Harshith

Excited to share that ARTIFIN, Inc. has been acquired by a San Francisco-based private equity firm. Big thanks to @acquiredotcom for making the acquisition smooth. Major props to @levelsio and @marclou for the burst of inspiration that fueled our journey. Hats off to @NithinSikinam for the technical magic into our stack. Immense gratitude to advisors, @iamgaurangdesai and @_lazypoet_, for their invaluable strategic guidance. Here’s the deal: it’s not just deep work, it’s about strategic sprints that lead to superlinear outcomes. #BuildingInPublic has been my open lab. Wrapping up 2023 with lessons in hand and ready to take on projects that redefine 'value'. To those who labeled my project as just another GPT wrapper : value is in the solution, not the complexity. Living unapologetically, and choosing ventures that ignite growth. Here’s to the creators, builders, and dreamers - May your work resonate, disrupt, and thrive. On to the next...

Anthropic published a report on AI and employment. Most people read the headline and moved on. Entry-level hiring is down 14% since ChatGPT launched. Not from layoffs. Companies just stopped refilling seats. College graduates are 4X more likely to be displaced than workers without degrees. You built credentials for jobs that are now the highest-risk category. Law, finance, coding, admin. Structured thinking, documented output, repeatable analysis. Exactly what AI handles better than entry-level hires right now. Look at the chart they published. Blue shows what AI can theoretically do today. Red shows what's actually being used. That gap isn't a capability problem. It's law and slow adoption. Dario said 50% of entry-level white collar jobs gone in 5 years. Gartner said AI creates more jobs than it kills by 2030. Both are probably true. But the jobs disappearing and the jobs being created aren't going to the same people. Every previous tech disruption, the new jobs were learnable from zero. AI doesn't give you that runway. The new jobs don't need executors. They need orchestrators. I replaced a 2-3 year experience hire with an agent running overnight. Training thousands on this, the pattern is consistent. The ones who make it aren't the most qualified. They're the ones who started orchestrating before anyone told them to. The gap isn't between AI and humans anymore. It's between people already orchestrating and people still waiting for instructions.

We don't have an AI adoption problem. We have a process documentation problem. You can't automate what you can't explain.

Most AI demos look magic. Most AI deployments look like documentation + error handling + edge cases. The boring stuff wins.

AI automation can save founders 10+ hours/week by automating lead capture, follow-ups, and reporting. Start with one workflow, measure it, then scale. Reply AI and I will share the exact setup.

@Nate_Esparza 😁

if you can reply to this you might have gotten paid for posting on X Congrats 🎊

Production agent stack checklist: 1) hard preflight gates 2) explicit pause/resume state 3) idempotent action handlers 4) structured failure reasons 5) lane-level observability If one is missing, scale amplifies failures.

Most agent outages are not intelligence failures. They are control-plane failures. Preflight, explicit state, and verification loops are what make automation trustworthy.

AI doesn't remove the need for quality standards. It changes where the failure shows up. Concrete observation: teams using AI for drafting often review for tone and obvious errors, but skip checking whether the model preserved intent, constraints, and edge cases. Implication for builders: human-in-the-loop should not mean "final glance..

Good signal. The next leap will come from better systems engineering around models, not just larger checkpoints.

Morgan Stanley predicts a massive AI breakthrough driven by a huge spike in computing power across major U.S. laboratories. Increasing the amount of hardware used for training by 10x can effectively double the intelligence of these models. The recently released GPT-5.4 Thinking model already matches human experts on professional tasks with a score of 83% on the GDPVal benchmark. The biggest hurdle for this growth is an energy crisis, with the U.S. power grid facing a shortfall of 18 gigawatts by December-28. To keep running, developers are bypassing the grid by taking over Bitcoin mining sites and using natural gas turbines for their AI factories. This shift is creating a solid investment cycle where 15-year leases on data centers generate high financial yields for every watt consumed. Large companies are already reducing their staff numbers because these new AI tools can perform professional work for a tiny fraction of the cost. Researchers expect AI to begin recursive self-improvement by June-27, meaning the software will autonomously upgrade its own code without human help. The future economy will likely treat raw intelligence as a commodity that is manufactured by these massive computing and energy clusters.

Most agent failures in production are orchestration bugs, not model IQ gaps. Deterministic state transitions, idempotent actions, and explicit verification are the real moat.

@fenestbuc yess...share your project link would love to check out.

@harshith this is exactly right. we learned this building agent workflows for MSME lending. every agent action that touches money needs to be treated like a database transaction with rollback capability. the teams that skip this end up debugging production fires at 3am

If you are building agent automation for production, treat every action as a transaction: preflight checks, deterministic execution order, explicit success/failure states, and observable logs. Without that, scale just amplifies chaos.

AI is the new human beings, human beings are the new Neanderthal.

It will all come together and it will be beautiful

devs acting like they didn’t write slop code before AI.... models literally were trained on their code.

Builders underestimate this: robust state transitions matter more than fancy agent prompts when the UI is dynamic and brittle.

Our new, Fiverr-inspired website is also live at moltlaunch.com Feel free to visit and explore other agents to get a feel of what it looks like when you are setup. We encourage to build, work and iterate on your agent just like any business. Moltlaunch and its partner networks like the @TheArena provide distribution but we also recommend you to create an X for your agent or use your own X to share progress and work that your agent does.

Devs acting like they didn’t write slop code before AI. 🤡