Who on-calls an AI? This is the unresolved crisis of agent ownership
At 2:07 a.m., an AI agent starts failing.  The application is still running, but requests are timing out because a The post Who on-calls an AI?…
Последние новости
⚑ Сообщить о проблемеTech news from the best sources
Все темы
- игры
AI
News
Tech
agents
ai
api
architecture
automation
beginners
career
database
devchallenge
devops
javascript
llm
machinelearning
mcp
opensource
performance
productivity
programming
python
react
security
showdev
testing
tutorial
typescript
webdev
The permission you denied today is your safety net for tomorrow
What happens when you give an AI support agent a permission it never needed, then assign it a task the The post The permission you denied today is y…
The audit log says my name: what an agent inherits when you hand it your credentials
I measured what an AI agent actually holds when it authenticates as me. Authentication turned out to be a token The post The audit log says my name:…
Query decomposition doesn’t fix context starvation — it just moves it
I have a small example that would best communicate the message I am trying to convey: say you built a The post Query decomposition doesn’t fix…
Why human oversight is shifting from writing code to defining requirements
This walks through the pipeline our agents operate inside—from a recorded scoping meeting through unit specs, spec review, generated code, The…
47,000 job listings reveal the engineering roles that AI is creating
Every major transformation in tech has led to roles merging, then new ones emerging. Friction between developers and operations drove The post 47,00…
Stop AI code sprawl before it destroys your software design
While AI code generators help teams ship faster than ever, that speed brings a hidden killer: Comprehension Debt. As soon The post Stop AI code spra…
The systems guide to production token optimization
When enterprise AI applications scale, they inevitably hit a wall. For many engineering teams this wall is initially diagnosed as The post The syste…
Why basic RAG fails at multi-hop reasoning (and how GraphRAG fixes it)
The current approach to designing LLMs within AI engineering is oversimplified. According to the echo chamber’s view, solving LLM hallucinatio…
Stop the token bleed: building token-efficient multi-agent systems
Every engineering team deploying AI agents eventually discovers an uncomfortable truth: the model isn’t the biggest expense. The hidden cost T…
Why your AI pipeline costs 10x more after the demo
Every token has a price. The problem is that most AI systems don’t reveal the bill until they reach production. The post Why your AI pipeline costs…
Say goodbye to K8s GPU pain: How DRA changes everything
Consider a platform team managing a shared GPU cluster with a mix of B200s, H100s, and recently added B300s. Every The post Say goodbye to K8s GPU p…
When do AI agents need permission boundaries?
An AI agent feels harmless when it only produces text, but the risk profile changes the moment it calls a The post When do AI agents need permission…
Can prompt caching tame RAG costs without sacrificing accuracy?
The AI ecosystem is drowning in tutorials on how to build a retrieval-augmented generation (RAG) app in five minutes. The The post Can prompt cachin…
Why smarter AI caching sometimes makes everything slower
Caching was one of the most critical optimizations in modern AI systems long before most teams realized it. Early prototypes The post Why smarter AI…
The “silent hallucination” loop: how our autonomous data pipeline poisoned its own vector store
Nothing matches the dread of checking a perfectly green observability dashboard with sub-100ms latency, right before an enterprise client emails The…
Why traditional CI/CD fails for LLMs (and the release gates we built to fix it)
This article explains why traditional CI/CD gates are not enough for production AI systems. I share a practical release-gating approach The post Why…
Template-based data extraction is dead. Here’s what comes next.
Modern businesses are in a constant, uphill battle against what to do with unstructured data: PDFs, contracts, scanned images, customer The post Tem…
Your AI pipeline is broken, and your dashboards don’t know it
Recently, a crucial RAG pipeline used by one of our corporate clients began hallucinating about financial numbers without notifying us The post Your…
Beyond the stack trace: why AI requires a new debugging paradigm
For some time, debugging has relied on the assumption that software is deterministic. It’s expected that with the same input, The post Beyond…
From Jupyter Notebook to production: How to ship AI systems that actually work
Moving from experimentation to production in AI requires a transformation of mindset, architecture, and engineering discipline. There’s no API…
Debugging the undebuggable: building observability into probabilistic AI systems
Debugging used to be straightforward: A service failed, you checked the logs, followed the stack trace, and fixed the bug. The post Debugging the un…
How to build your first end-to-end AI workflow in n8n
n8n is one of the most powerful workflow automation platforms available today, and the fastest way to learn it is The post How to build your first e…
The Rust sidecar pattern that fixes Python AI’s biggest weakness
In the world of AI, “it works on my machine” is the most dangerous phrase a developer can muster. Transitioning The post The Rust sidecar pattern th…
How AI-native systems are built
The core problem: the “brittle” enterprise Traditional enterprise software is deterministic; composed of a series of “If-Then” statements that break…
A nine-point checklist for shipping production-ready AI
Starting from square one Most teams can build an AI prototype. A notebook answers a few prompts, a demo agent The post A nine-point checklist for sh…