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README.md

@@ -41,11 +41,12 @@ two coding tasks at different difficulty levels:
 - **portscout** (easy): write a single-file concurrent port scanner using stdlib. create, test, done.
 - **logpulse** (hard): write a multi-file CLI log watcher with real-time stats, regex alerts, a fake log generator, a README, and self-test. requires file creation, debugging, and iterative fixes.
 
-four model configurations:
+five model configurations:
 - **Qwopus3.5-9B-Coder** (9B dense, 43 tok/s, fully in VRAM)
 - **Qwen3.6-35B-A3B MoE + thinking** (35B MoE, 3B active, 35 tok/s, ncmoe=30, reasoning on)
 - **Qwen3.6-35B-A3B MoE no-think** (35B MoE, 3B active, 35 tok/s, ncmoe=30, reasoning off)
 - **gpt-oss-20b** (21B MoE, 3.6B active, ncmoe=30, OpenAI Apache 2.0, native MXFP4 FFN)
+- **OmniCoder-9B** (9B dense, 42 tok/s, Tesslate, based on Qwen3.5-9B, fine-tuned on 425K agentic trajectories)
 
 ## key findings
 
@@ -63,6 +64,10 @@ four model configurations:
 
 7. **agent framework matters as much as the model.** Pi's 4-tool interface (read/write/edit/bash) produces fewer tokens per turn and simpler tool call schemas than Hermes Agent's patch-based approach. this reduces context pressure and makes structured output easier for smaller models.
 
+8. **agentic fine-tuning helps code quality, not agent loops.** OmniCoder-9B (Tesslate, 425K agentic trajectories on Qwen3.5-9B) produced the cleanest first-shot code of any model tested: 83-line portscout in <1 min with no self-fixes needed. but it failed on the hard task the same way Qwopus 9B did: ran a blocking command without timeout, then got stuck in a generation loop. code generation and agent loop management are separate skills, and fine-tuning on coding trajectories doesn't fix the latter.
+
+9. **thinking mode is a trap for 9B models.** OmniCoder-9B with default thinking mode burned all tokens on reasoning_content with 0 code output (same as Qwen3.6). the `-rea off` server flag is mandatory. once disabled, the model is fast (42 tok/s) and produces clean direct output.
+
 ## deep-dive benchmark: gpt-oss-20b (8 additional prompts)
 
 after the initial comparison, we ran 8 more prompts through gpt-oss-20b to stress-test reliability across different coding skills.