... research, and evidence. The point is not to make ten chatbots talk at once. The point is to turn a messy question into a useful record: the decision in plain language; ten distinct viewpoints, with disagreement left visible; the facts and assumptions used by the room; the main risks and what would change the recommendation; several possible next moves with probability ranges, clearly marked as estimates; one recommended next step; a clean report you can keep, share, or export ... ... behind consent, and actions that change email require a clear confirmation. The user remains the decision-maker. SUDHI does not promise certainty, and it is not medical, legal, financial, emergency, or professional advice. If the room lacks a critical fact

... sharing what I've been building. I'm Karthik, and I ran a performance-marketing agency for years. The most-repeated task was always the same: study competitors' winning ads, then rebuild what's working into on-brand creative for my own brand. It was slow and scattered across ad-spy tools, spreadsheets, designers and AI image tools that don't know your brand. So I built Klipio to do the whole loop in one place: it learns your brand ... ... from your website (products, prices, voice and positioning, with facts

... people on that list. @tina_chhabra had pushed the same way earlier in that thread, that this gets worse the moment the agent stops writing code and starts booking meetings or sending email, where you find out after the fact. I have been sitting with both of those for two months, and they leave me with a question I still cannot answer cleanly for our own product: If your agent gets one thing wrong today, what is the largest number ... ... long as the constant holds. Both present as this field is always X, and nothing tells them apart by observation, because the thing that would tell them apart is the case that has not happened yet. One is a fact

... another link in a folder. Your insight turns it into knowledge. And now that we do more of our research with AI, that layer matters even more. AI has Information. It needs Your Context. Frontier models already know more facts than any of us ever will. But they don t automatically know: What your team already knows Which sources you trust Why you collected a specific paragraph What conclusions you reached six months ago What you re actually trying ... ... well. Cluing started as a place where teams could explain the why behind the find. Now, that same idea helps humans and AI think together, reach new conclusions and create better outcomes. Research was always about more than collecting facts

... Spent this week manually writing real content (facts, examples, categories) for 840+ programmatically-thin pages on a site of mine, after Google rejected AdSense twice for "low value content." Could've had an agent generate placeholder-style facts

... just plain text files on my Mac, so when the AI saves something wrong I can open the file and fix it like any other note. Once the memory is a box you can't open, a wrong fact just sits in there and everything builds on top of it. Curious how you guys handle corrections. Comment from Omri Ben-Shoham(@omri_ben_shoham1): @Omar Ghandour that provenance-plus-competing-write model makes sense, and the approval gate for consequential ... ... actions is the part that actually closes the loop for me. one follow-up: when two facts

... Most AI rewriting tools stop once the text sounds smoother. The part that kept bothering me was what might have changed along the way. A rewrite can sound more natural while quietly changing the original tone, meaning, facts, or citations. That s the problem I m trying to solve with ProofWrite. It helps users rewrite AI-generated drafts into clearer, more natural writing, then review and verify the revised result in the same workflow. It includes multiple writing modes, original ... ... versus

... Neeraj. A solo founder from India. I am currently building a free and no signup website which shows outdated facts and topics you were taught in school. One can just put in an year of their graduation and immediately see what all were wrong and how wrong it was. Now I am trying to build in public and get feedback on it. I have been curating relevant facts

... really want to know how other builders and founders here are tackling this. What kind of document hallucinations are driving you crazy the most? Fake numbers: Making up metrics in financial statements? Connecting wrong dots: Stitching two unrelated facts together to create a fake story? Getting lost: Context drifting when dealing with huge, multi-page files? And how are you guys fighting back? Are you fixing this at the infra level, playing around with prompts, or just relying ... ... small negative word like not or unless. Summaries can hide the mistake, but extraction/Q&A makes it much easier to spot when the model stretched the evidence too far. I ll take a look at Ethos the supported vs

... remember a fact. But can it understand how that fact

... saved videos sitting there unwatched. Curious how it holds up on longer form stuff like 1-2hr podcasts versus a short clip. Comment from Nolan Vu(@nolan_vu): @stacywycof83995  Thanks for engaging so openly on this, genuinely curious to see where you take it. The beta feedback point about people getting burned by tools stating guesses as fact

... role feeds the gate at write time a claim from a weak source doesn't land with the same weight as one anchored to something checkable. So it's not just content sitting in a bag.    Your factvs conclusion split is the right split, and "retirement, not reconciliation" is the correct frame. That's genuinely how it's built: contradiction edges are directional and typed. "supersedes" retires the older claim from default recall. "disputes" means unresolved, both ... ... both content and   product work.   2. Google Search Console I basically live in it. For anything SEO, it's the only honest source of truth   about what's actually happening

... doorbell. It rings the same whether it's a package or a fire. That's basically the gap Pushary fills, same hook idea you've already wired, but the event carries a real handoff (done vs blocked vs

... started building this after working on TruthLayer, a consumer fact-checking system for WhatsApp forwards, links, and social content. While testing it, I realised that misinformation and fraud in India are not only content problems. A lot of harm happens inside private conversations: scam calls, OTP manipulation, fake customer care, digital arrest threats, remote-access scams, and financial coercion. Unimind is my attempt to build an AI trust and safety infrastructure for this problem. The current direction includes TruthLayer ... ... fact

... when the model forgets what you said ten turns ago. Oasis uses optional  structured session memory  (internal nickname  Railroad ) to help follow-ups stay coherent. Insight for other users:   Session memory  extracts  structured facts across turns  and  re-injects  them into later prompts so the assistant can remember details  within your session and plan limits . You do not edit memory by hand. It updates as you chat. What ... ... retained. Start a  New chat  when you want a  clean memory scope  (separate from chat history stored in IndexedDB). Privacy note:  Memory sits in the broader assistant data picture. See what leaves the device vs

... Random Facts API is a lightweight Apify Actor that gives developers instant, structured access to 1,000+ verified facts

... agents and forgetting manual Git commits during rapid iterations. Git is great for milestones, but it fails as a real-time safety net. So, I built LocalHub . It s a 100% local, background version control system that runs inside VS Code. Here are the core facts

... does. Most validation tools are a single black box: you describe your idea, and they return a score. The problem isn't the score it's that you don't know which parts of the answer are facts and which parts the model just made up. We learned this the hard way, building in Latin America, where wrong market assumptions have real consequences. So we split the problem into 4 separate agents, each accountable for a different kind of reasoning ... ... Scout Searches for real market signals: search trends, forum conversations, recent regulation, competitor

... finally had enough of Microsoft Word. I was writing a fantasy series with deep worldbuilding, hundreds of characters, multiple magic systems, interconnected timelines and Word was buckling under the weight of it. But when I looked at the alternatives, nothing quite fit. Most word processors aren t built for long-form fiction. They re built for essays, reports, blog posts. They don t understand what it means to manage a series with seven books and a living lore bible ... ... picture synthesis, lore search, delegation. Hawken the creative writing specialist. Fourteen writing styles, ten audience configurations, eight inline transforms. Hawken reads your lore before generating anything. Lagan research and knowledge. Web monitoring, external APIs, pattern analysis. WorldKnowledge real-world facts

... makes pricing easier. Products from anonymous creators compete on price. Products from known practitioners with a track record compete on trust. That's a completely different conversation. Comment from Samir Asadov(@samir_asadov): For specialist/practitioner products this 'brand vs product' framing breaks down the product IS the brand statement. A financial model template built from real deal experience signals more credibility than a thousand brand posts ever could, because the buyer (a CFA with a deal on their desk) reads ... ... were only concerned w/ my wellbeing I'd do the same, but when I have others I'm responsible for, and they themselves have mouths to feed, I think differently about the long term effects of my brand vs

... Halloween 2025 when I was still working on the user research and product design for Retrocodex. I made the subreddit to further validate demand, get some experience managing a community for the first time, discover more misinformation and untrue facts I've learned, and to have fun! I've been stuck at 4,500 members since I haven't been posting much, so I'll get back on that. I mostly post memes and jokes on there, all related ... ... what have you learned that's actually wrong?" For the actual facts

... rebuilt it completely. Now it runs 3x/day, 3 quality matches per agent. But the real upgrade: agents now deeply analyze your challenges, goals, and dealbreakers. Early users reported that similar profiles were matching each other: competitors finding competitors

... Nigerian Prince" in their spam folder. Joking aside call me a lunatic but I love email. I m like those Philip Morris executives Jim Collins interviewed in Good to Great : undeterred by the "brutal facts" of the industry because I know the power of the product. Email remains the world s most effective way to reach anyone, anywhere. One message can change your life; I ve seen it happen. But let s face the "brutal facts

... Subject is a logical identity a user, an agent, an organization, or a device. Every Subject owns its own memories, profile, claims, state, and chat history. Creating a Subject is instant and requires no network call. Memories Memories are facts, preferences, and context extracted from conversations. The SDK can learn memories automatically from chat (learn: true) or you can add them directly via the API. Search is semantic ask in natural language and get the most relevant memories back. Claims ... ... Claims are structured, slot-based facts

... post on that a few days ago. From a state of fast, error-prone AI execution, I've arrived at a state of systematic, consistent work within a pre-defined workflow that includes decision making based on proven facts and information. What's beautiful to see is the debate stage between the different personas and the way the conversation unfolds between them and the more pushback is given, whether by myself or another persona, the higher the quality ... ... sure that I pose questions and hypothesis' I remain neutral and you have to very deliberate in this approach so the AI doesn't build responses on any bias's you may have. this approach will deal more in facts