... this is how to wire it up for your own agent. 1. Instrument the agent The loop begins with seeing everything. Point your agent's OpenTelemetry exporter at Latitude and every interaction arrives as a trace, carrying the model calls, tool calls, retrieved context, latency, and cost. Related traces group into sessions, so a ten-turn conversation is a single object rather than ten disconnected rows. If your agent emits OpenTelemetry today, as most do, this is a new endpoint ... ... each unique message is embedded once no matter how many traces contain it. For the failures you would not think to search for, Behaviors cluster sessions by what the user was trying to do, and each topic

... filters, no pressure) Bloom short-form learning organized by topic (not doom-scrolling) Wrex fitness tracking + social fitness community Circles communities built around real interests Poses real-time AI camera coaching before you shoot Planes raw thoughts without viral pressure Chains threaded conversations that keep context Private Shelves save anything worth keeping The hardest challenge? Convincing people "one app for everything" isn't a compromise. That's the mental model

... Recently I updated Empirical's CLI documentation system. Before, I could have dumped the entire command reference into every agent session and called it a day. Instead, the installer adds a tiny instruction: empirical doc empirical doc <topic> empirical doctor That's it. The agent doesn't get the entire manual. It gets a pointer to the manual. When it needs help with memory commands, it runs: empirical doc memory When it needs installation help: empirical doc install ... ... interesting is that this is becoming a pattern throughout Empirical. The CLI uses on-demand documentation. Memory retrieval works the same way. Conversation context works the same way. Instead of shoving everything into the prompt and hoping the model

... nights and weekends. It's now live, completely free, and I'd love some honest feedback from the Product Hunt community. Cognilumin is a stateless AI infrastructure platform with three tools: Illuminator paste any complex topic and get a structured explanation with mental models

... supports 4 languages (EN/KO/JA/ZH) not just UI translation, but the analysis itself is generated natively in each language. Tech stack: Next.js on Cloudflare Workers, FastAPI + SQLite on Railway, Claude for narrative analysis + deterministic NLP pipeline (TF-IDF, sentiment, topicmodeling). Try it: novelcat.io pre-analyzed The Godfather screenplay sample, no signup needed. Still v0.1.0, would love feedback from the PH community ...

... discover page, extracts hashtag names, post counts, and engagement metrics. Receive clean JSON feed it directly into your recommendation engine, trend monitoring dashboard, or ad targeting model. Real world use cases Trend driven content recommendation for media apps. Competitive intelligence for brands tracking hashtag performance. Data driven marketing identify emerging topics

... with remarkable accuracy across decades. The models don't just add up they interact , creating insights that no single model

... under pressure. Protagoras Named after the greatest Sophist of Ancient Greece, this mode is a debate training partner . Bring any topic or position and spar with Protagoras: build stronger arguments, anticipate objections, craft effective rebuttals, and learn to think on your feet. Invaluable for students, lawyers, writers, and anyone who needs to communicate persuasively. Lattice of Mental Models

... Context Flow changes that. Now you can capture information from ANY website and instantly use it across ChatGPT, Claude, Grok, Gemini, Perplexity (or any of 30+ AI models in our memory studio). If AI is truly your second brain, it should follow you everywhere you go. Here's how it works: Create topic

... ready automation code @Tonkotsu is a fresh approach to coding with agents, in a clean, opinionated GUI @Usagebar is a macOS menu bar app that tracks your Claude Code 5-hour and weekly usage limits Best AI model for coding tasks? Hot topic

... Then, configure a conversational large language model in the plugin. If you re only curating articles, configuring just the conversational model

... share a similar underlying model: Questions are posted by users. Answers are submitted by other users. Threads grow over time through comments, edits, and votes. Content is served dynamically from databases. This approach has some strengths. It encourages participation, allows multiple viewpoints, and can surface niche expertise. However, it also introduces several systemic problems that become more severe at scale. Answers can become outdated as technology, facts, or best practices change. Threads often drift off-topic

... more than ever, when you look at new products launching on Product Hunt or anywhere else online, the conversation is dominated by one topic:  virality . There is an endless stream of tools and hacks promising to make your content "explode." It often feels like "going viral" has become the default KPI for every modern business. If you aren't trending, are you even growing? But is going viral a r eal, effective marketing strategy ? Pros: Massive awareness ... ... honest, watching the view counter spin is exciting. Cons: The "tourist" traffic:  Viral hits often bring curious onlookers, not qualified leads. They arrive, look, and leave without converting. Unpredictability:  You cannot build a reliable business model

... question that connects those dots: How do we actually make money - in a way that lets this become a self-sustaining business? If you re building in AI, that question is... not easy. I've been thinking about this topic a lot for Ting. I've seen pricing through a lot of angles in my career. At adidas (perceived innovation, even just more logos on item), Yeezy and drops more for hype and scarcity, and at TikTok and Snapchat ... ... card on file then expand the range and value of goods over time. Not to mention inflate the algo on commerce posts to drive the behaviour. And finally in video games I learnt a lot about free-to-play models

... mainstream adoption using Topical Analysis Cross-reference trending themes with your audience's established preferences from Channel Insights Create content at the intersection of rising demand and your unique expertise Google Trends data analysis shows early adopters of trending topics

... businesses that: Remembers everything you tell it, web digs you ask it to do, docs you attach, remembering conversations and aggregating them into topics for effortless interactions Keeps your data private with fully encrypted data & air-gapped AI inferencing, neither us or AI corporations see it when using our default "Tan" provider in the model

... various field operations, Preet tractor price has become one of the most discussed topics in 2025. The brand is known for offering strong performance at a budget-friendly cost, making it a perfect choice for small, medium and large-scale farmers. Preet Tractors is an Indian company that focuses on manufacturing tractors suitable for tough Indian farming conditions. The company offers tractors ranging from compact 2WD models

... preferences like please keep answers short , it forgets what you discussed last week, and it can t reliably recall that the person on the other side is an experienced dev at a startup, not a beginner. We have incredible models, but when it comes to long-term interaction with a human, they re basically goldfish. Today, I m excited to share something I ve been building to fix that: GetProfile is now open source a drop-in LLM proxy that ... ... Captures It receives conversations between your users and your AI via an OpenAI-compatible chat completions API. 2. Extracts It uses LLM analysis to extract structured traits (like expertise level, tone preference, interests) and important memories (events, milestones, recurring topics

... came across Paul Ekman's Basic Emotions Theory, which very succinctly lays out the basic human emotions, the feelings that arise from them, and the reactions. I couldn't find a single app that worked according to this model. Since I already had experience in mobile development (almost 10 years), and also knew professional psychologists, I decided I could try making it myself! What emotion tracking in Sphera gave me (and why the mood tracker doesn't work ... ... changed in my life. 7. A gentle development of positive thinking without toxicity has emerged Before working on the app, I greatly underestimated positive thinking; I thought it was either stupid or manipulative. But after studying this topic

... after shared lessons On-demand clarification without waiting for the next class Deeper engagement with topics that spark curiosity Ownership of their learning journey For Teachers: Reduced preparation time for visual materials More energy for interaction and guidance during class Better insights into student interests through their whiteboard explorations Scalable support as AI helps answer common questions The Future of Education: A Collaborative Model

... li09): For anyone curious about prompt and context engineering, we re hosting a free online session next Friday. Hira (who s brilliant on this topic) will break down real examples of how to craft reusable AI context. You'll learn: 1. The basics of prompts and why context makes or breaks them. 2. How large models

... cost you in time, money, and momentum? For context, I run DevVoid.org where I partner with founders on scalable builds. Always open to chat if this resonates. Comment from Jason Gelsomino(@jason_gelsomino): Agree 100%, great discussion topic. Thanks for sharing! The MVP struggle is real for sure. If the architecture isn t thought out carefully, scaling becomes very painful and expensive. In my experience, the best way to build an MVP is with scalability in mind from the start ... ... modular approach with clean code that can grow with the product. I would also recommend using well-supported frameworks and platforms to avoid reinventing the wheel. Prioritize core features first and build a roadmap and design the data model

... conversations, docs, recent tools) API key management for internal integrations Document intelligence (upload PDFs, DOCX, TXT, HTML) Locally hosted AI models privacy-first, zero data leakage See the working demo Real-world use case: We tested it by building a teacher s AI assistant in weeks. It can: Read & answer from uploaded docs Extract key concepts + break down topics

... seeing more and more  startup teams  and products thinking about the  sustainable business model  right from the get-go. So, what's different about the AI era? Is it the third wave? Where's its growth certainty? And how does it compare to devices and networks? I'll dive into that topic

... understand if what I have built with AI Model Match could be considered as complementerary with your solution. I focus mainly on release strategies for Prompt configurations and i see it very connected with what you are doing! I leverage Evals (done by other solutions) to automatically release new prompts configurations ;) please take a look to my solution and if you think it is valuable, we can discuss about this topic