AI Dev Reads
AI engineering posts picked from company and developer tech blogs such as Toss, LINE, Anthropic and OpenAI, each with a short summary. Tap to read the original.
Last searched Oct 19, 7:44 PM KST
Zero's BlogAdded Oct 19
What exactly is RAG?
Using a shopping-mall support bot as the example, it explains RAG, fetching only the relevant chunks for each question, with a LangChain code walkthrough. Citing Anthropic's Contextual Retrieval, it notes retrieval failures fell 35% with chunk context, 49% with keyword search added and 67% with reranking, and suggests just putting everything in the prompt when docs are under 200K tokens.
AgentsModels & evals
Does Claude really remember your name? LLMs and context
Each API call is independent, so the model remembers nothing between calls; chat apps just resend the whole conversation, which makes it look like memory. Send "my name is Youngsu" along and it uses the name; drop it and it says you never told it. The same applies to Claude Code's CLAUDE.md and memory files, which only help once they're loaded into context.
Models & evalsAgents
Hugging FaceAdded Oct 19
Welcome Beam: a 501B open model you can own, customize, and scale
Reflection released Beam under Apache 2.0: a 501B-parameter sparse MoE with 23B active per token and a 1M-token context, reporting 81.6% on SWE-bench Verified and 80.1% on Terminal Bench. It runs day-0 in transformers, vLLM and SGLang.
Models & evals
LangChainAdded Oct 19
The Most Common Questions About Agent Evals, Answered
Where to start with agent evals: offline vs online evals, evaluator types and LLM judges, benchmarks, multi-turn evals, and how to keep the cost down.
AgentsModels & evals
GitHub BlogAdded Oct 19
GitHub's model for secret detection: Finding more secrets with less noise
GitHub's context-aware classifier for leaked secrets in code raised weighted acceptance by 11.5% and cut median scan time by 86%, according to the post.
SecurityModels & evals
Run fast, private agents on-device with LFM2.5-24B-A2B
Liquid AI's 24B-parameter reasoning model activates only 2B parameters per token, so it runs fast on your own hardware: 184 tokens/s on an Apple M5 Max and 114 tokens/s on a Ryzen AI Max+ 395. The open weights are on Hugging Face.
Models & evalsAgents
토스 테크Added Oct 19
Is the leaderboard's #1 LLM also #1 at Toss? Building Toss Benchmark
Public leaderboards don't guarantee real-world performance, so Toss built a benchmark covering general Korean ability plus its own domain: real operating tasks, policy compliance (Toss IFBench) and commerce knowledge (Toss Knowledge). gemma-4-26B, first on the English math set, fell from 81.74 to 72.43 on the Korean translation and dropped to second.
Models & evalsCase studies