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I shipped an LLM efficiency + security kernel — and deleted my own best idea

A solo developer shipped BIOMA, a local LLM security and efficiency kernel written in Rust with a Python layer, after deleting their original 'mitosis' idea that failed to improve correctness in measured tests. BIOMA hardens LLM payloads in-process before they leave the machine, using mechanisms like metabolic context pruning and an atomic in-memory signaling substrate. The project is source-available under FSL-1.1-MIT and auto-converts to MIT after two years.

read2 min views1 publishedJul 13, 2026

Six months ago I set out to make LLMs "smarter" by orchestrating many of them

together. I measured it. It didn't work. Here's what I shipped instead — and why

the failure is the part I'm proudest of.

The plan was "mitosis": split a task across several LLMs, let them multiply and

compete, then synthesize the best answer. It sounds great in a pitch deck.

On ground-truth executed tests, it made correctness worse:

Every gain was ≤ 0. So I deleted it. The full evaluation — including the failure —

is in the repo's FINDINGS.md

.

The lesson: an idea that survives a pitch is not the same as an idea that

survives a measurement.

BIOMA is a small, local, provider-agnostic kernel (Rust core + a thin Python

layer) that sits in front of any LLM call and hardens the payload in-process, before it leaves your machine.

from bioma.firewall_client import CognitiveFirewall

fw = CognitiveFirewall(vault={"db_password": DB_PW})   # secrets to protect
h = fw.shield(history, "refactor this function")

import anthropic                                        # or google.genai, or openai
msg = anthropic.Anthropic().messages.create(
    model="claude-sonnet-5", max_tokens=1024,
    system=h.system or "", messages=[{"role": "user", "content": h.prompt}])

Three mechanisms, all measured:

Each context block gets a metabolic weight and a half-life; low-value blocks (old

logs, resolved chatter) are purged before dispatch.

0x0F

red alert → apoptosis.An atomic in-memory signalling substrate (~5µs) carries the alert state.

(Throughput benched at ~2M signals/s.)

Anthropic, Google, OpenAI, or a local model — same layer. You harden the payload

here and hand it to your SDK.

The license is FSL-1.1-MIT: the code is source-available (read it, run it,

build on it), free for any non-competing use, and it auto-converts to MIT after two years. I'm a solo dev — I wanted it visible and auditable without

BIOMA isn't magic. The whole thing is one discipline: measure everything, and keep only what survives the measurement — even when that means deleting the

Repo (Rust + Python, benchmarks, and the honest FINDINGS.md

):

https://github.com/jonathascordeiro20/bioma-framework

What would you attack first? I'll be in the comments — especially happy to go deep

on the firewall's saturation heuristic or the mitosis eval.

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