People will tolerate risk, cost, and friction if the product gives them durable leverage over time. This is not theoretical. It is happening right now. In the past month or two, people have been buying Mac minis specifically to run tools like openclaw or clawdbot locally. They are accepting security risk, brittle setups, and zero support. Not because the experience is good, but because the leverage compounds. These tools run unattended. They persist state. They produce outcomes while the user is offline. They meaningfully change what is possible day over day. That behavior should unsettle anyone building AI agents. The current AI ecosystem is flooded with capital and point solutions. Copilots for every role. Agents for every vertical. Yet most users are still doing the work of coordination. They manage prompts. They manage context. They manage retries. They manage integrations. We did not eliminate work. We shifted it into orchestration overhead. Most AI products are still demos with opinions. They help you complete a task once. They do not own outcomes. They do not compound. Many are, functionally, UX regressions with a model in the loop. The historical and current lesson is simple and uncomfortable: users will trade polish and safety for systems that quietly generate advantage over time. Adoption does not come from reassurance. It comes from inevitability. When opting out feels irrational because the system has become part of how results are produced. The real bar for AI agents is not autonomy or tool count. It is durable leverage: background execution, stateful memory, outcome ownership, and trust earned through repetition, not promises. Curious where others see the real failure right now. Product design? Incentives? Risk posture? Or are we still building agents that look impressive but do not actually change trajectories? #AI #AIAgents #ProductStrategy #ProductManagement #Startups #SaaS #UX

Amazon’s official postmortem, aws.amazon.com/message/10192…, on the us-east-1 outage reads like a deep dive into how fragile control planes can be when automation meets scale. A single race condition in DynamoDB’s DNS automation spiraled into EC2 launch failures, Lambda throttling, and NLB health-check chaos cascading across nearly every dependent service. It’s not that AWS is unreliable; it’s that our architectures are too tightly coupled to vendor control planes. When DNS, orchestration, and routing all depend on the same automation layer, “multi-AZ” isn’t the same as resilient. Here are a few lessons from the postmortem worth carrying forward 👇 DNS isn’t invincible. Independent DNS and health checks give you a fallback path when a provider’s control plane falters. Compute orchestration can fail noisily. Design workloads to recover even when new capacity can’t launch. Health checks can amplify failure. Add damping, delay, and edge-managed routing to avoid self-inflicted downtime. Dependencies cascade. The weakest link isn’t your app; it’s the invisible systems you assume “just work.” This outage reminded us that resilience isn’t just about redundancy, it’s about decoupling. I pulled together an Engineering Checklist for Control-Plane Resilience to help teams assess how exposed they are to these same failure patterns. Comment AWS and I’ll share the doc. aws.amazon.com/message/10192…

💸 How I’d Manage a $3M Seed Round I’ve raised over $50M in seed capital as a founder across my companies, but I’ll be honest, I didn’t really learn cash management until SVB crashed in 2023. That Friday, I was panicking. I had payroll due in days and was on the phone with every banker I knew trying to move funds out before things froze. That moment burned one lesson into me: as a founder, your cash strategy is your survival strategy. When you raise your first real round, it’s easy to focus on hiring, shipping, and runway math and forget that managing your cash is now a full-time job. Here’s how I think about startup treasury setup once that wire hits your account with four buckets: Operating, Reserve, Yield, and Contingency. 1️⃣ Operating: Keep 6–9 months of burn liquid. Use a modern bank like Mercury or Rho with sub-accounts for payroll, taxes, and payables. Automate categorization and approvals. 2️⃣ Reserve & Yield: Move the rest into yield-generating, safe instruments: FDIC-insured sweep accounts, short-term T-bills via Meow/Vesto/Arc, or government-only money-market funds. You can earn 4–5 % while staying fully insured or backed by Treasuries. 3️⃣ Credit & Liquidity: Even if you don’t need it, set up lines early. Ramp, Brex, or Mercury IO for corporate cards, and a small LOC with your bank after 6 months of deposits. 4️⃣ Contingency: Always have a backup bank. If your primary is digital, your secondary should be a traditional one (think Chase or BofA). Keep at least one month of burn there in case wires freeze or systems glitch. A solid setup like this can extend your runway by months and keep 100 % of your deposits insured. I broke this down (with a model and a 1-page cash management policy template) in a doc you can use with your board or CFO. 👉 Comment “CASH” below and follow me so I can send you the link.

The AWS outage on Monday showed what most of us already know but rarely act on: when your uptime depends entirely on one vendor’s control plane, your SLAs are a wish, not a guarantee. Resilience isn’t something you buy from AWS, Azure, GCP, or any vendor it’s something you engineer. Multi-cloud isn’t magic; it’s discipline. You trade vendor convenience for operational independence and that only works if you design for replication, parity, and orchestration from day one. Here’s what that looks like in practice 👇 1️⃣ Abstract state early Your database is what usually locks you in. Keep it portable and replicable. Use Postgres with logical replication, or distributed SQL systems like CockroachDB or Yugabyte that can mirror data across clouds. If you must use managed RDS-style databases, stream or dump changes to object storage (Cloudflare R2, GCS) for cold-standby recovery. Avoid provider-specific IAM in app code; use OIDC tokens or Vault for identity abstraction. 2️⃣ Make compute disposable Everything that isn’t state should be ephemeral and rebuildable. Package with Docker or Buildpacks no provider-specific runtimes. Keep build and run steps identical across clouds using `docker buildx bake` or multi-arch manifests. Include health and readiness probes so failover validation works everywhere. 3️⃣ Decouple configuration and secrets Your deploys shouldn’t break because an env var lives in one vendor’s secrets manager. Store config in portable formats (`.env`, 1Password, Doppler, Vault). Reference by key, not file path; keeps parity across AWS, Fly, Render, and Vercel. Rotate credentials outside your deploy tool; Prod can inject and validate automatically. 4️⃣ Make the edge your control plane DNS and routing should never live inside the same cloud you’re failing over from. Use Cloudflare, Akamai, or NS1 for weighted or geo-based routing. Cache static assets globally to survive origin downtime. Let routing policies handle user redirection when a region or provider goes dark. 5️⃣ Mirror deploy logic, not infrastructure Terraform across clouds quickly turns into YAML origami. Instead, aim for deployment symmetry identical build artifacts, consistent runtime environments, and a single declarative spec describing where and how to deploy. This outage wasn’t about AWS being unreliable. It was a reminder that resilience is earned, not inherited. Your deploys should move faster than your outages. Sign up for the beta 👉 [pushtoprod.ai](pushtoprod.ai) pushtoprod.ai

When AWS goes down, most websites go with it. Prod turns that single point of failure into an instant escape hatch. Tell Prod: Deploy this app to <pick a cloud> No YAML. No panic. Just uptime. 👉 Beta open at pushtoprod.ai pushtoprod.ai

We've been heads-down on Prod improving the DevEx. Here’s a quick demo: Took a small SvelteKit app (“NASA Space Explorer”) and pushed it to three providers, @Render, @Netlify, and @Vercel, by changing one word in the prompt. What Prod did behind the scenes: Detected SvelteKit Vite Swapped adapters for @Netlify and @Vercel (and showed a clean diff) Containerized for @Render Managed the @NASA API key and build steps No manual code edits 🎥 Watch the walkthrough: loom.com/share/40707e20d7f24… 🔔 Want early access? Join the beta: pushtoprod.ai #SvelteKit #WebDev #DevTools #PaaS #JavaScript #CLI @sveltejs @NASA loom.com/share/40707e20d7f24…

🎥 New demo alert: Watch how Prod deploys a full-stack app — Node TypeScript React Postgres — to Fly using nothing but natural language. 👉 Watch: loom.com/share/f3f4153736404… 👉 Sign up for the beta: pushtoprod.ai No Dockerfile. No YAML. No CI/CD setup. Prod’s AI agents: ✅ Analyze the code ✅ Detect the stack database needs ✅ Provision Postgres automatically ✅ Set up environment variables ✅ Deploy health check the app From source code → live app in minutes. If you’ve ever felt like deploying is harder than coding, this is the future we’ve been working toward. Let’s make deployment as seamless as building. loom.com/share/f3f4153736404…

🎥 Nathan Stehr just recorded a quick demo showing how to deploy a Python FastAPI app with Prod in a single natural language command. 👉 loom.com/share/51341410ebec4… No YAML. No config files. No DevOps bottlenecks. Prod is the AI agent powered CLI that: ✅ Analyzes your project automatically ✅ Sets up env vars secrets ✅ Builds a Docker image ✅ Provisions infra deploys If coding has leapt into the future with AI, deploying shouldn’t feel stuck in the past. 👉 Sign up for the beta at pushtoprod.ai #deployment #ai #aiagents #cli #docker #python #fastapi #render #Prod loom.com/share/51341410ebec4…

Coding has raced into the future with Copilot and Cursor, but deploying still feels stuck in YAML hell. That’s why we built Prod: AI agents that understand your code, manage infra, and deploy with natural language. Sign up for the beta: pushtoprod.ai pushtoprod.ai

🚀 Today, I’m excited to share something we’ve been quietly building and using at Meroxa for years: Prod, now open for beta at pushtoprod.ai At Meroxa, we had to deploy software into some of the most varied and resource-constrained environments, including defense and intelligence projects where mistakes aren’t an option. We built Prod to make those deployments fast, reliable, and human-friendly. Here’s why I think this matters for every developer: Deploying software still feels stuck in the past. Coding has leapt forward with Copilot, Cursor, and Claude, but deploying still means wrangling YAML, configs, and CI/CD pipelines. Prod isn’t just “AI that writes IaC.” Our AI agents analyze your code, provision infrastructure, manage secrets, and deploy, all from natural language. It fits where you already work. Prod is a CLI and a VS Code plugin, designed to sit right alongside the tools you’re already using. The vision is simple: every developer should be able to say what they want, and see their code live without friction. No DevOps bottlenecks. No config rabbit holes. Just momentum. 👉 If that resonates with you, I’d love for you to be part of the beta: pushtoprod.ai Let’s build a future where deployment finally feels as modern as coding. #devops #deployment #ai #aiagents #infrastructure #terraform #cloudformation #pulumi #vscode #cli #cursor #windsurf #claudecode #copilot #render #fly #netlify #heroku #vercel pushtoprod.ai

Watching our engineers grow and share what they’re learning is one of my favorite parts of building at Meroxa. In Part 2 of his “Java to Go” series, Haris Osmanagić dives into how Go organizes code—and why it just makes more sense in complex systems. Thoughtful, practical, and full of hard-won insight. 👉 meroxa.com/blog/from-java-to… Give it a read and show Haris some love. #golang #java #softwareengineering meroxa.com/blog/from-java-to…

Most AI use cases fall apart on one thing: access to real-time data. Fraud detection, recommendation systems, RAG pipelines, AI copilots, customer support agents—none of them are effective if they’re running on data that’s hours old. That’s the problem we solve. Conduit makes it easy for teams to build real-time data pipelines without needing to manage Kafka or write glue code. Ingest from Postgres Transform with AI Stream into Slack, vector DBs, analytics, or wherever action happens Whether you’re building an AI agent or training a model—your infrastructure should keep up with reality. 🧱 See working examples: github.com/ConduitIO/conduit… 📖 Read the breakdown: meroxa.com/blog/build-ai-tha… #AI #MLOps #RealTimeData #ConduitIO #DataEngineering #OpenAI #StreamingData #APIs #LLM #Startup github.com/ConduitIO/conduit…

🔥 The Dirty Secret About Large Language Models Everyone's obsessing over massive AI models while burning millions in compute costs. But here's the uncomfortable truth: bigger isn't better. It's about being smarter. At Meroxa, we're proving this daily. I just dropped a fascinating piece on how tiny, specialized language models real-time data streams are outperforming their giant counterparts at 1/10th the cost. Less hallucination. Better accuracy. Real-time knowledge of your business. The future isn't about more parameters – it's about better data, delivered in real-time. Ready to stop burning cash on oversized models? DM me or visit meroxa.com to see how we can help you build smarter, faster AI with real-time data streams. #AI #EnterpriseAI #llm #RealTimeData​​​​​​​​​​​​​​​​ #meroxa #conduit #conduitplatform

🚨 Let’s be honest—data warehouses are outdated. 🚨 They weren’t built for real-time streaming, and no amount of ETL magic can change that. If your business is relying on a warehouse for real-time analytics, you’re probably overpaying for stale insights and rigid systems. 💡 The Lakehouse is the future. It handles streaming data natively, scales effortlessly, and doesn’t lock you into proprietary nonsense. With table formats like Iceberg, Delta, and Hudi, it’s faster, cheaper, and way more flexible than the warehouse dinosaurs. At Meroxa, we’re helping companies ditch their warehouses and adopt Lakehouses without the headaches. Stop wasting money on legacy systems and start unlocking the full potential of your data. 👉 Read the blog to find out why Lakehouses are leaving warehouses in the dust #LakehouseArchitecture #RealTimeData #DataStreaming #ModernDataStack #TechLeadership #DataInnovation #BigData

AI-driven entrepreneurship can open doors to innovate business ventures and create new avenues for wealth creation for Black communities. My panel tomorrow will feature leaders who use AI to create new opportunities for their communities.

Extremely excited to be a part of this conversation around AI next week with @Senlaphonza @TyranceIi @DeborahNArcher and Meghan.

Lost so much respect for many of my Black and Latinx peers over the past few days because they choose to defend people that are aligning themselves with white supremacy. There is no nuance or grey area. We are at a critical juncture to defend the future for all those not in the majority. Your allegiance won’t protect or save you from their agenda because as history dictates they will eventually come for you.

Navigating #ApacheKafka and #KafkaConnect can be as frustrating as navigating #Panslabyrinth. This blog by @meroxadata’s CEO @devarispbrown covers how #Redpanda and @ConduitIO will lead #devs out of the maze and into the future of streaming data. meroxa.com/blog/data-pipelin…

Proud that @meroxadata is one of the 18 vendors selected to build a next-gen analytics platform for the Space Force. And yeah you read right, the total contract value is $900MM over 5 years🚀📈💰 spacenews.com/space-force-se…

So proud of the progress @CoactiveAI is making on all fronts(product, business, &company). Congrats on raising your Series A from @a16z & @BessemerVP. Thank you for allowing me to be a part of your journey 👼🏾🚀📈💰. businessinsider.com/coactive…