Monitor industry pain points and AI agent failures

Monitor Reddit (r/SaaS, r/startups, r/smallbusiness, r/Entrepreneur, r/consulting, r/sysadmin, r/devops, r/MachineLearning, r/ExperiencedDevs, r/accounting, r/healthcare, r/legaladvice, r/RealEstate, r/Construction, r/logistics, r/insurance), Hacker News, Twitter/X, Indie Hackers, and LinkedIn for people who are: 1. Describing a workflow they HATE doing manually and saying they'd pay for a solution — look for phrases like "I'd pay $X for," "why doesn't this exist," "someone please build," "I waste X hours per week on," "we're still doing this manually in 2026" 2. Describing cobbled-together workarounds using spreadsheets, Zapier chains, multiple tools duct-taped together, or hiring VAs to do something that feels automatable 3. Posting about firing or canceling an AI tool because it didn't deliver — what did they EXPECT it to do that it couldn't? 4. Teams of 10-500 people describing operational bottlenecks that cost them real money or time I do NOT want: - Generic "AI should do X" wishful thinking with no specifics - Consumer/personal productivity complaints - Anything about chatbots, content generation, image generation, or AI writing tools - Anything about LLM evaluation, observability, or monitoring I DO want: - Specific dollar amounts or time costs mentioned - Industry-specific problems (the weirder and more niche, the better) - Problems where the person describes attempting to use AI and failing — what broke? - Operational workflows involving coordination between multiple people, systems, or data sources Prioritize high-engagement posts (50+ upvotes, 20+ comments). For each finding, include: the exact pain point in their words, the source URL, what industry/role they're in, any dollar/time cost mentioned, and what solutions they've tried that failed. Monitor trade publications, industry-specific forums, government procurement sites (SAM.gov, GovWin), and niche subreddits for industries that are STILL running on paper, phone calls, fax machines, legacy software, or manual coordination in 2026. Focus on: 1. Industries spending $1B+ annually on manual labor-intensive processes that involve documents, decisions, coordination, or compliance — but where no AI startup has gained traction yet 2. Government agencies, municipal services, utilities, and regulated industries posting RFPs or job listings that reveal painfully manual workflows 3. Trade association conferences and industry publications discussing "digital transformation" as a future goal (meaning they haven't started yet) 4. Niche B2B software categories on G2 or Capterra where the top products have low ratings and users complain they're outdated Exclude: healthcare (oversaturated), fintech (oversaturated), real estate tech (oversaturated), marketing tech, HR tech, and edtech. Include: construction, agriculture, water/wastewater, waste management, mining, shipping/freight, funeral services, pest control, landscaping, property management maintenance, court systems, municipal permitting, food safety inspection, fleet maintenance, industrial equipment servicing, veterinary, dental labs, title/escrow, customs brokerage, and any other industry that sounds boring but moves billions of dollars. For each finding: the industry, the specific manual process, estimated market size if available, who the buyer would be (job title), and what technology they're currently using (even if it's literally paper). Monitor Reddit, Hacker News, Twitter/X, LinkedIn, and tech blogs for stories of AI agent deployments that went wrong, got rolled back, or disappointed users. I'm NOT interested in evaluation/monitoring — I want to understand WHAT TASKS people are trying to give AI agents and WHERE the agents fail. Look for: 1. Companies that deployed AI agents for a specific business process and had to revert to humans — what was the process and why did the agent fail? 2. Users describing multi-step workflows where AI works great for step 1-2 but completely breaks at step 3+ — what's the breaking point? 3. Industries where AI demos look amazing but production deployments consistently fail — what's the gap between demo and reality? 4. Specific tasks that require real-world coordination (between systems, between people, across time) where current AI architectures fall short 5. "AI can't do X" statements from domain experts who have actually tried — what is X? For each finding: the task/workflow that failed, the industry, what specifically broke (hallucination? couldn't access data? wrong sequencing? couldn't handle edge cases?), and what the human workaround currently looks like. Exclude anything about chatbot quality, content accuracy, or image generation failures. I only care about agentic task failures — AI trying to DO things in the real world and falling short. Monitor arXiv, tech blogs (from research labs at Google DeepMind, Meta FAIR, Anthropic, OpenAI, Microsoft Research, Nvidia Research), GitHub trending repos, Product Hunt, and Twitter/X AI research accounts for emerging capabilities that are becoming possible NOW but have no product built around them yet. Specifically track: 1. New model capabilities (tool use, computer use, multi-modal reasoning, long-context, real-time voice, video understanding) that just crossed a quality threshold — and the first creative applications people are hacking together 2. Open-source projects gaining rapid stars (500+ in a week) that solve a problem nobody had a name for before 3. Research papers being shared with high excitement that describe capabilities moving from "research" to "production-ready" 4. Developers building unexpected mashups — combining AI with hardware, robotics, IoT sensors, geospatial data, audio/video streams, physical infrastructure, or other non-obvious data sources 5. Any thread where someone says "wait, you can do THAT now?" — moments where a new capability surprises even technical people Exclude: incremental model benchmark improvements, new chat interfaces, AI art/music tools, and anything positioning itself as "ChatGPT but for X." For each finding: the capability, why it's newly possible (what changed), the hack/demo/project that showcases it, and what real-world problem it could solve at scale if productized.