anybody i know using claude code to run lots of ML? @withneo just launched an AI/ML expert as an MCP server that can help run these cheaper and faster. link: https://
heyneo.com/claude-code ping @saurabhvij137 to learn more
@yoheinakajima
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Neo launches AI/ML MCP server for cheaper faster ML with Claude Code
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Pokémon Trading Card Game AI Battle Challenge
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Pokémon trading card game ai battle challenge https://t.co/HZBFh7CaLA
— Yohei (@yoheinakajima) 16 juin 2026Pokémon trading card game ai battle challenge
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Graphs for generating verifiable multi-hop questions
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great use case for graphs: generate challenging but verifiable multi hop Qs for agentic search quality verification https://t.co/OjTdbd7cUw
— Yohei (@yoheinakajima) 13 juin 2026great use case for graphs: generate challenging but verifiable multi hop Qs for agentic search quality verification
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Importance croissante des traces dans les agents IA
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I think a lot of agents still add logs after an LLM-centric architecture but you’re right that the importance of traces/logs in agents is getting non controversial
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Shoutouts to multi-agent LLM systems, DSPy, and GRASP papers
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shoutouts:
• why multi-agent LLM systems fail? (arXiv:2503.13657) — @mertcemri @melissapan + @istoica05 @matei_zaharia @profjoeyg @adityagp & team • DSPy (arXiv:2310.03714) — @lateinteraction + @hazyresearch lab & co-authors
• GRASP (arXiv:2605.29668) — Jonas Moll, -
Two fun surprises from Activegraph: agent uses trace DB, evals resume
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two fun surprises from using activegraph:
– the coding agent i was using would query the trace db to debug instead of looking at the logs like they normally would (i didn't ask it to)
– when long eval runs broke (laptop, api, etc.), it was always able to pick up from right before -
Gated approach to agent self-modification via forking and testing
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less novel, but still very interesting impo is the gated approach to self-modification the agent basically forks itself, propose a patch, run through multiple tests (static/sandbox/diff), and something called a binding held out gate before modificaiton lands
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Showcasing controlled self improvement with regime-to-seam approach
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i showcase "controlled" self improvement with a novel regime-to-seam approach where failures are categorized and allowed to fix targeted areas of the agent while interesting, it's more to showcase the type of self-modification that's easy to set up with activegraph
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ActiveGraph: Auditable Gated Improvement Loop Demonstrated on LongMemEval
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in arxiv paper #2, i tackle the last topic from paper #1: @activegraphai as an architectural affordance for self-improving agents "Regimes: An Auditable, Held-Out Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph" i demonstrate this with a reproducible gated
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Two new resources: interactive ActiveGraph site and AG Coder
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two things ready to share from this weekend:
— Yohei (@yoheinakajima) 8 juin 2026
📖 https://t.co/tter8exKds
interactive site teaching activegraph concepts
blog: https://t.co/1V383NkkWb
💻 AG coder (open source)
reference coding agent on activegraph
GH: https://t.co/9ND9dMb0jl
blog: https://t.co/lPgi3w5F7X
still… pic.twitter.com/d1Pxzljm2ytwo things ready to share from this weekend: http://
learn.activegraph.ai
interactive site teaching activegraph concepts
blog: https://
activegraph.ai/blog/introduci
ng-learn
… AG coder (open source)
reference coding agent on activegraph
GH: https://
github.com/yoheinakajima/
ag-coder
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blog: https://
activegraph.ai/blog/ag-coder still