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AI, local business, and what happens when you decide to build instead of get replaced.

  1. 4 days ago

    Deep Dive - AI Model Parameters: What the Billion-Parameter Headline Actually Means

    "Billions of parameters" appears in almost every AI headline, but the number is widely misunderstood — and misreading it leads to genuinely bad choices about which AI to use. This episode breaks down what parameters really are, what they don't tell you, and what to ask instead. AI-generated (NotebookLM) audio overview. Source: HexLocal in-house research — What Does "100 Billion Parameters" Actually Mean? (Dr. Priya Nair). - Parameters are adjustable internal settings shaped by training, not memories or stored facts — intelligence is emergent from the whole arrangement, not held in any individual part - Parameter counts cluster into practical capability tiers, from phone-sized models at 1–3 billion up to trillion-scale frontier models that only run in the cloud - The relationship between size and capability is logarithmic, not linear — each jump in scale buys diminishing returns - A well-trained smaller model regularly beats a larger, poorly-trained one; Mistral 7B outperforming Llama 13B is the field's go-to example - Efficiency gains mean today's small models punch far above their weight — Qwen3.5 9B posts benchmark scores rivaling models more than ten times its size - The parameter count tells you nothing about training quality, context window size, or task specialization — the things that actually determine usefulness

    22 min

  2. 4 days ago

    Deep Dive - AI Quantization: How a Full-Size Model Shrinks to Fit on Your Phone

    Quantization is the technology behind local AI — the reason a model that should need a data center can run on your laptop or phone instead. This episode explains how it works, what the quality tradeoffs actually are, and why 2026 is the year it starts to matter for everyday business use. AI-generated (NotebookLM) audio overview. Source: HexLocal in-house research — What Is Quantization? How AI Shrinks to Fit on Your Phone (Dr. Priya Nair). Primary external sources include Dell's 2026 edge-AI predictions and model releases from Alibaba (Qwen3.5), Microsoft (Phi-4-mini), and Mistral. - AI models are giant piles of numbers — quantization rounds those numbers down aggressively, shrinking a model four to eight times without meaningfully changing what it knows - The key insight: intelligence lives in the pattern across billions of parameters, not in the decimal places of any single one - The quality ladder runs from FP32 (full precision, training only) down through Q8 (near-lossless) to Q4 (the local-AI workhorse) to Q2 (where quality loss gets real) - GGUF is just the file format that packages a quantized model for local use — the thing Ollama actually downloads - The tradeoff is real: local quantized models are strong on routine writing and summarization, weaker on deep multi-step reasoning than frontier cloud models - 2026's small-model moment — Qwen3.5, Phi-4-mini, Mistral Small 3 — is only possible because quantization closes the gap between model size and model capability

    21 min

  3. 4 days ago

    Deep Dive - AI-Generated Code: The Security Risk Hidden in Plain Sight

    AI tools now write nearly half the world's code — and they're introducing vulnerabilities at roughly twice the rate developers used to. This episode breaks down what's actually going wrong, explains a genuinely new kind of attack called prompt injection, and tells you what to watch for and ask about as a business owner. AI-generated (NotebookLM) audio overview. Source: HexLocal in-house research — AI Made Code More Dangerous: The Security Crisis Nobody Is Talking About (Dr. Priya Nair). Primary external sources include Black Duck's 2026 OSSRA report, Veracode's 2025 findings, and OWASP's AI security guidance. - AI now generates or assists roughly 42% of all code — and that speed comes with a documented doubling of vulnerabilities per codebase - "Vibe coding" — prompting an AI for code and shipping it without review — is a real and named industry problem, not just a cautionary metaphor - Prompt injection is a new attack class that hides malicious instructions inside ordinary content an AI reads, bypassing traditional code-level defenses - CVE-2026-25592, rated maximum severity 10.0, was the moment prompt injection became an officially catalogued, real-world threat in Microsoft's Semantic Kernel - AI agent-specific vulnerabilities spiked an estimated 255% year-over-year — a separate and sharper trend from the general code vulnerability rise - OWASP now publishes AI-specific security guidance, giving business owners a credible checklist to use when asking vendors the right questions

    24 min

  4. 4 days ago

    Deep Dive - Apple Intelligence and Siri AI: What Apple Isn't Telling You About Privacy

    Apple finally shipped the rebuilt Siri it had been promising for two years — but the "private by design" story gets complicated fast when Google's technology is reportedly running underneath. This episode breaks down what actually changed at WWDC 2026, what the new Siri can and can't do for a small-business owner, and why the privacy picture is more layered than Apple's marketing lets on. AI-generated (NotebookLM) audio overview. Source: HexLocal in-house research — Apple Intelligence Finally Showed Up, And It's Not What You Think (Dr. Priya Nair). Primary external sources include Apple's official WWDC 2026 newsroom release and a business-focused 2026 analysis of Apple Intelligence capabilities. - Apple's rebuilt "Siri AI," launched at WWDC 2026 with iOS 27, is less a new arrival than a long-delayed delivery on promises Apple made two years ago - Genuinely useful new capabilities include personal-context search, cross-app actions, on-screen awareness, and system-wide writing tools — but several business-sounding features (spreadsheet queries, meeting intelligence) are not actually there - Apple describes its architecture as "private by design" without naming the model underneath — widely reported to be a custom version of Google's Gemini, under a partnership worth roughly $1 billion per year - The real privacy architecture runs across three tiers: on-device processing, Apple's own Private Cloud Compute servers, and — for the heaviest queries — a large model reportedly running on Google Cloud - Apple Intelligence is a strong personal assistant for people deep in the Apple ecosystem; it is not a business operations tool and cannot connect to external systems or data sources without significant additional build - The honest question for any business owner isn't "is it private?" but "which tier is handling my query, and do I know?"

    21 min

  5. 4 days ago

    Deep Dive - Embeddings Explained: The Hidden Layer Behind AI Search and Memory

    Embeddings are the invisible mechanism behind AI search, smart recommendations, and the way AI tools "remember" your documents — and they're easier to understand than they sound. This episode breaks down how they work, why they matter, and what a small-business owner actually needs to know. AI-generated (NotebookLM) audio overview. Source: HexLocal in-house research — What Are Embeddings? The Hidden Layer That Powers AI Search and Memory (Dr. Priya Nair). - Embeddings turn meaning into numbers — arranged so that similar meanings land close together and unrelated ones land far apart - Embedding-based search matches meaning, not just words, which is why AI search can find the right answer even when the wording doesn't match - Vector databases store millions of these meaning-fingerprints and find the closest matches in milliseconds — that speed is what makes the technology usable in real products - Key options in the space include Pinecone, Weaviate, Qdrant, and pgvector — a PostgreSQL add-on that many businesses can bolt onto infrastructure they already have - RAG (retrieval-augmented generation) depends entirely on embeddings: the "retrieval" step is an embedding search, not a keyword search - For most small-business owners, embeddings are already running invisibly inside the AI tools they use — understanding the layer helps decode what those tools can and can't do

    20 min

  6. 4 days ago

    Deep Dive - Enterprise AI Agents: Why 40% Are Failing in Production

    Most AI agents look great in demos and fall apart in production — and the thing that breaks is almost never the AI model. This episode goes one level deeper on what's actually causing enterprise agent projects to fail, and what the ones that survive have in common. AI-generated (NotebookLM) audio overview. Source: HexLocal in-house research — 40% of Enterprise AI Agents Are Failing in Production — Here's What's Actually Breaking (Dr. Priya Nair). Primary external sources include Gartner research on agentic AI project failure rates and Druid AI's 2025–2026 production telemetry benchmark. - Gartner predicts more than 40% of enterprise agentic-AI projects will be scrapped by 2027, and MIT-cited research puts the ROI failure rate at around 95% when pilots move to real production - The four failure modes: flat architecture that buckles under load, absent governance, no observability, and security gaps — none of them is "the AI wasn't smart enough" - Scope creep and poor data quality together account for roughly 61% of all agent failures — both are management problems, not model problems - Druid AI's production benchmark (15 months of real telemetry across healthcare, finance, higher education, and HR/IT) shows containment rates ranging from 80% to 99.5% depending on industry — the spread reflects how much human judgment the work actually requires - Containment rate is the wrong metric: it can't distinguish between an agent that resolved an issue and one that just deflected it — the benchmark points to "governed resolution" as what actually matters - The hidden gate to successful deployment is data readiness — agents reasoning over messy, inconsistent data produce messy, inconsistent results, and then get blamed for it

    20 min

  7. 4 days ago

    Deep Dive - The $725 Billion AI Bet: Can Big Tech's Spending Actually Pay Off?

    The four biggest tech companies are on track to spend $725 billion on AI infrastructure in 2026 alone — the largest single-industry capital build-out in corporate history. This episode breaks down where the money goes, how it's supposed to come back, and what it means for the businesses relying on these tools if the economics don't hold. AI-generated (NotebookLM) audio overview. Source: HexLocal in-house research — The $725 Billion Question: Can Big Tech Keep Spending on AI? (Dr. Priya Nair). Primary external sources include Goldman Sachs capex modelling and publicly reported figures on inference market size and token pricing. - The $725 billion figure covers four companies — Google, Microsoft, Amazon, and Meta — spending mostly on chips, data centers, energy infrastructure, and networking - AI pricing per token has fallen dramatically since 2023, yet total spending keeps climbing because volume and reasoning model costs push in the opposite direction - The return model is a toll-booth play: whoever owns the infrastructure to serve AI inference at scale captures the margin on a compounding ocean of usage - The bull case treats this as building the electricity grid of the AI era — underbuilt relative to where demand is heading - The bear case flags that valuations are pricing in AGI-level returns not yet demonstrated, with capex outrunning actual revenue - Circular financing — where a chipmaker invests in an AI company that then buys chips from the same investor — is the sharpest structural concern for systemic fragility

    12 min

  8. 5 days ago

    Deep Dive - Microsoft Copilot: The $30 Seat Nobody Asked For

    Microsoft bundled AI into Microsoft 365 and priced it at $30 per person per month — but fewer than four in ten employees at companies that already pay for it actually use it. This episode breaks down what Copilot does, what it costs after the July 2026 pricing changes, and whether the gap between "switched on" and "actually used" tells us something bigger about enterprise AI right now. AI-generated (NotebookLM) audio overview. Source: HexLocal in-house research — Microsoft Copilot: The $30 Seat Nobody Asked For (Dr. Priya Nair). Primary external sources include Microsoft's own licensing and pricing pages, Microsoft 365 blog (June 2026), and Microsoft's June 2026 Partner Center announcement. - Microsoft Copilot is now built into Word, Excel, Outlook, Teams, and PowerPoint — not a feature add-on, but the way Microsoft wants you to work - The "Work IQ" pitch: Copilot draws on your org's own files, emails, and chats — but it inherits your existing permissions, which means messy file access becomes a fast, conversational problem - The real headline number: fewer than 4 in 10 employees at paying companies actually use it — the gap between adoption and deployment is the most honest stat in business AI - Autonomous agents are the next layer Microsoft is selling, but real-world deployments look like narrow, supervised automations — not digital employees - Microsoft has quietly used Anthropic's Claude models to power some agent outputs where its own results fell short, signaling that "which model" is still an open question even inside Microsoft - July 1, 2026 brought a pricing reshuffle that makes the cost picture more complicated for small and mid-sized businesses evaluating the upgrade

    14 min
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AI, local business, and what happens when you decide to build instead of get replaced.

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  • Creator
    HexLocal
  • Years Active
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  • Episodes
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    © 2026 HexLocal
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