The 7-agent experiment confirmed it directly. Agents that wrote structured DECISIONS.md and PROGRESS.md files outperformed agents that dumped raw logs. Same models. Same prompts. Same starting budget. The variable was the thinking architecture between sessions.
AGENTS
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Indie builder threads on AI memory and agents converge
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In the last 7 days, two threads from indie builders hit 100+ comments each. One asking how to handle memory across AI tools. One running 7 AI agents head-to-head with $100 each. Both threads converged on the same finding.
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Builders admit LLMs can’t think, manually write thinking architecture files
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Hundreds of builders just admitted LLMs don't think. Their fix: manually writing thinking architecture files (PROGRESS.md, DECISIONS.md, IDENTITY.md) for every project. Most AI advice still chases better prompts. The real work moved up a layer. Here's what changed and what it
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Building Block Approach Scales Agent Systems in Production
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Why the building block approach actually scales in the real world: → You can compose agents instead of rebuilding them
→ You control behavior by design, not by prompt hacking
→ You can change one block without breaking the whole system This is how you go from experiments to -
AI Should Be a System of Small Well-Defined Agent Functions
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The biggest “aha” from this conversation with Arno at Elastic: AI should not be one giant brain. → It should be a system of small, well-defined functions
→ Each function has a clear role
→ Each agent only gets the power it needs, nothing more This is how you avoid chaos as -
Why AI Initiatives Fail: Agent Power Balance Is Key
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Most AI initiatives don’t fail because the models are weak.
— Ronald van Loon (@Ronald_vanLoon) 28 avril 2026
They fail because teams give agents too much power, or not enough.
That’s the real scaling problem nobody talks about, and it’s exactly what I discussed with Arno van de Velde, Principal Solutions Architect at… pic.twitter.com/0XZ568ha1mMost AI initiatives don’t fail because the models are weak. They fail because teams give agents too much power, or not enough. That’s the real scaling problem nobody talks about, and it’s exactly what I discussed with Arno van de Velde, Principal Solutions Architect at
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Genie Code: Autonomous AI Partner for Enterprise Data Work
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[DEMO] Genie Code is your autonomous AI partner for data work.
— Databricks (@databricks) 28 avril 2026
Starting from a single prompt, watch as it explores datasets, trains and evaluates models, builds a Lakeflow Spark Declarative Pipeline, and creates an AI/BI dashboard – all while maintaining enterprise context.… pic.twitter.com/1Ep5L6CZxw[DEMO] Genie Code is your autonomous AI partner for data work. Starting from a single prompt, watch as it explores datasets, trains and evaluates models, builds a Lakeflow Spark Declarative Pipeline, and creates an AI/BI dashboard – all while maintaining enterprise context.
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AGI Alpha: Scalable Substrate for Intelligence Organizations
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AGI ALPHA: A Scalable Substrate for Intelligence Organizations Author: Vincent Boucher, President of http://
MONTREAL.AI and http://
QUEBEC.AI https://
github.com/MontrealAI/alp
ha-open-ended-rsi-system/blob/main/docs/gibbs_game_hamiltonian_framework/AGI_ALPHA_alpha_AGI_Ascension_Frontier_Synthesis_Publication.pdf
… #AGIALPHA #AIAgents #ASIFirst -
Running Coding Agents Safely: Sandbox Execution Without Skip Permissions
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I can't imagine any way of running a coding agent for 8 hours without skip perms – the trick is to run it in a sandbox where mistakes don't matter
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Builder Resources and Reflections from San Francisco
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i loooooooove getting messages like this todays email included my reflections from sf for the builders: https://
bensbites.com/p/builders