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
LY CorporationAdded Oct 19
Automating E2E regression tests with AI: turning one-click into a pipeline (LY Corp)
AI-generated E2E tests ran but checked the wrong things, so the team added spec tracing, gap questions, pre-run checks, deliberately wrong expectations to catch false positives, and human review packets. All 98 manual regression tests moved to Playwright, and coverage grew to 196.
Coding toolsCase studies
뱅크샐러드Added Oct 19
Why DDD matters more in the age of AI coding (Banksalad)
The more code AI writes, the more the structure around it matters. Banksalad split a Go credit service into business-level bounded contexts and four layers, and says coding agents now read several times less code per change and reviews stay narrower.
Coding toolsCase studies
토스 테크Added Oct 19
Building an AI platform so service teams can focus on product (Toss)
Toss's AI platform team pulled model integration, permissions, usage tracking and incident response out of individual service teams into one platform: a request portal, a model gateway, an MCP gateway for internal tools, input guardrails, Langfuse tracing and a CLI, applied by data tier (L1–L4) and SLA tier.
Infra & servingCase studies
Squeezing every CPU cycle and building a Milvus sizing rule (Toss)
Toss Securities' data infra team tuned Milvus for vector search: HNSW_SQ8 hit 316 QPS at recall@10 0.954 while cutting per-replica memory from about 335GB to 114GB, and pinning CPU and memory to the same NUMA node added 41% more QPS. They turned the measurements into a rule for how many servers to provision.
Infra & servingCase studies
카카오 테크Added Oct 19
Context design lessons from building a data-analysis agent (Kakao)
Kakao's agent finds the right data, writes and runs SQL, and explains the result. The post breaks context into system prompt, tool specs and message history, and shows with example prompts how, as models improved, step-by-step workflow rules moved out of the system prompt and into per-tool usage conditions.
AgentsCase studies
An AI support service that saved 300 hours a year (Toss Bank)
Toss Bank's jeonse-loan team built an AI assistant that answers questions in users' own words. Support calls fell by 550 a month, about 300 hours a year, and users who got an answer from AI were about 10 percentage points less likely to call. Surfacing common questions at each step raised AI usage up to roughly 5x.
Case studies
우아한형제들Added Oct 19
Rebuilding Baemin's e-contract screens: what we gave to AI and what humans kept (Woowa Brothers)
Most of the code for Baemin's new e-contract screens was written with Claude Code. AI handled view components and draft specs and tests; humans owned boundaries, lint rules and spec approval. It shipped about twice as fast with roughly 8.5x more test code per line, though four bugs surfaced in the first two weeks.
Coding toolsCase studies
How much to hand to the LLM: automating ad analytics reports with AI agents (LY Corp)
LINE Ads' data team automated a daily revenue-change report sent to Slack and email. Code handles calculations, candidate selection and consistency checks; the LLM interprets verified facts into prose; and a tool-calling agent explores causes only within allowed analysis dimensions.
AgentsCase studies
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