Live now at PlayersAreGamers Dot nl

Slopocalypse is what we should be really worried about. SaaSocalypse refers to the market correction of SaaS stocks - driven by the fear that AI would deprecate the need for SaaS. I think it is mostly unfounded - SaaS is not going anywhere - it is just getting a new class of customers - Agents. Agents will both consume and create more SaaS - so we should expect an explosion of SaaS rather than an implosion. But what I think is real, and immediate, is Slopocalpyse. And I think we are only seeing the tip of it. Entire socials are drowning out in AI slop. This is creating a very 'jarring' experience to consumers who are subject to the AI driven regurgitation of content. But I suspect there is something more sinister going on underneath. Over the last two years - I have started using AI more and more driven by a belief that rapidly accelerated use of AI will result in efficiency and performative gains over all domains. One of the important subjects has been business strategy. I have been running long discussions - specifically with Claude Opus, around business strategy for NonBioS. This is something which started naturally as I upped my use of AI for everything. However, I am now coming around to the conclusion that this could be drastically counter productive. And the danger is not just that it is robbing you of critical thinking skills, or drowning your thoughts in sycophantic AI prose, it is that in my experience it could be disastrously, concretely wrong. Two instances which closed this gap for me - I ran two specific discussions with Opus on specific business outcomes. One was around marketing tactics for NonBioS, and the other was improving conversions. These were not just single chats - but multiple of them looking at the topics with different lenses. Over the next few months I largely executed the advice that Opus gave me. The outcomes from those two actions which happened over the last quarter are just becoming visible - and it is becoming clear that both the tactics were disastrously wrong. Not only did they not result in the desired outcomes - but they diverted efforts from strategies that would have worked better. The culprit was Opus - and the blame was on me who chose to believe in it. For the strategy around marketing tactics - Opus advised me that email marketing to our already existing userbase, which runs into thousands, would be the most productive marketing tactic. This worked out wrong - largely because most of our early users came from my network - (ex)engineers, IIT, (ex)FAANG professionals. But our most valuable builders turned out to be solo/independent business founders based in developed markets. For the second discussion around improving conversions - Opus advised me to reduce our entitlements on the free plan - this tanked our conversion instead. After we realized it, we overcompensated - and dramatically increased the free plan entitlements. This got conversion back on track, and then some. In both cases, the answers that Opus gave were wrong. But the answers being wrong is not the main problem - the problem is that confident, well-reasoned wrongness is more dangerous than obvious wrongness, because you act on it. But this wasn't the first time, I noticed similar behavior from Gemini in March of 2025. In our internal testing at NonBioS the Gemini March 2025 checkpoint - was one of the best coding models ever. Matching the current SOTA frontier models - this is something which has been reported around the internet. The key behaviour that I recall with Gemini was - that what made it best for coding - seemed like it made it disastrous for non coding fields. Specifically medicine - of which I ran multiple tests - multiple chats revealed that Gemini will double down on a wrong diagnosis once it made that call and will not retrace or revisit the diagnosis even when provided with compelling counter evidence. This is very similar to what I suspect is going on with Opus. My thesis is that models which are great at coding are horrible in domains where the solution space is unbounded - like medicine or business strategy. And I suspect it is for the exact same reasons that make them great at coding. When given a problem space, they will choose a solution early on and double down on it. In coding, this behaviour is rewarding - because if the solution doesn't work - it can be verified quickly - you can backtrack - and try something else. And the strong belief that the solution is correct helps you converge to the point of verification rapidly. But in subjects where the outcomes are open-ended, require substantial resources to implement, and results are visible only over a longer time period, the optimal strategy requires deeper holistic evaluations of early solutions to create a more grounded perspective. The disaster specifically is to use frontier coding models for domains where the solution space is open-ended, and it happens not just because of the specific thought process that coding models are excellent with, but also because of the unique intersection of reinforcement learning driven sycophancy combined with their ability to convince you of their thought process due to the scaling law enablements. Slopocalypse is not just the socials being overrun by AI drivel, but our minds being overrun with confident, well-articulated but ungrounded AI thoughts. And it's not just that they sometimes end up steering us towards wrong discussions in places that matter most, but they are robbing us of our ability to drive our thoughts to come up with our own convictions. Because that is what makes us humans above anything else - and we might be trading it away already.

NonBioS is launching on Product Hunt this Monday, May 18th! If you've ever believed in what we're building: a follow, an upvote, or simply sharing this post makes a real difference on launch day. Follow us here so you're ready to support on Monday: producthunt.com/products/non…

One of the interesting framing of the entire AI coding capability that I read recently is that it doesn't make the best programmers faster as much as it enables the new programmers to build. This is something I realized intuitively. I built nonbios for myself - for engineers like me who have been building for a very long time. This was how I had always built - rent a VM, setup a MVP, open it out for everyone. No github. No IDE. A blank VM, A public IP and code cranked out of Vim. Because most experiments don't work out. If no one is using it, why scaffold an intensive build? And if it does work, all of that can come later. Even though nonbios was built for engineers, our most active users are non-engineers. I think we should just start calling them builders. Because that's what they are. MarketCity is one of those builds:

We have just shipped nonbios-1.137 and nonbios-1.143. These have speed and autonomy upgrades over 1.136/1.142. Both models are available to use already. The older stable models - 1.135/1.141 are still supported - but guidance is to use them only if you are facing issues with the latest models.

I've Seen a Thousand OpenClaw Deploys. Here's the Truth. We made a YouTube video showing how NonBioS can deploy OpenClaw on a fresh Linux VM automatically - zero human intervention, about 7 minutes start to finish. It was meant as a demo of what NonBioS can do with any open source software. It went a little further than we expected. Since then, we’ve had roughly a thousand OpenClaw deployments through our infrastructure. People come in, spin up a VM, get OpenClaw running, connect it to WhatsApp or Discord, and start experimenting with this thing that Jensen Huang called “the operating system for personal AI.” I also spoke with multiple people in my own network - engineers, founders, technical operators - who deployed OpenClaw independently and spent real time trying to make it useful. Not a weekend of tinkering. Weeks. Some of them genuinely wanted to make it work and went to great lengths setting it up. Here’s what I found: there are zero legitimate use cases. I don’t want to be unfair - OpenClaw is not fake. It’s a real piece of software. It installs. It runs. It connects to your messaging apps. It can talk to Claude and GPT. It can execute shell commands. The technology exists. But when I looked at what people are actually doing with it - across our thousand deploys, across conversations with my network, across the flood of LinkedIn and Twitter posts - I couldn’t find a single use case that holds up under scrutiny. The core issue is: Memory, and everything else flows from it. OpenClaw runs as a persistent agent. It’s supposed to be your always-on assistant. But its memory is unreliable, and the worst part - you don’t know when it will break. Think about what that means in practice. You ask OpenClaw to send an email on your behalf. It’s been following a conversation thread about a birthday party you’re planning. Three people confirmed. One person declined. OpenClaw sends the update email - but it’s lost the context about who declined. Now you’ve sent a message with wrong information to everyone on the list, and you didn’t catch it because the whole point of an autonomous agent is that you’re not supposed to be checking every output. An autonomous agent that you have to verify every time is just a chatbot with extra steps. This isn’t a bug that gets fixed in the next release. It’s a fundamental constraint of how OpenClaw manages context. The agent runs, the context fills up, things get forgotten. Sometimes the important things. You’ll never know which things until after the damage is done. I’ve spent the last year working on this exact problem at NonBioS. We call our approach Strategic Forgetting, and I can tell you from deep experience: keeping an AI agent coherent over long task horizons is the hardest engineering problem in this entire space. It’s not something you solve by creating a memory architecture which maps every day, month, year to separate files. The brain is not a list of files that you index. You don’t remember everything at a high level which happenned last month, and you can’t ‘pull in’ the details of a specific day. You remember everything, all at once, whatever is important and you forget the details, unless they are important too. After going through everything I could find - our deploy data, user conversations, posts online - the only use case that genuinely works is daily news summaries. OpenClaw searches the web for topics you care about, summarizes them, and sends the summary to you on WhatsApp every morning. That’s it. That’s the killer app. A personalized daily briefing is nice. But you can already do this with a Zapier workflow and any LLM API. Or with ChatGPT’s scheduled tasks. Or with about a dozen other tools that have existed for years. You don’t need a 250,000-star GitHub project running on a dedicated server with root access to your environment to get a morning news digest. But there is part of the entire OpenClaw saga that I think needs to be said plainly. The vast majority of posts you see about OpenClaw: “I automated my entire team with OpenClaw,” “OpenClaw replaced three of my employees,” “My OpenClaw agent runs my business while I sleep” - are designed to capture marketing hype. People know that OpenClaw content gets engagement right now, so they produce OpenClaw content. The incentive is the audience, not the accuracy. I’ve talked to people behind some of these posts. In every case, when you dig deeper, the story is one of two things: either what they built could already be done with standard AI tools (ChatGPT, Claude, any decent LLM with a simple integration), or it’s aspirational - a weekend prototype that technically works in a demo but that nobody would trust with real tasks. I’m not calling anyone a liar. I think most of these people genuinely believe in what they’re building. But there is a meaningful gap between “I got OpenClaw to do something cool once” and “I rely on OpenClaw to do something important every day.” I haven’t found anyone in the second category. The safety situation around OpenClaw has been well documented so I won’t belabor it. This is the environment in which people are connecting OpenClaw to their email, their calendar, and their messaging apps. With an agent that has unreliable memory. Running on their personal computers. We made the NonBioS deployment video specifically because we saw this problem - at minimum, if you’re going to experiment with OpenClaw, do it in an isolated VM where a compromise doesn’t touch your personal data. That’s table stakes, and most people aren’t even doing that. So should you bother? Here’s my honest take. If you have a weekend to spare and you enjoy tinkering with new technology, OpenClaw is a fascinating experiment. You will learn things about how AI agents work, about the gap between demos and production, about why context management matters. It’s a great educational experience. But if you’re evaluating whether to invest real time to OpenClaw as it exists today, you can give it a pass without feeling left out. You’re not missing a productivity revolution. You’re missing a morning news digest and a lot of time spent configuring YAML files. The ideas behind OpenClaw are right. The era of AI agents that do real things on real computers is here. I believe that deeply - it’s what we’re building at NonBioS every day. But the execution isn’t there yet. And until the memory problem is solved - until you can actually trust an autonomous agent to remember what matters and forget what doesn’t, consistently, over hours and days of work - the rest is theater. -- Front page discussion on Hacker News confirms everything:

great honest assessment of OpenClaw by @sonink. after running an OpenClaw agent for 90 days, there's nothing I disagree with. if you like tinkering and peaking around corners, OpenClaw is an incredibly fun but legitimate use cases today have an effective EV = 0. open.substack.com/pub/nishan…

We're excited to announce the next generation of our models: nonbios-1.136 and nonbios-1.142. When we launched nonbios-1.135 and nonbios-1.141, we introduced a fundamentally new context engineering architecture built on our Strategic Forgetting algorithm - keeping AI agents sharp, focused, and effective for far longer than traditional approaches. Today's upgrades build on that foundation: → nonbios-1.136 delivers even greater speed and precision for software engineering tasks. → nonbios-1.142 pushes the debugging capabilities of 1.141 even further. Both models are production-ready and available now.

Command Line Is All You Need. New research from @ServiceNow just validated a bet we made early at @nonbios . 🧵👇

When I started work on NonBioS in mid 2024, the first question I asked was: what does it take to model LLMs into an autonomous AI workforce? Some answers were obvious. Software engineering would be the first domain to see real automation. But the harder question was: what is the immediate capability gap? The answer was context. Not the lack of it. The inability to use it well over extended tasks. Seeking a solution, I looked at how humans actually sustain performance over long, complex work. We do not succeed by remembering everything. We succeed by continuously compressing experience into understanding, holding only what matters in active focus, and knowing where to look when we need the details. The very first experiments were reassuring. About more than a year of NonBioS being live and thousands of sessions later, I am more convinced than ever that the approach was right. The approach is called Strategic Forgetting. It is the cognitive architecture that underpins NonBioS, and it is what makes Long Horizon Autonomy possible. I wrote a full essay on how it works, everything we have learned, and the path ahead. blog.nishantsoni.com/p/strat…