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AI 资讯

All you need is... (r)evolution!?

This is just an opinion of what I experience and am witnessing, but looking at how LLMs scale feels like I've seen it before: with CPUs trying to outrun Moore's Law and break the rules of physics. Heat, power leakage, and diminishing returns made it increasingly expensive to squeeze out even small gains in clock speed. The GHz race shifted because it had to. For LLMs, more compute, more data, more parameters, and everything just keeps getting better? That curve seems to hit a ceiling and innovation needs to succeed the scaling race now. History does not repeat itself, but it rhymes. What learnings can we make from history to "predict" a potential future? History In the early 2000s, CPUs ran into a wall, a very physical one ^^ So makers adapted. Instead of crunching every single watt out of a single core, multi-cores became common. Athlon 64 x2, Pentium D, PS3 with its heavy Cell approach. From linear to parallel. From sequential to multi-threaded (and funny race conditions ;). Talks of distributed systems, SIMD/MIMD and new benchmarking spawned into what we have today. We still use CPUs, but differently. We still have Memory, but think about Cache, RAM, GPU or Unified. Same same, but different. Innovation because of limitation. Present I feel something similar is about to happen to gen AI. Yes, there are improvements in different areas, some in scaling, some optimisation, some performance, but the slope is becoming slippery. The last 12 months went from "Opus 4.5 is the pinnacle" to "What the hell is wrong with Claude?". The perfect (business) storm of scaling execution! But the low-hanging fruits have been eaten and the crops don't grow as fast anymore. Costs rise quickly, latency becomes a constraint, and even large context windows feel more like extensions than breakthroughs. What remains is more incremental, more expensive, and more complex. You could argue the whole venture of "agents" is the same multi-core experience repeating itself. A different kind of orch

2026-06-26 原文 →
AI 资讯

I Replaced 12 Developer Tools with ChatGPT (Here's What Actually Happened After 30 Days)

I have a confession. Somewhere around day nine of this experiment, I almost quit and went back to my old setup. Not because ChatGPT was bad. Because I was bad at using it. I kept typing half-questions the way I'd type into Google, hitting enter, and getting answers that were technically correct and completely useless. It took me about a week to realize the problem wasn't the tool. It was twelve years of muscle memory. This post is the long version of what happened when I tried to go a full month without my usual stack of developer crutches — Google, Stack Overflow, Regex101, JSONLint, a SQL formatter site, a commit message generator, a pile of bookmarked Docker cheat sheets, and a few other tabs I didn't even realize I kept open until they were gone — and replaced all of it with a single ChatGPT window. I work as a backend-leaning full stack engineer at a small e-commerce company. Python and Django on the server, a chunk of Node for a couple of internal services, Postgres, Docker, and an AWS setup that I inherited rather than designed. Nothing exotic. Which is actually why I think this experiment is useful — most of you reading this aren't working on some bleeding-edge ML pipeline either. You're maintaining stuff, fixing stuff, shipping features under deadlines that someone in another department picked without asking you. So here's what happened. All of it. The good parts, the embarrassing parts, and the parts where I quietly reopened Stack Overflow in an incognito tab because I didn't want my browser history to judge me. TL;DR I tried to replace 12 daily developer tools with ChatGPT for 30 days straight, tracking what worked and what didn't. Google search volume dropped by roughly 70%, but it never hit zero — and I don't think it should. Stack Overflow was the hardest habit to break, and also the one I missed least once I'd broken it. The small utility sites (Regex101, JSONLint, SQL formatters) were the easiest wins. ChatGPT replaced almost all of them outright. Do

2026-06-26 原文 →
AI 资讯

How I built multi-tenant Row Level Security with Aurora PostgreSQL for a B2B SaaS — H0 Hackathon

I'll be honest: I almost did multi-tenancy the wrong way. When I started building InspectIQ "a SaaS platform for Florida home inspectors" my first instinct was to add a tenant_id column to every table and filter it in the application layer. Every query would have a WHERE tenant_id = :current_tenant clause. Simple, familiar, done. Then I thought about what happens when you forget one. One missing WHERE clause. One endpoint that skips the filter. One inspector sees another inspector's client data. In a home inspection business, that's not just a bug — it's a HIPAA-adjacent nightmare and a trust-destroying moment with your first customer. So I did it properly from day one: Row Level Security at the database layer. What is Row Level Security? RLS is a PostgreSQL feature that lets you define policies directly on tables. When a user queries a table, the policy runs automatically, before your application code even sees the results. You can't forget to apply it. You can't bypass it with a careless JOIN. It's enforced at the lowest possible layer. For a multi-tenant SaaS, this is exactly what you want. How I implemented it Every table in InspectIQ has this pattern: ALTER TABLE inspections ENABLE ROW LEVEL SECURITY ; ALTER TABLE inspections FORCE ROW LEVEL SECURITY ; CREATE POLICY tenant_isolation ON inspections USING ( tenant_id = NULLIF ( current_setting ( 'app.current_tenant_id' , true ), '' ):: uuid ); The FORCE is important — it applies the policy even to the table owner. No superuser backdoor. The tenant context comes from the JWT. When an inspector logs in, their tenant_id is embedded as a custom Cognito claim. The FastAPI middleware extracts it and sets it at the start of every request: await session . execute ( text ( f " SET LOCAL app.current_tenant_id = ' { tenant_id } '" ) ) SET LOCAL scopes the setting to the current transaction. When the transaction ends, it's gone. No leakage between requests. Aurora PostgreSQL Serverless v2 I'm running this on Aurora PostgreSQ

2026-06-26 原文 →
开发者

Sometimes, health tracking accuracy is overrated

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. About three years ago, a doctor told me I had to lose abdominal fat. She didn't care about my lower […]

2026-06-26 原文 →
AI 资讯

Presentation: AI Works, Pull Requests Don’t: How AI Is Breaking the SDLC and What To Do About It

Michael Webster discusses the rise of headless AI agents and their impact on software delivery pipelines. He shares how massive, AI-generated pull requests create a severe bottleneck for human reviewers and introduce persistent technical debt. Learn how engineering leaders can leverage test impact analysis and automated validation pipelines to verify agentic output without sacrificing stability. By Michael Webster

2026-06-26 原文 →
AI 资讯

Of course Meta thinks gambling is the future

Meta is, by and large, a company built on other companies' ideas. It has almost perfected the strategy: wait for a new platform or social mechanic to take off, then either buy or clone it, put it next to Meta's unmatched user base and advertising engine, and watch the money pile up. Well, the next […]

2026-06-26 原文 →
AI 资讯

Anthropic’s Mythos mess is only getting worse

It's been two weeks since Anthropic took its Mythos-class models offline after a Friday evening ultimatum from the Trump administration. The company sprang into action immediately, sending a barrage of executives to Washington, DC. But updates have been suspiciously lacking, with no resolution in sight. Anthropic declined to comment multiple times this week about the […]

2026-06-26 原文 →
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