Japanese-language research, company posts and talks, summarised in English.
13 sources filed · JA 13
Seven contrasts: naming (orders vs ord), column comments, value meaning ('cancelled' enum vs sts = 9), type (datetime vs char(14)), foreign keys, soft delete (deletedat vs dflg = 'Y'), and price columns (unitpriceexcludingtax vs prca).
1.9xcost and output tokens for the obscured schema versus the readable one ($0.41 vs $0.21 per run)
FY2025: callers asked about eligibility for subsidised earthquake breakers and furniture anchors.
~40%calls the AI handled effectively in the FY2025 disaster-prevention pilot, against an 80% goal
The inherited B2C culture (good, cheap, fast, top-down execution) was too heavy and slow for B2B work, where staff must read each customer's needs and act on their own.
2022year Panasonic Connect was carved out as a B2B company in the holding-company restructure
Three questions per post: relevance (is the service the subject, not just named), one of eight categories, and five-level sentiment with an instruction to read sarcasm by intent.
~$5/monthJev classification cost; X API reads at $0.005 per post are the main expense
Genai's design aim is replaceability: accept lock-in at the interface, keep applications, inference (the main cost) and data separable.
~180,000central government staff the Prime Minister ordered to use Genai
Motivation: local machines run out of memory before people run out of attention when running parallel worktrees on a large monorepo; generic products fit the codebase and internal tools poorly.
55-60%share of the monorepo's daily PRs created through haro by October 2026
Data: five fictional projects and eight fictional engineers (40 pairs), drafted by Claude and reviewed by a person, including hard cases such as an overqualified tech lead and a vague self-assessor.
0.87 / 0.78 / 0.47Kendall tau-b against Opus 5.5 labels for Jev, Haiku 4.5 and the ruri embedding model
Structure: a mayor-chaired DX headquarters, one DX promoter per division, Code for Japan as support team.
~70%share of Saga City staff using generative AI (ChatGPT) in a June 2026 survey
Four skills: ship (ticket to merge, including CI fixes and replies to review comments), plan-checker (design review by other agents before risky work such as migrations or auth changes), pr-checker (parallel review by aspect, with a second agent challenging each finding) and devqa (runs QA scenarios in the dev environment and files bugs).
211PRs the author created and merged to main in 16 business days (24 Sep to 16 Oct 2026)
Cause types: bugs 40%, spec or usage 22%, customer data state 20%, external services 14%, undetermined 4%.
~700resolved support inquiries escalated to engineering, classified by cause and deciding evidence
Concepts are GitHub issues with Why, Outcome, Boundaries and AI Findings, sized minor, quick or full.
97% to under 10%share of merged PRs with a human approval, before versus after April 2026
Three steps repeat: a bandit picks the next candidate, balancing promising and unexplored ones; a proposer LLM writes a child hypothesis from archived parents; a judge (LLM or human) scores it.
~6,300US companies (August 2024 data) searched for acquisition candidates in the M&A task
No logs for the ~18-minute stall: it occurs before logd starts.
~990,000files in /private/tmp 9.5 hours after boot, almost all Claude Code scratch directories
11 sources filed · JA 11
Why flat RAG degrades with scale: old and current versions of a rule sit side by side and the old one can rank higher; top chunks fill up with fragments of the same long document.
96.7%score on 30 business questions over the company's own documents (29 correct, 1 wrong)
Three structural problems: inconsistent input (some branches used the system, some Excel, some had stopped), plans based on individual "experience and intuition", and a lead time that forces forecasting two to three months ahead.
~4,200 hoursannual manual input time for monthly sales plans across all branches of the food-service distribution division, now projected to be saved
Task: given a shuffled table describing a program's syntax tree, write the full program in a grammar explained in the prompt with eight fixed examples.
0/24 vs 24/24correct programs at nesting depth 8 with a bare `end` versus `end func foo` closing lines
Six small real fixes (three bugs, three features) from six public Python libraries, all made after July 2026, three runs each.
17/18 vs 18/18coding runs passing all tests: Qwen3.5-122B on vLLM versus Claude Opus 5.5 in Claude Code
Public pre-trained models lack service-specific vocabulary, spelling variants and model numbers; separate pre-training by each product duplicates data, training, evaluation and maintenance work.
0.895 / 0.853 / 0.760Moegi bert-110m scores on Shopping query, Shopping product and Search query classification
The environment went live in April 2023, about six weeks after GPT-4.
~5,000white-collar staff across the Nissin group with access to the internal GPT environment
Workload: synchronous and batch jobs reading text and images, about 40 million tokens a day.
about 40 milliontokens a day handled by Zenn's translation, review and spam-detection AI features
Trigger: overtime and staff turnover driven by documentation written after clinical work.
200 to nearly 300AI-generated nursing discharge summaries a month, about one per discharged patient
From September, bots opened 80-90% of answered threads in the dev-request channel; every post between 21:00 and 08:00 in September came from a bot.
80-90%share of answered threads in the development-request channel opened by bots in September
Same code, prompt and tools for each model; two runs each, up to 60 minutes and 30 model calls per run, with no carry-over between runs.
16candidate vulnerabilities reported across the four models
Two MASS cycles on Qwen3.6-27B raise score per output token 1.2-1.6x on MLR-Bench, ScienceAgentBench, DSBench and AstaBench.
1.2-1.6xscore per output token after two MASS cycles versus the base Qwen3.6-27B on four research benchmarks