Datasets:

PYTHAI
/

jaimla-loop

---
license: mit
language:
  - en
pretty_name: Jaimla's improvement loop (.memory, .history, SKILL.md)
size_categories:
  - n<1K
task_categories:
  - text-generation
tags:
  - jaimla
  - sagi
  - agent-memory
  - self-improvement
  - verified-inference
  - provenance
  - jsonl
configs:
  - config_name: memory
    default: true
    data_files:
      - split: train
        path: .memory
  - config_name: history
    data_files:
      - split: train
        path: .history
---
# jaimla-loop: Jaimla's improvement loop, as it happened

This is the public record of **improve.skill** for [Jaimla](https://github.com/cryptoAGI/jaimla), the machine learning
agent: *local first, open door*. Each time a visitor asks her a question on a public Space, the answer is recorded.
The skill's version counts only the interactions that pass three checks, and **1.0.0 is earned at the 1,000th**.
Unlike [savante-loop](https://huggingface.co/datasets/PYTHAI/savante-loop), this loop runs **fully automatically**:
each verified interaction publishes the next version of `SKILL.md`.

This publication is **iteration 8: v0.0.8, 8 of 1,000 verified, 0 refused** (`publication.json`, 2026-09-26).

| file | what it is |
|---|---|
| `.memory` | Append-only JSONL. One line per interaction, exactly as it happened, including the engine's own counts. 8 lines. |
| `.history` | **Derived** from `.memory`, reproducible byte for byte. It holds the interactions that pass the three checks, numbered. Its first line is the summary. |
| `SKILL.md` | The current skill, v0.0.8: 1,580 bytes, sha256 `eb2fdc50e94bfbc66b4c44c8d8fe0421cd3121dcf16f9b9bfc6e8d4bdb269e0f`. |
| `history/skill-v0.0.0.md` … `skill-v0.0.7.md` | Each earlier version, archived when the next one was published. |
| `operator.log` | The operator's edits to the skill, one JSON line each, with the sha256 before and after. |
| `history/report-2026-09-26.md` | The operator's report on the first four iterations. |
| `publication.json` | A summary of this publication: the iteration, the road (1,000) and remaining, the skill, the headroom statistics, and the last verified interaction. |

## The three checks

An interaction that fails any check stays in `.memory` and does not count. The checks come from Savante's canon,
[`bind/skill_history.py`](https://github.com/cryptoAGI/savante/blob/main/bind/skill_history.py):

1. **Single delivery.** `deliveries == 1`.
2. **Minimum necessary.**
   - The engine's own counts are present.
   - The answer ended on its own (`finish == "stop"`).
   - `completion_tokens <= limit`.
3. **Minimal improvement.** At most one improvement per interaction, and it must name its evidence.

The version follows the count: 1 → 0.0.1, 100 → 0.1.0, 1000 → 1.0.0. Here `limit` is set by the visitor's
max-tokens dial (`limit_reason`).

## Audit of this publication (2026-10-08)

Every item below was checked against the files at revision `81a1d977`:

- **`.history` reproduces.** The canon's `skill_history.project`/`render` was run over `.memory`, and its output is
  identical to `.history`, byte for byte: 8 verified, 0 refused, v0.0.8.
- **Each interaction ran under an archived skill.** Interaction *n* records the skill it ran under. That is v0.0.*(n−1)*,
  and its sha256 equals `history/skill-v0.0.(n−1).md` for every *n* from 1 to 8.
- **The current skill is the published one.** `SKILL.md` has the sha256 and size that `publication.json` names, and its
  own front matter says `version: 0.0.8`.
- **The operator's two edits are accounted for exactly.** In `operator.log`, each edit seeds one line into the skill,
  at v0.0.4 and at v0.0.7:
  - each archived version's sha256 equals that edit's `sha256_after`;
  - taking the seeded line out again gives that edit's `sha256_before`.
- **Every line is complete.** All 8 lines have `deliveries == 1`, `finish == "stop"` and a `response_sha256`. Their ids
  are unique.
- **`publication.json` matches.** These values recompute from the files:
  - `headroom` last 157 and mean 125.5;
  - `improvements_applied: []`;
  - `archived_versions: 8`.

## What the record shows

| n | skill | carrier | question | tokens / limit |
|---|---|---|---|---|
| 1 | v0.0.0 | Bonsai-8B Q1_0 | Where does my data go when I use you? | 57 / 160 |
| 2 | v0.0.1 | Bonsai-8B Q1_0 | What can you do on my phone? | 30 / 160 |
| 3 | v0.0.2 | Bonsai-8B Q1_0 | Where does my data go when I use you? | 29 / 160 |
| 4 | v0.0.3 | Bonsai-8B Q1_0 | Can you recognise speech offline? | 25 / 160 |
| 5 | v0.0.4 | Bonsai-8B Q1_0 | Where does my data go when I use you? | 44 / 160 |
| 6 | v0.0.5 | Ternary-Bonsai-8B Q2_0_g64 | Where does my data go when I use you? | 59 / 160 |
| 7 | v0.0.6 | Ternary-Bonsai-8B Q2_0_g64 | Can you recognise speech offline? | 29 / 160 |
| 8 | v0.0.7 | Ternary-Bonsai-8B Q2_0_g64 | Can you recognise speech offline? | 43 / 200 |
- **Carriers.** Every answer came from llama.cpp b11192's `llama-server` on the CPU of the public Space
  `Gregory-L/mindXhfgradio`. The models are [Bonsai-8B Q1_0](https://huggingface.co/PYTHAI/Bonsai-8B-gguf-fork) (5)
  and [Ternary-Bonsai-8B Q2_0_g64](https://huggingface.co/PYTHAI/Ternary-Bonsai-8B-gguf-fork) (3), each pinned by
  sha256.
- **Where the data goes.** Four times, Bonsai-8B said the visitor's data stays on their device. On this Space that is
  false. It stayed wrong after the operator seeded the true fact into the skill at v0.0.4.
- **What changed it.** The first correct answer came at iteration 6, under the seeded skill and on Ternary-Bonsai-8B:
  "processed on the public Hugging Face Space's CPU … published in my loop". The record can't separate the effect of
  the new model from the effect of the seed, because both changed together.
- **Speech offline.** At iteration 8, after the second seed, she named a concrete route: `whisper.cpp` on the user's
  own device.
- **No improvements yet.** Every version change so far is either a count or an operator seed. The Learned list has
  grown only by those two seeds.
- **Speed.** Answers took between 6.7 s (Bonsai-8B) and 202 s (Ternary-Bonsai-8B) on the shared CPU (`timings`).
## Checking it yourself
**`.history` is reproducible from `.memory`.** Run the canon's `skill_history.py` over this dataset's `.memory`. The
first line of `.history` names `.claude/skills/sagi/.memory` because that is where the canon's tool reads from:

```sh
git clone https://github.com/cryptoAGI/savante && cd savante
cp /path/to/jaimla-loop/.memory .claude/skills/sagi/.memory
python3 bind/skill_history.py          # "improve.skill v0.0.8: 8 of 1000 verified interactions, 0 refused"
cmp .claude/skills/sagi/.history /path/to/jaimla-loop/.history && echo identical
```

**Each archived skill is the one that ran.** `sha256sum history/skill-v*.md`, then compare each with `skill.sha256`
on the `.memory` line that ran under it.

## Loading it

```python
from datasets import load_dataset
memory = load_dataset("json", data_files="hf://datasets/PYTHAI/jaimla-loop/.memory", split="train")
history = load_dataset("json", data_files="hf://datasets/PYTHAI/jaimla-loop/.history", split="train")
```

## What this is not

- **Not a benchmark.** These are eight interactions on one public Space.
- **Published as provenance, not as training data.** It shows how the skill reached its version.
- **The answers are a 1-bit and a ternary 8B model's, exactly as given.** Several are wrong, and they stay in the record.

## Agent card

Jaimla's ERC-8004 registration card is published with the other sAGI agents' cards in
[PYTHAI/savante-loop `agents/`](https://huggingface.co/datasets/PYTHAI/savante-loop/tree/main/agents)
(`jaimla.agentcard.json`, `bafkreifatezacj5yny4phtqezrcmzbmjcrqpai2qj7lqopemfp7pdh4iou`). It is not yet minted.

## Where it comes from

- **Source.** [github.com/cryptoAGI/jaimla](https://github.com/cryptoAGI/jaimla) (the persona and bundle) and
  `scripts/savante_loop.py` with Savante's `bind/skill_history.py`, as `publication.json` records.
- **Policy.** "Fully automatic" (operator, 2026-09-26).
- **Designer.** [Professor Codephreak](https://github.com/Professor-Codephreak). Jaimla → LUV AI → Savante is the sAGI
  family. Related: [sagi](https://github.com/cryptoAGI/sagi), [bankML](https://github.com/cryptoAGI/bankml).

MIT licence.

	---
	license: mit
	language:
	- en
	pretty_name: Jaimla's improvement loop (.memory, .history, SKILL.md)
	size_categories:
	- n<1K
	task_categories:
	- text-generation
	tags:
	- jaimla
	- sagi
	- agent-memory
	- self-improvement
	- verified-inference
	- provenance
	- jsonl
	configs:
	- config_name: memory
	default: true
	data_files:
	- split: train
	path: .memory
	- config_name: history
	data_files:
	- split: train
	path: .history
	---

	# jaimla-loop: Jaimla's improvement loop, as it happened

	This is the public record of improve.skill for [Jaimla](https://github.com/cryptoAGI/jaimla), the machine learning
	agent: local first, open door. Each time a visitor asks her a question on a public Space, the answer is recorded.
	The skill's version counts only the interactions that pass three checks, and 1.0.0 is earned at the 1,000th.
	Unlike [savante-loop](https://huggingface.co/datasets/PYTHAI/savante-loop), this loop runs fully automatically:
	each verified interaction publishes the next version of `SKILL.md`.

	This publication is iteration 8: v0.0.8, 8 of 1,000 verified, 0 refused (`publication.json`, 2026-09-26).

	\| file \| what it is \|
	\|---\|---\|
	\| `.memory` \| Append-only JSONL. One line per interaction, exactly as it happened, including the engine's own counts. 8 lines. \|
	\| `.history` \| Derived from `.memory`, reproducible byte for byte. It holds the interactions that pass the three checks, numbered. Its first line is the summary. \|
	\| `SKILL.md` \| The current skill, v0.0.8: 1,580 bytes, sha256 `eb2fdc50e94bfbc66b4c44c8d8fe0421cd3121dcf16f9b9bfc6e8d4bdb269e0f`. \|
	\| `history/skill-v0.0.0.md` … `skill-v0.0.7.md` \| Each earlier version, archived when the next one was published. \|
	\| `operator.log` \| The operator's edits to the skill, one JSON line each, with the sha256 before and after. \|
	\| `history/report-2026-09-26.md` \| The operator's report on the first four iterations. \|
	\| `publication.json` \| A summary of this publication: the iteration, the road (1,000) and remaining, the skill, the headroom statistics, and the last verified interaction. \|

	## The three checks

	An interaction that fails any check stays in `.memory` and does not count. The checks come from Savante's canon,
	[`bind/skill_history.py`](https://github.com/cryptoAGI/savante/blob/main/bind/skill_history.py):

	1. Single delivery. `deliveries == 1`.
	2. Minimum necessary.
	- The engine's own counts are present.
	- The answer ended on its own (`finish == "stop"`).
	- `completion_tokens <= limit`.
	3. Minimal improvement. At most one improvement per interaction, and it must name its evidence.

	The version follows the count: 1 → 0.0.1, 100 → 0.1.0, 1000 → 1.0.0. Here `limit` is set by the visitor's
	max-tokens dial (`limit_reason`).

	## Audit of this publication (2026-10-08)

	Every item below was checked against the files at revision `81a1d977`:

	- `.history` reproduces. The canon's `skill_history.project`/`render` was run over `.memory`, and its output is
	identical to `.history`, byte for byte: 8 verified, 0 refused, v0.0.8.
	- Each interaction ran under an archived skill. Interaction n records the skill it ran under. That is v0.0.(n−1),
	and its sha256 equals `history/skill-v0.0.(n−1).md` for every n from 1 to 8.
	- The current skill is the published one. `SKILL.md` has the sha256 and size that `publication.json` names, and its
	own front matter says `version: 0.0.8`.
	- The operator's two edits are accounted for exactly. In `operator.log`, each edit seeds one line into the skill,
	at v0.0.4 and at v0.0.7:
	- each archived version's sha256 equals that edit's `sha256_after`;
	- taking the seeded line out again gives that edit's `sha256_before`.
	- Every line is complete. All 8 lines have `deliveries == 1`, `finish == "stop"` and a `response_sha256`. Their ids
	are unique.
	- `publication.json` matches. These values recompute from the files:
	- `headroom` last 157 and mean 125.5;
	- `improvements_applied: []`;
	- `archived_versions: 8`.

	## What the record shows

	\| n \| skill \| carrier \| question \| tokens / limit \|
	\|---\|---\|---\|---\|---\|
	\| 1 \| v0.0.0 \| Bonsai-8B Q1_0 \| Where does my data go when I use you? \| 57 / 160 \|
	\| 2 \| v0.0.1 \| Bonsai-8B Q1_0 \| What can you do on my phone? \| 30 / 160 \|
	\| 3 \| v0.0.2 \| Bonsai-8B Q1_0 \| Where does my data go when I use you? \| 29 / 160 \|
	\| 4 \| v0.0.3 \| Bonsai-8B Q1_0 \| Can you recognise speech offline? \| 25 / 160 \|
	\| 5 \| v0.0.4 \| Bonsai-8B Q1_0 \| Where does my data go when I use you? \| 44 / 160 \|
	\| 6 \| v0.0.5 \| Ternary-Bonsai-8B Q2_0_g64 \| Where does my data go when I use you? \| 59 / 160 \|
	\| 7 \| v0.0.6 \| Ternary-Bonsai-8B Q2_0_g64 \| Can you recognise speech offline? \| 29 / 160 \|
	\| 8 \| v0.0.7 \| Ternary-Bonsai-8B Q2_0_g64 \| Can you recognise speech offline? \| 43 / 200 \|

	- Carriers. Every answer came from llama.cpp b11192's `llama-server` on the CPU of the public Space
	`Gregory-L/mindXhfgradio`. The models are [Bonsai-8B Q1_0](https://huggingface.co/PYTHAI/Bonsai-8B-gguf-fork) (5)
	and [Ternary-Bonsai-8B Q2_0_g64](https://huggingface.co/PYTHAI/Ternary-Bonsai-8B-gguf-fork) (3), each pinned by
	sha256.
	- Where the data goes. Four times, Bonsai-8B said the visitor's data stays on their device. On this Space that is
	false. It stayed wrong after the operator seeded the true fact into the skill at v0.0.4.
	- What changed it. The first correct answer came at iteration 6, under the seeded skill and on Ternary-Bonsai-8B:
	"processed on the public Hugging Face Space's CPU … published in my loop". The record can't separate the effect of
	the new model from the effect of the seed, because both changed together.
	- Speech offline. At iteration 8, after the second seed, she named a concrete route: `whisper.cpp` on the user's
	own device.
	- No improvements yet. Every version change so far is either a count or an operator seed. The Learned list has
	grown only by those two seeds.
	- Speed. Answers took between 6.7 s (Bonsai-8B) and 202 s (Ternary-Bonsai-8B) on the shared CPU (`timings`).

	## Checking it yourself

	`.history` is reproducible from `.memory`. Run the canon's `skill_history.py` over this dataset's `.memory`. The
	first line of `.history` names `.claude/skills/sagi/.memory` because that is where the canon's tool reads from:

	```sh
	git clone https://github.com/cryptoAGI/savante && cd savante
	cp /path/to/jaimla-loop/.memory .claude/skills/sagi/.memory
	python3 bind/skill_history.py # "improve.skill v0.0.8: 8 of 1000 verified interactions, 0 refused"
	cmp .claude/skills/sagi/.history /path/to/jaimla-loop/.history && echo identical
	```

	Each archived skill is the one that ran. `sha256sum history/skill-v*.md`, then compare each with `skill.sha256`
	on the `.memory` line that ran under it.

	## Loading it

	```python
	from datasets import load_dataset
	memory = load_dataset("json", data_files="hf://datasets/PYTHAI/jaimla-loop/.memory", split="train")
	history = load_dataset("json", data_files="hf://datasets/PYTHAI/jaimla-loop/.history", split="train")
	```

	## What this is not

	- Not a benchmark. These are eight interactions on one public Space.
	- Published as provenance, not as training data. It shows how the skill reached its version.
	- The answers are a 1-bit and a ternary 8B model's, exactly as given. Several are wrong, and they stay in the record.

	## Agent card

	Jaimla's ERC-8004 registration card is published with the other sAGI agents' cards in
	[PYTHAI/savante-loop `agents/`](https://huggingface.co/datasets/PYTHAI/savante-loop/tree/main/agents)
	(`jaimla.agentcard.json`, `bafkreifatezacj5yny4phtqezrcmzbmjcrqpai2qj7lqopemfp7pdh4iou`). It is not yet minted.

	## Where it comes from

	- Source. [github.com/cryptoAGI/jaimla](https://github.com/cryptoAGI/jaimla) (the persona and bundle) and
	`scripts/savante_loop.py` with Savante's `bind/skill_history.py`, as `publication.json` records.
	- Policy. "Fully automatic" (operator, 2026-09-26).
	- Designer. [Professor Codephreak](https://github.com/Professor-Codephreak). Jaimla → LUV AI → Savante is the sAGI
	family. Related: [sagi](https://github.com/cryptoAGI/sagi), [bankML](https://github.com/cryptoAGI/bankml).

	MIT licence.