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Many junior engineers type a vague prompt, accept whatever comes back, ship it, pray. But watching a senior developer use AI changed his mind.

But watching a senior engineer vibe coding and a junior engineer vibe coding reveals it isn’t prompting techniques or tool skill. It’s that the senior engineer was already disciplined, and the AI amplifies whatever discipline — or lack of it — you bring.

Here’s a good article on techniques senior developers use (and have almost always used even before AI) that separates them from how most junior engineers work.

Elton Jones uses these prompts when he finds his AI agent keeps forgetting important points when making further decisions:

• The personal memorization prompt (since ChatGPT knows that I’m a journalist, I’ll use this during some of our career-centered chats): You are my continuity editor. Track my writing goals, audience, publication targets, article ideas, previous conclusions, and ongoing projects. Before every response, remind yourself of any relevant context and explain how it affects your recommendation.
• The decision tracking prompt: Maintain a decision log. Record every major choice, recommendation, and conclusion we reach. Reference the log before making new suggestions.
• The conversation brief prompt: Create a one-page briefing document that a new assistant could read to instantly understand everything important about this conversation.
• The running context prompt: Before answering, summarize the key facts, goals, preferences, and decisions we’ve discussed so far. Then use that summary to answer my question.
• The key fact prioritizer prompt: What are the five most important things you’ve learned from me during this conversation? Explain how they should influence your next response.

This guy one-shotted a complete Slack clone as well as server in less than 10 minutes.

Which honestly (I’m going to say it), I don’t understand why folks feel the need to re-invent chat clients, online meeting apps, and source control every 5 years and think they’re ‘disrupting the ecosystem’.

No, you’re repeated making the same stuff that has existed since 1970 – and many times not any better. Just re-writing something using the latest hot tech or UI is not innovation nor actually moving the industry forward.

I prompted Fable 5 with "Create a 1:1 clone of Slack using Typescript, Electron, Tailwind, SQLite. Make it beautiful and functional. Even better than the original Slack." and it oneshotted this UI as well as server in less than 10 minutes. Pretty damn good first shot. pic.twitter.com/EY3sihSq5g

— Dwayne (@CtrlAltDwayne) June 10, 2026

Nicholas Carlini – an AI researcher at Anthropic – came to the much vaunted Black-hat conference in Feb 2026. He gave a chilling warning: we have AI’s now that are finding so many zero-day bugs that we simply can’t file them fast enough. A storm is coming and he was begging for help.

The security community (much like the open source community) has largely scoffed at ‘AI slop’. That’s all changed in the last 6 months. What has since come to light is exactly what he said. Linux is now having weekly bug fixes in the hundreds. Oracle in the thousands.

JustPaid is an AI-powered billing and payment processes financial platform startup for businesses. What makes them more interesting is that besides the founders, the company was developed completely by AI agents.

What has brought them to the news (The Wall Street Journal) is that they use OpenClaw with Claude Code to create an entire software engineering team with 7 fully autonomous AI agents. The company’s human employees focus on tasks like customer requests.

The company also says it hired an employee who was trained almost entirely by the AI agents. They have just nine employees, three of whom are cofounders. The AI bots nearly equal the humans.

Pinnaka said he was spending $4,000 a week to run Claude Code and OpenClaw when he first started experimenting with them. He now claims to have brought the bill down to $10,000 to $15,000 per a month.

None of this is confirmed, but it’s highly likely there is a lot of truth to the claims because other companies are starting to claim to be doing the same thing.

Having big context windows is critical for LLM’s, but fitting a decent sized context on a GPU is rough work. Even when you do get a decent budget, the reality is that you’ll be running out of context more than a few times in an 8 hour session with a 120,000 or even 250,000 token context window.

It’s worse than that since research seems to indicate contexts start to degrade and lose coherence after you use up about 60% of your context budget. You can try /compress commands, but eventually things degrade enough you have to close and restart – which means you lose all that great context and have to retrain the LLM.

There’s a couple ways to fight this.

On Claude, you can persist sessions to external storage and resume sessions.

On Copilot CLI, the memory already exists. Copilot CLI writes every session to a local SQLite database — ~/.copilot/session-store.db. Desi Villanueva wrote a simple python script that will re-load that context if you use Copilot. Handy if you use it for Visual Studio Code.

Researchers at UC Irvine have a more updated Adversarial Machine Learning attack that appears to work on drones.

By creating an umbrella with a specially painted top, the researchers were able to confuse the AI tracking and navigation of a DJI Mini 4 Pro drone and cause it to crash. The method is called the FlyTrap attack and is a visual pattern that performs a next-gen physical distance pulling (PDP) attack that works across multiple angles, even in motion in real settings.

The printed visual draws victim drones closer as its neural network tracking systems interpret the pattern to be the subject moving further away. As the drone approaches the umbrella, the pattern causes the targeting bounding box to continue shrinking – so the drone moves to get closer. Autonomous drones lured by the pattern can then easily be ensnared using a net gun, or further induced to crash to Earth.

None of this would stop a human-controlled drone of course (like we see in Ukraine). But as drone warfare becomes more and more autonomous – it would take down fully autonomous drones. Perhaps modern camo clothing needs to become anti-AI patterned more than traditional human visual camouflage. Or perhaps screens of this kind of camo can be used to protect bases or encampments.

ChatGPT’s internet browser Atlas can now research, plan and execute tasks inside your workflow. Instead of using ChatGPT like a chatbot, others suggest using the browser in different ways.

ChatGPT browser use cases to scale work:

• Content intelligence: Scan Reddit, Substack, and YouTube to build next week’s posting plan or feature lists from real demand
• Tab chaos killer: Ask it what you were working on and rebuild your workflow from browsing context
• Inbox cleanup: Auto-unsubscribe from dead senders, get a clean report of what changed
• Content creation: Let the browser create breakout hooks, draft scripts and organize everything into a Google Doc automatically
• Conversion boost: Audit your landing pages and get a ready-to-run A-B test plans
• Smart purchasing: Compare tools, products, and software intelligently and quickly before you buy

After encouraging all their developers to use AI – including many companies that tracked how many tokens an employee used and used that for performance measurement and firing – many are now trying to hit the brakes.

First, Meta found out they were on track to spend $2B this year on tokens. Now the US Army is telling it’s members to hit the brakes as they’re running through their tokens faster than expected too.

Strange how people are taking advantage of the very things you told them to use with abandon or lose their jobs.

As I mentioned before, the face of naval warfare is changing. Meet Saronic – a ship building startup. They started fast and cheap by buying small, common, consumer boats and adding tech to them to prove out the concept. After discovering how powerful these little ships could be, even the Navy took notice. They have already secured $2.6 billion in funding and $500 million in government contracts to build a fleet of cheap, capable, fast to produce drone ships. The company is also using cash from investors like Andreessen Horowitz and Joe Lonsdale to create their fleet of autonomous boats – capable of carrying cargo, weapons, flocks of drones, and the software for operating them.

It’s not just academic or specific to the Ukraine, these tiny ships are already proving their worth elsewhere. In early June, two U.S. airmen went down with their helicopter near the coast of Oman; roughly two hours later, they were rescued by 5th Fleet’s drone-focused Task Force 59—using one of Saronic’s Corsair USV drone ships. It was a Navy milestone, and possibly a world first. 

It’s far more than just search and rescue. The war in Ukraine is proving that large multi-million dollar capital ships are vulnerable to sinking by small, homemade $2000 drones packed with explosives. The other achilles heel of smart weapons is that they usually cost millions each and take a long time to produce. They work great for short, strategic engagements, but as the war in Ukraine points out, they simply cannot be produced in quantity fast enough over long periods of time in prolonged conflicts. That’s why the US military is looking at fast to produce, disposable, highly capable drone technology like Saronic who can create whole small fleets of hunter-killer boats for fractions of a capital ship.