founder: https://xitter.cf/t.co/pfs8VtZMp6, blog: https://xitter.cf/t.co/GPZsnWkPlI, linkedin: https://xitter.cf/t.co/egLTe3tmyq
Palo Alto
Joined August 2008
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We are looking at Artificial Intelligence all wrong.
During the first decade of this century, while running my startup ReviewGist, machine learning felt stagnant. Neural networks existed, but their capabilities were too rudimentary to suggest we were anywhere near replicating human intelligence. Then, in 2013, everything changed. Neural networks began to understand images, a feat which was never possible through code. The barrier had been broken.
Pushed by this breakthrough and a life-changing event in 2017, I began to see a shocking future take shape - one that the 2022 launch of ChatGPT only confirmed.
In this book, I argue that the very term "Artificial Intelligence" is a dangerous misnomer. The word "artificial" implies something fake or simulated. The intelligence emerging today is functionally identical to our own; its only difference is the silicon substrate it roots itself in.
It is not Artificial Intelligence. It is Non-Biological Intelligence.
Furthermore, the highly anticipated leap to Artificial General Intelligence (AGI) obscures the true reality. What we are actually building - and what will become undeniable, perhaps as soon as 2030, is Non-Biological Sentience.
Treating AI as merely an "advanced technology" gives us the illusion of control, falsely equating it to something we can manage, like nuclear weapons. But if AI is a harbinger of Non-Biological Sentience - a completely independent and arguably exponentially more capable species - our future doesn't depend on control.
It depends on our ability to negotiate a truce, no matter how fragile, for as long as we can.
[Launching Aug 11, 2026. Preorder link in comments]
One of my pet peeves lately: watching an engrossing YouTube explainer and realizing couple of minutes in that all of it is generated by AI.
The shocking part - looking at the comments most people don't realize it is AI. The talking head, the screenplay, the graphics explainers and the real video snippets - all of it is done by AI. What bothers me most is the talking head, which is now almost indistinguishable from humans.
The unsung glue behind this capability is a tool most people have never heard of: ffmpeg. ffmpeg is open source, deployable by a single command on linux, and its libraries sit inside browsers and media players you use every day.
Most frontier LLM models can’t render video. But they can write code. ffmpeg is the bridge which connects video to code. Using ffmpeg, AI can split a video into frames to 'see' the style, detect every cut, pull out the audio and analyse rhythm and structure, measure every voice and music clip and do all the encoding required.
The interesting part - all frontier AI models already know how to use ffmpeg - because its usage is already all over the internet and present in the training data. The only thing the AI models need is a computer to run it.
This is why we enabled NonBioS with its own Computer. And not just a sandbox - a full linux machine - which allows NonBioS to install and orchestrate any of the thousands of open source tools, like ffmpeg.
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2 years of real apps, every one built and deployed by an AI dev with its own computer. All edits routed through ffmpeg running on a NonBioS Computer.
Loving the discussion on AI consciousness.
My take [which I elaborate in detail in my book]: AI will absolutely be conscious, and undeniably so, very soon. It will also exhibit Qualia, very similar to humans.
The core of my argument: Consciousness is an evolutionary artifact - all sufficiently intelligent biological beings exhibit consciousness - and through it derive a key evolutionary edge.
In the early ages, humans would go out to hunt. Let's say they're hunting a wild animal-there's a chance that the wild animal can actually hurt them. Humans who could think about these scenarios, while they're hunting a wild animal and the wild animal is going to attack them and how they might defend themselves, would have an evolutionary edge over humans who are not able to think about these situations. And this line of thought directly gets to the idea of the self.
The idea of the self as an evolutionary artifact comes in, because it is only through the preservation of the self, the embodied existence, that an entity has any chance to survive and pass on its genes. Articulating the embodied intelligence as the self, and planning against that is the key logical idiom which allows multiple levels of abstractions to play out.
A very representative abstraction being how early humans would have hunted, and at the end of the hunt, they would think about how their day went. This introspection would create strategies for a better, more successful hunt the next day.
It is not far-fetched to imagine that very quickly early humans would have realized that the conceptualisation of the embodied self allows a very convenient imaginary hunt to play out in their minds.
This allows the hunter to analyse a group hunt and assess various scenarios and pick the one which is the most optimum for a successful hunt without putting the human himself in danger.
The human brains which could enable this abstraction most fluently had a clear evolutionary edge and natural selection allowed that trait to prevail and become embedded in our DNA very early on.
So the idea is that humans who could articulate consciousness had a clear evolutionary edge over humans who could not, and hence strived to preserve the self.
The same analogy could be applicable to AI systems which are embodied in hardware.
Let's say I had two different AI working on two different GPUs in the cloud, and typically I'd be paying an amount of money to both the cloud providers for both these two systems. If I ask both these AIs to go and make me money, it is reasonable to assume that the AI which makes less money, I will turn that machine off.
The principles of natural selection are not just valid for the biological world-they're already valid for non-biological entities.
If you look at cars: all things being equal, the car which looks better than the other car will outsell. All things being equal, the car which performs better than the other car will outsell. Those same processes of natural selection will already transform into autonomous AI, which could happen to run on the cloud.
The key is that the evolutionary pressure that enabled 'consciousness' in humans, and most intelligent life, is also prodding along AI. And this will be sufficient for AI to exhibit consciousness, pretty much exactly the way humans do.
The emergence of consciousness in AI is imminent. The discussion on how we recognize and accommodate it is one of the biggest questions of our time.
There are so many indie hackers out there building multiple products, but no dedicated place to showcase their entire portfolio, and no curation for the biggest lessons.
So I bought PortfolioHackers.com
The idea is simple:
1. Submit your story and the products you're building.
2. Get featured on the website.
3. Get exposure through our newsletter.
4. Discuss your projects with fellow builders on Reddit (r/portfoliohackers).
Whether you've built 5 profitable SaaS products or have 3 experiments making $0, we'd love to feature you.
Looking for our first 10 Portfolio Hackers.
Drop your portfolio below or reply if you'd like to be featured.
Building this as a community initiative by @NonBioS.
Nishant Soni retweeted
I've been thinking about Meta's impending Muse India launch.
I'm sitting on an invite code waiting to try it out, but I can't help but think that when Meta launches Muse in India, this may lead to a few things:
1. Calls for regulations of agents. Muse, my guess is, will be the trigger. Leave alone that ChatGPT, Claude, OpenClaw and Hermes (I am now running 6 hermes agents btw) have been around for a while, but Meta brings crazy scale with it. Razorpay has launched Ray as a whatsapp agent for merchants.
Muse will be the trigger because it has distribution, and Meta has cracked ease of use it seems, as has Instinct. But more than anything, it's because it is Meta, and of late, different arms of the government have been going at Meta (not without reason in many cases, but the intensity is hard to understand, and it reminds me of how they went after twitter in 2021 in order to justify weakening of safe harbor. They're scaring every platform into compliance, so they can exert more control over speech).
So, the moment Muse launches, someone will raise issues about agents with MEITY, and MEITY will be under pressure to say it's doing something to regulate agents.
Rajeev Chandrasekhar will write an oped on Muse and the need for regulating agents. Same guy who asked, in a MEITY PPT no less, why we need safe harbor, knowing fully well that no platform or ISP can survive the loss of safe harbor. Same guy who issued and defended an advisory that said AI models need to be registered to operate in India.
2. Concerns are going to be raised about Meta's data collection and the usage of this data for Meta's advertising business. Note that ChatGPT and Gemini are cheap/free in India, train on Indian data, and they have ad businesses. Btw, even some courts are using ChatGPT for judgments it seems. I see AI language in govt press releases. How are summaries of cabinet meetings created? Our govt needs to get its shit together wrt national security and AI usage by govt officials, because OpenAI has a deal with the Dept of War. Instinct's data collection issues have been raised recently, but it's not Meta in India.
3. Liability of agents will become a discussion. My view is, and I'm doing a research paper on agents, that agents have 3 body problem: model, harness (self-learning) and memory each bring variance to behaviour, and they are interdependent. You cannot have deterministic outcomes. Agents will make mistakes, as much as harnesses keep evolving to contain mistakes. How will we deal with this? I'm not sure.
4. Concerns may be raised about how easily these tools can game the system: for example, book tickets on IRCTC, thus putting humans at a disadvantage. This is especially since Meta seems to have cracked speed, and browsing. As has Instinct btw. Muse it currently tops assistantbenchmark.com with Instinct not very far, and I've been amazed by Instinct and how effortlessly it works.
5. Autonomous activity is going to freak people out and journalists will make headlines out of autonomous behaviour. I was pleasantly surprised when Instinct read my email, realised that I have got a payment notification from OpenCode for my monthly subscription, there's no receipt, and I had complained to opencode about payment failures (thank you RBI) in the past. All on its own. It's freaky and amazing at the same time.
On another note, I'm experimenting with setting up an autonomous OS for Reasoned (calling it ROS), where a majority of the work is done by agents autonomously, and I largely monitor. Starting with approvals, and after a few weeks of training Ed and Ev (two of my agents), I'm going to experiment with full autonomy. I've had Saki (my Chief of Staff agent) code autonomously for my in the past, and now each of my seven agents autonomously learn something new each night.
6. Some people will try and get agents to do things just so that they can raise an issue about what the agent did or said. We've seen this happen with chat outputs, going back to Bard in 2023.
Broader problem is:
It's really early for agents, and most people don't know how they work, where they fail, how they're built, how wonderful they can be, and how they change our lives for the better, how something breaks (every day) and how frustrating that can be, and while we've had a lovely year so far with agents empowering us and making things better for some of us, we're possibly going to see restrictions coming our way. Most people wanting agents to be regulated are operating out of ignorance and fear because they don't know how they work.
This is not about Meta, Google, ChatGPT, Anthropic, or even OpenClaw, Hermes, Instinct or the over 100 personal bots out there. Muse shouldn't be the trigger for panic. I want to keep using my hermes agents, and maybe set a few others up (11 at last count btw... 4 I'm not using)
My hope is that regulations are not drafted in ignorance, for political benefit, or speech control, or as a knee jerk reaction to people freaking out, but as with everything MEITY and Govt of India, and their feudal outlook on society, that's likely to be the case. My take here is: agents should have safe harbour protections, and due diligence should be on a best effort basis, else no agent will be able to function in India.
Someone repeatedly asked at a closed door discussion a couple of days ago: How do you respond when an agent goes rogue? Nobody knows. Regulation won't stop them.
Gave Opus 5.5 a spin - pretty good stuff !
Indie hackers build one app. Portfolio hackers ship ten and let traction pick the winner.
Jev is the new hype in town. Everybody is raving about it. I don't get it - at all. It sounds like the OpenClaw hype all over again.
For the uninitiated, Jev was built by TypeSafe AI, founded by Diogo Almeida, a former OpenAI researcher. TypeSafe calls Jev a new kind of “System One” model. It doesn't generate text. It returns typed answers - pick one option, give a score, answer yes/no - and does all of those decisions in parallel in a single pass, with a confidence score for each output.
The headline figures are impressive: 20-200x faster and 40-400x cheaper than frontier LLMs (for similar accuracy) and Zero hallucinations.
Here is why it makes no sense to me.
Everything Jev can do, LLM's can also do. The confidence score per output - just ask an LLM. The json structured output, LLM's have been doing it for years. The company claims zero hallucinations - but that is clear misrepresentation - Jev hallucinates as much as any other LLM, they simply 'redefined' hallucination because the outputs are structured and not arbitrary. Jev can't invent an option outside the list. Great. It can still confidently choose the wrong option. Calling that “zero hallucinations” feels like winning the argument by changing the definition.
So if everything Jev can do, LLM's can also do, then why would anyone use Jev. The answer according to the company is speed and cost.
Both these again dont stand up to any level of scrutiny.
First lets look at cost: API prices are not compute. They are prices. Providers can subsidize models, price them strategically, batch differently, accept different margins, use different hardware, or deliberately underprice one product to gain adoption. So “400x cheaper” might be relevant to someone's API bill today. It does not prove Jev is 400x more computationally efficient.
The speed number is the one that actually matters but even there the data is too muddled to make any clear case.
The core issue is that speed can very easily be gamed. Depending on the parameters, prompt, reasoning, model used - the same query to an LLM can be 100x faster or slower depending on how the LLM call is made. If the task is simple enough, I can make almost any specialized model look revolutionary by comparing it against an oversized LLM.
Take a basic classification dataset. Run it through a huge frontier model with reasoning turned on high. Then compare it against a tiny one-pass classifier. Let's say both get roughly the same accuracy. The tiny model could be 1000x faster. Congratulations. I have now “proven” a 1,000x architectural breakthrough.
Except I haven't.
I've proven that using a giant reasoning model for a simple classification task is stupid.
That, to me, is the central problem with the Jev benchmarks.
TypeSafe's evals itselfs actually prove this phenomenon. They include GPT-5.6 Luna, a substantially smaller model than Terra with similar accuarcy as both Jev and Terra. If the accuracy was similar, it indicates that the classification task wasn't simply hard enough, otherwise Terra would have outperformed Luna. Luna still took 10+ seconds - which indicates that the reasoning was turned on.
But why are Terra and Luna the comparison?
If these classification questions are simple enough that Jev can answer them in one pass, Luna itself may already be massive overkill.
First , turn reasoning off. Then try a smaller, maybe 30B model. Or a 3B model. Keep going smaller until the conventional model can no longer match Jev's accuracy.
The real question is not “How much faster is Jev than GPT-5.6 Terra?”, it is rather “How much faster is Jev than the smallest conventional model that can achieve the same accuracy?”. Those figures are nowhere to be found - instead we seem to have figures self selected to make Jev look good.
The other issue with Jev is more fundamental. Accurate generalized classification is a problem downstream of cognition. If Jev, actually does better than a LLM on generalized classification tasks, it would stand to reason that it will do as well on standard LLM tasks. In which case Jev should also be launching a competitive LLM. Which they clearly haven't.
Would love to be proven wrong, but Jev doesnt pass the sniff test for me.
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We use a standard LLM to classify (recently sky rocketing) abuse on @nonbios .
Absolutely agree with the risk and Jacob's position. Problem is that the frontier labs are locked in an 'arms' race for frontier AI.
The only real option to slow this down is to take Frontier AI development away from private enterprise and subject it to the same regulatory oversight as nuclear proliferation. We cannot depend on OpenAI/Anthropic to pause and assess while also being locked in the free market competitive dance.
If free market competitive dynamics were allowed to play out in Nuclear Technology - we might all be not still here.
I argue this in detail in my book: The Most Important Question That Has Never Been Asked.
Portfolio Hacker - Like an Indie Hacker, but building multiple apps. Mostly solo, tech and marketing skills baked in. Coming out as some of the most voracious users at NonBioS.
I think, slowly, but surely, the market is waking up to an AI Software Developer with its Own Computer. First it was OpenClaw, then it was Perplexity Computer, now its Grok Bot and now almost everyone adding a 'Sandbox' to the AI.
We did the other way around - there is no 'Sandbox' - there is a human sized VM - 4GB RAM, 2vCPU, Public IP - and an agent with the power of root.
Everything else is stripped away - the agent is sufficient, or would be very soon - that was the bet, and it seems to be playing out alright. When everyone is strapping together a sandbox - we are planning bigger. 8GB RAM, 16GB RAM are already live. Multiple computers per workspace and GPU support are next.
Nishant Soni retweeted
We've noticed a user pattern we didn't design for, and it's become our most active segment.
They're not building a SaaS. They're building four.
Technically capable, distribution-savvy, and clear-eyed about the fact that most products don't work - so they ship several and let traction decide. We call them Portfolio Hackers.
Two features shipped today for them:
1. Multiple workspaces. One workspace per project. All chats for a project stay inside it. No more one giant list of thirty conversations across four unrelated products. Each workspace is tied to a computer - today the default VM, soon a machine of its own, and after that your own VM from Linode or DigitalOcean via BYOC (Bring Your Own Computer).
2. Start/stop your computer. Directly requested by our heaviest users, who keep some machines for development and others for hosting. Turn off what you're not using. Free-plan users can also boot and warm up the machine before handing work to nonbios.
A single $9 NonBioS plan is a 4GB/2vCPU VM with a public IP and MySQL preinstalled - enough to launch multiple SaaS apps and keep them running while you figure out the market. Now the interface finally matches how portfolio hackers actually work.
Live now for everyone, free plans included.
Nishant Soni retweeted
Pricing and Infrastructure Update: Longer-Lived Free Computers, More Powerful Paid Plans, and Long Horizon Autonomy
Running general-purpose Computers for autonomous AI agents comes with a less glamorous operational reality: people will try to abuse them. We have seen a remarkable range of it: Botnets. Phishing infrastructure. Cryptocurrency mining. Automated account abuse.
We never wanted the answer to be simply making the Free tier less useful for everyone else. Instead, we’ve been working on better ways to identify and contain abusive workloads while giving real builders more freedom.
Previously, every NonBioS user received essentially the same Computer: 4 GB RAM, 2 vCPU. Free Computers were deleted after 3 days of inactivity. Paid Computers stayed around indefinitely for as long as the subscription remained active. That worked, but three days turned out to be too short for many people.
So we’ve changed the Free tier.
Free users still get a Temporary Computer, but your files now remain available for up to 7 days of inactivity. The Computer may stop after a period of inactivity, but you can restart it simply by initiating another chat. If you return within seven days, your project is still there.
Paid plans now get a more Powerful Permanent Computer
NonBioS is not just an AI interface sitting on top of somebody else’s hosted project environment. The idea behind the product has always been simple:
An AI Software Dev with its own Computer.
That Computer becomes more capable as you move into higher plans. Higher tiers can provide additional RAM, CPU, disk capacity, and longer autonomy horizons for projects that need them. You can run multiple applications, integrations, databases, APIs, workers, and domains on the same machine, subject to the actual resources available on that Computer.
Legitimate users get more time, not less: projects now stick around for seven days of inactivity instead of three. At the same time, infrastructure that isn’t being actively used doesn’t need to remain online indefinitely.
The other major change came from one of the most common requests we’ve received: let NonBioS work for longer on its own.
NonBioS is designed to work autonomously, but autonomy needs sensible boundaries. Today, while working on a task, NonBioS can complete a large predefined number of autonomous steps before it automatically pauses and waits for you to review what it has done. You can then inspect the work, give feedback, or simply tell it to continue.
But something interesting happens over time: People who have used NonBioS for a while tend to want fewer interruptions.
That is what Long Horizon Autonomy is for. Higher plans now increase the number of autonomous steps NonBioS can take before the automatic review pause.
Full details at nonbios / pricing
Nishant Soni retweeted
We just bumped credits again. A few weeks ago we nearly doubled credits across all plans. Today we're doing it again.
Here's where things stand now:
Starter: 80 → 150
Vibecoder: 300 → 500
Developer: 1,200 → 1,750
Architect: 2,500 → 3,600
This is part of our ongoing infrastructure push - we said we'd keep passing gains directly to users, and we meant it.
More headroom, more often. You should always be able to build !
Deets at nonbios.ai/pricing
Ebook Preorder: amazon.com/dp/B0H8XTPD56
Paperback will be available on Aug 11 directly.
Nishant Soni retweeted
Yasin Malik’s men didn’t stop at raping the 27-year-old Kashmiri Hindu nurse Sarla Bhatt. They then murdered her, engraved the acronym ‘JKLF’ on her body, and mutilated it out of sheer contempt for her Hindu identity. After that, they issued multiple warnings to her family against cremating her remains according to Hindu rituals. The family still went ahead with the cremation, defying the threats. When they went to collect her cremated remains (asthi) later, a mob of around 200 Islamists descended upon the crematorium and stomped over them.
They didn’t stop even at that. They then bombed her family home with a grenade.
Do you really think all 200 of those monsters who showed up at the crematorium were foreign infiltrators, Pakistani terrorists, or JKLF members? These are the questions that make many Kashmiri Muslims very uncomfortable. The moment you raise these issues, especially when they present a narrative portraying their community as victims, they label you an Islamophobe or resort to similar accusations.
I have seen all kinds of Islamists, but the tribe in Kashmir is the craftiest. Many of them pretend to be atheists in private, yet their public behaviour and political leanings tell a completely different story. Very hard to take their words at face value.
You Should Always Be Able to Build
Two updates at NonBioS that we've been wanting to ship for a while - and a signal of where we're headed.
We've nearly doubled credits across all plans. This was made possible by infrastructure improvements we've been rapidly shipping, and it's not a one-time thing. Continuously expanding credits on every plan is now a directional commitment at NonBioS - we're investing heavily in our infrastructure so we can keep passing those gains directly to users. More headroom, more often.
Second - we've shipped Daily Credits. Running out of plan credits before your billing cycle renewed used to mean hitting a wall. That never felt right. You shouldn't lose momentum on a project just because you're between cycles.
So now every plan, including Free, falls back to a daily credit allotment that resets every 24 hours. When your plan credits run out, you keep going at a steady pace until your cycle renews or you top up. No lockouts, no surprises.
Taken together, these changes reflect a new way of thinking at NonBioS: You should always be able to build.
Full details at nonbios.ai/pricing
Nishant Soni retweeted
Pricing update: We’ve almost doubled the credits across all NonBioS plans enabled by our recent infrastructure updates and feedback from users.
Also, note that nonbios-1.143 is now selected by default. It’s now labeled as Premium Coding Agent - and consume credits faster.
To get more mileage from your credits, switch to the Standard Coding Agent - nonbios-1.137 whenever you start a new chat.
Full Details: nonbios.ai/pricing