惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 三生石上(FineUI控件)
D
Docker
GbyAI
GbyAI
宝玉的分享
宝玉的分享
Jina AI
Jina AI
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Vercel News
Vercel News
博客园_首页
Recent Announcements
Recent Announcements
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Hugging Face - Blog
Hugging Face - Blog
腾讯CDC
S
SegmentFault 最新的问题
Microsoft Security Blog
Microsoft Security Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
美团技术团队
V
V2EX
WordPress大学
WordPress大学
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
Visual Studio Blog
IT之家
IT之家
Apple Machine Learning Research
Apple Machine Learning Research
T
Tailwind CSS Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com

Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace
The mud and the mind
by Antonin · 2026-06-13 · via Hacker News - Newest: "LLM"

In my previous article I discussed about the necessary evil to change the coding tests to filter engineers.

As LLMs become the ultimate coders (producing boilerplate infinitely, instantly, and without fatigue) the software engineering workflow is rapidly shifting, and day-to-day work is moving heavily toward auditing and reviewing AI-generated code.
But because of this shift, a dangerous corporate fantasy is taking root: the belief that engineers should simply become project managers. But let us not confuse this evolution with a retreat.

Becoming an auditor does not mean keeping your hands clean.

Expecting engineers to simply shuffle tickets and prompt LLMs is a profound misunderstanding of the craft. An engineer’s primary output has never been code but has always been deep thought on how to efficiently solve a problem.

To solve a problem, you will need intuition.
To develop intuition you need experience.
And to gain experience you definitely need to put your hands in the mud and break your teeth on walls of complex archiecture.

You might ask yourself: “if writing boilerplate syntax is no longer the mud, so what is?”.
We need to redefine where the hard work actually happens in software engineering. The mud is no longer writing REST controllers, or scaffolding a CRUD application. The mud is profiling memory leaks in production. It is tracing unpredictable latency spikes across a distributed system. It is staring at obscure telemetry logs at 3 AM because your beautifully Claude-generated architecture introduced a silent race condition that only appears under heavy load.

That is where engineering happens and where engineers are needed.
That is the mud.

And this brings us to the ultimate reality check: accountability.
You can automate the creation, but you cannot automate the responsibility, because LLMs and Agents can write the boilerplate but they cannot suffer the consequences of a bad design. At the end of the day, you do, and this is what you get paid for.

Companies that confuse engineering with mere ticket-shuffling will inevitably build fragile, incomprehensible black boxes, and if an organization expects you to be a PM rather than a deep technical thinker, they have already lost the plot.
In this special case I have an advice: ignore them, and solve problems in places where the real problems to solve are.