Show HN: Open-Source DaiDocs now beats Graphify by 218x low tokens, 83% on LME
We have been working on making an Open-Source standard documents for AI LLM's and Agents. Just like an excel file, .dai (Document AI) is a new standard to keep memory local and use with any AI agents and LLM's. This new standard comes with lots of features such as more than 10x tokens savings (tested on LongMemEval-S) when comapred the .dai to raw files and scored 83% with gpt-4-0 and 92% with Fable 5! (This shows how well it remembers a very long conversation), as well as an increased context w
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Recorded metrics
points · 4 votes
2026-11-05 10:01:06.702Z
cumulative since publication
Research analysis
Project record
Repository: Kerneta/daidocs. Source account: We have been working on making an Open-Source standard documents for AI LLM's and Agents. Just like an excel file, .dai (Document AI) is a new standard to keep memory local and use with any AI agents and LLM's. This new standard comes with lots of features such as more than 10x tokens savings (tested on LongMemEval-S) when comapred the .dai to raw files and scored 83% with gpt-4-0 and 92% with Fable 5! (This shows how well it remembers a very long conversation), as well as an increased context w
Observed adoption & comparisons
Source votes: 4 votes (Nov 5, 2026 UTC; cumulative since publication); previous same-scope observation: 4 (Nov 5, 2026 UTC). These observations do not establish demand growth. Recent records to compare: “Show HN: Cerea – yet another self-hosted LLM chat front end” (Show HN, Nov 5, 2026); “Show HN: Lantern Lattice – the call graph from your compiler, for coding agents” (Show HN, Nov 5, 2026). Shared themes are navigation hints; they have not been verified as the same need or independent corroboration.
Adoption decision
Evaluate it against the tool or manual workflow it would replace. The stored record supports discovery; it does not yet establish reliability, lower operating cost or commercial adoption.
Unverified requirements
Not verified: license permissions, required hardware, installation steps, maintenance status and a representative task with its failure cases.
Next test
Choose one real task and a current alternative. Record the commit, environment, input, output, elapsed time and failure behavior; retain the result before recommending adoption.