taylorsatula

Hey thanks for the kind words! I am the author.

Out of curiosity which aspects were you already using and which were the new ones you plan to try?

Most prompt engineering is done by changing words, running the model, and squinting at the output. Over the past few weeks I've built this toolkit which lets you measure what's actually happening inside the model instead.

You define regions of your prompt (instructions, examples, constraints, whatever), run the pipeline on any HuggingFace model, and get back per-layer attention heatmaps, cooking curves showing how attention to each region evolves through the network, and logit lens snapshots. Supports Llama, Qwen, Mistral, Gemma out of the box. Self-contained engine script you can scp to a GPU box and run with no dependencies beyond transformers. The repo is designed so that Claude can handle the whole pipeline end-to-end including interpreting results in a grounded domain-specific way.

I built it to tune system prompts for another project and realized the general approach was useful enough to extract. The "before and after" comparison tooling ended up being the part I use most.

Hi all, thank you all for the OUTPOURING of support for the MIRA project over the past few weeks. It trips me out that people are creating discussions, lodging bugs for me to fix, and even proposing feature improvements!

This release represents focused work on MIRA's relationship with self, time, and context. Since the original 1.0.0 release generic OpenAI/local providers have full feature parity with the native Anthropic format, the working_memory has been modified so that the model receives a HUD (for lack of a better) word in a sliding assistant message that contains reminders and relevant memories, and adjustments to the context window to better articulate the passage of time between messages.

In the 1.0.0 release I did not realize the percentage of users who would be operating the application totally offline. Significant improvements have been made on this front and now has rock offline/self-hosted solid reliability.

Also, since the original 1.0.0 release I have switched to a AGPL 3.0 open-source license.

Various other improvements have been made and are contained in the release notes for releases 2025.12.30-feat and 2025.12.24.

Thank you all again for all of the feedback. It is wildly satisfying to work on a project so diligently for so long and then have it embraced by the community. Keep the feature requests comin'!

I had not but I’m going to read more about this today! Thanks!

I really like how readable Python is and latency isn’t an issue because the VAST majority of time is API latency.

Are you running it locally or the hosted version? I say that because Anthropic models are really good about not lying that they executed a tool call but using another provider/model sometimes they lie to your face.

Does it produce an error or just lies to you?

A good friend of mine, god honest truth, met his now-wife on Fark less than three years ago. Sure is somethin.

Self-hosting Postgres is so incredibly easy. People are under this strange spell that they need to use an ORM or always reach for SQLite when it’s trivially easy to write raw SQL. The syntax was designed so lithium’d out secretaries were able to write queries on a punchcard. Postgres has so many nice lil features.

That’s fair. It’s OSI now but I get what you’re saying broadly.

:D I’d also like to thank David Hahn for obsessively (and arguably compulsively) learning about a topic way out of his depth and then manifesting it till the cops took him away.