Blog de Joe Freeman

Documentation is the code you write when working with LLMs. The Agent is the compiler. Specs used to be written once and ignored. With LLMs in the loop, they become shared state - drafted, revised, and reconciled by humans and agents every time the system changes. Developer work in LLM-assisted development increasingly lives upstream of the editor: brainstorming, writing plans, and authoring skills. In practice, most of my time goes into shaping the problem so agents can take it from there—implementing features, generating tests, drafting documentation, and carrying parts of the SDLC forward without constant hand-holding. That upstream focus produces a growing body of specification: documents that explain not just what the system does, but why it is built the way it is. Every new brainstorming session, plan, or skill tends to spawn new docs and force updates to existing ones—merging overlapping specs, splitting bloated documents into sharper scopes, or retiring plans that no longer m...

I played with chatjimmy.ai. It was so fast that it felt fake.   The biggest bottleneck in modern AI isn't the math—it's the memory. Taalas is solving this by taking a radical approach: implementing AI models in the hardware itself. By utilizing a fabrication process with 4-bit transistors to implement FP4 natively in silicon, they effectively achieve one parameter per transistor. Because the model is built into the chip, they completely bypass the traditional memory wall. The result is a hyper-dense architecture that delivers 1000x better performance than conventional GPUs. To experience the latency of hardwired AI firsthand, check out their demo at  chatjimmy.ai . HC1 demonstrates the power of Taalas hardcore model silicon technology, delivering 17k tokens per second per user on Llama 3.1 8B model.  I can hardly wait for translation devices and personal assistants with this kind of speed. Hype or game changer?  I don't know, but I'm excited. Limitations The model i...