Blog
From the ConjureForge team
Thoughts on autonomous development, agent architecture, and the future of software.
Why autonomous agents beat autocomplete — and why that matters for shipping software
Autocomplete tools are optimised for the wrong thing. They make the next token faster. We think the bottleneck is not typing speed — it is the planning and iteration loop. Here is why we built ConjureForge around a full Plan→Build→Test→Reflect cycle instead.
Full post coming soon →
Introducing ConjureLoop: the autonomous build agent at the heart of ConjureForge
ConjureLoop is not a chatbot that writes code. It is an autonomous agent that receives a brief, builds an execution plan, runs each task, tests the output, reflects on failures, and retries — without you needing to sit there and babysit it. This post walks through the architecture.
Full post coming soon →
Project memory: the hardest unsolved problem in AI coding tools
Every AI coding tool we tested had the same failure mode: after about 30 messages, it forgot what it built. Files hallucinated. Imports broken. Architecture decisions reversed. Here is how we solved this with a combination of episodic memory, project wiki, and the Memory Palace embedding store.
Full post coming soon →
Forge Credits: building a transparent, predictable billing model for AI usage
AI billing is a mess. Token counts are confusing, costs are unpredictable, and most tools give you a nasty surprise at end of month. We designed Forge Credits to fix that — one unit of account for every AI action, visible before you run it.
Full post coming soon →
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