Marc Mojica

🤯 Eric Schmidt challenges the 3-year superintelligence prediction. The "San Francisco narrative" suggests agents will quickly lead to recursively self-improving AI. This rapid recursive improvement is particularly potent for scale-free problems like coding or mathematics. While the path to superintelligence is clear, the exact timeline remains a point of debate among industry leaders. 💡 Investing in foundational AI research and ethical development is crucial regardless of the exact timeline. What practical steps can businesses take today to prepare for advanced AI systems, whether in 3 or 7 years? #AI #Superintelligence #AGI #FutureOfWork #TechLeadership Thanks to ai.rise.co for sharing this video 🙏

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The principles behind the term 'context engineering' are not new, but the terminology serves as a valuable abstraction for addressing the key challenges in developing effective AI agents. Understanding context engineering can help us navigate the complexities involved in AI development. For more insights, check out the article linked below. https://lnkd.in/g8aSn6r5

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Is Anthropic's latest Fable model release the template for new models going forward? 1️⃣ grant access from all surfaces - Claude Desktop, Web UI, Claude Code, and API - for a limited, introductory time (Fable is available until 6/22) 2️⃣ grant access from API only thereafter - let's be realistic: money is made with token consumptions via APIs, not subscriptions. 3️⃣ re-introduce that model to all surfaces when there's a newer model to replace it, thus repeating the cycle (note: this is TBD) So latest models come with a premium price due to API-only access. Older models ride along on the subscription model. Does this work for y'all? I'm genuinely curious: where are you on the "dang that's expensive" to "company's gotta make money" scale?

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The Hallucination Minimizer: a simple way to stop trusting the wrong AI answers Hallucination is not a rare edge case. It still happens with the most advanced language models. Why? Because LLMs • do not have access to all relevant data • optimize for plausibility, not truth • are trained to be helpful, even when they should say “I don’t know” This becomes dangerous when AI is used for market analysis, strategy work, or competitive intelligence. So I built something to fix this. 👉 The Hallucination Minimizer It is a custom GPT that does not answer your question directly. Instead, it rewrites your prompt to actively reduce hallucination risk. What it does for you: • adds missing context • installs explicit guardrails • forces critical reflection before answering • increases specificity where it matters most Here is the link to the GPT: https://lnkd.in/dRP8ta_f

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As a Shopify app developer, if Hookdeck didn’t exist, your options would look something like this 👇 1. No infrastructure at all Most apps do this at first: no queue, no retry strategy, just synchronous webhook calls. It’s fine for a dev store with a few orders, but when a single merchant grows or Shopify retries about 1000 updates, your app falls behind. By changing one webhook URL to Hookdeck, everything is automatically queued, retries occur on failure, and you gain full visibility into every request, all without needing to modify any code. 2. AWS EventBridge or Google Pub/Sub Most Shopify devs aren’t running massive AWS or GCP stacks, and if they try, they’ll lose hours wrestling with IAM permissions, tangled policies, and access rules long before a single webhook even reaches their system. Then come the dashboards, messy configuration, vague logs, and retries that need to be glued together with three other services. Hookdeck combines the parts you would need to cobble together (EventBridge, SQS, CloudWatch, DynamoDB) but focuses entirely on reliable webhook delivery. 3. Build your own (RabbitMQ, etc.) Launching RabbitMQ on Fly or Heroku feels simple at first, but once your queues start filling and memory hits its limit, the system fails, forcing you to chase lost events, spend hours debugging, and pay far more in compute than the app is actually worth. Hookdeck exists to remove all the webhook headaches from your life: we handle automatic queuing, retrying failures, and logging every request so your app always processes every event without you having to touch a line of infrastructure code.

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Why Even as Context Windows Get Bigger, RAG Still Wins (And probably always will). We’re entering an era of massive context windows. Models like GPT-5.2, Claude 4.5, Gemini 2.5, and Grok 4 can now ingest hundreds of thousands — even millions — of tokens in a single prompt. That’s a remarkable engineering achievement. But here’s the uncomfortable truth for enterprise AI: bigger context windows don’t eliminate the need for Retrieval-Augmented Generation (RAG). Imagine answering a business-critical question by rolling an entire warehouse into the conference room. - Every policy binder. - Every contract. - Every regulation, SOP, audit log, and customer record — stacked floor to ceiling. That’s a million-token context window. Impressive? Yes. Practical? Not even close. Now imagine something better. You ask a question — and a professional librarian instantly pulls the exact 3–5 documents, opens them to the right paragraphs, and places them directly in front of you. That’s Retrieval-Augmented Generation (RAG). Same knowledge. Radically better execution. 📚 Why the Librarian Keeps Winning 💰 Cost Feeding an LLM a million tokens per query is like renting warehouse space for a single lookup. RAG retrieves only what matters → 80–95% lower inference cost, every time. 🎯 Accuracy When everything is in context, nothing is prioritized. RAG surfaces the most relevant passages, dramatically reducing hallucinations. ⏱️ Freshness Warehouses go stale. RAG pulls live, up-to-date regulations, contracts, and data at query time. 📈 Scale Even 10 million tokens barely scratches enterprise knowledge measured in terabytes. RAG scales without exploding context windows. 🔐 Control & Safety In healthcare, finance, legal, and government, “maybe the model noticed the right clause” is unacceptable. RAG enforces source control, redaction, permissions, and auditability by design. At Inference Analytics AI Analytics, our agentic AI studio platform combines vector databases + knowledge graphs specifically for sensitive-data environments. Retrieval-first architectures aren’t optional — they’re table stakes when mistakes mean lawsuits, fines, or worse. Large context windows are powerful. They’re just not a replacement for a librarian who knows exactly where to look. The winning setup? Don’t move the warehouse. Query it intelligently.

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SearchAI Index Server is a self-hosted, OpenSearch 3.5-compatible search + vector engine — full-text, kNN and hybrid search, aggregations and facets, with native snapshots. https://lnkd.in/eG5tvt8p

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🤖 Build a Support Agent with Vercel AI SDK — Scrimba 💡 “Good systems don’t just respond; they anticipate.” 👤 Completed by: Seven Grant 📅 Date: January 16, 2026 ⏱️ Duration: ~2 hours ✅ Grade: 100% 📌 Key Takeaways 1. The Vercel AI SDK abstracts LLM complexity without limiting control. 2. 🧠 A support agent is fundamentally a decision system, not a chatbot. 3. ⚡ Streaming responses significantly improve perceived performance. 4. 🔧 Tool calling enables agents to act, not just answer. 5. ✍️ Clear system prompts outperform long, verbose instructions. 6. 🛡️ Guardrails are as important as capabilities in customer support. 7. 📚 Embeddings are best used for retrieval, not reasoning. 8. 🌐 Web search integration fills documentation gaps in real time. 9. 🧩 Context management directly impacts answer quality. 10. 🎯 Latency is a UX issue, not just a technical metric. 11. 🧱 Modular architecture makes agents easier to extend and debug. 12. 📐 Deterministic outputs matter in support workflows. 13. 🚨 Error handling should be explicit, not implicit. 14. 🔍 AI agents should escalate uncertainty, not hallucinate confidence. 15. 📊 Observability is critical for production-ready agents. 16. 🔁 Prompt iteration is an engineering discipline. 17. 🤝 A well-designed agent reduces cognitive load for both users and teams. 🧠 Reflection This course reinforced that effective AI support agents are less about “intelligence” and more about structure. The Vercel AI SDK enables fast iteration while preserving architectural rigor. What stood out most was how small design decisions—prompt clarity, context boundaries, and escalation logic—compound into measurable improvements in reliability and trust. This wasn’t just a tutorial; it was a blueprint for building AI systems that are actually usable in production. #VercelAISDK #AIEngineering #CustomerSupportAI #ContinuousLearning #SevenGrantTravels

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NEW: Alphabet and Nvidia VC arms back vibe coding startup Lovable at $6.6 billion valuation --- The VC arms of Alphabet and Nvidia have invested in Swedish vibe coding startup Lovable’s $330 million Series B at a $6.6 billion valuation, the company announced on Thursday. The news confirms an earlier story from CNBC, which reported on Tuesday that Lovable had raised at that valuation, trebling its valuation from its previous round in July, and that the investors included U.S. VC firms Accel and Khosla Ventures. CapitalG, one of Alphabet's VC divisions, and Menlo Ventures led the round. Alongside Accel and Khosla, Nvidia venture arm NVentures, actor Gwyneth Paltrow’s VC firm Kinship Ventures, Salesforce Ventures, Databricks Ventures, Atlassian Ventures, T.Capital, Hubspot Ventures, DST Global, EQT Global, Creandum and Evantic also participated. The fresh funds take Lovable’s total raised in 2025 to over $500 million. Full story: https://lnkd.in/evaTwFvd

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