In yesterday's discussion by @aaronoleary, there were a few thoughts about using robots at home.
In this context, several questions occurred to me.
For example, what will happen to the future of humans if we delegate most of the manual and mental work to machines? How will we handle our free time? How will people be rewarded?
Over the past few days, you may have noticed a new account on Product Hunt called "Curious Kitty" commenting on some launch posts. It was created on 20th December 2025.
First observation: it behaves like a bot. It only asks questions (no opinions, no praise, no criticism) and those comments get visually highlighted, similar to how awarded comments stand out on Reddit. (screenshot attached)
I'm preparing an update to my article about the top alternatives to Product Hunt: https://www.tetriz.io/blog/top-4... So far I have been covering MicroLaunch, Fazier, BetaList & AppSumo. Which other platform do you think deserves to be mentioned?
I run explicare, a GDPR-compliant transcription tool in Germany, so this tension is basically my day job. I want to open it up as a discussion rather than push a conclusion.
Here's the practical reality for a European company. Most of the best speech and AI services run on US infrastructure. The moment audio, transcripts, or personal data leave the EU and land on a US server, you're in GDPR territory: lawful basis, transfer mechanism, sub-processors, all of it. For many of our enterprise and public-sector users, that alone rules out half the market before accuracy or price even come up.
The legal ground keeps moving, too. The EU-US Data Privacy Framework is still valid today, but a French challenge is now pending at the European Court of Justice, and a US Supreme Court ruling this June, plus the oversight board (PCLOB) losing quorum, have weakened the exact safeguards the framework was built on. A "Schrems III" case is widely expected. If you plan a product roadmap around that, you're building on sand.
Been in a good thread here about tracking brand mentions across ChatGPT/Perplexity for buyer-intent prompts. One thing that keeps coming up: model responses vary run to run, so a single "yes, we're mentioned" or "no" doesn't tell you much.
Curious how people are handling this, running prompts multiple times and tracking frequency? Logging exact prompt wording to control for drift? Something else entirely?
Trying to build a more reliable method before I trust any of this data enough to act on it.
Hey guys!!! I've been trying quite a few no-code AI tools lately, and honestly, the space is moving so fast that it's getting hard to keep up.
Right now I've used things like Lovable, Base44, Bolt, Replit Agent, and recently Autocoder. They all seem to have different strengths. Some are better for quick MVPs, some are stronger at UI, while others feel more capable when projects start getting larger.
I'm curious what everyone else is actually using day to day.
If you could only keep one no-code AI agent, which one would you pick, and why?
Every day, the PH feed is packed with shiny new SaaS tools most of them browser-based, many of them AI-infused. It s exciting, no doubt. But compared to a time not so long ago, something seems missing: local desktop apps.
They re rare now, and it makes me wonder are native apps still worth building, or have they quietly slipped into the realm of nostalgia?
After all, web apps offer clear benefits for both users and makers or investors. Users don t have to install anything, updates are seamless, and their data is accessible from any device with a browser. For investors, the advantages are just as compelling: a single tech stack, easier user onboarding, lock-in effects, and plenty of levers for driving growth and virality.
Large language models are becoming increasingly capable reasoners. They can identify patterns across huge datasets and derive complex conclusions from established premises. But genuine scientific breakthroughs often require something different: reframing the problem and proposing premises that did not previously exist.
Google DeepMind researcher Tom Zahavy separates scientific discovery into three modes of inference: induction finds patterns in data; deduction derives conclusions from rules; and abduction proposes a new explanatory framework for a surprising result.
Today's LLMs are strong at induction and rapidly improving at deduction. The paper argues that they still lack the abductive jump needed to originate new foundational hypotheses. Given Einstein's equivalence principle, a model might derive much of the later mathematics. The harder step is inventing that principle when observations are sparse.
