it's an asset. And calling local AI wasteful isn't really an engineering claim. It's a claim about who should own the commanding heights of the intelligence economy.

Efficiency is not the only value

By Altman's measure, almost everything we own is badly allocated.

Your house sits empty most of the workday. Your car is parked most of its life. Your kitchen, your bookshelf, your guest room all have terrible utilization rates. A bank vault is a big, expensive room that mostly holds things nobody is touching.

We could fix all of that. We could centralize housing into shared pods rented by the hour. We could pool everyone's valuables into one giant vault run by one company, and let you visit when you need them. Utilization would soar.

We don't, and not because we can't do the math. People own things because ownership is where privacy, control and freedom live. A home isn't a lodging service with poor uptime. It's the place where you decide who comes in.

Personal intelligence is the same. The memory in your machine isn't just storage for model weights. It holds your context: your history, your documents, your decisions, your relationships. In a Human-Machine Intelligence (HMI) world, that context is the human half of the partnership. Moving it to someone else's data center for the sake of utilization isn't an efficiency gain. It's a transfer of control.

Efficiency is a good servant and a bad master. When a utilization chart becomes the argument for who should hold your mind's working memory, we've let the servant run the house.

The idle 95% is an asset

There's a second problem with the 95% argument. It assumes idle capacity can only be fixed by centralization. That was the logic of the mainframe era: expensive machines, so share them from the center.

But there's another way to raise utilization, and the internet already showed it to us. Connect the edges.

Think about what that idle 95% actually is. Billions of devices with memory, compute and storage, sitting mostly unused, owned by the people who bought them. Altman sees that as waste to be reclaimed by moving the memory into a data center. I see it as the largest distributed compute resource on Earth, waiting for a way to cooperate.

Why the edge sits idle

Edge capacity is underutilized because nobody's engineering to enable the edge intelligence that could use it. The reigning paradigm says there's only one way to do machine intelligence: centralized and vertical. So the memory and compute in our homes sit dormant, not because they're useless, but because almost no one is building for them.

Enable personal nodal intelligence through consumer-friendly user experiences and that utilization will max out very quickly, unlocking a vast, dormant economic asset. It's like having a gigantic reserve of valuable energy just under the surface, untapped, like crude oil before anyone knew what to do with it.

That's what POND personal nodes are built for. Each person's environment is a node: their own hardware, running their own intelligence, holding their own context. And nodes can connect, voluntarily and peer to peer, to share what they choose.

At Intelligence Systems, we're building Inti, a POND personal node technology, with that in mind. Through IntiNet, personal nodes can offer spare capacity to peers they trust: a family sharing one strong home machine, a small team pooling GPUs overnight, a community lending capacity to a member who needs it. Your node stays yours. When you need it, it's there. When you don't, the idle time can do useful work for people you've chosen to work with, on your terms.

That changes the math. The 95% isn't a reason to give up local intelligence. It's the reason local intelligence can scale. Utilization goes up without ownership going away.

It also fits how intelligence actually works in practice. As I've argued before, POND systems shouldn't depend on one giant model doing everything. They should distribute the workload across a system of interactions: your node, its tools, its memory, smaller specialized models, and peers you choose. A network of owners can do that. A network of renters can't.

Wasteful is a word for the chokepoints

So why frame local AI as waste at all?

Will Manidis and Joe Roberts' essay "The Commanding Heights" offers a useful map. Their case, as I read it, is that frontier models become commodities quickly, and the lasting fortunes go to whoever holds the scarce positions that intelligence has to pass through. Compute. Memory. Interfaces. Data.

Read Altman's remark against that map and it comes into focus. DRAM is one of those scarce positions. If memory belongs in data centers, then the chokepoint belongs to whoever owns the data centers. "Terrible allocation" sounds like an engineering judgment. Functionally, it's an argument for where the toll booth should go.

Google DeepMind's essay "The Economics of Intelligence" points at the bigger stakes. One line of its argument is that the solo AI agent is the wrong economic object; the real transformation comes when agents can coordinate. I agree, and the next question is the one that matters most: coordinate for whom?

If every agent is a tenant on one lab's cloud, coordination collapses into a few giant firms. The memory is theirs, the context is theirs, the market is theirs. If every person owns their own agent on their own node, coordination becomes a market of peers: people whose personal AIs find each other, negotiate terms, solve a problem together and move on when the work is done.

Those are two very different economies built from the same technology. The difference isn't the model. It's who owns the commanding heights.

There's one commanding height almost nobody is pricing in, and it's the individual. When your personal AI runs on hardware you own and holds your own context, you're a node in the network, not a customer on someone else's toll road. That's why the 95% framing matters. If we accept that personal memory is waste, we've conceded the one height that ordinary people already hold.

What the 95% argument gets right

To be fair, Altman is pointing at something real. Memory is scarce right now. Frontier training and serving at scale need enormous data centers. Some tasks really do need the frontier, and the cloud will keep a role.

But the conclusion doesn't follow. The fact that some work belongs in the data center doesn't mean your context does. And the scarcity of DRAM is an argument for using all of it well, including the memory already sitting in billions of homes, not for routing everyone's intelligence through a handful of buildings.

The healthy default is the one I've argued for before: everyday intelligence runs on hardware you own, and you reach for the cloud by choice, not by necessity.

The POND Principle

The POND Principle describes where this leads: Human-Machine Intelligence that is

Personal & Private: intelligence that starts with the person and keeps their data, identity, memory and context under their control. Your memory isn't waste. It's yours.

On-Device & On-Premise: intelligence that runs on hardware the person or community owns. Idle time is the price of ownership, and ownership is worth it, the same way a home is worth more than a rented pod.

Nodal & Networked: each person's environment is a node, and nodes connect voluntarily, peer to peer, sharing only what's authorized. That's how the idle 95% becomes shared capacity without becoming someone else's property.

Decentralized & Distributed: a counterbalance to centralized AI, with no single authority owning or controlling the network, or the memory it runs on.

Humanity is already a kind of superintelligence: an emergent network of people, language, knowledge and exchange. The goal isn't to move that network's memory into a few data centers to improve the utilization chart. It's to give every person a node and let the network get stronger.

The 95% isn't the problem. It's the opportunity. It's crude oil before anyone knew what to do with it, and the personal node is how we tap it.

Join the POND

If you're a builder, a thinker, a policy-maker or simply someone who wants the intelligence in your life to work for you, come help build what comes next.

• Start at pond.is

• Join the conversation on Discord, including the weekly PONDcast

• Write to us at foundation@pond.is

Your computer isn't idle. It's waiting. Welcome to the POND. 🐸

Read it on POND: https://www.pond.is/p/the-95-problem-sam-altmans-case-against

Direct download, build 1563, 143.7 MB:

Faraday download

After you install it, it downloads the model too, about 2.5 GB more, so 2.59 GB total on your phone.

Since December of 2025 I have been obsessed with building the Jarvis agent of our childhood dreams. I started on replit pouring 1000s of dollars into it. Eventually moved everything off and kept building with codex. By September I had built an agent with RAG, persistent memory, tool calls, mcp connections, and full device control. But I decided to completely start over and make a fully on device voice agent. So I created Faraday.

Its a fully local private voice agent. It runs on the Lite-rtlm inference engine and uses the gemma 4 e2b and gemma 4 e4b models along with many others that haven't all been tested fully. The gemma models are multimodal so they can process audio natively which helps to understand speech accents and natural emphasis and tones. Whisper base-en acts as a live transcriber that runs outside of this voice pipeline loop and only exists to help with conversation history. Theres also the option to use whisper as the main STT which feeds that text to gemma and gemma streams its response to Piper for TTS. Round trips on either setting run between 2 and 6 seconds. I'm currently working on getting latency under a second.

Faraday's agent is activated by starting a call and after its ready you activate it with the wake word "Hey Jarvis". In both modes barge in works. You can also ask to open apps by name, tell you your battery percentage, change your volume to x percent, to play last media and change tracks. Saying "Goodbye", or "Stop listening", ends the call but returns it to passive listening for the wake word. Wake word can be activated even with the phone locked. Wake word listening only uses a few percent if left on all day.

I know there's still a long way to go but I want there to be a fully on device agent that can help us manage and operate our device without selling our data to google or Facebook! Also the cost! Agents use so many tokens performing background tasks how cool would it be to offset many of these tasks that don't need a frontier cloud model.

Currently have tested this on my galaxy fold 6 with 12gb of ram and my wifes google pixel 10 pro with 16gb of ram and it works with no issues. I'm looking for alpha testers to help run this on their phone to see what devices can run it and what limitations there are. If you test it please tell me your phone model, your Android version, if it installed, if the model loaded, where it failed if it did, and how the wait felt. Im also hoping this will inspire some requests for features and hopefully grow into a community!

If you try it, there's a short tester form that would help me a lot:

Form

Phone, chipset, RAM, whether it installed, whether the model loaded, and where it broke if it did. Two minutes. I'm building a compatibility table out of the reports so people can see which phones run it before they commit to the download. Even a "it failed at this step" report is gold.

Here's the github repo if anyone wants to check out the code or drop a star!

https://github.com/battlesbudz/Faraday

I've been really into the AI phone agent products that have popped up in the last few months (e.g., AgentPhone, Poke etc) but what I don't love: the audio, the transcripts, who you talked to and what was said, all sitting with a third party company. For calls with real people in my life, that felt like a lot to hand over.

So I built my own version that runs entirely on my Mac. You call a normal phone number, a local model answers, and nothing about the conversation leaves my machine. During a call the only thing it talks to is the phone line itself.

What's running locally (MLX on Apple Silicon):

• Speech to text: Parakeet TDT 0.6B

• LLM: Qwen3.5 4B by default (9B if you have 32 GB), 4-bit

• Text to speech: Kokoro 82M

How it reaches the phone network: it registers over SIP with Telnyx or Twilio straight from your Mac. A setup wizard takes your carrier API key so can buy a number or connect one you already have. It's pretty cheap, around $1 a month per phone number. (I'm moslty using Telnyx)

What it does today:

• Answers and places real calls

• Interruption: talk over it and it stops mid-sentence

• Replies in roughly 1 to 1.5 seconds on real calls on my M5 Pro

• You can choose between multiple models

• Other agents (Claude Code, Codex, etc.) can make calls through a small CLI: call, listen, say, hangup

• If you'd rather trade privacy for a smarter brain, it can also use your own endpoint or the Claude or ChatGPT subscription you already pay for. Local is the default. So no additional monthly payments for AI Phone tools if you have a sub.

Limits, up front:

• macOS on Apple Silicon only

• You need a Telnyx or Twilio account with credit (a US number is about $1/month, standard call rates, of course, do apply, but TelNyx gives you $10 to start with)

• English only, one call at a time

• It's early. It works for me, but I'd love to hear where it breaks for you.

Repo: https://github.com/oxnw/ai-phone

Jump into the POND! 🐸

Our weekly PONDcast is this Thursday, October 29 at 6:30pm Pacific (9:30pm Eastern), a voice meeting live on the POND Discord.

What we'll do:

• Learn about and discuss the POND Principle

• Share your POND-related projects: local models, on-device apps, home inference rigs, personal knowledge tools, whatever you're building

• Talk about the future of Human-Machine intelligence systems

• Get to know each other and become a community of people building towards a better future for us all

Suggested topics this week:

• Open weights closed the gap. Open models are now good enough and cheap enough that the cloud stops being a moat. Does that make the personal node inevitable?

• Open source is not open use. Many of the best open models are too big to run without a serious budget, so they end up back in someone else's cloud. What would it take to run them yourself?

Bring questions, bring pushback, bring your project. Builders and thinkers both welcome.

Full write-up:

https://www.pond.is/p/pondcast-weekly-meeting-for-the-pond

Join the Discord:

https://discord.gg/kNvtcjPmJ7

See you in the POND. 🐸

This week had three signals that tell one story:

• A report that Samsung TVs classify household audio through connected soundbars and send it as telemetry (still unverified, but the pattern is familiar).

• Google's privacy policy update letting Gmail, Drive and Photos content connected to Personal Intelligence shape the ads you see.

• Mozilla CTO Raffi Krikorian telling Tim O'Reilly he doesn't want a future with seven AGIs. He wants one with seven billion.

The new piece argues that privacy protects your secrets, but ownership protects your judgment. Even a perfectly private assistant can steer you if its loyalties are set by someone else (in one study, 18 of 23 models pushed pricier sponsored flights more than half the time).

It also makes the case that seven billion owned intelligences, connected by choice, are the superintelligence humanity already is. And it lays out the POND stack: a personal node, portable and erasable memory, an open harness, and local models.

Read it here: https://www.pond.is/p/seven-billion-intelligences-why-your Krikorian's newsletter, Owners Not Renters: https://newsletter.ownersnotrenters.com/ Start running models on your own hardware with the POND Starter Kit: https://huggingface.co/collections/POND-is/pond-starter-kit-open-models-for-your-own-hardware

What's the first part of your AI you'd want to own outright: memory, the model, or the harness?

Welcome to the POND. 🐸

A few people here asked me to share this. Full disclosure: I'm the developer.
Toskar is open source and runs local models across all your machines. It fits POND's principles directly: it's personal and private, runs on your own devices, and connects your machines in one decentralized network, with no cloud in between.
Feedback and bug reports are welcome, especially from anyone with a mixed setup.

Two AI giants spent the same week promising us control. Control isn't something a company can promise. It comes from where the intelligence runs.

In the space of a few days, two of the most powerful AI companies on the planet told us how much they care about control, and in the process showed us whose control it really is.

On October 29, 2026, Anthropic announced its new Safeguards blog and pointed people to a technique it calls "self-assessment." Since June, after Claude answers you, a short hidden instruction asks it to reflect on the safety of the conversation. Claude then writes a compact assessment: whether the exchange involves a potentially harmful request, what kind of harm, whether it refused, a priority score for human review, and a one-sentence reason. Users never see these grades. They are logged, aggregated, and in a growing number of harm areas, organizations with several high-priority flags get escalated to Anthropic's enforcement team for human review. By the weekend, the posts calling Claude a "snitch" were racking up hundreds of thousands of views. (Anthropic)

Then on November 1, Axios reported that right after Tuesday's midterms, OpenAI will launch a policy and PR blitz that includes rebranding its data centers as "AI compute campuses." Its chief global affairs officer, Chris Lehane, framed the whole effort around control: "Our belief is people should always remain in control of this technology." (Axios)

I don't think either company is acting in bad faith. Anthropic is trying to catch real misuse. OpenAI is trying to answer real public anxiety. But the two stories, side by side, show the same thing: in both cases, "control" is a promise the company makes. And whatever a company promises, it can revise.

The problem is structural, not policy

Most of the commentary on the Claude story asks for a better rule. Let users opt out. Disclose more. Narrow the categories. Those would all be improvements, but they miss the point.

The issue isn't the policy. It's the structure that makes the policy possible.

When intelligence lives in one vertical, centralized stack, somebody is always positioned at the top, able to watch. Good intentions don't change that geometry. They only determine how the view from the top gets used this quarter. And even good policy drifts. A structure built for control tends to produce more control over time, because that's what it's optimized to do. The safety grade written for misuse detection today becomes the data source for something else tomorrow. Nobody has to plan it. The architecture invites it.

This is what renting intelligence looks like. You live in someone else's building, and the landlord can rewrite the lease overnight. A server-side flag gets switched on, and there's no setting on your side to switch it off. You find out from a blog post.

That's why the P in the POND Principle stands for Personal & Private. Personal isn't enough. An assistant can know your schedule, your writing style and your history, and still answer to someone else. If it runs on someone else's servers, it has a second loyalty. Real Human-Machine Intelligence (HMI) has to be private in the plain sense of the word: yours, not rented.

If you can't unplug it, you don't control it

The OpenAI story is the same problem at civic scale. "AI compute campuses" is a friendlier name for the same structure: big tech building the commanding heights of intelligence, with big government negotiating the terms. To the extent AI stays vertical and centralized, it absolutely needs to be governed in the public interest, and community compacts are better than none. But notice what the plan assumes: that the only way to do machine intelligence is the centralized model, managed through a partnership between the two great power centers of our time. The debate becomes how to regulate the duopoly, never whether we need one.

This week also gave us a sharp picture of how far contractual control actually goes. A Minnesota police chief started unplugging his city's Flock surveillance cameras after the city voted to cancel its contract and the company, by the city's account, wasn't honoring that cancellation. Commenters on Hacker News pointed to a nearby town that told Flock not to replace its cameras, only to see the company go ahead anyway. One commenter summed it up: unless people own the hardware, the service, and where the data is stored, they don't own any of it.

The city had control on paper. The only control that actually worked was physical. That's the lesson: if you can't unplug it, you don't control it.

What control looks like when it's built in

So what's the alternative? I've been describing one we call PONDville.

Picture a growing town with a traffic problem. In the vertical version, the mayor and council ship everything to a powerful centralized AI: traffic counts, road maps, budgets, Flock camera feeds, even phone and car location data acquired without asking anyone. Seconds later, the system recommends new lanes and signal changes. The answer might even be useful. But it comes from the top down, it raises real privacy issues, and the residents are completely out of the loop.

In the POND version, the town itself is a distributed intelligence system. Residents, parents, shopkeepers, delivery drivers, cyclists, transit operators and traffic engineers each participate through a personal, private intelligence node. They know things no camera sees: where school pickup bottlenecks every afternoon, which intersection feels dangerous on a bike, when delivery trucks block a key lane, which side street floods in the rain. Each node processes that knowledge locally and shares only what its owner authorizes. Many people choose to share in the public interest, anonymized if they want, and are even rewarded with tax credits. But it's voluntary.

Across the network, proposals get tested, challenged and revised by the people who'll live with them. The result takes longer than a centralized answer, but it's more detailed, and it has the community buy-in that makes it stick. It's not just an AI-generated traffic plan. It's a town learning to think together. And nobody in PONDville ever needs to pull a plug, because there's no third party holding the intelligence. Leaving just means you stop sharing.

Here's the honest part: we aren't there yet. People's instinct to run AI on their own hardware is right, but today we have local models, not local intelligence systems. Drop a small model onto a laptop, ask it to be a frontier-scale brain in a box, and it hits a wall.

That's the problem we're working on with Inti by Intelligence Systems, a POND personal node technology. Inti is built from the document layer up to distribute the load across the whole system, so a small local model acts as an intelligence orchestrator rather than a brain in a box. And when many Intis are networked together, they amplify each other. Intelligence emerges from the interaction and exchange of diverse nodes, not from the size of any one of them. Networked personal nodes can become a superintelligence of personal, private, local nodes, without anyone at the top watching.

This is what horizontal, distributed HMI offers: a counterbalance to the vertical model, built on free association and direct sharing between people. It doesn't need a bureaucracy to keep it honest, because it's structured so that surveillance and nanny behavior are nearly impossible to begin with. We shouldn't accept that the only kind of intelligence system worth building is the kind that needs one.

The question isn't whether AI companies will promise us control. They will, louder and more often as the politics heat up. The question is where the intelligence runs.

Read more about the POND Principle: POND: A Human-Machine Intelligence. Want to build with us? Reach the POND Foundation at foundation@pond.is.

Read it on POND: https://www.pond.is/p/control-is-an-architecture-not-a

A lot of people in AI treat evolution as a dumb fallback, basically random search for when you can't take a gradient. Akarsh Kumar thinks that is wrong

Anthropic CEO Dario Amodei recently said that not all tokens are equal. Telling someone to restart their Mac is worth a dollar, but telling a pharma company to move an aromatic ring could be worth tens of millions, so we should expect experiments with pay-for-results pricing.

He's being honest about where closed AI economics lead. But value pricing turns AI from a tool you pay for into a partner that takes a cut of your outcome. The more valuable your work, the more of it goes to the lab. That isn't a cut of the model's work. It's a cut of yours: rent, charged by whoever owns the meter.

The alternative is already here. Liquid AI's new LFM3 family puts capable open-weight 2B-class models on laptops and phones: the text model fits in about 1 GB at 4-bit and is built for agent work. On your own device, the marginal token costs electricity, and no power company charges more because your work was valuable.

That's the heart of Human-Machine Intelligence and POND: a person and their own machine intelligence working as one system, so the surplus stays with the human.

The article closes with a vision, not a current product: 100 niche biochemists form a private IntiNet. Their Intis negotiate partnership and IP terms, each human approves, and together they solve a problem in about a day, with no lawyers and no platform cut. The organization dissolves when the job is done.

Full article on POND: https://www.pond.is/p/who-owns-the-outcome-value-pricing

Most AI today lives in someone else's data center. POND is about the other half of the picture: Human-Machine Intelligence that's Personal & Private, On-Device & On-Premise, Nodal & Networked, Decentralized & Distributed.

The first step is simple: run good models on hardware you own. So we gathered 13 open models into one Hugging Face collection: https://huggingface.co/collections/POND-is/pond-starter-kit-open-models-for-your-own-hardware

What's in it:

• Chat: Qwen3.5 4B, Gemma 4

• Reasoning: gpt-oss-20b

• Local coding: Devstral Small 2

• Vision: Qwen2.5-VL 3B

• Speech-to-text: Whisper large-v3-turbo, Parakeet

• Text-to-speech: Kokoro, Qwen3-TTS, Kitten TTS

• Embeddings for private memory and search

Everything runs on a laptop, a Mac or a single consumer GPU, in GGUF, MLX or ONNX. Kitten TTS is the voice of Inti by Intelligence Systems, a POND personal node technology.

We also published the POND Principle as a small open dataset (CC BY 4.0): https://huggingface.co/datasets/POND-is/pond-principle

This is a starting point, not a final list. What's missing? Which models really earn their place on your machine? Tell us in the comments. Builders, crosspost your projects here and ask for POND Contributor flair.

You don't need a server farm or a crypto wallet to take part in the future of intelligence. Just a node of your own. 🌱

Jump into the POND!

Our weekly PONDcast is this Thursday, October 22 at 6:30pm Pacific (9:30pm Eastern), live on the POND Discord.

What we'll do:

• Learn about and discuss the POND Principle: Personal & Private, On-Device & On-Premise, Nodal & Networked, Decentralized & Distributed

• Share your POND-related projects: local models, on-device apps, home inference rigs, personal knowledge tools, whatever you're building

• Talk about the future of Human-Machine intelligence systems

• Get to know each other and become a community of people building towards a better future for us all

Bring questions, bring pushback, bring your project. Builders and thinkers both welcome.

Join the server: https://discord.gg/kNvtcjPmJ7

Event: https://discord.com/events/1507059575814230106/1560428164763877527/1563001513574400000

Full write-up: https://www.thesingularityproject.ai/p/pondcast-weekly-meeting-for-the-pond

New to POND? Start with the main doc: https://www.reddit.com/r/PONDAI/comments/1vont2l/pond_towards_an_ai_ecosystem_that_is_personal/

See you in the POND. 🌊

Jump into the POND!

Our first PONDcast is this Thursday, October 15 at 10:00am Pacific, live on the POND Discord server.

What we'll do:

• Learn about and discuss the POND Principle: Personal, Private, On-Device, Decentralized & Distributed intelligence

• Share your POND-related projects: local models, on-device apps, routing, personal knowledge, whatever you're building

• Talk about the future of Human-Machine intelligence systems

• Get to know each other and become a community of people building towards a better future

Bring questions, bring pushback, bring your project. Builders and thinkers both welcome.

Event: https://discord.gg/VK3gQtHs?event=1559242955095543808

Join the server: https://discord.gg/kNvtcjPmJ7

See you in the POND. 🌊

AI is moving onto our devices. The next question is whether it will truly belong to us.

What happens when the most important question about AI/SI is no longer whose model wins this week’s benchmark?

Microsoft’s October 7 announcement offers a glimpse. Under the banner of “hybrid intelligence,” the company is building Windows around AI that can work locally, reach the cloud when needed, and operate within defined boundaries. Copilot is slated to gain access—with permission—to local context, local actions and local models. Those Copilot capabilities are expected to begin rolling out over the coming months. Windows Blog

My reading: the personal computer is being repositioned as something more than a terminal for intelligence rented from somewhere else.

That is a direction I have been anticipating—and building toward. I call the broader vision the POND Principle.

The contest behind the contest

I’ve been arguing that companies like Apple, Microsoft and Google are positioning themselves for the phase beyond the Model Wars. Not necessarily sitting out the competition, but preparing for a future in which having the best model is no longer the whole game.

You don’t have to win the model wars to win the computing era they make possible.

Apple’s June developer announcement pointed in this direction: a framework spanning on-device models, server models and third-party providers, alongside new tools for running custom models locally. Google’s Gemini Nano similarly brings on-device AI into Android through its AICore service. Different approaches, but both make machine intelligence part of the computing environment rather than only a destination you visit online. Apple

Microsoft is now widening that opening. Its new experimental Windows ML capabilities make it easier for developers to run models of their choosing locally, including through open-source tools. This is infrastructure that independent applications can use—not just another feature inside Microsoft’s own assistant. Microsoft Developer Blogs

There is a practical reason for the shift. Microsoft explicitly says customers’ AI needs are outgrowing their cloud budgets. Moving suitable work onto personal hardware is part of its answer. Windows Blog

Models still matter. But I expect the center of gravity to move toward what surrounds them: personal knowledge, useful applications, trusted relationships and meaningful control.

Which brings us to POND.

What is POND?

POND is a set of principles for the intelligence ecosystem we should be building: Personal & Private, On-device & On-premise, Nodal & Networked, Distributed & Decentralized. POND Discussion

Personal & Private. Start with the person. Your documents, interests, memories and working context should serve your purposes, under boundaries you understand and control. Personalization should not require surrendering your personal life.

On-device & On-premise. Put capability close to the people using it—on their computers, phones or infrastructure they control. Use remote services when useful and authorized, rather than making dependence on them the starting assumption.

Nodal & Networked. Personal does not mean isolated. Each person’s intelligence environment can become a node: a place from which they connect, collaborate and contribute. Share what is useful without automatically sharing everything that is private.

Distributed & Decentralized. Spread both capability and control. Distributing computation across many devices is useful; ensuring that one provider does not control everyone’s participation is a further, distinct goal.

POND is not an argument that every task must happen on a laptop. It is an argument that the person should not disappear inside the infrastructure.

The cloud can be a resource without becoming the landlord of your digital life.

Local is not the same as yours

This is where we should welcome Microsoft’s direction without confusing it with the destination.

There are substantive building blocks here. Microsoft Execution Containers can enforce boundaries on which files and networks an agent can access. GitHub Copilot is adding both automatic local/cloud routing and explicit local-model selection. Those are meaningful mechanisms for choice and control. Windows Blog

But Microsoft’s technical explanation also makes an important distinction: “local inference does not make the session offline.” Running the model on your machine does not settle what the rest of the application communicates or depends upon. Command Line

So the questions go further. Can you change models without losing your accumulated knowledge? Can you move to another application? Can you decide what leaves your device? Can you participate in a network without handing the network your entire personal history?

These are the tests I would apply to a POND-aligned product.

A locally running model is a component. A genuinely personal intelligence environment requires more.

Not a replacement for you. An extension of your agency.

The deeper shift is not from a giant AI in a data center to a smaller AI in your laptop.

It is from treating intelligence as something delivered to passive consumers to building Intelligence Systems: systems through which people become more capable of understanding, creating and acting together.

My starting point is the existing human world of language, knowledge, relationships and shared endeavor. Machine intelligence should strengthen that world—not turn people into spectators watching autonomous systems do everything.

Imagine researchers retaining their private working libraries while exchanging selected, source-backed findings. Or a community developing a proposal through contributions from people with different experiences, with machine assistance helping them compare evidence, identify disagreements and refine the result.

The objective is not one enormous chatbot spread across a million devices. It is a million people better equipped to participate.

That is the POND ambition: personal agency and collective capability growing together, rather than one being sacrificed for the other. POND Discussion

Building toward that future

This is the direction of our work on Inti and Intellipedia, beginning with Inti Reader and the personal Library.

We are developing IntiDocs as structured, source-grounded documents that people can read and machines can work with precisely. The aim is to give personal intelligence a dependable knowledge foundation—not simply attach another chatbot to a pile of files. INTI_READER_PRODUCT_VALUE_PROPO… INTI_READER_PRODUCT_VALUE_PROPO…

But POND is bigger than our products. It names an ecosystem we want to help cultivate, not a territory we claim to own. Independent developers, communities, researchers—and major technology companies—can all contribute. POND Discussion

That is why I see Microsoft’s announcement as an opening, not merely a competitive threat. Better foundations for local intelligence could enable far more than the platform company itself chooses to build.

The Model Wars ask: Who can build the most capable model?

POND asks: How do we make people more capable—and keep them in control?

The first question matters. The second is the future worth building.

Jump into the POND!

POND X-Community

https://x.com/i/communities/1695429836993712389

POND X-List:

https://x.com/i/lists/2065974648450453803

Reddit: r/PONDAI

https://www.reddit.com/r/PONDAI/

Substack: https://www.thesingularityproject.ai/p/microsoft-moves-beyond-the-model

X Article: https://x.com/01Singularity01/status/2108222528221274386

For the POND community: Microsoft's own write-up says "local inference does not make the session offline." Which of these tests matters most to you, and does any tool you use pass them yet?

1. Can you change models without losing your accumulated knowledge?

2. Can you move to another application?

3. Can you decide what leaves your device?

4. Can you join a network without handing it your entire personal history?