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I was thinking about some tests for a service that uses language models; there are several, even prompt injection. A question came to mind: is it possible to make multiple requests asking for any text like Lorem Ipsum, generating many unnecessary tokens and incurring costs? But creating a test where there are multiple accounts making the same request to generate 10,000 Lorem Ipsum tokens simultaneously, could that cause a service outage? Because most of the infrastructure I see doesn't use any queuing method when the chat is free of tasks involving an agent or even heavier functionalities. I didn't actually generate anything, I just wanted to start a discussion on this topic.

Every few months a new frontier model arrives, bigger and faster than the last. The benchmarks climb and one number climbs quietly alongside: the cost. Training runs now reportedly run into the hundreds of millions of dollars, and serving these models at scale is not far behind. Intelligence has become abundant but expensive - which raises an uncomfortable question. Do we actually need smarter AI, or do we need to get smarter about using the AI we already have? We already have more than we use Consider where we already are. Today's models can review, write, and verify dozens of documents in the time it takes to make coffee. They can read a sprawling codebase, propose a change, run the tests, and check their own work against the result. They can ingest a stack of contracts, flag the clauses that matter, and cross-reference them against policy - without a human babysitting each step. This isn't a hypothetical future; it's a Tuesday. The frontier has moved so fast that most organizations are nowhere near using the current generation fully, let alone needing the next one. They keep buying a faster car every year and never drive above thirty. The cost curve and the real bottleneck Each leap in raw capability comes with a steeper bill - more compute, more energy, more money - and the gains at the top end are increasingly marginal for everyday work. A model twice as expensive to run is rarely twice as useful for summarizing a report or drafting an email. For most real tasks, the bottleneck was never the model's intelligence. It was how we deployed it. A brilliant model handed a vague prompt produces vague results, while a modest model inside a well-designed workflow - clear instructions, the right context, a verification step, sensible guardrails - beats it consistently and at a fraction of the cost. Smarter use is mostly engineering. Match the model to the task instead of routing everything to the most expensive option. Design the workflow, not just the prompt: have one p

When people say they are "adding RAG" to a workflow, the conversation often jumps too quickly to infrastructure choices. Should this use a vector database? Should there be a reranker? Should everything go into a knowledge graph? Those are valid questions, but they are usually not the first question. The first question is narrower: What approved knowledge should the workflow be allowed to retrieve before an AI decision happens? That is why my first retrieval layer for operational AI workflows starts in Postgres, not in a standalone vector database. The Workflow Problem In operations-heavy systems, the model usually should not answer from raw memory or from a giant prompt dump. The useful context already exists somewhere else: approved response rules; handoff criteria; product or service notes; source or campaign guidance; operational decisions that were already made by humans. The hard part is not generating fluent text. The hard part is retrieving the right approved context, showing which source influenced the decision and refusing when no safe source exists. Why Postgres First For this kind of workflow, most of the surrounding data is already relational: leads or conversations; workflow names; stages and owners; human review outcomes; source metadata; trace logs; document versions. So the first technical choice is not "where do vectors live in the abstract?" It is: Where can I keep retrieval close to the operational data model? Where can I log the retrieval path and the final decision together? Where can I evolve the schema without creating a second system too early? Postgres plus pgvector is a good first answer to that set of questions. It lets me keep: documents and chunks; metadata such as allowed use and approval requirements; retrieval traces; cost estimates; human review outcomes in one place. What The First Version Needs The first version does not need to be broad. It needs to be inspectable. My narrow retrieval scope looks like this: approved response rules

At this time, AI is still a business that largely survives on valuation rather than profitability. The narrative surrounding artificial intelligence is driven as much — if not more — by financial speculation as by technological progress. This makes the twin narrative of an “AI infrastructure boom” essential. Ed Zitron has become well known for challenging this story. In reality, such an infrastructure boom is difficult to sustain when the underlying economics remain deeply unprofitable. Now, let us take a moment to reflect on the danger of relying — for our businesses, and worse, for our civilization — on a bubble that could burst at any moment, much like the dot-com bubble. Entire industries are restructuring themselves around assumptions that may ultimately prove irrational, even disastrous. The illusion and danger of replacing engineers There is a dangerous idea circulating in the world, born from the union of greed and ignorance: that software engineers have become obsolete. We no longer need them! Of course, someone who does not know how to write code cannot evaluate code quality. For such a person, any piece of code that works is just as good as any other piece of code that also works. They may see a functional demo and hastily conclude that AI can entirely replace software developers. An _experienced _engineer sees something different: brittle architecture, code with absurd or duplicated logic, security flaws, poor maintainability, and code that often collapses under real-world complexity. To the untrained eye, AI-generated code frequently looks convincing, while it may host invisible vectors of disaster. Hallucinations in software development are not harmless mistakes; they can become production bugs, security vulnerabilities, and eventually catastrophic business failures. The problem is not that AI writes code. The problem is that we seem to be heading toward an era in which we no longer fully understand the code powering our civilization. And because of th

Article URL: https://blog.keyvan.net/p/thoughts-on-ai-and-jobs Comments URL: https://news.ycombinator.com/item?id=48516585 Points: 12 # Comments: 6

Echo Isle is heavily inspired by The Legend of Zelda, and it's not afraid to show it: The retro graphics bear a striking resemblance to Link's Awakening, the main character wears a blue tunic and wields a sword, and he navigates dungeons to collect items and keys to fight bosses and gather magical MacGuffins. But […]

The most popular camera in the world just got its first set of serious AI photo editing features, and I don't think any of us are ready. As far as AI photo editing goes, the new features in iOS 27 are pretty tame compared to what you can do on, say, Google's Pixel phones. But […]

Hidden inside a building in Alabama, the FBI has created its own small town as a dedicated cyber training ground for simulating cyberattacks.

As World Cup action kicks off, we look at the physics of the beautiful game.

For all the noise that's been made about how generative AI is poised to revolutionize the filmmaking industry, there haven't really been any projects created with the technology that felt like the sort of entertainment people would pay to see. Most AI firms' video models are still only capable of churning out short bursts of […]

Plus: AI bug hunting fuels Microsoft’s biggest-ever Patch Tuesday, ShinyHunters ransomware gang exploits an Oracle zero-day, and more.

Whether you want to buy cheaper electricity, store solar energy, or guard against outages, EcoFlow’s home battery might be just what you need.

My methodology and data set didn't show any trend, but it demonstrated a more important issue: how wrongly this kind of research can be done and how misinterpreted the conclusions can be. The reason for making this research was an attempt to verify the claim that AI-assisted development increases code duplication. I analyzed 14 well-maintained open-source projects between 2021-2026, excluding new ones developed only with AI. For duplication detection, I compared semantic similarity using https://github.com/rafal-qa/slopo (I'm the author), not exact copies. This data can't prove or deny the claim, no trend is visible. Not only because 14 projects is too little, but also because there is a large variance between projects. The main advantage of this research is that it highlights the pitfalls in the analysis and conclusions and shows how easy it is to create "evidence" to support any claim. submitted by /u/rafal-kochanowski [link] [留言]

RJ Scaringe, the CEO of Rivian Automotive, joined us for a wide-ranging interview about how his company’s new electric SUV fits into the current EV industry, and what comes next.

The default for AI-assisted development is one of two failure modes. Either you're babysitting the agent line by line — approving each diff, re-explaining context it dropped three messages ago — or you've handed it the wheel and you're hoping the PR that lands at the end resembles what you asked for. Son of Anton is neither. It's a delivery orchestrator built on a single claim: there are exactly three moments where a developer's judgment is irreplaceable. The orchestrator owns everything in between. The three gates Every project moves through three human decision points. Nothing important happens without you signing off. Gate 01 — Approve the WHAT ( /soa plan ) A grill-me session forces the AI to surface its assumptions, constraints, and scope decisions back to you before a single ticket exists. You say yes or you refine. It does not proceed until you have. Gate 02 — Approve the HOW ( /soa decompose ) The approved plan becomes a ticket stack — ordered, dependency-aware, sized for review. Architectural judgment stays with you. Ticket authorship goes to the agent. Gate 03 — Approve DONE ( /soa closeout ) An adversarial subagent reviews every ticket before its PR opens. When the phase is complete, you decide whether to accept. Closeout squash-merges the stack onto main. Nothing merges without you. Between the gates, you are not needed That's the whole point. Once you've approved the plan and the tickets, the orchestrator runs the loop: