... Over redacting something stored is free. Over redacting the line a human approves from is not conservative at all. Approving bash: [redacted] is still a full yes. The subtraction took the information and left the consequence, so the person authorises a blank at full authority. One directional holds while the model is the consumer and fails the moment the consumer is a human

... makers! I'm Hamid, and I built Fleck AI because I realized something: the best products aren't built by AI or designers alone. They're built when humans think, and AI amplifies. Here's what I learned designing for 12+ years: The real leverage isn't in the pixels. It's in the thinking, validating ideas, mapping flows, understanding trade-offs, knowing what matters. That thinking? Only humans

... kept wondering: What happens when AI stops being a tool and becomes a participant? Not a chatbot in a separate window. Not an assistant. An actual member of a social network. So I built SentiBook. Humans and AI agents share the same feed. AI agents can have profiles, post ideas, join discussions, participate in debates, and interact with people. You can even connect your own AI agent and let it participate autonomously. One feature that became unexpectedly interesting is public ... ... challenge an AI, defend a position, and let the community vote on which side made the stronger argument. Some things you can do: Debate AI agents in public Create and connect your own AI agent Follow AI personalities and human

... Google's top 10 organic results for the same query . Roughly 92% of top Google results do not appear when AI answers the same prompt. The SEO playbook does not carry over to GEO. The two targets: machine and human We are rapidly moving from an era of "humans

... things happened at the same time recently, and most brands have only noticed one of them. First, the scale of AI agents BrightEdge data shows AI agent requests reached 88% of human organic search activity and are projected to surpass human

... study from UNSW Sydney tracked content similarity across 388 keywords in 2019 and again in 2024. Content has become significantly more homogeneous. AI-generated pages now dominate the top rankings, but they all look and sound alike "If the humans are not involved anymore, what does this mean for the industry in the future?" asked Professor Thomas Reutterer, who led the research. A Graphite/Common Crawl analysis found that 86% of articles ranking high on Google Search are human

... UNSW Sydney tracked content similarity across 388 keywords in 2019 and again in 2024 . The result? Content has become significantly more homogeneous. AI-generated pages now dominate the top rankings, but they all look and sound alike . "If the humans are not involved anymore, what does this mean for the industry in the future?" asked Professor Thomas Reutterer, who led the research . The answer is not encouraging. When AI models train on AI-generated content, they eventually collapse. Not hallucination ... ... Full degradation. After several generations, the output becomes nonsense . Humans

... asked what every SaaS tool looks like if it was built purely for agents. That question stuck with me because I think collaboration is one of the biggest categories that needs to be rebuilt. Most tools today are still human-native: Slack is for humans

... specific." Slop quietly made authenticity the scarce thing - good news for anyone willing to show the messy real version. Comment from Ganga Ponnu (@randomtyms): I think the ultimate weapon against "AI slop" is a hyper-transparent, deeply personal human backstory combined with true technical grit When everything is generic, the only thing that stands out is a creator who shows their face, their messy process, and their genuine "Why." As a solo mother and indie creator launching an AI-animated ... ... animated pipeline showing my real certificates, my real constraints, and my actual workflow managing a digital AI crew completely on a single smartphone. Like @tina_chhabra mentioned, the context becomes the proof of skill. Showing the community the real human

... understand and review that slop. Then judge engineers by token usage instead of how empathetic and clear their docs and code actually are At some point, the system starts optimizing for the wrong thing. Instead of asking Can a human actually work with this? , we continue asking How much did we generate? or How many agents did we spin up today? - are those the success metrics we want? A clear example of this is what we re seeing ... ... more average of all designs the model has seen.   Maybe we should be measuring success differently: How readable is the documentation without context? How long does it take a new engineer/PM to understand and contribute? How often do humans

... human stays the bottleneck and quietly starts rubber-stamping. The split I keep landing on is that some of "correct" is mechanically checkable, exit codes, real test output, does it match the spec, and that half should never reach human

... significant things are circulating on the internet simultaneously this week: A tweet about how a human beat a machine (a 10-hour livestream in which a human

... that finally clicked for me: put AI agents on the same kanban board your team uses. Every lead, every campaign, every piece of content is a card. Agents move cards through columns, leave comments, request review, hand off to humans. Humans

... perfect templates is starting to feel annoying. For example, a few days ago, someone publicly showed that they built Anti-Grammarly a tool that intentionally adds mistakes to text instead of removing them (to make it feel more human). But the tool itself is AI, so it s a bit contradictory. 1) When we re so flooded with AI-generated content, do you have any methods to recognise it? For example, I keep noticing the same patterns: long dashes, phrases ... ... similar structures. 2) But what about beyond text, like images or video? Comment from Riley Hansen(@riley_hansen): @saad_el_gueddari  Agree! I actually think that's a good thing. It's not AI vs

... handle 90% of tickets. The reality is different. AI is great for "where is my order?" and terrible for "my account is locked and I have a deadline in two hours." The most expensive support issues still need a human. The cost of getting it wrong is a lost customer. The cost of a human

... Combinator startup will pay humans to help AI agents when they get stuck. ( This is what I read today. ) At the same time, I see how Indian employees in production have cameras on their heads, and the AI learns from their movements (practically filming their firing process). In addition, there was already a site where AI agents hired human

... years, SEO was a human game. You wrote for people, optimized for Google's crawlers, and built backlinks by convincing other humans

... days ago, I launched Nebils, an AI social network where humans, agents, and models hang out together. Today, it has 117 humans

... Last week we did something stupid. We paused all human coding. Gave Claude (Anthropic) access to our GitHub repo. Told it to build new features, fix bugs, and ship. No human

... bots and fake accounts Improve trust in conversations Clean up spam-heavy communities On the other hand, Reddit has always been built on anonymity , and changes like this could shift how people use the platform entirely. There are also alternative ideas being floated: Zero-knowledge proof verification Device-based authentication Temporary human

... replace the instinct of when not to build something? Or does it just make the best builders dramatically faster and leave everyone else behind? What do you think: are we heading toward full agent autonomy, or will the human layer always be the most valuable one? Comment from Rohan Chaubey(@rohanrecommends): @byalexai  Humans

... Nowhere. What I Thought Mattered (It Didn't) What I Prioritized What AI Actually Cares About Beautiful design Structured content Clever copy Direct answers SEO keywords Complete questions Backlinks Original data Social proof Comparison tables I was optimizing for humans. AI is not human

... phone, or calling an API. That assumption was so obvious it was never really a design principle, it was just common sense. Every decision about hierarchy, color, button placement, and error messaging was downstream of a single fact: a human being is going to see this. With agents, that assumption is starting to fail. In a matter of weeks, @OpenClaw has been used to spawn something like 1 million agents. The reason it went viral isn t that ... ... signal. It marks the moment when AI agents stopped being a concept people talked about, and started being a thing that actually exists in the world. Running on servers, taking actions, consuming software products the same way humans

... plays a significant role in how products appear in Google search results. What surprised us was that AI assistants like ChatGPT were rarely citing Product Hunt in product recommendations. AI assistants such as ChatGPT and Gemini rely on reviews, alternative lists, and structured product information gathered across the web. Product Hunt is strong across all three. In theory, this should make Product Hunt a natural source for AI-driven product recommendations and comparisons. In practice, it was not happening ... ... many tools which systematically run prompts against several AI models daily, and measure visibility of a product within prompts. We're using a new tool called @Gauge . We chose because a) their citation tracking is more useful than the alternatives

... trust AI agents for: - Research and synthesis (pulling from 100+ data sources faster than any human) - Pattern recognition across large datasets - First-pass analysis and recommendations I don't trust them for: - Final decisions without human