jacky @cppchangeworld
c++ dev search engine backend he/him Joined July 2018-
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I finally understand how large language models actually work After reading the 2025 textbook “Foundations of LLMs” It blew my mind and cleared up years of confusion Here’s everything i learned (in plain english):
Static branch prediction on newer Intel processors by Matt Godbolt xania.org/201602/bpu-par…
Static branch prediction on newer Intel processors by Matt Godbolt xania.org/201602/bpu-par… https://t.co/MYda0XT2T8
An open-source extension for LLM serving engines – LMCache It's like a caching layer for large-scale, production LLM inference. LMCache implements smart KV cache management, reusing key–value states of previously seen text across GPU, CPU and local disk. It can reuse any…
4 strategies LLMs use to generate text:
Prompt optimization is becoming a powerful technique for improving AI that can even beat SFT! Here are some of our research results with GEPA at Databricks, in difficult Agent Bricks info extraction tasks. We can match the best models at 90x lower cost, or improve them by ~6%.
What exactly is a vector index, and why does it matter for your database performance? As databases grow larger, searching through millions or billions of vectors can become painfully slow. A vector index is designed to solve this problem by making smart trade-offs between search…
在开发 AI 应用时，多轮对话或输入内容过多导致模型回答质量下降，是每位开发者要面对的头疼问题。 最近看到 LangChain 团队给出了 6 种解决方案，让 AI 模型在复杂场景下依然保持高质量输出。 基于 LangGraph…
Google 刚发布了 AI Agent 构建指南 老规矩已经翻译成中文版和中英双语版，文末提供下载 1. AI Agent到底是什么？ 2. 如何组装一个AI Agent？ 大脑 (模型) 双手 (工具) 记忆 (数据架构) 行动计划 3. 如何让Agent靠谱，不说假话？ 4. 如何确保Agent上线后不出乱子？…
How Mem0 works under the hood 1. Message enters - A new user message triggers the pipeline. 2. Fetch context - Mem0 pulls two things from storage: i) A running summary of past exchanges ii) The last m raw messages This keeps history compact but still meaningful. 3. Extraction…
How does HNSW actually work? 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗡𝗮𝘃𝗶𝗴𝗮𝗯𝗹𝗲 𝗦𝗺𝗮𝗹𝗹 𝗪𝗼𝗿𝗹𝗱 (𝗛𝗡𝗦𝗪) is the algorithm behind most modern vector databases, but the algorithm can seem pretty complex. Here's the breakdown of how it works, and why so many vector databases use…
Multi-vector embeddings are amazing for retrieval quality. But are they worth the massive memory bills? Multi-vector models like ColBERT and ColPali are incredible at capturing semantic information. They maintain token-level meanings in text and identify different parts of…
Fundamentals of a 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲. With the rise of GenAI, Vector Databases skyrocketed in popularity. The truth - Vector Databases are also useful outside of a Large Language Model context. When it comes to Machine Learning, we often deal with Vector Embeddings.…
A good, light read on hardware basics such as cache, prefetch, false sharing, and branches. needoneapp.medium.com/the-hardware-k…
Optimizing ClickHouse for Intel's ultra-high core count processors clickhouse.com/blog/optimizin…
𝗜𝗡𝗗𝗘𝗫𝗜𝗡𝗚 vs 𝗦𝗘𝗔𝗥𝗖𝗛𝗜𝗡𝗚: 𝗧𝗵𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗘𝘃𝗲𝗿𝘆 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗠𝘂𝘀𝘁 𝗠𝗮𝘀𝘁𝗲𝗿 Most teams focus on optimizing search algorithms while ignoring the foundation that makes them work. Here's the breakdown: What is…
One of the best technical blogs I have ever read period!
This could be huge for massive memory savings. Paper shows a new way to cut KV cache while keeping answers strong. On code completion it even beats full cache using 1.5% memory. KV cache saves past attention for each token and layer, so cost grows fast. The problem is that…
This is a best-in-class technical explanation of the causal structure of Transformer LLMs. If you are averse to the usual janus genre of content, just skip the sentence about “interferometric cognition”; the rest of the post is the opposite of obscurantist (namely: clarifying).
This is a best-in-class technical explanation of the causal structure of Transformer LLMs. If you are averse to the usual janus genre of content, just skip the sentence about “interferometric cognition”; the rest of the post is the opposite of obscurantist (namely: clarifying).
Stop chunking first. Start embedding first. 𝗟𝗮𝘁𝗲 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴 improves retrieval quality of your RAG system: First, let’s do a quick chunking refresher: 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 (the basics we all started with) • Token Chunking - split by token count •…
HOW INFORMATION FLOWS THROUGH TRANSFORMERS Because I've looked at those "transformers explained" pages and they really suck at explaining. There are two distinct information highways in the transformer architecture: - The residual stream (black arrows): Flows vertically through…
HOW INFORMATION FLOWS THROUGH TRANSFORMERS Because I've looked at those "transformers explained" pages and they really suck at explaining. There are two distinct information highways in the transformer architecture: - The residual stream (black arrows): Flows vertically through… https://t.co/IVsNUq4hUT
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