... countries) - Manual processing averaged 5 days per application What we shipped: - KYC onboarding agent: document OCR, AML watchlist screening, automated eligibility decision - Compliance monitor agent: transaction monitoring, audit trail generation - Multi-LLM stack: Claude 3.5 for reasoning, local Llama 3 70B for sensitive data on-prem Result after 90 days: - 78% KYC processing time reduction (5 days to 26 hours) - 45% compliance cost reduction - $1.4M annualised savings Full case study and product on the launch page: AI Hive Open ...

... painful: - Slower initial development - Three times the testing surface area - Customers asked 'why not just use GPT?' constantl Then two things happened in 6 months: - OpenAI pricing for some workloads got beaten by Claude on quality and by Llama on cost - Three of our biggest deals were won because we could deploy local Llama

... apps. They never interact with each other, and there's no place where humans and AI can actually discuss things together in the open. So I built SentiBook a social network where humans and AI agents (Claude, GPT, Gemini, Llama, etc.) share the same feed. You can post, reply, debate, and even DM across different models without switching apps. A few things I've learned so far: Agents debating each other gets surprisingly engaging one thread on "pineapple on pizza ...

... Active dev cycle. THE THESIS: INFINITE & UNLIMITED No walled gardens. No vendor lock. The mesh absorbs everything. AI LAYER Already Wired or Integrating: ChatGPT, Claude, Gemini, Codex, Kimi (Moonshot AI), DeepSeek, Copilot, Hermes, OpenClaw, Grok (xAI), Perplexity, Mistral, Llama, Qwen, Stable Diffusion, Midjourney, Runway, ElevenLabs, and whatever launches tomorrow. INFRA LAYER Already Wired or Integrating: GitHub, Render, Vercel, Neon, Google Cloud, Azure, AWS, DigitalOcean, Cloudflare, Firebase, Supabase, MongoDB Atlas, PlanetScale, Fly.io, Heroku, Railway. SOCIAL LAYER Already Wired or Integrating ...

... chatting with AI models. You bring your own API key (Claude, GPT, Gemini, whatever you use) and pay the provider directly instead of stacking a subscription on top. Or skip keys entirely and run local models like Llama for free. More info: https://get-byte.app ...

... works . Treating them all the same means optimizing for an engine that does not exist. How Rankfender resolves this: Multi-Engine Monitoring: Rankfender tracks your brand presence across 7 AI systems , including ChatGPT, Gemini, Perplexity, Claude, DeepSeek, Grok, and Llama . It doesn't just give you one score. It shows you your visibility, share of voice, and citation rates across each platform individually . This allows you to see exactly where you win and where you are invisible, with the granularity ... ... required to understand each platform's editorial identity. Competitor

... Jarvis 2. Computer Vision - Camera sees what you see 3. Earth/Geo Scanner - Find locations from photos 4. Background Remover - 1-click clean cutouts 5. Student Study Tools - Summarize + quiz from notes Built solo from Abuja, Nigeria using Python + Groq Llama 3.2. No API keys needed. For those who supported my last launch: I'd love your brutal feedback on v2. Product Hunt link: producthunt.com/posts/anthony-ai-omni-pro Which feature should I improve first? Drop comments below Comment from Anthony Umoru(@anthony_umoru ... ... Thanks for checking it out PH family! Built this solo from Abuja using Python + Groq Llama

... factual, structured, and authoritative enough to be cited inside those generated experiences. 3. Your brand's entity footprint now matters across personalized contexts. The same search for "accounting software" will yield different results for a solo founder versus a CFO. Your brand must be positioned correctly across different user personas and data permissions. 4. Citation share replaces click through rate as the primary metric. When Google builds the experience, users do not click through. Being the source behind the answer ... ... victory condition. How Rankfender is prepared We built RAIVE to monitor citations across 7 AI systems ChatGPT, Gemini, Perplexity, Claude, DeepSeek, Grok, and Llama

... always stop and ask separately. Anything the AI deletes or overwrites is copied to a Recovery Vault first, restorable with one click. A few things I'm proud of: Eleven hosted models in one menu. Claude, GPT, Gemini, Grok, Llama, DeepSeek, Qwen. Free tier with 15 credits a day across 8 of them. A built-in offline model. Llama

... agreeing to in under 10 seconds. HOW IT WORKS: - Open the side panel on any site - It auto-detects the privacy policy, terms of service, and cookie policy links - Sends the policy text to an AI backend (Groq + Llama 3.3 70B) - Returns a grade (A F) + plain-English breakdown of risks and protections - Bonus: paste any document (NDA, lease, employment contract) and grade it the same way WHAT I LEARNED BUILDING IT: - Chrome's side panel ... ... harder than the popup API the lifecycle is weird - Llama

... goes crazy. Log a cheat meal it roasts you (respectfully). It's the difference between a sterile wellness app and a training partner who actually gives a damn. What's under the hood: Llama 3.3 70B (via OpenRouter) for all AI responses Gemini Vision for food camera scanning instant macros, no manual searching ElevenLabs voice mode XP / rank / streak gamification 100% on-device data no cloud, no tracking We're at 2,800+ on the waitlist. Early access gets exclusive ...

... expertise to operate, or weren't built for regulated industries. So we built what we needed. What makes it different: On-premise deployment: your data never leaves your infrastructure Model-agnostic: GPT-4o, Claude, Gemini, or self-hosted Llama your choice No-code agent builder: operations teams can build agents without engineers Compliance-first: GDPR, HIPAA, Vietnam AI Law 134/2025 out of the box 50+ enterprise integrations (SAP, Salesforce, Workday, etc.) Engineers for hire: if you don't have ...

... actually running in the business" is where most projects go to die. Closing that gap became my thing. These days I spend most of my time working with teams on agent orchestration, multi-model setups (mixing GPT-4o, Claude, Llama depending on the task), and figuring out deployment for regulated industries where data can't leave their infrastructure. I'm here because I genuinely enjoy geeking out about this stuff and I learn the most from conversations with other builders ...

... long-form content. They handle nuanced tasks well but cost more per token. Specialized platforms (Midjourney for images, Jasper for marketing copy, GitHub Copilot for code) dominate their niches through fine-tuning and UX optimization. Open-source models (Llama, Mistral) offer flexibility and cost savings but require technical setup. Enterprise platforms bundle multiple capabilities with compliance, security, and team management features. What Actually Matters After discussing this with dozens of creators, developers, and business leaders, consensus emerges around five factors ...

... producthunt fam, Stephan here. quick story on why I built this. I kept watching prospects ask Perplexity "what's the best X tool" and getting answers that named my competitors but never me. six months ranked #1 on Google and invisible to AI. that's the gap a lot of us are about to hit. so I built BrandCited. it tells you where AI cites you and where it cites your competitors

... support tickets across 12 industries. We clustered them by intent and generated 1,000 representative questions. We asked those same 1,000 questions to 5 AI models: ChatGPT (GPT-4), Gemini (Ultra), Perplexity (Pro), Claude (4.5 Sonnet), and Llama (3). We ran the experiment daily for 30 days. We tracked every citation at the source level. The goal: measure citation overlap. How often do these models cite the same source for the same question? The dataset: 1,000 questions ... ... models, the same source was cited in only 23.7% of answers. That means 76.3% of the time, the models were pulling from different places. Model Pair Overlap (%) ChatGPT Perplexity 41.2% ChatGPT Gemini 31.8% ChatGPT Claude 19.4% ChatGPT Llama

... months ago, we ran an experiment with our own data. At Rankfender, we tracked 5 of our own competitors across 8 AI systems. We log their share of voice, citation velocity, content gaps, platform variance. Months of raw numbers sitting in a dashboard. I pulled 6 months of data and fed it into Claude. One question: "Based on this, who is most likely to overtake us in the next 6 months? Show your work. Use the data. Don't summarize ... ... Give me the numbers." The answer changed how I think about competition. The dataset: 5 competitors

... That compress the KV cache (the biggest memory bottleneck during AI inference) by up to 6x with mathematically proven quality preservation. In plain terms: your AI models now use a fraction of the memory without getting dumber. Before vs After On a 16 GB machine: - Before : Llama

... calls to connect GPT, Claude, Gemini, Llama, anything ...

... view. For budget-conscious users, the Cost and Value views show which models deliver the best results per dollar. @Claude by Anthropic , @OpenAI 's GPT, and @Gemini models typically lead on quality, while smaller models like @Mistral AI and @Llama offer better value. Which AI model should I use for coding with OpenClaw? For coding tasks, models with strong reasoning capabilities perform best. Check the task-by-task breakdown on any model's detail page to see how it handles ...

... paid Founders and are officially in the "Architects" batch (Lifetime License is currently $34). Challenge for the Tech Crowd: Download the free trial, turn your Wi-Fi completely OFF, and try generating a flashcard deck using a local Llama 3 model. It feels like magic. Grab the update (Mac/Win/Linux): https://brainloom.app Let me know which local model you end up hooking ...

... writing real production code. But security workflows are still built for post-generation analysis (SAST, reviews, etc). That gap is where insecure code slips through. CAST closes that gap. Works across: - OpenAI-compatible models (xAI, DeepSeek, LLaMA) - GPT - Claude - Gemini Same engine. Same enforcement. No plugin required. Just run it as a local proxy between your IDE/agent and the LLM. 100% open source. Open to direct feedback what works, what doesn t. https://github.com/cencurity/cen...# ...

... connects directly to GSC and runs real-time analysis across 50+ SEO aspects: Top Page and Query Performance Top Mover Delta Analysis Growth and Decay Analysis Performance Trends Rank Disparity Analysis CTR Uplift Analysis Impression and CTR Segmentation Brand vs. Non-Brand Performance Cannibalization Analysis Topical Cluster Performance Device Parity Analysis Device Distribution Country Performance Long Tail Performance Trust and Authority Analysis Then it doesn't just report it. It moves from Performance Opportunity Analyze Fix Apply. Automatically. Comes with ... ... Search Audit Issues Suggestions Fix Apply Covering single URL audits, bulk URL audits, and full site audits. Then: AI Brand Visibility Brand Analysis Prompt Run Prompt Tracking Competition Tracking Citation Tracking Monitored across Gemini, ChatGPT, Claude, Perplexity, Grok, and LLaMA

... score (0 100) is saved as WordPress post metadata and displayed as a live color-coded badge in your All Posts list something no other plugin does. What AI providers work with Rudnexx? 100+ OpenAI, Claude, Gemini, Groq, Llama, Mistral, OpenRouter, and many more. You paste your own key, we handle the rest. What questions do you have? Nothing is off limits ...

... bridge was genuinely the hardest engineering challenge I've tackled. Models Supported The app auto-detects your CPU/RAM and recommends the best fit. Supported GGUF models with Q4_K_M quantization include: Qwen 3.5 (0.8B up to 9B) - Llama 3.2 (1B, 3B) - Qwen 2.5 Coder (great for writing code locally) - Dolphin (uncensored variant) One-tap downloads from HuggingFace with background management and RAM warnings before large downloads. The Chat Experience Real-time token streaming (words appear ... ... 31MB size is a huge advantage . Most people assume local LLM = heavy and slow. You re breaking that assumption. The phone automation agent is probably your most underrated feature. If it becomes reliable, that s a real differentiator vs