Why AgentKits Memory's Hybrid Search Has to Solve Japanese Twice
How AgentKits Memory fuses BM25 and vector search, and why Japanese, Chinese, and Korean queries need a second decision in the keyword half.
Guides, news, and tutorials from AityTech on manufacturing AI, factory DX, legacy code modernization and software development.
How AgentKits Memory fuses BM25 and vector search, and why Japanese, Chinese, and Korean queries need a second decision in the keyword half.
Why Legacy Dragon parses JCL, with condition codes, PROC nesting, and GDG windows, as a first-class language in the same AST.
Why voice and language breadth, not speed, is the hard problem for on-device browser TTS, and how PrivateAI's 322+ voices compare.
How the AgentKits Marketing Kit's selector loads at most five of its 28 skills to avoid context rot, and why it is simpler than the research proposal.
Why Legacy Dragon treats EBCDIC and Shift-JIS character encoding as a parsing-layer concern, not a pre-processing step added later.
Why cloud AI is priced per token while on-device tools have no such bill, and what that buys, and does not, for PrivateAI.
Why the agentkits-marketing repo holds two plugins, Content Factory and Campaign Manager, while marketplace.json lists only one, and what it signals.
Why AgentKits' Engineer Kit (40 skills, Preparing) was built after the Marketing Kit (18 agents, 93 commands, 28 skills, Ready).
Why RBS's 2012 meltdown and TSB's 2018 migration failed on unseen impact, and why Legacy Dragon's AST graph is built for impact analysis.
What Chrome 148's stable Summarizer, Translator, and Language Detector APIs on Gemini Nano cover, and where PrivateAI fills the gap.
How AgentKits ships its README and 23-module course in ten languages with a Claude Agent SDK tool, and why an LLM suits the job.
Why generative COBOL-to-Java rewrites are not reproducible, and why Legacy Dragon starts from a deterministic AST graph instead.
Why PrivateAI's browser-only architecture sidesteps the shadow AI leakage behind ChatGPT's 410 million DLP violations in 2025.
Why Legacy Dragon parses VB6 and PowerBuilder with the same AST-based approach it uses for mainframe COBOL, the quieter legacy estate.
How AgentKits' installer supports 18 AI coding tools from one .claude/ source, and how it relates to the AGENTS.md standard.
Why Legacy Dragon's AST graph matters for capturing the knowledge of retiring COBOL engineers as well as for migration speed.
Why AgentKits Memory stays in the local-MCP lane among Mem0, Zep, Letta, Cognee, and LangMem, and what that trade-off costs and buys.
How WebGPU makes browser-based AI fast and 2026 data-localization law makes it valuable, and why on-device tools like PrivateAI fit.
Why Legacy Dragon ships as a single binary for COBOL and JCL shops with tight network controls, with no npm install or cloud API call.
Why AgentKits' Marketing Kit groups agents by TOFU/MOFU/BOFU instead of technical domain, a deliberate bet on depth over breadth.
Why semiconductor and precision manufacturers cannot use cloud drawing SaaS, and how Zunavi makes on-premise the default architecture.
How Zunavi's feature flags separate reliable capabilities from assistive ones that need human verification, and why that is enforced twice.
Why tracking the latest drawing revision by deleting and reinserting rows breaks, and how PostgreSQL, Prisma, and one flag replace it.
Why parsing is the bottleneck in legacy modernization, and how AST-based tools for COBOL, JCL, and PL/I change the economics.
What AityTech's git history shows about its product portfolio: conventional commits, an agent-driven workflow, and where AI tooling pays off.
How PrivateAI's browser-based OCR, text-to-speech, speech-to-text, background removal, and translation tools run without a server.
Zunavi (図ナビ) is our in-development, on-premise-first platform for managing manufacturing drawings, specs, and approvals.
A look at the AityTech lineup of PrivateAI, Legacy Dragon, AgentKits, Yomite, and StudyKits and its privacy-first, on-device philosophy.
Our blog: guides, news, and tutorials on iOS development, web platforms, AI solutions, and open-source tools from our studio in Japan.
How we architect macOS applications that process AI workloads entirely on-device — no cloud uploads, no data collection, no compromise on capability.