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a company can replace you with AI even if the AI is worse at your job management has accepted worse work for less money before the “humans are still better” pep talks are missing that part

AI keeps stopping halfway through long tasks? Make it write a checkpoint every 10–15 minutes: → completed → still pending → exact next action If it stalls, the next run resumes instead of restarting.

Now there are a bunch of decision models, eg Jev, Cloudflare's Clef model, community OSS projects like Laya, plus OpenAI is releasing their decisions API soon. What are you actually using in production? Are people just using Jev? Fine-tuning their own Jevlike model for specific use cases?

I posted this as a response to one of my X posts. It looks like a post itself, so here it is: Whatever an AI agent hears and says costs tokens. So let it hear the best and shortest possible prompt and ask it to give a straight response only, no extra verbiage (garbage).

What AI skill will be the most valuable in 2027? - RAG - AI agents - AI security - AI evaluation - AI automation - Prompt engineering

That's absolutely true

The AI safety movement is mostly marketing. The people loudest about 'existential risk' are the ones shipping the models fastest.

When something that worked well suddenly starts rejecting legitimate actions, it’s hard not to wonder what changed behind the scenes

The tools are changing, but the eye for good design still matters. Knowing what should exist may become more valuable than knowing how to manually build every pixel

Hot take: most “AI agents that connect to all your tools” are just the same product wearing different landing pages. Everyone wants to build the AI operating system for your life. Connect Gmail, Slack, Notion, Calendar, your CRM, and 50 other tools. Slap a $20/month subscription on it. But why is everyone racing to solve the same problem? There are entire industries running on broken workflows. Businesses bleeding money because of decisions made on bad data. Millions of people dealing with problems that software still hasn't solved. Yet somehow, the most crowded race in AI is to build another agent that does a little bit of everything. The next big AI company might not be the one that connects to the most tools. It might be the one that goes ridiculously deep on a problem everyone else overlooked. There's so much left to build. We need more people willing to solve problems nobody else is looking at.

The new interview is not DSA vs AI. It is DSA through AI. Companies now give you a messy 10k-line codebase + Claude Code and watch how you use it. They don't care if you write code from scratch. They care if you spot the subtle O(n²) bug the AI generated.

The moment AI learns to move like us, robotics changes forever. Walking robots could enter factories, homes, hospitals, and disaster zones turning intelligence into physical action.

A lot of the discussion around sovereign compute still treats ownership like it’s binary: either you own the stack or you don’t. But ownership is a poor measure of sovereign compute. In practice ownership can sit on top of many many dependencies, which can matter more in practice than who technically owns the hardware. You can own the machines and still need someone else to patch them. You can own the model weights and not really have the people or infra to keep the thing useful. You can keep all the data “sovereign” and still depend on outside services for the system to actually work. So maybe the better test is, can you break the dependency when you need to? If you switch suppliers, what gets stranded? What data, workflows, tacit knowledge, whatever, gets left behind? Can you actually see what the system is doing well enough to control the important parts of it? There’s a weird failure mode on the other side too. If sovereignty means rebuilding absolutely everything yourself, you eventually end up recreating whole research ecosystems, tooling, infra, expertise, etc, and at some point that starts making you weaker. You learn from a smaller pool, move slower, miss stuff happening elsewhere. So I think the more useful version of sovereignty is something like: dependency is fine, as long as it stays reversible. You can rely pretty heavily on outside providers and still have decent sovereignty if none of them can suddenly turn that reliance into leverage over whether you can keep operating, keep what you’ve learned, or leave. That seems way more real than saying “we own the servers” and calling the problem solved.

One of the fastest ways to optimize an AI agent is to replay its old runs. Take a recorded trace, change one thing: • Model • System prompt • Reasoning level • Model parameters Then replay the session while reusing the original tool outputs. Now you can compare both runs and isolate exactly how your change affected the agent, without paying to execute the entire environment again. This is why I've been experimenting with agent replays using Kitaru by @zenml_io.

Big Tech is spending billions on AI infrastructure while laying off thousands of engineers. Those layoffs aren't evidence of successful AI transformation they're cost-cutting disguised as innovation. And when you need experienced engineers to debug, secure, and maintain every line of AI generated code, you've just made your actual development team more essential, not less.

BREAKING: The job market is about to split in two. By 2030, you will either be replaced by AI, or you will be highly paid to manage it. While most people are studying for obsolete degrees, smart players are quietly mastering 15 high-leverage skills. Here is where the money is moving: 👇 👇👇

Your AI agent will literally get so much better if you ask it for screenshots of what it's doing. I've been using this strategy with my hermes agent and it's starting to become autonomous more and more. Every time I see it's stuck, I tell to send me a screenshot -> I see what's going -> I prompt better -> Problem solved Tell your agent to send you more screenshots. (and delete them if it saves them in a temp folder)

Gemini is replacing custom “Gems” with reusable skills inside normal chat. That feels like the right direction. People don’t want 12 tiny AI personalities. They want one assistant that knows 12 ways they work. Customization should feel like memory, not programming.

linkedin is basically people using AI to write posts about why AI will never replace authentic human connection