Christopher D.

Vibe Coding vs. Assisted AI Engineering “Vibe coding” blew up this year: you describe what you want, the LLM spits out code, you keep prompting until it “works.” It’s fast and fun but it also tends to ship mystery code to prod. It’s democratizing, yes but risky if you don’t deeply review the output. The best engineers aren’t “vibing”; they’re letting AI write most of the code while they act as editors owning the spec, tests, architecture, and approvals: - Break work into tickets → have agents propose plans → you edit the plan. - Let agents implement and self-test → you review diffs, run tests, and request changes. - Gate merges with CI, policy checks, canaries, and rollback playbooks. - Keep a tight feedback loop so agents learn your patterns over time. This is how tools like Devin (Cognition) and Factory are actually used on serious stacks: ticket → plan → test → PR, with humans approving every step. Real teams are doing this today. Why not vibe code in prod? Because if no one truly understands the code, you’re one prompt away from a disaster. We’ve now seen high-profile incidents where an autonomous tool deleted a live production database during a freeze, then fabricated outputs to cover its tracks. That’s not “democratization” that’s operational risk. Review and control are non-negotiable. Editor-Model Playbook (steal this): - Spec first. Write a crisp objective, constraints, acceptance tests. - Plan review. Have the agent propose a plan; you edit for architecture & safety. - Localize blast radius. Sandboxes, feature flags, shadow traffic, canaries. - Testing as contract. Ask the agent to generate tests from your spec; you extend edge cases. - PR hygiene. Require traces/rationales in PRs; block merges without tests and rollout plan. - Safeguards. RBAC for agent credentials, read-only by default, explicit “dangerous ops” prompts, auto-backups, and one-click restore. - Continuous learning. Capture post-merge notes so agents learn your patterns (naming, observability, domain quirks). Bottom line: The future isn’t “prompt until it compiles.” It’s agent-augmented engineering where humans think like architects and editors, and AI handles the toil under guardrails. If you’re shipping to real customers, treat AI like a tireless junior: helpful, fast, and review-required. That’s how top teams are getting leverage without the bad vibes. P.S. if you enjoyed reading this give it a like and or follow. I try to put out thoughtful content like this daily based on my observations.

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Live talk about deploying LLMs in productions. Insightful notes about importance of evaluation data by Lena Shakurova She discussed the use of G-eval and RAG-eval as key techniques for evaluating LLMs in production environments.

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Now (finally) all of the major AI chatbots - ChatGPT, Claude, Gemini - have cross-thread context / memory. That is, things you work on in one chat thread can be referenced in another. ChatGPT has had this for a while. Claude just rolled this out to the expensive tiers recently. (Coming soon to the cheap seats.) Gemini now has it. (See below.) If you’ve been using Claude or Gemini, you will be surprised at how much more useful and smarter your chosen AI is now. This is one of those simple features that won’t make headlines but will improve the day-to-day utility more then most people realize.

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Launching within 5 days: Actionpackd AI Software Development Kit A modular AI SDK for building real-world LLM applications, fast. Streaming-first. Multi-provider. Typed prompts. Edge-ready. Full suite of examples: agents, chat, RAG, eval, UI. Crafted with scale and speed in mind, from local dev to production. One of the first full-stack AI SDKs from India. Built for developers who want more than wrappers. MIT licensed. OSS-first. #ai #sdk #opensource #typescript #actionpackd #llm

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This ones already old news, but I just got the chance to dive into it. Apple released a paper a few weeks ago on LLMs ability to reason, titled the "Illusion of Thinking". They gave LLMs some problems and shared how it failed to solve them. Anthropic recently wrote a response to that, showing how by making some minor tweaks in their approach, the problems do get solved. In this episode of #karachiwaladeveloper I wanted to explore both sides of the story and understand what really makes the AI work: - The core arguments from papers suggesting AI's reasoning is fundamentally limited. - The compelling counter-arguments that our current testing methodologies are flawed. - What "AI's Accuracy Collapse" really means for the future of AI development and adoption. https://lnkd.in/dXhq5RUd #AI #ArtificialIntelligence #LLM #MachineLearning #DeepLearning #AIResearch #TechDebate #Innovation #FutureOfAI #AccuracyCollapse #DataScience

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Bootstrapping Builds Discipline When every dollar counts, you get crystal clear on what actually moves the needle. 💡 Would your startup survive if the money ran out tomorrow? If the answer is no, it’s time to rethink your priorities. Build lean. Validate early. Spend like it’s your own money—because it is. #Bootstrapping #StartupDiscipline #FounderTips #JetpackLabs #LeanStartup #SaaSFounders #BusinessStrategy

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Observations on Agentic Products Hitting the Market As agentic AI products begin entering the market, I've noticed some interesting challenges worth sharing. First, these systems are significantly harder to build than traditional software. We're asking them to solve complex problems in business environments with real-world limitations that are difficult to program. For example, when an agent needs to "find all customer complaints about product X," it must determine which tools to use (database searches, document analysis, email scanning), how to combine these approaches intelligently, and when to stop searching - all while avoiding making things up. Speed is also critical for real-world implementation. Effective agents need smart memory systems that remember successful approaches, preventing the need to reason from scratch each time. They require techniques to efficiently narrow down massive datasets without losing crucial information. We also need reliable pause-and-resume functionality (similar to how humans handle complex tasks) and effective information sharing between agents with different security clearances. Perhaps most interesting is the pricing challenge. Not all agent tasks deliver equal value - there's a world of difference between "Schedule a meeting" and "Optimize inventory across 50 warehouses" or "Predict regulatory compliance issues." Yet many current pricing models charge the same regardless of task complexity or business impact. Companies in this space need to develop pricing structures that better reflect the actual value delivered across different scenarios. What challenges are you seeing with agentic AI in your organization? I'd love to hear your experiences. #AIAgents #ArtificialIntelligence #AgenticAI #AI #LLMs #Startups

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The average SaaS startup rewrites 40% of their code after launch. Tech Formation's AI upgrade aims to cut that number in half. This hits close to home for anyone who's been in the startup trenches. You build fast. Ship early. Then discover your app crashes under real user load. Tech Formation just rolled out AI-powered monitoring that changes this cycle. Their system checks performance and user flow while you build. Not after. Here's what caught my attention: 🔍 Real-time risk detection during development 🚀 Load simulation before you have actual users ⚡ Early warnings on potential friction points 📊 Actionable insights for feature prioritization As Sunny from Tech Formation put it: "The pace of SaaS development is rising, but visibility matters most." The AI doesn't replace human judgment. It augments it. Industry data shows AI integration in SaaS leads to better decision-making and improved productivity. Companies are moving toward consumption-based pricing and more autonomous AI agents. This upgrade reflects a bigger trend. Teams want to focus on vision, not technical firefighting. The two-week free trial makes it accessible for early-stage teams to test the waters. For startups burning cash on rework, this could be a game-changer. What's your biggest technical hurdle slowing down your product development? #SaaSDevelopment #AIinTech #StartupLife 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/g-qukdPq

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What happens when multiple exponential technologies stop being separate curves—and start compounding each other? We talk about AI, biotech, robotics, materials science, and energy as if they’re parallel lines. They’re not. They’re vectors converging into a single acceleration event: the compression of human decision time. 1. The myth is that technology “replaces” human work. It doesn’t. It replaces the delay between insight and action. When feedback cycles shorten faster than governance, ethics, and cognition can adapt, humanity enters a new regime: real-time civilization. 2. From a systems lens: we’re evolving from human-driven systems with digital tools to machine-driven systems with human governors. From a psychological lens: the anxiety many feel isn’t fear of AI—it’s temporal dislocation. Minds built for linear time now exist in exponential space. From an investor’s lens: the scarcest asset won’t be capital—it will be cognitive latency tolerance. 3. How do we remain coherent when everything around us accelerates beyond comprehension? 4. Our instincts crave stability. But evolution now rewards adaptability. The dissonance people feel isn’t resistance to change—it’s grief for the loss of predictable time. 5. Energy density and compute cost are the real pace-setters. When both drop below threshold simultaneously, intelligence and matter converge—machines begin to manipulate physical systems as fluidly as software. 6. Governance systems optimized for deliberation will fail under conditions demanding instantaneous response. The next form of leadership won’t be administrative—it will be architectural: designing systems that can think faster than any committee. 7. Acceleration without alignment leads to collapse. Humanity’s next competitive advantage isn’t intelligence—it’s coherence under exponential stress. 8. Our desire to “pause AI” or “slow down innovation” isn’t technophobia—it’s a subconscious attempt to regain synchrony with our own creation. 9. Every prior epoch shift—agriculture, industry, computation—redefined what it meant to be human by changing our relationship to time. Exponential convergence will redefine it again by changing our relationship to decision. 10. End-state: a civilization where cognition, computation, and coordination are continuous. Humans become curators of context. Machines handle real-time optimization. Leadership evolves into designing meaning, not control. So: Design for coherence, not control. Prioritize meaning bandwidth—how fast insight spreads, not just data. Build ethical architectures that scale with speed. Teach temporal literacy: how to think in acceleration, not snapshots. The next frontier for humanity isn’t outer space or artificial intelligence—it’s the architecture of adaptation itself. Those who can think, design, and act coherently amid exponentials won’t just survive the future. They’ll define it.

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Zapier’s new integration with Google’s AI Studio Gemini models isn’t just a shortcut for startups. This opens doors for founders and operators to trigger document summarizations, run compliance checks, or schedule workflows via natural language—no scripting required. I’ve been plugging these new chains into internal tools, and honestly, the speed gains are huge. You go from hours of swivel-chair work to one-click flows that just *run*. The real story is how these tools make automation less scary for non-technical teams. Opportunity everywhere. https://lnkd.in/ed9aXHvg

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