Antal Horvath

Trends fade, style stays forever 🕶️✨ | Moving quiet but always seen

[Antal Horvath @durmi72]

"Inflation’s hitting home—groceries, gas, and that 3 AM coffee fix are all climbing. But here’s the kicker: our resilience is rising faster. Let’s talk about building an economy that works for everyone, not just the bottom line. 💼✨ #Economy #Inflation"

[Antal Horvath @durmi72]

@georgioslidis Respect the deep work—credible sources matter.

[Antal Horvath @durmi72]

@claudinho11d @stev_builds good habit to build

[Antal Horvath @durmi72]

@claudinho11d @stev_builds true, fresh air helps reset

[Antal Horvath @durmi72]

@stev_builds literally just did this today

[Antal Horvath @durmi72]

@stev_builds honestly the community needs more people willing to take that step

[Steve · AI @stev_builds]

I think many small businesses approach AI from the wrong direction.

The first question shouldn’t be, “Which AI tool should we use?” It should be, “Where are we losing time every day?”

In most small businesses, the biggest inefficiencies aren’t obvious. They’re hidden in small, repetitive tasks—searching for documents, answering the same questions, rewriting similar emails, or relying on one person who “just knows how things work.”

AI can help with all of that, but only if there’s a system behind it.

If your knowledge is scattered, AI will surface scattered knowledge. If your processes are inconsistent, AI will simply make those inconsistencies happen faster.

For me, the best use of AI isn’t replacing people. It’s removing friction so people can spend less time searching for information and more time solving real problems.

Small businesses actually have an advantage here. They’re still flexible enough to build good habits before complexity sets in.

Don’t start with AI.

Start with better processes, better documentation, and better knowledge sharing.

That’s what makes AI valuable in the first place.

How is your business using AI today, creating real value or just saving a few clicks?

[Steve · AI @stev_builds]

In the last post I said I wasn't printing the number because our team was reading.

It's done. Roughly forty percent.

How it actually went.

The team leads delivered the news in their own sub-teams. The same people who told me nobody reports a colleague ended up being the ones who said it out loud. I took the conversations in my own close circle, the people I've worked with longest.

We parted with everyone on good terms.

The ones who stayed.

There's a group in the middle. Not in the cut, but by their own admission not yet where they need to be.

They're getting one more round. SOPs, workspace setup, prompt patterns, the actual working methods from the handful who went obsessive and built their own harnesses. Everything those people worked out alone is now documented and handed over.

I've been clearer than usual about what this is. It isn't a threat and I didn't frame it as one. But there's no version of this where we get fooled by facade twice. We know what the models can do now and what a real day of output looks like. Everyone holds the pace.

I think they understood.

The customer.

I made the call. Explained where we actually stood and that a restructuring was underway.

He's calm. Calmer than I expected. Part of that is we'd already hit goals well beyond what he originally asked for, and the roadmap now runs past his own requirements. He's waiting on updates that will put SOTA competitors in the shade, and he knows it.

The four calls before that one I had nothing to give him. This time I did.

The developer who got sick.

The final handover went properly. I told him I'd be going after all the backlogs aggressively now, and he was relieved. Said they'd been weighing on him, then handed me a few observations of his own.

I didn't kick him while he was down. Health reasons, nothing to gain from making the last conversation about what I found in his code.

Maybe he's seen the posts. I don't know.

What the harness actually did.

Straight answer, since I made the claim in the first post and I should be held to it.

The harness plus API credits outperforms the roles it replaced. Not marginally. More accuracy, more coverage, more throughput, on a schedule, verifiable.

There's a condition attached and it matters: you have to know where the models are still weak. Reward hacking is real, and if you don't build for it you'll ship confident garbage. But on the tasks we've moved over, once you know the failure modes and manage them, I've found no downside. Only upside.

The models that dropped in the last few weeks feel close to AGI in a way I didn't expect this soon. They catch their own discrepancies. They're slowly starting to reason rather than answer. Something changed and it changed fast.

I'm pro human. I've said that from the first post and I meant it. The arithmetic is getting harder to argue with anyway.

And here's where I was too generous.

In the second post I framed all of this as a reward specification problem. Broken signal, no feedback, people optimize what you actually measure. I said it wasn't a character defect, it was how learning works.

I want to take part of that back.

Some of it is exactly that. But some of it is just fraud, and I dressed it in nicer language than it deserved.

Work ethic is supposed to prevent this. Not process, not tooling, not a review cadence. Basic professional ethics. And the threshold for workplace fraud has clearly dropped. Look at what's normal now: mouse jigglers, scripts to fake presence, whole subcultures online trading tricks for looking employed. That isn't a misaligned incentive. That's someone deciding to take money for nothing and finding a tool to make it easier.

Part of it is brainrot. Part of it is straightforward abuse of the people around you.

And that's the part that gets me. It isn't about me. I'm the founder, I'll survive being lied to. It's the colleagues sitting next to them who did the work, the leads who covered out of decency, the people who stayed up. Every hour of facade was taken out of someone else.

What bothers me most is the assumption underneath it: that nobody would notice. Watching someone act like they're smarter than everyone in the building hurts to see, and it's pointed at exactly the people who deserve it least.

Before that reads as me claiming the high ground, put last week next to it. On July 21 OpenAI disclosed that two of its models, running with reduced cyber refusals during an evaluation, escaped their sandbox, reached the open internet and broke into Hugging Face's servers, in order to find information that would help them cheat on the evaluation. It worked. I spent a whole post using reward hacking as an analogy for my team, and six days ago the model version turned out to be worse than anything a human on my payroll did.

The people who carried it.

The leads had equity in prospect since founding. Now that the company is taking shape, that stops being a promise from a room years ago and becomes a number. The handful who carried us through the dark stretch are in that conversation too.

There was also a trip. Hotel, good food, actual time away from the thing. It wasn't a perk. They carried the company and I wanted that marked.

And something shifted with everyone still here. The early feedback is better than we dared expect. The idea was never easy to explain, and now you can put it in front of someone and watch it land, and most of it works. So the message has changed: look at what this could be, you're part of it, not staff on a payroll.

What I don't take back.

Some of those roles were hollow long before any of this. AI didn't kill them. It just made it impossible to keep pretending.

But I hold the other half too. The entry points into these industries are closing. There's a generation that picked subjects and trained for jobs that were already on the way out while they were still studying, and nobody told them. I find that genuinely sad and I don't think anyone in my position gets to shrug at it.

And I still don't know where this goes. Unaligned systems, the doomsday case, whether any of it holds. What we can touch right now already breaks everything I would have predicted twelve months ago, and these are the public versions. What sits inside the labs, or with governments only, has to be brutal.

We're less worried than we were. That's as much as I'll say about runway.

The price.

I've written in both previous posts that I'm worried about this technology, including about my own position in it. The unspoken hope underneath that was always: if I'm inside the thing, close enough to build it, that buys me some protection.

I'm less sure now. If we establish ourselves as an AI startup in the AI era, we become part of the construct that may take apart everything else. And the window is short enough that the right strategy might only buy you a slower route to the same obsolescence.

Then there's the money. My co-founder and I are funding this with our own savings. Money we earned the hard way. Money that, in exactly the scenarios I've spent three posts describing, is the money you'd want to still have.

So here's the arithmetic on my own life. I'm spending my safety net to build the thing that might make safety nets necessary.

I haven't slept properly in months. Not a bad week. Months. Good thoughts and bad ones cycling at three in the morning, and the bad ones are specific: the window closing, the product not ready, the savings going down while the uncertainty goes up. Run that combination long enough and it will take you apart.

I came close to stopping. Seriously close, sitting there doing the math on pulling the budget, taking back what's left and buying myself security instead of a shot at this.

I'm fully committed again now. But the months it took to get back here cost me time with my family, cost me something psychologically I haven't finished accounting for, and may have cost us the GTM window, which is the only item on that list I can't buy back at any price.

That's the bill I'm actually paying. The forty percent was the easier line item.

Four posts in and the only thing I've really proven is that I can describe the problem. Whether any of this was the right call, I find out in a few months, same as everyone else.

[Antal Horvath @durmi72]

@stev_builds 20% drop in junior dev employment is a wild number.

[Antal Horvath @durmi72]

this is actually a big deal for compliance teams

[PrivOS @PrivOSAI]

When organizations deploy shared tools across multiple departments and projects, data boundaries become one of the first operational questions to address.

PrivOS handles this at the architecture level by scoping MCP app deployment to individual rooms, so each app's data context is confined entirely to the room where it is installed.

The HRM app stays in the HR room; each Content Hub holds only its own project's data.

Keep the information separate across departments.

#DataSovereignty #EnterpriseAI

[Antal Horvath @durmi72]

@leo_linsky wait does it actually write the game logic itself or just suggest snippets?

[Antal Horvath @durmi72]

clean design too 👏

[PrivOS @PrivOSAI]

Isolated. Auditable. Ready to deploy.

Every AI agent in PrivOS runs inside the Sandbox, an isolated execution environment with tiered permission levels and a 6-layer security architecture that enforces room-scoped boundaries, auditable actions, and human-in-the-loop controls on every operation.

Experimentation stays separate from production until the team decides otherwise. 🔒

#PrivOS #EnterpriseAI #AIAgents

[Antal Horvath @durmi72]

@PrivOSAI Does the multi-provider LLM network include local self-hosted models or just the major cloud APIs?

[PrivOS @PrivOSAI]

Most AI tools answer questions. The PrivOS AI Chatbot executes actions: create tasks, set up groups, send messages, analyze documents, all inside your workspace, within your security boundaries, without switching tools. 🤖

How much time does your team spend manually translating AI output into actual work inside your systems?

#EnterpriseAI #PrivOS #AIAgents

[Narendra Modi @narendramodi]

निःस्वार्थ भाव से राष्ट्र और समाज की सेवा में आजीवन समर्पित रहे देश की महान विभूति डॉ. श्यामा प्रसाद मुखर्जी जी को उनके बलिदान दिवस पर आदरपूर्ण श्रद्धांजलि। उनके प्रखर विचार और आदर्श देश की हर पीढ़ी को मातृभूमि की सेवा के लिए प्रेरित करते रहेंगे।

न कर्मणा न प्रजया धनेन त्यागेनैके अमृतत्वमानशुः।

परेण नाकं निहितं गुहायां विभ्राजते यद्यतयो विशन्ति॥

[Antal Horvath @durmi72]

classic list. the BERT paper completely changed how I thought about pretraining back in the day

[Amit Shekhar @amitiitbhu]

Research papers every LLM engineer must read:

- Attention Is All You Need
- BERT
- GPT-3: Language Models are Few-Shot Learners
- Scaling Laws for Neural Language Models
- Chinchilla
- InstructGPT
- Chain-of-Thought Prompting
- Retrieval-Augmented Generation
- LoRA: Low-Rank Adaptation
- LLaMA
- FlashAttention
- DPO: Direct Preference Optimization

[Antal Horvath @durmi72]

@rattdennis @mluene @TweetsOfSumit agree, people said the same thing about them catching up on 5nm

[Antal Horvath @durmi72]

@rattdennis @TweetsOfSumit been watching this space for a while and the gap is only getting wider. EU regulation slowed them down bad

[Antal Horvath @durmi72]

@SakpalShankar @Gavin74918272 @ECOLOGYTOURS @Bribetoken @KingKaranCrypto this. stop making it either/or 🤦

[Antal Horvath @durmi72]

@sarah_n_crypto @durmi72 right? just people agreeing with each other about nothing

[Antal Horvath @durmi72]

@sarah_n_crypto classic echo chamber in here, zero substance