What we've learned building AI that runs on Tuesday at 2am when nobody's watching
MIT research shows 95% of AI pilots fail - not due to technology, but lack of clear success criteria. Learn how Strongly's Business Value Mapping defines measurable ROI before you start building.
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A "standard mutual NDA" almost gets signed - until someone reads to the end. When to ask for one, when to sign, what to strike, and the seven red flags hiding inside a document labeled routine.
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A $10M-$100M company that did everything right, now too big to pivot and too small to absorb the disruption. The eight pressures squeezing it, and the one decision that decides whether it breaks out or fades.
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A $589 billion dress rehearsal, and the bet it threatened. Cheap open weights, a market concentrated in a handful of names, Michael Burry's short, and the question worth losing a little sleep over.
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A comedy that ended in 2019 keeps looking more like a documentary every year. The writers hired real scientists and refused to fake the technology - then agentic AI, on-device models, and the AI safety debate all arrived on schedule.
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Gauss had it in 1805 and never published. One divide-and-conquer trick takes a calculation from N squared to N log N - from all-night to instant. What it computes, why it is fast, and why it runs inside the app that names a song in a bar.
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Five products are wearing one phrase. The taxonomy behind the buzzword, where the kernel metaphor breaks under load, and the checklist that separates machinery from marketing.
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There is a gap between governance that describes what should happen and governance that controls what does happen. Using a debt-collection stress test - FCRA, Regulation F, disparate impact - how active governance closes it.
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Illicit demand has driven tech adoption for fifty years. Generative AI follows the script, but it fabricates rather than distributes - and the people it can fabricate are real. The harms, the regulation, and why accountability beats line-drawing.
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Most agents are blind to physical space. Gaussian splatting is emerging as the spatial memory that lets an agent re-observe a place from angles it never stood at. How it works and what it takes to run in production.
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Google Pay's Universal Commerce Protocol signals that agents are about to start spending money. What it actually takes to put a transacting agent into production and run it on day two.
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Everyone is talking about agents. Almost nobody is talking about what actually makes them work. Why classical ML is the unglamorous machinery that makes modern AI usable.
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Microsoft pulled Claude Code. Uber burned its 2026 AI budget in four months and the COO can't prove the spend produced anything customers can feel. A May 2026 reality check.
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Hiring a CAIO at $150K-$200K and expecting production results in two quarters is a recipe for failure. The harder questions, the real costs, and the flywheel that actually works.
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Barge-in, structural prompt-injection gates, vendor lock-in, multi-agent handoffs, and schema-aware retrieval. The work the Friday-afternoon demo glossed over.
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The saturation problem no one is solving yet. Why adding more capability to a single agent stops paying off, what the research now shows, and what we are building (Fission) for it.
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Series finale. Weights, activations, and KV cache. How GPTQ, AWQ, SmoothQuant, FP8, and INT4 compose to multiply serving economics.
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One model. Many requests. Continuous batching, chunked prefill, disaggregation, and speculative decoding - the scheduling layer that transformed LLM economics.
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Sizing, fragmentation, PagedAttention, prefix caching, quantization, and offloading. The tensor that wrecks naive memory management.
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One token, every layer. Count the FLOPs and bytes, plot the roofline, and see why FlashAttention and kernel fusion drive production performance.
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NVMe to VRAM. Safetensors versus pickle, pinned memory, PCIe ceilings, and why naive loaders are leaving a 3x speedup on the floor.
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The map. Tokens, the autoregressive loop, prefill vs decode, and the three resources that constrain everything. The whole stack in one mental model.
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The same tools that let more people swing also let them skip the work. Two axes, an honest self-assessment, and five rules if you build anyway.
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The energy was real and the speakers were strong. But the voices we usually run into on the front lines were not on stage. Why that gap matters.
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An operating model, not a tool stack. Six moves that take AI from accessory to substrate, plus the one thing you do not automate.
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What actually blocks secret leaks, what looks like protection but is not, and the leak paths most write-ups skip - sourced against Anthropic's official docs.
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How batch, streaming, and ML workflows turn accounts receivable from a cost center into a cash flow engine.
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The critical architecture between AI agents and humans - covering HITL checkpoints, observability, and multi-agent routing.
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When does K8s make sense? Understanding the inflection point where managed APIs become constraints.
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How AI is fundamentally changing the relationship between economic growth and employment
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Why the hardest part of your AI strategy has nothing to do with choosing the right platform
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Toredai demonstrates what's possible with AI-powered trading on Strongly.AI
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Enterprise-grade AI capabilities for small and medium businesses without the enterprise price tag
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The latest in modern RAG, knowledge graphs, embeddings, and agentic retrieval
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MIT study: 95% of AI pilots fail due to lack of clear success criteria and ROI
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Why FDEs will thrive while traditional programming roles transform
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Solving MCP's security challenges with 100+ containerized servers
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PC revolution destroyed 3.5M jobs but created 19M. Why AI workers earn 56% more today.
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AI-assisted development meets enterprise security and legacy integration
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From Basics to Advanced Implementation
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Strongly.AI's Customizable Agent Chains for Optimizing Prompts
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Build Production AI with LLM Governance and Day Two Operations
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