Dan Fox

Part 2 of my ongoing series From Vibe Code to Production-Ready is live! This one dives into Claude, Gemini, and MCPs (Zen + Serena) -- not just as shiny tools, but as a framework for moving from quick prototypes into reliable systems: Command patterns, context, and the practices that make "vibe coding" scale. Part 1 explored why vibe coding eventually breaks down, and Part 2 is the bridge: where experimentation starts becoming engineering. Part 3 will explore agent command patterns: Compacting, Context, and Claude Code Ops Read here: https://lnkd.in/e8-dnaYC #AI #MCP #ClaudeAI #Gemini #AgenticAI #Engineering #ProductDevelopment #VibeCoding #FromPrototypeToProduction #TechLeadership

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New for Business Insider: AWS’ capacity+performance issues cost real money w/ some AI customers (eg. Epic Games, Thomson Reuters, Figma) moving workloads to Google and Anthropic, an internal document shows. Highlights the financial toll of AWS’s capacity crunch. https://lnkd.in/gxZUhNe8 #Amazon #AWS

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Everyone's waiting for AGI to arrive. Meanwhile, they're missing what's already here: the ability to build systems that think like we do through orchestration. The job isn't writing prompts or fine-tuning models. It's orchestration. Think about how your brain books a flight. Memory recalls preferences. Attention focuses on constraints. Experience evaluates options. Caution validates. Motor control executes. Multiple specialists coordinating seamlessly. That's intelligence. The future of software engineering is designing these systems-defining roles, choreographing collaboration, establishing guardrails, building adaptive workflows. You're not coding every step anymore. You're composing a team of capabilities that figures out the steps themselves. Stop waiting for AGI. Start designing better orchestration. The engineers who understand this will build systems that are more reliable, adaptable, and capable than anything a single model can achieve. Are you ready to be the composer? https://lnkd.in/gBw2FY2T

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Good thoughts from Tom Capper here on the middle-ground of what "GEO" actually means from a tactical perspective. Couple of key points: - Off-site strategy (and citations) will only get more important - Attribution is getting more difficult, and LLM traffic might be misleading - We're going to have to get better at measuring brand awareness https://lnkd.in/g58tU4Cf

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The simultaneous emergence of Claude Cowork and Clawdbot (now rebranded as Moltbot) is a clear signal of where consumer AI is headed this year: genuine end-user productivity through semi-autonomous agents. The days of purely prompt-based, one-shot results are giving way to systems that can handle real, ongoing work based on high-level requests. These tools are starting simple, but their trajectory is unmistakable: they will likely evolve quickly into capable digital assistants running 24/7. If recent AI trends hold, most of us will soon dedicate at least one (if not several) always-on machines or cloud instances to handling our routine personal and professional chores. This shift could free up significant time and mental energy, allowing us to focus on higher-leverage activities with far greater returns. Security, however, remains the single biggest hurdle. Granting AI agents access to local files, email, calendars, or messaging apps introduces real risks such as prompt injection, data leakage, unintended deletions, or worse. While tools like Cowork use sandboxes and careful permissions, and self-hosted options like Moltbot keep data local, vulnerabilities persist and demand vigilance. For now, the wisest path is cautious experimentation: start small, isolate sensitive data, monitor closely, and contribute feedback to help shape safer, more reliable agents. These early steps are opening exciting new chapters for everyday users. AI, it appears, won't just answer questions, but will truly work alongside us.

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I thought this was a good and very reasonable talk by Eleanor Millman on how to prioritize projects for a platform engineering team: https://lnkd.in/gaYtUHRH A few insights I took from it: - Unlike product features which can be more directly tied to revenue, platform engineering work is a couple steps removed. But that doesn't mean it's unimportant! Far from it, platform engineering projects can make everyone at the company more efficient, able to produce higher quality software, etc. - While "urgency" is always going to be a factor, you don't want to just be putting out fires, you want be able to prioritize work which is "high impact" ... even if that impact isn't felt immediately. - Good rule of thumb: prioritize "highest impact for lowest effort". - The talk has some ideas on what factors to choose for impact, and how to blend them. For instance "speed of development" is one factor, "cloud cost optimization" might be another. The weighting of different impact factors can change over time, depending on the needs of the business. I would say that a lot of companies don't have much methodology here but I can really see the value in codifying it. You can always change the methodology or the weighting if it's spitting out results that don't pass the smell test or you really feel it is leading the company astray. Clarity can give the org more freedom to put resources behind projects that would otherwise never be taken on. When things are unclear, prioritization still happens implicitly, but the decisions tend to be a lot more random and fear-based and the org is worse off as a result.

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Anthropic recently shipped Legal Skills for Claude Desktop's Cowork, which triggered a market selloff. We ran its contract review feature against our own custom, legally engineered pipeline. The SaaS contract, playbook, and model (Opus 4.6) were all the same. The results were not: - Findings: 222 vs. 18 (12.3x delta) - Track changes applied to DOCX: 52 vs. 0 - Quantitative risk score: 93/100 vs. "CRITICAL" (a label, not a number) - Market benchmarks with percentiles: 12 vs. 0 - Comparable contracts analyzed: 4 vs. 0 - Deliverables produced: 7 vs. 1 The pipeline also produced a send-ready negotiation email, a strategy report with 10 priorities each with target/fallback/walk-away positions, 8 anticipated pushback scenarios with prepared counters, 15 talking points citing market data, and analytics with risk scoring, playbook alignment, and market percentile benchmarks for every major term. That said, Claude Desktop's output, while narrow, did correctly identify the 8 most dangerous provisions from the Provider's perspective: liability cap carve-outs, MFN pricing, escrow, uncapped indemnification. For a 5-minute investment, that's genuinely impressive. But even here, the depth gap was critical and obvious. Only the pipeline connected Section 13.3(f), which carves out insurance-covered losses from the cap, to the insurance requirements in Section 16. The practical effect: Provider's own insurance policy limits become the real liability cap, not the negotiated number in Section 13. You negotiate a $1M cap. You carry $10M in cyber insurance. Your actual exposure is $10M. A single pass reads Section 13 and Section 16 independently. Sixteen specialists cross-referencing each other catch the interaction. The same pattern repeats throughout. A 5-day outage simultaneously triggers SLA credits, DR termination, escrow source code release, and unlimited liability — a cascade across four sections. Three separate payment withholding mechanisms plus an express prohibition on service suspension effectively eliminates revenue protection. These mechanisms are not an accident. They're a coordinated conspiracy woven into the contract. And only AI specialists, working in parallel and synthesized together, are built to find it. This is our fifth controlled experiment. The pattern holds across drafting, triage, legal research, redlining, and now contract review. The model is not the product. The architecture around the model is the product. If you want to see what that looks like for your legal work: Taylor Legal Engineering, LLC. Full interactive report with methodology, pipeline architecture, and all comparison data: https://lnkd.in/eyiaXcdd #LegalTech #LegalEngineering #AI #ClaudeDesktop #ContractReview #LegalAI #LegalOps #MultiAgentAI #SaaS

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"Invention and failure are inseparable twins," Jeff #Bezos once said, and this insight completely transformed how I approach innovation. We often celebrate breakthroughs while hiding the messy iterations that preceded them, creating a dangerously incomplete picture of the creative process. The most innovative companies don't just tolerate failure - they systematically extract learning from it, building institutional knowledge with each unsuccessful attempt. #Amazon's culture of embracing "high-quality failures" reminds us that the goal isn't to eliminate mistakes but to ensure they move us forward rather than in circles. 146/365

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